Machine-Learning on Vin Patel

Artificial Intelligence Against Money Laundering

Vin Patel — Mon, 19 Dec 2022 11:05:28 +0000

Key takeaways

Every good thing has an unintended consequence, and the digitalization of the finance industry is no exception.
FinTech are catching up and attempting to scale their resources and technology to meet the demand for their services while remaining compliant with regulations.
In this article, we have discussed how AI helps in detecting money laundering and makes the fintech industry secure for online transactions.

Every good thing has an unintended consequence, and the digitalization of the finance industry is no exception. The fintech industry is expanding at a breakneck pace to meet customer demands and provide them with online services. However, this demand has increased the likelihood of cybercrime.

Online banking has made money more accessible, and criminals have taken advantage of this opportunity to launder illicit funds and fund terrorism at a faster and larger scale around the world. And, with the amount of data available in the world, particularly in the financial industry, it is impossible to detect fraud by manual techniques.

To combat the drawbacks of digitalization, artificial intelligence is being used as a digital weapon to detect financial fraud and make online banking a more secure option for everyone.

Using AI for Anti-Money Laundering (AML) #

Artificial intelligence and machine learning are two dynamic fields of study that adapt intelligently to market changes and detect emerging risks. They can be quickly and painlessly integrated into existing compliance programs. Early adopters are gaining significant efficiencies while also assisting their institutions in meeting rising regulatory requirements.

It is necessary to understand the risks first before implementing any solution. Money mules, who are recruited to move funds, knowingly or unknowingly, as part of a money-laundering scheme, or Surfing, which involves moving large sums of illicit money through a series of smaller transactions, are examples of the dangers.

These refined money launderers pose a serious threat to financial institutions around the world, and their activities have far-reaching societal consequences. As a result, socioeconomic disparities such as terrorism, drug trafficking, and human trafficking pose a threat to social structures and order, as well as open and fair trade.

Using specialized algorithms, AI tools can detect money laundering in a variety of ways. In essence, these algorithms analyze large amounts of data and raise a red flag if anything suspicious is discovered, such as unusual transactions or account activity.

Here are a few ways in which AI can detect suspicious behavior:

AI can also help with customer due diligence and know your customer processes, allowing them to be completed more quickly and thoroughly.

For AML purposes, AI can provide financial institutions with a wider range of customer data that can be used in risk assessments, suspicious activity reports, and investigations.

When dealing with potentially suspicious activity, algorithms can be used to pre-fill reports with relevant data and standardize language and terminology, saving AML employees valuable time.

Financial institutions can use AI to manage their massive amounts of unstructured data. Banks must be able to analyze their unstructured data as part of transaction monitoring, sanctions screening, and other activities to be AML compliant.

Challenges Involved in AML #

Traditional financial institutions have had years to develop their anti-money laundering (AML) programs, gradually adapting to changing regulatory requirements. FinTech are catching up and attempting to scale their resources and technology to meet the demand for their services while remaining compliant with regulations. Here are some of the challenges that fintech companies face when implementing AI.

Digital banks require actionable insights quickly to develop and improve their anti-money laundering (AML) and counter-terrorist financing (CFT) frameworks, but they face some significant challenges in ensuring AML compliance. The Financial Conduct Authority (FCA) announced in 2021 that it was investigating Mono for possible non-compliance with AML/CFT regulations, which should indicate that Fintech are being given more attention. To begin with, their reliance on online banking makes them vulnerable when it comes to approving an account or transaction, necessitating risk assessment procedures. Then there’s the massive amount of data that needs to be processed quickly, which includes a variety of data types ranging from IP and geolocation data to other personal information obtained from apes and digital devices. It’s difficult to sift through so much data to find actionable, relevant, and timely insights, especially when compliance processes are typically repetitive, data-intensive, and inefficient.

Using AI to Drive Efficiencies in Operational Hotspots #

Modern AI techniques can sift through massive amounts of data collected across various bank departments, effectively replacing manual investigation. This enables the modern bank to investigate literally every transaction that occurs in a fraction of a second, particularly in these areas.

These compliance processes can be automated with AI solutions, including:

Identity verification #

Verification checks and KYC procedures are essential for ensuring that your customers are who they claim to be. To identify AML risks with high accuracy and at the earliest opportunity, banks must understand how to contextualize relationships between individuals and business entities.

This could include biometric data and scans of official documents as part of digital identity verification. Additional measures, such as a video KYC check, can be implemented because some fake documentation appears to be very convincing.

Banks should consider the benefits of slowing down onboarding to reduce the risk of hasty and inaccurate approvals. Banks will be one step ahead of the competition by incorporating layers into the initial scoping process.

Monitoring the activities #

Customers must also be accurately and efficiently screened against international sanctions and watch lists by digital banks. They must also be able to recognize and track changes in the status of politically exposed persons (PEPs) and their family members and close associates.

Monitoring transactions #

It is critical to track and understand customer transactional behavior to ensure that it is consistent with expectations. Suspicious activity, such as unusual transactions completed at unusual frequencies or volumes, or transactions involving high-risk jurisdictions, necessitates constant vigilance.

Conclusion #

Over half of respondents have integrated AI or machine learning into their AML compliance processes, are piloting AI solutions, or plan to implement them within the next 18 months. The two main reasons for using AI and ML in AML processes are to improve the quality of investigations and regulatory filings, which is cited by 40% of respondents, and to reduce false positives and associated operational costs, which is cited by 38%. In this article, we have discussed how AI helps in detecting money laundering and makes the fintech industry secure for online transactions.

Related reading

Artificial Intelligence — Will It Outpace Humans?

Vin Patel — Tue, 06 Dec 2022 12:49:57 +0000

Key takeaways

***The Artificial Intelligence (AI) discourse comprises two camps.
It could collate and sift through its learning, selecting the choice that had the highest potential for a positive outcome.
There is no crystal ball to state if one will overtake the other, but for now, we will just have to allow technology the upper hand and to let AI steadily improve and run amok in our society.

The Artificial Intelligence (AI) discourse comprises two camps. One who view it as the hope of tomorrow; a breakthrough which, combined with human capabilities, can augment and transform every walk of life. Then there are those for whom AI is the harbinger of a dystopian future. One that, more often than not, resembles some variation of the Matrix. The reality? AI is still in its infancy. It is evolving because we are continually seeking ways to simplify our existence. In that case, could we go too far? Could AI, at any point, become smarter than us or is its existence dependent on the cognitive abilities and native intelligence of humans?

A central tenet of the 4th Industrial revolution, AI has been more transformative than anything we have ever seen, at times challenging what it means to be human.

From consumer teach to enterprise-grade solutions, artificial intelligence is firmly ingrained in our innovations. It might be easier to collate a list of the part of modern society that is yet untouched by AI than the other way round. AI is influencing every facet of our daily lives, helping improve efficiencies and augmenting human capabilities.

The Science Behind Artificial Intelligence #

In recent years, artificial intelligence has been popularized by software’s like Sir and Google Duplex, though it has been in the works since the 1950s. The human brain has been the primary inspiration for AI researchers, with the quest for replicating cognitive abilities of the brain, being the most elusive of the goals.

As it stands, machines are already observing, analyzing, and learning from data and mistakes just like humans can. Instead of being programmed in what to think, AI uses statistics, mathematical algorithms, and artificial neural networks to automatically model and find patterns in data to predict an outcome. Given the amount of data we generate from the touchpoints in our daily life, it is little wonder the use of artificial intelligence has exploded in recent times.

Abstract Thinking – Can AI Emulate It? #

Recently Google released a speaking AI that mimics human conversation, and a google subsidiary, Deep Mind, created an AI that beat two world champions in a board game called ‘Go’. The reason the latter is remarkable is that ‘Go’ is one of the most complex strategy games. There are so many potential moves and game states that it makes chess look like toddler play. Yet, ‘Alpha Go’, Deep Minds innovation, conquered it by keeping the memory of the games it has played and studying them as experience. It could collate and sift through its learning, selecting the choice that had the highest potential for a positive outcome. Does that sound a lot like a human brain, learning from its experiences and using that to decide future interactions?

But given all this, the reality is more humbling. While there is substance behind AI’s hype, its underlying hypothesis still centers around the availability of data. The most sophisticated of machine learning algorithm is at its core a computer software that requires the right data sets to problem solve. From facial recognition software that compares billions of pixels to AI-powered Rob-analysts in banking software’s, the same rules apply.

Technology evolves, progresses, and becomes more efficient. Deep neural networks enable AI algorithms to analyze data through multiple layers with greater accuracy. Generative adversarial networks let AI create data based on the data set it has trained on, while reinforcement training lets machines learn from feedback-based trial-and-error interactions. However, the fundamental principle remains the same – if you can translate the task into data, AI will do it.

But can one break down common sense into data? Can we create datasets that enable AI to think in abstracts when situations do not follow the predetermined course? How do we mathematically define, curiosity?

Artificial Intelligence vs Our Cognitive Abilities #

In continuing with the argument around data, one thing we can certainly say–Humans are dreadful at storing massive amounts of data and are terribly slow in processing larger sets of information. In the time it would take a human brain to translate 200 words, an AI-based algorithm would translate at least a million. The fact is computers surpass us in any operation that involves mathematical computation or data processing.

However, the human brain is exceptionally adept at deciding based on common sense, instinct and scarce information. We can transfer learning across parallel events, use limited information to determine the next best course of action, and when all else fails, we use creativity to construct new responses. What comes naturally to humans is the hardest to translate into machine learning.

The Future as It Stands #

Artificial Intelligence is the cornerstone of our existence and is here to stay. Machines do obviate the need for a human workforce in some sectors, but they are not a complete replacement. The future does not lie in the hands of either. It is based on the collaborative and complementary intelligence of both; the cognitive abilities, leadership, and creativity of one combined with the speed, scalability, and quantitative strengths of the other, is the way forward for businesses and humanity alike.

For sure, as technology advances and gives AI the wherewithal to handle generalizations on its own, the heated debate on its regulation needs to take center stage. Black-box models of AI can increasingly make predictions and decisions as humans do, but without the ability to communicate the reasoning behind it. Their outcomes could potentially be on patterns which we as humans cannot perceive. How does one then justify a course of action determined by a machine?

In either case, we are at a dawn of technological evolution – one where man and machine will need to learn to live together, where the existence of one entirely depends on the other. There is no crystal ball to state if one will overtake the other, but for now, we will just have to allow technology the upper hand and to let AI steadily improve and run amok in our society.

Related reading

What is GPT-4 and What Does it Mean for Businesses?

Vin Patel — Tue, 29 Nov 2022 14:01:45 +0000

Key takeaways

The extent of AI-powered tools and technology penetrating our day-to-day life is undoubtedly unfathomable, and just like its predecessors, GPT-4 is here to amaze the virtual world.
This clearly implies that the impending model is capable of performing tasks at a much faster rate and is able not just to comprehend vast amounts of data but also to generate better, quicker outcomes.
For now, these language models are here to aid the writers and make lives easier.

The extent of AI-powered tools and technology penetrating our day-to-day life is undoubtedly unfathomable, and just like its predecessors, GPT-4 is here to amaze the virtual world.

In 2020, OpenAI released GPT-3, which leverages AI and Deep Learning to generate content. In 2021, Sam Altman, CEO of OpenAI, hinted that the company is planning to reveal GPT-4 around July-August this year. However, the company’s discreet call and no-revelation-talks have caused quite a stir among the AI fraternity, leaving hardly a trail of information on this hyped product.

Even though this language model is yet to be released, there is not an ounce of doubt about its ability to excel its predecessors, and AI enthusiasts cannot stop talking about it. Here we get into the details of GPT-4, its expected features and its use cases for businesses across the globe.

What Exactly is GPT-4? #

The AI company, OpenAI has produced a series of autoregressive language models in the form of a Generative Pre-trained Transformer, more commonly referred to as GPT. These models started pouring in some time back, with the last one released in 2020 by the name GPT-3.

These language models are able to generate varied kinds of texts on the internet and are responsible for popularizing the “AI-generated content”. These employ machine learning to produce auto-generated texts and queues from even a miniature input text, allowing users to generate large amounts of content.

OpenAI revealed that the size of GPT-4 will almost remain the same as GPT-3, and there is enough evidence to support the fact that larger is not always better when it comes to AI-enabled products.

Technically, GPT-4 is composed of 100 trillion parameters which are almost 500 times more than its forerunner, NLP. This clearly implies that the impending model is capable of performing tasks at a much faster rate and is able not just to comprehend vast amounts of data but also to generate better, quicker outcomes.

How is GPT-4 Useful for the Virtual Market? #

Just like the predictions made by the users, GPT-4 is certainly going to perform tasks in a far more powerful manner than GPT-3. This implies that the latest model is expected to be apt and prompt and will be able to understand users’ requirements better.

GPT-3 worked with natural language and took some time to generate correct output, but GPT-4 shall be capable of consuming more input in the form of a higher number of symbols and generating more human-like results.

For the virtual world and businesses, GPT-4 is coming with the promise of making AI-generated content more accessible and popular. This means that the internet may see an outpour of “robot-written content”. GPT-3 had already achieved auto-generated email responses and hyper-programmed text generation, and GPT-4 is trained to do it all with more agility and precision.

For brands and companies leveraging this tool, content curation will become a piece of cake as long-form auto-generated content in a timely manner would yield faster results in less time and labor. The main intent of the language model is to come as close to human-like ability as possible, and with so many parameters, this upcoming product seems very true in achieving this trait.

Even for the programmers and coders, code versions of GPT-4 are sure to generate codes and programs within minutes and are guaranteed to wipe out the low-skilled coders from businesses.

Closing Thoughts #

While the applications of GPT-4 are too many to predict and enlist, there is a growing concern that the popularity and accessibility of such tools are going to flood the web with auto-generated content, making it difficult to distinguish between the original and plagiarized content forms.

Also, it raises another important question. Will the writer be sent home? Is AI-generated content the real king, after all? Certainly not. Even technically, GPT-4 is only a text-only model that makes it a smart content curation tool but nowhere like a human. And yes, if we are able to achieve multimodal AI(which is not nearly impossible in the upcoming decade), then the future of regular writers may be hanging on the thread. For now, these language models are here to aid the writers and make lives easier.

Related reading

Questions and answers

What Exactly is GPT-4? #

How is GPT-4 Useful for the Virtual Market? #

Intelligent Test Automation: Why Should You Use It?

Vin Patel — Tue, 29 Nov 2022 12:29:12 +0000

Key takeaways

Intelligence test automation mainly refers to the software automation testing process combining Robotic Process Automation (RPA) with artificial intelligence and machine learning.
Moreover, the above scenarios can be mimicked easily using their historical information.
Customers have a good user experience that increases returning customers, contributing to a profitable business.

Intelligence test automation mainly refers to the software automation testing process combining Robotic Process Automation (RPA) with artificial intelligence and machine learning. It also uses AI algorithms that make the software more stable, reduce errors, and are more efficient.‌‌

To integrate an analytical feature into the lifestyle of software testing, intelligent algorithms are executed by AI and machine learning. The results are automatically evaluated within their unique intelligent testing automation scenario.

How Does Intelligent Test Automation Work? #

The key to advancing the deployment and development is Intelligence Test Automation. Software Development Lifecycle (SDLC) permits automation during different phases. Unit testing occurs at the code level, while the system and integration testing occur during an integrated environment as the features develop.

Intelligent test automation is said as a combination of AI processes, machine learning and other traditional automation practices. The combinational result is one of the smarter ways to test applications and software at higher efficiency with almost no error.

What Are The Risks of Insufficient Software Testing? #

Quality insurance like user acceptance, regression, performance, and mobile testing should be considered while developing software. This reduces the cost to both user and the owner. Insufficient software testing leads to developer complications, poor consumer experiences, and stakeholder disapproval.‌‌

Some of the risk that frequently takes place is:‌‌

Excessive expenses - Testing software during its development process costs less than when it is already developed.
Increased risks of security breaches- When software is not tested properly, risks of security breaches or crashes are higher.
Increased risks of harmful bugs - Proper software testing can detect and prevent bugs before they create issues.
Dissatisfied customers - Customers demand software updates run their current software more efficiently. Failure of this can cause dissatisfied customers, resulting in decreased returning customers.

Why Should Teams Adapt To This New Method? #

Software companies need to adapt to Intelligence Test Automation when technology is rapidly evolving. Testing software in the development process ensures quality. Intelligence test automation comprises a model-based testing approach to automatically generating and maintaining test cases.‌‌

This also allows organizations to encourage end-to-end testing practices for broader test coverage, reduced maintenance costs, and more efficient test cases.‌‌

Some major reasons for teams to adapt to these new methods are listed below-‌‌

**Instant feedback-**The intelligent test result analysis provides instant feedback that eventually improves the response time. Majorly, it’s because of the use of AI-driven smart analytic, which quickly identifies all the critical areas and can also implement test cases automatically. There needs to be more clarity on AI control over highly repetitive tasks only apart from the cases with the same input and output, which is totally wrong; AI can take over both seamlessly. Moreover, the above scenarios can be mimicked easily using their historical information.
Optimizing test suite- This comes as a major issue due to overengineering, resulting in a huge backlog. Without proper optimization, testing processes and software development can waste time, effort, and resources. However, with a properly optimized test suite, AI-driven testing can focus on adapting the cycle as it develops and also helps in clearing the backlog. Test suite optimization removes duplicates and improves traceability by identifying repetitive and unrequited test cases.
Less hectic work - Teams can offload lots of time-consuming work as with Intelligent test Automation, AI and ML supports them and work on their behalf.
Smarter decisions- It can reduce surplus data and transform them into actionable insights. Moreover, AI/ML-based implementation majorly focuses on test data generated by automation suites. Smart decisions to use this technology to analyze the test results and detect bugs or quality of the test cases make testing smooth. This data can also be taken as a key parameter to decide the next action point. Automation testing with such smart approaches can help in prescribing a solution in a defined time for any action failure or critical faults.
Faster releases - With Intelligent Test Automation, the software launch process increases as manual processes are swapped out with autonomous ones. To fetch insights on the root cause of test failures, intelligent test automation can play a crucial role by diving deep into the automation suite. Software release gets faster by finding a preventive approach for the defect occurrence.

Conclusion #

Intelligent test automation is a step toward successful software as it ensures quality, efficiency, easier scaling, and mitigates many different risks. Customers have a good user experience that increases returning customers, contributing to a profitable business.

Related reading

The Future of Wealth Management? Teach or Hybrid?

Vin Patel — Tue, 22 Nov 2022 18:54:34 +0000

Key takeaways

But such seemingly simple goals can face some tirelessly complex hurdles.
With urbanized goals like sustaining a lifestyle, education planning for kids, healthcare costs of ageing parents, or even buying a new home, today’s consumer needs more appropriate real-time advice than ever before.
The paradigm shift in the wealth management landscape is occurring rapidly, and these digitalization trends bear testament to the massive change that the wealth management space is facing.

With futurists looking to re-invent the asset management value chain and consumers demanding more client-centricity, can tomorrow’s wealth management solution be led solely by teach? Digitalization is promising a massive upgrade to long-term financial planning. What can this mean for you as an investor?

An investor usually has two broad objectives: to grow their capital and, second, to protect what they already have. But such seemingly simple goals can face some tirelessly complex hurdles. From trying to understand asset classes and keeping track of portfolio performance to changing investment buckets based on market trends, an average investor often has a lot to contend with. Combine this with increased uncertainty and the rising cost of risk, and you have a consumer who is frayed at the edges and is demanding a lot more in the way of client-centricity.

As per a report published by Deloitte in 2017, we are about to witness the largest wealth transfer in history over the next 40 years, with almost USD 50 trillion changing hands from one generation to another. This makes it even more essential for institutions to gear up to address re-wired Millenials’ demands. With increased globalization, it’s vital to recognize that digital transformation is no longer a choice but an imperative for sustainability. This is something wealth managers have now realized.

Risking disruption to re-invent their value-chain, wealth management is now following in the footsteps of consumer-centric organizations like Amazon, Apple and Netflix, to name a few; to empower a digitally-savvy investor with tools and methodologies that up until now were the purview of a select few.

The Emergence of Wealth Teach #

Just as fintech transformed financial services, Healthtech promises a revolution in the investment management industry. It encompasses a range of different channels - from B2C rob-advisors to micro-investing platforms that allow users to regularly save small sums of money to even B2B providers that offer digital brokerages and wealth advisory. Healthtech is attempting to create a fullyoperational wealth management bank ‘in your pocket’.

With cutting-edge technology, firms like Wealthfront, Addepar and InvestCloud are helping investors benefit from Artificial Intelligence (AI) and deep-data-driven analytic to receive real-time personal financial management at a fraction of the cost.

No Longer A One-Size-Fits-All #

Thanks to Machine Learning, advisory firms are infusing data and analytic to serve clients holistically by generating relevant, timely, and actionable insights for clients and advisors alike.

Embedded at the heart of Healthtech is a behavior-based segmentation based on risk appetite, interests, and financial goals. This brings a more intuitive experience to the investor, going beyond just recommending portfolio allocation based on risk profile or asset under management (AUM). Instead, more customized offerings are made available based on client personas and interests. With this outside-in approach, technology is bridging the chasm between business objectives and customer expectations.

Taking Rob-Advisory To The Next Level #

But as consumer sentiment evolves, the new generation of wealth customer is looking beyond just growing their capital. With urbanized goals like sustaining a lifestyle, education planning for kids, healthcare costs of ageing parents, or even buying a new home, today’s consumer needs more appropriate real-time advice than ever before. This is perhaps where traditional wealth management has sometimes fallen short of its objectives. With analyst, stock picks undermining market indexes more often than not, and underdog plays making headline-worthy moves, advisors often find themselves dragging their feet when it comes to generating timely advice and meeting client goals.

For Healthtech innovators, this is a core focus area - bringing granular level needs-based advisory, real-time, to the mass market. By integrating economic trends and even social media analytic, whose singular stand in recent times has moved markets beyond measure, this type of advice becomes more relevant than ever.

Combining this with click-of-the-button UI, Healthtech brings in user-friendly omnichannel support to simplify investing and at the same time, give control back to the investor. For instance, an investor can individualize their financial planning through goal-based bucket creation to set aside a long-term retirement corpus while having an aggressive short-term bucket for saving up for a down payment on a house. If there are changes in market sentiments in the short term, the investor can shift money from one bucket to another. This is perhaps the most significant takeback from this value chain: more control, better individualization and better expectation management.

Taking it a step further, a personalized dashboard gives you 24/7 access to your portfolio with the ability to compare performances across different asset classes ranging from commodities, mutual funds, energy, real estate, and even art. But where does this leave the traditional human element of financial planning advisory?

The Convergence of Teach And The Human Element #

Wealth management has been a long-standing stalwart of a personal advisory model. But algorithms are slowly taking some of the more fundamental functions of human advisors. However, that doesn’t entirely render the human element obsolete. On the other hand, it augments the offerings while still retaining the traditional model’s personal and emotional component through a more hybrid approach.

Human advisory’s significance is invaluable for clients with material assets to invest in, or even when it comes to tax planning and estate management. This is why the digitalization of the Wealth Management space is so unique. The pairing of the human relationship with automation to create an omnichannel service makes financial planning a lot more approachable for the masses.

Wealth Management 2.0 #

The paradigm shift in the wealth management landscape is occurring rapidly, and these digitalization trends bear testament to the massive change that the wealth management space is facing. With the ramification of UI, the enormous shift in demographics now controlling wealth and the new and digitally re-wired customer at the helm, Healthtech seeks to bring far-reaching changes to a system that up until was the purview of a select few. We, for one, are excited at the potential of this change.

Welcome to Wealth Management 2.0.

Related reading

Why Is ELT Better For Cloud Data Warehousing?

Vin Patel — Tue, 22 Nov 2022 18:50:10 +0000

Key takeaways

Modern data warehousing, such as cloud and other hybrid environments, provides integrated machine learning solutions to store data that enables both customer insights and business intelligence (BI) to help make faster business decisions.
Redshift lets its users automatically add clusters in times of high demand.
That’s why cloud-based data warehouses and ELT go hand-in-hand with regard to performance, scalability, and lower costs as compared to the on-premise databases and ETL.

Introduction #

Modern data warehousing, such as cloud and other hybrid environments, provides integrated machine learning solutions to store data that enables both customer insights and business intelligence (BI) to help make faster business decisions. Data warehousing refers to collecting data from several different sources, such as business applications, mobile data, and social media. Then this data is used to deliver valuable business insights and analytical reports.

The heterogeneous data obtained from various sources are first cleansed and then organized into a consolidated format in the data warehouse. Enterprises and organizations use data warehousing tools and Database Management Systems (DBMS) to access the data stored on the warehouse servers to support their operational business decisions.

The market growth of data warehousing is attributed to factors such as the increasing amount of data generated by enterprises and a growing need for BI to gain a competitive advantage. The huge volumes of data produced by different businesses are exerting tremendous pressure on their existing resources, thus forcing them to adopt data warehousing solutions for flexible, efficient, and scalable storage. This data can be leveraged using advanced data mining and BI tools to provide valuable business insights to users for strengthening their customer retention, increasing their operational efficiency, helping in better decision-making, and increasing their revenue streams.

Storing data on-premises can become quite expensive if the computing power and storage have different scalability. Data-driven organizations are the early birds to swap the traditional on-premises with cloud data warehouses that are more agile and flexible. That’s because the latter can instantly scale themselves to deliver high or low computing needs as required, thus, making them highly cost-effective.

Such companies are finding that their ETL (Extract Transform Load) tools are less adaptable to the hybrid environments, and demand more upgrades, ultimately making them turn the costlier investment in the long run. The scalability and computing power of ETL is proving to be Achilles’ heel. Cisco reports that 94% of all workloads will run in some form of cloud environment by 2021.

Numerous business enterprises are now considering the benefits of cloud data warehousing, which include on-demand computing, multiple data type support, flexible pricing models, integrated BI tools, and unlimited storage. SMEs (Small and Medium Enterprises) are rapidly adopting the cloud warehousing model due to low infrastructure needs and affordable costs. As the last decade saw a rapid growth of cloud adoption rates across many industries. Today, cloud data storage accounts for 45% of all enterprise data, and the number could grow to 53% by Q2 of 2021.

The available cloud data warehouses that allow companies to manage their analytical data by storing and cost-effectively processing data are changing. The shift from the on-premises servers toward the could data warehouses is sparking a shift from ETL to ELT (Extract Load Transform). ELT is an alternative technique compared to the traditional ETL, as this process involves pushing the transformation component of the process to the target database for better performance. This capability is quite useful for processing the massive data sets required for big data analytic and business intelligence (BI).

Let us now discuss an efficient process to move and transform data for analysis, which is crucial for business growth and innovation in this data-driven world.

Loading a data warehouse can be an extremely time-consuming process. ELT is the process of extracting data from one or multiple sources and loading it into a target data warehouse instead of transforming the data before it is written.

The ELT process involves streamlining the tasks of modern data warehousing and managing big data so that businesses can focus on data mining for actionable insights. ELT capitalizes on the target system that does the data transformation. With ELT needing only raw and unprepared data, this approach needs fewer remote sources than other techniques.

ELT reduces the time data spends in transit and boosts efficiency, as it takes advantage of the processing capability that is built into a data storage infrastructure. Though this process has been in practice for some time now, it is now gaining popularity with the more widespread use of Hadoop and cloud-native data lakes.

What is ELT? #

It stands for extract, load, and transform- the processes a data pipeline uses for replicating the data from a source system into a target system such as a cloud data warehouse.

Extraction is the first step in which data is copied from the source system.
Loading is the next step, where the pipeline replicates data from the source into the target system, which could be a data lake or a data warehouse.
Transformation is where the organizations can run whatever transformations they need once the data is in the target system. Usually, they will transform the raw data in different ways for use with different tools or business processes.

ELT and its Infrastructure #

ELT is a modern variation of the older ETL process in which the transformations occur before loading the data—running the transformations before the load phase results in a more complex data replication process. ETL tools need processing engines to run the transformations before loading the data into a destination. While with ELT, businesses use the processing engines in the destinations for efficiently transforming data within the target system. So, the removal of an intermediate step streamlines the data-loading process.

Use case of ELT #

As the ETL transforms data before the loading stage, it is an ideal process when the destination needs a specific data format. This could include when there’s a misalignment in the supported data types between the source and destination or due to limited ability to scale processing in a destination quickly or security restrictions that make it impossible to store the raw data in a destination.

Liv Up, the Brazilian food teach start-up, provides an example of the effectiveness of ELT in data warehouses. The company integrated data from a variety of sources, such as Google Analytic, MongoDB, and Zendesk, into its data warehouse. Though the process was effective in the past, it was a bit cumbersome.

Data from MongoDB was quite challenging as it needed to translate NoSQL data into a relational data structure. The company took about a month to write the code for their data pipeline with traditional ETL. That’s when they started to look for reducing the time to value and get their data to its destination more quickly.

So, the company turned to Stitch, a cloud-first, developer-focused platform that helps to expedite the data replication process. Acquired by Talend in November 2018, the company operates as an independent business unit. Hundreds of data teams rely on them to securely and reliably move their data from the SaaS (Software-as-a-Service) tools and databases into the data lakes and data warehouses.

It took about 8 hours a week to extract and load the company’s data for Stitch. Liv Up benefited from the ability to build its transformation phase to easily leverage the BI tools that were integral to their company.

ELT is a better approach when the destination is a cloud-native data warehouse like Google BigQuery, Amazon, Snowflake, Microsoft Azure SQL Data Warehouse, and Redshift. That’s because these organizations can transform their raw data at any time, when and as required for their use case, and not as a step in the data pipeline.

A few popular cloud data warehouses #

1. Amazon Redshift #

The approach of Amazon Redshift is a PaaS (Platform-as-a-Service) that is highly scalable. It has the provision the clusters of nodes to customers as their computing and storage needs evolve. Each node has an individual CPU, RAM, and storage space. To set up Redshift, you must provision the clusters through AWS (Amazon Web Services). Redshift lets its users automatically add clusters in times of high demand.

2. Google BigQuery #

The best thing about the architecture of BigQuery is that you don’t need to know anything about it. It is serverless; that’s why its underlying architecture is hidden from the users (in a good way). It can scale to thousands of machines by structuring computations as an execution tree. BigQuery sends queries through a root server, intermediate servers, and finally, leaf servers with local storage.

3. Microsoft Azure SQL Database #

Azure SQL Data Warehouse is an elastic, large-scale DWaaS that leverages the broad ecosystem of SQL Server. It uses a distributed MPP (massively parallel processing) architecture that is designed to handle multiple operations simultaneously by several processing units that work independently and have their own dedicated memory and operating system. It collects data from databases and SaaS platforms into one powerful, fully-managed centralized repository. The computing and storage as billed separately, so they can scale independently.

4. Snowflake #

Snowflake is a DWaaS (Data Warehouse-as-a-Service) that operates across multiple clouds, including AWS, Microsoft Azure, and soon, Google Cloud. It separates the storage, computes, and services into detached layers, allowing them to scale independently. These automatically managed storage layers can hold structured or semi-structured data. The compute layer contains clusters, each of which can access all data but work independently and concurrently to enable automatic scaling, distribution, and rebalancing.

ELT Advantages for Businesses #

The explosion in the types and volume of data to be processed by businesses can put a strain on traditional data warehouses. Using an ETL process to manage millions of records in new formats can be quite expensive and time-consuming. This is where ELT offers numerous advantages over ETL:

1. Faster Time to Value #

Generally, ELT provides a faster time to value, which means business intelligence is available far more quickly. Whereas ETL needs a time-intensive and resource-heavy transformation step before loading or integrating data.

2. Scalability #

ELT tools are used along with the cloud data warehouses, which are designed to auto-scale in the case of increased processing loads. The cloud platforms allow for almost unlimited scale that too within seconds or minutes, while the older generations of on-premises data warehouses need the organizations to order, install, and configure new hardware.

3. Flexibility #

You can replicate the raw data into your data lake or data warehouse and transform it when and however you need them with the ELT process, as it is adaptable and flexible. This makes it suitable for a wide variety of businesses, goals, and applications.

4. Simplifies management #

It separates the loading and transformation tasks, lowers the risk, minimizes the interdependencies between the processes, and streamlines project management.

5. Leverages the latest technologies #

It harnesses the power of new technologies to push improvements, compliance, and security across the enterprise. It also leverages the native capabilities of modern big data processing frameworks and cloud data warehouses.

6. Future-proof data sets #

ELT implementations can be directly used for data warehousing systems. However, most of the time, it is used in the data lake approach, where data is collected from a range of sources, combined with the separation of the transformation process to make it easier to make future changes to the warehouse structure.

7. Lowers the cost #

Cloud-based ELT can result in a lower total cost of ownership, as an upfront investment in hardware is often unnecessary.

Though ELT is still evolving, it offers the promise of unlimited access to data, less development time, and significant cost savings; thus, it redefines data integration.

How ELT Works? #

It is becoming increasingly common for data extraction from its source locations to get them loaded into a target data warehouse for transforming them into actionable business intelligence. This process is called ELT and involves the following steps:

1. Extract

This step is the same in both ETL and ELT data management approaches. The raw streams of data from virtual infrastructure, software, and applications are consumed in either their entirety or as per predefined rules.

2. Load

This is where the ELT differs from its earlier cousin, ETL. Instead of delivering the raw data and loading it to a temporary processing server for transformation, ELT delivers the data directly to the destination storage. This helps to shorten the cycle between the extraction and delivery of the data.

3. Transform #

The data warehouse sorts and normalizes the data and keeps part or all of it accessible for customized reporting. The overhead for storing a huge amount of data is higher, but it offers more opportunities for data mining for relevant BI in near real-time.

Why is ELT Better? #

Let us take a closer look at the difference between ETL and ELT processes. The primary difference between ETL and ELT is the amount of data retained in the warehouses and where the data is transformed.

In ETL, the transformation of data is done before loading it into a warehouse, which enables the analysts and business users to get the data they need faster. Moreover, they don’t have to build complex transformations or persistent tables in their business intelligence tools. Whereas, in ELT, the data is loaded into the warehouse or data lake as it is, without transformation before loading. This makes it an easier job for configuration as it only needs a source and a destination.

The ETL and ELT approaches for data integration differ in the following ways:

1. Load Time #

It takes significantly longer to get data from the source system to the target system with ETL, whereas it is faster with ELT.

2. Transformation Time #

ELT performs data transformation on-demand, using the computing power of the target system, which significantly reduces the wait times for transformation as with ETL.

3. Data Warehouse Support #

ETL is a better approach for legacy on-premise data warehouses and structured data, while ELT is designed for the scalability of the cloud.

4. Complexity #

Typically, the ETL tools have an easy-to-use GUI (Graphic User Interface) that simplifies the process. While ELT needs in-depth knowledge of BI tools, masses of raw data, and a database that can transform the data effectively.

5. Maintenance #

ETL requires significant maintenance for updating data in the warehouse, whereas, with ELT, data is always available in near real-time.

Conclusion #

Both the ELT and ETL processes have a place in today’s competitive scenario. Understanding the unique needs and strategies of a business is the key to determining which process will deliver the best outcomes. Businesses need a data warehouse to analyze data over time and deliver actionable BI. So, should you deploy your data warehouse on-premises at your own data center or in the cloud? The answer depends on factors like cost, scalability, control, resources, and security.

Some businesses may deploy a data warehouse on-premises, in the cloud, or a hybrid solution that combines both. An organization choosing an on-premises data warehouse must purchase, deploy, and maintain all the hardware and software.

However, as a cloud data warehouse is a SaaS, having no physical hardware, a business will pay for the storage space and the computing power they need at a given time. A business pays for the storage space and the computing power they need at a given time. Scalability is simply adding more cloud resources, and there’s no need to employ people or maintain the system as those tasks are handled by the provider. That’s why cloud-based data warehouses and ELT go hand-in-hand with regard to performance, scalability, and lower costs as compared to the on-premise databases and ETL.

Related reading

How Does OpenAI's GPT 3 Work?

Vin Patel — Tue, 22 Nov 2022 17:32:41 +0000

Key takeaways

The GPT 3 can write texts, poems, translate paragraphs, chat, code, design, create answers, and much more.
Interestingly, the output data is similar to the human writing style.
However, it has opened many doors to an AGI world and got the ML community dreaming!

What if you had a technology that could write the best write-ups? Seems something from Science Fiction, right? But guess what, OpenAI’s GPT-3 can do that.

The GPT 3 can write texts, poems, translate paragraphs, chat, code, design, create answers, and much more. It’s like having a personal writing assistant! Currently, it is one of the largest language models in the world. OpenAI released GPT 3 in June 2020. Since then, it has been the talk of the Machine Learning community.

Developers around the world developed startups based on GPT-3.Copy. AI is one such GPT-3 powered AI which can generate copies for social media posts, ads, and more. You need to select the copy type and describe your product. And within a few seconds, it will generate results.

But What Exactly is GPT-3? #

According to Greg Rain, the CEO of Raizlabs, ‘GPT-3 is the largest leap in artificial intelligence we’ve seen in a long time, and the implications of these advances will be felt for a really long time’.

OpenAI’s GPT-3 is the successor to GPT-2.OpenAI is an AI research center that works on Artificial General Intelligence (AGI), which may help humanity. It has been developing AI technologies over the years. The GPT-3 is from the GPT series of API.

The early mentions of Generative Pre-Training (GPT) are in its research papers. It discusses how Artificial Intelligence can work on unlabeled data. Most of the available AI technologies work on labelled data.

However,in the GPT 3 paper, the researchers say that the 175 billion parameter language model can predict future texts on unlabeled data. If you use GPT 3, you can assess the words and use them to teach the AI. The AI system will then start predicting future words based on unlabeled data. You have to repeat the procedure until you generate the desired output.

So, this makes it suitable for any writing! It can generate any copy with a few instructions.

According to Greg Rain’s article, ‘The original GPT only worked on 7000 books data. However, the GPT-3 worked on 410 billion data by crawling the world wide web. Not only that, but it also uses data from books, Wikipedia, and a lot more. So, it is over 45 Terabytes of text!’

For now, the GPT-3 is in an experimental stage. Selected developers are getting early access to it. Samar Mor is one such developer who got early access. He said, ‘The main difference I see with GPT 3 is that, unlike other AI applications that are narrow, its performance is general and human-like.’ He used it to developAirpaper that generates structured data from any document, PDF, or image!

How Does GPT-3 Function? #

The GPT-3 generates texts using a neural network system. It is considered the most accurate compared to its predecessors in the GPT series. As it uses 175 billion parameters, it is capable of learning on its own. It means GPT-3 can do any work without training!

It is highly efficient due to its in-context evaluation. Once the user feeds the information, the AI analyses the texts. Then it provides the most probable statistical data using a calculation. For this, it uses the massive data that exists on the internet.

It doesn’t understand the meaning of the words it produces, but it generates the correct sentence. With a bit of language adjustment, GPT-3 can even write stories, blogs, essays, jokes, and more. Interestingly, the output data is similar to the human writing style.

Here are some examples!

The developer demonstrates how he used GPT 3 based technology to describe the react app he wants. The AI generations the function (code) to build the app (To-do).
Here, GPT -3 is developing paragraphs based on the initial hints.
The user instructs GPT 3 to tweak the writing style of a sentence, and it does its magic!

Many people shared their experiences with this Artificial Intelligence on Twitter. If you type GPT-3 on the Twitter search bar, you will find many examples of how it works. Do you know it can also write a song with just the title and artist’s name? Not only that, but it can work as an ideas generator for businesses.

Recently Designer Jordan Signer used this technology to build a plugin and named it “Designer”. This plugin produces app designs from descriptions.

What Are The Limitations of GPT-3? #

Although GPT-3 looks promising and is one of the greatest AI revolutions, it has shortcomings. Even Sam Altman, the founder of OpenAI, has pointed out its limitation.

Here are some of its shortcomings.

Still in its Beta version #

The GPT-3 is still in its experimental version. It has a lot of potential, which we have already seen. But the hype around it is shadowing the problems that come with this model.

Doesn’t understand the meaning of the output #

This model uses statistics to dish out the most probable sentence or code. However, it doesn’t understand what it is presenting. Therefore, it lacks semantic understanding. GPT 3’s data is what it crawled on the internet, so it generates sexists, racists, and biased output!

Not anywhere close to AGI #

Many developers have praised the model as the start of AGI, but it is not so. It generates human-like sentences because of its massive datasets. Structurally it is similar to its predecessor, GPT-2. The only difference is the resource it used for training.

As it doesn’t have semantic understanding, proper reasoning skills, and lacks generalization, it doesn’t have ‘human like’ writing skills. It can generate faulty output with biases. So, it’s far from something AI enthusiasts will consider in AGI.

Future of GPT-3 #

Till the time GPT-3 is in its experimental phase, it is difficult to say much about its future. However, as you can understand from the hype around it, it has a lot of potentials. The fact that many startups are using it to develop AI apes is evident of its future. But the fear is its human-like functioning! The output data is close to what human writes, so this creates the chances of misuse.

The caber-world is already dealing with fake news, false information, and Deepfakes. With technology like GPT-3 in the scene, it can cause more problems! Therefore, there needs to be a regulation. OpenAI has already said about the dangerous impact of GPT-3, if not regulated.

For now, only after proper inspection are the developers getting access to this technology. OpenAI is strict with this model’s use. So, we can’t say much about this model’s future. However, it has opened many doors to an AGI world and got the ML community dreaming!

Related reading

Questions and answers

But What Exactly is GPT-3? #

According to Greg Rain, the CEO of Raizlabs, ’ GPT-3 is the largest leap in artificial intelligence we’ve seen in a long time, and the implications of these advances will be felt for a really long time ‘.

How Does GPT-3 Function? #

The GPT-3 generates texts using a neural network system. It is considered the most accurate compared to its predecessors in the GPT series.

What Are The Limitations of GPT-3? #

Although GPT-3 looks promising and is one of the greatest AI revolutions, it has shortcomings. Even Sam Altman, the founder of OpenAI, has pointed out its limitation.

Everything You Need to Know About Programmatic Advertising

Vin Patel — Tue, 22 Nov 2022 16:50:50 +0000

Key takeaways

Programmatic advertising is like quantum physics in marketing, everyone has heard of it and talks about it, but only a few people understand it.
It is only open to the advertisers who get invited to the auction.
Predictions expect that most of the advertisement campaigns will use this within two years.

‘All the forms of programmatic trading will become the key to the automated movement.’

Original diagram for Techfastly by Vin Patel.

You might have heard from other people in different organizations about how programmatic advertising has changed the landscape of the digital era and not fully understand it. Programmatic advertising is like quantum physics in marketing, everyone has heard of it and talks about it, but only a few people understand it.

Here, we will explain programmatic advertising, the process, its benefits, and why digital marketers turn their campaigns programmatic.

What is Programmatic Advertising? #

In simple terms, programmatic advertising is the use of software to buy Ads. It makes buying and selling Ads automatic by replacing human interactions with artificial intelligence and machine learning.

Advertising effectively and catching the right audience’s attention is a tough job. This is where programmatic marketing comes to play.

Since it started in 1994, it has gained immense popularity with a high success rate for running advertisement campaigns. Using an algorithm to boost advertisements takes digital marketing to a different level. This process increases buyers’ and sellers’ efficiency, accuracy, and transparency. The entire process happens within a few milliseconds. Surprised? Read more to learn how the process works.

How Does Programmatic Advertising Work? #

Original diagram for Techfastly by Vin Patel.

The designed algorithms, like machine learning and artificial intelligence attribute to the efficiency of programmatic advertising.

Machine learning is a process to improve computers to learn and enhance through the gained experience without the need for any explicit coding. You can understand it as extra supplements to enhance them to be artificially intelligent, like human beings.

Although computers? artificial intelligence isn’t comparable to a human’s cognitive abilities; it is rapidly advancing and gaining popularity in every sector, even digital marketing. The simplest example is when search engines like Google predict search suggestions close to what you might need rather than what it would generally anticipate based on your past searches.

How is AI Used in Programmatic Advertising? #

Artificial Intelligence analyses the user’s behavior, which optimizes the marketing campaigns so that the users are more likely to convert into customers.

Let us try to understand this concept through an example.

You are an owner of a clothing brand that you wish to advertise digitally. You supposedly make use of software or a platform to run your ad campaigns programmatically. Here are a few details about the possible consumers that the software will help you recognize.

Location
Demographics (Age or Gender)
IInterests and Hobbies
Time or Weather
Your Goals
Device (TV, smartphones, etc.)

After receiving data on these factors, the campaigns are organized with alignment to the above elements so that the chances of visitors converting into potential customers are higher.

The flow of programmatic buying goes like this:

A person visits a website that advertises programmatically.
The automated bidding start, and the publisher puts an auction for an advertisement on a supply-side platform (SSP).
This platform lets the buyers know about the website, user, and Ad space. Interested advertisers offer bids.
The Ad space allows Demand Side Platforms (DSP) to make bids. When the SSP receives information about the Ad Space, it gets data about the user’s cookies and analyses it.
The analyzed data gets shared with the DSP. It acts on advertisers to choose Ad Space according to the user’s interests and the advertiser’s budget.
The highest bidder with the most suitable bid gets the ad placed on the website. The various DSPs placed their bids on behalf of the advertiser, and the SSP chose a winner.
The SSP displays the winning Ad bid on the website. If the Ad interests the user, they click on the ad and turn into customers of the advertiser.

Original diagram for Techfastly by Vin Patel.

All this process happens in milliseconds when the user clicks the website and the webpage loads.

Types of Programmatic Media Buying #

There are four types of Programmatic Media Buying.

1. Real-Time Bidding (RTB) #

This is a real-time open auction environment open to everyone. In this process, bids on an Ad Space place, and the highest bidder gets the slot. RTB is easy to set up and optimize, but this process lacks transparency. The advertisers don’t know the exact websites where their Ads get placed.

2. Private Marketplace #

This process is similar to RTB except for a few points. It is only open to the advertisers who get invited to the auction. Also, the advertiser knows precisely where the Ads get published.

3. Preferred Deals #

Before the Ad impressions are made available for open and private auctions, advertisers choose them at a fixed price. Both parties negotiate the pricing and targeting. The advertiser gets a look at the publisher’s Ad inventory and isn’t bound to make purchases.

4. Programmatic Guaranteed #

This process is a bit traditional that doesn’t have any auctions. Here, both parties negotiate on a one-on-one basis. After a successful negotiation, the Ad impression is directly sold to the advertiser.

Programmatic Guaranteed is opted for by advertisers with a substantial budget. They have options to choose from Ad space inventories to prices, audience, and frequency.

There are a few places where you can run marketing campaigns.

Display Ads (online visual Ads published on the header, footer, or sidebar of webpage)
Video Ads (Ads that appear while playing YouTube videos, pops up in online articles, or appears in search suggestions )
Audio Ads (Ads on apes like SoundCloud, Pandora, and BBC)
Social Media Ads (Ads displayed on social media platforms)
Native Ads (appear in different locations of a webpage)
Digital Out home Ads

How Much Does Programmatic Advertising Cost? #

Programmatic advertising costs way less when compared to traditional methods for advertisements. The price ranges from 0.50$ to 2$ CPM (cost per mile). Traditional campaigning costs a minimum of $10.

However, it is a rough figure and depends on several factors and parameters. If you wish to have a rough idea about the cost of advertising programmatically, you can use the IAB Programmatic Fee Transparency Calculator.

Benefits of Programmatic Advertising #

Programmatic marketing has many benefits besides low-cost, automation, machine learning and artificial intelligence.

1. Real-time Insights #

Opting for traditional advertising techniques takes a lot of time to get results, and you cannot determine the efficiency. These problems get solved with programmatic advertising. It provides insights into your marketing campaigns and the target audience.

2. Enhanced Targeting Abilities

One of the best things about programmatic marketing is personalization. Using the user’s cookies regulates strategies to gain more conversions.

3. Greater Transparency

The advertiser knows about the Ad impression, the target audience, and the Ad performance through this system. They can also easily track the spent amount and optimize their campaigns for better results.

4. Reduction of Ad Fraud

Due to the greater transparency of the advertising process, Ad fraud has been reduced. There are major programmatic advertising platforms that block bot traffic and ad fraud in their system.

5. Less Wastage

Your campaigns reach your target audience efficiently, and fewer ad impressions get wasted on the wrong audience. Also, tackling Ad fraud more effectively ensures the least wastage of campaigns.

The Future of Programmatic Advertising: Final Thoughts #

Original diagram for Techfastly by Vin Patel.

Programmatic advertising is one of the growing sectors of the market. Talking about the future, it has already arrived. According to a report, almost 88% of display Ads in the United States will be forecasted programmatically in 2021.

Not only does programmatic marketing eases the campaigning process through automation, but it also is more efficient as it recognizes and targets potential users. Also, small brands can opt for programmatic marketing even if they have a low budget.

Programmatic advertising is expanding exponentially every year. Predictions expect that most of the advertisement campaigns will use this within two years.

Related reading

Questions and answers

What is Programmatic Advertising? #

How Does Programmatic Advertising Work? #

Programmatic Advertising The designed algorithms, like machine learning and artificial intelligence attribute to the efficiency of programmatic advertising. Machine learning is a process to improve computers to learn and enhance through the gained experience without the need for any explicit coding.

How Much Does Programmatic Advertising Cost? #

Programmatic advertising costs way less when compared to traditional methods for advertisements. The price ranges from 0.50$ to 2$ CPM (cost per mile).

Deep Learning Techniques for Object Detection

Vin Patel — Tue, 22 Nov 2022 16:40:10 +0000

Key takeaways

A report by McKinsey states that by 2030 the global economic activity of Artificial Intelligence would easily sum up to 13 Trillion USD.
It finds and classifies a variable number of the given object or an image.
Owing to the robust development of deep neural networks, the performance of these detectors have improved rigorously over the years.

Understanding the Term - Deep Learning #

A report by McKinsey states that by 2030 the global economic activity of Artificial Intelligence would easily sum up to 13 Trillion USD. The field of AI became crucial when the machines could complete those tasks that typically required human intelligence. It also includes machines that can learn to acquire skills and gain experience without any human intervention.

Deep learning is another subset of machine learning where artificial neural networks (ANN) and algorithms are inspired by the human brain to learn from massive datasets. Deep learning consists of neural networks which have deep layers that enable learning. Using deep learning techniques, we could solve any problem which requires “critical thinking”.

Why Deep Learning Is Growing Quickly Since Past Five Years? #

At present, the amount of data that we generate every day is striving and estimated to be 2.6 Quintilian bytes. The enormous amount of data generated each day is why deep learning is thriving at a steady rate. As the deep learning algorithms need a lot of data to learn from, it increases the data creation as well as the learning capabilities in recent years.

Also, more data creation means more deep learning algorithm advantages from robust computing power and proliferation of artificial intelligence as a service. Now, let’s move on to one of the most trending deep learning techniques, object detection.

Object Detection with Deep Learning Methods #

Object detection is the procedure to describe a collection of related computer vision modules that includes identifying objects in digital pictures. It tends to locate the presence of objects with bounding boxes, classes, or types of any particular object in an image. When an image is added in the system with one or more objects; it adds a class label for each bounding box. A bounding ‘box? is an imaginary rectangle that creates a collision box for that object and aids as a point of reference for object detection.

The algorithms of object detection produce a list of object categories that are present in the image with an axis-aligned bounding box. It indicates the position and scale of every object category. The two most used methods of object detection are direct object predictions and predictions on a grid. Direct object detection detects and classifies multiple objects. It finds and classifies a variable number of the given object or an image. Few common examples of direct detection are face detection, counting, and visual search engine applications. The predictions on the grid works when the model is properly trained on datasets with full images. Here each grid point independently helps in predicting whether it contains an object’s center or not and if the bounding box dimension is for an object or what object class it is.

The two Most Popular Algorithms of Object Detection are YOLO (You Only Look Once) & SSD (Single Shot Detection)

YOLO is a real-time object detection algorithm that combines a mufti-step process, utilizing a single neural network for performing prediction and classification of bounding boxes for object detection. YOLO is heavily optimized for detection performance and runs faster when two separate neural networks classify and detect objects distinctly. So, basically, YOLO is based on the idea of segmenting an image into multiple small images.

Original diagram for Techfastly by Vin Patel.

SSD is designed for detecting an object in real-time. It utilizes a region proposal network for creating boundary boxes and utilizes those boxes for classifying the objects. So, far below the real-time processing needs, SSD speeds up the process by reducing the need for a region proposal network. For accuracy, SSD applies few improvements which include default boxes and mufti-scale features.

Original diagram for Techfastly by Vin Patel.

The Role of Focal Loss in Object Detection #

Focal Loss is something that tries to handle the class imbalance problem by assigning more weights to hard or easily misclassified examples like a noisy background picture. It reduces the loss contribution from examples and improves the significance of correcting the misclassified examples.

Focal Loss is important because sometimes training is inefficient as most of the locations are easy negatives that have no useful learning. It is proven essential for training the imbalanced datasets in object detection tasks.

Final Thoughts on Deep Learning Techniques for Object Detection #

Object detection is the key ability of most AI and deep learning tactics as mobile robots and general autonomous machines are aggressively being deployed for the purpose of object detection. Hence, object detection systems are gaining more momentum and nan-robots or robots explore the areas which have not been seen by humans yet. In such cases real-time open-world learning ability is critical.

One of the main focus of visual object detection is to detect objects that belongs to specific class targets with absolute localization in an image input. Owing to the robust development of deep neural networks, the performance of these detectors have improved rigorously over the years.

Related reading

5 Ways Companies Improve UX with AI

Vin Patel — Tue, 22 Nov 2022 16:37:38 +0000

Key takeaways

Artificial Intelligence (AI) has been bringing massive transformations in various industries in recent years.
For example, it can change the brand’s logo or the content that goes on the website.
AI is also helping UX professionals develop better user-friendly designs for websites and applications.

Artificial Intelligence (AI) has been bringing massive transformations in various industries in recent years. We see AI impacting different parts of life and making possible things that were once fantasy.

Although there is a fear that AI will take away our jobs, it is not so. On the other hand, AI will help us progress in different sectors. According to Forbes, by 2022, AI will create 58 million jobs.

So, we should try to welcome these changes and make life easier. Another sector where Artificial Intelligence is bringing revolution is the UX (User experience). In this article, you will get an idea of how AI helps in improving UX for businesses.

What is UX, and Why is it Important? #

User Experience (UX) is how one user feels when he or she interacts with any system. It can be a website, mobile application, device or software.

Most businesses are shifting their focus to creating a better customer experience. Almost all successful companies are known for an exceptional understanding of the audience’s interests and needs. To satisfy customer requirements, it is essential to have an understanding of their likes and dislikes.

Therefore, companies try to collect data on customer requirements and experience to create the right products and services. The product or service must make a good first impression. According to Forester Research, a good UX design on a website can boost conversion up to 400 percent.

So, most companies are also going for a proper UX design for their websites and services. UX is important because it helps create a positive experience for potential customers. When they have a positive experience, they remain loyal to the brand for many years. All these years, this job fell in the hand of the marketing research teams.

However, in recent years, many companies have been bringing in AI and machine learning technologies to create a better user experience for their customers.

How AI Improves UX? #

If you study the goals of AI and UX closely, you’ll find that they are similar. They are designed to study human behavior and interpret it. With the interpretation, they find out what is the next thing the customer might do.

In this way, predictive analysis is done, which is utilized by companies to map out their plans properly. But how exactly can AI help UX? Let’s take a look at a few ways.

Intelligent Algorithms #

AI works on algorithms. With the help of these algorithms, AI can collect information about website visitors or application users. Then it uses these data to optimize the system for future use. It keeps on learning and adjusting the data about the user experience. The ultimate goal is to provide users a personalized and engaging experience.

Deep Learning #

AI uses a process that we know as deep learning. With this mechanism, it can analyze thousands of different variations and generate alternatives. This information can be used in UX for adding buttons or graphics on a website or an app.

For example, with AI, we learn at what places a user clicks the most on a website. We can enhance those areas with striking graphics or a button. As most users are clicking on certain regions, AI can understand others will do the same.

With this information, UX can be enhanced by changing the website’s design by adding a few things here and there. Deep learning technologies help us get behavioral data which helps AI to improve the UX of any system.

Hotjar is a popular AI tool that helps you learn where exactly people are clicking on the website. In this way, one can easily find the spots to enhance.

Original diagram for Techfastly by Vin Patel.

Testing using AI #

UX professionals need to study various metrics to improve the whole user experience of any product or service. AI can help with this! It has the right tools that help in evaluating and track the UX metrics, such as:

Location of users
Bounce rates
User Flow
Categories or Product reviews
Session times
Pages view
and more.

With the help of these metrics, the UX professional will understand how they can improve the user experience. Google Analytic is one such product that uses AI to provide these insights into a website.

For example, a UX professional learns that most website visitors are clicking on an image of a product. So, it would be better to enhance that picture or bring it to the front page. In this way, the users don’t have to go through the pages to get the product.

No Biases in Testing #

When you use AI for UX, there are no chances of any biases. AI only shows the reality of what users want or do with the product or service. So, there is no need for A/B testing or split testing.

In A/B testing, personal reasoning can impact the result. Say there is an A/B test with a result showing that sample A has received more attention. However, the professional may still want to go for B as they may think that users will like it more.

A/B testing is not transparent. The professional is unable to see what are the reasons a person chose a particular sample. However, when you use AI for such metric testing, there are no biases.

AI will show only the reality. It will give data on what people like and also show information about why they do so. As AI relies on hard data, you will likely get a clear picture of what the users want.

In this way, UX professionals will deal with unbiased data when working with AI.

Ways Companies can improve UX with AI: #

AI can help UX professionals to understand the importance of unbiased user data. This user data is the key to creating a positive experience for the customers.

Here are the five ways AI can help improve UX:

1. Emotion AI #

Do you know AI can detect emotions as well? The mechanism is called Emotion AI or affective computing. Gadgets with emotional intelligence can understand the emotional channels of communication as well.

It helps the AI learn and interpret verbal and non-verbal human communication. For example, when a person smiles, the AI can tell whether it’s a happy or sad smile.

Original diagram for Techfastly by Vin Patel.

This technology includes:

Machine learning algorithm
Facial recognition algorithm
Data analysis

UX professionals can learn about the emotional responses to a company’s products, content, website or services with emotion AI. The technology can be easily used on the footage of CCTV recordings in stores to understand people’s emotions while looking at different products of a particular brand.

If the businesses learn the people’s reactions to their brand elements, they can take the UX professionals’ help to make necessary changes. For example, it can change the brand’s logo or the content that goes on the website.

With enhanced content on the website, the company can expect better engagement. In this way, emotional AI can help optimize different elements of a company.

One good example of emotion AI in action is Affectiva’s emotional car software. It can detect when a driver is angry or unfocused while driving. The AI installed in the system will then take control of the car and prevent any accidents.

2. Complex Data Analysis #

Even these days, UX professionals depend on heat maps and A/B testing to learn all about user engagement. However, AI can easily provide more in-depth information regarding the user experience.

UX teams can use AI deep learning mechanisms to analyze and track huge data sets. AI guarantees accurate behavioral data, which is not possible through traditional methods. Many businesses are making use of behavioral data in their UX processes.

For example, Joseph Turner, a shirt retailer, took the help of Segmentify’s AI solutions to expand their business. With AI support, they could easily understand what users liked and disliked. They made 2.6 times higher conversions with the help of AI solutions.

With the AI-produced data, they could analyze which places to optimize on their website. And this resulted in 20% more items in the basket.

3. Personalized Advertising #

Although people think UX professionals only have to deal with the website or the landing page. However, the truth is these professionals start working from the first moment a product or a service is introduced to the public.

The struggle to get better engagement starts from the first introduction, which is from advertising. Advertising helps grab the attention of users online to a certain product. When intense competition exists in the market, it is essential to make better ads.

Now a person has thousands of brands to choose from. The trick is to become a superstar in front of that person so he or she goes for your product. Moreover, with only an eight-second attention span to impress, the job is more difficult than before.

AI can help UX designers come up with more personalized and creative ads for customers. As most of the leads are now available online, social media ads are more common.

Original diagram for Techfastly by Vin Patel.

AI produces more in-depth data on the users when they interact with the brand’s social media pages. So, every time a user likes a page, comments on posts or displays any interest in any product, the AI will collect the data.

Then it will interpret the data to segregate the audiences into distinct groups with certain attributes. The UX designer can then look at this information to design the ads accordingly.

For example, the AI data shows that most of the users showing interest in the brand and its other product ads are between the ages of 16-20. So, when the next ad is planned, it should be optimized for this age group. In this way, the ad will gain better attention from this age group.

4. Chatbots #

Chatbots are the most popular ways in which AI can improve the user experience of the brand. These are virtual assistants who talk to the user when there is no real customer support.

Using natural language processing, chatbots can speak just like human beings. In this way, they help businesses double their customer service metrics.

According to Capgemini, 75% of the businesses that have used chatbots have reported a 10% increase in customer satisfaction.

Moreover, chatbots can work 24/7, unlike their human counterpart. So, it can also handle unruly customers who take out their frustration on customer care service professionals. However, if you fear chatbots will take over customer care professionals’ jobs, you are wrong.

No matter what, during any conversation with the bot, the human professional will have to take over. It helps in maintaining the human touch the users want from any company. Therefore, in the sector of customer service as well, AI can help UX professionals very well.

5. Automation #

UX testing can use automation during the research process to make work faster. AI automation can detect the problems that users experience during interacting with any product, service or website.

With the correct automation tool, the company can reach out to the user who has a problem with anything related to the company. The AI collects the data and presents it to the company. In this way, the UX professional can use the data to prepare a better system for future users.

Automation in UX testing can help in resource-saving, cost-reducing, performance testing and more.

Amazon is one of the best examples of using automation in UX. It has gained a 35% increase in its revenue with the help of personalized recommendations. You must have come across these recommendations when using the Amazon shopping website.

Original diagram for Techfastly by Vin Patel.

The AI here records all the data about you, including your past buys, age and location, to provide personalized product recommendations.

Conclusion #

Artificial Intelligence is still evolving. Now it has limited assistance for UX professionals. Although, whatever assistance AI provides is helping businesses grow rampantly.

Almost all the top business giants are using AI’s help to get their things going. It helps in saving money and also makes the work more efficient. However, no company can grow without giving a better user experience. So, recently most companies have been using AI to enhance customer experience.

From chatbots to personalized recommendations, we see a lot in all the UX elements. AI is also helping UX professionals develop better user-friendly designs for websites and applications.

Related reading

Questions and answers

What is UX, and Why is it Important? #

User Experience (UX) is how one user feels when he or she interacts with any system. It can be a website, mobile application, device or software.

How AI Improves UX? #

If you study the goals of AI and UX closely, you’ll find that they are similar. They are designed to study human behavior and interpret it.

Everything You Should Know About Cybersecurity Automation

Vin Patel — Tue, 22 Nov 2022 16:15:31 +0000

Key takeaways

The year 2020 saw the rapid spread of coronavirus across different countries of the world, with the World Health Organization announcing it as a pandemic situation.
SOAR and SIEM (Security Incidence and Event Management) are related in a couple of ways- these two technologies collect and apply data from different sources for analyzing and identifying any strange, irregular, or suspicious activities.
References: - Gartner - https://www.ccn.com/facebook-blackout-90-million-lost-revenue/ - http://web.archive.org/web/20230330051909/https://www.carbonite.com/blog/article/2015/10/downtime-costs-small-businesses-up-to-$427-per-minute.

The year 2020 saw the rapid spread of coronavirus across different countries of the world, with the World Health Organization announcing it as a pandemic situation. The governments of many countries had to declare complete lockdowns to prevent the spread of Covid-19. This forced companies to create remote workforces and operate off the cloud-based platforms to continue running their businesses.

All this to say that the cybersecurity industry has never been more important. With the ongoing coronavirus mutations that are rapidly spreading among people in different countries, remote working is forecasted not to stop anytime soon. Moreover, as remote workers will continue to be a target for cybercriminals, cloud breaches will tend to increase. So, cybersecurity remains an issue for the worldwide businesses that are now involved in remote working.

Today all businesses irrespective of their sizes are seeking methods to improve their efficacy and profitability from IT activities by investing in cybersecurity. Irrespective of how people think about automation- whether they feel it has a positive impact on their businesses or kills cybersecurity jobs- this does not divert the truth that automation is set to cause changes in cybersecurity jobs. That’s due to the many ways for achieving the set goals of businesses today, which include cybersecurity automation in their business operations, which is regarded as a way to “make the playing ground level” for caber attackers and cybersecurity pundits. The key objective is to minimize and possibly eliminate caber threats. Cybersecurity automation is meant to:

Integrate artificial intelligence (AI) with machine learning (ML) techniques into the system to improve the analytic capabilities of the businesses
Make the collection of data faster, easier, and more effective
Eradicate tedious, time-consuming tasks to relieve the IT security pundits so they can prioritize high-level tasks.

Every business organization wants to have cybersecurity automation integrated into its information infrastructure. It allows them to relocate human resources to other departments where their efforts are needed more to improve the efficiency of the company.

Cybersecurity Automation: What is it? #

Cybersecurity automation entails substituting the manual processes/operations with automated systems to enable caber threat detection and prevention while enhancing the response intelligence of the organizations to protect themselves from caber-attacks. In other words, the automation of cybersecurity helps to:

Minimizes the human involvement in handling the sensitive security activities
•Predict any potential risks and caber threats and retaliate to them effectively

Today, companies across the world have reported a rise in cybersecurity expenditure in their business operations. As per Gartner’s Forecast Analysis, the worldwide cybersecurity solutions will reach $170.4 billion in 2022. [1] Also, the cybersecurity automation industry that includes the application of AI and ML is expected to grow soon rapidly.

Cybersecurity automation is critical to all companies as caber-attackers are increasingly launching sophisticated attacks on businesses.

So, what sort of cybersecurity automation tools are required to incorporate into the security systems/processes of a business? Let us now discuss the automation solutions and platforms attested by cybersecurity experts.

Cybersecurity Automation Solutions and Platforms #

1. Robotic Process Automation (RPA) #

It involves using both physical and virtual or software robots in the industrial processes to handle repetitive duties. For cybersecurity automation, RPA uses automated systems/tools/platforms to manage or handle intellectual functions such as inspections, scanning, and low-level instance responses.

Further, it may include extraction and collection of data, basic threat detection, and various cognitive functions. Integrating RPA into businesses has many advantages from logical, threat, and compliance perspectives.

RPA increases cybersecurity efficiency by eliminating the burden of performing manual, repetitive, and tedious tasks. It assists in reducing the involvement of humans, which is considered the most significant cybersecurity vulnerability. That’s because knowingly or unknowingly, humans are the biggest threat to the caber health of businesses. So, eliminating the human aspect when handling the organizational data makes it more secure. All in all, applying RPA in businesses helps to reduce cybersecurity vulnerabilities to a large extent.

RPA assists in performing device discovery and inventory and aids in identifying the exposed attack surfaces to counter the caber attacks
It minimizes the detection of threats and time of response through the application of automated diagnosis and alert notifications
It enables automatic updates and patches rollout, hence increase the cybersecurity of businesses
This technology provides a never-ending security coverage
It refurbishes cybersecurity teams with inadequate talents
It reduces human involvement in handling sensitive data
It reduces the commitment of IT security pundits so that they can concentrate on other high-level tasks

Additionally, businesses that have adopted RPA stay compliant with various regulations including, PCI DSS (Payment Card Industry Data Security Standard) and GDPR (General Data Protection Regulation) standards. For instance, RPA can be used for notifying any data breach, and data documents, roll out informed consent alerts, and data aggregation.

Nonetheless, companies should not be overly dependent on RPA for all their cybersecurity solutions, especially those that require high cognitive and analytical abilities, which should be a mix of high-cognitive learning methods/techniques/technologies and human intervention.

2. SOAR and SIEM #

SOAR (Security Orchestration and Response) is a combination of IT technologies enhancing cybersecurity with minimal human involvement in low-cognitive activities. It is meant to advance the major cybersecurity affiliated functions like orchestration and automation of security, and security response. This technology performs three tasks by enhancing the management capabilities of risks and vulnerability, responding to cybersecurity instances, and automating the security activities.

SOAR and SIEM (Security Incidence and Event Management) are related in a couple of ways- these two technologies collect and apply data from different sources for analyzing and identifying any strange, irregular, or suspicious activities. Though these two solution technologies usually operate side-by-side to offer data security, they differ in several ways.

SIEM is a more manual operation compared to SOAR as it needs manual retaliations to perform different tasks such as the normal system upgrades and technology tweaks, rule setting, improvement in efficiency, increase detection effectiveness, and signature optimization. However, SIEM solutions are limited to detect known threats and are quite inaccurate to detect new and unknown threats.

Compared to SIEM, SOAR is more heterogeneous. It uses the SIEM alerts and automatically retaliates them. It uses cognitive techniques such as AI and ML tools for learning which enables them to detect both the known and the unknown threats. It helps to automate and enhance the security operations as well as retaliate to the caber threats and potential risks by wiping out repetitive tasks and introducing the orchestration of operations within an organization.

The main function of SOAR security orchestration helps prevent phishing attacks. Robotic automation in data collection, analysis, and improving operations reduces the time of risk detection and response time to these phishing attacks.

Both these technologies are usually applied hand-in-hand due to their similarities. By integrating both the SOAR and SIEM cybersecurity technologies ensure that the tools protect the organizations against several caber threats.

3. Certificate and Key Management #

Application of Secure Sockets Layer (SSL) certificates and keys is a cybersecurity threat for an organization. For instance, the existence of blind spots in the network, that is the poor visibility within the network where shadow certificates cause operation outages, security breaches, and network downtime.

Network outage affects businesses in a couple of ways such as noncompliance penalties, loss of revenues, loss of reputation, loss of customers, etc. A 14-hour outage cost Facebook an estimated $90 million in March 2019. [2] While the loss in revenue for small businesses due to downtime drops to the lower-but-still-significant tune of $137 to $427 per minute. [3]

Certificate and key management platforms enhance the business’ operations. These tools help the business owners to detect/identify/understand all digital certificates existing within their network irrespective of their type, date of issue, brand, or date of expiry. The certificates identified by these tools include SSL and TLS (Transport Layer Security) certificates, client certificates, IoT (Internet of Things) certificates.

Certificate management platforms enable cybersecurity automation where tedious and time-consuming operations involving manual management of thousands of keys and certificates are automated through the following:

Automating the three-month, two-month, and one-month expiry notifications of certificates
Automation of issuing, installing, renewing, and revoking certificates
Automating the creation of users
Automating the generation of reports

Having a valid and sturdy SSL certificate is a crucial element of an organizations caber health. Using SSL certificates in the modern world of technology has become so extensive that managing them is a handy task for companies. That’s because the businesses have lists of the number of SSL certificates owned by the enterprise, the issuer, the number of public keys associated with the certificates, and the people allowed to access the keys. Therefore, the certificate management tools play a critical role by automating the discovery process for the SSL certificates.

4. Custom Software Solution Development #

Every business differs in terms of operations and needs from the other. Though the available cybersecurity automation technologies are significantly critical to businesses, now companies are finding it better to build their own solutions customized to meet the peculiar requirements of their business. For instance, it might be an activity that the company’s workforce could handle, but the management would want to delegate them to a third party.

Wrap Up #

It is becoming increasingly difficult for businesses to secure themselves from caber threats and mitigate attacks due to their sophistication. Today, security teams all over the globe are facing the hurdle of effectively managing millions of notifications generated by the security systems/tools/platforms. To investigate these threats, caber experts must execute manual, repetitive and, time-consuming tasks. Fortunately, cybersecurity automation solves these problems experienced in day-to-day business operations. Automation and integration of cybersecurity in business operations are becoming significant to save resources such as data, revenue, and reputation for many organizations worldwide.

References:

Related reading

Artificial Empathy: The Last Step of Humanizing Machines

Vin Patel — Tue, 22 Nov 2022 15:35:29 +0000

Key takeaways

This scientific inclination is most transparent in the post-human rise of humanoid robots.
Presumably, the prognosis of advanced emotional AI promises empathic fridges that are able to recognize your mood and suggest appropriate food.
Erich Fromm, an expert on human nature and the nature of emotions said: *The danger of the past was that men became slaves.

The growth of Artificial intelligence has been under the spotlight of technology throughout the past decade, and emotional AI may be the very pinnacle of science or the human’s aspiration to understand and reconstruct nature and, finally, himself. This scientific inclination is most transparent in the post-human rise of humanoid robots.

The enthusiasm inwrought in the study and the development of AI resulted in rapid progress that can be felt in the average daily life today worldwide in the form of smartphones, navigation, all the way to online banking. The technology in our hands is changing and evolving every year.

Still, even though Artificial Intelligence is growing and improving (mainly through the machine learning processes), the gem that is still unattainable to the AI capacities are emotions and emotional intelligence.

Can Machines Develop Emotions? #

Many argue that machines can’t develop human-like emotions, even though there are human activities in which a human can nevermore beat even a basic computer, like calculation for instance.

The fact is that machines are master calculators because the calculation is based on a system. On the other hand, emotions can’t be translated or fit into any precise system. Even though emotional expressions and gestures can be observed and classified, these kinds of scientific methods can’t get the hang of something as spiritual as emotions.

The important factor that plays a role in the odds for AI to evolve into real ‘EI’ is the fact that grasping emotions and the way they work is so complex it remains a challenge even for the competence of our own language.

At the end of the day, robots are still complex machines that come down to operating by programming languages built on binary code.

Ethical Problems #

AI is a controversial subject on its own, but if AI becomes empowered with emotional intelligence, the complexity of the problem deepens as AI becomes more powerful and possibly more out of control.

Some of the potential suspicions include privacy issues, loss of work in certain areas, even more, widespread surveillance, security issues, “AI awakening” (meaning that the intelligence could become so autonomous that we can’t control it anymore), etc. Many studies address possible troubles regarding Virtual personal assistants (VPA), like Alex and Google Assistant, especially the ones regarding privacy.

However, the emotional development of AI could have some benefits as well, such as in medicine, education, mental health, etc. For instance, emotional AI referred to as ‘Empathic AI mirrors’ in the form of robotic playmates, showed positive results with children with autism.

Artificial Emotional Intelligence: AI And Autism #

Uses of emotional AI #

With the flourishment of empathy in AI, many industries and fields of life would reap the benefits, from the gaming and car industries to healthcare, and retail, and it would even improve cooking. Presumably, the prognosis of advanced emotional AI promises empathic fridges that are able to recognize your mood and suggest appropriate food.

One of the major benefits it would bring to healthcare is monitoring patients at all times and therefore avoiding any oversights. Some games could detect the recipients reactions and adjust the game mode and difficulty according to them. Facial recognition and emotion detection are already being applied by the apes such as Instagram.

Pro et contra #

Machines learn from us, but this time maybe we can learn something from machines. If experiencing love, anger or jealousy is the biggest challenge for them, could we presume that those are the very things that significantly define us as human beings? If the answer is yes, maybe we should take our emotional lives far more seriously and nurture them more, instead of attempting to create artificially emotional beings when we don’t understand our own emotionality.

On the other hand, maybe we will discover new knowledge about the nature of our own emotions through striving to create an artificial one. Opinions regarding AI are divided, not only among the lairs, but within the scientific circles as well, but the discussion is intensifying furthermore when we add emotional intelligence to the picture, especially because of the ethical problems.

Lovotics

A CBC article titled ‘Can a robot love you back?’ introduces a robot called lovotics (which is a coinage of love and robotics), a robot whose aim is not completing some trivial assignments, but to practice the very crown of emotions - love.

Dr. Samani, the director of AI technology in Taiwan, even studies reciprocal love between humans and robots. These love-oriented robots look more like toys than advanced machines, but, in love, it’s always more important what’s on the inside. In this case, it’s a sophisticated endocrine system that navigates digital hormones based on interactions with humans.

Samani himself says that ‘a robot is a piece of machine’ and that it is actually not capable of ‘experiencing’ love, but what it does is more of a simulation of love or affection. Therefore, the robot remains within the limits of completing a function. It is more than questionable whether if we could use the word love to describe this machine programmed to give off an illusion of affection, at best.

Conclusion

It seems that we eagerly embraced the not so long ago future-like technologies entering our everyday lives in the forms of phones, unnecessarily diverse home devices, etc. But the truth is that maybe the world has immersed far too easily into creating new forms of consciousness and interacting with them. Additionally, bear in mind that all those mass-produced trivial devices are made of plastic that is one of the most urgent environmental threats today.

As we just might be witnessing the very pinnacle of this technological evolution, maybe it is the perfect time to consider all the pros and cons, as well as all the potential repercussions of this ground-breaking, but also possibly vain and dangerous invention, regardless of whether if robots are our friends or our enemies.

Erich Fromm, an expert on human nature and the nature of emotions said: The danger of the past was that men became slaves. The danger of the future is that men may become robots. True enough, robots do not rebel.

Related reading

Questions and answers

Can Machines Develop Emotions? #

Many argue that machines can’t develop human-like emotions, even though there are human activities in which a human can nevermore beat even a basic computer, like calculation for instance.

A CBC article titled ‘Can a robot love you back? #

’ introduces a robot called lovotics (which is a coinage of love and robotics), a robot whose aim is not completing some trivial assignments, but to practice the very crown of emotions - love.

Transforming Global Economy through Disruptive Cloud

Vin Patel — Tue, 22 Nov 2022 12:49:40 +0000

Key takeaways

In an age of emerging technology, staying abreast is impossible.
Other examples of companies that evolved through digital transformation include Walmart, Disney, PayPal, National Geographic, and Starbucks.
With time, technology has evolved faster than anything else and has outspread uncountable intelligent ways to create businesses that bring disruptive changes on the global economic scale.

In an age of emerging technology, staying abreast is impossible. It is common to find that the leading value creators of recent years are all reliant on technology. It is undoubtedly a major disruptive force across the entire global economy. Almost every factor that contributes to the global economy is affected by technology. Instead of being a layer of the economy, technology is becoming the heart of it. The most significant evolutions in the economy are often accompanied by process of “creative destruction,” in which profit pools shift, industries restructure, and incumbent firms are replaced. According to Joseph Schumpeter, an economist from the early 20th century, such changes entail “creative destruction.” As you read further in this article, you will see how emerging technologies have a disruptive byproduct on the global economic scale and a brief on the growth of cloud infrastructure in non-technical companies.

Disruptive technologies often showcase a rapid change in price and performance compared to alternative approaches. They change how business is done, and needs are met despite entering an established market. Disruptive technology allows people to break down barriers and empower people who may have been ineffective. It can also spread more interaction and responsiveness to communications platforms.

Technology Drives The Future of All Industries! #

When you think about all leading industries, the only buzzword that comes to mind as the driving force is ‘Technology’. Technology is in itself the number one industry. Any industry’s expected success is largely due to the way technology has altered and will continue to alter. In the past decade, cloud computing, machine learning, artificial intelligence, and big data have been cited as the sub-sectors that have made the biggest impact and are continuing to do so. In terms of massive changes witnessed among multiple industries, technology is leading in almost every one of them. In the energy industry, technology has shown miracles in the form of energy trading. It increases the use and efficiency of sustainable energy sources. On the other hand, in financial companies, it’s financial technology or cryptocurrency that’s leading the way. Healthcare industries hold a strong output due to implementing technology using subsectors such as biotechnology, health data management, personalized health solutions, advanced insurance solutions, etc. Also, disruptive technological methods have introduced growth and sustainability in retail, construction, and hospitality.

Digital Transformation: A Disruptive Technology Method That Brings Massive Change To All Industries #

Digital transformation, also known as digital business transformation, refers to the strategic use of digital technologies for streamlining and accelerating business processes. Many technical and non-technical companies are accepting digital transformation for big growth. For example, IKEA has recently put one step forward in the field of technologically growing by digitally transforming through an online platform. Without this transformation, IKEA would not have opened another sales channel. Other examples of companies that evolved through digital transformation include Walmart, Disney, PayPal, National Geographic, and Starbucks. A study performed on 1,200 mid to large-sized US companies found that the average budget for digital transformation in 2018 was $14 million, and spending on digital transformation worldwide is expected to reach $1.78 trillion by 2022. Digital transformation is expected to add $100 trillion to the global economy by 2025. Digital transformation can have varying impacts on your company. However, digitization often brings these benefits:

Reduced operating costs
Optimized operations
Increased revenue
Improved customer satisfaction
Lowered fragmentation

Cloud-based Infrastructure #

Since the mid-2000s, cloud technology and platform business models have driven the technology industry globally. Among all technological advancements, cloud technology is one such strategy that has now spread to every big and small business and has played a major role in the economic sector. In the past few years, cloud computing has become increasingly popular for many reasons. It allows almost any application or service to be delivered over the Internet or a network, without the need for expensive local hardware or software. The market value of top cloud-native infrastructure software vendors is increasing rapidly. With cloud-based services, businesses can streamline performance and support growth and development with a much more scalable and reliable IT infrastructure. Historically, cloud computing has gone through multiple phases - it started with grid (or utility) computing, then came the “application service provision” phase, followed by what is now called SaaS. Software companies using cloud-native infrastructure can pay for cloud computing resources as they use them, rather than making expensive capital investments. Every new trend in cloud services has possibly changed the way a business runs in terms of product consumption and services, data storage, and communication. Additionally, cloud computing allows users to access their data remotely by moving data to the cloud. The enterprise to consumer expansion of cloud services does not appear to be slowing down any time soon. Companies can launch new ventures within the cloud without spending more on the infrastructure or employees. This strategy allows competing with larger, existing incumbents. Companies that use cloud technology and platform business models mainly apply them using data and analytic. The ability to monitor and analyze large data sets can help businesses better respond to real-time consumer feedback and improve their product offerings. Apart from cloud-based services, a few latest disruptive technologies are reported to have developed over the last decades. Such disruptive technologies include artificial intelligence, energy storage, next-generation genomics, blockchain, E-commerce, streaming entertainment, social media networks, GPS systems, smartphones, ride-sharing apes, etc.

Conclusion #

In summary, you now know that high-technology production was limited to financially developed countries in the past. Today, disruptive technology acts as a force that is more easily transferred to developing nations, where high-teach products can be combined with low costs. This displays the extremely rapid transitions of time and technology. With time, technology has evolved faster than anything else and has outspread uncountable intelligent ways to create businesses that bring disruptive changes on the global economic scale.

Related reading

Predictive Analytic AI-Powered SaaS Platform: Explained

Vin Patel — Tue, 22 Nov 2022 11:09:45 +0000

Key takeaways

The world as we know it is changing thanks to predictive analytic enabled by AI technology.
Attempting to ‘predict’ previous events can demonstrate its usefulness.
Developers that want to stay ahead of the curve should consider this and brainstorm ways to include predictive analytic AI into their products.

The world as we know it is changing thanks to predictive analytic enabled by AI technology. Today, every industry has access to a huge amount of data. Finding gems that solve your business challenges, on the other hand, is as tough as sailing across the ocean in a fleet. The majority of existing technologies lack the ability or speed to analyze large data crowds and get insights from them. Predictive analytic tools that are AI-driven come in handy in this situation. These solutions have the potential to provide vital data insights for each company. Such SaaS technologies can be extremely beneficial for businesses with limited finances and time-sensitive initiatives.

In general, SaaS systems are diversified and growing in popularity with each passing day. Customer success teams are common in SaaS organizations, which is a well-known fact. And developers believe that boosting client satisfaction will boost income. Predictive analytic and SaaS systems can boost your company’s success.

Key Statistics #

According to McKinsey, AI may unlock $2.6 trillion in marketing and sales value from $7.2 billion in 2020, representing an almost 200 percent growth.
The global predictive analytic industry is expected to expand to $21.5 billion by 2025.

AI-Powered Predictive Analytic SaaS Platforms’ Techniques #

Here are six of the most popular predictive analytic SaaS platforms to get you started.

Quantitative Research #

Quantitative research is a technique for identifying patterns of behavior that relies on mathematical and statistical modelling. It is numerically based and can be applied to any field in which numbers can be calculated.

Machine Learning #

All advanced AI technology is built on this foundation. Machine learning helps machines to increase their ’thinking’ abilities by learning from the patterns they process.

Neural Networks #

Neural networks are systems that technologically imitate human neural networks. They’re utilized to solve difficult AI challenges. The system is named after how it is arranged, comparable to human brain networks. It facilitates machine learning by utilizing specific algorithms.

Predictive Modelling #

Predictive modelling employs past event statistics to forecast the future. It’s vital to keep in mind that this type of modelling can be used in any scenario. Attempting to ‘predict’ previous events can demonstrate its usefulness.

Google Analytic #

Google Analytic is a widely used data analytic platform that uses machine learning from Google to provide insights into your data. The tool’s AI-powered advanced analytic form the foundation of many firms’ reporting.

Helix #

Helix creates technology and SaaS products that connect diverse datasets and extract advanced research insights from big data using artificial intelligence, machine learning, and other new technologies.

Predictive Analytic Platforms Powered by AI Are the Future of Technology #

AI-driven technology is causing a lot of buzz right now, and for a good reason. Although most people are unaware of it, AI is present in almost every aspect of our life, including technology. This presence is only growing because, according to McKinsey research, AI can handle more than 400 long-standing business difficulties, even at the current stage of development.

Small businesses and entrepreneurs can use the SaaS predictive analytic system to take advantage of AI processing power. In today’s highly competitive economy, this service is very important. Without a doubt, technology will advance in response to the increased need.

It’s vital to remember that new platforms must be compatible with existing technology. There are also legal ramifications to take into account. To protect both the developer and the user from legal action, all SaaS systems must include a well-thought-out agreement. As AI technology becomes more widely used, the problem becomes more complex.

How Do Predictive Analytic SaaS Platforms Deliver Insights? #

The main benefit of using predictive analytic SaaS tools for your business is that you might receive results that go beyond merely showing you what’s happening right now. They also advise on how to deal with the circumstance. As a result, a predictive analytic AI-powered platform generates real-time insights that may be deployed to achieve a competitive advantage.

The following are some examples of how predictive analytic insights might be applied in business situations:

Creating tailored marketing efforts for a variety of audiences
Developing product enhancements that anticipate changes in client needs allows you to stay ahead of the curve.
Analyzing the competitors to come up with a unique selling proposition
Predicting business results in the face of changing global economic conditions
Identifying areas for improvement and automating many operations within the company.
Getting the most out of available resources

Bottom Line #

From e-commerce to healthcare, AI is revolutionizing every industry. It’s difficult to imagine a company succeeding without big data. AI-powered SaaS services will continue to gain popularity as it is impossible to derive strong insights from data using only human intelligence. With machine learning technology improving with each application, we can expect breakthroughs soon. Developers that want to stay ahead of the curve should consider this and brainstorm ways to include predictive analytic AI into their products.

Related reading

What is the Best Way to Use Autograd to do Gradient-Based

Vin Patel — Tue, 22 Nov 2022 11:07:46 +0000

Key takeaways

In machine learning, testing a prediction model is very essential.
- Gradients are included in the package to aid in the completion of different matrix transformations.
Let us have a look at how it may be applied to logistic regression.

In machine learning, testing a prediction model is very essential. In the majority of circumstances, we assess the prediction models by maximizing different loss functions with respect to the model’s parameters. We can use gradient descent to optimize these loss functions when going in-depth. Autograd is a Python tool that can assist us in optimizing numerous functions related to machine learning models or basic mathematics. In some cases, this package can be used in place of the well-known NumPy library. We will look at the Autograd package and see how it can be utilized in this article. Let us start with the definition of Autograd.

What is Autograd? #

Autograd is a gradient-based optimization library. Autograd is a Python module that can help us discern between Bumpy and Python code. Features like loops, ifs, recursion, and closures and be handled by the Autograd package. Apart from these, it can also have many step-wise derivatives of functions. Most of the time, we see that the Autograd package supports both the backpropagation and forward propagation methods, which are also known as reverse-mode differentiation and forward mode differentiation in mathematics, respectively. This means that this package can pick gradients from a function with scalar values.

We may mix and match these two ways using this package. This package’s main goal is to enable us to execute gradient-based optimization. With this package, we may utilize gradients for the following operations and functions:

Mathematical operations - a variety of gradients are used to optimize the majority of mathematical operations.
A wide range of array manipulations can be performed using different gradients available.
Gradients are included in the package to aid in the completion of different matrix transformations.
Gradients for a wide range of linear algebra and Fourier transform algorithms are included in the package.
Modules are available for N-dimensional convolutions.
The use of complex numbers is permitted.

Using the following lines of code, we can install this package:

!pip install autograd

We are now ready to use this package for our work after installing it in the environment.

Autograd Implementations #

Now we will have a look at some of the functions of the Autograd package.

Evaluating the hyperbolic tangent function’s gradient #

We will look at how to evaluate the gradient of the Tanh function in this implementation. Let us start by defining the function.

import autograd.numpy as agnp
def tanh(x): 
 y = agnp.exp(-2.0 * x)
 return (1.0 - y) / (1.0 + y)

We used the module autograd.bumpy, which is a NumPy wrapper in the autograd package, in the preceding code. We have also built a tan function. Take a look at the above-mentioned function’s gradient.

from autograd import grad 
grad_tanh = grad(tanh) 
grad_tanh(1.0)

Output:

In the above code, we created a variable to contain the tanh function, and for evaluation, we imported a function named grad from the autograd package. Let us look at the difference in finite range.

(tanh(1.0001) - tanh(0.9998)) / 0.0002

Output:

0.6300094945832502

We also have the option of differentiating the functions as many times as we like by calling a module called elementwise grad.

from autograd import elementwise_grad
x = agnp.linspace(-10, 10, 100)

In the preceding code, we called our module and constructed an array with random values ranging from -10 to 10. We will now try to draw derivatives for the previously given function.

plt.plot(x, agnp.tanh(x),
 x, elementwise_grad(agnp.tanh)(x),
 x, elementwise_grad(elementwise_grad(agnp.tanh))(x),
 x, elementwise_grad(elementwise_grad(elementwise_grad(agnp.tanh)))(x),
 x, elementwise_grad(elementwise_grad(elementwise_grad(elementwise_grad(agnp.tanh))))(x)) 
plt.show()

Output:

Original diagram for Techfastly by Vin Patel.

We can see how the function varies for our defined x here.

Note that at the start of the implementation, we created a tanh function, and in the most recent iteration, we used the autograd function.

We looked at how to utilize autograd modules in the previous example. Let us have a look at how it may be applied to logistic regression.

Logistic regression loss function optimization #

Let’s define a sigmoid function.

def function(x):
 return 0.5 * (agnp.tanh(x / 2.) + 1)

Defining a function for predictions:

def function2(w, i):
 return function(agnp.dot(i, w))

Defining loss function for training:

def loss_function(w):
 preds = function2(w, i)
 label_probabilities = preds * targets + (1 - preds) * (1 - targets)
 return -agnp.sum(agnp.log(label_probabilities))

Defining the weights and input:

i = agnp.array([[0.52, 1.12, 0.77], 
 [0.88, -1.08, 0.15],
 [0.52, 0.06, -1.30],
 [0.74, -2.49, 1.39]])
w = agnp.array([0.0, 0.0, 0.0])

Defining the target:

targets = agnp.array([True, True, False, True])

Defining gradient function for training loss:

training_gradient = grad(loss_function)

Optimization of weight using the gradient descent:

w = np.array([0.0, 0.0, 0.0])
print("loss in initial:", loss_function(w))
for i in range(100):
 weights -= training_gradient(w) * 0.01
print("loss after training:", loss_function(w))

Output:

loss in initial: 2.772588722239781

loss after training: 1.067270675787016

We have seen an example of logistic regression for weight optimization that we pushed in between utilizing the autograd package’s components.

Related reading

Machine Learning 101 Continued IV- Insurance Data set

Vin Patel — Tue, 22 Nov 2022 10:18:44 +0000

Key takeaways

In the previous post, I shared with you my trial with six different machine learning models and being able to measure their accuracy scores.
As this is a common problem faced in machine learning, we will use a pre-programmed command ‘Label-Encoder()’ method.
!Diagram: Diagram Original diagram for Techfastly by Vin Patel. The above table does provide evidence that data points contained in strings when converted into numeric values have assisted in increasing the accuracy of the algorithms.

Preparing for a Real-Life Machine Learning Project #

In the previous post, I shared with you my trial with six different machine learning models and being able to measure their accuracy scores.

In the preceding Insurance dataset post, I had left out Strings data (text). In this post, we will revisit the scores of the models after including the string data, which will help us in determining the values in these strings that the algorithms would enjoy.

As always, we follow the following series of steps:

Step 1- Load the Data.

Step 2- Check Data Types & Standardize the data types.

Step 3- Split the data into Test & Training sets.

Step 4- Run multiple algorithms and compare the Accuracy Score.

Step 1: Load the Data #

This time, we load the large CSV file which has both strings and integers. We have also kept the same logic based on which we arrived at the different labels of ‘Excellent/Good/Poor’ drivers.

As before, we use the print() command to verify if the pd.read_csv works successfully. The screenshot below proves that this is the case.

Original diagram for Techfastly by Vin Patel.

We now wish to view the different data types the various ‘features’ are in.

Pandas support a data type method (d.type) that allows us to query the data type in a single line of code. To verify whether we executed the d.type command, we once again rely on the print() command. The output on the screen confirms the data types in our file to be a mix of integers, float, and objects (strings). Algorithms in scikit-learn understand only numeric data. So, we need to convert all object data into numeric data.

As this is a common problem faced in machine learning, we will use a pre-programmed command ‘Label-Encoder()’ method. Using this method converts string data into numerical data.

Original diagram for Techfastly by Vin Patel.

We implement the ‘Label-Encoder’ in the following manner:

From sklearn import reprocessing

#import the necessary module from sklearn import reprocessing# create the Labelencoder object = reprocessing. LabelEncoder()#convert the categorical columns into numericencoded_value = le.fit_transform([“basic”, “premium”, “extended”])print(encoded_value)

Please note, this method assigns the numeric values to the classes. The classes are assigned with values in the order of their first letter, as in the original list.

The output on the bottom right of the screen tells us that ‘basic’ transformed into 0, ’extended’ transformed into 1, and ‘premium’ transformed into 2.

Original diagram for Techfastly by Vin Patel.

Now, to transform all the different columns and all the unique entries, we use the following code:

print("‘State Code’: “,file[‘State Code’].unique()) print("‘Education: “,file[‘Education’].unique()) print("‘Gender: “,file[‘Gender’].unique()) print("‘Location Code: “,file[‘Location Code’].unique()) print("‘Marital Status: “,file[‘Marital Status’].unique()) print("‘Sales Channel’: “,file[‘Sales Channel’].unique()) print("‘Vehicle Class: “,file[‘Vehicle Class’].unique()) print("‘Vehicle Size’: “,file[‘Vehicle Size’].unique())

And upon running the program, we can view the unique classes for each column in the screenshot below:

Original diagram for Techfastly by Vin Patel.

We now use the encoded_value = le.fit_transform([“basic”, “premium”, “extended”]) command, and convert all the strings into numeric values. Upon running the program, we observe that there are no errors:

Original diagram for Techfastly by Vin Patel.

To verify if the data conversion has taken place appropriately, we use the head() function to examine the first few lines of our data set. To replicate all the remaining columns of string, we use the following code:

file[‘State Code’] = le.fit_transform(file[‘State Code’])file[‘Coverage’] = le.fit_transform(file[‘Coverage’])file[‘Education’] = le.fit_transform(file[‘Education’])file[‘Gender’] = le.fit_transform(file[‘Gender’])file[‘Location Code’] = le.fit_transform(file[‘Location Code’])file[‘Marital Status’] = le.fit_transform(file[‘Marital Status’])file[‘Sales Channel’] = le.fit_transform(file[‘Sales Channel’])file[‘Vehicle Class’] = le.fit_transform(file[‘Vehicle Class’])file[‘Vehicle Size’] = le.fit_transform(file[‘Vehicle Size’])

Upon printing the file (print(file.head())), we can see that all strings got converted to numeric outputs.

Original diagram for Techfastly by Vin Patel.

Step 2: Train the Data #

Like we did in the previous exercise, we load the file into an array.

Once the data loads into an array, the data can split into ‘Training’ data and ‘Testing’ data. As we did last time, we will train the data on 80% of the data set and test it on the remaining 20%.

To do so, we use the following commands:

array = file.values

X = array[:,0:17]

Y = array[:,17]

Upon running the program, there are no errors reported, as shown in the screenshot below:

Original diagram for Techfastly by Vin Patel.

To confirm the correct selection of X (features) and Y (labels), we use the print command:

print(X)

Original diagram for Techfastly by Vin Patel.

print (Y)

Original diagram for Techfastly by Vin Patel.

We re-use the commands that we used the last time to split the data to ‘Train’ and ‘Test’. We import the module from the sklearn library. We use a ‘random state’ in our data splitting to ensure that the different algorithms we use receive the same amount of data to avoid any bias. from sklearn.model_selection import train_test_split X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=0.80, random_state=1) Running the library does not result in any errors, as evidenced in the screenshot.

Original diagram for Techfastly by Vin Patel.

At this point, our data has successfully split into Training and Testing data.

Step 3: Run Multiple Models #

Again, as we did in the last post, we use the following six models for testing and evaluation:

Linear Models:

o Logistic Regression (LR): The model looks for a pass/fail relationship between variables.

o Linear Discriminant Analysis (LDA): Looks for a linear relationship between two or more classes of objects.

Non-Linear Models #

o K-Nearest Neighbors (KNN): A pattern recognition used for both regression and classification

o Classification and Regression Trees (CART): Use of ‘decision trees? to go from observations to conclusions

o Gaussian Naive Bayes (NB): Calculation of pre and post probabilities approach

o Support Vector Machines (SVM): Supports labeling of unlabeled data in an unsupervised environment

To run these six models concurrently, we can treat these models as a list of models to be used using [].

Add the following to your code:

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

#importing the models

models = []

models.append((‘LR’, LogisticRegression(solver=‘liblinear’, mufti_class=‘ovr’)))

models.append((‘LDA’, LinearDiscriminantAnalysis()))

models.append((‘KNN’, KNeighborsClassifier()))

models.append((‘CART’, DecisionTreeClassifier()))

models.append((‘NB’, GaussianNB()))

models.append((‘SVM’, SVC(gamma=‘auto’)))

#adding the models

To append these models to a list named models, we must import the models from sklearn. We once again run our code and find no errors. Hence our code should look like as follows:

Original diagram for Techfastly by Vin Patel.

Step 4 - Check Accuracy Score for each Model #

To check the accuracy of each of the six models, we create a list titled results and names.

results = []

names = []

for name, model in models:

fold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)

cv_results = cross_val_score(model, X_train, Y_train, cv=fold, scoring=‘accuracy’)

results.append(cv_results)

names.append(name)

print(’%s: %f (%f)’ % (name, cv_results.mean(), cv_results.std()))

We will also have to import:

from sklearn.model_selection import cross_val_score (to Evaluate a score by cross-validation) and

from sklearn.model_selection import StratifiedKFold (Provides train/test indices to split data in train/test sets)

Upon running the program, we can see the results for the different models we used in the screenshot below:

Original diagram for Techfastly by Vin Patel.

If we compare the results of the previous exercise and this, we will find that with an increase in data ingestion with the algorithm, the accuracy of predictions has increased.

Original diagram for Techfastly by Vin Patel.

The above table does provide evidence that data points contained in strings when converted into numeric values have assisted in increasing the accuracy of the algorithms.

Related reading

Machine Learning Operations (MLOps) – What is it, Importance

Vin Patel — Tue, 22 Nov 2022 09:24:17 +0000

Key takeaways

MLOps, or DevOps for machine learning, is a set of practices that combines software development (Dev) and operations (Ops).
This can be done using various metrics such as accuracy, precision, and recall.
If you are looking for ways to improve your machine learning workflow, then MLOps is worth considering.

MLOps, or DevOps for machine learning, is a set of practices that combines software development (Dev) and operations (Ops). The aim is to shorten the systems development life cycle and deliver value faster.

In simple terms, it can be said that MLOps is the application of DevOps principles to machine learning. It helps manage, monitor, deploy, and maintain machine learning models throughout their entire lifecycle.

What is MLOps or Machine Learning Operationalization? #

Machine Learning Operationalization (MLOps) is the combination of tools and processes to manage end-to-end workflows for developing, training, testing, and deploying machine learning models.

MLOps aims to make it easier for data scientists and machine learning engineers to work together by automating the process of building, training, and deploying machine learning models.

Types of MLOps #

Here are five types of MLOps:

Continuous integration: #

Continuous integration automatically builds and tests the code whenever a change is made. This helps to identify errors early and fix them quickly.

Continuous delivery: #

In continuous delivery, the code is automatically deployed to a production environment after it has been tested. This lowers the possibility of errors and guarantees that the most recent modifications are always accessible in production.

A/B testing: #

A/B testing compares two or more machine learning models by deploying them in a production environment and measuring their performance.

Canary release: #

A canary release is slowly rolling out changes by releasing the machine learning model to a small number of individuals first before progressively expanding the user base over time.

Blue/green deployment: #

Blue/green deployment is a way of quickly rolling back changes to a machine-learning model by deploying two model versions in parallel. The version not being used (the green version) is updated with the latest changes, while the other version (the blue version) remains unchanged. If there are any errors in the new version, they can be quickly rolled back by switching to the other version.

The MLOps Project Flow’s Five Steps #

The MLOps project flow can be divided into five steps:

Data collection: #

The first step is to collect data from various sources. Then, machine learning models can be tested and trained using this data.

Data reprocessing: #

The next step is to reprocess the data. This includes tasks such as cleaning, normalizing, and transforming the data.

Model training: #

The next step is to train machine learning models on the preprocessed data. This can be done using various algorithms and techniques.

Model evaluation: #

The next step is to evaluate the trained models. This can be done using various metrics such as accuracy, precision, and recall.

Model deployment: #

The final step is to deploy the trained models into a production environment. Again, various tools and technologies can be used to do this.

Benefits of Machine Learning Operationalization Process #

There are many benefits of MLOps, which include:

Improved collaboration between data scientists and engineers: #

MLOps helps to improve collaboration between data scientists and engineers by providing a framework for them to work together.

Reduced cycle time: #

MLOps helps reduce the cycle time by automating the building, training, and deploying of machine learning models.

Increased accuracy: #

MLOps helps to increase the accuracy of machine learning models by providing a way to test and deploy them in a controlled environment.

Improved scalability: #

MLOps helps improve the scalability of machine learning models by providing a way to deploy them in a production environment automatically.

Reduced risk: #

MLOps helps reduce the risk of errors by building, testing, and deploying machine learning models automatically.

Increased efficiency: #

MLOps helps to increase the efficiency of the machine learning development process by providing a way to automate it.

MLOps Tools #

Numerous tools are available to assist with the implementation of MLOps, which include:

Jenkins: Jenkins is a tool that can be used to automate the process of building, testing, and deploying machine learning models.
Puppet: A puppet is a tool that can be used to manage the configuration of machine learning environments.
Chef: A chef is a tool that can be used to automate the process of provisioning and configuring machine learning environments.
Ansible: A tool that can be utilized is called Ansible to automate the process of deploying and managing machine learning environments.
Google Cloud Platform (GCP): GCP is a cloud platform that provides various services for building, training, and deploying machine learning models.
Amazon Web Services (AWS): AWS is a cloud platform that provides various services for building, training, and deploying machine learning models.
Kubernetes: Kubernetes is a tool that can be used to orchestrate the deployment of machine learning models.
Docker: A technology called Docker can be used to package and deploy machine learning applications.

Final Thoughts #

MLOps is a great way to improve the collaboration between data scientists and engineers, reduce the cycle time, increase the accuracy of machine learning models, and improve scalability. If you are looking for ways to improve your machine learning workflow, then MLOps is worth considering.

Related reading

Predictive Care Enabled by AI: An Overview

Vin Patel — Fri, 18 Nov 2022 14:23:13 +0000

Key takeaways

Data is becoming increasingly important in driving innovation.
In dharma and medicine, you may estimate how long a clinical trial would take in each population based on the population’s disease occurrence, previous trial participation, and the number of trial sites required.
It’s also important to remember that no model is completely error-free or capable of replacing experts.

Data is becoming increasingly important in driving innovation. Industries collect an increasing amount of data as technology progresses and changes human behavior. The value of that data comes from our ability to extract it in order to gain actionable, relevant insights - insights that can help us work smarter by accelerating better outcomes while remaining equitable and inclusive of the people we serve.

Predictive care, which combines a variety of statistical approaches such as predictive and data modelling, machine learning, artificial intelligence (AI), and deep learning to assess facts and forecast future unknown outcomes, is of particular interest. It is widely employed in a variety of fields, including business management, retail, travel, and sports, as well as science, healthcare, and pharmaceuticals.

Mastering the element of predictive care will be a vital component in improving the quality of care for patients as artificial intelligence (AI) technology becomes more ubiquitous in the medical field.

Providers must operate efficiently, effectively, and precisely in order to give better care to individuals. Integrating predictive care into the healthcare system could help achieve those objectives while also providing some relief to medical practitioners. On the other hand, predictive analytic systems must be appropriately trained to minimize future care inequities.

What is Predictive Care? #

Predictive care uses modelling and forecasting approaches to determine what is highly likely to happen in the future. Physicians, researchers, medical specialty societies, pharmaceutical corporations, and everyone else involved in healthcare can then use those projections to deliver the best possible care for specific patients.

Predictive care uses a number of modelling techniques, ranging from basic hierarchical linear models to modern machine learning and AI algorithms, to produce the most reliable predictions possible. Machine learning is a subset of AI that involves generating models to describe data with greater precision as more data is introduced. AI is a collection of technologies that can think and adapt itself.

Historical data is used in predictive care to train algorithms that can predict future outcomes. These models can build predictive models and find patterns or trends in data using statistics, machine learning, and/or AI. Understanding simple or complex links between several variables and one or more outcomes is a key insight. It can, for example, provide a credit rating in finance based on a history of loans or payments as well as a debt-to-income ratio. It can measure the risk of acquiring diabetes or other diseases in a clinical setting by taking into account health status, diet, age, and family medical history.

How Does Predictive Care Work? #

Predictive care uses machine learning and data mining technology to analyze risks and forecast outcomes. The system will use historical data and statistical modelling to determine individual patient outcomes.

Predictive care is showing promise in a number of medical fields as technology advances. Scientists at James Cook University have made headway in finding techniques to keep premature babies alive using predictive care.

Doctors can assess each premature newborn’s hazards and assign a score using the Neonatal Artificial Intelligence Morality Score (NAIMS). The risk score assists doctors in determining the optimal course of therapy for each infant as well as any potential risks that may occur along the way.

Predictive Care In Healthcare #

Predictive measurements can be beneficial in a variety of areas within healthcare. For example, in a supply chain, current in and outflow could be used to predict when resupply is required. You might forecast when machinery would fail in manufacturing to assist decide service intervals. In dharma and medicine, you may estimate how long a clinical trial would take in each population based on the population’s disease occurrence, previous trial participation, and the number of trial sites required.

Payers can profit from predictive analysis by projecting service use for a specific group. Identifying solutions to save costs and increase revenue could be beneficial to healthcare administration. Another intriguing area is precision medicine, which could anticipate treatment effectiveness based on a patient’s medical history or genomic results, allowing for the avoidance of unneeded therapies and the provision of high-quality care.

Predictive algorithms are used in medicine to help doctors make more personalized and targeted treatment decisions for specific patients. We and others have utilized image algorithms on medical images to comprehend better aggressive diseases and guide therapies, including forecasting better patient outcomes. Particularly in digital pathology, image comprehension and processing have come a long way. During the epidemic, we saw the emergence of artificial intelligence (AI) systems to aid clinicians in making quick diagnoses.

Benefits of Predictive Health Care #

Predictive care in healthcare employing machine learning tools and techniques has a number of benefits, including boosting company efficiency and supporting clinicians in providing health care services to each patient.

Choosing an Appropriate Location for New Clinics and Hospitals #

It takes a lot of effort to open a new clinic or medical center. The first step is to choose the ideal location for the business. If management makes a mistake here, it could have ramifications throughout the company, resulting in losses. In order to deliver valuable services to the public, a hospital must be located closer to the target audience, be conveniently accessible, and carve out a niche among competitors.

Predictive care can assist management in assessing potential sites based on a variety of factors. Predictive care in healthcare can show you the benefits and drawbacks of opening a clinic in a specific area by looking at how competitors are doing and examining the location’s accessibility (among other factors).

Outbreaks and Pandemics: Predicting and Analyzing the Risk #

The pandemic brings up another significant role of predictive care in healthcare research articles. Researchers and scientists can forecast outbreaks and the spread of contagious diseases using historical and real-time data. This can assist governments in taking proper actions to manage the outbreak and reduce the number of people killed in society.

Different governments employed a variety of such techniques to track the spread of the coronavirus in an attempt to limit it and reduce the number of impacted areas.

Improving Business Operations for Efficient Hospital Administration #

Hospital administration is possibly the most difficult of all. Even little blunders and misunderstandings might result in life-threatening scenarios. Everything has to be perfectly in tune and streamlined. However, saying it is easier than doing it.

Using sophisticated technology, however, is conceivable. In the case of healthcare insurance, predictive care has resulted in patients, hospitals, and insurance companies working together to process claims and avoid difficulties. Delays in processing and approval of claims can be eliminated, allowing patients to receive treatment more quickly.

Healthcare centers may have a stress-free work environment by automating recurrent processes, allowing staff to focus on providing courteous and efficient patient service.

Conclusion #

It is past time for medical companies to include predictive care into their healthcare systems and develop models that can provide reliable insights and forecasts. It’s also important to remember that no model is completely error-free or capable of replacing experts.

Related reading

Questions and answers

What is Predictive Care? #

Predictive care uses modelling and forecasting approaches to determine what is highly likely to happen in the future.

How Does Predictive Care Work? #

'PeopleLens', Microsoft's Newest AI Technology, is Helping

Vin Patel — Fri, 18 Nov 2022 14:22:22 +0000

Key takeaways

For visually impaired children, social interaction has always been a barrier because of their inability to spot the real-time gestures made by the speakers in conversations.
The users are able to sense the position of the speaker using an actual 3D map which keeps on reconfiguring in real-time with movements and changes in the gaze of the speaker.
With the leading giant innovating in this area, we can expect to see more companies sinking their feet in these waters and putting technology to a good cause.

For visually impaired children, social interaction has always been a barrier because of their inability to spot the real-time gestures made by the speakers in conversations. With experience over the years, blind children are able to develop better communication and social skills.

What if there was a way to instill confidence and increase social communication among the visually impaired kids? What if there was a technology that would enable them to converse with peers in a better manner, giving them a better sense of the gestures made by the speakers?

The advanced implications of AI have enabled this dream technology to be a reality as Microsoft unveiled its new device called ‘PeopleLens,? which makes the lives of blind children a lot easier and socially active.

What is PeopleLens’ How Was It Developed? #

PeopleLens is an advanced AI device that allows children with low vision or vision impairments to interact and socialize among groups. The technology was developed by the leading giant Microsoft and is aimed to bring out a revolution in how the visually impaired perceive and interact with the people around them.

The researchers combined studies from psychology and language and speech therapy and monitored the paralympic participants, closely observing how they were able to connect with their peers. This led to the formulation of this gadget that uses People Map and enables the children with less vision to locate and track the voices of people in their surroundings.

How Does It Work? #

The technology works through an augmented reality system and complex machine learning algorithms that send identification and judgment signals. The people must register themselves by clicking pictures at certain angles through the phone connected to this AR device. The pictures are then converted into a series of vectors that are placed over a virtual map or mental map. This allows the child to get an idea of the position and distance of the people he/she is interacting with.

The images of people sitting or interacting with the wearer are taken into account and analyzed by AI algorithms. The different cameras allow the machine to process the images and detect the pose and orientation of the speaker.

The users are able to sense the position of the speaker using an actual 3D map which keeps on reconfiguring in real-time with movements and changes in the gaze of the speaker. The data or the names of the people talking are then read aloud in a spatialized audio, giving the child the exact sense of distance and the relative position of their peers.

Benefits of PeopleLens #

As complex as the working sounds, the benefits of this new technology are immense.

This has enabled the children with visual impairment to lead a better life by enabling them to see.
The social interaction of such children is bound to improve, and the wearers can understand their peers in a better way when they are using PeopleLens.
The children using this device also get notified of other objects like chairs or tables, apart from recognizing the people sitting in space.
The company aims to bring about a change in the way people with less vision interact with their peers, giving them a better idea of who is talking to them, who is gazing in their direction, whose heads are turned away and so on.
The teachers and parents can also communicate better and foster better relationships with this teach at hand.

Conclusion #

The possibilities with this thoughtful technology are endless as this technology can be modified to serve a lot many industries and sectors. This has opened up a world of opportunities for visually impaired children who struggle with their everyday lives.

Microsoft has certainly taken a commendable step in this direction by opening doors for more features and allowing children with less vision to feel more connected and empowered. With the leading giant innovating in this area, we can expect to see more companies sinking their feet in these waters and putting technology to a good cause.

Related reading

Questions and answers

What is PeopleLens’ How Was It Developed? #

How Does It Work? #

Artificial Intelligence, Making its way in the Aviation

Vin Patel — Fri, 18 Nov 2022 11:38:48 +0000

Key takeaways

Passengers should anticipate greater touchless check-in and security processing, symptom screening, heat maps to preserve social separation, and other automated standards and new functions of the airport in the post-Covid-19 crisis.
Using this data, AI systems may recognize dangers in a variety of contexts.
The aviation sector has great potential to grow and become safer thanks to artificial intelligence.

Passengers should anticipate greater touchless check-in and security processing, symptom screening, heat maps to preserve social separation, and other automated standards and new functions of the airport in the post-Covid-19 crisis. In other words, they will encounter more robots and fewer people.

Smart technologies will be crucial in helping airports, and airlines get through this crisis, which is expected to hasten the use of automation and AI in the aviation industry.

Whether you’re ready or not, artificial intelligence (AI) and machine learning (ML) is being used in aviation. According to experts in the field, the usage of AI/ML can boost productivity, safety, workload, and speed to enable more advanced technology like data ecosystems and autonomous vision-based navigation.

AI in the Aviation Sector #

AI technologies and their computational abilities for data interpretation may be useful in a wide range of processes and activities, including analytic, software configuration, customer support, and many more. A number of industries, including finance, marketing, retail, and health, continue to show potential for AI.

Artificial intelligence (AI) can help businesses in the aerospace industry speed up production while also addressing safety problems. In addition, AI systems process massive amounts of data considerably faster than humans by examining data from several sources.

This allows aerospace companies to examine various elements more effectively and efficiently. AI in airplanes also helps with creating applications that monitor and conserve fuel, pinpoint problem areas, and support air traffic control. For corporate executives, understanding and utilizing the applications of AI have become essential.

Applications of Artificial Intelligence in the Aerospace Industry #

The applications that employ AI technology to disrupt the aviation industry are listed below:

Better Fuel Economy

Aerospace industries place a premium on fuel quality, and even a slight decrease in aircraft fuel consumption can significantly impact a business’s profitability and sustainability.

The deployment of AI-powered devices may result in a reduction in fuel use. For instance, a French startup, Safety Line’s machine learning application, can help pilots improve their climbing trajectories before each flight. The ascending process uses the most gasoline, so enhancing this stage results in significant financial savings.

2. Practices & Training #

AI can be used to construct flight training. AI simulations can be combined with interactive virtual frameworks to give pilots a complete simulation environment.

Simulators with artificial intelligence capabilities may also be used to collect and assess academic data, such as biometrics, to develop customized training regimens based on students’ academic performance.

The next significant application of AI will be to help pilots while they are flying. AI-powered cockpit changes can gradually improve a flight path by monitoring and alerting the pilot to crucial conditions such as fuel levels, aircraft conditions, weather, and other factors.

In the future, aircraft may be supplied with stunning cameras that are powered by image processing techniques, expanding the pilots’ field of vision and improving the quality of their performance.

3. Using AI To Enhance Air Traffic Management (ATM) #

The ATM becomes one of the most important factors when it comes to air safety because there are so many flights taking off, flying, and landing at the altitude that these planes can travel. In areas like flight planning, conflict prediction, and safety evaluations, the ATM can profit from AI.

In addition to minimizing delays, AI technologies can enhance trajectory prediction and safety. This trajectory prediction can aid in creating safer air travel routes in congested airspace. To prevent deadly incidents, researchers are creating conflict and complexity prediction technologies that can foresee unsafe circumstances based on the historical data that is supplied to them. By reducing the occurrences of ionosphere mistakes, where the atmospheric shift might impact the satellite navigation system’s signals, AI will also aid in improving the prediction.

4. Improved Client Experience #

Commercial aviation places a specific emphasis on client satisfaction and service excellence. AI is one way for airlines to increase consumer engagement and provide first-rate customer service. For instance, AI-based automated platforms called chatbots can react to customer inquiries in real time and in a manner resembling human interaction. Online chatbots can save clients time and effort by automating customer care. There are several ways to do this, including:

Personalized and accurate advice on the customer’s purchase choices
Chatbots with AI assistance provide quick and helpful support
24-hour automated assistance simplify client interactions

5. Identification of Travelers #

Security is a critical responsibility for commercial airlines, and AI can offer effective safeguards to ensure passenger safety. Smart cameras with AI capabilities may use facial recognition to detect suspicious people at airports.

To train AI systems for this, images of people with criminal records can be employed. Smart cameras with AI capabilities could be deployed in airports to spot criminal activity.

6. Threat Identification #

Using computer vision systems, advanced analytic, and geospatial signal processing, AI can discover and classify hazards and dangers. AI systems can be trained for this purpose using images and videos from a range of aerial vehicles and satellites.

The images and videos taken could be categorized as normal or suspicious. Using this data, AI systems may recognize dangers in a variety of contexts. AI-based solutions can be useful for business, civic, and commercial purposes.

Corporate leaders may help pilots make informed decisions based on spatial and situational information by integrating AI into the aircraft industry.

7. Fraud Prevention #

Airlines reportedly lose about $1 billion a year as a result of payment fraud, according to IATA estimates. Several fraudulent tactics include:

Using stolen credit cards.
Requesting compensation for “lost” luggage.
Setting up phony online travel companies, and so forth.

Both fraud management strategies and fraud cases are becoming increasingly sophisticated. Airlines use sophisticated, machine learning (ML)-based analytic tools to evaluate data from numerous sources, identify fraudulent transactions, and improve passenger authentication and payment security.

8. Automated Messaging #

Travellers become anxious when a disruption like a flight delay or a luggage loss happens. Customers will probably not choose this airline for their subsequent journey if they don’t receive a response or explanation of an issue from an airline representative in a timely manner. It is important to respond to consumer inquiries quickly, even more so than the actual procedures done to resolve a problem.

By applying algorithms for interpreting natural language or unstructured text, AI software accelerates and streamlines the operations of customer support representatives.

Creating chatbots is another method to automate and enhance customer care. Today, several airlines use AI-powered chatbots to improve customer service by assisting travellers with flight booking and management, luggage tracking, queries, and other needs. Bots can update flight information, lead consumers to certain services or outlets, and more, freeing up humans to focus on more worthwhile tasks and minimizing human contact.

Customer service automation, such as chatbots, can understand basic inquiries and answer in a conversational, informal manner. Airports may offer 24/7 customer support and lessen the human touch by using chatbots.

Famous Airlines using AI to Improve Operations #

As you can see, there are many ways that airlines may employ artificial intelligence (AI) and data science to automate their processes, cut costs, and improve customer happiness. Here, we’ll merely briefly discuss a few actual instances.

Alaska Airlines

According to numerous industry-recognized rating systems, including the American Customer Satisfaction Index and JD Power North America Airline Satisfaction, Alaska Airlines has consistently ranked among the top carriers for customer satisfaction (ACSI). Since the 1990s, it has also been a leader in innovation, beginning with the introduction of self-service kiosks, GPS technology, and online ticket sales. And it continues to pave the path.

Alaska Airlines inked the agreement for the usage of the Flyways AI technology in May 2021. A flight monitoring and routing application help dispatchers create fresh, effective routes and make informed judgments. The technology generates predictions based on data and offers aircraft operations and routing suggestions. Flyways helped the airline cut miles, conserve 480,000 gallons of fuel, and avoid producing 4,600 tonnes of carbon emissions during the course of the six-month trial period.

2. Delta Airlines #

In May 2017, Delta revealed that it would invest an estimated $600,000 in four automated self-service bag-checking kiosks, one of which would use facial recognition software. The airline said facial recognition technology would be used to authenticate customer identity by matching customer faces to passport images and choosing Minneapolis-St. Paul International Airport to debut the four self-service kiosks.

3. United Airlines #

A partnership between United Airlines and Amazon Alex, dubbed “United skill,” was revealed in September 2017. According to reports, the app enables Alex users to converse in natural language to obtain answers to the most typical questions concerning United flights.

4. Southwest Airlines #

Southwest Airlines, based in Dallas, is the biggest domestic airline in the US and the biggest low-cost airline worldwide. TripAdvisor’s 2020 Travelers’ Choice Awards named Southwest the top US airline.

Achieving high-level customer service requires you to listen to your consumers and make adjustments to your operations in response to their feedback. In its 2014-opened Listening Center, Southwest does precisely that. Forty professionals from the Customer Relations, Marketing, and Communication divisions keep an eye on social media feeds to help the airline address new problems as soon as they arise.

The team can monitor social media sentiment regarding Southwest, its rivals, and the airline industry as a whole, analyze the most frequently discussed business-related topics on social media and keep up with news from traditional media thanks to sophisticated software that collects relevant keywords from across the web. The airline uses social media insights to stay up to date on trends and offer consumers tailored promotions.

Summary #

AI and machine intelligence can deliver the best client experience imaginable through automation and self-service apes. Utilizing data also enables aircraft companies to contemplate larger discounts and competitive positioning.

One of the most important issues facing the aviation sector is air safety. It may be the only situation in which individuals are willing to compromise their privacy to ensure the highest level of air safety. AI is advancing aviation safety and assisting airlines to enhance their customer service, lessen the enormous problem of delays, enhance the environment of the airspace, and enhance passenger security. The aviation sector has great potential to grow and become safer thanks to artificial intelligence.

Related reading

Acceleration in Adaptation of IoT in Modern Day Businesses

Vin Patel — Thu, 17 Nov 2022 12:33:38 +0000

Key takeaways

It’s difficult to leave the “Internet of Things” out of a list of cutting-edge and paradigm-shifting technologies in a world dominated by artificial intelligence, data, and ever-evolving connection technology.
Sharing of perceptions and data** Almost every organization relies on data gathering and transmission, and the advent of IoT has completely changed how data is processed.
The fact that IoT technology is still in its infancy and its applications are expanding across various industries suggests the possibility of introducing additional features and advantages in the future.

It’s difficult to leave the “Internet of Things” out of a list of cutting-edge and paradigm-shifting technologies in a world dominated by artificial intelligence, data, and ever-evolving connection technology. IoT is actually one of the most significant technologies in use today because it is essential to the development of numerous other technologies, including machine learning.

Nearly every element of our life creates data, which is captured, shared, and tracked in order to provide us with fresh perspectives, increase efficiency, and enable businesses to make wise decisions. Enterprise leaders are beginning to grasp the value of the Internet of Things in enterprises. Healthcare, energy, automotive, manufacturing, agriculture, and other sectors are implementing IoT. It has numerous uses, including an air track, doorbell cameras, security systems, thermostats, and many others. The Industrial Internet of Things industry will continue to expand annually as more businesses adopt the IoT.

Why is the Internet of Things (IoT) so Important? #

IoT has emerged as one of the most significant 21st-century technologies in recent years. Continuous communication between people, processes, and things is now possible thanks to the ability to connect commonplace items—such as household appliances, automobiles, thermostats, and baby monitors—to the internet via embedded devices.

Low-cost computers, big data, the cloud, analytic, and mobile technologies enable sharing and collecting of data by physical objects with minimal human intervention. Digital systems can monitor, record, and modify every interaction between connected entities in today’s hyper-connected environment. The physical and digital worlds collide, but they work together.

What Technologies Have Made IoT Possible? #

Although the concept of IoT has been around for a while, it has only become a reality thanks to various recent technological advancements.

Availability of inexpensive, low-power sensor technology: More manufacturers may now use IoT technology thanks to reasonably priced and trustworthy sensors.
Connectivity: It is now simple to link sensors to the cloud and other “things” for effective data transfer thanks to a variety of network protocols for the internet.
Cloud computing infrastructures: Businesses and consumers may now get the infrastructure they need to scale up without having to manage it, all thanks to the expansion of cloud platforms.
Analytic using machine learning: Businesses may acquire insights more quickly and easily thanks to machine learning and analytic improvements, along with access to diverse and enormous volumes of data stored in the cloud. The development of these complementary technologies pushes the limits of IoT, and the data generated by IoT feeds these complementary technologies.
Artificial intelligence with speech (AI): Natural language processing (NLP) is now available on the Internet of Things (IoT) devices, including digital personal assistants like Alex, Cortana, and Sir. This has made IoT devices more appealing, practical, and inexpensive for usage at home.

Long-Term Adoption of IoT Will Change the IT Workforces of Businesses #

The operational technology (OT) and information technology (IT) teams are naturally merging together as businesses concentrate on accomplishing this transformation. Due to the increased enterprise adoption of IoT, both domains are increasingly collaborating, which has an impact on the priorities and workload of internal technology teams.

The transition to Industry 4.0 is continuing, and it appears that corporations are more at ease than ever with their part in it. Companies are increasingly incorporating IoT into their business strategies, according to new data from the Information Services Group (ISG), and they’re not only swiftly updating their IoT plans—their scope is also “becoming more ambitious” as a result. Now, businesses are receiving long-term IoT care.

In practice, this implies that companies are investing in connected device sensors, whether they are in automobiles, buildings, or tools and equipment, and utilizing the layers of analytic available for each use case to produce actionable data sources that “derive higher-level business insights.”

The value of the IoT technology market is anticipated to reach $1.39 trillion by 2026, according to Mordor Intelligence. Several things are probably to blame for this phenomenal increase, including:

The COVID-19 epidemic has expedited the development of data analysis, smart home technology, and remote monitoring.
Businesses are vying with one another to create superior artificial intelligence products. These frequently call for an IoT-related network of cutting-edge sensors and edge computers.
IoT networks are more effective compared to centralized systems at completing some jobs.

Industries, including manufacturing, logistics, oil and gas, transportation, energy, mining, aviation, etc., have all been significantly impacted by IoT. In fact, a fourth industrial revolution has already begun, marking a new phase in the management and structure of the industrial value chain. The fourth industrial revolution (industry 4.0) is based on caber-physical systems, also referred to as smart machines. By the end of 2023, 94% of organizations are expected to be using IoT.

Acceleration in IoT Adoption #

The survey claims that 79% of firms intend to invest sizable sums of money into at least one IoT project in the next two years. In fact, in the next two years, the typical large organization plans to invest in nine different IoT use cases. The majority of businesses want to invest in IoT for asset/plant performance improvement, with 42% of those surveyed planning significant investments. It should come as no surprise that the willingness to invest (more) and overall ROI have a strong association. 97% of businesses that have implemented IoT solutions for asset/plant performance optimization—technologies that let users operate and maintain assets at higher levels of equipment effectiveness—have already demonstrated a profitable return on investment.

But even less common use cases are anticipated to experience significant growth. For instance, more than one-fifth of businesses want to make significant operational investments in augmented reality (augmented operations). The conclusion is clear: IoT technology will be adopted more widely in the upcoming years, according to all indications.

Benefits of the Internet of Things (IoT) for Businesses #

Let’s examine a few strategies firms can use to enhance key areas of their operations by utilizing the Internet of Things and its cutting-edge technologies in the future.

Cost reduction

Internet of Things technology will be more specifically designed to support the success of those organizations as they deploy IoT devices to optimize operations and boost profitability. IoT devices are already having an effect on business profits, from cybersecurity to workplace productivity.

Maintenance expenses can be reduced when IoT devices and sensors are used to maintain corporate equipment at peak performance. Office equipment on-the-fly troubleshooting detects issues before they have an impact on staff and employees, avoiding the inconvenience and expense of significant repairs.

One advantage the Internet of Things delivers to your operations and maintenance process is the reduction of expensive extended downtime for repairs.

2. Sharing of perceptions and data

Almost every organization relies on data gathering and transmission, and the advent of IoT has completely changed how data is processed. IoT apes allow for broader access to personal data and track user connection patterns with the device. As the application learns from the patterns, it gets wiser and offers a better user experience.

IoT products let businesses analyze this data at the same time in order to grow their operations. The information can be used to study consumer wants, the buying cycle, the scope for advancements and innovations, and marketing and advertising tactics.

3. Moving real-time insights #

Real-time data from processes, devices, and people via sensors is revolutionizing business since it can be a game-changer in the visibility into what is happening. The important industries are already experiencing innovation, particularly in the retail sector.

Retailers can increase profitability by managing sales and inventory effectively by collecting real-time buyer insights. In order for devices like systems and smart cameras to work together, the Internet of Things is crucial. A corporation can then make well-informed decisions and acquire novel business models that aid in achieving its goals.

4. Deliver multimedia services more easily #

An omnichannel sales strategy streamlines the entire purchasing process, making it popular nowadays. This service is significantly aided by the Internet of Things (IoT), which also enhances the user experience.

Thanks to the installation of sensors on internet-connected equipment, businesses or manufacturers can monitor operations, status, and service levels from remote places and offer prompt support as and when consumers require it.

5. Accessibility, effectiveness, and output #

These advances will enable enterprises to operate more affordably, resulting in a significant landscape expansion due to the affordability of insertion and the increased viability of production. Smart desks, enabled by IoT, provide workers with the optimum workspace. To guarantee that people are seated for maximum comfort and efficiency, smart desks monitor user preferences and learn from them.

These upgrades will make it possible for businesses to run more efficiently, leading to a huge landscape expansion as manufacturing and insertion become more feasible. For instance, IoT-enabled smart desks give staff the ideal workstation. Smart workstations adjust and vary based on individual preferences, guaranteeing constant user comfort and efficiency.

6. Enhanced security and safety #

IoT has a huge potential in high-risk EHS businesses where events like collisions with large items, falls, and electrocutions put human lives in danger. Due to their ability to support remote operations, enable collaboration between human employees and robots, and give rich low-level data, such as high or low temperatures, IoT applications are equipped to offer safe, dependable, and practical solutions to improve safety conditions in the workplace.

IoT technology also provides a creative design for an autonomous system that localizes, watches, and warns site employees who work in dangerous areas, particularly in hostile and distant areas where the difficulties and risks associated with these facilities are raised. Assuring security and defense against physical threats like fire accidents in the workplace, the IoT also sends notifications for abnormalities or other acts that risk the enterprise.

Drawbacks of IoT #

The Internet of Things has advantages that are particularly wanted for boosting business profitability, but it also poses some risks. Let’s take a quick look at the drawbacks of IoT for the purpose of fairness.

Security holes

The most pervasive issue impeding IoT development as a whole is insufficient security measures. Because smart gadgets capture and transmit private information that may have serious repercussions if revealed, there is always a concern about data leaks. However, IoT solutions rarely adhere to all applicable data protection standards, encryption protocols, and other laws and technologies intended to prevent unwanted access to sensitive data. They also rarely have sufficient anti-tampering measures. Inadequate data protection may have expensive, terrible, or even catastrophic results, such as identity theft, the theft of company secrets, the loss of tools or products, sabotage, etc. Because of this, experts who can guarantee that the installed hardware and software systems have no vulnerabilities and are well-protected against any hacking attempts must create and execute IoT solutions in the company.

2. Connection and workload

With many gadgets linked to the internet, network and bandwidth limitations are becoming a problem. The majority of IoT features require lower latency for efficient performance, and others may call for local servers or service providers to offer new bandwidth and QoS to handle workloads with specific needs. Even without considering the cost of labor, putting this into practice could not be economical.

By using edge data centers to handle some of the computing workloads, businesses may increase the devices’ processing power and boost connectivity performance. Due to their proximity to the network edge, they will assist decrease latency and speeding up information processing. The ability to address connection concerns by extending network services into remote locations is another benefit of edge data centers.

3. Dependence on a network #

The extensive network of connections between different devices and accessibility to the global network are the essential components of the Internet of Things. IoT devices consequently also need an infrastructure that guarantees continuous cable and wireless connection with high throughput, low latency, and ongoing Internet access. Therefore, to use IoS, a corporate firm must first offer the full complement of necessary networking hardware, including cables, routers, hubs, local data storage devices, etc.

4. Demands for high skill levels #

IoT solutions need responsible, knowledgeable experts who are well aware of the scope and potential repercussions of their job. IoT solution deployment, setup, upkeep, and scale changes in a commercial operation require highly trained administrators, who may be difficult to discover and hire given their high salary expectations. All staff members should obtain the appropriate training and manuals before dealing with the implemented network of smart devices. Therefore, even though IoT decreases the need for human resources, the staff that is still on-site must be well-trained to avoid interfering with the operation of smart devices and causing a “snowball effect.”

Conclusion #

By offering data-driven insights into the effectiveness and performance of their processes and systems, opening up fresh perspectives on customer behavior and pain spots, and generating new business prospects, IoT enables organizations to innovate and flourish.

Because of this, businesses have a lot to gain from implementing IoT, despite the fact that there are difficulties before, during, and after deployment. However, organizations can use the technology’s disruptive potential by combining technological know-how, support infrastructure, and cybersecurity protocols. The fact that IoT technology is still in its infancy and its applications are expanding across various industries suggests the possibility of introducing additional features and advantages in the future.

Related reading

Social Media Analytic using NLP

Vin Patel — Thu, 17 Nov 2022 12:13:10 +0000

Key takeaways

We all use a language that both parties understand to communicate.
Few businesses develop their own platforms for sentiment research.
Anyway, considering that NLP is way faster than humans can give it an edge start in the world of the future, and who knows, in the future, machines might be able to ‘read between the lines’.

Understanding Natural Language Processing(NLP) #

We all use a language that both parties understand to communicate. What happens, though, when humans wish to talk to computers?

Today, computers play a crucial role in our lives, yet they only comprehend machine language. Because it is highly challenging for humans to understand the meaning of so many 1s and 0s, it makes it very challenging for us to communicate with them.

The idea of Natural Language Processing (NLP) was first brought to the field of technology in the 1950s. It is a part of artificial intelligence that aids a computer in interpreting or manipulating the natural language that humans use when they write or speak.

NLP is one of the technologies that is evolving the fastest. It uses a combination of machine learning, deep learning, and statistical models, and is made possible by the abundance of big data, powerful hardware, and sophisticated algorithms.

Today, social media has become an integral part of everyone’s life, and the amount of unstructured data produced by social media every day is massive and insane! It is not possible to consume and understand this data manually.

The volume and nature of information available to NLP researchers today have undergone a radical change due to the emergence of social media. It has altered how big data can be used.

It is feasible to identify correlations between demographic data, language use, and human engagement using data from social media sites like Twitter, Facebook, YouTube, blogs, and discussion forums. Researchers may learn to recognize these data, follow current topics and forecast the spread of illness using statistical and ML approaches.

Here are some applications of NLP that make it necessary for social media:

NLP aids researchers in determining which subjects are popular on social media.
Information extraction and retrieval are aided by NLP.
NLP is useful for screening adult content or inappropriate content.
NLP aids in compiling all the data necessary to draw any conclusions.
NLP is useful for spotting rumors.
Sentiment analysis and detection are made easier by NLP.

Role of NLP in Sentiment Analysis #

We must first comprehend sentiment analysis or sentiment detection to appreciate NLP’s function in this process. Sentiment analysis is a sort of text mining technique that finds and extracts subjective information from the source material. By keeping an eye on online interactions, businesses may use sentiment analysis to understand better the social sentiment surrounding their brands, products, or services.

Simply said, sentiment analysis identifies the public’s opinions, beliefs, feelings, orientation, etc., on social media. Determining the sentiment is often reliant on syntactic information; for example, the words’ sad’, ‘bad’, ‘difficult,’ etc. may convey negative emotions, and words like ‘successful’, ‘great’, and ‘interesting’ convey positive sentiments.

Few businesses develop their own platforms for sentiment research. Large training data sets and in-house expertise are needed. However, it can be profitable for companies that have highly unique needs that cannot be satisfied by current systems. In certain circumstances, businesses frequently create their own tools using open-source libraries.

HuggingFace, SpaCy, Flair, and AllenNLPare NLP libraries that can do sentiment analysis. Additionally, some low-code machine language tools, like PyCaret and Fast. AI, support sentiment analysis. Transformer models like BERT, XLNet, and GPT3 can be used for deep learning sentiment analysis. GPT3 can even analyze sentiment without any practice data.

Since there have been so many privacy scandals and data breaches in recent years, data privacy has become one of the most hotly debated topics. The widespread usage of AI models makes user privacy even more vulnerable since a sizable chunk of the user data that AI models consume is expressed in natural language.

NLP and privacy have come into contact during the past several years due to the numerous academics that have offered NLP-based solutions to these data privacy issues.

Everyone may engage with others and express their opinions freely on social media, but we are frequently unaware of the amount of sensitive and private information exposed. The user’s ignorance of privacy dangers is the leading cause of this.

How Does NLP Assist in This Circumstance? #

A client-side privacy awareness mechanism may warn users of potentially private information leakages in their communications, and privacy protection frameworks are constructed where ML models are designed to recognize messages that hold sensitive information.

During data pre-processing, NLP techniques are used to eliminate stop words, replace each word with a widely used synonym using the WordNet lexical database, and stem each word to condense the dictionary of terms to words in their root form.

According to a method and supporting tool provided by Canfora et al*.*, it is possible to automatically intercept the private information supplied in a social media post. They employed syntactic structures to identify unique recurrent patterns used by users to convey particular privacy leakage classes in natural language, and they automatically categorized the classes.

Tokenization, lowercase conversion, stop-word elimination, and stemming are the characteristics they utilized. They guarantee that sentence classification performance beats cutting-edge ML approaches and is unaffected by the features used or the training set. They also created a browser add-on to protect users’ privacy when they post on Twitter.

Recent developments in deep learning and machine learning (ML) have made it possible for computers to do many valuable tasks with NP.

Additionally, technology has made it possible for humans to create computer programs that can carry out tasks like language translation, semantic comprehension, and emotion recognition.

The fact that computers don’t yet have the same innate comprehension of natural language as humans do provides a drawback. Anyway, considering that NLP is way faster than humans can give it an edge start in the world of the future, and who knows, in the future, machines might be able to ‘read between the lines’.

Related reading

Debunking The Biggest Machine Learning Myths

Vin Patel — Thu, 17 Nov 2022 12:02:04 +0000

Key takeaways

Depending on who you ask, machine learning (ML) will either be a blessing or a bane for the business.
Even something as easy as automated emails, reports, address book updates, reminders, and phone call scheduling may be done with machine learning.
I hope this article was able to answer questions about the most popular machine learning myths.

Depending on who you ask, machine learning (ML) will either be a blessing or a bane for the business. On the one hand, it will give digital processes a wide range of new capabilities, from automated workflows to self-managing infrastructure. On the other hand, it will result in employment losses and render businesses helpless to remedy problems when they arise.

While searching for the two sides of ML, we realize that there are a lot of myths surrounding the technology. Although the truth likely lies somewhere between these two conclusions, it is important to dispel some myths about technology to fully understand what machine learning can and cannot do. Let’s begin by asking the mandatory question of what machine learning really is.

What is Machine Learning? #

Machine learning (ML) is a branch of artificial intelligence (AI) that allows computers to automatically learn from information and previous experiences while seeing patterns to generate predictions with minimal human involvement.

Through machine learning techniques, computers can function independently without explicit programming. Applications that use machine learning (ML) are fed new data and are capable of growing, developing, and adapting independently.

Machine learning extracts meaningful information from massive amounts of data by using algorithms to find patterns and learn in an iterative process. Instead of depending on any preconceived equation that may serve as a model, ML algorithms use computing methods to learn directly from data.

Top Myths about Machine Learning #

Artificial intelligence and machine learning are the same things. #

Artificial intelligence and machine learning are concepts that are frequently used interchangeably. Both, however, are distinct from one another and not interchangeable. Natural language processing, robotics, and computer vision are all included in the artificial intelligence field. Using statistics and data predictions, machine learning teaches itself about patterns.

Building a machine learning platform is simple, and anyone can do it. #

Many believe you can quickly construct any platform by simply Goggling machine learning. However, machine learning is a unique technique that demands expertise. You need to understand how to demarcate data, create an exact algorithm, prepare the data for testing and training, and, most importantly, understand the production system if you want to learn machine learning. One needs practical experience with machine learning patterns and algorithms to become an expert in this field.

Without human intervention, machine learning can function independently. #

People think that a machine can learn a system without using actual programming codes. However, people are responsible for creating algorithms for machine learning solutions. Therefore, human assistance is a necessary component of machine learning and cannot be completely disregarded.

ML is only for large enterprise. #

Many SMEs and startups view machine learning as unapproachable; they believe it is primarily intended for large businesses. But this assumption couldn’t be further from reality. We do not dispute the associated expenditures. However, investing a lot of money in machine learning is not necessary.

Even something as easy as automated emails, reports, address book updates, reminders, and phone call scheduling may be done with machine learning. ML doesn’t always have to perform laborious tasks. For small businesses, it can handle repetitive chores and save time, money, and effort.

Deep learning and machine learning are similar. #

Deep learning is actually a branch of machine learning and a very complex, multilayered neural network. To comprehend data, artificial neural networks (ANNs) attempt to imitate the human brain; deep learning algorithms are said to be built on neural networks. At least three neural networks should be used in the deep learning method.

Contrary to what is often believed in the market, deep learning is not a solution to ML issues and does not operate in the same manner as ML models.

Building a deep learning model results in a predictive system that can generalize and adapt to unique business scenarios. Deep learning makes predictions about the future based on historical and current data, whereas machine learning processes data sets to derive useful insights. A highly developed deep learning model is flexible and can adapt to changes in the industry.

Data cleaning is not required for ML model implementation. #

An IBM estimate from 2016 shows that inadequate data management skills and poor data quality cost US firms $3.1 trillion annually. Now, over five years later, we can envision the magnitude of losses businesses endure as a result of poor data quality.

The machine learning model is first put into practice by cleaning the data. It has a consistent database-style structure and formatting.

The accuracy of ML is insufficient to predict black swans. #

An unplanned catastrophe known as a “black swan” can significantly impact a company. While some individuals assume machine learning is always correct, others disagree, particularly when it comes to black swan events. How does a computer forecast the most unforeseen occurrences or events?

It is feasible, after all. Machine learning models are actually very accurate at predicting the unforeseen. They can spot patterns and trends that humans and conventional computations cannot. It also depends on the team and management, who may choose to dismiss the prediction in the belief that there was a flaw in the system or a missed evaluation.

Conclusion #

Even while machine learning’s full potential has not yet been realized, it is now in play. The bulk of businesses now employs machine learning techniques. Future developments in machine learning and deep learning are likely to be quite significant. In this article, we covered some of the common myths surrounding ML. This myth-buster was necessary as people are still unaware of what ML can do. I hope this article was able to answer questions about the most popular machine learning myths.

Related reading

Differences Between Artificial Intelligence (AI) and Machine

Vin Patel — Wed, 16 Nov 2022 12:22:26 +0000

Key takeaways

Artificial Intelligence and Machine Learning are making sweeping changes in the technological world and are always making a buzz in the teach industry.
Thus the agent ( reinforcement learning algorithm) learns continuously through iteration.
The Verge, another big player in the teach industry, believes that AI might be a powerful way to discover the universe.

Artificial Intelligence and Machine Learning are making sweeping changes in the technological world and are always making a buzz in the teach industry. Companies are using both terms as synonyms to create hype around their products and brands, but they are doing it all wrong.

What Are Artificial Intelligence (AI) and Machine Learning (ML)? #

Machine Learning #

Tom Mitchell defines machine learning as “The study of computer algorithms that allow computer programs to improve through experience automatically.”

In simpler words, ML is a subset of AI that works based on huge amounts of data, where the machine learns from patterns and experience and does not require external human coding.

Artificial Intelligence #

AI is a technology that aims at creating smart machines that possess human intelligence and thinking patterns. This technology is applied to systems to simulate human emotion into a machine. They use emotionally intelligent data to better understand the working of the human brain.

These AI systems use smart algorithms such as Computer Vision, Robotics, Machine Learning, Deep Learning, Neural Networks, and Voice Technology.

How Is Machine Learning Different From Artificial Intelligence? #

AI is the superset of ML. AI and ML are neither synonymous nor two different entities. Machine learning is the subset of AI. It is used for creating intelligent systems to generate predictions and make decisions based on historically fed data. The ultimate goal of AI is to produce smart machines, whereas ML is a method to achieve it.
AI is used in system software, and ML is put in service for online recommended systems. The term AI has been in every teach junkie’s mouth since the development of system software such as Sir, Google Assistant, Alex, and Alpha Go. In their early days, these systems created a buzz in the teach industry, and with this, the term “**Deep learning”**gained popularity.

Deep learning is the classic case of “necessity is the mother of all inventions”. The term AI was coined in 1956, and since then, the subject has constantly been evolving and becoming increasingly complex. Fifty years ago, any chess game on a computer was considered a high level of AI, whereas now it’s nothing more than a basic coding formula.

Deep learning delivered results when typical rule-based coding was unable to perform tasks, and it also took over new fields such as voice and face recognition, natural language processing, and image classification. Deep learning combined with profound learning, a subset of machine learning, establishes a system that can be termed smart.

Machine learning, on the other hand, works for a specific domain only. For example: If I create a machine learning model to detect fish images, the system might become unresponsive toward images of octopuses. This was until a new and stronger type of machine learning was created. This newer type and stronger version of ML is called Reinforcement Learning.

Reinforcement Learningfocuses on maximizing accuracy and minimizing error. It is done by continuously collecting observations from monitoring the interactivity within its environment. Thus the agent ( reinforcement learning algorithm) learns continuously through iteration. The most advanced example of reinforcement learning is superhuman computers that are capable of beating humans at video games.

Types of Artificial Intelligence and Machine Learning #

Artificial Intelligence and Machine Learning are broad fields of study and have types and subtypes.

Artificial intelligence is broadly divided into three broad types: #

Narrow/ Weak Aartificial** Iintelligence** This is designed to perform a single task and to get better at its execution with time. It can include any task which has already been performed and minimize the inconveniences while its execution. Most AI that exists today comes under the category of Narrow AI.
General Artificial Intelligence Often mentioned as “The True AI”, general AI prioritizes machine comprehension rather than a single task-to-task performance. It focuses on making a machine perform on a wider level, just like a human being would do!
Strong Artificial Intelligence We are yet to become WestWorld (a TV show based on AI). All the mystic aura that surrounds the word AI, in general, comes from the myths of AI from stuff like robots, fully automated cars and making a machine with equal thinking and functioning capability.

Machine learning has the following types: #

Supervised Learning**** This machine-learning model works based on explicit examples. It learns from the past and uses it to make future predictions. This works in an environment where the data is marked with correct answers, and the algorithm produces the already-known correct outputs.
Unsupervised Learning**** Unsupervised Learning lacks correct outputs in the environment because it is impossible to attain, obtain or observe the correct labels, and therefore it generates output based on data exploration and patterns (if there are any).
Semi-Supervised Learning (SSL) As the name suggests, it takes a mid-way between supervised and unsupervised learning. Most AI systems require a combination of both techniques to solve a problem, and semi-supervised learning works best in those cases.

Scope of Artificial Intelligence and Machine Learning #

Machine Learning is a faster-growing area of study, especially some of its interesting subtypes like reinforcement learning giving better results than ever, whereas Ai, on the parallel, encompasses a lot as a subject and has a vast scope. The future of ML can be the generation of recommended systems that are hundred percent (absolute state) accurate and have zero error frequency. Although the future of AI is quite uncertain as this world still hasn’t seen the house guard robots that the early AI manifesto promised.

As companies use the term AI to market their brand, services and products to create hype in the teach market without a complete understanding of it, their systems will eventually falter. According to TechTalks (a major teach website), when their systems start to fail, they will hire humans to do the jobs. Can this mistrust cause another winter of AI in the market? Melon Musk says that AI will be “vastly smarter” than humans in the next five years. He also said that it was the biggest threat to humans. The Verge, another big player in the teach industry, believes that AI might be a powerful way to discover the universe.

Related reading

Questions and answers

How Is Machine Learning Different From Artificial Intelligence? #

AI and ML are neither synonymous nor two different entities. It is used for creating intelligent systems to generate predictions and make decisions based on historically fed data.

Can this mistrust cause another winter of AI in the market? #

Melon Musk says that AI will be “vastly smarter” than humans in the next five years. He also said that it was the biggest threat to humans.

How Is Web 3.0 Blockchain Technology Fuelling a Six Trillion

Vin Patel — Wed, 16 Nov 2022 12:21:28 +0000

Key takeaways

Web 3.0, as its name implies, is the most recent or developing phase of the interne’s evolution.
According to insiders in the industry, as the Web3 ecosystem matures, demand for Web3 developers will rise.
Web 3.0 blockchain will significantly impact both old and new businesses.

Web 3.0, as its name implies, is the most recent or developing phase of the interne’s evolution. The foundation of Web 3, or Web 3.0, is the idea of building a completely decentralized environment. This emerging field of technology thinks that the power of artificial intelligence, machine learning, and cutting-edge technologies like blockchain can resolve the issues facing the modern internet and online ecosystem.

What Drives The Growth of Web3.0? #

As per Market Research Future, the market for Web3.0 blockchain technology will be valued at more than $6 trillion in 2023. From 2023 to 2030, Web3.0 is expected to increase at a CAGR of 44.6%. Consequently, the market for Web3.0 blockchain technology will be worth more than $6 trillion. Almost every industry is anticipated to use Web3.0 blockchain, including financial services, retail and e-commerce, media and entertainment, healthcare, IT, government, and energy.

Many people think Web3.0’s fate is inseparably related to the tumultuous market of cryptocurrency. The reason behind this is that it depends heavily on the blockchain. However, that is not the case. Cryptocurrencies are only one aspect of the emerging sector.

Gartner reports that although the value of cryptocurrencies fell in the first few months of 2022, decision-makers cannot assume that the same is true for Web3.0 technologies. The research and consultancy business claims that Web3.0 technology will soon pass the threshold of maximum acceptance, and sectors ranging from food safety to aviation maintenance will tokenize their apps. The study suggests that CEOs, decision-makers, and IT executives should consider themes like the metaverse, the decentralized app market, artificial intelligence, video games, and machine learning. These technologies will accelerate Web3.0’s development.

How is Web 3.0 Essential for The Financial World? #

Understanding human emotions and thoughts will be one of Web 3.0’s key characteristics. Artificial intelligence, also known as AI, will be heavily utilized to filter data and information and customize search results based on user preferences. Integrating blockchain with IoT for logistics, supply chains, and manufacturing line operations is attracting the attention of the industrial sector.

The banking, financial operations, and insurance industries are some significant proponents of Web3.0 blockchain technology. The weight of the global economy is propelling Web3.0 into a new age as the new technology is anticipated to overhaul the established financial institutions.

Web 3.0 will further broaden the scope of engagement, including secure data transfers, richer information flows, and frictionless payments. This will occur due to web3, allowing us to communicate directly with any machine without using intermediaries who charge fees. From the widespread use of NFTs and cryptocurrencies to the expansion of decentralized autonomous organizations and blockchain-based infrastructure and storage, rapid Web3.0 innovations are taking place everywhere.

Web 3.0 Blockchain Teach #

The blockchain’s advantages are intriguing. It increases customer acceptance because it is simple to use, extremely inexpensive, and far quicker than conventional decentralized operations. Decentralized networks are deemed safer by the cybersecurity industry because it takes coordinated efforts to compromise a blockchain operation.

Blockchain is one of the technologies driving web 3.0, along with others like IoT and AI. More specifically, because it redefines the data structures in the semantic web’s backend, blockchain is the basis of web3. Since there is a historical increase in the demand for Web3 developers, 2021 will witness a rise in Web3 development. According to insiders in the industry, as the Web3 ecosystem matures, demand for Web3 developers will rise.

Blockchain-based Web 3.0 has the potential to be highly beneficial in many ways. With the help of blockchain technology, transaction speeds can increase surprisingly. Blockchain technology has enough potential to drastically change how the internet will function in the future, from the first seven transactions per second (TPS) of Bitcoin to the current 50000 TPS of Solano. Blockchain technology can significantly enhance several key industry sectors, including supply chains, digital health care, and digital transactions in general. Blockchain technology has enough potential to ultimately alter how we view social media platforms today.

Different Blockchain Applications and Innovations in Web 3.0 #

The primary end users of this new technology are anticipated to be the retail and e-commerce industries. They will gain from safer transactions, new services and apes, and innovative purchasing and selling methods. Developers are promoting blockchain as a remedy for privacy worries, rising as more users worldwide refuse to divulge their personal information to significant internet firms.

The blockchain is being used in various ways by other businesses. Healthcare, for instance, is rethinking how to maintain and store electronic health records. A significant element of web 3.0, speech recognition software, is exemplified by Sir.

Web3 is now used by the artificial intelligence platform Wolfram Alpha. An excellent illustration of a web 3.0 social network website is Steemit. It is a decentralized rewards system that exclusively utilizes the social media platform of the Seem Blockchain. IDEX is a well-known decentralized marketplace for selling web 3.0 compatible ERC-20 tokens.

Users can build their addresses or even engage with the network because everyone has access to the blockchain. One cannot exclude users from this network because of their social makeup, gender, income, or geography. Thanks to this capability, users will find it much simpler to move their wealth or assets anywhere in the world quickly.

Conclusion #

The world of developers is presenting Web 3.0 as a movement that will take back internet control from the five major teach giants. This is because blockchain has a lot of use cases for cryptocurrency, blockchain, and the metaverse. Web 3.0 would see our data reside on blockchain networks, where people rather than centralized organizations would hold it as it is today.

Web 3.0’s impact on businesses will make them more transparent and user-centric. There will be a radical transformation in everything wrong with how corporations handled customer data in the past. Web 3.0 will fundamentally transform the way we communicate and collaborate online, and this transition will affect everyone. Web 3.0 blockchain will significantly impact both old and new businesses.

Related reading

Questions and answers

What Drives The Growth of Web3.0? #

As per Market Research Future, the market for Web3.0 blockchain technology will be valued at more than $6 trillion in 2023. From 2023 to 2030, Web3.0 is expected to increase at a CAGR of 44.6%.

How is Web 3.0 Essential for The Financial World? #

What is Hyper-automation & How is it Used in Businesses?

Vin Patel — Wed, 16 Nov 2022 10:52:51 +0000

Key takeaways

Although it may sound like science fiction, hyper-automation can rapidly discover, validate, and automate a wide range of business and IT operations.
It reduces human error and fatigue while enabling robots to mimic human intellect.
Your employees will be more engaged in their job as they seek to solve challenges and develop creative solutions instead of being bogged down by low-level, repetitive tasks.

Although it may sound like science fiction, hyper-automation can rapidly discover, validate, and automate a wide range of business and IT operations. The result of machine learning, packaged software, and a range of tools used to execute a task is referred to as hyper-automation. It may employ cutting-edge technology such as artificial intelligence (AI) and machine learning to assist workers and streamline work processes.

The sophistication of the automation (i.e., find, analyze, build, automate, measure, monitor, and reassess) and the instruments that may be automated are referred to as hyper-automation.

What Role Does Hyper-automation Play? #

Most firms have a range of IT and business automation solutions. Many of these tools are script- or rule-based in nature or are designed for specialized uses (i.e., onboarding automation). These tools allow ad hoc automation for simple actions. A new solution is deployed whenever the HR or marketing departments need to automate a set of responsibilities.

The problem with this strategy is that it leads to the organization becoming a patchwork of separated automation that is riddled with gaps. This makes establishing automated, end-to-end operations to meet business goals difficult and costly for IT.

Automation refers to simple task automation, whereas hyper-automation is a comprehensive strategy that seeks to automate and coordinate as much as possible to transform processes (digital transformation) and build new ones that would have been impractical to accomplish using conventional methods.

With the aid of AI and ML tools, businesses are increasingly pursuing unified, enterprise-wide automation programs to cut costs and increase productivity.

Benefits of Hyper-automation #

Hyper automation can free up internal resources, improve security, and help firms save money. Benefits like these can be leveraged to boost sales, marketing, and customer service.

When using hyper-automation, your business can:

1. Compile and examine a variety of data: #

Your business may benefit from the new insights by improving its decision-making procedures and customer service.

2. Increase employee engagement: #

Make sure that repetitious tasks are carried out skillfully and consistently to increase employee engagement.

3. Utilize low-code and no-code digital technologies: #

You can save money and time by automating tasks that would normally require manual intervention.

4. Boost agility: #

Increase agility by providing more timely, precise information for better decision-making.

Integrating Hyper-automation in Your Company #

As was already mentioned, hyper-automation enables the integration of advanced technologies like AI and machine learning with core automation and RPA. When you employ hyper-automation, your business might gain from it in a variety of ways.

1. Enhanced effectiveness and flexibility: #

It is best to use hyper-automation to boost output. This is made feasible by assigning low-value tasks to automation systems, which frees up people’s time for higher-value tasks. It helps you to grow your company while having quick and effective decision-making skills.

2. Increasing ROI and cost-cutting: #

You may increase earnings while cutting expenses through hyper-automation. In addition to happier consumers, greater staff morale, productivity, and income are also financial advantages of hyper-automation. By 2024, organizations should be able to cut operating expenses by 30%, according to Gartner.

3. Precision and quickness: #

Hyper automation speeds up and increases the accuracy of work processes while also enabling the automation of intricate business procedures. Artificial intelligence, natural language processing, deep learning, and predictive analytic make end-to-end automation conceivable.

It reduces human error and fatigue while enabling robots to mimic human intellect. Speed and effectiveness are significantly increased as a consequence.

4. Enhanced client support: #

Hyper automation assigned difficult, time-consuming jobs to bots. This enables staff to concentrate on bettering the client experience by understanding their preferences. They may also provide each consumer with a solution that is specifically personalized, thanks to it.

5. Processes, robots, and the digital workforce: #

Hyper automation produced by intelligent process automation within an organization enables it to function without human input. A trait that all instances of automation capabilities have is artificial intelligence (AI), machine learning (ML), natural language processing (NLP), intelligent optical character recognition (OCR), and AI visual recognition.

The technologies let robots observe, analyze, and assess jobs, improving their Hyper automation capabilities. Hyper automation may be utilized for process mining and job delegation thanks to its capacity to map out business processes.

How Does Hyper-automation Work? #

Like every company’s endeavor, the degree of success is based on the planning process. Creating a hyper-automation strategy could help you streamline your objectives and actions. Your plan for hyper-automation may be divided into four steps:

1. Choose your objectives: #

You must first identify the precise goals you hope to accomplish with Hyper automation integration in order to create a sound plan. Adopting a thorough strategy to a holistic vision is necessary for the implementation of a successful business process automation. A detailed strategy must include every employee, department, and process inside your company.

2. Improve your system’s scalability: #

A sophisticated technological architecture called hyper-automation enables a business to increase its automation capabilities. It calls for a significant level of automation.

Or, to put it another way, every opportunity needs to take scalability into account. Which domains will benefit most from the introduction of robotic process automation (RPA) is decided at this stage.

3. Carry out research: #

Research is done internally or with the aid of outside specialists. Finding processes that are prepared for automation is required. It typically ends up in bureaucracy-heavy areas like finance, accounting, and human resources.

A technical evaluation of a few chosen RPA providers, as well as proof-of-concept and knowledge-gathering platforms, are also part of this process.

4. The method, the test, and the application: #

Businesses make sure the necessary personnel are prepared and trained for the RPA deployment. Additionally, it outlines the steps that must be taken to test and manage the IT infrastructure. Every step of the implementation process is recorded, documented, and completed under specified rules.

Conclusion #

The benefits of hyper-automation will enable your employees to receive the most recent business and market information, enabling them to do their duties to the best of their abilities.

Your employees will be more engaged in their job as they seek to solve challenges and develop creative solutions instead of being bogged down by low-level, repetitive tasks.

#

Related reading

Questions and answers

What Role Does Hyper-automation Play? #

Most firms have a range of IT and business automation solutions. Many of these tools are script- or rule-based in nature or are designed for specialized uses (i.e., onboarding automation).

How Does Hyper-automation Work? #

Like every company’s endeavor, the degree of success is based on the planning process. Creating a hyper-automation strategy could help you streamline your objectives and actions.

How AI-Generated Art Is Changing the Future of Art?

Vin Patel — Tue, 15 Nov 2022 12:47:58 +0000

Key takeaways

You probably experience the power of artificial intelligence (AI) every day when you access social media or make an online purchase.
Software developers like those behind Stable Diffusion think it is up to users to utilize the program properly and not infringe on other people’s intellectual property.
Artificial intelligence is expected to be quite helpful in many aspects of our lives, including healthcare, money, education, and many more.

You probably experience the power of artificial intelligence (AI) every day when you access social media or make an online purchase. Numerous businesses employ AI to enhance business processes and automate customer experience stages. Recently, the media has begun to explore AI art, specifically art made with neural networks.

Art is personal. It can survive as many meanings as there are points of view, if not more. The definition of art is constantly changing, and new frontiers are always being explored.

The idea behind AI is to enable computer replacement for humans so that simple and even certain complicated jobs can be completed without human intervention. Given how many businesses are investing in artificial intelligence, it should come as no surprise that there are AI-powered art generators that can produce original works of art.

According to flattering coverage from publications like the New York Times and The Atlantic, a new star is emerging in the art world, along with other recent museum and gallery exhibitions: the machine. Additionally, it has helped to popularize a wide variety of philosophical issues pertaining to using computers in artistic creation.

AI Expanding into Artistic Domains #

Although artificial intelligence has been around for a while, technology has recently experienced a comeback in the field of fine arts. AI is a perfect fit for the creative industries since it can be utilized to develop content efficiently, expedite workflows, and improve creative outcomes.

AI is already being utilized in the art sector to support artists in a variety of ways, including assisting them in switching from analog to digital tools, enhancing their creativity, and providing novel experiences for their consumers. From Andy Warhol’s usage of the Polaroid camera to Leonardo da Vinci’s silver nitrate sketches, artists have historically added technology to their work to enhance it. Now, AI is being used in novel and creative ways to support artists. AI can be utilized to help with various tasks, including helping artists who may have reached a creative roadblock come up with fresh ideas and analyze the art market.

What is AI-Generated Art? #

Since A. M. Turing, the founder of computer science, utilized a machine to produce an abstract work of art in the 1950s, artificial intelligence has been employed to create works of art. More recently, in 2016, the world’s first AI-generated exhibition was completed by the Berlin-based art collective Central. “This is Not an Exhibition” exhibited works produced by “The Curator,” an algorithm that was taught to recognize and duplicate the textures, colors, and patterns present in pieces in the MoMA’s online collection.

The algorithm used information and photos from the MoMA collection to produce original works of art. These works of art were, in a sense, artificial intelligence-generated imitations of human creations. The exhibition provided evidence that artificial intelligence (AI) might be applied to produce original works of art.

Several businesses have developed in recent years that offer AI software that aids in creating graphics using text suggestions. AI-generated art has taken off since Stable Diffusion’s publication, despite the fact that tools like Mid Journey and DALL-E have been available for some time. Stable Diffusion was made widely available in August of this year by the business Stability.ai. This amazing software can make artwork in the manner of any artist using any subject or background by gathering photographs from all around the internet. Without using a pencil or brush, anyone may use this tool to create stunning artwork and graphic designs.

In order to improve the artwork they produce with stable diffusion, several users are even developing integrations with other programmed like Photoshop. AI is also being utilized to produce stunning works of art, not just hilarious photos for social media. There are several ethical concerns about artists receiving credit for their work because tools like Stable Diffusion gather photographs from various websites. Some creatives even worry that artificial intelligence will totally supplant humans in the field of art. Although AI art is a technological marvel, it poses several crucial issues regarding the future of content management and art as a profession in the digital sphere.

Who Then Owns AI-Generated Art Once It Has Been Produced? #

The next issue AI art raises is whether or not artists can file a lawsuit if their “style” is included in AI-generated artwork. According to current American law, there is no protection without human invention. Additionally, copyright rights are now unavailable to AI generators because they are not human.

Software developers like those behind Stable Diffusion think it is up to users to utilize the program properly and not infringe on other people’s intellectual property. The regulations governing the use of an artist’s work to create new works of art and the copyrights associated with such works of art still need to be made clearer.

The U. S. Rights Office refused to grant an artist the copyright to artwork produced on the Creativity Machine platform back in February, claiming that “human authorship is a precondition to copyright protection.” The regulations governing AI artwork have lately become more complex due to a case in which a graphic novelist submitted their graphic novel (made using AI) for copyright and was granted it.

Companies like Getty Images believe they have no choice but to prohibit AI artwork from being added to their website while the legalities are still unclear. It can be difficult to determine when you’re on the right side of the law because the burden of duty rests with each individual user. As much as possible, businesses seek to protect their users from the risk of unintentionally breaking the law.

But not all legal restrictions may apply to digital artists who use these AI art producers. What kinds of material may and should be produced is another issue. Unfortunately, the artist or topic of the artwork also suffers if these AI art platforms are utilized for unethical behaviors like copyright infringement or producing deep fake images. Defamation can result from deep fakes, which raises an intriguing issue about moderation.

The Potential of Artificial Intelligence Is Endless #

You can produce a variety of artistic media, including music, poetry, stories, and more, by employing AI generators. The most consistently striking photographs are those produced by AI, though.

The AI gets to work and makes art for you once you feed it cues (lines of text, photos, or whatever it needs). Any prompt you can conceive of, from Spongebob as a Japanese samurai to an opera production of Othello in space, can be fed into the AI generator. Your limit is only the sky. Even while some AI studies with visual composition have been disappointing, the potential is very evident. Additionally, like anything that uses AI, you can be sure that it will improve with time.

In several sectors, artificial intelligence is already advancing. Even the production of traditional violins could benefit from AI. AI has advanced significantly from just ten or even five years ago. The ability of AI to combine simulation and reality. Designing areas of that nature, such as homes or rooms, is already used to construct 3D environments. So using the same technology to produce art experiences is a logical next step.

For the purpose of creating virtual reality as art, AI can quickly create realistic 3D photos and films. You might find yourself in the metaverse perusing an exhibition of AI-generated artwork, such as framed images that are hung on moving walls around you or artwork that changes every time you look away to give the impression of movement. Even art exhibitions that you can engage with while viewing them in virtual reality can be created with AI. You wouldn’t be able to do this in a museum, but you would be able to touch the image in front of you, feel the AI creation’s texture, and envision the paint beneath your fingers. It’s reasonable to assume that the potential for AI in art is limitless. Its only restrictions are those imposed by people’s imaginations.

End Note #

Because of the nature of art, it can be understood in a variety of ways by a wide range of people. Both science and the arts are experiencing exciting times now. However, we are neither in the midst of an artistic revolution nor at any imminent risk of seeing machines take the place of artists.

It is frequently forgotten that (non-trivial) art develops similarly to science: by building upon a past of invention and discovery, sometimes taking small steps, and other challenging and overturning paradigms. Therefore, before we assume AI-generated art will replace people in the creative process, let’s give it another thought. However, it does seem that a work of art is still art, whether a human or a machine made it.

It will take time to see if AI art ever sparks a revolution that calls into question how we create art in general, but based on the history of modern art in general and computer art in particular, this appears likely to occur.

Artificial intelligence is expected to be quite helpful in many aspects of our lives, including healthcare, money, education, and many more. It is always improving. Why, then, do we applaud AI when it is addressed in regard to those fields but have the propensity to mock it when it enters the art world?

Related reading

How Chatbots Disruption Benefits Healthcare Industry?

Vin Patel — Fri, 11 Nov 2022 11:28:49 +0000

Key takeaways

Introduction Smart algorithm-powered interfaces conversing via text or audio are becoming more and more popular.
Chatbots can even provide links to training material, videos, and other relevant information, making it easy for employees to access necessary information.
The future is yet to be determined but the healthcare industry could definitely leverage them to improve patient care as well as customer service.

Introduction

Smart algorithm-powered interfaces conversing via text or audio are becoming more and more popular. The recent surge in chatbots has led to their prominence in several industries, including retail, entertainment, social networks, finance, and healthcare. Chatbots are particularly widespread in customer services; they could help perform all the repetitive and lower-level activities of a representative.

A chatbot uses machine learning algorithms with an NLP (natural language processing) layer built-in to communicate like humans with users via text or audio. Healthcare providers, doctors, and medical assistants are now leveraging these artificial intelligence solutions to streamline patient care and reduce unnecessary expenses. There have been various cases where a digital personal assistant or a chatbot has helped patients with their queries. The patient may not even be aware that the human-like medical representative is actually an intelligent conversational machine in many cases.

Healthcare Chatbots: Future Analysis #

The demand for healthcare services has reached an all-time high in the wake of the COVID-19 pandemic. Doctors are in short supply around the world, and chatbots have proved to be an essential tool in healthcare delivery. Chatbots could help patients detect symptoms, manage medications, assist with chronic health issues, and schedule their appointments at healthcare facilities.

According to ‘The Global Chatbots in Healthcare Market Report’ by BIS Research, the market is expected to grow at a significant CAGR of 26.29% during the forecast period from 2019 to 2029. Healthcare chatbots, thanks to improved internet access, a rise in smart device adoption, and advancements in speech recognition and NLP, will see significant growth in coming years.

Governments of various countries are investing money in the healthcare sector, resulting in better healthcare infrastructure and increased digital technologies usage.

Why use Chatbots in Healthcare? #

Healthcare chatbots are growing at a CAGR of 26.29%
38% of the users believe that chatbots offer good experience
33% have rated chatbots as friendly and accessible
Clinical chatbots have a satisfaction rate of 73%
Healthcare bots will save $3.6 billion worldwide

The Importance of Chatbots in The Healthcare Industry #

Amidst the new coronavirus (COVID-19) pandemic, the CDC and WHO have suggested using chatbots to distribute information about symptoms, recommend behavior changes and provide emotional support. If we implement chatbots properly, we will be able to actively share current information that enables suitable health practices and reduces the psychological harm caused by anxiety and social isolation.

It is possible that chatbots could lead to a major healthcare overhaul. They can enhance efficiency and accuracy. Here are the key advantages of a healthcare chatbot:

Reduce waiting time: Time is essence, specifically in medical issues. Chatbots can answer your queries within seconds. Patients need not wait in line, call or email hospitals to get answers to their questions; they can just type in their queries and get an immediate response.

Daily Medication Reminder: Doctors prescribe medicines to help patients with various health conditions. A research study suggests that over 50% of patients do not take their medication on time.

Chatbots can provide not only timely medical advice but also send daily reminders to patients for their medication.

Appointment Scheduling: Scheduling an appointment over a phone call could be time-consuming and even frustrating sometimes. The call operator will need to get details about the patient, the doctor they need to see, and manual check if the slots are available. It leads to an additional administrative cost. A well-employed chatbot could assist patients in booking their appointment with the right doctor.

Improved Internal Communications: Chatbots aren’t limited to helping patients. They can be used to facilitate better internal communication among employees. Chatbots can even provide links to training material, videos, and other relevant information, making it easy for employees to access necessary information.

Billing and Registration: Healthcare facilities can use our platform to integrate with backend billing, insurance claims, and inventory management systems. This allows quick and easy generation of invoices and payments.

Health insurance providers could utilize the bots to answer common queries related to coverage, claims, and procedures.

24*7 Availability: Doctors may not be available all the time for every patient. Bots can be available 24/7 personally for you and assist you throughout your recovery. They can provide medical information, give tips, remind you about medication, and care for your overall health.

Famous Healthcare Chatbots #

There are quite a few variants of healthcare industries. Here, we are mentioning some of the chatbots characterized by their biggest features.

Real-Time Healthcare Assistance #

Elomia #

An AI-powered virtual therapist trained using thousands of consultations conducted by doctors. The virtual therapist could take care of the users emotional well-being and can help patients with:

Depression
Anxiety
Lower self-esteem
Sleep Issues
Relationship Issues

Youper: Self-Guided Therapy #

A mental health app that uses AI and is backed up by an army of healthcare professionals. It helps users with personalized conversations and offers:

Personality Assessments
Emotional State test
Mood Tracking
Mindfulness

Babylon Health #

A chatbot that gives medical advice before you have to go in for an appointment. Here is how it works

The rob-doctor talks to you
If needed, it redirects you to a video conference with a Babylons full-time physician
The physician prescription is sent to your nearest pharmacy right from the app

Note: The app also stores consultation notes for future reference.

Ada Health Companion #

Ada Health is a diagnostic tool that requires users to fill out a detailed health assessment based on their symptoms.

Ada helps you have basic conversations while also inputting information from a massive database of relevant inputs and cases. It feeds into a larger machine-learning model to provide an accurate estimation of your overall health and pinpoint the causes of your problem, as well as possible solutions.

Buoy Health Data-Backed Discussions #

Used as a diagnostic tool. It helps users self-assess for their symptoms. Here is how it works:

Talk about your symptoms
Get clinical insights on your symptoms
Choose the best care option based on your symptoms

Note: They will follow-up with you if you allow the access and they also have an ‘Buoy Community’ where they can connect you with people experiencing similar symptoms.

SafedrugBot #

Aimed to help out physicians. The bot has three different commands:

Information on Drugs
Active Ingredients
Alternative Drugs

In the future, it can help identify allergies and foreign prescriptions.

Content-Rich

CancerChatbot’s #

An AI medical chatbot software for helping people learn about different types of cancer, various treatment options and the long-term outlook.

This bot crowdsources the information from relevant sources on various types of cancers, organizes them, and makes it available for users in an approachable way. Cancer-related information is regularly provided to users via a chatbot, which also answers users’ queries and runs them through a database of cancer research data, and treatment trials.

Your MD #

A symptom diagnostic chat tool with a database of information. It can be accessed via various platforms like

Skype
Telegram
Facebook
Slack
Android App
iOS App

Reminders #

Florence #

Used to remind patients to take their medication. They need to enter the medicine name, quantity, and the time patient has to consume it. It has several other features like:

Track weight and periods
Send prescription refill reminder
Daily health tips
Moderate mood
Locate nearest physician or pharmacy

Conclusion #

Chatbots are undoubtfully beneficial in the healthcare industry. They are an important part of the future of health IT. In the wake of the pandemic, these bots have multiplied. Although, they offer a lot of advantages; there are concerns about data privacy and security. The future is yet to be determined but the healthcare industry could definitely leverage them to improve patient care as well as customer service.

Related reading

The Kingmaker at NETFLIX: Data-Driven Predictive Modeling

Vin Patel — Fri, 11 Nov 2022 09:59:55 +0000

Key takeaways

The day was May 19 of the year 2019, and the very last episode of Game of Thrones was just about to air.
With an ever-increasing global member base and Content catalog, Netflix looks up to data and predictive modeling techniques for employing quality checks accurately and efficiently.
- Capture and keep user attention for as long as possible by providing a highly addictive and hyper-personalized user experience.

The day was May 19 of the year 2019, and the very last episode of Game of Thrones was just about to air. Emotions ran high and wild as the opening credits streamed in for an audience of eight that night.

A few seconds in, and we realized that the audio and video were off by a couple of seconds.

It was anger, despair, and irritability wrapped in one.

Since we were too invested in the finale by then (mainly Jon Snow), we faithfully cursed the streaming service in mufti-colored tones, vowed never to use it again, and decided to battle through by turning ON the subtitles.

This incident occurred, way before I started subscribing to Netflix, and now I just can’t seem to get enough. Netflix is my window into new lives, cultures, and experiences. Its high-quality content, great user experience, and on-point recommendations seem nothing less than magic.

So, How did Netflix actually make all of this happen? And more importantly, How does Netflix (a comparatively costlier alternative) manages to poach a subscriber amidst the sea of free, pirated, and cheaper video-on-demand alternatives?

Simple Answer: Data-driven Predictive Modeling Techniques. #

Need Details? Read ahead. #

Predictive modeling is a way to answer ? What is most likely to happen?, based on what has already happened (Historical data) and happening right now (Current data). It essentially maps out different probable outcomes that are likely to occur, using Machine Learning, Artificial Intelligence, Data Mining, Statistics, and whatnot.

The success or failure of such predictive analytic is heavily biased towards the abundance of quality data availability - which is used to select, train, and test various predictive modeling techniques. And, with its millions of subscribers streaming countless hours every single day,

“Netflix is Data-rich, Very rich!”

Some of its data sources include Member Ratings, Member Information (location, language, watch duration), Video data (play duration, box office performance, critic reviews), User device data (type, features), Metadata (actors, directors, genre, parental rating), Real-time user actions (scrolls, mouse-overs, clicks, time spent on a given page, etc.), Social network data, Search terms entered by members, etc. [Sources as listed in various blogs, interviews and Quota questions answered by Netflix data engineers]. This abundant and versatile data is utilized by Netflix to understand, predict, and optimize its various product features using data-driven predictive modeling techniques, to enhance and personalize your experience.

Everyone Loves Netflix Recommendations #

Netflix knows its customers? expectations intimately!

The Million Dollar Netflix prize (launched in 2006), first brought attention to Netflix’s committed focus on improving its recommendations engine. Flashforward to 2021, and today Netflix personalizes millions of different products for every single one of its million subscribers using data-driven predictive modeling techniques.

This user-experience optimization which started with member ratings has evolved to optimize content titles, the imagery used to portray these titles, synopses, metadata, trailers, the number of titles displayed in a row, etc. And, all of this happens while competing continuously with the new incoming titles and changing user behavior. Given the global scale of Netflix’s audience, this engine is fine-tuned to be in line with the cultural, racial, and overall societal sensitivities of the audience as well. For example, an American staple might not sit well with an Indian audience and vice-a-verse. Xavier Amatriain, Former Engineering Director at Netflix, says this in a WIRED interview:

? We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior. All that data is fed into several algorithms, each optimized for a different purpose. In a broad sense, most of our algorithms are based on the assumption that similar viewing patterns represent similar user tastes. We can use the behavior of similar users to infer your preferences.?

Predictive Quality Control #

Butcher this and the churn rate skyrockets in no time!

When it comes to monitoring content quality (audio, video, subtitles, closed captions, etc]), Netflix sets the bar quite high. It imposes several Quality Checks (QC) consisting of various automated and manual inspections across its massive digital supply chain. Checks such as missing translation, censored content, incorrect frame rate, luminance shift, and audio loudness are some of the issues identified and replaced during QC to avoid any disruptive and disturbing streaming experience.

With an ever-increasing global member base and Content catalog, Netflix looks up to data and predictive modeling techniques for employing quality checks accurately and efficiently. The machine learning model is trained using the past QC data and predicts the probability of an asset not meeting the Netflix quality standards.

Global Expansion #

As of 2021, Netflix is available for streaming in over 190 countries.

As Netflix continues to expand globally, a variety of new content categories, catering to different languages (dubbed audio and subtitles) and cultural preferences has to be added to its library every single day. Apart from the massive data and engineering effort that goes into installing and maintaining such data servers throughout the world, a global expansion of such a humongous scale also adds to the diversity of the receiving networks and devices. This poses a myriad of new technical challenges. How do Netflix deals with such expansive exertions has been elaborated in the succeeding paragraphs?

Personalized Streaming Experience #

Not only ‘WHAT, but Netflix also optimizes ‘HOW you watch your content?!

Apart from suggesting what to watch, Netflix also tries to optimize another key QoE (Quality of Experience) metric that is ‘How you watch your content?. It accomplishes this by utilizing video performance data such as playback failures, rebuffed rate [Time required to replenish local client buffer from the server], nitrate, etc. This data is used to study and train predictive models determining the impact that each performance parameter on an individual member’s streaming experience. Ultimately, these predictions are used to fine-tune algorithms governing the QoE of a member for delivering a high-quality streaming experience.

Chaitanya Ekanadham, the Senior Research Scientist at Netflix, elaborates:

‘As we expand rapidly to audiences with diverse viewing behavior, operating on networks and devices with widely varying capabilities, a ‘one size fits all? solution for streaming video becomes increasingly suboptimal. For example: Viewing/browsing behavior on mobile devices is different than on Smart TVs. Cellular networks may be more volatile and unstable than fixed broadband networks. Networks in some markets may experience higher degrees of congestion. Different device groups have different capabilities and fidelities of internet connection due to hardware differences.?

Optimizing Content Caching #

Even before the credit rolls on, Netflix caches and plays Next!

Now, as the credits start rolling, a user will always have a choice to continue with Netflix’s recommendations or let Netflix choose the ‘Next?. To reduce the time latency between consequent video plays and facilitate a smooth binge-watching experience, Netflix employs predictive caching models. These models are trained using historical member viewing data and other contextual variables, to predict the most likely content, that might have been played next by the user. Since caching is largely dependent on the size and bandwidth available at an intermediate internet hop, the use of a predictive model makes an intelligent data-driven guess as to ‘What to cache next? from the massive Netflix catalog.

Original diagram for Techfastly by Vin Patel.

Mohit Vohra, Director, Content Delivery, says:

‘Maximizing caching efficiency at the closest possible locations translates to lesser network hops. Lesser network hops directly improve user streaming quality and also reduces the cost of transporting network content for both ISP networks and Netflix. Furthermore, maximizing caching efficiency makes responsible and efficient use of the internet.?

Predictive Content Delivery #

No one loves the buffer wheel!

Internet video-on-demand streaming services are akin to tunnel delivery, where both sending and receiving devices/networks in question play a crucial role. And, Netflix heavily invests in utilizing state-of-the-art predictive modeling algorithms and technologies for optimizing user experience from both ends. However, some of the factors that usually impede Quality of Experience (QoE) at the user end are the poor quality of a subscriber’s Internet connection, characteristics of the delivery network, algorithms being run on the playing device, etc. To overcome these, Netflix makes use of the historical user data pertaining to the network (bandwidth, round-trip time, stability, predictability) and device (firmware update, UI changes) for a better characterization of the user device and network, and adjusts accordingly.

Original diagram for Techfastly by Vin Patel.

Netflix has 203.66 million subscribers worldwide*,* with 5,415 content titles in their US library alone. One of the major factors contributing to this success is Netflix’s utilization of its abundant data combined with extensive use of predictive modeling techniques to learn, predict, and improve its member experience continuously. The various QoE (Quality of Experience) metrics discussed above impact each other as well as member interaction.

To summarize, By using predictive modeling techniques, Netflix is able to:

Scale various processes to cope up with the growing demand.
Re-allocate crucial resources to further improve the member experience.
Personalize content even in the face of poor quality networks or devices (Important when dealing with new and uncertain markets).
Capture and keep user attention for as long as possible by providing a highly addictive and hyper-personalized user experience.
And much more, which we might not know about.

So, the next time you wonder as to why Netflix continues to be on the top or why you simply can’t stop watching it, recall this:

Netflix is continuously using data and algorithms to outspend and outproduce its competition, and the scale of hyper-personalization it provides to your viewing experience keeps on pumping those nasty endorphins!

Related reading

The Secret Behind Netflix's Use of AI For Better Show

Vin Patel — Fri, 11 Nov 2022 09:56:41 +0000

Key takeaways

You turn on Netflix after a hectic day at work to find the show you want to watch.
With more than 140 million subscribers globally, Netflix finds it challenging to provide the highest streaming quality to its users.
As a result, Netflix will recommend more relevant content as you spend more time there.

You turn on Netflix after a hectic day at work to find the show you want to watch. Do you remember the most recent Netflix movie you watched? Netflix seems to have the appropriate answer to the issue of what to watch next. No, the reason is not coincidental; it is based on data.

The pandemic, according to studies, is boosting the expansion of the worldwide OTT industry even more, and the compound annual growth rate (CAGR) is predicted to reach 19% by 2026. As the OTT platform king, Netflix is renowned for offering a vast selection of high-quality content. The secret to Netflix’s success is AI and machine learning technology, which provide audiences with more sensible and appropriate recommendations.

AI-Powered Recommendation Systems #

An AI-powered recommendation system is a tool for data filtering that predicts what items and products are relevant to the user from a catalogue using data and algorithms. Recommendation systems combine approaches that are collaborative and content-based. As a result, it suggests a similar pattern of these attributes. The content-based method now draws on the intrinsic items the user has already expressed interest in. A collaborative approach, on the other hand, examines all of the service’s users and makes recommendations to a new user based on the pertinent items of existing users who share similar tastes.

Recommendation engines utilize algorithms to offer clients service or product recommendations. These engines have recently begun using machine learning techniques, which improve the accuracy of the item prediction process. In addition, the algorithms adapt based on the information collected from recommendation systems.

Typically, hybrid recommended systems are used by Netflix. It begins by contrasting the watching and searching patterns of people with similar interests.

Use Cases of AI/Data/Machine Learning at Netflix #

Multiple algorithms make up the NRE, which filters content according to a user’s profile. The algorithm uses 1,300 recommendation clusters to sift more than 3,000+ titles based on each user’s tastes. Since personal recommendations account for 80% of Netflix viewing activity, they are practical and pay off.

Personalized Movie/Web Show Recommendations #

Netflix and AI work together to enable audience preferences and support developing specialized playlist recommendations. Netflix is a top-rated streaming service. More than 200 million paying subscribers use the service in 190 different nations. Although they are the best and most valuable streaming service in the world, a secret lies beneath all of their success. Netflix’s recommendation engine is highly sophisticated. They even have a system created specifically for the streaming platform. The Netflix Recommendation Algorithm, or NRE for short, is what it is known as. This program dramatically influences the success of Netflix.

Optimal streaming quality #

One critical factor that directly affects bounce rates is the streaming quality. With more than 140 million subscribers globally, Netflix finds it challenging to provide the highest streaming quality to its users. However, with AI and machine learning, Netflix can anticipate future demand and place resources in critical server locations years in advance. As a result, audiences can stream high-quality video uninterrupted even during peak hours by pre-positioning the video assets closer to the subscribers.

AVA: Generating Necessary Thumbnail Images #

To collect stills from the many thousands of available titles, Netflix established AVA. These stills will eventually become the illustrative visuals the firm utilizes to increase audience engagement.

According to Netflix, AVA analyses contextual metadata for each frame of every title in its library. After this, it finds objective cues that ranking algorithms use to find structures that satisfy the service’s artistic, creative, and diversity goals before they can be used as thumbnail images. These elements, according to Netflix, include:

Identifying faces, estimating poses, and analyzing emotions.
Motion estimate, including camera movement and motion blur.
Identify a camera shot, including determining the filmmaker’s intention.
Determining the importance of non-human subjects while detecting objects.

Utilizing Big Data in Netflix #

Every day, millions of people watch Netflix, and the company receives a large amount of data that needs to be kept and evaluated. The online streaming service provider uses various data, including user reviews, information from social media, search phrases, etc.

Artificial Intelligence For Video Meta-data #

Metadata rich in content must be available to make content searchable. Metadata includes details on what and how the actors are presented and their emotional state and temperament. It is present in every frame of the videos in the content repository. By properly collecting metadata, Netflix and AI can pinpoint these feelings and create personalized highlights to point the user toward the show they are most likely to enjoy, increasing viewing and user satisfaction.

Artificial Intelligence For Production of Content #

The first is to speed up and improve content development efficiency, reducing costs and time-to-market, which is essential in a world where video consumption is currently king. This is among the top uses of artificial intelligence that Netflix will make in 2022.

Image Personalization for The Audience #

As viewers navigate through the company’s thousands of titles, Netflix employs artificial intelligence and machine learning to anticipate which visuals will best captivate each viewer. It is among the best uses of artificial intelligence that Netflix will make in 2022.

Bottom Line #

The customized grid of recommendations provided by Netflix’s suggested engine, worth over $1 billion annually, is only tailored to the viewers’ preferences. Although Netflix has an algorithm-based recommendation system, machine learning and AI are the primary factors that make these recommendations more relevant. As data is gathered, the algorithm gets smarter. As a result, Netflix will recommend more relevant content as you spend more time there.

Related reading

Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World eBook: Iansiti, Marco, Lakhani, Karin R.: Amazon.in: Kindle Store

Top Five AI-ML Books For Business Leaders

Vin Patel — Fri, 11 Nov 2022 09:23:21 +0000

Key takeaways

Lakhani Released: 2020 The central idea of the whole book is to provide information about AI and how it has been changing the business industries.
It would be helpful for readers to use these codes as references and develop new codes.
After reviewing all these books, I want to conclude that these are fantastic resources to understand AI power and how we can use it in business transformations.

According to a recent new forecast by Gartner, Inc., the worldwide hyper-automation would enable the software market up to $596.6 billion by 2022. The prediction is enough to say that technologies become a dire need for the highly competitive market. Among emerging technologies, Artificial Intelligence (AI) and Machine Learning (ML) are positioned at the top to bring automation to businesses. Business leaders are seeking to implement AI & ML to transform their traditional business processes. They are looking for resources to understand how it works. Here is a list of books that would help understand how AI and ML could be game-changers for their business. #

(1) Competing in The Age of AI: Strategy & Leadership When Algorithms And Networks Run The World #

Author: Marco Lansiti & Karin R. Lakhani

Released: 2020

Review & Features: #

The central idea of the whole book is to provide information about AI and how it has been changing the business industries. The authors have explained that AI, networks, and data analytic can remove traditional business constraints like scope, scale, and learning and support companies to design new business models.

The book has covered the concept “Age of AI” to point out that AI has been transforming businesses’ working styles and changing economic models. Authors urged that “AI is becoming the universal engine of execution”. AI is bringing new companies operating execution models by integrating different software, algorithms, and operating systems. The authors used the example of “Amazon” to articulate this concept and compared it with Walmart.

The transformation in competition is possible through the emergence of various digital operating models. The book has provided examples of some companies competing with each other. Snapchat, Facebook, Tencent (WeChat), and TikTok have adopted scalable digital operating models to stay in the competition.

The explanation about other concepts like AI factory, Strong and Weak AI, Framework, Strategic Collisions, and Ethics has been described with real-life examples. The authors took three industries, including entertainment: YouTube, Netflix; banking: Ant financial; and hospitality: Airbnb, to explain the recurring pattern. The author pointed out that Weak AI is more impactful to enhance business growth.

The book is perfect for every business leader who is thinking of transforming the business to compete in the market.

Amazon Link: #

(2) The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You #

Author: Mike Walsh

Released: 2019

Review & Features: #

The book is not like a traditional book. It has been considered a practical approach to learning AI, automation, and algorithms to transform everyday operations. According to the author, we live in a world where wonders happen every day because of technology. Still, we assume that robots are replacing human jobs. The author argues that it is not true; the fact is that human beings are unwilling to adopt the changes.

The author has extensively researched, interviewed business leaders, industry experts, and scientists to explore the potential of human intelligence in the future. He provided a simple definition of algorithm leader, “An algorithm leader is someone who has successfully adapted their decision making, management style, and creative output to the complexities of the machine age.”

To encourage human beings and push them to become ‘smart’, the author developed the “10 Principle” shown in the picture. The author explored varied industries, cultures, and history to understand how to get success using AI. His principles are surrounded by futuristic ideas such as machine learning, probabilistic thinking, disruptive innovation, decentralization, and digital ethics.

Through this practical knowledge, the author pointed out that first, change your mind, change the work style, and then you can change the world. This change is not stuck for business purposes only but also applicable to personal life.

The book articulated the power of algorithms and how it plays a vital role in every aspect of the business. This book is best for anyone who wants to learn from real-life experiences and those who want to learn only specific principles.

Amazon Link: #

The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You eBook: Walsh, Mike: Amazon.in: Kindle Store

(3) Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions #

Author: Matt Daddy

Released: 2019

Review & Features: #

“Business data science refers to a modern standard for data analysis to implement it for businesses”. The book aims to provide an essential concept of machine learning for solving business problems and help grow the business economy. The author has provided a toolset for business data science by integrating machine learning, statistics, and economics concepts.

The uncertainty, regression, regularization, validation, classification, controls, and factorizations are building blocks of machine learning. All of these concepts have been explained with various examples.

According to the author, “Machine learning is the field that thinks about how to automatically build robust predictions from complex data”. It has been pointed out that ML and statistics are closely related to each other, which is inspiring new ideas for designing new ML algorithms to solve any business problems.

Another feature of the book is snippet codes of R language to explain the data analysis methods. It would be helpful for readers to use these codes as references and develop new codes. Technologies have been changing everything, and it is forcing business leaders to make wise decisions. Applying the prediction concept and developing a system would help understand the future risk and make better decisions.

Henceforth, the book is an excellent resource for learning the principles, applications, and practices to explore business data sciences.

Amazon Link: #

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions eBook: Daddy, Matt: Amazon.in: Kindle Store

(4) Artificial Intelligence for Business Leaders #

Author: Ait K Jha

Released: 2020

Review & Features: #

Readers do not require prior knowledge of AI and relevant topics. They do not need to learn complex algorithms and code to apply AI to drive their business. The book has covered all essential theoretical concepts to explain AI and how it can change every business. The author has provided a lucid definition of all concepts. For instance, “Machine Learning is a kind of Artificial Intelligence that provides computers the ability to learn without being explicitly programmed.” This simple definition of Machine Learning is easily understandable by anyone.

Another exciting feature of the book is that the author explained AI use-cases and practical examples to find out how AI makes business operations easy. Strategy formation before technology implementation is challenging for every business leader. The book has covered “Enterprise and Economic strategy” to help business leaders create effective business strategies.

Apart from this, most business leaders try to find other details about the technology, such as how disruptive it is, how it generated results in the past, where it failed, and what would be the future, or who is providing the services. The author has used examples of big companies like Amazon, Apple, Microsoft, and Google to describe all these.

All this information would be beneficial for readers to make a wise decision before adopting AI. In my opinion, the book is best not only for business encaustics but also for technology leaders to get the updated information in one place.

Amazon Link: #

Artificial Intelligence for Business Leaders: ARTIFICIAL INTELLIGENCE and MACHINE LEARNING BOOK FOR MANAGERS, LEADERS || ZERO CODING WITH SIMPLE Explanation… Intelligence for managers and leaders 1) eBook: Jha, Ait K: Amazon.in: Kindle Store

(5) The AI-First Company: How to Compete and Win with Artificial Intelligence #

Author: Ash Montana

Released: 2021

Review & Features: #

The author wrote this book to encourage the formation of AI-First companies with capabilities of data gathering, processing, and using it in the required place. The author himself founded, The AI-First Company to support this encouragement.

The author highlighted that AI-First companies are trillion-dollar companies that would take top place among all industries. These companies follow the design principle to develop predictive models which perform prediction and automation of business functions. And, the book would help them to understand the practical implementation of AI.

The cornerstone of the book is its framework which consists of eight parts. The author explained each part through a single chapter. For instance, the first part brought a new concept, “Data Learning Effects (DLE)” designed using network, scale, and learning effects. The author has designed the following formula:

DLE = Economics of scale to data + data processing capabilities + data network effects #

Also,

Output = function (data, data processing capabilities, and data network effects) #

Besides this part, the next parts are related to lean AI, strategy formation, team building, execution, and advantages. All these parts would help readers to integrate AI with existing business processes to automate it. The recent conversation of Author Christian Lute, founder of Crate.io) has also pointed out the importance of AI and ML in business.

The book is unique to find out the systematic steps of AI implementation in the existing business.

Amazon Link: #

The AI-First Company: How to Compete and Win with Artificial Intelligence eBook: Montana, Ash: Amazon.in: Books

#

Final Thoughts #

The availability of thousands of books always gets us confused, especially those who do not have a strong background in technology. I have recommended five AI and ML books for business leaders, executive managers, data scientists, and engineers through this article. All these five books cover theoretical concepts, original use-cases, original principles, and real examples related to ML and AI. After reviewing all these books, I want to conclude that these are fantastic resources to understand AI power and how we can use it in business transformations.

Related reading

Microsoft MOLeR: A New Age of Artificial Molecule Generation

Vin Patel — Wed, 09 Nov 2022 11:28:29 +0000

Key takeaways

The advancement of technology not only improves an individual’s global position but also makes life easier.
Due to its low-level, atom-by-atom generation approach, it struggles to recreate essential structures, such as complicated ring systems.
MoLeR is substantially quicker than all baselines for both training and inference, thanks to a simplified formulation and concurrent training on all generation steps.

The advancement of technology not only improves an individual’s global position but also makes life easier. Medical science has improved world health more than ever before. The world is rapidly moving toward technology and innovation. Every day we hear about a new idea or discovery. The fascinating aspect of this relationship is that each new technology masters its predecessor.

One of the many applications that have become a reality, thanks to technology, is the discovery of therapeutic medications.

MoLeR is a graph-based model that naturally supports scaffolds as an initial seed of the generative technique of molecules. MoLeR beats state-of-the-art methods on unconstrained molecular optimization problems and outperforms them on scaffold-based tasks, according to the findings of MoLeR developers, while being an order of magnitude faster to train and sample from than existing approaches. It’s also visible how a variety of seemingly minor design choices affect overall performance.

The MoLeR Model: #

The molecules are represented as graphs in the MoLeR model, with atoms acting as vertices connected by edges corresponding to bonds. The model is trained using the auto-encoder paradigm, which consists of an encoder’s graph neural network (GNN) to compress an input molecule into a so-called latent code’and a decoder to reconstruct the original molecule from this code. The reconstruction method is designed to be sequential because the decoder must decompress a short encoding into a graph of any size.

A partially produced graph is extended by adding new atoms or bonds in each phase. The decoder in the model makes predictions at each step simply on the basis of a partial graph and a latent code rather than relying on previous predictions. MoLeR is taught to build the same molecule in a variety of different orders because the order of construction is random.

How MOLeR Optimize The Process of Molecule Generation? #

Even after training the model, as mentioned earlier, MoLeR has no concept of “molecular optimization.” It may, however, use an off-the-shelf black-box optimization method to do optimization in the space of latent codes, similar to other ways. This is not achievable with CGVAE because it employed a considerably more sophisticated graph encoding. It chooses Molecular Swarm Optimization (MSO) for working because it produces state-of-the-art results for latent space optimization in other models, and it works quite well for MoLeR. On novel benchmark tasks that are akin to true drug discovery projects using big scaffolds, the developers discovered that optimization with MSO and MoLeR outperformed existing methods.

The Evolution That Led Up to the MoLeR Model: #

CGVAE, a generative model of molecules developed by the researchers, performed well on simple synthetic tasks. However, the CGVAE model’s applicability in practical drug development was limited by two issues:

It can’t be naturally limited to merely looking at molecules with a certain substructure (called the scaffold). Due to its low-level, atom-by-atom generation approach, it struggles to recreate essential structures, such as complicated ring systems.

Because the decoder must decompress a short encoding into any size graph, the reconstruction technique is meant to be sequential. In each phase, they add new atoms or bonds to the partially constructed graph.

Rather than depending on prior predictions, the model’s decoder simply produces predictions based on a partial graph and a latent code at each step. The researchers explain that pharmacological compounds are typically composed of larger structural motifs rather than random atom combinations, comparable to how sentences in spoken languages are composed of words rather than random sequences of letters. MoLeR, unlike CGVAE, learns to extend a partial molecule using entire motifs after first recognizing these common building blocks in data (rather than single atoms). Because the sequence in which the molecule is formed is arbitrary, MoLeR has also been trained to build the same molecule in several orders.

As a result, MoLeR not only takes fewer steps to produce drug-like compounds, but it also does so in a manner that is more similar to how biologists think about molecule construction.

A scaffold is a critical component of a molecule that has previously shown intriguing features. Drug development approaches usually concentrate on a small subset of the chemical space, identifying a scaffold first and then only looking at compounds that contain the scaffold as a subgraph. The design of MoLeR’s decoder permits the smooth integration of an arbitrary scaffold by employing an random scaffold as an initial state in the decoding loop. MoLeR is well suited for targeted scaffold-based exploration since it learns to complete arbitrary subgraphs via randomized generation order during training.

Although MoLeR has no concept of “molecular optimization,” the researchers note that optimization in the space of latent codes can be done using an off-the-shelf black-box optimization method. In this study, they used Molecular Swarm Optimization (MSO), which offers cutting-edge findings for latent space optimization in other models. Their findings demonstrate that it is also effective for MoLeR. The researchers used MSO and MoLeR to optimize new benchmark tasks that are similar to genuine drug development projects requiring large scaffolds and found that this combination outperformed current models.

Results: Efficiency of MoLeR #

The number of molecules processed per second is used to quantify the speed of different models in training and inference. For this comparison, we do not subsample generation stages, so every model processes all of them, even though MoLeR can only learn from a subset of them. MoLeR is substantially quicker than all baselines for both training and inference, thanks to a simplified formulation and concurrent training on all generation steps.

Related reading

The Changing Scenarios of Digital Enterprises in 21st

Vin Patel — Wed, 09 Nov 2022 11:22:26 +0000

Key takeaways

Introduction In today’s teach-savvy generation of modern computing inception, AI has been a far-fetched objective, yet each day appears to bring us closer to that goal with new cognitive computing models.
Numerous cognitive computing drawbacks have been identified, posing substantial obstacles to broader implementation, including the following: SecurityWhen digital devices manage sensitive data, the issue of security inevitably arises.
All enterprises and humankind as a whole will benefit from initiating the transition process and adopting innovative technologies to ensure a bright and much more efficient future.

Introduction

In today’s teach-savvy generation of modern computing inception, AI has been a far-fetched objective, yet each day appears to bring us closer to that goal with new cognitive computing models.

Coming from a synthesis of cognitive science and based on the fundamental premise of simulating the human thought process, cognitive computing as a concept and its applications are sure to have far-reaching implications for our personal lives and industries such as healthcare, insurance, and more. The advantages of cognitive technology much outweigh those of conventional artificial intelligence systems.

According to David Kenny, General Manager, IBM Watson – the world’s most powerful cognitive computing framework — “AI is only as intelligent as the people who educate it.” The same cannot be said of the most recent cognitive revolution.

Cognitive computing processes employ a combination of artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis, and contextual awareness to solve problems in the same way as people do. IBM defined cognitive computing as an advanced system that learned rapidly, reasoned purposefully, and interacted naturally with humans.

Artificial Intelligence vs. Cognitive Computing #

While the fundamental purpose of artificial intelligence is to find the optimal algorithm to solve a problem, cognitive computing takes a step further by attempting to replicate human intellect and wisdom via the analysis of a variety of elements. Cognitive computing is an entirely distinct notion from Artificial Intelligence.

Cognitive computing emulates and learns from human cognitive processes. #

Unlike artificial intelligence systems that solve a specific problem, cognitive computing learns through pattern analysis and offers appropriate actions to humans based on their knowledge. In the case of artificial intelligence, the system takes absolute control of a process and uses a pre-defined algorithm to finish a job or avoid a situation.

In comparison, cognitive computing is a distinct discipline in which the computer acts as assistance rather than the performer of the work. Thus, cognitive computing enables people to do faster and more accurate data analysis without worrying about the machine learning system making incorrect conclusions.

Cognitive computing does not obliterate people. #

As said previously, cognitive computing’s primary goal is to aid people in decision-making. This endows humans with exceptional analytical precision while also assuring that everything remains under their control. As an illustration, consider artificial intelligence in the healthcare system. A system powered by AI would make all treatment decisions without consulting a human doctor. However, cognitive computing would enhance human diagnosis with its own set of facts and analysis, improving decision quality and adding a human touch to essential operations.

Going Cognitive: Cognitive Computing’s Benefits #

Modern computer technologies are poised to transform current and historical process automation systems. according to Gartner, cognitive computing will reshape the digital world in ways that no other technology has in the past two decades. By enabling the analysis and processing of massive volumes of volumetric data, cognitive computing enables the use of a computing system for meaningful real-world applications. Cognitive computing provides a slew of advantages, among which are the following:

Analyze Data with Precision #

Cognitive systems are incredibly efficient in collecting, juxtaposing, and cross-referencing data to understand a scenario efficiently. In the healthcare industry, cognitive systems such as IBM Watson assist physicians in collecting and analyzing data from a variety of sources, including previous medical reports, medical journals, diagnostic tools, and historical data from the medical community, thereby assisting physicians in providing a data-backed treatment recommendation that benefits both the patient and the physician. Rather than replacing physicians, cognitive computing accelerates data analysis through robotic process automation.

Streamlined & Expanded Business Processes That Are Efficient #

Cognitive computing enables real-time analysis of developing trends, identifying business possibilities, and resolving crucial process-centric concerns. A cognitive computing system like Watson can streamline operations, mitigate risk, and adapt to changing situations by analyzing massive amounts of data. While this equips firms to respond appropriately to uncontrolled variables, it also aids in the development of lean business processes.

Enhancement of Customer Interaction #

By incorporating robotic process automation, the technology may be leveraged to improve client relations. Customers can obtain contextual information from robots without interacting with other staff members. Because cognitive computing enables businesses to present consumers with only relevant, contextual, and meaningful information, it enhances the customer experience, increasing customer satisfaction, and engagement.

Cognitive Computing Issues: Obstacles to a Better Future #

Each new technology has challenges during its existence. Even though cognitive computing can transform lives, humans oppose innovation because of fear of change. Numerous cognitive computing drawbacks have been identified, posing substantial obstacles to broader implementation, including the following:

SecurityWhen digital devices manage sensitive data, the issue of security inevitably arises. With the ability to process and analyze enormous amounts of data, cognitive computing faces substantial data security and encryption challenges.

With the proliferation of connected devices, cognitive computing will need to consider the implications of a security breach by building a foolproof security strategy that includes a system for identifying suspicious behavior to maintain data integrity.

AdoptionThe primary impediment to every new technology’s success is voluntary adoption. To ensure the success of cognitive computing, it is critical to building a long-term vision for how the new technology will improve procedures and enterprises.

The adoption process may be simplified by collaboration amongst many parties, including technology developers, corporations, governments, and individuals. Simultaneously, it is critical to have a data privacy policy that will further accelerate the adoption of cognitive computing.

Management of ChangeChange management is another critical issue that cognitive computing must address. People are reluctant to change due to their inherent human nature, and because cognitive computing can learn in the same way that humans do, people are concerned that robots will eventually replace humans. This has had a significant influence on growth prospects.

Cognitive technology, on the other hand, is designed to function in concert with people. By injecting information into the networks, humans will foster technology. This makes it an excellent illustration of a human-machine relationship that people must accept.

Protracted Development CyclesOne of the most significant obstacles is the time required to construct scenario-based applications using cognitive computing. Cognitive computing is presently being developed as a generic solution — which implies that it cannot be applied across numerous industrial segments without the support of large development teams and a significant amount of time.

Protracted development cycles make it more difficult for smaller businesses to build cognitive capacities independently. With time, as development lifecycles decrease, cognitive computing will undoubtedly take on a more significant role in the future.

Conclusion #

Adoption of cognitive technology begins with the discovery of manual tasks that can be automated utilizing the technology as part of the digital evolutionary cycle. Numerous businesses, such as IBM, have previously pioneered the cognitive technology space, which is now powering a slew of genuinely digital corporations worldwide.

Each minute, additional data is evaluated to acquire insight into past occurrences and to optimize present and future activities. Not only can cognitive technology aid in prior analysis, but it also aids in far more accurate prediction of future occurrences via predictive analysis. Due to the strong and adaptable nature of the technology, the future opportunities and pathways in both the B2B and B2C segments are enormous. IBM Watson is already leveraging the power and benefits of cognitive computing in the finance and healthcare industries. It is thought that in the future, such technology would assist humans in becoming more efficient, delegating routine analysis and focusing on creative tasks.

To maximize the benefits of cognitive computing, firms must do a thorough study of their processes, data, talent model, and the market in which they operate. Beyond cost reduction, we believe that one of the greatest potentials for cognitive technology is the capacity to create value, as well as the ability to restructure work and boost efficiency by streamlining a variety of procedures.

Regardless of the difficulties and obstacles, the benefits of cognitive technology cannot be underestimated. All enterprises and humankind as a whole will benefit from initiating the transition process and adopting innovative technologies to ensure a bright and much more efficient future.

Related reading

The Healthcare Industry Endorses Predictive Analysis Now!

Vin Patel — Mon, 07 Nov 2022 12:21:49 +0000

Key takeaways

Statistics, machine learning, data mining, and other statistics-related technologies have already accomplished the inconceivable in the IT industry and are quickly developing every day, extending their wings across many technological areas.
These biosensors are discretely attached to the patient’s chest and collect, store, measure, and transmit respiratory and heart rates as well as contextual data including posture, activity level, and ambulation every minute.
This article focuses on the use of predictive analysis in monitoring a patient’s health.

Statistics, machine learning, data mining, and other statistics-related technologies have already accomplished the inconceivable in the IT industry and are quickly developing every day, extending their wings across many technological areas. Predictive analysis has also been used in the healthcare business to embrace these technologies.

Predictive analytic is a field that evaluates historical and real-time data to create predictions about the future using diverse approaches such as modelling, data mining, statistics, and artificial intelligence (AI). Predictive analytic in healthcare may benefit the sector in a number of ways, and some of the use cases have practically changed the industry.

How does predictive analysis function in healthcare? #

Every case that healthcare staff deal with requires them to handle a vast quantity of data from many sources. Patients’ electronic health records, medical imaging, screening findings, and different administrative data are all included. Furthermore, they are frequently required to make quick judgments based on this knowledge. Every time they connect with their physician, patients want a consistent, informed experience. They want a smooth, customized experience whether they’re getting a basic check-up or visiting a specialist.

Predictive analytic tries to warn physicians and caregivers about the likelihood of events and consequences before they happen, allowing them to prevent rather than treat health problems. We now have algorithms that can be supplied with historical as well as real-time data to generate meaningful predictions, thanks to the emergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Predictive algorithms may be used to help clinicians make better decisions for individual patients as well as to influence treatments at the cohort or community level. Basically, predictive analysis solves functional and management problems in hospitals.

Use cases of predictive analysis: #

Advanced predictive analysis methods can perform wonders like improving chronic disease management, reducing overhead costs, being a great assistant in medical research, and many more. Here we read about the use cases of predictive analysis in the healthcare industry:

Predictive analysis detects early signs of patient health deterioration: #

In the ICU, where a patient’s life may rely on quick action when their health is likely to deteriorate, predictive insights can be very useful. The number of patients requiring acute care in the ICU has increased since the coronavirus pandemic, highlighting the need for technology to assist caregivers in making quick decisions.

Because patients’ vital signs are continually monitored and evaluated, predictive algorithms can assist in identifying patients who are most likely to require care within the next 60 minutes. This enables caretakers to intervene at an early stage in the patient’s condition, depending on minor symptoms of deterioration. Predictive analytic may also estimate the likelihood that patients will die or return to the ICU within 48 hours if they are discharged from the ICU, assisting caregivers in deciding which patients can be discharged.

In a hospital’s general ward, where patients’ deterioration sometimes goes unreported for lengthy periods of time, predictive analytic can assist discover early warning indicators of unfavorable outcomes. As wearable biosensors become more widely used, care professionals may be able to detect early indicators of patient deterioration as patients travel through different acuity levels in the hospital. These biosensors are discretely attached to the patient’s chest and collect, store, measure, and transmit respiratory and heart rates as well as contextual data including posture, activity level, and ambulation every minute.

Predictive analysis helps in chronic disease prevention and management: #

Predictive techniques like remote patient monitoring and machine learning can help hospitals make better decisions by providing risk grading and threshold alarms. This feeds into personalized messaging with the predictive analytic data cloud, reminding patients to refill medicines or assisting them if they have problems getting refills or going to the pharmacy.

Predictive analysis uses big data to find solution to the problem. The system may provide a specific risk score to a person based on test results, patient-generated data on their lifestyle, and biometric data. This score indicates the likelihood of a problem occurring in the near future. It’s also more likely to spot early indicators of deterioration and report them to a doctor.

Furthermore, AI may notify a care manager when a patient is lagging behind on their treatment plan and suggest targeted outreach. These are the types of flawless patient experiences that encourage better compliance plans and, as a result, better health results.

Predictive analysis delivers predictive care for at-risk patients in their houses: #

Many patients are now discharged without long-term health monitoring following a hospital stay, putting them at risk of adverse events and hospital readmission that may have been averted with proper preventative measures. Falls at home are particularly prevalent among the fragile and old, and they are a primary cause of fatal and non-fatal injuries. It’s yet another way for predictive analytic to help turn a reactive healthcare strategy into a proactive one.

Predictive analytic may leverage data from a variety of sources, such as hospital-based electronic medical records, fall detection pendants, and previous usage of medical alert systems, to identify seniors who might need emergency transportation in the upcoming month. This enables healthcare practitioners to contact a senior citizen prior to a fall or other medical crisis, avoiding needless hospital readmission and lowering transportation, acute care, and rehabilitation expenditures.

Predictive analysis can make predictions based on genetic data: #

Adults with genetic abnormalities account for at least 10% of the population. Early detection of certain of them can aid in their management and avert issues later in life. However, because the human genome is a complex system, assessing genetic data is a difficult task.

Predictive analytic may be used to examine and compare a person’s genetic data to a database of potential faults and disorders. It can even be utilized as early as the newborn stage to alert parents to the problem their kid may be suffering from.

Bottom-line: #

Predictive analytic in healthcare is projected to grow in popularity since it has the ability to improve clinical care delivery and equipment maintenance. But, since as useful as predictive algorithms are, their influence is ultimately determined by domain specialists? physicians, nurses, engineers, and hospital managers? who understand how to assess probability in the context of a patient or healthcare system. As a result, professional input, as well as cutting-edge analytical capabilities, are required in the creation and deployment of such algorithms. This article focuses on the use of predictive analysis in monitoring a patient’s health.

Related reading

How to Use Intelligent Document Processing

Vin Patel — Thu, 03 Nov 2022 13:02:52 +0000

Key takeaways

In each stage of document data integration; data science methods such as computer vision, text recognition, machine learning, and natural language processing, increase human understanding of unstructured data.
Any data that doesn’t match up is detected and sent to a person to be reviewed and corrected.
As we re-imagine how we work, new workflows become revolutionary business partners.

In each stage of document data integration; data science methods such as computer vision, text recognition, machine learning, and natural language processing, increase human understanding of unstructured data.

Intelligent Document Processing (IDP) is gaining popularity because it offers game-changing methods for automating data extraction projects that were previously difficult, if not impossible, to complete.

What Has Changed Recently in Document Processing? #

What’s new is integrating various tools into a single platform solution, completely changing how we work. New data sources improve business outcomes and open the road for human-driven innovation.

This is a novel method of gathering and extracting data. All of the major technology companies are developing sophisticated tools, but the difficulty is that they aren’t all available on one platform.

Intelligent Document Processing platforms are advanced software machines that classify data from any text-based source and feed it into the data supply chain.

What Makes an Intelligent Document Processing Platform So Special? #

Every step required to convert paper or digital documents into appropriately labelled data is included in intelligent document processing technologies.

IDP platforms must meet the following criteria:

They must be sector agnostic.
Accommodate both structured and unstructured data.
Integrate with cloud and on-premises content management systems
Scale to process billions of extractions every day
Create a graphical user interface for training and categorization

Each Stage of Document Data Integration and How Intelligent Document Processing Platforms Handle It? #

Document capture #

The platform integrates with scanning hardware to digitize tangible material such as paper or microforms. Because not all documents are digital, a solution to speed up historically sluggish scanning operations is necessary.

Data is ingested from digitally born content such as text files, PDFs, and office productivity documents via built-in integrations.

Image processing #

Computer vision algorithms provide image processing, which prepares a document for effective OCR and preservation. The IDP platform will produce two versions of digitized documents: one for machine reading and one for on-screen viewing in a content management system.

OCR (Optical Character Recognition) #

Accurate OCR is required for machines to read text on documents. The utilization of several OCR engines is one of IDP’s most essential features. By synthesizing the data from numerous engines until near-perfect accuracy is attained, a “layered” method eliminates the need for better OCR.

NLP #

Find paragraphs, sentences, or other language pieces in your papers that express specific meaning using Natural Language Processing (NLP). NLP uses techniques including sentiment analysis, part-of-speech tagging, named entity tagging, and feature-based tagging to speed up data discovery.

Classification #

The majority of business documents are collections of pages containing various types of information. Machine learning and other intelligence-based techniques are used to train IDP classification engines to recognize documents.

Automatic document recognition is a crucial step in deciphering the contents of a document. The days of manual data entry for categorization are long gone.

Extraction #

The artificial interpretation of information by the software is critical to successful data extraction. Because artificial intelligence (AI) is only as knowledgeable as its training, the system must be able to find and classify all expected information within a page. This includes recognizing parts of natural language documents and extracting certain data items such as dates, names, and numbers, among other things.

Data Validation #

To be trustworthy, every extracted data must be verifiable. IDP solutions are unique because they validate data using external databases and pre-configured lexicons. Any data that doesn’t match up is detected and sent to a person to be reviewed and corrected.

Integration #

The needs for data integration are quite varied. IDP platforms must integrate with all downstream applications since they are essential sources in the data supply chain. This comprises databases and document repositories in the cloud and on-premises. For portability, labelled data and metadata are connected to human-readable versions of the data.

How to Use Intelligent Document Processing to Your Advantage? #

The development of document data literacy is the key to success with IDP platforms. Before training software to integrate data, a significant amount of work must be spent learning what data is provided and the business risks that are associated with that information.

It is necessary to engage subject matter experts who use the information to do work to attain document data literacy. Their in-depth knowledge of the business value and interpretation of the data on the documents they work with guarantees that the correct data is extracted and that the appropriate action is taken with it.

What’s the Difference Between Document Capture and Intelligent Document Processing? #

The most significant difference between IDP and standard capture is the potential of development in document processing. Over a decade ago, the main players in traditional document capture stopped inventing solutions.

There are two explanations for this:

To begin with, such technologies were developed during a time when preserving compute was a priority. Their software architecture was not designed to handle the scalability requirements of today’s data-intensive applications. And, because many of these platforms have evolved through acquisition, a platform-wide software re-build to match IDP’s needs would be prohibitively costly.
The second reason is that traditional document capture firms have a huge customer base. They’re successful as is, and they don’t want to disturb their customers’ present operations by requiring an upgrade. Instead of focusing on capture innovation, they have shifted their focus to other technologies such as robotic process automation or rebranding to look to have IDP capabilities.

Categories of IDP Vendors #

To make it easier for you to explore the landscape, we’ve divided the vendors into categories:

Vendors of innovative IDPs Vendors of legacy IDPs
IDP merchants who specialize in a specific niche
Systems integrators who provide IDP services
Providers of technology with IDP components

Vendors in each category have produced solutions that are distinct from one another because they approach the IDP dilemma through different lenses.

Suppliers of IDP Technology #

General-purpose technology components like OCR and computer vision are available on the major technology platforms. Because these components aren’t whole IDP solutions, we’ve merely included them in this piece because some businesses prefer to develop capabilities internally using existing technological components.

Extract by Amazon
Google Cloud Vision is a service provided by Google.
Computer Vision in Microsoft Azure

The Engine for Transformation is Intelligent Document Processing. #

Data plays a key role in transformation in all organizations. They obtain important insight needed to disrupt their market by producing something new by gaining new sources of data or finding new techniques of analysis.

The most crucial aspect of “becoming digital” is data. It has been predicted that data will become the new trend, and that moment has come. Because digital transformation produces value-added services, products, operational strategies, and capabilities, data is the single most crucial component for innovative success.

Final Thoughts #

Data is a key enabler of digitalization, and companies that invest in intelligent document processing will remain on the cutting edge of innovation and growth. IDP supports the modern industry by integrating a steady stream of useful data into software applications. As we re-imagine how we work, new workflows become revolutionary business partners.

Related reading

Questions and answers

What Has Changed Recently in Document Processing? #

What’s new is integrating various tools into a single platform solution, completely changing how we work. New data sources improve business outcomes and open the road for human-driven innovation.

What Makes an Intelligent Document Processing Platform So Special? #

Every step required to convert paper or digital documents into appropriately labelled data is included in intelligent document processing technologies. IDP platforms must meet the following criteria: - They must be sector agnostic.

How to Use Intelligent Document Processing to Your Advantage? #

What’s the Difference Between Document Capture and Intelligent Document Processing? #

Industry 4.0 Changing Lives: The Training Workers' Case

Vin Patel — Mon, 31 Oct 2022 05:28:57 +0000

Key takeaways

The expansion of digitization is making the global transition to Industry 4.0.
It makes them better communicators, improves the influx of innovative ideas, and increases their adaptability and ability to embrace new career challenges.
It is a revolution organizations need to prepare for so that their competition in the cut-throat marketplace doesn’t outrun them.

The expansion of digitization is making the global transition to Industry 4.0. Termed as the future factory, it is changing how organizations manufacture, enhance, and deliver their products by integrating smart technologies like cloud, artificial intelligence, IoT, and analytic.

Let’s dive in and understand how the new industrial revolution will affect us or if it is just another marketing buzzword.

Introduction to Industry 4.0 #

Industrial revolution 4.0, or the fourth industrial revolution, is the advancement of the industrial 3.0 revolution with the addition of smart devices and systems, virtual reality, and wireless connection.

It vividly describes digital transformation with technological developments and advancements, including machine learning, artificial intelligence, smart cCities, IoT, edge computing, cloud computing, and smart automation.

The four foundational technologies that constitute the building blocks of Industry 4.0, according to McKinsey are:

Data, connectivity, and computation power (includes cloud, blockchain, sensors, and Internet of Things)
Intelligence and analytic (includes machine learning, artificial intelligence, and advanced analytic)
Interaction of humans and machines (includes automation, robotics, augmented reality, virtual reality, and chatbots)
Advanced Engineering (includes renewable energy, nanoparticles, and additive manufacturing processes like 3D printing)

The first three industrial revolutions focused on inventing and innovating novel machines and advancing electronics and production. The advancements in automation and wireless connectivity will revolutionize the workflow, enable better connections with smart devices, and level up production and living standards.

Impact on Workforce #

Since the first robot Animate was created, employees have expressed concerns about losing their jobs to robots. Several sci-fi movies helped visualize robots running factories and businesses, but the reality is far different and is not the reflection of reality.

The Hays Global Skills Index shows how middle-skilled jobs would disappear. Still, it also describes the creation of jobs in higher and lower-skilled occupations, proving that automation would create more jobs instead of deleting them.

Research by the World Economic Forum supports these findings, which showed that automation creates around 133 million jobs and only cuts 75 million jobs. The employees in the phased-out jobs could be unskilled and assigned new roles in alignment with the new industrial revolution.

Global Trends Associated with Industry 4.0 #

Industry 4.0 promotes several projects, but the most vital trend is increased localization. This means that the production houses, manufacturing, and factories are getting closer to their customer base. Other global trends to look out for include:

Enhanced Cybersecurity and privacy
Industrial robots
3D printing
AR
Increased transparency
Cloud computation

Training Employees for Industry 4.0 #

Some methods for training your employees to adopt Industry 4.0 are:

Upskilling and Deskilling Employees #

The idea of deskilling employees might seem intimidating, but it offers more benefits than hiring a new workforce. Besides increasing employee satisfaction, it is a more cost-effective and faster process.

Searching for new employees, onboarding, and making them familiar with the workspace is time-consuming. In contrast, with a few inputs, it is easier to train existing employees within the organization.

Re-training for Soft Skills #

Soft skills prove as crucial as other skills in defining the competitiveness of the employees in their job. It makes them better communicators, improves the influx of innovative ideas, and increases their adaptability and ability to embrace new career challenges.

Some soft skill domains for training can include:

Emotional Intelligence
Collaboration
Creativity
Persuasiveness

Enhance Training Techniques #

To prepare to adapt to the digital revolution, your organization needs to improve its training methods. Two examples of achieving this goal are using AR and online performance support.

Implementing novel training techniques can give employees a better idea of what they would work with, improve the appeal of their prospective careers, and increase employee satisfaction.

How to Meet the Employees? #

Industry 4.0 training involves addressing individuals where they are, irrespective of their countries or cities.

Some of the training patterns we see in version 4.0 are just upgraded versions of training we’ve already seen. The following are some of them.

AR Training

In 2017, a study published in the Harvard Business Review discovered that augmented reality was already enhancing factory workers’ productivity.

Microlearning

Mobile devices are used to give quick information and instruction that is intended to help workers acquire new skills as they are required. These technologies provide information precisely when and where employees need it since they are made to be delivered in the manner in which the brain learns best.

On-the-job Training #

Employees learn while working by undergoing on-the-job training. Employ a strategy that is cumulative so that workers can gradually acquire the various skills they need.

As workers adapt to the requirements of industry 4.0 technology, this kind of training might also lead to decreased downtime.

Online Checklist #

Using digital checklists, employees can learn the procedures and systems required for a specific job or location.

Digital Support #

Mobile-first digital performance support gives employees immediate access to industry 4.0 technologies.

VR Training

While upholding quality standards and increasing output, virtual reality training can assist in lowering manufacturing costs. This training is urgent, given the global shortages and rising demand.

Companies that Implemented Training Modules for Workers for Smart Manufacturing #

Several organizations deny the impact of the fourth industrial revolution on how they conduct business. Even if companies understand the importance of Industry 4.0, they are either unprepared or struggling to identify talent, knowledge, and opportunities that come with the new industrial evolution.

Only some firms understand the essence of embracing industry 4.0 and have implemented training modules for workers for smart manufacturing. There are two ways of deskilling the existing workforce in manufacturing: By doing it within the organization or by outsourcing the training process.

Some organizations that have embraced Industry 4.0 and training methods for their employees are Schneider Electric, Bosch, SAP SE, ABB, and Hitachi.

What’s Next? #

According to Custom Market Insights (CMI), the market cap of Industry 4.0 was 118.11 billion US dollars in 2021 which is projected to reach approximately 155.76 billion US dollars by 2030, showing a CAGR growth rate of 14.3 percent.

These statistics are proof that Industry 4.0 isn’t just a buzzword. It is a revolution organizations need to prepare for so that their competition in the cut-throat marketplace doesn’t outrun them.

Related reading

The Impact of AI in Satellite Imagery

Vin Patel — Thu, 04 Aug 2022 03:04:59 +0000

Key takeaways

With the advent of artificial intelligence (AI) and machine learning (ML), satellite imagery has evolved into something far more exciting and valuable.
There are two groups when it comes to the application of ML models in satellite imagery.
AI, particularly the fast-evolving sub-disciplines like a machine and deep learning, offers the key to rendering satellite photo processing smoother, more robust, and more universally accepted.

What do we know about satellite imagery? The most basic definition of satellite imagery is that it is a mechanism by which we receive photos of the Earth taken from space with the help of certain technology. These satellites are like eyes in the sky. With the advent of artificial intelligence (AI) and machine learning (ML), satellite imagery has evolved into something far more exciting and valuable.

Scientists receive a lot of data from these satellite images daily. When this data is tested properly and evaluated, it can provide valuable insight into strategic initiatives, problem-solving, and disaster relief efforts.

Uses of Satellite Imagery #

There is an ever-increasing number of satellites orbiting the planet Earth. They take millions of photographs from space every day and send them back to the scientists on Earth. Satellite imaging allows for unprecedented distant surveillance of our environment while also generating crucial data on existing conditions on the surface. They also help scientists detect changes in the Earth’s climate, any kind of resource scarcity, the extent of urbanization, etc. Satellite imagery is also used to measure agrarian and industrial growth, human expansion in certain areas and other worldwide developments.

AI Applications for Satellite Imagery #

In terms of technology, the world is moving at a breakneck speed. The amount of data sent from satellites for processing and analysis has increased significantly. The number of climatological photos available for human analysis is increasing at an exponential rate. Now the problem arises when the number of climate researchers and meteorological data analysis professionals cannot be increased at the same rate. Under such circumstances, automation is required. Herein comes the uses of AI and machine learning.

Satellite imagery AI applications have been broadened to numerous levels. This is primarily for machine learning research to extract critical information for AI model development in the same fields. There is a need to recognize diverse things from high altitudes for field surveys or urban development. In these conditions, AI models produced through machine learning or deep learning are required for usage in space surveillance via satellite photos.

Satellite imaging, in combination with aerial images and AI, could be used to observe and measure events like topmost tree thinning, loss of undergrowth native vegetation, etc. Applications of AI through machine learning can also be used to measure alterations in water levels. Furthermore, the development of new vegetation in areas that are being revived can also be tracked by artificial intelligence technology.

Artificial Intelligence allows for the analysis and correlation of a vast number of climate variables. The use of both terrestrial and geographical data is critical for establishing connections between all these parameters. This is to gain a better awareness of global challenges and offer solutions. It gives crucial aspects for interpreting the trajectory of the environment and its influence on our existence to all policy-makers (NGOs, governments, and regular citizens).

Application of ML Models and Algorithms in Satellite Imagery #

Satellite imagery technology has advanced significantly in recent years, mainly due to advances in machine and deep learning. Machine learning approaches enable engineers to enhance picture data in addition to image editing. Machine Learning algorithms have shown to be an effective technique for interpreting satellite data at any resolution and providing more nuanced insights. There are a few hurdles in applying Machine Learning to satellite photos in its early phases, along with the extremely huge file size of satellite imagery and the file formats being specifically built for geo-referenced photographs, which makes Big Traditional data Processing applications challenging. However, analyzing the concept of the time value of a solution and building a powerful framework facilitates the construction of programmed to solve these restrictions.

Machine Learning is also used heavily for satellite pictures by Earth Observation companies like DigitalGlobe and Planet. Planet Analytic is a specialized Machine Learning system that employs Machine Learning algorithms to scan regular satellite images, recognize and classify structures, locate topographical and geographic characteristics, and reliably observe even just the smallest changes that occur. The data feed is readily incorporated into procedures and provides remarkable insights into practically any location on the planet.

There are two groups when it comes to the application of ML models in satellite imagery. They are:

One-level Satellite Data Applications

Mufti-level Satellite Data Applications

We will discuss them one by one along with examples of each category.

One-level Satellite Data Applications #

Machine learning algorithms are used directly in satellite imagery in “one-level” applications. This type of satellite data is used to observe small objects and detect changes through the use of AI.

For example, change detection algorithms heavily depend on machine learning in satellite imagery. Agriculture, farmland use, public development (e.g. road segments), environmental (deforestation, water reserves), and global disaster monitoring are all examples of change detection use cases. Change detection derived from remote sensing (RS) data is a widely used way of identifying alterations on the Earth’s surface.

- Change Detection Algorithms #

The practice of finding differences in the condition of an object or phenomenon by watching it at different times is known as change detection. Satellite images are used to detect digital changes. In simple words, a change detection algorithm is the ability of a system to correctly perceive external input, learn from it, and apply what it has learned to fulfil specific goals and tasks. This is achieved through flexible adaptation. Several AI techniques are inspired by biological phenomena. They mainly concentrate on the advent of modern deep learning methods, innovative network topologies, and sophisticated machine learning methods.

- Computer Vision Techniques #

The fundamental use of satellite data is to enable computer vision to detect diverse objects. Computer vision is a branch of artificial intelligence (AI) that allows machines and programs to extract useful data from virtual photos, videos, and other sensory inputs. Computer vision can also participate in executing certain measures to make predictions based on that data.

Municipalities, government bodies, emergency crews, the armed forces, and other civil authorities require reliable information on structures, route sections, and urban area boundaries. A variety of integrated, fully prepared workflows using convolutional neural nets (eg. FasterRCNN, YOLO) have made object detection easier. However, there are several difficulties with satellite images that make this job more difficult. The potential for adapting these technologies to various scales and important assets in satellite photography is enormous.

Mufti-level Satellite Data Applications #

Information retrieved from satellite data is only a sequence of parameters in a more complicated ML system in mufti-level applications. It can range anywhere from recognizing cars in parking lots to predicting crop yields, detecting oil inventories, etc. With the help of mufti-level satellite data applications, scientists can also track the economic activities of several countries throughout the world. It is especially applicable for those areas that are difficult to reach, such as China.

As an example of a mufti-level satellite data application, we can take into account agricultural field mapping from the air. Data from satellite photos can aid the AI model in predicting crop productivity and prices. The AI model, which is based on satellite imagery, has shown to be a very effective tool for yield estimation.

Final Thoughts #

The ability of scientific and commercial communities to evaluate satellite images is still far outpacing its accessibility. While AI has made tremendous progress in satellite imaging, there is still a long way to go. Satellite imaging data obtained due to machine learning is an invaluable resource for addressing several problems. These problems can be as diverse and essential as assessing the effects of global warming, forecasting agriculture yields, etc.

The impact of artificial intelligence in satellite imaging can also be seen in measuring environmental development toward the Sustainable Development Goals (SDGs). AI, particularly the fast-evolving sub-disciplines like a machine and deep learning, offers the key to rendering satellite photo processing smoother, more robust, and more universally accepted.

Related reading

How Artificial Intelligence Is Helping With Space

Vin Patel — Thu, 04 Aug 2022 03:00:58 +0000

Key takeaways

Introduction Artificial intelligence (AI) is increasingly vital in space operations and exploration, from evaluating the landscape on Mars to boosting communications between satellites and ground stations.
In time-sensitive scenarios, an AI/machine learning system could offer solutions in minutes, rather than the hours or days required by more traditional methods.
To realize AI’s immense promise, we must also ensure that AI systems are built and maintained safely, and that commanders and operators have the necessary training and expertise to trust this disruptive technology.

Introduction

Artificial intelligence (AI) is increasingly vital in space operations and exploration, from evaluating the landscape on Mars to boosting communications between satellites and ground stations. It has a capacity that has a plethora of uses and holds enormous potential for the data-dense and complex environment of space.

Despite broad unfamiliarity, artificial intelligence is a technology that is revolutionizing every aspect of life. It is a versatile tool that helps people to reconsider how we combine information, evaluate data, and use the ensuing insights for better decision making. Our goal with this thorough overview is to educate policymakers, opinion leaders, and interested observers about artificial intelligence and to highlight how it is already affecting the world and raising critical questions about society, economy, and governance.

The huge amount of data presently being given in real-time by Earth observation assets (e.g., the Copernicus sentinel network) and astronomical missions (e.g., the Kepler Space Telescope) provides intriguing new datasets and necessitates the use of novel machine learning techniques. Indeed, remote sensing is driving deep learning innovation for large-scale image processing tasks, whereas AI is being applied in a wide range of astrophysical applications, including planet discovery, galaxy classification, and cosmological modelling.

AI usage is also aiding less theoretical applications in the space sciences. Machine learning techniques are being used by mission planners to optimize spacecraft trajectories, conserving fuel and increasing mission lifespan. Closer to home, scientists are increasingly finding space junk and possibly hazardous near-Earth objects. Solar physicists are using artificial intelligence to anticipate the magnitude and direction of solar flares, which may cause billions of dollars in damage to satellite constellations, power grids, and telecommunication networks throughout the world if they go undetected.

Today’s space environment is congested, complicated, and contested—no longer a safe haven for US or partner space assets. AI has the ability to dramatically improve domain awareness and command and control decision-making, as well as to boost the resilience of satellites and their networks.

To realize the full potential of these prospective improvements, however, we must strengthen the security and trust in AI technology. Consider the assessments made by AI that assist humans in making decisions. Can commanders and operators have confidence that the algorithms that underpin these analyses were developed objectively, using suitable data and without bias? Can they be certain that the data they are utilizing has not been tampered with or modified by adversaries? These are key questions to address to ensure safety and mission-critical assets are not jeopardized.

Let’s discuss the potential applications of AI in the space sector one by one.

Improving The Space Domain Awareness #

Space is becoming increasingly congested. Over 2,600 active satellites, over 34,000 objects 10 centimeters or larger, and over 900,000 pieces of space junk between 1 and 10 cm orbit the Earth today. All are orbiting in distinct orbits, traversing distinct planes, and travelling at varying speeds. Developing a comprehensive image of this complicated environment is a critical first step toward securely working in space and safeguarding space assets.

To begin, organizations can compile a complete catalogue of known and observed Earth-orbiting objects using accessible data and an AI system. This same artificial intelligence system may continuously monitor and assess the likelihood of collisions, warning satellite and spacecraft operators in the event of an increased risk.

This is how such a scenario could unfold. Once operators identify a vulnerable satellite using their “space catalogue,” AI can assist them in determining the best course of action for securing that satellite. This AI has the potential to mix traditional modelling and simulation with a deep learning network and collision avoidance algorithms to rapidly generate a list of possible moves to avoid the space object.

As is the case on Earth, each possible avoidance technique has a number of advantages, disadvantages, and associated consequences. For instance, one course of action may result in a reduction in fuel costs while minimizing operational impact. Another possibility is that operators will be able to “see ahead” in order to avoid downstream interference or collisions.

Organizations can program an artificial intelligence/machine learning system to propose the most optimal avoidance techniques depending on the mission’s most pertinent criteria. Users—the “people in the loop”—can then utilize their judgement and mission knowledge to select the most appropriate maneuver to keep important space assets safe.

In time-sensitive scenarios, an AI/machine learning system could offer solutions in minutes, rather than the hours or days required by more traditional methods. This is the ability of AI to accelerate domain awareness — and thereby avoid costly collisions — in today’s more congested space environment.

Accelerate Command and Control in Decision Making #

Another area in which AI has significant potential is command-and-control decision-making, particularly when assets are threatened with little time to react.

Consider a scenario in which an operator must defend space assets from an anti-satellite (ASAT) attack launched from direct ascent. The operator may have only minutes to decide what to do in such a case. AI and data analytic have enabled decision-makers to accomplish a previously near-impossible task: efficiently analyzing massive volumes of data and rapidly arriving at a set of probable actions.

The AI system analyses ASAT trajectory data in order to determine potential targets. It then rapidly develops a variety of alternative courses of action, which may involve navigating, employing countermeasures, or engaging in offensive or defensive operations. The system sifts through several different courses of action using machine learning, taking into account connected effects and downstream repercussions. Operators and commanders are then presented with a timely menu of optimum options, accelerating command-and-control decision-making and strengthening space defense in mission-critical scenarios.

Strengthening Resilience Through Machine Learning and Automation #

Satellite constellations and the networks that connect them are growing in size and complexity in response to business demands for worldwide communication and data transportation. Additionally, these networks are growing more susceptible to sophisticated kinetic and non-kinetic threats.

Operators can reduce these dangers and strengthen the resilience of space networks and constellations by integrating AI into their systems. Organizations can employ artificial intelligence to rapidly examine data for network vulnerabilities. They can then use AI algorithms to “heal” or “self-adapt” in response, ensuring that all network nodes are reconnected. Additionally, organizations can incorporate self-learning algorithms into satellites to make them more self-sufficient and durable in the event that up-link and down-link contacts with ground operations are lost.

Additionally, artificial intelligence (AI) can automate the monitoring of a satellite’s “health status,” the resolution of anomalies, and the execution of defensive operations against threats. By automating these processes on satellites, operators may focus on more difficult, mission-critical tasks.

Building Trust Through Algorithm Development and Operator Training #

As with any new technological use, whether in space or elsewhere, security and trust are critical to acceptance and effectiveness. The development of AI algorithms is the first step toward AI security. Organizations must assure the provenance of the data used to train the algorithms, that algorithms are built with as little bias as possible, and that security is maintained throughout the software development and data storage processes.

Furthermore, enterprises owning space assets and systems will need to train operators in AI and machine learning, which involves knowing how AI systems are constructed and designed. Operators must also be fully aware of the capabilities and limitations of their AI-powered systems. Only via extensive training and education, as well as the implementation of safe processes, can operators and decision-makers have enough confidence in AI systems to employ them to better their missions.

Conclusion #

Deterring and defending our space assets has become both a requirement for national security and a significantly more difficult undertaking as the space environment rapidly evolves and proliferates with new users, new capabilities, and increasingly sophisticated threats. AI solutions offer a transformative opportunity for protecting, improving, and enhancing space missions and assisting the United States in maintaining space dominance by improving space domain awareness, accelerating command-and-control decisions, making satellites and their networks more resilient, and more. To realize AI’s immense promise, we must also ensure that AI systems are built and maintained safely, and that commanders and operators have the necessary training and expertise to trust this disruptive technology.

Related reading

Quantum Computing and the Rise of Machine Learning

Vin Patel — Wed, 03 Aug 2022 20:03:47 +0000

Key takeaways

Quantum computing has developed in recent years as a novel computing model that has the potential to take traditional computers to a whole new level.
It will be important if we are to handle the massive amounts of data we create and solve extremely complicated issues.
These signs are enough to provide the promise of a bright future that lies ahead of us in the field of quantum computing.

Quantum computing has developed in recent years as a novel computing model that has the potential to take traditional computers to a whole new level. All technology-related media outlets have been publicizing all minor and potential advancements in this sector. Even though this is an exciting period for the field, the field itself remains a mystery.

The mystery behind quantum computing remained in the closet for many years until the day it was discovered. And since that day, everything has changed in the computing world. A lot of things became extremely easy, and the machine learning scenarios took a leap in just a few years.

Saying this wouldn’t be wrong that quantum computing will revolutionize machine learning for years to come.

Let us begin with what quantum computing really is.

What is Quantum Computing? #

Quantum computing is a field of computing that focuses on developing computer technology based on quantum mechanics (which explains the behavior of energy and material on the atomic and subatomic levels). Traditional computers can only encode/decode information in bits that have a value of 1 or 0, limiting their capabilities.

But this is where quantum computing distinguishes itself; it uses quantum bits, also known as quits. The theory used here is that subatomic particles can exist in one or more states.

The assumption of quantum computing is that it can be useful in a variety of applications that are deemed critical in today’s technological environment, ranging from cybersecurity to medical applications to machine learning. One of the primary reasons for the field’s popularity is its broad range of applications.

Quantum computing is an exciting thing when we talk about machine learning (ML). It combines with ML and gives us Quantum Machine Learning as a product.

What is Quantum Machine Learning? #

Let us go a little back in time and look at machine learning in a broader context. Machine learning is made up of two components: data and algorithms. So, when we talk about quantum machine learning, do we mean the data, algorithms, or both?

At points where classical machine learning raises its hands and refuses to help us, the above-mentioned quantum computers help us with quantum machine learning.

QML is an umbrella term for four kinds of scenarios. They are:

Tensor network and de-quantized recommendation system algorithms are examples of quantum-inspired classical algorithms processing classical data.
Classical methods, such as neural network-based quantum states and optimizing pulse sequences, are applied to quantum data.
Quantum algorithms, such as quantum optimization algorithms and quantum classification of classical data, are applied to classical data.
Quantum algorithms, such as quantum signal processing and quantum hardware modelling, are applied to quantum data.

Now that we are acquainted with quantum machine learning, let us look at how it can change the future, i.e., how it can improve the present machine learning scenarios to create a better future.

The Future is All Quantum #

Quantum computers are capable of quickly resolving complex problems #

If one of these industry giants succeed in developing a commercially viable quantum computer, these quantum computers would likely be able to finish computations that would take thousands of years to do using traditional computers. Google now claims to have a quantum computer that is 100 million times faster than any current machine. It will be important if we are to handle the massive amounts of data we create and solve extremely complicated issues. The key to success is converting our real-world problems into quantum language.

Our data sets’ complexity and scale are expanding faster than our computer capabilities, putting significant pressure on the fabric of computers. While computers today struggle or are unable to tackle particular issues, the power of quantum computing is predicted to solve these same difficulties in seconds. It is expected that improvements in quantum computing technology would help artificial intelligence, particularly machine learning, and will continue to do so. Quantum computing methods enable us to improve on what machine learning can currently do.

Solutions will be optimized by quantum computers #

Another way quantum computing will assist usher in a revolution will be to sample data and optimize all types of issues we face; from portfolio analysis to the best delivery routes to help identify the best treatment and pharmaceutical routine for each individual.

With the rise of big data, we have modified our computer architecture, necessitating a novel computational method to deal with large data. It is not just greater in scope, but the challenges we attempt to tackle are extremely complex. Quantum computers are more suited to solving sequential problems. The ability they provide businesses and even consumers to make better decisions may be all that is required to persuade firms to invest in this emerging technology whenever it becomes available.

Quantum computers could spot patterns in large data sets #

Quantum computing is predicted to be capable of rapidly searching massive, unsorted data sets for patterns or abnormalities. Quantum computers may be able to examine all objects in your database at the same time and detect these abnormalities in seconds. While this is technically conceivable today, it requires a parallel computer to look at each record one by one, which takes an inordinate amount of time and may never happen depending on the size of the data collection.

Quantum computers might aid in the integration of data from various data sets #

Furthermore, due to the integration of extremely diverse data sets, major advancements are predicted when quantum computers become accessible. Although this may be challenging initially without human interaction, human engagement will assist computers in learning how to integrate data in the future. So, if a research team wanted to compare various raw data sources with unique schema linked to them (terminology and column headings), a computer would need to grasp the relationship between the schemas before the data could be compared. Breakthroughs in the study of the semantics of the natural language are required to do this, which is really difficult in artificial intelligence. Humans, on the other hand, may provide input that trains the system for the future.

Quantum computers promise to enable rapid analysis and integration of massive data sets, improving and transforming machine learning and artificial intelligence capabilities.

End-Note #

Quantum computing offers enormous promise for enhancing and altering the way technology works today. Despite the fact that quantum computing is not a new topic at all — it is as ancient as computing itself — it has garnered traction and a lot of attention in recent years.

However, quantum has recently progressed from pure theory to practice. That is the cause for the unexpected surge in media and scholarly interest. For the first time in quantum history, anybody may access and run tiny to moderate programs on quantum computers.

These signs are enough to provide the promise of a bright future that lies ahead of us in the field of quantum computing. Hope all these promises come true in the future.

Related reading

From Bits to QuBits

Vin Patel — Wed, 03 Aug 2022 19:57:37 +0000

Key takeaways

Every day personal computers, phones and tablets spin a fascinating array of cybernetical illusions.
‘Multiple states’ mean that if you’re running an algorithm using quits, you might need only a single quit in a superposition to replace all those classical bits.
The age of quantum computers should have similarly sweeping and valuable consequences.

Every day personal computers, phones and tablets spin a fascinating array of cybernetical illusions. Click on a tiny icon of a browser, and in pops a window that lets you behold the world at your fingertips. Sitting in Canada, but want to map your holiday through the interweaving streets of Rome? No Problem! ‘Maps’ is always at hand. Switch over to the satellite feed to get the lay of the land and even the closest gelato shop. Oh wait, it’s new times. Let’s hold a team meeting or catch up for a coffee with a friend, virtually, of course.

Ever wondered about the wizardry at play? Ultimately everything you see on your screens is being painted - one tiny dot at a time.

Dozens of times a second, the screen is swept by a stream of bits. A constantly changing sequence of binary data tells each of the million pixels whether, at any moment, it should be red, green, or blue, or a combination to make many others. When you click on a file, drag it into a folder and then zip it and send it away to a colleague on the other side of the world, the whole activity seems so effortless. Yet, in the background, we’ve issued a sequence of commands that get translated into ‘bits’ that eventually generate packets that stream through our high bandwidth networks.

Sustaining this illusion requires a staggering amount of information processing. Imagine sending signails bit by bit through the internet where it is decoded and dispatched effectively so that they animate the correct block of the screen on the other side of the world. Now visualize multiplying these actions at least 4 billion times over per second (assuming that at least half of the world has access to the internet and a device). The sheer scale of processing happening here is staggering. Even on actions that are limited to your personal computer, the amount of processing that is taking place in the background is boggling.

So how long can we hold up to the processing demands of the world? It’s not just about faster streaming; we are talking about cryptocurrency, machine learning and all the new and upcoming trendy fields. Let’s face it – even with all the sprawling data centers, we will one day run short.

But if our progress is anything to go by - big things happen when computers get smaller. Remember the iPod and the Smartphone? Momentous change also occurs when these devices go faster, way faster. And quantum computing is about pursuing perhaps the most significant performance boost in the history of technology. The essential idea? Exploiting the befuddling physics of subatomic scales to shatter all the myopic processing hurdles of today.

Quantum Timeline #

1980 – Physicist Paul Benioff theorizes Quantum Computing

1981 – Nobel winner, Phycisist, Richard Feynann coins the term quantum computing

1985 – David Dutch creates a blueprint of a quantum computer

1994 – Mathematician Peter Show writes an algorithm that could tap a quantum computers power to break the widely used form encryption

2001 – Researchers at IBM-Stanford University use a 7-quit computer to factor the number 15

2011 – D-Wave announces the first commercially available quantum computer

2016/2017 – IBM announces a 5 quit processed for developers and by 2017, upgrades that to a 16 quit processed

2019 – Google Sycamore declares quantum supremacy

2021 - A Japanese teach company achieves quantum communications over optical fires exceeding 600 km in length, a new world record

Bit’s and Bytes

Anyone who opens the most basic high-school text on computer theory almost immediately learns two comfortingly simple facts: Any kind of data that goes through the circuitry powering your device can be translated into a combination of two symbols - 1 and 0.

This data is handled using basic operations called AND, OR, and NOT. It’s the underlying principle on which every single computer in the world works, including the supercomputers. But this singular state has been both a boon and a hurdle.

Even with all the computational prowess in our hands, calculations that involve dense computation simply take too long.

Enter – Quantum Computers. They hope to bring in substantial enhancements in many disciplines. Longer-lasting batteries for electric cars, advances in chemistry or even enabling new medical treatments, the promise is multifold. Quantum computing has garnered some serious momentum in the last few years.

The appeal of quantum computers lies in their potential to help to quickly answer questions so challenging that it would take decades for today’s computers to solve.

A computer made up of quit (quantum equivalent of bits) is simply a collection of circuits. The crucial difference between quantum computing and classical computing is that bits are binary. They are either zero or one. Quits, on the other hand, can be present in multiple states at once through quantum entanglement. The process is aptly termed superposition. ‘Multiple states’ mean that if you’re running an algorithm using quits, you might need only a single quit in a superposition to replace all those classical bits. Sequence several quits together into a quantum circuit, and the possibilities are stupefying.

The inspiration for a quantum computer came about much earlier than one would imagine. In the early 1900s, Quantum mechanics emerged as a fundamental theory in physics to describe the physical properties of nature through the microscopic world of atoms and subatomic particles. This led to advances such as transistors, lasers, and magnetic resonance imaging.

The idea to merge quantum mechanics and computational theory arose in 1982 with physicist Richard Feynman. He reasoned that computing based on classical logic could not tractably process calculations describing quantum phenomena. Quantum physics has so many variables (much like nature) that we cannot keep track of them on a computer. But, although his idea generated the field of quantum simulation, it didn’t spark much research activity at the time.

However, in 1994, mathematician Peter Show devised a quantum algorithm that could efficiently determine the prime factors of large numbers, sparking a surge in interest in quantum computing. For laymen, this is a classic so-what moment. But when we realize that the security of nearly every online transaction today relies on an RSA cryptosystem that hinges on the un-solvability of the factoring problem, the situation takes a different turn.

How do the Quits fit in our future? #

Mathematicians are still canvassing what might be accomplished with all this quantum power when it finally grows up. Ordinary computers are great for solving “easy” problems — questions that can be answered in a reasonable amount of time, like predicting the path of a hurricane or simulating the effect of a nuclear explosion.

Then there are “harder” problems – where solutions are difficult to find because it feels like searching for a needle in a haystack, but once a solution is found its easier to verify its authenticity. One such problem is the factoring of large numbers. With the threat that a quantum computer can potentially break any current cybersecurity system, the race has taken a different turn. Eventually, we will need a new generation of encryption technology to protect sensitive data from potential quantum computer attacks, and teach companies would give an arm and a leg to be at the forefront of that evolution.

Given this and more, companies are progressively investing in this space. Microsoft’s Azure cloud has released quantum tools, as have Google and Amazon’s respective cloud platforms. IBM is even offering online access to some of its quantum processors, so anyone can experiment with them. In 2019, Google demonstrated that its 54-quit quantum computer could solve in minutes a problem that would take a classical machine 10,000 years. But this ‘quantum advantage’ applied only to an extremely narrow situation. Long term, though major technology giants see themselves generating cashflow by charging corporations to access data centers packed with supercooled quantum processors.

On the other hand, non-teach experts expect quantum computing to help us understand biology and evolution, cure cancer, and even take steps to reverse climate change. But all said and done, quantum computing is still in an embryonic state, and its real-world application still feels a while away.

If you are wondering what’s in it for the rest of us? Well, the dawn of PC’s has helped make life safer, comfortable, and more convenient. The age of quantum computers should have similarly sweeping and valuable consequences. Perhaps it could result in a Smartphone-Q? Who knows…

Related reading

Questions and answers

Sitting in Canada, but want to map your holiday through the interweaving streets of Rome? #

Switch over to the satellite feed to get the lay of the land and even the closest gelato shop. Let’s hold a team meeting or catch up for a coffee with a friend, virtually, of course.

So how long can we hold up to the processing demands of the world? #

It’s not just about faster streaming; we are talking about cryptocurrency, machine learning and all the new and upcoming trendy fields. Let’s face it – even with all the sprawling data centers, we will one day run short.

Remember the iPod and the Smartphone? #

Momentous change also occurs when these devices go faster, way faster. And quantum computing is about pursuing perhaps the most significant performance boost in the history of technology.

If you are wondering what’s in it for the rest of us? #

Well, the dawn of PC’s has helped make life safer, comfortable, and more convenient. The age of quantum computers should have similarly sweeping and valuable consequences.

How Effective Is It in Cryptocurrency Trading?

Vin Patel — Wed, 03 Aug 2022 18:51:21 +0000

Key takeaways

Cryptocurrency is a rapidly evolving concept that many people are aware of, but few understand.
- **Examining the Flow:**The study of a specific asset’s money flow is the single most effective way of determining its long-term success.
In the future years, the world of blockchain may begin to open up to Machine Learning practitioners.

Cryptocurrency is a rapidly evolving concept that many people are aware of, but few understand. The fascination with cryptocurrency technology grows day by day, and machine learning research has never been more rapid. The world of cryptocurrency and blockchain is becoming increasingly intriguing, as the prices of Ethereum and Bitcoin have reached all-time highs in 2021.

The value of Bitcoin increased more than 250,000% between 2012 and 2021. The industry is still booming. For example, in the United States, only 6% of adults own or use them, and about 15% of investors are women. The number of Bitcoin users is expected to skyrocket.

Following the massive rise of blockchain, entrepreneurs worldwide are constantly investing in blockchain & cryptocurrency. Nonetheless, machine learning provides some of the best tools because prior information and knowledge of the cryptocurrency sector, other connected industries, human nature, and demographics are some of the best data to extrapolate growth tendencies. News cycles and search volume are the most powerful known correlates of cryptocurrency value. Let’s first understand the main skeleton of the crypt trading method. Hopefully, this article will provide you with a basic understanding of how machine learning is utilized in cryptocurrency trading.

How Do Cryptocurrencies Work? #

Have you ever gotten a paper token instead of a little change from your next-door shop, which he would take the next time you visit him? Imagine a digital versionof that token, and you have your cryptocurrency.

The main distinction is that there is no owner-issuer in this case, and it would be accepted universally, at least theoretically. Cryptocurrency is a digital or virtual coin protected by encryption, making counterfeiting almost impossible.

Cryptocurrencies use blockchain technology. They are digital tokens that may be used to pay for products and services on the internet. Like any other fiat currency, such as the US dollar or the Indian rupee, they have a predetermined fixed value. Cryptocurrencies are mined digitally, using supercomputers to solve challenging computational mathematics tasks. Their mining is time-consuming, expensive, and only seldom profitable.

More or less, every cryptocurrency is supported by blockchain technology. It’s a decentralized public ledger or register that keeps track of every transaction that’s taken place in that coin. Instead of being maintained by a central server, the ledger is accessible to the public and may be hosted by anybody who wants to help secure it. The transactions are structured as “blocks.” Cryptocurrency miners verify these transactions to confirm their validity. This is done to minimize currency double-spending and to ensure that the input and output expenditures are equal.

Cryptocurrency and Machine Learning #

Searching for “machine learning crypt trading bots” on Google returns almost 12 million results. Machine learning is used intensively in the stock market nowadays for trading. As a result, it’s no surprise that machine learning techniques are being used to develop trading algorithms for the cryptocurrency markets. Those who have invested in cryptocurrencies have great difficulty in predicting the market’s trajectory. As a result, in recent years, machine learning and AI-assisted trading have piqued the curiosity of crypt investors. These investors utilize this method to rule out the possibility that the bitcoin market’s inefficiencies might be leveraged to make bizarre profits by predicting its movements.

How is Machine Learning Analytic Performed? #

The type of analysis used in machine learning depends on the problem statement. There are some popular types mentioned below:

**Supervised Learning:**Items in the dataset are classified into predefined categories, such as “successful” vs. “failed,” or which emotions are conveyed in a text message.
Unsupervised Learning: Items in the dataset are assigned to categories that were not created at the outset of the analysis, such as segmenting clients based on relevant characteristics during marketing campaigns.
**Semi-supervised Learning:**This algorithm is used for similar objectives to supervised learning; however, both labelled and unlabeled data are used to train the model. It is used generally when acquiring the complete labelled data became expensive, such as detecting face on a webcam
**Reinforcement Learning:**Trial and error are performed in this type of algorithm to determine which activities yield the biggest rewards.

Following the selection of the general type of analysis, the specific model is developed using an input dataset known as the training data. Following the development of the model, it is evaluated on one or more additional datasets known as the validation data or the test data. Validation data are sometimes defined as a subset of the training data saved from the start development of the model and used at the end to fine-tune the model. The data used to test a fully-developed model is referred to as test data. Because of the market’s volatility, they noticed that no single model was very predictive. One strategy is to combine a variety of models. According to the study, aggregations of various models are more effective and outperform the market.

Smart Crypt Trading with Machine Learning #

**Examining the Flow:**The study of a specific asset’s money flow is the single most effective way of determining its long-term success. Machine learning enables individuals to predict value fluctuations in an asset by studying how known entities transfer money and comparing it to previously known data sets. Investors will be able to safely leap before the disaster strikes if they are aware of this. There are various applications for this within the crypt sector, making machine learning something that will be extremely important to effective trading in the future.
**Analyzing Trading Behaviors:**Individuals may use machine learning to understand the performance of assets in the cryptocurrency market deeply. It may also be used to learn about an individual investor’s trading tendencies.
**Fraud Detection:**Machine learning is being brought to use inside the crypt sphere as one of the most advantageous techniques for detecting fraudulent transactions. This is because the nature of cryptocurrencies allows dealers to remain more or less anonymous. Because of this characteristic of crypt exchanges, any dangers posed by stammers are irreversible. As a result, a considerably more aggressive strategy to preventing fraud is required. Machine learning employs a preventive system that includes both computers and human analysts who work together in a feedback loop to solve problems. Researchers have created a novel and fast way of identifying malicious code using deep learning techniques to characterize and compare code. SiCaGCN is the name of the system, and it has shown promise in terms of detecting foreign bitcoin mining codes. The system guards against unauthorized applications abusing and misusing computer resources.
**Intensive use of Chatbots:**With several high-quality additions, such as chatbots, AI and ML have significantly added value to the daily lives of investors. Chatbots have advanced the way trading is done since it is easier for traders to engage with the chatbot and access the history of the statements.

The Disadvantages of Machine Learning in Crypt Trade #

**Unpredictable Results:**The models do not always provide such a direct correlationbetween their predictions and the actual results. Overfitting in machine learning refers to the practice of feeding a statistical model with more data than is required. Trading algorithms tend to be given excessive amounts of past data. That isn’t inherently bad, but overfitting can lead to inflexible trading methods in both current and future conditions.
**Setting Parameters Can Be Time-Consuming:**Machine learning can only be used in trading following a specific pattern; machine learning is ineffective if there is no pattern. The cryptocurrency market is a volatile one. To specify your approaches, which might be quite sophisticated, you will need a thorough understanding of machine learning and crypt trading. This can take a long time. Aside from that, correctly configuring and managing these technologies requires a significant amount of effort.
Trading All the Time:While some people prefer to trade all day and invest in many currencies or assets at the same time, others believe that this is a significant disadvantage of adopting crypt trading bots. True, they can help you increase profits, but if you’re not careful, they can swiftly empty your wallet.

Automation, no matter how sophisticated, is not without flaws. Experts believe that trusted algorithms would fail — and that this will result in a “series of cascade failures” for financial institutions of all sizes. It happened before during the trillion-dollar stock market crisis of 2010, which was caused by an algorithmic miscalculation. Even for small-scale transactions, a poor internet connection might be disastrous. Trading takes place in a fast-paced atmosphere, and a dependable IT infrastructure is essential for staying ahead of the competition.

Conclusion #

In the realm of blockchain and cryptocurrencies, machine learning has a place. The application of machine learning techniques extends beyond cryptocurrency price forecasting and trading. With such a high rate of adoption in trading, it is apparent that automation is here to stay. Its objectivity, accessibility, and quickness make it a valuable and powerful weapon for traders. Automated trading systems, on the other hand, should not be left unattended. Preventing a domino effect of algorithmic errors requires careful research and intelligence.

As blockchain and machine learning continues to evolve, we should expect to see tremendous innovation in each domain. In the future years, the world of blockchain may begin to open up to Machine Learning practitioners.

Related reading

Automated Trading and AI in Crypt Market

Vin Patel — Wed, 03 Aug 2022 18:47:00 +0000

Key takeaways

Introduction In today’s world, cryptocurrencies have become the most fascinating buzzword.
Exchanges and banks often compensate for the supply-demand imbalance by charging high fees.
Any bot that trades bitcoin on an exchange has to be trustworthy.

Introduction

In today’s world, cryptocurrencies have become the most fascinating buzzword. Digital currency, which has drastically gained popularity and has expanded to a greater extent. It has become a highly effective financial medium, capable of yielding enormous profits on cryptocurrency exchanges or while investing in these assets over time. There is now an exact increase in consumer rates in digital money transactions, and it isn’t difficult to identify a solid pattern and benefit greatly from this value growth.

Automation has now deepened its roots in almost every aspect of human life and a fast-growing industry like the crypt market, the potential to automate the trading interaction is more appealing.

But, the question is how AI can be used in the crypt market? #

Brokers with coding abilities and programming expertise can build their crypt robots or improve their existing ‘Expert Advisors’ to make sophisticated coin trading more efficient. The robot will exchange cryptographic resources on its own, providing its owner with a great profit.

Despite this, there is a major danger that newbies and unskilled crypt-merchants are mostly ignoring for unknown reasons. Hundreds of trading instrument calculations are now frequently available on the Internet, with little concern for their real quality. It’s not easy to make a mistake in their settings or to immediately start a wasteful bot. As a result, the business will close quickly. As a result, crypt robots should be approached with caution, and various tests on trial accounts should be carried out. It’s critical to be vigilant and choose a trading consultant intelligently when it comes to crypt-trading. Otherwise, you risk taking up a viral miner and losing money.

Traditional financial markets are being aggressively examined using machine learning and artificial intelligence. However, some businesses have been forced to close owing to issues that we will investigate further. As a result, similar technology may be employed in the crypt-trading space. What are the differences in telecommunications between using ME (mathematical expectations) and AI (artificial intelligence)?

The tremendous volatility of cryptocurrencies increases the risks of cryptocurrency trading while simultaneously making it more profitable than any other kind of investment. Prices change often throughout the day, allowing traders to earn steady profits provided they are properly estimated. Massive amounts of data must be analyzed to evaluate the patterns of the always-changing cryptocurrency market, which artificial intelligence and machine learning systems can help with.

The Main Advantages of AI and ML***:***

Ability to analyze large amounts of data
Learning ability
High work speed
Accuracy

Artificial intelligence is currently being utilized to manage billions of dollars in traditional assets like stocks and bonds. Though the use of ME and AI is not that widespread, it is expected that the use of these technologies in the crypt market will gain new heights in the future.

Forecasting The Cryptocurrency Market Using AI #

Augur’s decentralized technology for creating peer-to-peer prediction markets is based on the expertise of its network members. It does, however, allow it to make quite accurate bitcoin projections. The Forecast Foundation, a non-profit organization, established Augur to make public forecasting more accessible.

In contrast, the NeuroBot platform is based on the work of neural networks rather than human experience. With their help, it creates projections about the behavior of the bitcoin market. The system monitors and compares currency rate fluctuations, as well as forecasting future changes. The inventors of NeuroBot claim that their technology can appropriately assess changes 90% of the time. By combining technical and basic analysis, the system’s creators hope to enhance it.

Such platforms can make life much easier for rookie cryptocurrency traders who haven’t yet had the chance to learn everything there is to know. Of course, prognostic systems can’t predict everything, but they can handle the vast bulk of technical market research and do it with remarkable precision.

Market Sentiment Analysis Using AI #

The examination of a huge quantity of data is required to calculate the sentiment of the bitcoin market. This holds for articles, blogs, forums, and even the comments left beneath them. Senna, a blockchain-based artificial intelligence platform does the analysis automatically and responds quickly. The system also employs machine learning in its operation. Senna has also partnered with CryptoScanner, a platform that utilizes artificial intelligence to anticipate market sentiment and then shares the results with traders to help them create trading strategies.

Finding Parties To Make Deals #

One of the concerns is the lack of liquidity in the bitcoin market. Digital currencies are inappropriate for business payments due to their extreme volatility. Exchanges and banks often compensate for the supply-demand imbalance by charging high fees. To address the issue, platforms such as TradeConnect, which uses machine-learning technology, are being used. It uses a side-matching system to help consumers choose the right broker and bank for them, allowing them to trade directly and save money.

Individual companies specialize in developing artificial intelligence solutions for the bitcoin sector. The Money Token team, for example, created Amanda, an AI assistant who will lend to bitcoin users. From the time the loan is applied for until it is fully repaid, the virtual assistant will be able to offer loans against bitcoin collateral and provide complete support.

Security When Using Crypt Bots #

You must be cautious when entrusting someone with the API keys to your exchanges. Only trustworthy services are worthy of this distinction. After all, an API key is a form of permission that allows you to access your account, including trading and fund withdrawals. The fact that any bot needs the API to work isn’t a sign of deception. Give any platform a careful investigation before you start working with it. Always use two-factor authentication to limit account access.

Advantages & Disadvantages of Using a Crypt Bot #

Advantages of Crypt Bots #

Round-the-clock trading. A bot never gets tired or bored of doing the same task 100s of times.
Absence of human factor. It might happen sometimes that people make mistakes in performing certain operations like that of entering details into a system.
Quick solution or execution of an operation according to the set of instructions provided.
Convenient diversification and risk-sharing.

Disadvantages of Crypt Bots #

Bots having degraded performance and ineffective strategies. Using such strategies can be a big loss to newbie users.
The bot irrespective of its work efficiency has to be monitored and updated regularly and thereby adjusted according to the function.
There are bots with low-quality software. Before choosing software for these purposes one must find out a source that is 100% authentic.

Advantages of Automated Cryptocurrency Trading #

During its heyday, Mt-Gox, the now-defunct cryptocurrency exchange, made significant use of a specialized crypt robot known as Willy to manipulate the value of Bitcoin units. This bitcoin trading bot was the first to raise and then lower Bitcoin values. This happened between 2012 and 2014, when the bitcoin market, as well as all other forms of decentralized money, were largely obscure and disliked.

Naturally, the current state of the crypt industry has ruled out the use of bots to influence the price of decentralized coins in this way. The bulk of robots are used to automate exchange trading. The implementation of given algorithms is the outcome of a set of advantages of the activities carried out by these crypt bots. The key advantages are mentioned below:

The trader is relieved of the necessity to continually watch the virtual currency market and determine the best times to start positions.
Using crypt robots to trade digital assets removes the element of fear and emotional state from trading choices. The majority of new crypt-traders lose money owing to psychological and emotional instability, as well as a disregard for their trading strategy’s guidelines.
Chart patterns, candlestick patterns, quote directions, and trend reversal points are all recognized by the bitcoin bot, which is frequently undetectable to the trader.
A well-tuned trading robot is more successful at analyzing charts, making more efficient trades, and looking for the ideal entry opportunities.
The above characteristics are just a few examples of how automated bitcoin trading may be beneficial. There are several more benefits to such programs that may benefit any trader.

Conclusion #

A variety of issues are preventing AI from becoming widely used. Information is scarce because the bitcoin market is still new compared to the traditional stock market, and AI competence is usually limited by the amount of data available to humans. Another difficulty is that very accurate and complex machine learning algorithms need costly and sophisticated computer equipment, which is only available to huge businesses. All of this, however, may be remedied by studying and practicing AI and machine learning techniques at a reputable AI school.

It’s essential to recognize that the nature of a cryptobot is defined by the connection between its performance and its true worth. Any bot that trades bitcoin on an exchange has to be trustworthy.

Related reading

Advancing Healthcare with Medical Image Processing

Vin Patel — Wed, 03 Aug 2022 12:34:50 +0000

Key takeaways

Artificial intelligence has been a far-fetched goal of computing since the advent of the computer, but new cognitive computing models may be getting us closer than ever.
The application, in general, enables users to interact with MRI, CT, and other forms of medical imaging data in a number of ways, including the development of models using CAD-designed implants and equipment.
Computers will expand our knowledge and talents when they attain the capacity to think like humans.

Introduction #

Artificial intelligence has been a far-fetched goal of computing since the advent of the computer, but new cognitive computing models may be getting us closer than ever.

The results of cognitive computing, a fusion of cognitive science (the study of the human brain and how it functions) with computer technology, will have far-reaching ramifications in our daily lives, healthcare, industry, and more. One such important fields of study is Medical Image Processing which has aided in advancing the healthcare industry.

What is Medical Image Processing? #

Medical image processing is the study and application of three-dimensional image files of the human body, typically obtained from a computed tomography (CT) or magnetic resonance imaging (MRI) scanner, to diagnose diseases, guide medical procedures such as surgery planning, or for research purposes. Radiologists, engineers, and doctors employ medical image processing to better comprehend the anatomy of individual patients or groups of patients.

How Cognitive Computing Has Made It Possible #

Artificial intelligence (AI) and cognitive computing breakthroughs have had a substantial influence on medical imaging in recent years. Deep learning and other advanced AI algorithms have a wide range of applications in medical image processing and analysis, including image reconstruction, tumor detection, feature extraction, segmentation, and classification, as well as wound healing assessment and detecting cardiovascular abnormalities. The Image Processing

System has a vast range of benefits.

What Are The Benefits of Medical Image Processing? #

The main benefit of medical image processing is that it allows for in-depth but non-invasive investigation of internal anatomy. In order to improve patient treatment outcomes, develop better medical equipment and medicine delivery systems, and arrive at more accurate diagnoses, 3D models of the anatomies of interest can be built and investigated. It has become one of the most essential tools for medical advancement in recent years.

The higher image quality, along with better software tools, enables for exact digital restoration of anatomical structures of diverse sizes and properties, such as bone and soft tissues. Measurement, statistical analysis, and the creation of simulation models with correct anatomical geometries, for example, provide for a better understanding of interactions between human anatomy and medical technology.

How Does Medical Image Processing Work? #

The initial stage in medical image processing is to extract raw data from CT or MRI scans and rebuild it into a format that can be used in appropriate software. A common input for image processing is a 3D bitmap of greyscale intensities with a vowel (3D pixels) grid. The strength of signals from proton particles during relaxation and after the application of extremely high magnetic fields determines the greyscale intensity in a CT scan; however, the greyscale intensity in an MRI is determined by the strength of signals from proton particles during relaxation and after the application of extremely high magnetic fields.

Medical users frequently modify the rebuilt image volume to segment out and change specific anatomical regions of interest, such as tissue and bone. At the 2D and 3D levels, users may perform a variety of image processing tasks, including:

Image filters are used to reduce and remove undesirable noise and artefacts.
Cropping and resampling input data to make picture processing easier and faster
Automated approaches based on AI-based machine learning algorithms are being used to detect distinct anatomical areas utilizing segmentation tools.
Using measurement and statistical techniques to quantify various aspects of picture data, such as centerlines
Importing CAD models of implants or medical equipment in order to investigate how they interact with specific anatomies.
Exporting models for physics-based simulation, further design work, or 3D printing actual duplicates of the anatomy in question

Where & When Does Medical Image Processing Fit In The Product Portfolio? #

Simpleware software provides a wide range of medical applications, from fundamental research through multiple clinical processes. The application, in general, enables users to interact with MRI, CT, and other forms of medical imaging data in a number of ways, including the development of models using CAD-designed implants and equipment. Simpleware ScanIP’s capabilities, for example, are used by device engineers to tackle problems like organizing surgical procedures and analyzing the efficacy of alternative implant designs, as well as exporting models for simulation and design.

Cognitive Robotics goes beyond medical image processing #

After medical image data has been processed, Simpleware ScanIP comes with a variety of add-on modules that allow you to do more with it. Customizing steps and automating time-consuming or repetitive tasks are also options. Users in the medical field, for example, can:

For 3D printing, export STL files from processed medical pictures.
For size and location, combine CAD-designed implants with anatomical imaging data.
Create volume meshes for physics simulations using Finite Element and Computational Fluid Dynamics, such as impact or stress and strain.
Continue product development by translating processed picture data into CAD-friendly NURBS and interfacing with popular CAD software.

Putting Medical Image Processing into Practice #

Patient-specific hemodynamic models of difficult aortic dissections, developed at University College London to better comprehend life-threatening vascular illnesses, provide a fantastic example of how medical image processing works recently. To analyze CT scans and create models suitable for CFD analysis using Simpleware software, the following methods were followed:

Aortic dissection CT images are collected from patient-specific instances.
Simpleware ScanIP is used to rebuild patient geometry, which includes noise processing and segmentation of regions of interest such the dissected aorta and branches.
Smoothing procedures are carried out automatically using scripting to reduce pixelation artefacts.
Surface models of the dissected aorta are created and loaded into ANSYS® software for CFD simulations, which include intraluminal pressure and wall shear-stress-based indices.
The outcomes of the simulation provide hemodynamic information that may be utilized to aid future clinical understanding.

Conclusion #

We now have personal digital assistants (such as Sir and Google) on our phones and computers, but they are not genuinely cognitive systems; they have a pre-programmed set of responses and can only respond to a limited number of requests. However, in the not-too-distant future, we will be able to talk to our phones, computers, autos, and smart homes and receive a true, meaningful answer rather than one that has been pre-programmed.

Computers will expand our knowledge and talents when they attain the capacity to think like humans. We are on the verge of entering an era in which computers may augment human intelligence and knowledge in whole new ways, much like the protagonists of science fiction films rely on computers to make accurate forecasts, gather data, and draw conclusions.

Related reading

Sentimental Analysis — The Key to Changing Perceptions

Vin Patel — Wed, 03 Aug 2022 12:27:04 +0000

Key takeaways

When people think about data and computing, the first thing that comes to mind is Artificial Intelligence (AI).
Such systems can track data from multiple repositories to give better insight and assistance regarding engagement.
Now is the time to invest in these technologies, as the benefits will become multifold with time.

When people think about data and computing, the first thing that comes to mind is Artificial Intelligence (AI). In the marketing sphere, AI has gained a considerable foothold, evident from the many SaaS products being launched. That being said, there is one aspect that is still amiss. Personalization. How can a company genuinely show that they care about their consumers when their products don’t reflect that? That’s where cognitive computing comes into the picture.

Cognitive computing uses a combination of AI, neural networks, sentimental analysis, natural language processing, and the like to solve real problems. To implement a genuinely human-centric approach, it’s essential to use technology that mimics human emotions and actions better. This is precisely what cognitive computing does, and this is what differentiates it from AI-led approaches.

In addition to that, quantum computing and cognitive computing will soon be synonymous with companies such as IBM taking the next step forward. This could give cognitive computing the push needed in the marketing world as data-driven marketing seems to be getting more popular with time.

But before that, let’s see how cognitive computing works to bridge the human-computer gap.

Bridging the Human-Computer Gap in Marketing #

When it comes to using computing technologies, the one thing that sets cognitive computing apart is that it accounts for human emotions. This makes it the perfect choice for marketing purposes since it goes beyond the generic responses. To do this, most marketers use sentimental analysis — a method used to gauge the audiences sentiments and leverage them for your business.

An excellent example of sentimental analysis in action is a humanoid robot. They act like “cognitive assistants” and help you out with your immediate needs. You can find them either at conferences/ events where they’re checking you in, within hotel lobbies acting as your concierge, or even at nursing homes, helping elderly patients with their needs. The applications are far and wide but what’s important to note is that robots can understand our emotions now, all thanks to complex cognitive computing capabilities.

Even cognitive computers such as IBM’s Watson can improve business processes and customer interactions over time but use robotic process automation. They can identify emerging patterns and give marketers the competitive edge they need in the market. As cognitive computing allows the system to provide valuable and contextual information to its customers, they increase engagement over time.

Now that we know that human-computer interactions are being bridged let’s look at how sentiments play a role in that context.

Leveraging Sentimental Analysis for Your Business #

Sentimental analysis is a process in which you gauge the emotions, attitudes, opinions of your audience concerning either your product, service, or brand from the business perspective. In today’s day and age, most customers expect personalized marketing content, which forces you to go above and beyond in your marketing efforts. But with sentimental analysis, it’s easier to gauge the emotions and attitudes of your customers and leverage that for your business.

For the most part, sentimental analysis focus on three aspects — engagement, decision, and discovery. Such systems can track data from multiple repositories to give better insight and assistance regarding engagement. This fosters the ability to have more profound and meaningful dialogues with humans, engaging the customer. Concerning decision making, they use reinforcement learning that helps it make better decisions through a closed loop of learning. Through deep learning and unsupervised machine learning models, cognitive computers tend to discover patterns and behavior that would not have been obvious otherwise. Using these three aspects, you can build relationships with your customers that would not have been possible otherwise.

A study showed that 68% of users are more likely to buy a product if it provides some form of personal value. This personal value cannot be gauged without genuinely understanding the customer, their needs, and what kind of situations they are in. To do this, sentimental analysis tends to be the best bet for business owners. Here, the analysis can either be direct (social media & feedback) or indirect (automated sentimental analysis). When it comes to marketing, it can be used to track buyer sentiment, improve the ideal buyer persona and even manage any potential crisis — all powered through cognitive computing. That being said, it’s always important to employ a mix of direct and indirect analysis because, at the end of the day, a machine can only do so much.

The Quantum Computing Push in 2021 #

As mentioned earlier, cognitive and quantum computing have been said to go hand in hand with each other. Many quantum computers are being developed for this purpose because they offer a particular competitive advantage over other technologies. In conjunction with AI, marketers could spring forward in their ability to connect with their customers. As quantum algorithms can improve machine learning models (data clustering), customer and brand relationships will become more intuitive and thorough.

Many companies have already begun adopting these technologies because of their value in process optimization and marketing. It’s clear that the quantum push is already happening, with many mergers and acquisitions taking place in 2021. Some of them include Honeywell and Cambridge Quantum Computing, Rigetti Computing and Supernova Partners Acquisition Company ($1.5 billion), IonQ and DMY Technology ($2 billion), IBM and Raytheon Technologies, and many more.

These companies are spearheading the next generation of cognitive computing, which could become super beneficial for the marketing industry as a whole, especially for the early adopters of the technology. It’s is an amalgamation of all they need to succeed, from discovery to the decision to engagement.

Final Takeaway #

Cognitive computing is the key to better and more competitive marketing strategies. Most marketing techniques rely on consumer behavior, and to ‘decode’ said behavior; it’s essential to build on models that can interpret actual human emotions. The use of sentimental analysis will only increase with time and will ensure that cognitive computing is truly the pinnacle of competitive marketing. Now is the time to invest in these technologies, as the benefits will become multifold with time.

Related reading

Adoption of Cognitive Computing Across Various Industries

Vin Patel — Wed, 03 Aug 2022 12:20:53 +0000

Key takeaways

One of the most comprehensive amalgamations witnessed in recent times – cognitive computing, is a unique blend of artificial intelligence, natural language processing, machine learning, neural networks and other scientific disciplines.
These systems are designed to parse through data, share health information, improve patient outcomes, and personalize patient care by helping medical professionals make faster, cost-effective decisions.
In summary, cognitive computing bears true potential to transform the way technologies such as artificial intelligence and natural language processing are deployed.

One of the most comprehensive amalgamations witnessed in recent times – cognitive computing, is a unique blend of artificial intelligence, natural language processing, machine learning, neural networks and other scientific disciplines. Essentially cognitive computing is an advanced framework that combines the aforementioned and uses computerized models to imitate the human thought process. Note however, that it is very different from proper AI-enabled systems – cognitive computing studies patterns and analyzes factors to help humans take a relevant course of action.

Given its incredible ability to think and process like humans, cognitive computing is heavily used in decision-making across major verticals. To that end, even prominent companies have been working to invest in cognitive computing solutions – consider Kanverse.ai for example. The firm, recently in May 2021, announced that it is launching the Datolite release of its AI-powered IDP (Intelligent Document Processing) Product for Enterprise. This IDP is built on the Kanverse Cognitive Automation platform that integrates artificial intelligence with Business rule framework, Optical Character Recognition (OCR), and Automation to deliver a product for digitizing the entire document processing for enterprises.

Ultimately, the aim of cognitive computing is to stimulate human thinking within computerized models. Over the years, it has been deployed across many verticals, with path-breaking results. IBM’s Watson for instance, has been used in the medical realm, to collect information about specific disorders, including but not limited to basic details, patient history, journal articles, diagnostic tools, treatments, etc., analyze the same, and eventually provide a recommendation. This literally changes the course of how humans perceive AI-enabled tools – through cognitive computing, interacting with tools could become a much more seamless process.

The following paragraphs provide a gist of the use of cognitive computing across major industry domains:

How cognitive computing is revolutionizing the banking sector #

The BFSI industry has been prompt in adopting technologies that make financial processing easier and less complicated. The realm of banking comes with its own set of processes – fund transfers, cash deposits and withdrawals, bank statement accountability, and most importantly, resolving customer queries. Although most financial institutions have been quick to deploy virtual assistants and bots for the customers convenience, it has become obvious that people would prefer interacting with something with a slightly more human touch. Here’s where cognitive computing steps in to ensure seamless consumer experience.

Banks and other financial establishments are leveraging cognitive computing to automate banking and investment products, manage a large amount of transactions, and use evidence-based information to provide tailored services to customers. Using cognitive computing helps banks examine and analyze data from a wider array of resources so they are able to mitigate risks better. Banks can also use CC for fraud detection – to predict what may happen, even before an activity actually occurs.

The predictability factor of CC is particularly useful in trading. As a matter of fact, data and analytic provider Previn states that close to 1,360 hedge funds leverage computer models to make the majority of their trades. Many a time, high frequency trading is completely automated, in which case business models can analyze large amounts of data and use the same to improve trading outcomes, all by themselves.

Wipro’s recent partnership with Finastra to accelerate digital transformation across APAC corporate banks is an example of the BFSI sector has been incorporating cognitive computing in its processes.

Cognitive computing is on its way to transforming the healthcare sector #

The incorporation of technologies, such as Big Data, AI, imaging, has brought about a tremendous change in the medical sector. Healthcare companies have lately been leveraging cognitive computing to make vital decisions regarding a patients condition, diagnosis, and treatment. CC is also being used by companies to as a clinical support tool – in the future, it is likely to be used in monitoring systems that scrutinize episodes in patients suffering from asthma, Parkinson’s disease, diabetes, and more, without human intervention.

As the complexity of diseases and the number of patients in the medical ecosystem increase, healthcare personnel often find themselves in trouble as they have to deal with a vast amount of health-related data in less time. Not to mention, this data is pretty fragmented and does not provide a concise insight into a patients medical history and decisions to be taken for the patients overall well-being. To that respect, cognitive computing systems step up as an efficient solution. These systems are designed to parse through data, share health information, improve patient outcomes, and personalize patient care by helping medical professionals make faster, cost-effective decisions.

IBM’s Watson is one of the best examples where cognitive computing is being used to process data and model possible solutions. IBM has worked with a lot of hospitals in partnership, especially Cleveland Clinic that has been using IBM’s Watson cognitive computing technologies across its clinical and administrative operations.

Cognitive computing – Bringing about a change in the education domain #

Cognitive computing can prove to be a game-changer in the field of education. With the arrival of the pandemic, the education sector has undergone a massive change. Online schooling became more commonplace, pushing the education technology industry trends to a considerable extent. Amid this scenario, the rise of cognitive assistants will prove to be highly helpful for teachers, students, and other support staff.

Cognitive computing systems can act as a personal tutor for students, as they guide them through their coursework and help them make decisions about which course to opt for next. They can also personalize course materials for students, help them resolve problematic situations in the course, and play the role of career counselors – a job that is crucial in educational institutions.

Cognitive computing solutions are also incredibly useful for teachers. An analysis of the education system worldwide often points to the problem of teachers not being able to answer queries satisfyingly, from students owing to their varying abilities and learning aptitudes. To that end, cognitive computing systems can help teaching staff, as they have the ability to digest large data sets and engage with humans in natural language. For instance, if a student asks a specific doubt to a teacher, he/she can feed it into the cognitive system, which will not only provide the right answer but also explain the steps en route.

The efforts put into implementing cognitive computing in education are quite visible off late. For instance, recently, in August 2021, the department of science and technology (DST) announced a funding of USD 134,848 to establish a cognitive computing hub at IIIT (Indraprastha Institute of Information Technology Delhi), Delhi.

An overview of how cognitive computing is deployed in the retail industry #

Personalized customer experience has been, since long, a cherished dream for retailers. While the e-commerce sector has been quick to adopt novel technologies to ensure a seamless consumer experience, introducing cognitive computing systems into the mix may change the game altogether.

These solutions can be used to create personalized promotions, recommend products during online shopping, generate in-depth customer insights, check on workforce utilization and optimization, and optimize inventory. As online shopping continues to gain precedence, retailers will need to incorporate more and more cognitive computing solutions in their processes to offer personalized, seamless user experiences. These solutions will help retailers track patterns in customer data, derive customer insights, and lead to improved customer interactions.

Apart from that, cognitive computing provides the advantages of predictive analytic, voice assistance, demand forecasting, social media engagement, price optimization, and personalized recommendations. It is also incredibly beneficial for consumers, as it provides then with self-help applications such as self-checkout payment platforms, price checkers, information kiosks, and mobile payment apes to improve their online shopping experiences.

The Future of Cognitive Computing #

There’s no doubt about this, cognitive computing is here to stay. Most experts are certain that it’ll lead to exciting growth opportunities across major use-cases. It has the ability to revolutionize key industry domains, as they scramble to resolve their current challenges. Advancements in cognitive computing will encourage companies to be more receptive to the technology and increase its adoption. In summary, cognitive computing bears true potential to transform the way technologies such as artificial intelligence and natural language processing are deployed.

Related reading

Cognitive Manufacturing and The Beginning of a Revolution

Vin Patel — Wed, 03 Aug 2022 11:08:41 +0000

Key takeaways

Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes.
Our factories will become more efficient and productive, as well as less wasteful, as a consequence of the support of smart machines that continue to grow wiser as they have access to more data.
Manufacturing must grow into cognitive manufacturing in order to fully advance to Industry 4.0 and beyond.

Introduction #

Have you ever imagined a world without technology or new inventions? Let us try to think of one such world, where there were no such inventions to revolutionize mankind or to ease the lives of people. Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes. Imagine a world functioning on needles and thread. Does it sound weird? Yes, it does. Do you know what’s even worse? Imagine a world without online shopping. In the time that I am finishing this line, I’m sure a thousand more orders have already been placed on various online shopping apes in my locality alone. If I tell you to imagine these, then basically I am telling you to travel through time, and it won’t be a time travel back to just a few years, but you would have to go centuries back. But here the thing is, we talk about technology and its effects in almost everything, especially in easing our lives but then we just forget about one thing that has really been made easier by technology and inventions: it is the manufacturing of products.

When we talk about manufacturing, we need to talk about a kind of technology that is ready to revolutionize the industries and also this whole world, “Cognitive Computing.” Implementation of cognitive computing has bought a new revolution alongside, i.e., Industry 4.0.

Let us dive in now and try to understand what cognitive computing is.

What is Cognitive Computing? #

#

Cognitive computing is the application of thinking, language processing, machine learning, and human capabilities to assist ordinary computers to solve issues and analyzing data more effectively. A computer system can handle complicated decision-making processes by learning patterns and behaviors and becoming more intelligent.

A cognitive computing technology platform adapts and makes sense of information, including unstructured information such as natural speech, using machine learning and pattern recognition.

Now that we know what cognitive computing is, let us try to know what cognitive manufacturing is.

What is Cognitive Manufacturing? #

#

Cognitive manufacturing employs cognitive computing, the Industrial IoT, and sophisticated analytic to optimize manufacturing processes in previously unimaginable ways. It assists enterprises in improving basic business indicators like productivity, product reliability, quality, safety, and yield while minimizing downtime and costs.

Why Does Cognitive Manufacturing Matter? #

#

Cognitive technologies delve deeply into a manufacturing process and business environment to extract information with real-world value for a company.

Cognitive manufacturing uses cognitive computing, the Industrial Internet of Things, and sophisticated analytic to digitize, analyze, and improve manufacturing processes in previously unimaginable ways.

Cognitive manufacturing is effective because it integrates sensor-based data with machine learning and other artificial intelligence skills to discover patterns in structured and unstructured data from plant, company, and industrial systems. It collects pertinent data in real-time and applies analytic to produce unparalleled levels of understanding and insights into the production process. It automates reactions based on its findings and provides actionable information as well as continually updated knowledge to production decision-makers.

Cognitive manufacturing has revolutionized the industry. It is said that it has made the world ready for industry 4.0. Let us dive in a bit more and try to understand what industry 4.0 is.

What is Industry 4.0 #

#

We are in the midst of a substantial transition in the way we make products as a result of factory digitalization. This change is so compelling that it is being dubbed Industry 4.0 to signify the fourth manufacturing revolution. From the first industrial revolution (mechanization via water and steam power) to mass production and assembly lines via electricity in the second, the fourth industrial revolution will build on what was started in the third with the adoption of computers and automation, enhancing it with smart and autonomous systems powered by data and machine learning.

Now, and in the future, as Industry 4.0 develops, computers are linked and communicating with one another in order to make choices without the assistance of humans. Industry 4.0 and the smart factory are made feasible by a mix of caber-physical systems, the Internet of Things, and the Internet of Systems. Our factories will become more efficient and productive, as well as less wasteful, as a consequence of the support of smart machines that continue to grow wiser as they have access to more data. Finally, it is the network of these digitally linked machines that produce and exchange information that results in the ultimate power of Industry 4.0.

Cognitive Manufacturing and Industry 4.0? #

The transcontinental railroad, the cotton gin, electricity, and other technologies had long-lasting effects on civilization. The same thing happened when we learned about the breakthrough Artificial Intelligence and its applications, and Industry 4.0 was in the works. Businesses all across the globe were striving to implement the concept of Enterprise Cognitive Computing (ECC). It employed artificial intelligence (AI) to improve corporate processes by embedding algorithms into apes that assist the organizational process. Cognitive Computing applications can automate repetitive, formulaic operations and give accurate results in large magnitudes.

Cognitive Computing paved the door for a technology known as Cognitive Manufacturing, which is notably valuable for manufacturing firms. It is also a cognitive method to integrate AI algorithms with industrial procedures in order to accomplish desired results.

Cognitive Manufacturing uses cognitive computing, industrial IoT, and sophisticated analytic to digitize, comprehend, and improve manufacturing processes. It enables manufacturing processes to collect data from various sources and analyze this data in order to provide important knowledge about processes, machine work efficiencies, employee efficiency, and workload statistics with a thorough analysis that can render suggestions to optimize each department of the manufacturing paradigm.

By utilizing AI algorithms on diverse operational applications, it enhances productivity, scalability, dependability, and security. Cognitive manufacturing completely leverages data across systems, equipment, and processes to gain actionable information throughout the whole value chain from design to support. Manufacturing must grow into cognitive manufacturing in order to fully advance to Industry 4.0 and beyond.

End-Note #

#

A few hundred words ago, I made you imagine a world without technologies, online shopping, and necessary inventions. Let us now end that time travel and think about the delivery of online shopping goods with drones. That is what we call a future in front of our eyes. One such future is cognitive computing and this when combined with the manufacturing sector gives rise to an industrial revolution. This is what we call the future. The beginning of industry 4.0. And cognitive computing is what is making this world ready for a kind of time travel we couldn’t have imagined. So, however turbulent it is going to be, trust the power of teach. It is going to be a safe journey.

Related reading

Questions and answers

Have you ever imagined a world without technology or new inventions? #

Let us try to think of one such world, where there were no such inventions to revolutionize mankind or to ease the lives of people. Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes.

Do you know what’s even worse? #

Imagine a world without online shopping. In the time that I am finishing this line, I’m sure a thousand more orders have already been placed on various online shopping apes in my locality alone.

How Are Technology Leaders Using Data and Machine Learning

Vin Patel — Fri, 29 Jul 2022 18:55:15 +0000

Key takeaways

Businesses need to be ultra- vigilant in the contemporary world embodied by cutthroat competition to stay ahead in the race.
Identification of Tangible Goals – Identifying new business prospects with the help of big data necessitates a well-defined goal.
The key is to understand how machine learning and data can be appropriately used to meet each challenge and goal.

Businesses need to be ultra- vigilant in the contemporary world embodied by cutthroat competition to stay ahead in the race. Adopting, employment and mindful dissemination of upcoming technologies like AI, big data, and machine learning can help companies stay afloat. Data science and machine learning have impeccable impacts on businesses all over the globe, and they are rapidly turning critical for the differentiation and survival of technology bigwigs. The big claims about big data and machine learning as transformative instruments in the hands of businesses have accuracy and honesty in them. So, let us analyze the role of big data and machine learning in helping technology leaders to help identify new business opportunities.

The Implication of Big Data in Gauging New Business Opportunities #

The valuable data insights that help a company or individual understand their target audience and anticipate their preferences are known as big data. In 2020, 44 zettabytes of data were generated. Large-scale companies are incredibly valuable because they have the ability to merge diverse data sets into useful sources for AI algorithms to affect and comprehend human behavior. Thus, there is no question as to why technology leaders use big data in various ways to elevate their businesses and identify novel business opportunities. Experts believe that big data can help technology leaders outperform their competition through innovation. A myriad of companies is co-opting big data to discover their flaws.

By 2017, the adoption of big data in companies had reached 53 percent. In 2022, data will play a huge role in enabling companies to understand their target demographics and preferences. Every entry point in digitalization and modernity is inundated with new data that describes human behavior more accurately. Even more importantly, audio-visual mediums such as videos and phone calls are being used to collect more meaningful data. An in-depth examination of these data points can shed light on consumer habits, personality traits, and significant life experiences. Technological giants have leveraged such strategies for product improvement, business strategy, and marketing campaigns to cater to their target clientèle.

Since its proliferated use in 1997, big data has helped businesses and technology giants pave out new growth opportunities. Some of the veritable ways through which big data can help enterprises to include:

Creation of New Revenue SystemsBig data provides companies with invaluable insights that are premised on the market and consumer analysis. Technology giants and leaders have used big data to make additional revenue by selling non-personalized trend data to massive corporations operating within the same domain. Thus, big data provides an ideal additional route for revenue.
Technological InnovationOne of the best parts about adopting and scrutinizing big data is the plethora of backup technologies available at the company’s dispense. For instance, a relatively simple cloud-based phone system enables companies to easily store and retrieve extensive communication-related data such as call logs, customer information, client conversations, feedback, and the like. The availability of cost-effective ancillary digital mechanisms such as Office 365 for extensive data collection and storage makes its utilization even more feasible for businesses. Likewise, the ample presence of analytical tools can help companies at various stages of their functioning. For example, technological pioneers combine big data and analytic tools to understand the most viable way to spend money on customer acquisition, determine how much each customer is spending on the services, and so forth.
Data Safety and Risk AnalysisTechnology leaders appropriate big data tools and technologies to map out a company’s complete data landscape and use it to analyze all the internal and external threats to their business model even before they materialize. Knowledge is one of the most potent weapons businesses have to ace within their domain. The much-needed knowledge can be derived from big data and its various tools, making a company better equipped in all aspects, including safety. More and more industries are now focusing on big data, making attaining security more doable and within reach.
AutomationThrough robotic process automation, Big Data has the incredible ability to solve internal shortcomings and operations. Companies may create a highly automated and efficient decision-making system using enormous data sets. Automating data collection and storage and data security are now possible thanks to scalable IT infrastructure and declining cloud computing prices.

How to Harness the Power of Big Data #

Since 2012, a whopping 40 percent of the large global corporations led by technological leaders have made considerable investments in big data strategies. However, for newbies, the utilization of big data can be overwhelming and perplexing. Nevertheless, fret now as we have a few tips to share. Companies need to evaluate the following factors before finalizing big data solutions.

Putting Together an Experienced TeamThe first step in being successful in the precarious game of utilizing big data is to put together a team of expert data collectors, analyzers, and strategists. Going by the example of technological leaders, curating a diverse group that is equipped to deal with the nuances of modern analytic methods, manipulate big data sets, and understand overarching business goals is essential.
Identification of Tangible Goals – Identifying new business prospects with the help of big data necessitates a well-defined goal. Data and analytic should be used to support the company’s overall objectives, such as increasing profit margins, building brand recognition, and gaining market share.
Capturing the Right Data – Once the underlying business goal is identified, the next step is to have a concise and lucid understanding of the data itself before its application. Identifying, capturing, and tracking the correct data is the backbone of the entire big data mechanism. Consequently, relying on inaccurate data can wreak havoc and push the company to an abysmally new low.
Application of Proper Analytic Methods – Pioneer data analysts are skilled in translating big data into valuable insights with mind-blowing efficiency and accuracy. Visual summaries and crisp data reports that are easy to understand by laypersons facilitate the supporting teams to process the data and make meaningful business decisions quickly.

Now that we have discussed the intricate relationship between big data and business opportunities, let us move towards machine learning and observe its impact on new business avenues.

The Significance of Machine Learning #

The nearly-infinite quantity of data available to humankind coupled with factors like affordable data storage, growth of less expensive and more powerful processing tools, and so forth have propelled the development of machine learning or ML. Technological leaders and novices worldwide are compiling more innovative ways of analyzing mammoth-sized complex data with faster delivery, better accuracy, and seamless results. Machine learning tools enable organizations to identify profitable opportunities and potential risks. So, let us look at the top advantages of machine learning in the business sphere.

Better Customer Experience #

Machine learning helps companies attract more people to their products and services and help increase conversions. In other words, machine learning facilitates an organization to better understand their customers’ browsing experience and behavior so that the company can provide them with precisely what they need. For example, the recommendation engine in the renowned OTT platform Netflix helped the steaming site attract a vast audience and increase profits by effectively encouraging binge-worthy shows. A 2018 research reveals that 80 percent of Netflix viewers follow the recommendations extended by machine learning. In a nutshell, some of the benefits of personalized customer experience by machine learning include:

More extensive revenue generation, courtesy of higher customer engagement and retention
More pronounced customer satisfaction that facilitates brand loyalty
Less time invested in research through traditional methods
Cost-effectiveness by erasing office staff and pieces of equipment from the process

Effective Automation #

Delegating mundane and repetitive tasks to the machine increases production speed. Moreover, opting for machine learning also eliminate manual data errors data duplication and ensures a higher quality of work. Furthermore, the company does not even need a hi-teach developer to reprogram the systems repeatedly whenever there is a change in the workflow and process. Thus, the combination of machine learning and automation has various advantages for a business, including –

Faster service
Reduction in spending on software management
Saving money by distributing repetitive tasks to machines
Use human resources for more critical jobs
Removal of manual error
Reduction in mundane administrative tasks

Robust Predictive Abilities #

ML predictions have two significant advantages to help a business identify new avenues. The first is customer choice predictions that are premised on customer data. The machine learning system recognizes typical and atypical behaviors using customer data, which is then utilized to predict the changing demand for a company’s products and services. Likewise, large enterprises can tweak their ML systems to process enormous amounts of market data to arrive at unique and insightful forecasts. Thus, a company will identify trends faster and attain the fat-mover advantage.

Reasonable Resource Planning #

Through predictions made via machine learning, a company can estimate the resources needed to meet changing demands for its products and services. Advance knowledge can help a company in inventory and process management. As such, a company can viably have enough products during high-demand periods, decrease the risk of wastage, save money by knowing the optimal type and amount of product to keep on hand, and likewise.

Better Internal Company Changes #

The scope of machine learning is not just limited to customer acquisition and marketing campaigns. Machine learning is a tool that can help a company set and manage workflow, follow staff progress, and maintain internal corporate values. Few of the fundamental merits that machine learning offer in terms of internal adaptability include –

Wiser task prioritization and work distribution
Higher transparency of work among employees
Heightened productivity
Seamless integration of new technologies into the existing systems

Better Adaptation to Market Fluctuations – Technology leaders like Google, Amazon, and Apple are always a step ahead of the rest because they adapt quickly to market changes. As we have already discussed, the ML predictions provide a company the opportunity to veritably improve its business model and a competitive edge over peers. Some of the advantages include

• Machine learning extends insights into the market movement, allowing a company to know the global trends well in advance.

• ML ensures the basis of efficient sales and marketing stratagems in the future

• It helps companies save money, time, and human resources in advance.

• Lastly, machine learning allows a company to adapt to changing environments faster.

Efficient Data Management – Big data means that manual collection and data usage is complex and nearly impossible. In such a challenging context, machine learning comes into the picture. Machine learning helps a company distinguish relevant data from peripheral and ancillary information and spam. Through this mechanism, Mail was able to eliminate 100 million spam messages per day in 2019. Some of the benefits of machine learning for extensive data management include –

• Saving of time by dividing important work from subsidiary work for employees

• Increase in productivity as employees can focus on higher priority tasks

• Heightened user engagement by offering customers only relevant information

End Note

In conclusion, we can safely assert that machine learning and big data can help technology leaders and novices identify new business opportunities. The provision of an efficient automation solution coupled with other phenomenal advantages like saving time, money, and resources, predictive competencies, and so forth are undeniably advantageous for businesses of all types and sizes. A wide assortment of industries such as the healthcare sector, manufacturing sector, and so on are co-opting the combination of machine learning and big data to avoid mistakes and better cater to their customers. Thus, machine learning and big data are here to say. The key is to understand how machine learning and data can be appropriately used to meet each challenge and goal.

Related reading

The World of Web 3.0 and Its Influence on Social Media

Vin Patel — Mon, 04 Jul 2022 13:13:00 +0000

Key takeaways

Humans seek new ways to develop and grow their lives and surroundings.
Through semantic web concepts and natural language processing, computers will be able to comprehend information like humans.
Based on decentralized networks, Web 3.0 will revolutionize the economy, transform the delivery and consumption of goods and services, and alter the way businesses operate, thereby transforming culture, society, and individuals.

Humans seek new ways to develop and grow their lives and surroundings. We live in a fast-paced world where almost everything is digitized and developed. The technology revolution has helped humans explore the endless possibilities of creating and enhancing platforms for better user experiences. It all began in 1989 in the form of Web 1.0, and today we are on the verge of achieving a new height in terms of Web 3.0. In 2014, Gavin Wood, the co-founder of Ethereum and inventor of Polkadot, introduced the idea of Web 3.0. It focuses on bringing changes to the world of social media and cryptocurrency trading. In the course of Web 3.0’s development, you will experience a multitude of benefits and transformations, but the world is yet to be familiarized with all these changes. As time passes and developments take place, you will soon be aware of it. For now, this article will introduce you to Web 3.0 and how it’s a game-changer in terms of decentralizing social media.

But before you read further, here’s exactly what you need to know about Web 3.0.

What is Web 3.0? #

In comparison to Web 1.0 and Web 2.0, the latest Web 3.0 is totally contrasting. Web 1.0 users could use the internet mainly in a read-only mode, and no interaction with the virtual world was available. It then evolved into a higher version with enhanced features in Web 2.0. It allowed users to interact with the internet because of developments in web technology such as JavaScript, HTML5, css3, etc. During this era, companies created interactive web platforms like YouTube, Facebook, Twitter, and many more. Web 2.0 could not guarantee a decentralized platform offering users total privacy.

But today, Web 3.0 is contemplated to be the latest evolution of the internet. It uses artificial intelligence, blockchain, and decentralization techniques to process data with near-human intelligence. These characteristics of blockchain contribute to Web 3.0’s features, and they have a dramatic impact on social media operations. According to experts, in Web 3.0 users will reach a point on the internet where every company is run by a decentralized group, specifically termed DAO (decentralized autonomous organization). The DAO runs on the blockchain using an algorithm that does not require a manager or third party.

Features of Web 3.0 #

The first key feature is decentralization. With Web 3.0, machine learning and artificial intelligence drive decentralization, while blockchain technologies enable blockchain transactions. Blockchain helps to centralize data storage while establishing trust with the virtual world. Consequently, it lets users access data relevant to their preferences without requiring permission from third parties. This would allow users to have greater control over the information that companies like Google, Twitter, and Meta hold.

The second feature is entirely trustless and permissionless. It implies direct interaction between participants without needing an intermediary or a governing body’s permission. Consequently, it lets users access data relevant to their preferences without requiring permission from third parties.

Thirdly, Web 3.0 uses artificial intelligence and machine learning. Through semantic web concepts and natural language processing, computers will be able to comprehend information like humans. By applying semantics to search and analysis, web technologies can be improved for creating, sharing, and connecting content. In addition to this, Web 3.0 uses machine learning that mimics how humans learn by using data and algorithms.

The fourth feature is connectivity and ubiquity. In Web 3.0, the internet will be accessible to anyone anywhere at any time. It uses the internet of things (IoT) to launch various smart devices. The internet-connected devices will no longer be limited to PC and smartphones. The future internet-connected devices will not only be limited to computers and smartphones like Web 2.0 but also bring forth many new types of devices that work on the fundamentals of IoT.

With a decentralized method, Web 3.0 will divide the ownership of channels among all participants. These new centralized networks will be based on blockchain technology, the same as Bitcoin and Ethereum. It is effectively a transparent digital ledger that can record transactions that cannot be altered. This decentralized technology even tinkers with data privacy by allowing you to choose whether you want your information to be available to the public.

Web 3.0 comes with a new form of mobile transactions in the form of DApp. DApp will bring a paradigm shift in the world of social media. Now here a question arises on how DApp social media varies from the existing social media of Web 2.0?

You might be familiar with social media applications like YouTube, Twitter, Facebook, and Instagram, which are part of Web 2.0. But with Web 3.0, decentralized applications would provide better transactions between creators and developers using blockchain technology. The social media platforms you currently use earn huge money through the content posted by creators, yet, the creators receive minimal value for it. Web 3.0 social media platforms like Steemit, Hive, Hyper, Rest.news, Mirror.xyz etc. will alter this and provide better financial rewards to the content creators. DApps offer built-in payment layers, which eliminate undesirable third parties and unwanted expenses.

Content creators can also sell their content or monetize their content using NFTs. You can purchase and sell NFT tokens to own unique digital items. NFT tokens represent almost any kind of digital item, including arts, graphics, music, videos, images, etc. Several notable NFT sales have occurred recently, including Jack Dorsey, CEO of Twitter, selling his first tweet and Melon Musk selling a song about NFTs. You can use platforms like OpenSea, Nifty Gateway and Rarible for NFT transactions.

One of the major advantages of Web 3.0 will be that you can own and control the social media content you create, and the value you create can also be divided as per the agreement between them and the platforms. Even the multifaceted means of generating income will enable creators to reach out to a larger audience. Web 3.0 ensure that it provides an enhanced user experience by analyzing users’ social media posts, comments, and likes which are vital to enable content recommendations. According to experts, Web 3.0 will give you the freedom and authorization to have complete access to your data. You can easily access your data from anywhere and at any time.

Finally, Web 3.0 will feature better and more advanced data privacy since third parties will not be involved. Based on decentralized networks, Web 3.0 will revolutionize the economy, transform the delivery and consumption of goods and services, and alter the way businesses operate, thereby transforming culture, society, and individuals.

Related reading

Implementing Web 3.0 in Businesses

Vin Patel — Mon, 04 Jul 2022 13:09:25 +0000

Key takeaways

The internet is a spectacular technology that has delivered on all of its promises over the last few years.
Web 3.0 technology will provide people control over their data.
Overall, Web 3.0 allows businesses to operate more freely, develop faster, and provide a better customer experience.

The internet is a spectacular technology that has delivered on all of its promises over the last few years. The internet has transformed education, private communications, urban development and management, and, most importantly, the way we do business in a brief time. The best aspect is that the internet is still in its development stage, and the future potentials are bright.

If you ask any expert about the state of the web, they’ll tell you that we’re on the verge of Web 3.0. But what is Web 3.0, and how will it affect our lives, if at all? The third generation of online technology, known as Web 3.0, is here to stay. Although the technology is still in its early stages and there is no universally accepted definition of what it is or what it implies, it will be one of the most critical technologies for organizations in the future. So what impact will Web 3.0 have on how we use the internet and on businesses? Let’s find out.

What Is All The Buzz Around Web 3.0? #

The definition of Web 3.0 differs from situation to situation, although it is generally regarded as the third generation of the internet. Web 3.0 is often referred to as the semantic or geographical web. If you divide the interne’s growth into two halves, the first being plain online pages and the second being applications, social media, and mainstream adoption, the emerging blockchain-based web would be internet 3.0. It is a more user-friendly, safe, private, and well-connected internet.

Web 3.0 is distinguished by creating a virtual space within the internet via websites such as immersive digital shopping malls, which allow users to interact with one another and purchase things.

The ‘semantic web,’ which analyses and uses user data and behavior to provide a more personalized online surfing experience, has emerged due to leveraging the power of big data as well as machine learning. A fantastic example of this in action may be found in advertising, where advertisements for things you are actively interested in following you across the web. It allows personalization and the semantic web, which provides a foundation for information to be repurposed and shared more readily among programmed, communities, and businesses.

However, several experts predict that Web 3.0 will provide a quicker, more intuitive web experience, with the potential to forego command-line search keywords in favor of organically speaking to the likes of Google.

Critical Competitive Advantages of Web 3.0 #

1) Semantic Web #

The semantic web will help in ’educating’ the computer on what the data means, allowing artificial intelligence to grow and fully use the material. The main idea is to create a spider network of information throughout the internet to help people comprehend the meaning of words via search and analysis.

2) Improved Personalized Browsing Experience #

Web 3.0 gives everyone a significantly more personalized browsing experience. Websites may automatically change according to our device, location, and any accessibility needs we may have, and web apes will become extremely aware of our usage patterns.

3) Artificial Intelligence (AI) #

Along with blockchain technology, AI is the newest and most impactful technology on the market right now. In computer science, artificial intelligence, often known as machine intelligence, is simply intelligence or learning by computers. When combined with natural language processing in Web 3.0, computers can now discern information like people to give faster and more relevant results. Consequently, they grow more intelligent to meet the needs of various users.

4) The Advantages of Blockchain #

Integrating business technologies such as blockchain technology can improve the commercial applications of Web 3.0. For example, blockchain in Web 3.0 enables the cost-effective shifting and transfer of digital assets across company borders. Blockchain advantages include eliminating minor criteria such as gender, regional location, and many more.

5) IoT Smart Gadgets #

Smart IoT gadgets can only be improved by incorporating Web 3.0 for intelligent and smooth functionality. This Web 3.0 application gives new chances to become agile and adaptable to take risks efficiently and successfully. Company technology must provide new avenues for exploring and expanding the business sector.

6) 3D Graphics #

As the internet progresses from a simple two-dimensional web to a more realistic three-dimensional cyberworld, Web 3.0 will significantly affect its future. Web 3.0 websites and services, such as online gaming, e-commerce, and real estate, make extensive use of three-dimensional design.

7) Enhanced Customer Relations #

When implementing Web 3.0, every transaction is recorded in a decentralized ledger visible to all parties involved, making businesses directly accountable to their consumers. Businesses may leverage this transparency to foster trust and long-term consumer connections. In addition, customers know that the information is legitimate and has not been changed since blockchain data is immutable.

8) Insights Based on Data #

A Web 3.0 firm must have data-driven insights to satisfy the target audience based on market research and adequate data. Business technologies such as AI, machine learning, IoT, and other Web 3.0 applications make it simple to obtain essential information regarding the target audience.

How Have Major Companies Adopted Web 3.0 To Gain A Competitive Edge? #

Web 3.0, powered by today’s most disruptive technology - Blockchain – will revolutionize internet infrastructure and influence how organizations function. Web 3.0 technology will provide people control over their data. For example, people will be able to choose whether to share or sell their information. Advertising fraud will also be eliminated by Web 3 technologies that authenticate impressions and click with the user’s identification.

Streaming services like Amazon Prime and Netflix are top-rated and profitable these days. This sector, however, confronts hurdles linked to copyright concerns and equitable reward distribution to marketers and content providers. Smart contracts in web 3.0 can aid in the resolution of these difficulties. Unlike Web 2.0, Web 3.0 offers the prospect of filling the gap between a user’s online presence and their actual person, which means you’ll be able to leave anonymous comments on postings and make purchases without purchasers knowing your identity. This implies that users may make transactions and download information and photos without their true identity being revealed.

Blockchain technology streamlines the login process for your website’s visitors. For example, consider how often you’ve used your Facebook profile to sign in to websites. Blockchain technology works on the same principle as Facebook, but the difference is that, unlike Facebook, nobody owns your data but you. From a commercial perspective, your customers will still be able to sign in quickly on your website, and you will never have to worry about safeguarding their data for them because it’s already more secure.

Since blockchain technology is recognized for its vital security requirements, Web 3.0 may potentially prevent hacking. According to IBM, blockchain is very less likely to be hacked due to end-to-end encryption of documents. Another layer of protection that prevents hackers from attacking your website is making the data anonymous and demanding authorization to view it. Blockchain technology is already being used for security by companies like Apple, Google, and Facebook. Some companies already implementing Web 3.0 in their business models are as follows:

BitPay

BitPay is a crypt payment management tool for individuals and companies. Founded in 2011, BitPay enables retailers to accept Bitcoin payments for various goods and services. It offers hosted checkout, shopping cart plugins, billing solutions, mPOS software, etc. In addition, it provides payment cards and a digital wallet to customers for payment management.

Helium

Helium is a bitcoin mining solution based on hardware. It links low-powered equipment to a single gateway and transmits encrypted data over the internet. It also pays people who link-time machines and gateways. It has received stock backing from many renowned venture capital companies. Helium was founded in 2006 in San Francisco.

Terra

Terra is a payment infrastructure built on the blockchain. It provides a price-stable cryptocurrency that is utilized as a payment method. In addition, it provides a decentralized asset whose value is derived from transaction fees collected on the Terra network. This company was established in 2018 in Seoul, and it provides a reserve comprised of decentralized assets. The token may be used to pay with cryptocurrencies on e-commerce sites.

Web 3.0 is the future of business, and understanding how it works is crucial for entrepreneurs, corporations, and organizations. Businesses cannot afford to fall behind on current market trends. Business choices must be made with prudence, keeping in mind that the future is now and continually renewing.

Is Web 3.0 The Future of Businesses? #

In several ways, the internet has revolutionized the world. Web 3.0 promises a significant transformation to the internet, stressing user privacy – a problem that has been discussed but not effectively handled for some time. Web 3.0 is critical because it allows businesses to simplify their processes by eliminating intermediaries and connecting computers directly. This innovation improves collaboration and communication among employees, partners, and customers, resulting in a more efficient company.

Web 3.0 is all about web users creating more digital material than ever before. This is why businesses who are already on the Web 3.0 bandwagon first are more likely to win over their consumers by providing innovative, secure content solutions and an improved onboarding journey. Overall, Web 3.0 allows businesses to operate more freely, develop faster, and provide a better customer experience.

Related reading

Questions and answers

What Is All The Buzz Around Web 3.0? #

How Have Major Companies Adopted Web 3.0 To Gain A Competitive Edge? #

Is Web 3.0 The Future of Businesses? #

The Origin of Web 3.0: Is It the Future of The Internet?

Vin Patel — Mon, 04 Jul 2022 13:02:13 +0000

Key takeaways

Since the rise of blockchain, we’ve all heard the term “decentralization” floating about.
The Semantic Web 3.0 claims to better organize “the world’s information” than any Web 2.0 application.
As a result, Web 3.0 has the potential to be the Interne’s transformative future.

Since the rise of blockchain, we’ve all heard the term “decentralization” floating about. The general public is aware that the notion of currency has been decentralized via blockchain and that the product is cryptocurrency.

What if it was pointed out to you that the Internet, too, can be decentralized? Web 3.0, the subject of our talk today, makes it feasible.

Web 3.0 describes the evolution of web usage and interaction, particularly the Web’s metamorphosis into a “Database.” The next phase of the Internet will end major businesses’ and governments’ monopolistic supremacy.

What is Web 3.0? #

Web 3.0 was initially named Semantic Web by its original creator Tim Burners-Lee. It is a sort of Internet that will operate on decentralized networks such as Ethereum or Bitcoin and employ blockchain technology.

It will be a platform that is not controlled by any single entity, yet everyone can trust it since all users will follow the same set of hard-coded rules known as consensus protocols. Making the Internet smarter and processing data with near-human intelligence will highly benefit users. It will create an open, trustless, and permissionless network.

It is truly claimed as the “future of the internet”.

How is Web 3.0 Formed? #

Keep in mind that the layers of web 3.0 are recent additions to the current Web 2.0 framework. It is done to remove all constraints imposed by web 2.0, which limited users to merely reading, writing, and creating on the Internet. In web 3.0, however, all algorithms will strive to read individual data and tailor the Internet to the particular user, making it way more convenient!

While web 2.0 alters currently commoditized personal computer technology in data centers, web 3.0 pushes the data center out to the edge, bringing edge computing into our hands. Edge computing comprises the first layer of the semantic Web. In 2025, phones, computers, appliances, sensors, and autos will generate and consume 160 times more data than in 2010.

Data is decentralized in web 3.0, which means that users will have ownership over their data. Different data producers can sell or exchange their data using decentralized data networks without losing control, endangering privacy, or relying on middlemen. The second layer of web 3.0 is made up of decentralized networks. You may log in securely and anonymously over the Internet using Internet Identity.

Artificial intelligence (AI) and machine learning (ML) algorithms have advanced to the point that they can now make helpful and occasionally life-saving predictions and acts. When built on top of emerging decentralized data structures that provide today’s internet behemoths access to a plethora of data, the possible applications extend far beyond targeted advertising to areas like precision materials, medication development, and climate prediction. As a result, AI is Web 3.0’s third and final layer.

#

How does Web 3.0 Work? #

Blockchain is at the core of Web 3.0 technologies. Cryptocurrency and NFTs both employ the same technology. Web 3.0 proposes that data be stored in decentralized storage across the Internet rather than on a restricted number of servers as in Web 2.0 apes. Uniswap, Dark Forest, Foundation, and PoolTogether are just a few of the decentralized Web 3.0 programs (lapps) that operate on the Ethereum blockchain.

What Makes Web 3.0 the “Future of the Internet”? #

The Semantic Web 3.0 claims to better organize “the world’s information” than any Web 2.0 application. It improves online technology by letting users produce, distribute, and link content through search and analysis based on the ability to comprehend the meaning of words rather than keywords or numbers.

In Web 3.0, cryptocurrencies are envisioned as a way to recompense content providers, who would get a token every time someone viewed their work. Smart contracts will facilitate application sharing, transactions, and censorship-resistant peer-to-peer file storage. It would involve a complete shift in corporate behavior, as well as consumer and producer empowerment.

Because it aspires to blur the boundaries between real and virtual worlds by reimagining graphics technology and placing three-dimensional (3D) virtual worlds or metaverses into sharp relief, some visionaries refer to Web 3.0 as the Spatial Web. Three-dimensional design (3D) is widely used in Web 3.0 websites and applications.

Not just in futuristic gaming applications like Decentraland but also in other fields like health, real estate, and e-commerce, 3D visuals provide a new degree of immersion. Museum tours, computer games, e-commerce, geographical settings, and other applications all employ 3D visuals.

Lastly, The foundation for Web 3.0 will be different. We’ve become accustomed to the “browser and website” approach. Browser plugins assist us in a variety of ways, including reducing web pages, filling in passwords, and adblocking.

Individual users will begin to conceive of what we now refer to as a browser as a form of an operating system that executes applications on the semantic Web. What we now call plugins will become apes, and while they will still be able to display documents and videos, some will be much more advanced. Users will be able to purchase and create bots that serve them directly in Web 3.0, similar to how hackers and some websites do now.

Can Web 3.0 Live Up to Its Name? #

Decentralization will aid the Interne’s growth by allowing it to be accessed from everywhere, perhaps allowing individuals who have never used the Internet to do so.

Many of the shortcomings of Web 2.0 will be addressed in Web 3.0. Because of semantic metadata, information is better connected with Web 3.0. As a result, the user experience advances to a new degree of connectivity, utilizing all available data.

Web 3.0 essentially takes the Internet one step further by making it available to anybody, everywhere, at any time. Because IoT will bring forth a variety of new sorts of smart gadgets, Internet-connected devices will no longer be restricted to PCs and cellphones, as they were in Web 2.0. Your identity, the majority of the goods you own, all of your data, and every software capability you have a right to utilize will all be connected and able to work together in the end. As a result, Web 3.0 has the potential to be the Interne’s transformative future.

Related reading

Questions and answers

What is Web 3.0? #

How is Web 3.0 Formed? #

How does Web 3.0 Work? #

Blockchain is at the core of Web 3.0 technologies. Cryptocurrency and NFTs both employ the same technology.

What Makes Web 3.0 the “Future of the Internet”? #

Customer service in the digital age. Do we still need human

Vin Patel — Mon, 04 Jul 2022 12:18:01 +0000

Key takeaways

Customer service has been of utmost importance for businesses for a long time, given how critical customers are to help a company reap successful long-term results and distinguish its brand from the competition.
Building customer relationships ensures increased employee morale, reduced customer attrition, excellent sales, and high turnover.
It is more than likely that customer service in the digital age may be characterized by a mixture of efficient bots and human staff, harnessing the best of both and helping businesses retain as many consumers as possible.

Customer service has been of utmost importance for businesses for a long time, given how critical customers are to help a company reap successful long-term results and distinguish its brand from the competition. What commenced in the form of written communication and telephone conversations to face-to-face interaction has today transformed into a full-fledged, value-driven service portfolio for customers.

Conventional strategies are no longer sufficient to address the changing needs of today’s market. With changing customer behavior, the stakes are significantly higher, and businesses need to work harder to provide increasingly beneficial yet cost-effective and, to a considerable extent, personalized services to customers.

In today’s world, driven by consistent digitalization, ensuring that customers are happy is more of a challenge than ever. With happier customers comes better trust in the brand and, consequently, more loyalty and advocacy. As per reliable reports, people may be four times more likely to buy a product referred by a known person. There’s an 18% increase in loyalty from a referred customer instead of the one obtained from a different method.

As of today, there are almost 4.4 billion active internet users worldwide, a number that will only serve to increase in the coming years. With a considerable percentage of the global population going online, businesses have access to a wide number of digital customers, paving the way for successful strategies to be coined – such as interacting with customers via live chat, virtually enhancing purchase experiences, and so on. Digitalization has revolutionized customer experience and can have a profound impact on customer behavior, in turn, impacting the brand value of a business.

Statistics About Companies Adopting Digitization #

Businesses had been adopting digitization way before the pandemic; however, it cannot be denied that the onset of COVID-19 acted as a tremendous trigger, pushing companies to rapidly scale up the deployment of digital strategies to accelerate their business. At present, digital transformation is at its peak and has led to the deployment of new business models implementing modern technologies such as machine learning, chatbots, data analytic, and blockchain. Companies across major end-use sectors have been rapidly integrating IT solutions in their ecosystem, generating new challenges and opening up numerous growth opportunities.

Back in 2021, IDC predicted that 30% of organizations would increase innovation and generate new models to digitize their companies. According to IDC, 60% of enterprise executives are of the opinion that IoT will have a major role to play in their companies’ digitalization.

Gartner had predicted that by 2023, close to 70% of organizations will use value stream management to enhance development and operations pipeline flow, leading to better, faster customer service. In addition, the highest growth of digital transformation was expected to be from devices (close to 14%) and enterprise software (approximately 10.8%), owing to companies providing innovative workplace solutions.

InformaTech predicts that AI software revenues may cross USD 100 billion globally by 2025. Close to 60% of companies may deploy artificial intelligence solutions to help employees in the near term, states Deloitte, while Forester says that digital transformation investments rose at over 3X the rate of overall IT expenditure.

With speedy internet becoming rather commonplace, more and more companies have been adopting digitization, investing heavily in business intelligence platforms to enhance customer experience. In 2021, Flexera claimed that around 37% of IT initiatives undertaken by companies globally focus on improving customer experience. The software company had also stated that 49% of companies across the globe prioritize cybersecurity when it comes to technology initiatives, while around 54% prioritize digital transformation.

According to a survey of 1,200 mid to large-scale US companies, the average digital transformation budget was pegged at USD 14 million in 2018, while digital transformation expenditure worldwide may cross USD 1.78 trillion in 2022. In addition, IDC claims that around 65% of global GDP may be digitalized by 2022.

With most industrial enterprises and startups having already adopted strong digital transformation strategies and a substantial percentage planning to adopt one soon enough, the digital ecosystem worldwide is set to proliferate at an appreciable rate. As customers form the nucleus of digitalization, businesses are expected to continue the adoption of digital solutions to enhance the customer experience in an increasingly competitive marketplace.

How Has Pandemic Affected Digitization and Its Adoption? #

The impact of the coronavirus pandemic on the adoption of digitization has been overwhelming. Many companies had strong security and IT mechanisms in place and had already been working to transition to a digital ecosystem. For these establishments, the onset of the pandemic made it easier to transition to a virtual working environment quickly. For many others, especially the ones with rigid legacy systems in place, an aging infrastructure, and bandwidth constraints, the pandemic was a brutal wake-up call to quickly digitalize their business models and create a new ecosystem in place for the future. This rang true in the case of government firms with severe capacity issues and the lack of a mobile-friendly presence. In effect, the pandemic forced several companies to accelerate the adoption of digital solutions, which included the deployment of virtualization and the cloud.

According to a report by Celerity, around 63% of industry leaders claimed that the pandemic prompted them to adopt digital transformation strategies sooner than they had planned. A similar report by IBM states that a mammoth 96% of leaders reported COVID-19 to fast-track their digital transformation plans by around 5.3 years.

One of the biggest changes sparked by the COVID-19 pandemic was observed in the payments industry. With social distancing becoming the norm due to nationwide lockdowns, the online payments sector depicted a meteoric rise, paving its way to becoming one of the fastest-growing markets worldwide. Having observed consumer interest in virtual payment models, several companies, even the ones with an originally conventional model, transformed their payment systems into consumer-friendly, digitalized versions.

In essence, the pandemic played a crucial role in speeding up the adoption of digital solutions, as contactless payment volumes witnessed an exceptional rise in demand across retail outlets, pharmacies, grocery stores, departmental stores, vegetable and fruit sellers, recharge vendors, and many others including prominent players across the and EdTech sector.

Given the dire situation of the healthcare ecosystem post the onset of the pandemic, the demand for telehealth and online consultations depicted an upsurge as well. Hospitals and medical establishments raced ahead to embrace digital transformation to monitor the increasing influx of patients. As per McKinsey, during 2020, the use of digital tools for health support by customers saw a rise of 15%, while it was predicted that around 37%of consumers might very well use telehealth in the future.

In addition, the research firm asserted that around post the pandemic, the use of telehealth increased from 11% to 76%. While it has had devastating effects overall, the pandemic has certainly helped push the implementation of digital tools across several industrial sectors.

Importance of Relationships with Consumers #

As the popular old adage goes, ‘The customer is always right’. Well, technically, this need not be true; however, it brings home an important point – customer satisfaction is highly critical for a business to be successful. At the end of the day, retaining consumers for your product/service is essential if you want the business to make money at all. Establishing strong, loyal relationships with customers, to that end, is one of the most vital strategies companies must adopt.

Building customer relationships ensures increased employee morale, reduced customer attrition, excellent sales, and high turnover. However, just establishing a liaison with the customer does not produce successful results – maintaining customer relationships is important. To that end, it is critical that companies increase focus on customer relationship management (CRM) as a vital tool. Customer acquisition is important, yes, but customer retention is more critical today.

Having good relationships with customers mandates consistent improvisation in company services. Customer behavior and expectations change periodically with changing trends and a transforming marketplace. Alongside, continual improvisation is vital so as understand changing consumer needs and fulfill them accordingly. A single negative experience or a seeming lack of concern can completely phase the customer out of the transaction ecosystem and negatively impact the company as well.

According to a survey conducted by PwC a year ago, 1 in 3 customers out of the 15,000 that were surveyed, chose to abandon a good brand after just one bad experience, while close to 92% may completely abandon the business after two negative experiences. In consequence, sour customer relationships can very well kill the brand sooner than one may realize.

A vital point that forms a part of good customer relationship management is paying attention to customer support. Customer feedback about a product/service, no matter how trivial, must be placed on a high pedestal. At the end of the day, customers want to feel heard, want to feel that they matter. A company that ensures appropriate customer satisfaction has more of a chance of retaining customers than a company that ignores customer feedback.

In today’s bustling work culture, paying attention to changing customer needs and behavior is more important than ever before. Businesses need to adopt suitable CRM software to understand customer requirements and how to fulfill the same effectively while maintaining brand awareness. As per estimates, using effective customer relationship management tools can help increase sales conversion rates by an astonishing 300%.

How Have Chatbots and Other Services Taken Over Customer Services? #

With an increasing number of brands trying to make their presence felt amid a cutthroat marketplace, the landscape of customer service has entirely changed in the last half a decade. Companies across myriad industry verticals are leveraging AI to ensure effective customer support. To that end, chatbots – virtual assistants, have made quite a reputation for themselves by handling multiple customer service pain points at the same time. For instance, chatbots are used today to resolve customer queries without human intervention, help customers 24/7, and handle multiple customers at once.

These AI-powered bots are suitably developed to use existing information and resources to help resolve customer questions. They are designed to recognize and answer query variations and can also be trained to provide instant responses in various voices and tones.

Because of their 24X7 availability, chatbots have quickly become a popular channel for customer inquiries. Users can promptly engage with chatbots in a live session and get all their questions resolved without having to meet an actual customer service executive face to face. This also enables customers to get their queries quickly resolved on demand, reducing the load on companies’ service teams. In effect, chatbots can take over simple question responses that usually take up 70%-80% of customer service representatives’ day. This time can be efficiently spent on resolving a more detailed customer query, leading to proactive customer support.

A vital reason why chatbots are taking over customer support is the financial strain. By installing a chatbot just once, businesses are able to save tons of money by not having to hire numerous customer service personnel. This comes as a cost-effective solution for understaffed teams in companies.

The increasing proliferation of digitization indicates that the humanizing technology trend is well on its way to becoming mainstream. Take Alex, for example, the virtual assistant that has probably had more conversations with customers than they may have had with actual people. To that end, companies are finding a way to revolutionize customer service by adopting digital solutions and making them as personable and empathetic as possible.

Chatbots are thus being carefully programmed to understand the customer’s pain points and voice changes and then resolve queries accordingly, in a deeply sympathetic voice that resonates with the brand in question. Chatbots are reshaping the entire dynamics of customer services by discarding redundant traditional messaging platforms and being available round the clock to transform even first-time visitors into permanent customers.

Are Human Interactions Still Needed? #

Contrary to popular consensus, digitized tools and virtual assistants may not exactly phase-out actual customer service entirely; human interactions may still be the need of the hour. In the aftermath of the pandemic, numerous companies turned to computerized customer service platforms and chatbots; however, it has been observed that many users are getting increasingly annoyed at having to communicate with a mechanized voice.

As per a survey comprising 2,000 consumers by outsourcing company CGS, almost 40% of British survey respondents claimed that speaking with a human customer service representative was preferable to having to converse with a virtual assistant. The same viewpoint was shared by United States survey respondents, 38% of which wanted companies to bring back human customer service.

Despite convenient online access and reliability, close to 83% of consumers claim that they prefer human interaction as opposed to digital assistants, according to the Global Consumer Pulse Research study from Accenture Strategy. This is because no matter how efficient and knowledgeable a virtual assistant is, it cannot match the convenience and understanding provided by a human customer service representative. Many are of the opinion that bots may have ready-made answers for standardized questions, but they will never be able to fully understand what the customer wants or solve the query the way the customer asks.

The biggest reason, however, for the retention of human customer service is the fact that users still want to feel needed, understood, and valued. Personalization is important, and so is active listening and empathetic communication that will help a company understand their customers well. Human interactions not only resolve the query in hand, but also help establish a long-term, personal relationship with users, thereby helping to increase customer satisfaction by up to 20% or more. It is more than likely that customer service in the digital age may be characterized by a mixture of efficient bots and human staff, harnessing the best of both and helping businesses retain as many consumers as possible.

Related reading

Questions and answers

How Has Pandemic Affected Digitization and Its Adoption? #

How Have Chatbots and Other Services Taken Over Customer Services? #

With an increasing number of brands trying to make their presence felt amid a cutthroat marketplace, the landscape of customer service has entirely changed in the last half a decade.

Are Human Interactions Still Needed? #

Contrary to popular consensus, digitized tools and virtual assistants may not exactly phase-out actual customer service entirely; human interactions may still be the need of the hour.

DataStax, The Real-time Data Company, Unveiled “Change Data

Vin Patel — Mon, 04 Jul 2022 09:42:22 +0000

Key takeaways

The best part about the new CDC for Astray DB is that it’s now powered by advanced streaming technology and is built on the latest Apache Pulsar.
In regard to this, the enterprise architect of Alpha Ori Technologies, Praveen Viswanath, said that Alpha Ori Technologies develops real-time data solutions for maritime shipping.
Through DataStax, every business could be able to muster live data and rapidly construct the smart, top-notch apes needed to come to be a data-driven firm.

The best part about the new CDC for Astray DB is that it’s now powered by advanced streaming technology and is built on the latest Apache Pulsar. It uses the event streams to process and deliver database changes in real-time. This real-time data changes then becomes available for use across data lakes, search, artificial intelligence, data warehouses, and machine learning.

With this advanced and powerful integration of event streaming with high-performance, high-scale Astray DB mufti-cloud databases, any organization gets enabled to build more reactive and smarter applications. Applications that are fueled by real-time or, we can say, connected data.

According to the chief product officer at DataStax, Ed Snuff, the CDC for Astray DB is an entirely new and unique concept. The major areas it taps for the performance of the world’s most scalable database and combines with the next-generation event streaming. Undoubtedly, working with the next-generation event streaming technology will fuel today’s data-intensive applications with the best solutions for a seamless workflow.

Currently, only a fraction of real-time data is being leveraged, even though businesses have a huge urgency to use it. This is major because of the failure of legacy architectures that cannot meet the performance and scale requirements of real-time data processing. Due to this, we see the final result as data uploaded in batches which ultimately creates chronically stale data.

In regard to this, the enterprise architect of Alpha Ori Technologies, Praveen Viswanath, said that Alpha Ori Technologies develops real-time data solutions for maritime shipping. Also, he added that they are extremely excited about the availability of CDC for DataStax Astray DB. This will help real-time analytic by providing a real-time source of streaming data. He said CDC help our maritime clients save money on fuel, mitigate maintenance-related risks, and reduce carbon emissions by enabling them to alert and deliver analytic rapidly.

According to the report made by McKinsey, one of the seven characteristics predicted to define data-driven enterprises is the Data-Driven Enterprise of 2025, where data is processed and delivered in real-time. However, companies often choose between speed and computational intensity, which can delay more sophisticated analyses and inhibit the implementation of real-time use cases. WorkJam is the place of the business governor for actual forerunner orchestration: plan, duty administration, activities, communications, staffing, and several others.

The director of the R&D at WorkJam had earlier stated in an interview that they’re always hunting for different means to simplify their functions and operations with the help of Change Data Capture (also known as CDC). This will allow them to produce smarter and more responsive connected applications; live data would power.

With the help of Change Data Capture for Astray DB, companies could homogenize on the enormous scalable with no downtime Astray DB whatsoever. Apart from this, any modifications to the database are distilled and furnished in real-time to different portions of the data system without having any sort of downtime between rate, scale, and accessibility. This smooth incorporation facilitates corporations to initiate their real-time data to raise accumulative business significance from regions encompassing e-commerce, crime detection, consumer satisfaction, trades, stock and transport, and logistics.

The principal analyst at DBInsight stated that Through the CDC, Astray DB also unites functional and streaming data to evolve to be a bidirectional data center for live data. He added that the CDC would enable their consumers to combine Astray DB with cloud analytic places such as the Snowflake or the junction for Android application with further limited resistance.

DataStax is a live data corporation. Through DataStax, every business could be able to muster live data and rapidly construct the smart, top-notch apes needed to come to be a data-driven firm. DataStax Astray DB differently mixes the strength of Apache Cassandra—the highly scalable database across the globe—with the developed Apache Pulsar streaming teach in a harmonious bunch provided as assistance on any cloud.

Several giant corporations, comprising names such as ESL Gaming, Verizon, Audi, and several others, depend on DataStax to express the strength of real-time data to provide instant virtual experiences that could gain a victory in modern markets and shift businesses.

Apache Cassandra, Apache, Cassandra, Apache Pulsar, Pulsar, and Apache Kafka are either enrolled brand names or brands of the Apache Software Foundation or its associates in the Canadian region, the United States of America, and several different regions.

Related reading

Demystifying Machine-Learning Systems

Vin Patel — Wed, 06 Apr 2022 22:10:36 +0000

Key takeaways

Machine learning is increasingly infiltrating everything, including self-driving cars, voice recognition, chatbots, smart cities, and so on.
Reinforcement Learning: Reinforcement learning is seen to be the last chance for real artificial intelligence.
There is no question that machine learning will revolutionize numerous industries, including education, business, and health care, making the world a safer and better place.

Machine learning is increasingly infiltrating everything, including self-driving cars, voice recognition, chatbots, smart cities, and so on. Because of the amount of big data and the importance of data analytic and predictive analytic, machine learning has become a must-have technology.

When computers are exposed to new data, they are intended to learn independently and complete tasks without the need for human interaction. It means that when a computer or system developed with machine learning encounters a new pattern of data, it will detect, analyze, and adjust accordingly, producing the intended result without the need for humans.

The intricate and powerful ‘pattern recognition’ algorithms lead them where to seek what is at the heart of machine learning’s self-identification and analysis of new patterns. As a result, the need for machine learning programmers with considerable experience dealing with complex mathematical computations and applying them to big data and AI is increasing year after year.

Machine learning’s evolution #

Machine learning is not the same as it used to be due to advances in computer technology. It evolved from pattern recognition and the concept that computers might learn without being programmed to do specific tasks; artificial intelligence researchers wanted to see if computers could learn from data. Because models may change autonomously when they are exposed to new data, the repetitive feature of machine learning is critical. They use past computations to make consistent, repeatable judgments and consequences. It is not a new science, but it is gaining popularity.

While many machine learning techniques have been around for a long time, the ability to automatically apply complex mathematical computations to massive quantities of data repeatedly and faster each time is a relatively new phenomenon. Here are a few well-known machine learning applications that you may be familiar with:

The self-driving Google car is an example of the fundamentals of machine learning.
Online recommendation services like Amazon and Netflix are examples of machine learning applications in everyday life.
Combining machine learning with language rule generation helps Twitter understand what consumers are saying.
Fraud detection is much easier with ML algorithms.

What are the types of challenges that machine learning can solve? #

There are three types of machine learning problems:

1. Supervised Machine Learning: Supervised Machine Learning methods are used when you have previous data with results (labels in machine learning language) and wish to predict the outcomes for the future. Supervised Machine Learning challenges may be further classified into two types:

Classification Issues: When you wish to categorize outcomes into multiple groups. For example, determining if the floor needs to be cleaned/mopped is a categorization challenge. The outcome can be classified as either Yes or No. Similarly, whether a consumer will fail on their loan or not is a categorization challenge that every bank is interested in.

Regression Problem: These challenges fall under the Regression umbrella when you want to know the expected quantity. Determining how much cleaning is necessary, for example, is a regression problem. A regression problem is determining the expected amount of default from a client.

2. Unsupervised Machine Learning: There are situations when you do not want to anticipate an outcome precisely. You want to do some segmentation or grouping. A bank, for example, may want to categorize its customers in order to understand their behavior better. This is an unsupervised machine learning problem since we are not expecting any results.

Reinforcement Learning: Reinforcement learning is seen to be the last chance for real artificial intelligence. It is a somewhat more sophisticated topic than typical machine learning, but it is as important for the future. This is as excellent an introduction to reinforcement learning as you can find elsewhere.

How do machines learn? #

Machines learn in the same way that people do. Humans learn via their experiences, training, and teachers. They sometimes employ knowledge that is given into their brains, and other times they make judgments by analyzing the present circumstance in light of their previous experiences.

Similarly, machines learn from the inputs that tell them which is correct and which is incorrect. Then they are given data to analyze depending on the training they have received thus far. In other circumstances, people have no notion which is right or wrong and just make a conclusion based on their own experiences. We will investigate the many learning principles and methodologies.

Who is using it? #

Machine learning may be used for any business that deals with massive amounts of data and has several difficulties to solve. For example, machine learning has proved to be incredibly valuable to firms that are making the most use of the technology in the following domains:

Financial services #

Banks and other financial institutions use machine learning technology for two primary purposes: identifying relevant insights in data and preventing fraud. The data can help investors locate investment possibilities or decide whether to trade or not. Data mining may also be used to identify clients with high-risk profiles, and caber surveillance can discover early signs of fraud.

Government #

Government organizations have a particular need for machine learning for public safety and utilities since they have a variety of data sources that may be mined for insights. Analyzing sensor data, for example, identifies chances to increase efficiency and save money. Machine learning may also help detect fraud and reduce identity theft.

Medical care #

Machine learning is a rapidly rising trend in the health care business with the development of wearable gadgets and sensors that can analyze a patient’s health in real-time. Medical experts can also use technology to analyze data in order to find patterns or red flags to provide better diagnosis and treatment.

Retail #

Retail websites that propose things based on prior purchases use machine learning to examine your purchasing history. Retailers use machine learning to acquire data, evaluate it, and customize a shopping experience, create a marketing campaign, optimize pricing, item planning, and get consumer insights.

Oil and gas #

Oil distribution is being simplified in order to become more efficient and cost-effective. In this field, the number of machine learning application cases is large? and rising: discovering new energy sources, identifying and analyzing minerals in the earth, and predicting the failure of refinery sensors.

Transportation #

Transportation business relies on making routes more efficient and detecting possible difficulties to boost revenue; therefore, data analysis is critical to uncover patterns and trends. Machine learning’s modelling capabilities and data analysis are valuable tools for delivery firms, public transit, and other transportation enterprises.

Conclusion #

Machine learning is unique in its own way. While many experts are concerned about the rising reliance and visibility of machine learning in our daily lives, on the bright side, machine learning may accomplish marvels. And the world is already experiencing its magic - in health care, banking, automotive, image processing, voice recognition, and a variety of other industries.

While many of us are concerned that robots will take over the world, how we develop successful yet safe and manageable machines is entirely up to us. There is no question that machine learning will revolutionize numerous industries, including education, business, and health care, making the world a safer and better place.

Related reading

Questions and answers

What are the types of challenges that machine learning can solve? #

There are three types of machine learning problems: 1. Supervised Machine Learning: Supervised Machine Learning methods are used when you have previous data with results (labels in machine learning language) and wish to predict the outcomes for the future.

How do machines learn? #

Machines learn in the same way that people do. Humans learn via their experiences, training, and teachers.

Who is using it? #

How Artificial Intelligence is Evolving the Future

Vin Patel — Thu, 17 Mar 2022 22:52:22 +0000

Key takeaways

Across industries, artificial intelligence (AI) is revolutionizing how we communicate, consume information, and receive goods and services.
This is done by collecting data from numerous previously segregated sources, such as linked medical records, home devices, and, increasingly, non-medical data, that remained separated in 2020.
After a decade, we can see all of these and more applications of AI in improving healthcare.

Across industries, artificial intelligence (AI) is revolutionizing how we communicate, consume information, and receive goods and services. AI is already transforming the patient experience, how physicians practice medicine, and how the pharmaceutical sector operates in health care. The quest has only just begun.

What Role Does Artificial Intelligence Play in Healthcare? #

AI Solutions For Doctors #

A variety of AI applications are already being utilized by providers to “improve human performance on cognitive activities.”

For example, by evaluating data and applying machine learning algorithms to diagnosis and treatment plans, AI is assisting doctors with decision support or even making decisions for them.

Two examples of existent applications in this area are:

Diagnostic imaging interpretation: Medical imaging devices enabled with AI is now integrated with some algorithms that make image reading faster and more accurate using deep learning and categorization technologies. This includes X-rays, CT scans, MRI tests, and others.

Precision health: It is a newer AI Solution as compared to other solutions out there. It is based on patient data which is obtained through genetic information, wearable gadgets, and advanced electronic health systems which focus on preventive care. Such technologies can identify potential threats and recommend preventive measures which are based on the patient’s information, such as their lifestyle, their biometric healthcare data, and also their environment.

AI-assisted Patient Care #

With a rising emphasis on patient experience and involvement in healthcare, it is only natural that AI has been adapted to assist both patients and providers. Patients now have more authority and influence over their care courses thanks to the adoption of AI-enabled interfaces.

Here are two instances of patient remedies that make use of artificial intelligence:

Virtual health assistants: With the use of augmented reality, speech and body recognition technologies, and cognitive computing, patients can interact with the virtual persona built for them. Such virtual health assistants provide a more tailored experience for the patients. It helps in making them learn how to better manage their health.

Customer service bots in healthcare: This AI application is an interactive chatbot that responds to patient remarks and queries about administrative issues such as bill payment, appointment scheduling, and medicine refills. It uses natural language processing, concept extraction methods, and sentiment analysis to provide the given features to the patients.

We know that when patients are engaged in their care, they do a better job of managing their chronic diseases and their outcomes improve, yet remaining in regular communication with individual patients adds a time-consuming strain to already overburdened medical personnel.

What Does The Future Hold? #

As with any rapidly evolving technology, predicting what the future holds for AI can be difficult at times. What is known is that medical data is becoming more digitalized and standardized, implying the availability of huge, mufti-site, or even multinational data pools with consistent information. Machine learning will become even more capable of automating a growing number of jobs when trained with this plethora of data, boosting both the precision and variety of such systems.

Because AI algorithms might also additionally research indefinitely, customers withinside the future will automatically gain from operations they did in the past, supplying their patient’s consent to proportion this information. Systems will routinely adapt to their environment and enhance with every passing day.

AI can now find patterns in massive volumes of data that are too subtle or complex for humans to see. This is done by collecting data from numerous previously segregated sources, such as linked medical records, home devices, and, increasingly, non-medical data, that remained separated in 2020.

The first key consequence in 2030 will be the ability of health systems to deliver fully proactive, predictive healthcare.

Predictive Care Enabled By AI #

AI and predictive analytic assist us to apprehend greater approximately the numerous factors in our lives that affect our health, which includes wherein we’re born, what we eat, where we work, what our nearby air pollutants degrees are, and whether or not we’ve got get entry to secure housing and a solid income.

By 2030, healthcare structures might be capable of are expecting while someone is vulnerable to growing a persistent condition, for example, and propose preventative steps before the disease worsens. Diabetes, congestive heart failure, and COPD are all on the decline as a result of this advancement.

Connected Hospitals Mean Connected Care #

Along with predictive care comes another innovation in terms of where that care is delivered. A hospital by 2030 will no longer be a large structure that treats a wide range of diseases; instead, it’s going to focus on the seriously unwell and enormously complicated procedures, whilst much less pressing instances are monitored and handled via smaller hubs and spokes, inclusive of retail clinics, same-day surgical treatment centers, professional remedy clinics, or even people’s homes.

A shared digital infrastructure connects these areas. This network, in addition to employing AI to identify patients in danger of deterioration, can also reduce bottlenecks in the system and guarantee that patients and healthcare professionals are guided to where they can be best cared for or where they are most needed.

Location is now not the glue that holds this community together. Instead, it’s far the reviews of the people it serves with a view to deciding the 1/3 essential distinction in 2030.

Better Patients-staff Relation #

For patients, research has long demonstrated that they can have a direct impact on whether or not they recover.

In 2030, AI-powered predictive healthcare networks are assisting in the reduction of wait times, the development of personnel workflows, and the remedy of the ever-growing administrative burden.

AI produces experiences that adapt to the professional and the patient by learning from each patient, diagnosis, and operation. This now no longer most effective improves fitness outcomes, however additionally decreases expert shortages and burnout, permitting the gadget to be financially sustainable.

This system spans communities, connecting people, locations, hardware, software, and services to form true networks of care that improve lifelong health and well-being.

Conclusion #

With a slew of difficulties to address, driven by well-documented factors such as an aging population and rising rates of chronic disease, the need for more innovative healthcare solutions is evident.

Despite the significant media attention, AI-powered solutions have taken minor efforts toward tackling major concerns, but have yet to make a big overall impact on the global healthcare industry. If several critical difficulties are addressed in the coming years, it has the potential to play a leadership role in how future healthcare systems run, augmenting clinical resources and assuring optimal patient outcomes. The ideas discussed above are not far from becoming accomplished. After a decade, we can see all of these and more applications of AI in improving healthcare.

Related reading

How Did The Role of AI and ML Evolve in Software Testing?

Vin Patel — Mon, 07 Mar 2022 21:50:14 +0000

Key takeaways

A software test is a procedure that determines whether an electronic product meets standard requirements and whether it is defect-free.
This is because AI and machine learning-based testing rely on the self-learning model.
The primary goal of incorporating AI and machine learning into the testing ecosystem is to automate repetitive tasks that waste manual time and effort.

A software test is a procedure that determines whether an electronic product meets standard requirements and whether it is defect-free. It entails using manual or automated tools to evaluate one or more properties of interest by executing software/system components. In contrast to actual requirements, software testing aims to find errors, gaps, and missing requirements. Software testing is necessary because it allows bugs or software errors to be identified early and corrected before the final product is delivered. A well-tested software product ensures dependability, security, high performance, saving time and improving customer satisfaction. When it comes to eliminating repetition and ensuring refinement, ML and AI are the technologies (cognitive) that emerge in our minds. AI and ML are causing a paradigm shift in software testing with deep learning-based algorithms. Various AI and machine learning (ML)-powered automation testing tools are now available on the market. These tools streamline software testing and allow teams to deliver more refined products.

In this article, we will talk about how AI and ML can be leveraged in the process of software testing and how their role is constantly evolving in the field.

The Utilization and Role of AI and ML in Different Stages of Software Testing #

The three major stages of any software testing process are test case creation, analysis, and case maintenance. Let us look at how AI and ML play a role in software testing at various stages.

Test Case Creation #

Test Case is a set of steps designed to ensure that your software application’s specific features and functionality work as expected. For any test scenario, a test case consists of a series of test steps; data, preconditions, and postconditions. Testing engineers use test cases to compare expected and actual results in order to determine whether a software product meets the needs of their customers.

You can write the cases manually while the system automates them using AI and Ml technologies. You can use automatic test case creation to identify and create test cases without human intervention. This can be used for both visual and functional testing. The use of AI and machine learning to generate test cases is still in its early stages. With more research, the future appears bright, and we will have more efficient and accurate test case creation methods.

Test Case Analysis #

You can use machine learning and computer vision to conduct visual testing of your test cases for test case analysis. You can use this information to compare your current product version to previous versions and make necessary changes. Using AI and ML, you could, for example, identify high-risk parts of the source code and communicate this information to the QA engineers. Then, before deploying the applications, the developer would keep this in mind, and your test cases would be more reliable.

Test Case Maintenance #

It is essential for manual testing teams to constantly monitor all test cases and modify them in accordance with the current features of the system. Users provided input from manual testing activities in the traditional test management and maintenance system. However, you can now use AI and machine learning to automate testing, automatically pushing the results into test management and maintenance systems. This is because AI and machine learning-based testing rely on the self-learning model. As a result, if the current system undergoes any changes, it detects them and updates the test cases in real-time.

Approaches to AI and ML Implementation in Software Testing #

There are different approaches to AI and ML implementation in software testing. Some of them are -

Visual Testing #

Visual testing or user interface testing is a way for developers to ensure that a web or mobile ape’s UI meets the original requirements. It accomplishes this by using AI and ML-based image processing and screen comparison algorithms to test a website or application’s overall look and feel. Percy is one popular tool for visual testing.

Differential Testing #

The objective of differential testing is to observe the differences between similar applications by applying the same input. Coding-related issues, security vulnerabilities, regressions, and other issues can be found using AI and ML-based self-learning algorithms. It’s one of the newest methods for detecting changes.

Self Healing Automation #

Self-healing test automation tools use AI and machine learning techniques to update changes in an application’s environment automatically. It monitors changes in the UI and adjusts the test cases accordingly. It also aids in the elimination of the need for test case upkeep. The technology allows tests to adapt and update themselves. Mail and Testis are two famous automation tools.

Declarative Testing #

Declarative testing concentrates on what you want to achieve with this test rather than how the process will work. It’s more important to figure out what you’re testing for than it is to figure out how you’re going to test. It also allows you to compare the ape’s final state to the expected state. This testing aims to automate tedious, error-prone, and repetitive tasks to boost productivity.

Benefits of Integrating AI and ML in Software Testing #

Visual Validation #

Pattern recognition and image recognition capabilities in AI aid in detecting visual bugs in applications through visual testing. It aids in ensuring that all of the visual elements are engaging and functional.

Accuracy #

Manual testing results in a lot of human error, especially when repetitive tasks are required. Automation testing eliminates these human-caused errors. However, with the help of artificial intelligence (AI) in automation testing, repetitive tasks are handled more efficiently, and accurate results are recorded.

Saves Time and Money #

When there is a change in the source code, software tests must be repeated. Manually, this becomes time-consuming and requires a significant amount of effort from testers. On the other hand, repetitive tasks are handled correctly, quickly, and efficiently with AI-driven tests.

Decreased Defect #

AI and Ml help in the early and rapid identification of bugs, reducing defects, and increasing product reliability.

Conclusion #

With cutting-edge technologies like AI and machine learning, software testing has taken on a whole new meaning. It is becoming more precise and efficient than it has ever been. Software testers no longer have to deal with the same tasks repeatedly. Instead, they are responsible for defining procedures and establishing a testing road map. The primary goal of incorporating AI and machine learning into the testing ecosystem is to automate repetitive tasks that waste manual time and effort.

Related reading

Predictive Analytic in Combating Cybercrime

Vin Patel — Fri, 11 Feb 2022 01:30:00 +0000

Key takeaways

Today, cybercrime is a trending word across industries and has become a devastating threat.
Predictive analytic uses the method of attack graphs to handle such a heavy task.
For way better help in cybersecurity, companies can too accomplice with analytic firm to work and imply predictive analytic.

Today, cybercrime is a trending word across industries and has become a devastating threat. Cybercrime is on the rise mostly due to the uncountable and latest range of methods used for attacks. The rapid and dynamic development of methods to assault cybersecurity has made defending the network from this type of crime a harder task. Nevertheless, as an extract of the great saying “Darkness cannot drive out darkness, only light can”.

Predictive analytic has proved to be a ray of hope against cybercrime. Do you think that a prior warning can help prevent these caber-attacks? Fortunately, there is an advanced way of doing so. As a prelude, I would like to introduce you to the concept of predictive analytic.

What is Predictive Analytic? #

Analyzing historical data and using analytical techniques, such as statistical modeling and machine learning, to predict future outcomes is called predictive analytic. Predictive analytic is termed as a solution because it is designed to handle growing volumes of data and simultaneously predict the possibility of an attack. With the aid of sophisticated predictive analytic tools and models, people can predict the future of their businesses. In addition, predictive analytic can be used to detect and cease various types of criminal behavior before they cause serious damage. By studying user behavior and actions, an organization can notice anything out of the ordinary, such as cybercrimes. As predicted outcomes have grown, security professionals have begun to leverage predictive analytic. Individuals run multiple models simultaneously and combine the outcomes to find the most likely result. The greatest preferences of predictive analytic are its capacity to distinguish inconsistencies.

How Predictive Analytic Work? #

The most commonly used methods in predictive analytic include:

Identifying Loopholes in Network #

Cybercrime is a threat to all industries with varying impacts. For example, the methods used for attacking healthcare industries will differ from the methods used in software or aerospace industries. These variations depend on the data available, and it is always an important step to implement ways of protecting data together with restricting cybercrime from taking place. Predictive analytic use tools which are based on identifying network vulnerabilities. These tools help identify and observe malicious activities on a network and use this analysis to anticipate possible threats. It is important that these systems are up to date and specifically capable of identifying sophisticated attacks buried under the heaps of data in order for them to be effective.

Handling Massive Data with Attack Graphs #

Predictive analytic deals with a huge data collection which can be exceptionally difficult to cross through and then process, analyze for required insights. These data results as an overall collection from databases, programs, devices, and servers.

Predictive analytic uses the method of attack graphs to handle such a heavy task. All paths of entry and exit to a cybersecurity network are represented by a caber-attack graph. They delineate ways in which an attacker can misuse the vulnerabilities of a network.

Lists Preventions to Avoid Future Attacks #

Predictive analytic focuses more on preventive future attacks rather than explaining the reason and areas of attack in the past. It provides a smart move for you to identify weak areas along with solutions to identify patterns and detect abnormalities that can be easily avoided with certain preventions to save your data. Predictive analytic combines with hacker bots to detect and monitor network activity. It can also detect immediate irregularities in the data flow.

Predictive Analytic Tools #

Here are three of the most popular predictive analytic tools:

? SAP Predictive Analytic — SAS — Microsoft Azure Machine Learning

These tools use big data, statistical algorithms, and machine learning methods to predict the probability of real-time and future results based on past data. The main objective of predictive analytic tools can be summarized in four points:

? Detect cybercrime — Optimize the operation of network stream — Improve overall performance and protect data — Reduce future risks

Industries Using Predictive Analytic To Prevent Cybercrime #

Predictive analytic is widely used among all industries to improve business with merely a minimum or negligible percentage of existing risk. It is a highly valued technology in industries like Retail, Marketing, Healthcare and Banking. Among these, banking and financial industries use predictive analytic to avoid cybercrimes. There are huge amounts of data and money in the financial industries which face extreme risk on a daily basis. Apart from detecting cyberattacks and protecting data, the banking sector takes the help of predictive analytic for optimizing customer-based decisions, avoiding fraud, enhancing risk management, and providing better customer solutions.

What Does the Future of Predictive Analytic Hold? #

In conjunction with being a commanding subject for study and research at this time, predictive analytic has already made a position in terms of security in each large and small company. Security being the most elevated need of each company, predictive analytic have combined with branches of artificial intelligence, modelling etc., and have come up as the foremost successful shield for cybersecurity. There’s continuously less chance for such caber crimes to end totally in near future but the possibility and accessibility of these security technologies has multiplied up. These innovations have a mind-blowing potential to form our virtual and computerized world free of all wrongdoings.

Conclusion #

By involving predictive analytic as a potential part, an organization can recognize the wide range of cybercrime taking place every day. Predictive analytic apply statistical algorithms for organizations to predict future caber-attacks in real-time. Organizations can take an active part in sorting these cybercrime assaults by involving a group of experts and analysts who can research how often it is possible for cybercrime to occur. Analysts can gather internet data measurements and apply predictive analytic techniques to identify any vulnerabilities in their cybersecurity that could be exploited by cybercriminals. By combining machine learning with predictive analytic, companies can move forward with the efficiency of cybersecurity and intelligently dodge all approaching threats. This implies they are able to form a total analytic and visualization arrangement which helps hold up all information and reports. For way better help in cybersecurity, companies can too accomplice with analytic firm to work and imply predictive analytic.

Related reading

Questions and answers

What is Predictive Analytic? #

Analyzing historical data and using analytical techniques, such as statistical modeling and machine learning, to predict future outcomes is called predictive analytic.

What Does the Future of Predictive Analytic Hold? #

In conjunction with being a commanding subject for study and research at this time, predictive analytic has already made a position in terms of security in each large and small company.

How is AI Changing Cybersecurity Landscape

Vin Patel — Wed, 26 Jan 2022 20:25:00 +0000

Key takeaways

Cybersecurity has been among the head banging issues for most organizations and rightly so with top companies losing 25$ every minute due to cybersecurity breaches.
The traditional ways of tracking malicious events may not prove to be as effective, especially with the caber attackers using advanced technologies to break through network gateways.
Human analysts, thus, require the assistance of artificial intelligence technology in order to ensure integrated cybersecurity across all the touchpoints and defeating caber threats before they cause unrepairable harm to organizations.

Cybersecurity has been among the head banging issues for most organizations and rightly so with top companies losing 25$ every minute due to cybersecurity breaches. With the advances in technology, companies are putting various tools and systems to work when it comes to managing security of data and enterprise assets. However, the hackers have access to the same technology which is being used to commit countless data breaches every single day and exposing vital company details every single minute.

Introduction of Artificial Intelligence is steadily changing the cybersecurity landscape and giving institutions an extra edge over the hackers and caber sinners. Companies, irrespective of their services, are storehouses of huge number of details, records, personal data and sensitive information that must be protected at all costs. The constant bombardment of cybercrimes and data breaches have compelled the organizations worldwide to adopt and establish an automated system of detecting such threats and ensuring complete safety and security of their data. Machine learning, simulating human brain is, therefore, being looked upon as a savior that can help analysts predict the attacks beforehand and consolidate the security framework of their organization.

Artificial Intelligence in Cybersecurity #

Threat Detection #

The threat to organizations is huge and comes in all forms and sizes and relying on signature rules is no less than a blunder. With so many suspicious activities going on in the web, companies are bound to stumble upon threats and malicious activities that can crash complete operational functions of any company.

If used properly, AI has the power to detect any unwanted messages or threats in seconds by simply scanning the web. Risk identification and management is a whole lot easier when AI based programs stand like a guard to any suspicious IP addresses, fraud websites, loopholes or similar suspicious malware. With AI, companies can follow predictive risk management rather than reactive solutions while dealing with vulnerabilities or breaches.

Incident Prediction #

What can be better than dealing with threats and vulnerabilities? Detecting the future ones. Cybersecurity management is such a challenging topic that it can get overwhelming for even the experts to keep tack of all the activity that is happening over the web. Moreover, a human can get nowhere closer to a bot when it comes to scanning hundreds, in fact thousands of documents in a single day.

AI operated devices can capture incidents and offer insights into the security system that not only help in dealing with the incoming threats but also expose the areas which are most likely to be affected or breached in the future. With AI, companies have a resourceful insight that can help them detect the next movement of hackers and build firewall around the data before the attack even happens.

Securing Network #

Most of the threats across mid-sized businesses are seen to arise through network pathways. The traditional ways of tracking malicious events may not prove to be as effective, especially with the caber attackers using advanced technologies to break through network gateways.

As opposed to the obsolete ways of securing network bandwidths, artificial intelligence can accelerate the process of identifying unrestricted threats from entering a network, regardless of its size. This allows huge networking endpoints to stay secured and free of any risks or potential hacks, all from a centrally regulated AI program.

Authenticated access #

Companies usually rely on passwords and pins to protect their internal communication channels and data networks. If any hacker is able to enter a local network where the employee’s data, files, personal information and even credit card credentials are stored, it can be a disaster for the organization.

With strong AI processes like facial recognition and biometric scans, organizations can be assured of complete security at all time without having to deal with any potential security breach. AI based programs automatically identify a wrong access and instantly block it without any human intervention.

Prompt Response #

When most organizations in the previous decade relied on getting security by taking prompt action after the threats entered their system, the present security management has opened a wide horizon. This also required a human expert to mitigate risks, perform repetitive sequences and develop quick response if ever the security framework was threatened.

However, Artificial intelligence technology not only identifies the risks but also takes proper action in a matter of minutes to stop the attacks. Even as the employees doze off or are in the middle of a vacation, the threats are dealt with and diluted without any human supervision, drastically reducing the response time to unpredictable and discrete threats.

Artificial Intelligence Over Humans #

It is quite understandable to argue that machines can never be treated as equal to humans but contrary to that belief, Artificial intelligence is by all means a front runner in the race of decision-making and learning. The threats are so huge and the data so vast that it is impossible for mere manual ways to protect even a simple network, let alone the whole organization. The signature-based approach used previously by giant companies are no longer effective in shielding the walls of digital infrastructure of a company.

Firstly, artificial intelligence is able to handle and screen a huge amount of data which can never be expected of a human or even a team of hundreds. This ease of targeted screening and exposing threats from an ocean of data and information is a good enough reason for companies to make the switch to an AI based software. Secondly, AI algorithms can operate by learning over time and detect patterns over long operational cycles. This means that once AI recognizes a pattern of past attacks, it maps and clusters them and accurately detects the threat pattern in future, giving companies the assurance that the repetitive attacks or threats are not successful in causing destruction.

Human analysts, thus, require the assistance of artificial intelligence technology in order to ensure integrated cybersecurity across all the touchpoints and defeating caber threats before they cause unrepairable harm to organizations.

Related reading

How Human-centric AI Solutions can Help in Solving Business

Vin Patel — Fri, 21 Jan 2022 15:30:00 +0000

Key takeaways

Every day, you have to decide whether a quaint little coffee shop in some town deserves a loan or not.
We are only helping everyday decision-makers to be better at their jobs by using human-centric AI.
All the dangers and concerns of the spiralling growth of AI can be avoided by encouraging human-centric AI methodologies.

Imagine you run a bank that lends money to coffee shops. Every day, you have to decide whether a quaint little coffee shop in some town deserves a loan or not. If this task is assigned to a traditional AI system, the machine will probably check the financial statements, the credit rating of the owners, assets and liabilities of the shop, etc. It might also check analytic like the number of repeat customers and the coffee flavors that make the most profit.

But is that all the information required to make that all-important business decision to lend or not to lend? How about checking the commitment and enthusiasm of the owners to scale up their business into a multinational coffee shop chain? How about other factors like the camaraderie between the partners of the coffee shop?

Gauging such factors that come easy to a professional banker, is quite tough for most AI systems. This is one among the several instances where human-centric AI systems would help us in solving our complex business problems.

What is Human-centric AI? #

Human-centric AI never attempts to take the human being out of the business equation. Instead, it empowers humankind to be better at their jobs. You can be a better doctor, teacher, or like in the earlier example - a better banker by using human-centric AI systems.

There are two streams of research currently happening in this field. An ambitious long-term idea is to model deep learning systems by simulating how the human brain works. The other, more promising, stream of research is to include more human input during the entire life cycle of the AI system.

As a child, all of us were incapable of functioning properly in this society. We learned the rules of the world bit by bit from our parents, siblings, teachers, family members, friends, and strangers. We didn’t have to see pictures of all breeds of dogs to learn to differentiate a dog from a horse. It’s the same with a human-centric AI system. Rather than supplying the AI system with a brute force list of endless data, a curated set of data is provided to human-centric AI systems.

Human Beings and Learning Phase #

A standard machine-learning algorithm uses large chunks of data in its learning phase. Later, the decisions made by the AI are hugely influenced by the data that is used in the learning phase. So, the quality of data used becomes highly important. But who decides what data is supplied to the AI?

Let’s again consider the example of granting loans to coffee shops. In a human-centric AI system, expert bankers will be teaching the system how they decide to approve or reject a loan application. This list of experienced bankers should be diverse enough to represent all genders, ethnicity, and background to prevent the AI system from being biased towards any community.

In the learning phase of the AI system, experts from various fields can be used to improve the accuracy of the system. Fields like sociology, philosophy, economics and game theory would contribute towards improving human-centric AI systems.

Human Beings and Execution Phase #

Once the initial learning phase is complete, a human-centric AI system begins to arrive at decisions for each business scenario. However, we still need the discretion of a human being to validate the decisions made by the AI system. Any disagreement between the expert and the AI system provides an opportunity for perfecting the system to a better state.

Such validation steps are integral to any human-centric AI system. Remember, the goal is not to remove human beings from the decision-making process. We are only helping everyday decision-makers to be better at their jobs by using human-centric AI.

Human-centric AI makes explainable decisions. This makes it easy for humans to validate the decisions. Coming back to our earlier example of lending to coffee shop owners, consider a scenario in which the new AI system advises not to grant a loan to a small coffee shop owner named Martha. The system will also explain why Martha doesn’t deserve a loan. Let’s say the AI explains that it’s too risky to loan Martha because of her old age. A human being will be able to identify that it is not ethical to deny Martha a loan because of her age. Here comes the most important feature of human-centric AI - being ethically sound.

Adding Ethics to AI #

In addition to solving a business problem, human-centric AI ensures fairness in its operation. In fact, human-centric AI is the answer to all our dystopian concerns about AI taking over our planet.

All industries can benefit from human-centric AI systems. In the medical field, an inexperienced doctor can quickly identify even the smallest hints of pneumonia, which otherwise, could only be identified by senior doctors. This would bridge the gap between the rich and poor in terms of expert medical care. Telecoms engineers can detect patterns of problematic networks and take urgent actions without affecting emergency services using a human-centric AI system. Logistics managers can decide on the most efficient and environmentally friendly route to be taken for a particular consignment. The possibilities of human-centric AI to solve business problems ethically are endless.

In Sales and Marketing #

Even with extensive data collection and analytic, companies struggle to understand the needs of their customers. It’s not rare to see mufti-million dollar marketing campaigns that ticked all the right boxes fall off the cliff during execution. Often, this happens due to the over-reliance on numbers rather than emotions.

Many products sell due to subjective qualities like beauty, image, and brand perception. It’s not easy for a data analytic tool to measure such nonobjective data. Even for a traditional AI, efforts to measure the aesthetic appeal of a prospective product have not been successful in an industry environment. Human-centric AI can solve this prediction problem efficiently and help in determining the market perception of a product or service.

Most business problems are sales problems at the root of them. So if human-centric AI could determine whether what you’re doing is going to sell in the grander sense of the word, it could be of help in solving such business problems.

Conclusion #

Human-centric AI can solve complex business problems with high accuracy. We can also ensure ethically correct practices by pursuing this technology. All the dangers and concerns of the spiralling growth of AI can be avoided by encouraging human-centric AI methodologies.

Related reading

Web 3.0 and Crypt Adoption: A Way Bigger than Expected

Vin Patel — Fri, 07 Jan 2022 15:30:00 +0000

Key takeaways

And when I am away from the apartment, or away from the office, then how can I monitor my places.
Whatever progress is seen in technology in today’s era, it has been integrated by Web 3.0.
Investing in cryptocurrencies will be seen as mere byproducts and the concept will prove to be a way bigger.

What will your life be like if the devices and connectivity are removed? You have to pick up your friend at the chaotic airport area. You have to order food from some restaurant. You need to send money to any of your relatives urgently. And when I am away from the apartment, or away from the office, then how can I monitor my places. Considering all these things, now understand the power of Web 3.0.

We are becoming dependent on the web day by day. Increasingly, the functionality is improving. Because in the beginning websites were static pages and contained all the basic information. But these static pages were the repository that you used to get the information you wanted.

The next advancement gave birth to Web 2.0. It gave you a more interactive version. Your conversation with other content creators started. Like-minded communities began to congregate in large numbers on social media websites such as Facebook, YouTube and Flick. The affinity created by it led to increased collaboration around the world.

You may not be aware but you are reaping the benefits of Web 2.0 and 3.0 simultaneously. Web 3.0 includes internet-enabled mobile devices and the merging of sensors, built-in crypt wallets, and self-sovereign identities. And this is the web of tomorrow.

Web 3.0 connects mobiles, appliances, cars, home entertainment systems, music all these devices and decentralized networks (cryptographic keys, distributed databases). It also incorporates advancements in reasoning and machine learning (which yields products like Alex, Sir, Cortana). Sir and Google Assistant will give you weather information in the coming times. They will also inquire about an available dinner reservation from your mobile before sending cryptocurrency to a friend who works in Ireland.

Web 3.0 and the Internet of Things #

Tessa’s over-the-air fix is an example of Web 3.0 syncing with the ‘Internet of Things? (IoT). Owners in America had received a recall notice that a charger plug needed to be fixed as it caused the fires in the car. There was no need to schedule servicing and owners did not even realize that Tesla had remotely upgraded software that remedied 29,222 cars of the hazard. It is the flow of connectivity that is shifting from Titbits to “life-enhancing” activities that have a clear value proposition. Immediate cost saving is apparent in IoT applications and yields brand differentiation and loyalty.

Gateway to Crypt and Web 3.0 #

Now let’s talk about the integration of cryptocurrency wallets in the web 3.0 browser. Crypt Wallet holds crypt coins and provides a secure, private, digital space. It acts as a single login for all Web 3.0 decentralized applications (called Lapps).

If you are thinking about bitcoin, it is not an asset that you would use for day-to-day transactions. Crypt coins also include specific gaming coins that are used to purchase in-game virtual goods such as ‘skins’, weapons or vehicles.

On many platforms, if time is spent in the game, you will get tokens that can be used that you earn on in-app purchases to create a “circular economy”. You can buy, sell and keep the sword as a memento. Tokens can also be rented out to another player. Crypt facilitates the transactions and the goods are stored in your wallet.

Whatever progress is seen in technology in today’s era, it has been integrated by Web 3.0. Whether you are gaming or trading or buying next-generation sporting event tickets, you need a crypt wallet.

Just to reiterate, Web 3.0 brings openness and transparency, so does blockchain. Blockchain means the insights organized in the form of blocks and the cryptographic hash assigned make them secure and immutable.

As Web 3.0 becomes an absolute reality, resources, content, applications and protocols will become accessible to all. Decentralized options will make the world more inclusive.

Blockchain is paving a way to democratize the Internet. Thus, DApps and smart contracts will eventually automate specific processes. And this is where the top crypt players come into play and will bring the best of technology to contribute to the Web 3.0 ecosystem.

Crypt Investments #

There is a strong connection between blockchain technology and Web 3.0 which further leads to a specific cryptocurrency and investment-specific approach.

For example, Ethereum is one of the most popular Web 3.0 blockchain whose role is to help developers with decentralized apes. Because of this, it has largely come under the radar of long-term crypt investors. Apart from this, there are many more examples when it comes to the relevance of Web 3.0. And some of these chains include The Graph, Filecoin, Helium, LivePeer, and more.

Because of this high-level crypt-internet connection into the world, a lot of investments are going to be busted in this deep well and that will not be financial superpositions. The entire crypt investment sector is backed by teach-based facts. You can easily find many precedents.

Mainstream Crypt Adoption #

Because the technology is multivariate and complex, a complete merger can take time. Some of the top crypt players driving the Web 3.0 boom such as Helium, Flux, Kaduna, and more, follow the tried and tested, proof-of-work consensus model.

The Web 3.0 incentive token is expected to attract some well-rounded attention as the global bias shifts towards non-fungible tokens and creator monetization. The global streaming industry is estimated to be worth $250 billion by the end of 2026. Although developers are developing decentralized platforms and apes and give a kickstart, you still have to wait for proper mainstream adoption.

Wrapping Up #

Crypt players are opening up to Web 3.0 and its own ecosystem can breed the requisite applications and resources. Even if trading, exchange value and investment are kept separate, crypt players will still be directly connected to the Web 3.0 renaissance. Web 3.0 will change the thought stream of those people in the coming time who are not welcoming this concept. Investing in cryptocurrencies will be seen as mere byproducts and the concept will prove to be a way bigger.

Related reading

Questions and answers

What will your life be like if the devices and connectivity are removed? #

You have to pick up your friend at the chaotic airport area. You have to order food from some restaurant.

Tessa’s over-the-air fix is an example of Web 3.0 syncing with the ‘Internet of Things? #

Owners in America had received a recall notice that a charger plug needed to be fixed as it caused the fires in the car. There was no need to schedule servicing and owners did not even realize that Tesla had remotely upgraded software that remedied 29,222 cars of the hazard.

How Data Integrity and AI Work Together To Empower Trusted

Vin Patel — Mon, 03 Jan 2022 23:30:00 +0000

Key takeaways

Artificial intelligence (AI) and machine learning (ML) are unquestionably becoming more relevant to businesses looking to gain a competitive advantage via digital transformation.
Data visibility and observability are made possible by this form of automation.
Companies that wish to promote digital transformation and outperform competition must manage data integrity successfully.

Artificial intelligence (AI) and machine learning (ML) are unquestionably becoming more relevant to businesses looking to gain a competitive advantage via digital transformation. More than 75% of enterprises value AI and machine learning over other IT investments, and they’re employing data scientists in large numbers to help them succeed. However, most of these projects are compartmentalized within discrete business functions rather than tackling digital transformation across the company.

Traditional analytic cannot handle the magnitude and complexity of data that enterprises now have access to. We need new methods to analyze this deluge of data, and AI and machine learning can help us do just that. To educate ML models, we also require incredibly rich and complete data sets, which we now have thanks to the exponential rise of data. As of 2020, IDC expects 64.2 zettabytes of data were generated or replicated, with 10.6% of labelled data being relevant for analysis or input into AI/ML.

Nonetheless, before AI/ML can be used to transform an organization, organizations must first resolve the data integrity problems that are used to drive AI/ML results.

Challenges in Data Integration and Quality #

Let’s start with how a company saves its information. Customer and employee data, for example, are typically stored in difficult-to-access data stores. A conventional IT infrastructure for a company includes a range of enterprise applications and databases, several data centers, and fresh data generated in the cloud. These variables contribute to data silos, which are challenges to guaranteeing data consistency and accuracy. Many businesses are hampered by data silos, which restrict them from gaining quick and effective business insights from relevant data.

Even though business leaders use high-quality data to make choices, a survey found that more than 45 percent of freshly created data records contained at least one serious inaccuracy. Data bias and terrible business judgments are caused by incomplete, duplicate, and inaccurate data coming into machine-learning pipelines and analytic. Data can also be prone to inaccuracies, especially when it comes from a variety of sources and providers. Standardizing, verifying, and validating data before AI/ML can use it is typically a time-consuming, human process. Therefore, data scientists still spend most of their time preparing data rather than fine-tuning models or visualizing it.

As we discuss the necessity of data quality for AI, it’s also vital to discuss AI for data quality. The use of automation in data pipelines can improve the date’s correctness and consistency. Using AI/ML to identify data changes, alert data drift, and establish quality criteria, for example, can assist enhance data quality, automating data pipelines, and minimizing manual workload. Data visibility and observability are made possible by this form of automation.

Data governance is also crucial to address in the context of data quality. Trust in data stems from the capacity to demonstrate with absolute certainty how the data was prepared, track the lineage of data back to its raw source, and give rights management and auditing capabilities.

Enriching Data with Context and Meaning #

Data management has always worked on making data precise and consistent, but this isn’t enough to make data relevant. I’ve noticed that firms in various industries fail to take advantage of third-party data to provide essential context to their internal data. Before you can truly trust the market intelligence you gain from that data, you must evaluate it in context? not just who and what, but also where, when, and why.

Location is an essential category of third-party data. Insurance businesses, for example, use geography to underwrite policies, analyze and forecast risk involved with catastrophic events like hurricanes and wildfires, and set pricing strategies. Financial services firms use location data to confirm and accurately attribute transactions to a specific store or merchant and better understand subsidiaries and parent corporations. Telecommunications companies are preparing 5G rollouts based on location data, AI/ML, and analytic to provide a new location and context-aware services to millions of endpoints. Points of interest and mobility data are also included in data enrichment, allowing for the analysis of traffic movements into and around a location.

Demographics is another critical source of third-party data since they aid in client segmentation, tailored outreach, and the development of new services and products. By offering a more comprehensive view of clients, demographic data can also assist firms in reducing data bias. Retailers mix location with demographics and consumer insights to help comprehend their clients, give greater personalized experiences, and determine their purchasing proclivity and generate fresh product recommendations.

While it is true that enriching data using third-party enrichment data set benefits AI/ML models, it is also true that AI and ML are becoming increasingly vital for their development. The use of AI and machine learning speeds up and scales up the process of creating data sets as well as proposing which data sets to utilize for enrichment.

Obtaining Data Integrity To Get Confidence In Business Decisions #

These steps, taken collectively, make up data integrity: data integration, quality of data and governance, data enrichment, and location intelligence. Insufficient data has a massive negative influence on AI and machine learning. For business insights obtained from AI/ML to be trusted, data must be delivered with optimal accuracy, context, and consistency. It is impossible to rely on data and the data-driven business insights based on it without data integrity.

Final Words #

CEOs and business leaders are rightfully concerned about the data integrity to make decisions. Data integrity is essential for trusting the results of sophisticated analytic and the business decisions that follow. Companies that wish to promote digital transformation and outperform competition must manage data integrity successfully.

Related reading

A Note on Text Mining

Vin Patel — Fri, 17 Dec 2021 11:11:00 +0000

Key takeaways

As individuals get more familiar with technology, all institutions are progressively migrating to electronic forms.
Stemming is the act of converting words to their stems, or standardized forms, by creating fundamental forms of words to recognize them by their root word forms.
This article has taught us about text mining, its procedures, methodologies, and real-world applications.

As individuals get more familiar with technology, all institutions are progressively migrating to electronic forms. Massive amounts of data are now being sent via the internet in the form of digital libraries, archives, and other textual information sources such as blogs, social media networks, and e-mails. Although it may appear to be more practical, organizing and dictating such a large quantity of data can be a time-consuming process. Even data mining technologies are incapable of managing unstructured textual data since it necessitates the investment of time and effort to extract information.

Natural Language Processing (NLP) is one of the most advanced machine learning technologies that is growing in every sector. Text mining is a subset of NLP that provides tools and strategies for delving into unstructured data to discover important patterns and insights. They are necessary for textual data analysis. This article is an introduction to text mining, in which we will learn about its processes, methods, and applications.

What is Text Mining? #

Text mining is the technique of extracting important information from standard language text data. This is the information that we produce through text messages, papers, emails, and files written in common language text.

Text mining is generally used to extract useful insights or patterns from large amounts of data. Text mining is a collaborative topic that incorporates information retrieval, data mining, machine learning, statistics, and computational linguistics techniques. This is concerned with natural language text saved in semi-structured or unstructured forms.

Text mining seeks facts, connections, and confirmation from large amounts of unstructured textual data. This collected data is then transformed into a structured format that can be examined or shown immediately using HTML tables, mind maps, charts, and so on. Text mining uses a variety of techniques to process the text for this purpose.

The Process of Text Mining #

The process of Text Mining is tedious and includes a lot of steps. We have simplified and presented the process below:

Text Reprocessing: #

The first process under text mining is text reprocessing. It is used on a huge number of documents that contain unstructured and semi-structured data. It converts a raw text file into a well-described sequence of linguistically relevant units.

The following steps are performed under text reprocessing:

Text Cleanup performs a variety of functions, such as eliminating advertising from web pages and cutting out tables and figures, among others.

Tokenization is the process of converting sentences into words by removing spaces, commas, and other punctuation marks.

Filtering removes words with no important content information, such as articles, conjunctions, prepositions, and so on. Even terms that are often repeated are deleted.

Stemming is the act of converting words to their stems, or standardized forms, by creating fundamental forms of words to recognize them by their root word forms.

Lemmatization reorganizes the word to the correct linguistic root, which is the verb’s base form. Throughout the procedure, the initial step is to comprehend the context, followed by determining the POS of a word in a phrase and finally identifying the ’lemma.'

Linguistic processing employs Part-of-Speech (POS), Word Sense Disambiguation (WSD), and Semantic structure.

Text Transformation: #

Text transformation generates features after the feature selection procedure. Feature generation reflects texts based on the words they include and the occurrences of those words, where the order of the words is unimportant. It makes use of bag-of-words or vector space models.

In this context, feature selection refers to the process of selecting the subset of relevant characteristics that will be utilized in the creation of a model. It reduces dimensionality by removing redundant and unneeded elements.

Text Mining: #

After the transformation, the text is mined through several techniques of mining like classification, clustering, summarization, etc.

Methods Used in Text Mining: #

Numerous approaches are being developed to tackle text mining challenges; they are essentially appropriate information retrieval based on user requirements. Some typical approaches based on information retrieval techniques are as follows:

Term Based Method: #

The term-based technique inspects the document based on terms and benefits from productive computing performance while capturing the theories for term weighting.

Phrase-Based Method: #

Phrases are more ambiguous and contain more sorts of semantic information. Documents are expected based on phrases in this technique since they are less dubious and more convenient than individual terms. Some of the factors that limit performance are as follows:

As a result of the secondary analytical characteristics of words

A rare event

Extensive duplication and a lot of noise

Concept-Based Method: #

This method concludes terms at the sentence and document levels; such text mining techniques are based on the analytic examination of words and phrases. An analytical analysis is used in this case to assess the word significance in the absence of a document.

Pattern Taxonomy Method: #

Documents are evaluated using this approach based on patterns, which are generated in a taxonomy by applying a relation. Data mining techniques such as association rule, frequent itemset mining, sequential and closed pattern mining may be used to identify patterns.

Applications of Text Mining: #

The following are some real-life applications of Text Mining:

Customer Service: #

There are several options for a consumer to provide feedback, including chatbots, customer surveys, online reviews, support requests, and social network accounts. Combining feedback with text analytic technologies can result in rapid improvements in customer happiness and experience.

Risk Management: #

Text mining can provide information about industry trends and financial markets in risk management by controlling sentiment data and extracting information from analyst reports and whitepapers.

Maintenance: #

Text mining has the potential to automate decision-making processes by detecting patterns that correspond with issues, as well as preventative and reactive maintenance operations.

Healthcare: #

In medical research, a manual inquiry is expensive and time-consuming, but text mining provides an automated alternative for extracting useful information from medical literature.

Spam Filtering: #

Text mining provides a way for filtering and removing such emails from inboxes, improving user experience, and lowering the danger of caber threats.

EndNote: #

As technology progresses and changes at a rapid pace, data quantities, much of which is unstructured, continue to grow. This new deluge of big data necessitates that most businesses combine structured and unstructured data to provide extensive visibility and insights into their company and operations.

Text mining is a multidisciplinary topic that combines computational linguistics and natural language processing for extracting non-trivial information from unstructured textual input using text mining algorithms. This article has taught us about text mining, its procedures, methodologies, and real-world applications.

Related reading

AI & Cryptocurrency - A Powerful Pair

Vin Patel — Thu, 16 Dec 2021 15:55:00 +0000

Key takeaways

We really need to discuss the fascinating and famous phenomenon going on for the past few years: cryptocurrency.
NeuroBot claims an accuracy of up to 90% and plans to further improve the system with technical and fundamental analysis.
And even if a bot is configured properly, it will not guarantee profits.

We really need to discuss the fascinating and famous phenomenon going on for the past few years: cryptocurrency. Digital coins have become an incredibly profitable investment tool to date, and it comes with the potential to generate huge profits on cryptocurrency exchanges. It is barely a speculative investment, even for the long term. As of today, if we talk about the client’s premium in digital money trades, there is a good and precise growth indeed.

The mechanization of “exchange interaction” becomes inevitable and widespread if development occurs rapidly in any fragment. The same is the case with cryptocurrency. Brokers with the coding ability and good programming knowledge can build their own crypt robots. A broker can also work on improving the existing expert advisors, which will lead to a productive exchange of advanced coins. Robots will have no foundation for exchanging cryptographic resources. And from this proprietor will achieve remunerative pay.

But the focus must also be on greater danger. Whether the amount of computation is a handful or the large scale that is required to computerize the exchanging trade instruments, you always get them on the internet. Thus the actual quality always remains questionable. There is always the possibility of error in settings. Simply put, a fast loss hangs over the store. The gist of these things is that crypt robots should be thought through very carefully.

One needs to be vigilant as to who should be a trading advisor. Otherwise, it is always easy to contact a viral miner and allow it to use up your system resources and thus lose money from your trading account. You won’t even know, and your coins will end up in a fraudsters purse.

In recent years, these traditional financial markets have seen active use of machine learning and artificial intelligence. Due to the nuances, some companies had no choice but to shut down and announce that they would later disclose it.

Due to the high volatility of cryptocurrencies, crypt trading certainly involves many risks, but it is also more profitable than any other form of investment. Although fluctuations are a big deal throughout the day, eventually, a steady income reaches the trader. To analyze patterns in a dynamic market like crypt, a high level of information needs to be processed. And the role of artificial intelligence and machine learning systems will certainly matter here.

Artificial intelligence can handle large amounts of data with high working speed, thereby managing traditional assets, bonds, and stocks, which are worth billions of dollars. Still, ME and AI systems cannot fully take their place in the crypt market. But these technologies are actively entering the market.

Predictions About The Cryptocurrency Market using AI #

Augur’s decentralized platform is based on the experience of network participants. But fairly accurate predictions come from it. It is the Forecast Foundation, a non-profit organization that developed Augur and aimed to make it an accessible source of public forecasts. In contrast, the Neurobot platform is based on the functioning of the neural networks and not on the user experience with the ability to make predictions on the dynamics of the crypt market. The system itself keeps an eye on exchange rate fluctuations and puts forward predictions for the next day. NeuroBot claims an accuracy of up to 90% and plans to further improve the system with technical and fundamental analysis. For a novice crypt trader, such platforms are meant to make life easier, with little or no know-how in the beginning. Obviously, it is not wise to rely on such predictive platforms, yet they can contribute to the lion’s share of technical analysis.

Market sentiment #

Tracking cryptocurrency sentiment requires a huge amount of data. This requires a combination of blogs, articles and forums and the comments within them. AI and Blockchain-based platform Senna has a tendency to automatically analyze and generate results in no time. The system also implements elements of machine learning. Senna with the CryptoScanner app uses AI to determine market sentiment so that traders can use the results to plan their strategies.

Business payments #

The main problem that can be sighted in cryptocurrency is the lack of liquidity. Exchange rate fluctuations fail to make digital currency attractive as a suitable alternative for commercial payments. Heavy fees from exchanges and banks compensate for the difference between supply and demand. A platform like TradeConnect uses machine-learning technology to handle such issues. Platforms like TradeConnect come out to deal with such problems. TradeConnect uses machine learning and side-matching protocols to uncover the right broker and bank. Individual companies are specifically targeting AI platforms and an example of this is the Money Token team that created AI assistant Amanda. The groundbreaking concept provides loans to the crypt community. The system is designed to lead the borrowers in a proper way which goes from loan application to automated status check to repayment.

Security when using crypt bots #

Sharing API keys of exchanges requires trusted services with a good reputation. The API key allows using your account to trade and withdraw funds through it. If a bot is using the API, it is not in itself a sign of fraud. Before using any platform, do thorough research about it. Learn about security methods and use two-factor authentication when limiting account access.

Final Words #

A bot is not dependent on sleep so can work round the clock. The boat is purged of more human factors such as mistakes or emotions while doing trading operations. Bots can trigger instantaneous automatic execution according to predefined set parameters. In contrast, Bots can be a disadvantage with outdated strategies. Thus, due to lack of experience, the user may incur a loss. Set-and-forget is harmful, and bots must be constantly monitored. Don’t choose bots with low-quality software. And even if a bot is configured properly, it will not guarantee profits. Prices and earnings depend on other factors as well.

Related reading

Automation and AI Perform Tasks That Once Required Our

Vin Patel — Fri, 10 Dec 2021 20:30:00 +0000

Key takeaways

From utopian to dystopian, the scenarios of the impact of artificial intelligence (AI) on our societies are varied.
The insidious, short-term decline in our capabilities can have serious, long-term consequences for human health, corporate governance, and city life, to name a few.
It is a deep hope: strengthening our mental abilities also strengthens what makes us human beings.

From utopian to dystopian, the scenarios of the impact of artificial intelligence (AI) on our societies are varied. Which one will prevail? As stated by the Villain mission, such “are a delicate matter. Our governments and companies’ leaders are struggling to define the necessary strategies for these different scenarios. Still, their mission is to combat the negative impact of AI and new technologies on our cognitive capacities.

AI seeks to understand how human cognition gets interpreted by creating cognitive processes that mimic human beings. As a result, it has improved the speed, precision, and efficiency of the way we think and work.

However, its prolonged use also weakens our cognitive abilities over time. Indeed, suppose we automate our cognitive tasks such as problem-solving and decision making. In that case, we reduce our innate or unaided capacity to “translate information into knowledge and knowledge into know-how” (1).

There are growing case studies of the negative impact of AI and new technologies on a range of human cognitive abilities. For example, the constant use of computational systems that analyze and recommend courses of action could reduce the understanding of the competitive situation and the ability of leaders to develop medium and long-term strategies (2).

In terms of spatial conceptualization skills, the constant use of computer-aided design software can reduce architects’ sense of scale.

The insidious, short-term decline in our capabilities can have serious, long-term consequences for human health, corporate governance, and city life, to name a few. Therefore, we must fight against the threat of today to protect our life of tomorrow.

We believe in the virtuous circle that can constantly reassess, redefine, and evaluate AI.

The black boxes of “deep learning.”

The five professions most threatened by artificial intelligence

Let’s start with reviewing current technologies used by businesses and cities to reassess their impact on cognitive abilities in the short and long term. Then, let’s redefine these technologies and their designs so that citizens or workers achieve their efficiency and productivity goals without sacrificing their cognitive abilities. If this is not possible, this redefining phase should make it possible to fluidly alternate from a mode that aims for efficiency to a model that makes it possible to strengthen our cognitive capacities.

Finally, let’s use the AI?and data analysis applied to these new designs to better assess their effectiveness and, if necessary, initiate a new Reassess / Redefine / Evaluate cycle that allows us to make our lives easier without affecting capacities of our brains.

Protect the cognitive sense #

To illustrate such a strategy, the Lew Kwan Yew Center for Innovative Cities in Singapore has developed a new intelligent GPS design. Conventional GPS provides turn-by-turn directions, which the driver can follow almost without thinking. So reassessed, GPS is a cognitive risk factor, as sticking to navigation regularly reduces hippocampal activity with potential long-term risks of Alzheimer’s disease. The intelligent GPS could thus allow a driver to receive new information related to visual and spatial cues, for example: “turn right at the red building,” or “turn left at the Ferris wheel after the airlift,” protecting thus his cognitive and spatial sense of the urban environment. The driver could then have the option of switching between smart GPS mode and conventional GPS in cases where they need quick turn-after-turn instructions.

To such a device, we could add analysis possibilities and a playful approach to help the driver see if his navigation improves over time and encourage the continued use of this mode of interaction.

Such a strategy forces us to decide which human cognitive capacities are essential and must be protected as a priority to develop solutions with AI that enhance these capacities while reaping the benefits of efficiency and effectiveness increased productivity.

From this perspective, such a strategy is much more than a strategy to protect our cognitive capacities. It is a deep hope: strengthening our mental abilities also strengthens what makes us human beings. It’s about becoming more human in the age of AI.

Related reading

How Is Facebook Utilizing Artificial Intelligence

Vin Patel — Sat, 04 Dec 2021 16:01:00 +0000

Key takeaways

If you recall the early days of Facebook photo tagging, you’ve witnessed firsthand how far the company’s artificial intelligence features and capabilities have advanced.
Its News Feed is regulated by artificial intelligence and may grow even more reliant on the technology shortly.
With so many AI-based projects in place, Facebook can overcome new obstacles and forging new routes.

If you recall the early days of Facebook photo tagging, you’ve witnessed firsthand how far the company’s artificial intelligence features and capabilities have advanced. In the terrible old days, the social network was prone to propose inappropriate tags for individuals in photos’or worse, mistake a chair or futon for your closest buddy.

Facebook’s picture recognition capabilities have advanced dramatically due to artificial intelligence and machine learning. The technology automatically analyses what happens in photographs that lack human descriptions or tags, enabling users to search for photos using keywords even if they are not annotated. As with other big technology companies, Facebook is investing heavily in AI research and development. Facebook employs hundreds of employees working on artificial intelligence, and according to, CEO Mark Zuckerberg has “tripled his company’s investment in processing power for AI and machine learning research” in recent years.

Facebook now utilizes artificial intelligence to help people find the correct content across text, images, and videos and impact how its advertising product functions. This is crucial for advertisers attempting to reach Facebook’s almost two billion users. Some of the major ways in which Facebook is deploying AI-

Textual Analysis #

The majority of data posted on Facebook is still text-based. While video may have a higher data capacity in gigabytes, text may still be just as rich in terms of insights. While a picture may be worth a thousand words, if all you want to do is answer a fundamental question, 1,000 words are sometimes unnecessary. Each piece of data that is not required to answer your query is noise, and more critically, a waste of storage and analysis resources.

Facebook employs a technology called DeepText that is created to extract meaning from the words we publish by learning to evaluate them contextually. Neural networks assess the relationship between words to determine how their meaning changes due to their proximity to other words. Due to the nature of semi-supervised learning, the algorithms do not require reference data? such as a dictionary? to explain the meaning of each word. Rather than that, it teaches itself by the way words are employed.

Facial Recognition #

Facebook trains it to recognize people in images using a deep learning program called DeepFace. It claims that its most advanced picture recognition engine is more accurate than humans at determining if two separate photographs are of the same person? with a 97 percent success rate compared to 96 percent for people.

It’s safe to assume that the application of this technology has generated controversy. Privacy advocates argued it went too far since it would allow Facebook? based on a high-resolution image of a crowd? to assign identities to many of the faces, obviously impeding our ability to move freely in public. Previously, the social media giant used a less advanced version of the facial recognition program that did not use Deep Learning. Since the technology initially made news, Facebook has been relatively silent about its development and may be presumed to be awaiting the result of current privacy lawsuits before disclosing anything about their plans to roll it out.

Targeted Advertising #

Facebook decides which advertisements to present to people using deep neural networks? the cornerstones of deep learning. This has always been the foundation of its business, but by assigning machines to learn as much as possible about us and to cluster us in the most insightful ways possible when serving us ads, it hopes to maintain a competitive edge against other high-teach competitors such as Google that are vying for market supremacy.

Creating Applications #

Facebook has even chosen to delegate the duty of determining which procedures may be enhanced using AI and Deep Learning to robots. A system named- Flow has been built that utilizes Deep Learning analysis to perform simulations of 300,000 machine learning models each month, allowing engineers to validate concepts and identify efficiency possibilities.

Artificial intelligence has already significantly influenced how Facebook operates and how each Facebook user interacts with the network. Its News Feed is regulated by artificial intelligence and may grow even more reliant on the technology shortly. Machine learning algorithms at the firm are becoming increasingly adept at comprehending text and pictures. And the advertisements you view may be strongly influenced by the preferences and insights revealed by AI to the company’s data scientists.

The platform’s application of AI across all aspects is anticipated to accelerate. In 2016, Facebook launched FBLearner Flow, an internal platform for sharing machine learning expertise and code. At a high level, FBLearner Flow enables the transfer of algorithms and models from one element of a business’s operations to another, accelerating the growth of machine learning. This’along with the company’s tremendous advancements in artificial intelligence’has some significant ramifications for marketers.

Implications For Marketers #

Pay close attention to how Facebook’s News Feed evolves over the next couple of years. #

Marketers rely on the News Feed to reach customers with everything from advertisements to organic content. If artificial intelligence begins to determine entirely what consumers want to see, marketers may lose insight into how those judgments are made.

The aim is that super-effective AI would make it even easier to target the right audiences with sponsored advertisements. However, this may not apply to organic content if the material shared by your brand does not outperform that picked by algorithms. On Facebook, marketers may need to rely far more on sponsored targeting than on organic shared engagement.

Evaluate the value of your Facebook content #

Consumers and Facebook now have more significant influence over platform content than at any point in history. Facebook’s AI advancements expedite changes on a large scale, possibly giving consumers a greater capacity to filter out undesirable communications. Posts and updates of mediocre quality will not suffice. Brands must be ruthlessly honest about the value they generate through their content. Assume that it is insufficient at the moment and devise a plan to rectify the situation.

Investigate AI solutions that can assist you in determining your return on investment #

Depending on the changes made by Facebook and other internet companies as a result of artificial intelligence, you may have to fight fire with fire. Keep an eye on artificial intelligence (AI) technologies that evaluate campaigns and propose lucrative actions. These may give critical advice on producing a sufficient return on investment on Facebook and other social media platforms. It pays to begin by demoing products that pique your interest.

Conclusion #

Zuckerberg has committed to employing artificial intelligence to address some of the company’s and customers’ most pressing issues, particularly in seven key categories: Discriminatory Speech Terrorism, Nakedness, Graphic Brutality, Junk mails, Imitation Accounts, Automatic Image Recognition, Machine Translation, Talking Pictures, and Chatbots are examples of how AI is used.

In a word, artificial intelligence is here to stay and will undoubtedly have a significant influence on how Facebook serves both users and marketers. Although Facebook has always been coy regarding future breakthroughs, the company is constantly leveraging technology to deliver new features and services each year. With so many AI-based projects in place, Facebook can overcome new obstacles and forging new routes. After all, the invention is a never-ending process.

Related reading

The Role of Artificial Intelligence In The Cryptocurrency

Vin Patel — Mon, 29 Nov 2021 22:29:23 +0000

Key takeaways

In today’s world, cryptocurrencies have become the most fascinating buzzword.
Digital currencies are inappropriate for business payments due to their extreme volatility.
Any bot that trades bitcoin on an exchange has to be trustworthy.

Introduction #

Automation has now deepened its roots in almost every aspect of human life and a fast-growing industry like the crypt market, the potential to automate the trading interaction is more appealing.

But, the question is how AI can be used in the Crypt market?

Brokers with coding abilities and programming expertise can build their crypt robots or improve their existing Expert Advisors to make sophisticated coin trading more efficient. The robot will exchange cryptographic resources on its own, providing its owner with a great profit.

It’s critical to be vigilant and choose a trading consultant intelligently when it comes to crypt-trading. Otherwise, you risk taking up a viral miner and losing money.

As a result, similar technology may be employed in the crypt-trading space. What are the differences in telecommunications between using ME (mathematical expectations) and AI (artificial intelligence)?

The main advantages of AI and ME:

Ability to analyze large amounts of data.

Learning ability.

High work speed.

Accuracy.

Forecasting The Cryptocurrency Market Using AI #

Market Sentiment Analysis Using AI #

Finding Parties To Make Deals #

Security When Using Crypt Bots #

Give any platform a careful investigation before you start working with it. Always use two-factor authentication to limit account access.

Advantages & Disadvantages Of Using A Crypt Bot #

Advantages Of Crypt Bots: #

Round-the-clock trading. A bot never gets tired or bored of doing the same task 100s of times.

Absence of human factor. It might happen sometimes that people make mistakes in performing certain operations like that of entering details into a system.

Quick solution or execution of an operation according to the set of instructions provided.

Convenient diversification and risk-sharing.

Disadvantages Of Crypt Bots: #

Bots having degraded performance and ineffective strategies. Using such strategies can be a big loss to newbie users.

The bot irrespective of its work efficiency has to be monitored and updated regularly and thereby adjusted according to the function.

There are bots with low-quality software. Before choosing software for these purposes one must find out a source that is 100% authentic.

Advantages Of Automated Cryptocurrency Trading #

The trader is relieved of the necessity to continually watch the virtual currency market and determine the best times to start positions.

Using crypt robots to trade digital assets removes the element of fear and emotional state from trading choices. The majority of new crypt-traders lose money owing to psychological and emotional instability, as well as a disregard for their trading strategy’s guidelines.

Chart patterns, candlestick patterns, quote directions, and trend reversal points are all recognized by the bitcoin bot, which is frequently undetectable to the trader.

A well-tuned trading robot is more successful at analyzing charts, making more efficient trades, and looking for the ideal entry opportunities.

The above characteristics are just a few examples of how automated bitcoin trading may be beneficial. There are several more benefits to such programs that may benefit any trader.

Conclusion #

Another difficulty is that very accurate and complex machine learning algorithms need costly and sophisticated computer equipment, which is only available to huge businesses. All of this, however, may be remedied by studying and practicing AI and machine learning techniques at a reputable AI school.

It’s essential to recognize that the nature of a cryptobot is defined by the connection between its performance and its true worth. Any bot that trades bitcoin on an exchange has to be trustworthy.

Related reading

What are Outliers in Machine Learning and How to Remove

Vin Patel — Thu, 21 Oct 2021 18:08:00 +0000

Key takeaways

Imagine, you are working hard and very carefully while collecting important data.
Outlier analysis, for example, becomes essential in some data analytic applications, such as credit card fraud detection, since the analyst may be interested in the exception rather than the rule.
Outliers are a part of every machine learning program, but they need to be identified and removed.

Introduction #

Imagine, you are working hard and very carefully while collecting important data. Suddenly, you come across some bad data in your calculation and the whole job gets awry. These errors are called ‘outliers? in machine learning - something that everyone, especially data scientists finds very frustrating. Outliers in machine learning are harmful to the data collection process and can distort your observations. It is important to detect and get rid of these outliers beforehand. This is what we are going to learn in this article.

What are Outliers in Machine Learning? #

An outlier is a data point that stands out from the rest. They reflect measurement mistakes, poor data collection, or simply variables that were not considered when collecting the data. According to Wikipedia, it is a ‘distant observation location from other observations.’ Outliers have the potential to distort your results and provide incorrect information.

What are the Different Types of Outliers? Point Outliers #

Anomalies in comparison to the bulk of observations in a feature. In a nutshell, a data point is called a global outlier if its value falls significantly outside the bounds of the data collection in which it is discovered.

Contextual Outliers #

Anomalies are observations that are deemed unusual in a certain situation. A data point is called a contextual outlier if its value deviates considerably from that of the other data points in the same context. It is important to note that the same result may not be deemed an outlier if it occurs in a different environment. Because time series data are records of a certain amount across time, the “context” is nearly always temporal. It should come as no surprise that contextual outliers are prevalent in time series data. Contextual Anomaly levels are not outside of the usual global range but are out of the seasonal rhythm.

Collective Outliers #

A group of anomalous observations that look near to one another due to their comparable abnormal value.

A subset of data points within a data set is deemed anomalous if their values as a group deviate considerably from the overall data set, but the individual data points’ values are not abnormal in either a contextual or global sense. In time-series data, this might appear as typical peaks and valleys happening outside of a time period when the seasonal sequence is normal, or as a collection of time series that are in an outlier condition.

What Causes Outliers in Machine learning? #

The following are the most typical sources of outliers in a data set:

Errors in data entry: Outliers in data can be produced by human mistakes such as those made during data collection, recording, or input.

Error in measurement: Outliers are most commonly found in this context. This occurs when the measurement instrument employed is found to be defective.

Errors in experimental design (data extraction or experiment planning/execution)

On purpose (dummy outliers made to test detection methods)

Errors in data processing #

Errors in sampling #

Natural outlier (data novelty, not an error): When an outlier is not caused by a human mistake, it is referred to as a natural outlier. This category contains the majority of real-world data.

How to Get Rid of Outliers in Machine Learning? #

Outliers cannot be identified when collecting data; you will not know which numbers are outliers until you begin analyzing the data. Many statistical tests are sensitive to outliers, thus being able to detect them is a crucial component of data analytic. The interpretability of an outlier model is critical because choices aimed at dealing with an outlier require some context or explanation.

In reality, outliers can be useful indications at times. Outlier analysis, for example, becomes essential in some data analytic applications, such as credit card fraud detection, since the analyst may be interested in the exception rather than the rule. Simply said, when you identify outliers, you have three options: accept them, rectify them, or remove them. If there is a probability that the outlier will not have a substantial impact on the outcome, you may choose to “accept” it. Otherwise, you have the option of ‘correcting’ it or deleting it. However, you should only delete data points that are clearly incorrect.

So, here are some ways in which outliers can be removed from machine learning:

Observation Deletion #

To avoid skewing your research, it is sometimes advisable to delete certain entries entirely from your dataset. We remove outlier values if they are the result of a data input error, a data processing error, or if the number of outlier observations is very tiny. Trimming at both ends can also be used to remove outliers. However, when we have a tiny dataset, removing the observation is not a smart idea.

Value Transformation #

Outliers can be eliminated by transforming variables. The variance produced by extreme values is reduced by these modified values. Log transformation Scaling Box-transformation Normalization of cube roots

These approaches reduce the size of the dataset’s values. If your data has too many extreme values or is skewed, this approach can help you normalize it. However, these methods do not always produce the greatest outcomes. These approaches result in no data loss. In all of these methods, the box cox transformation produces the best results.

Imputation #

We can impute outliers in the same way that we may impute missing numbers. In this approach, we may utilize the mean, median, and zero value. There is no data loss because we are imputing. In this case, the median is suitable since it is unaffected by outliers.

Separately Treating #

If there are a large number of outliers and the dataset is tiny, we should consider them individually in the statistical model. One method is to consider both groups as separate entities, construct distinct models for each, and then merge the results. However, when the dataset is huge, this approach becomes laborious.

Conclusion #

Getting a sudden error while writing or executing a code can be heavily frustrating. Outliers when show up, act like a bug for your hard drafted code. It can be a mood killer when you are coding for 6 or 7 hours straight and then they show up.

These were the ways how one can remove outliers from machine learning. Hope now you know the concept of outliers. Outliers are a part of every machine learning program, but they need to be identified and removed. So, now that you know how to do the same, happy coding.

Related reading

Questions and answers

What are Outliers in Machine Learning? #

An outlier is a data point that stands out from the rest. They reflect measurement mistakes, poor data collection, or simply variables that were not considered when collecting the data.

What Causes Outliers in Machine learning? #

The following are the most typical sources of outliers in a data set: Errors in data entry: Outliers in data can be produced by human mistakes such as those made during data collection, recording, or input.

How to Get Rid of Outliers in Machine Learning? #

Edge AI: The Future of Artificial Intelligence

Vin Patel — Sun, 17 Oct 2021 01:17:00 +0000

Key takeaways

The modern world is surrounded by technologies and we are spending more time on our devices.
It enables users to understand how their data is being collected.
It allows professionals to access vital medical records and process capabilities to recommend treatments.

Introduction: #

The modern world is surrounded by technologies and we are spending more time on our devices. It is increasing the need to bring essential computation on our devices to help businesses serve more technology users. Artificial Intelligence (AI) evolution is taking place and cloud computing is one of its essential elements. It is also accelerating the market for edge computing, which is expected to reach 1.12 trillion by 2023.

How does Edge AI work? #

Edge AI is still evolving but has a wide range of potentials. It is a technique to proceed AI algorithms on a hardware device locally without any connection for a device with Edge AI to operate. It enables devices to process data and make decisions independently. Real-time operations like data creation, decision and superfast still hassle-free actions are prime features of Edge AI. Its local data processing avoids the risk of cloud data storage and streaming thus protecting the privacy of the data.

Edge AI provides a new way of performing AI and Machine Learning (ML). Generally, models are trained for a specific task in a typical ML setting. It is programmed to find patterns, evaluate and validate the test dataset with similar properties of the trained dataset model. The cloud infrastructure with computing and data storage resources comes with the data transfer challenges and at the cost of latency because of bandwidth limitations.

The real-time prediction needs are increasing for smart and sustainable developments. Industry 4.0 trends having Edge AI technology has autonomous systems and smart Internet of Things (IoT) devices. The ML model with Edge AI in the edge device does not need connection to the outside world. Though time for training a model on a dataset and deploying it to production remains the same for cloud computing, it reduces the latency cost and maintains the data safety.

Importance of Edge AI: #

As technologies are emerging with new features and advantages, they are accepted based on what important role they play in continuing business safely, smoothly and in a protective environment. Some of the important factors of Edge AI are:

? Edge AI provides intelligence to sensors. Surveillance cameras to record and store images for hours and use them while necessary. But Edge AI algorithms allow these systems to proceed real-time data, detect and process suspicious activities in real-time. It provides inexpensive as well as efficient service to users.

? Edge computing increases the system’s capacity to process images and data in real-time. For example, the Edge AI system can process traffic signs, road directions, pedestrians, and other vehicles for driverless vehicles and improve secure transportation levels.

? Smartphone users are increasing exponentially. Edge AI solutions incorporated in smartphones allow analyzing the images and videos to generate audiovisual stimuli and detect scenes and spaces.

? Industrial IoT is facing security issues that can be improved inexpensively with Edge AI.

? The network connection speed is increasing with developments in communication technology. 5G technology is hyped but to bring it to beneficial applications, it should have great speed and very low latency in data transmission, which is possible with Edge AI.

Benefits of Edge AI: #

? Privacy: With the increased concerns of users about their private data location, companies are offering AI-enabled personalized features in their applications. It enables users to understand how their data is being collected.

? Security: Cloud data is increasingly becoming sensitive due to distributed architectures. Edge AI nodes in different AI-enabled devices like phones, speakers, robots, and tablets can find the suitable security mechanism for each device.

? Latency: Edge AI is chosen due to reduced latency. Both network level and device level services are getting more distributed, and thus latency concerns are increasing while sending data across devices and networks.

? Load balancing: Edge AI is crucial to load balance multiple endpoints to maintain end-to-end application resilience.

? Cost reduction: Edge AI-enabled devices such as wearable technologies improve user experience by reducing the cost and latency. It interacts in real-time to make payments or monitor sleep patterns, and so forth. Moreover, it reduces the required bandwidth that leads to a reduction of contracted internet service costs.

? Low maintenance: Edge technology devices have automatic monitoring and do not need specialized care by AI developers or data scientists.

Potential application of Edge AI in Various Industries: #

Though Edge AI technology is still in the development process, it has gained popularity in different sectors. It has potential applications in various industries; some of them are autonomous vehicles, smart cities, industrial manufacturing, healthcare, and the financial industry. It is also applicable in AI virtual assistants and augmented reality devices.

? Autonomous vehicles: Though we are not expecting driverless cars immediately, the automotive industry has already invested billions of dollars in the development of autonomous vehicle technology. These vehicles should operate safely and based on real-time data analysis regarding their directions, surroundings, communication with other vehicles on the road as well as weather conditions. Moreover, manufacturers should receive their data regarding usage, maintenance alerts, and interface with local municipal networks.

New computing solutions need to be adopted by the autonomous industry to avoid interference caused to these vehicles due to the data traffic produced by personal computers, cell phones, and other connected devices. Edge computing enables autonomous vehicles to collect, process, and share data between vehicles as well as to other broad networks in real-time without latency. When driverless vehicles are combined with edge data center networks to collect and relay crucial data to emergency response services, municipalities, and auto manufacturers, they provide unparalleled reliability disabling network infrastructures.

? Smart cities: The development of smart cities is becoming a reality with a massive sensor network collecting data on utility, critical infrastructures, services, and traffic patterns every day. The collected data is stored and analyzed to get meaningful information. It allows city management officials to solve problems faster. There are limitations to conventional cloud solutions as they respond slowly to a multitude of devices in operation outside of the network. On the other hand, edge AI allows regulating device utilities and public services while responding to conditions in near real-time. The integration of edge computing with a connected network of the IoT in smart city infrastructure has the potential to transform the way people live and utilize services in the city.

? Industrial Manufacturing: One of the most benefited industries from the technological revolution is none other than the manufacturing sector. The integration of edge computing and data storage into industrial manufacturing equipment enables manufacturers in energy efficiency, better predictive maintenance, and reduced manufacturing cost with reliability. Edge AI promotes smart manufacturing practices.

? Finance: The importance of customer experience is increasing with growing competition in the marketplace. Edge AI computing integrated with smartphone apes allows banking institutions to target their services to customers better.

? Healthcare: The integration of the healthcare industry with IT solutions has faced various challenges, but edge AI offers new opportunities to healthcare professionals in delivering patient care. To provide appropriate treatment, healthcare professionals should be able to access patients’ critical health information in real-time.

Edge AI-enabled medical devices gather and process patient health data and help throughout the course of treatment and diagnosis. Patients in rural areas are miles away from the nearest healthcare provider. Edge computing solutions collect, store and deliver real-time information remotely. It allows professionals to access vital medical records and process capabilities to recommend treatments.

Related reading

7 Amazing Benefits of Adding Chatbots To Your Website

Vin Patel — Thu, 23 Sep 2021 15:30:00 +0000

Key takeaways

You like or hate it when you run your business website successfully; you need to automate some of its processes.
Also, there are zero chances of human emotions and errors that can affect customer relationships.
Hence if you also want to get such advantages over your competitor, it is better to look for the perfect chatbot application.

You like or hate it when you run your business website successfully; you need to automate some of its processes. Chatbots have gained popularity in recent years in the business world as they automate the chatting process with your customers and clients. According to Marketsandmarkets, the global market of chatbots crossed over $700 million in 2016. It was expected to increase with a rate of over 35% by 2022.

Today several businesses integrate chatbots with their business website and try to improve the customer experience. Such chatbots benefit customers because when they type their query, then chatbots automatically reply to instructions. Hence customers and visitors of your website get an instant response and feel personalized. Thus, there are several advantages of integrating Natural Language Processing chatbots. They are based on deep learning and decode the input effectively.

But businesses may feel confused whether they should use chatbots for their website or not. Here we have covered some crucial benefits of adding chatbots to your website. If you read it, then you will have clear information to integrate chatbots.

What are Chatbots? #

A chatbot is a programmed chat application that is specially designed to interact with the website visitor. They respond to the message of the visitors conversationally by mimicking human behavior. These tools are just like chat applications, but they respond with their programmed response rather than human support.

In short, you can say there is an essential guide who assists visitors of the website navigate things easily. They act like virtual assistance but are entirely automated software. When you integrate chatbots into the website, then it assures you to respond to the text as soon as possible with a smooth process.

7 Benefits of Adding Chatbots To Your Business Website #

#1. Chatbots simplify the things on your website #

Your website is the place where your audience can know everything about your offerings. It holds information about each product, company, reviews, and much more. And customers of today’s do not want to waste and wait for their precious time. Hence they always look for the magic button where they can find essential information just in seconds. Thus, with the digital age, the advantage of chatbots can not be ignored. Hence, using NLP chatbots for your business website can surely improve your customer’s experience.

#2. It will save you hard-earned money #

According to the survey, over 265 billion requests businesses receive from their customers. And it cost over $1.3 trillion to serve these customers with customer representatives. But if you want to cut down this massive expenditure from your expenditure budget, looking for the chatbot can be the solution. It will allow your business website to serve visitors 24/7/365 without spending big money on resources and customer representatives.

Chatbots offer real-time features by which visitors can get a response to their queries. Also, there are zero chances of human emotions and errors that can affect customer relationships. Thus, cutting down costs is also a benefit of chatbots when you integrate with your website.

#3. Make your brand stronger in front of customers #

Do you want to look like a strong brand in front of your customers? You’re trying to do the same and do your best to satisfy the customer with your services. But still, you may not be getting a good result; hence using chatbots can seem to you as the ideal solution. Because after integrating chatbots on websites, 7 out of 10 customers reacted positively. That means brands can win customers and become solid brands for the ideal product.

Chatbots reasonably support the visitors even if customers use lousy language. Thus, here the human mind does not affect, and customers also get a polite and positive reply.

#4. You can target a wider audience #

Over 40% of top businesses use chatbots these days, and if you are a startup, you should use this too. Almost every social media platform has integrated its platform with the ideal chatbots in today’s world. Hence they get help in managing colossal customer support. Using the relevant chatbot application to your site will surely take your business website to the next level. It automatically effectively markets your product or services. Thus, if you want to target a wider audience and maximize your sales, you should use chatbots like Skype, Telegram, Facebook Messaging, etc.

#5. Enhance your lead generation process #

A well-established website of the company also generates leads and makes sales. But after implementing chatbots to the website, you can see the state and changes. As per the report of Chatbot magazine, over 65% of millennial positively said they like to purchase products of brands using chatbots. The chatbots can follow up with them better and give some ideal packages for their needs. Thus, once you integrate the chatbot into your website, it helps the website visitor to buy the product.

#6. Improve engagement with customers #

It’s another benefit of chatbots because they can effortlessly increase engagement with the customer. As you all know, engagement is an essential aspect to retain the customer for a longer time. The advanced AI chatbots give proactive responses that reduce the chances of presenting irrelevant information to the visitor.

Also, as the customers get real-time solutions hence, they feel more engaged. And they are more likely to buy the products you offer to them as they think they will get good customer support in the future. Hence if you use chatbots on your website, then it will also increase customer engagement.

#7. Chatbots keep you updated with the latest trends #

Many business websites and social media platforms use chatbots, so they stay relevant in their business. Over 50% of customers and visitors of the websites want to get services 24/7, and if you do so, you will have a more customer base. Moreover, some leading platforms like Facebook Messenger, Skype, and other platforms support chatbots for their audience. Only Facebook and Kik support about 30,000 chatbots, and their audience base feel free to communicate with the brands easily.

There are several benefits of chatbots when you integrate one on your website. Hence if you also want to get such advantages over your competitor, it is better to look for the perfect chatbot application.

Related reading

Questions and answers

What are Chatbots? #

A chatbot is a programmed chat application that is specially designed to interact with the website visitor. They respond to the message of the visitors conversationally by mimicking human behavior.

Do you want to look like a strong brand in front of your customers? #

You’re trying to do the same and do your best to satisfy the customer with your services. But still, you may not be getting a good result; hence using chatbots can seem to you as the ideal solution.

How Conversational AI Help Drive More Sales

Vin Patel — Wed, 22 Sep 2021 15:30:00 +0000

Key takeaways

With the evolution of technology every day, the process of doing business is also changing.
Thus, they can manage users to stay on one website for a longer duration.
Conversational AI provides a unique experience to the customers and can enhance a spark of interest in online engagement and shopping.

With the evolution of technology every day, the process of doing business is also changing. In recent times, many changes have taken place in e-commerce sales and customers’ way of buying and selecting different products and services. From visiting a store by themselves, checking out item stock availability to purchasing unlimited products/services from online stores with a few clicks, and tracking the order status, everything has now become digital. According to the research of Statista, “the number of global digital customers will increase to 2.14 billion by the end of 2021. With these rapid changes, e-commerce industries also need to pace up with the customers’ growing demand. The lack of proper customization and personalization with users reduces sales but also leads to the downfall of business’ goodwill.

What is Conversational AI #

Conversational AI is a technology that acts as a bridge between customers and enterprises. By using enhanced technologies such as Natural Language Processing, Automatic Speech Recognition, and Machine Learning; it tries to solve the needs of both e-commerce sites and consumers. Many retail stores and e-commerce companies are shifting to various forms of conversational AI such as chatbots, messaging apes, voice assistants, visual search engines and many others to provide 24/7 support to consumers’ requirements, offer additional services and leverage more sales. Conversational AI targets that chunk of consumers’ experience that can be beneficial for extra lead to retail and e-commerce services.

Different Forms of Conversational AI For The Retail and E-commerce Industry #

Chatbots Chatbots with Natural Language Processing (NLP) capabilities are excellent and versatile that engage with customers and provide instant answers to their queries. According to Hubspot, 47% of customers are open to make a purchase via chatbots. They can handle a large number of queries and are available 24/7 live for human interaction and user experience. It is not based on one-way communication, furthermore, it allows customers to engage in an organic way by resolving their issues by giving relevant responses.

Messaging Apes The different messaging apes like WhatsApp, WeChat, Facebook Messenger, Slack and more help to make conversions with consumers easier without the need to navigate to other phone apes or sites. As per Juniper stats - over 70% of chatbots availability will be retail-based by 2023, and chatbot e-commerce transactions will stretch out to $112 billion annually.

Voice Assistant Voice search option has gradually decreased the text-based search for online and e-commerce platforms, and thus, are also minimizing the consumers’ efforts. The customers can use voice-based search assistants for their needs instead of keypad searches. The AI shopping bots will listen to the voice, and voice search mechanisms will provide the assistant for particular queries.

Now, the question arises of how industries can use and implement Conversational AI for better productivity and efficient interaction with customers.

Ways Conversational AI Can Improve Customer Engagement #

NLP Built Bots Bots built with Natural Language Processing understand the intent behind the customer queries and engage in more human-like conversation with users. NLP uses models that help bots to gather information to create more personalized messages for the customers. Thus, they can manage users to stay on one website for a longer duration. The use of chatbots as digital assistants on a website or any other social media platform is a prominent way of creating new buyers.

Mufti-lingual Bots A Mufti-lingual bot uses multiple languages to converse with customers. They understand the context behind languages and, therefore, can be a powerful asset for all e-commerce and retail businesses around the globe. The use of these bots by companies makes them accessible to a vast number of users not only for purchasing various products/services but to create brand awareness among customers. By engaging in a particular language with potential customers can broaden e-commerce sales and enable them to reach a wider audience for long-term relationships and growth.

Omani-channel conversation It is a mufti-channel approach to sales that provides and focuses on a smooth customer experience with integrated and seamless shopping. Nowadays, customers want to save time, personalized service, knowledge about online products before ordering, track record of item delivery, trust on social media and other platforms. All these needs can be catered through omnichannel conversation to lead generation for retailers and other companies.

Virtual Sales Agent Virtual selling has become the need of an hour in this situation of the Covid-19 pandemic. A virtual sales agent is an employee who works remotely or independently to communicate with customers to provide rich and reliable services. Virtual selling enables e-commerce companies to expand their sales services, promotion and marketing. The buyers are often confused regarding product pricing or features. The reliable recommendations and individual services provide value to the buyers. This allows users to choose products according to their wish that results in loyal consumers and develop trust between retailers and customers.

Advantages of Using Conversational AI #

Conversational AI application makes it easy to navigate different phone apes and websites for consumers to easily track and look out for what they require. It also allows convenience and deeper learning to the retail and e-commerce industry to reach their prospective customers and clients. It arranges the natural conversation to customers wherever they live across the globe over whichever platform or channel they choose. Some enterprises that use chatbots for delivering and personalized services to buyers include Sephora, Apple Watch, Domino’s pizza and H&M. The main pros of using Conversational AI are:

Seamless payment method

Easy decision-making support

Fast and quick service

One-to-one attention

Real store shopping experience

Those are the gone days of using traditional methods to increase sales and profits for your business. Several studies have shown that messages through chatbots have 85% of average opening rates with a Click-through rate (CTR) of 40%. Now, with help of this cost-effective alternative and strategy, your business can change potential buyers to revisiting consumers and can also give a boost to sales. Conversational AI provides a unique experience to the customers and can enhance a spark of interest in online engagement and shopping.

Related reading

How To Secure IoMT Devices?

Vin Patel — Tue, 14 Sep 2021 20:16:00 +0000

Key takeaways

There was a time when healthcare was all about manual recordings and heavily reliant on the doctor’s opinion.
Most organizations tend to be lax in their attitude towards IoMT device security, which is evident from the fact that only 51% of device manufacturers in the U.
It’s high time that they implement IoMT device security as a prime strategy in their development process because a life-saving device is only as good as its ability to save a life.

There was a time when healthcare was all about manual recordings and heavily reliant on the doctor’s opinion. Come 2021, and instead, the tables have turned. There hasn’t been another time in history where doctors and other healthcare practitioners are more reliant on technology to improve the lives of their patients. The Internet of Medical Things (IoMT) industry has connected healthcare with technology in one of the most ingenious manner. As per the Frost and Sullivan IoMT forecast to 2021 report, it’s expected that almost 40 to 50 billion IoMT devices will be part of the network by the end of the year. Right now, 48% of IoMT devices use mobility to connect to the cloud, and that number is expected to rise to 68%, with most of them using artificial intelligence and machine learning to spear ahead.

Given that the industry will only see a considerable boom year after year, many concerns are surrounding the IoMT technology in itself. With device security incidents involving data breaches and device misuse increasing with time, it’s high time we understand the risks associated with IoMT devices and what we can do to mitigate them.

What are IoMT Devices? #

Simply put, IoMT devices are those that improve the entire healthcare network and infrastructure. It’s very common to see that when these devices are spoken of, only those that help the patient are accounted for. But that’s far from reality. IoMT devices include both?those that help patients and those that help healthcare providers. Examples include health monitors, lab devices, nurse calling monitors, sensors, controllers, and the like. The current forecast of the market for 2022 is $52.2 billion and is expected to rise as the years pass by. With its increasing impact on people’s lives, it won’t be surprising if it surpasses its forecasted valuation as well.

Under IoMT again, there are multiple classes? Class I, Class II, and Class III. Class I includes non-invasive devices that post low to moderate risk to the patient, such as manual stethoscopes, enema devices, etc. Under Class II, we can include those that are invasive in nature and do post a moderate to high risk to the patient. Examples of those include air purifiers, infusion pumps, etc. Class III, better known as?active devices,? literally helps the patient live. These could be your heart monitors, pacemakers, pulse generators, etc. With our increasing reliance on such devices, the concern of device security seems more real with time.

Risks of IoMT Device Security #

The main concern with IoMT devices is that a huge amount of personal data is being generated daily. With such intimate data being recorded and saved in cloud servers, device security is expected to be top-notch. Yet, that isn’t always the case. For example, the Wannacry ransomware attack in 2019 was deployed in over 150 countries affecting 300,000+ devices. Even a year after the cyberattack took place, Microsoft’s serves still continue to find vulnerabilities in such systems.

If that isn’t horrifying enough, another attack, Bluekeep took place in 2020 in the US, where a vulnerability in the Microsoft Remote Desktop Protocol service was exploited to steal medical records. Since these are deployed as worms that take over the system in the sneakiest of manners, it’s very hard to identify and fight them. The most concerning part is that patient medical records are valued at millions to the right stakeholder, and it’s just being sold like hotcakes. In most cases, hospitals have had to turn away patients because of such cybersecurity attacks.

Here, we have nobody else to blame apart from ourselves. Most of these device security issues can be mitigated by employing simple defenses. In 56% of most healthcare security breaches, it has been found that simple things such as using different passwords would’ve mitigated the whole issue altogether. That being said, deniability is the main source of these issues. Most organizations tend to be lax in their attitude towards IoMT device security, which is evident from the fact that only 51% of device manufacturers in the U. S. use Food and Drug Administration (FDA) regulations to protect their gadgets. The sheer lack of ignorance and accountability is costing people their lives.

How Can We Secure IoMT Devices? #

Given all the risks and concerns that were put forward above, it’s reasonable to wonder whether or not we can completely protect ourselves from such cybersecurity attacks. It is a possibility with the right defense, such as those listed below:

i. Comprehensive IoMT management: It’s very easy to forget about the basics because it seems so obvious. But most organizations are unaware of the complex network that they make use of. The first thing they must do is create a live inventory for all their assets. It can include information about what devices are present, where they are present, what security gaps exist, etc.

ii. High-end encryption: It goes without saying that encryption is key. Any data that is created, stored or even passed through the network must be encrypted. It reduces the likelihood of breaches, although that is highly dependent on how strong the encryption is, so make sure that it’s fool-proof.

iii. Network segmentation: Segmentation is the simple process of dividing the IoMT network with encryption at each level of the network. This ensures that even though vulnerabilities might be present in one part of the network, the others aren’t affected. Only authorized users can access these networks and even their authorization levels can vary between different parts of the network.

iv. Regular software updates: No network can be protected forever, no matter how much you encrypt and segment them. Eventually, the vulnerabilities will show, and they will need to fix. This is why it’s important to carry out regular penetration testing to see how vulnerable the current system is and fix any bugs or introduce new security features. Data integrity is key and an integral part of secure IoMT devices.

v. Accountability: It’s always best to just admit it if there are any bugs or gaps in the network and fix them as soon as possible. No system is completely fool-proof but it can be managed well. For example, there are many vulnerabilities present in the Microsoft systems, but they continually identify and fix any gaps that they discover in their accountability testing.

Secure IoMT Devices — A Possibility? #

In short, it’s a yes and no. IoMT is the future of healthcare, and companies all across the world are jumping on the bandwagon. This also means that every single day, tons of data is being generated but not necessarily protected. Most cyberattacks also use existing vulnerabilities in such devices, making it virtually impossible to keep up with them. With more people becoming aware of the device’s security concerns, the general public could soon become risk-averse, drastically reducing its valuation over time. Securing IoMT devices can be a simple process yet, many organizations fail to implement the necessary precautions. It could be because they either don’t consider it an issue, don’t have the budget for it, or don’t have a strategy in place. It’s high time that they implement IoMT device security as a prime strategy in their development process because a life-saving device is only as good as its ability to save a life.

Related reading

Questions and answers

How Can We Secure IoMT Devices? #

Secure IoMT Devices — A Possibility? #

IoMT is the future of healthcare, and companies all across the world are jumping on the bandwagon. This also means that every single day, tons of data is being generated but not necessarily protected.

Data Privacy Challenges in Building an IoT Solution

Vin Patel — Mon, 23 Aug 2021 20:34:30 +0000

Key takeaways

Today we live in a digital era where everything is getting digitized and connected to each other.
However, as IoT is an emerging technology, there are still several challenges in building IoT solutions for several applications.
Additionally, network firewall, custom security strategies and token-based authentication should be implemented on all aspects of every IoT application in the solution.

Today we live in a digital era where everything is getting digitized and connected to each other. As a result, modern technologies alter the way people used to live, work, commute and communicate. These technologies, like Artificial Intelligence (AI), Internet of Things (IoT), Information & Communication Technology (ICT), Machine Learning (ML), Computer vision, Blockchain, and Data Analytic, are still evolving but making remarkable transformations in industries. Among these technologies, IoT has proven potential to connect the physical and virtual world on a single platform and thus gaining popularity in every sector adopting advanced technologies.

IoT #

IoT stands for Internet of Things. The ’thing? in IoT can be any physical device such as smartphone, computer, washing machine, wearable device, television, headphone, lamp, building, vehicle or any other possible thing that can be thought of. The IoT technology will connect any systems, devices, objects that can be connected in the near future. These things are embedded with sensors, software, and other electronic components to transfer the data. The internet interconnects these devices and makes a giant IoT network of connected ’things?. There are three relationships in the IoT network viz

Things-things (machines to machines)

Things-people (devices to people)

People-people (people to people)

Significant Challenges in Building IoT Solution #

The integration of several devices connected through the network is prone to vulnerabilities, threats, and risks. Some standard IoT privacy and security problems are discussed below:

? Authentication and Authorization

Existing systems and software platforms are getting upgraded with technological advancements, thus increasing the date’s security and safety. But accordingly, hackers are becoming more dangerous in attacking or hacking systems leading to cybercrimes such as identity thefts, personal information misuse and others. This is because most of the devices have weak and predictable passwords. Additionally, many sensors, devices and systems have default authorization credentials that are easy to hack through the network.

? Transport Layer Insecurity

IoT infrastructure has different layers of configurations and systems such as hardware, communication, application, and communication layers. Systems in these layers communicate with each other to transmit and receive data. Transport layers must be secured to prevent data leakage. Data encryption is the best solution available today to transfer data safely over the internet, but most devices fail at this step. It leads to insecure data transfer and increases security issues of IoT solutions.

? Insecure User Interface

IoT devices such as smartphones, laptops, personal computers or even web interfaces have user interfaces to manage the device as well as to make appropriate use of the data collected. However, these devices are prone to security issues such as data leakage, cross-site scripting, or identity misuse. Therefore, it is vital to maintain proper security measures while operating and handling IoT solutions.

? Disregarding Privacy Concerns

There should be safe code practices and a proper set of rules while handling business logic and services data. Furthermore, private, confidential and sensitive information regarding the organization should be kept safe to avoid possible threats like cyberattacks. Therefore, organizations and governments must implement security methods while adopting IoT solutions in sensitive data handling.

? IoT Security and Privacy Laws

Businesses look for customer privacy and secure IoT devices in compliance with IoT security and privacy laws. A few rules and regulations have been passed globally, such as the California Consumer Privacy Act (CCPA), European Union General Data Protection Regulation (GDPR). These acts are dedicated to protecting consumer privacy, but there are differences in scope, requirements and penalties for noncompliance for both laws. Moreover, there is no single globally accepted rule or regulation regarding IoT solutions that make it difficult for IoT solution manufacturers to produce solutions in compliance with these acts.

IoT Data Privacy Challenges #

IoT enables uninterrupted and fast machine-to-machine communication that has already attracted several industries, governments, and enterprises. They have initiated developing and implementing IoT solutions and aim to enhance the productivity and growth of organizations. However, as IoT is an emerging technology, there are still several challenges in building IoT solutions for several applications. For example, there are no universal standards for IoT solutions. Moreover, concerns regarding data privacy and application security are not addressed yet. IoT solutions will be used to collect, store, exchange and analyze massive data efficiently and securely. Thus, it is important to implement strategies in protecting all components of IoT solutions from evolving security threats.

IoT interconnects billions to trillions of intelligent things with unique identities and addresses. These things process and communicate information about the physical environment and about themselves. These IoT systems are used in different applications such as advanced building management systems, pervasive healthcare, public surveillance, innovative city services, participatory sensing applications and data acquisition. The collection of vast amounts of data through these systems about people’s private lives and personal information raises serious data privacy concerns. These issues may have unwanted consequences like a failure of new services, costly lawsuits, reputation damage and so forth.

IoT-related projects have experienced colossal data privacy issues previously, like a public boycott of the Italian retailer Benetton in 2003, the revocation of the Dutch intelligent metering bill in 2009 and the recent outcry against the EU FP7 research project INDECT. IoT is dependent on different technologies like wireless sensor networks (WSN), mobile applications & platforms, web personalization and Radio Frequency Identification (RFID).

IoT features are changing with the latest technology developments. Simultaneously, the number of ways of interacting with smart things are growing rapidly and making networking more complex. Upcoming features will be exciting and attentive but will aggravate data privacy issues and introduce unknown threats. These known, as well as strange privacy issues, must be considered in reference models accounting for specific entities and data flows. In addition, the perspective of existing privacy legislation regarding evolving and unique IoT solution features should be considered while building IoT solutions.

Privacy is one of the most important majors for people, and it is becoming prominent with increased usage and efficiency of electronic data processing. It was first defined in 1968 by Weston as ’the right to select what personal information about the person is known to what people?, and it is still equally valid. Before electronic data usage, data privacy concerns were limited and primarily controllable. For example, it is challenging to prevent cyberattacks and hacks in electronic data over the internet. Therefore, IoT solutions should guarantee — Awareness of privacy risks by IoT solutions collecting data — Understanding and control of the use and dissemination of personal data — Individual control on collecting and processing personal information by IoT solutions

Thus, enterprises should focus on the security of IoT solution components like communication channels, embedded software, data, and other various devices that are prone to data privacy concerns. Multiple-factor authentication and authorization processes should be implemented to ensure user identity and reduce opportunities for hackers to misuse confidential user information. Additionally, network firewall, custom security strategies and token-based authentication should be implemented on all aspects of every IoT application in the solution.

Related reading

Why Your Business Needs NLP Chatbots?

Vin Patel — Wed, 18 Aug 2021 23:18:34 +0000

Key takeaways

Chatbots are a game-changing invention in the realm of automation and machine learning.
Without Natural Language Processing, chatbots would be unable to give much assistance.
It gives you technological benefits to sustain in the market by saving you time, effort, and money, which leads to higher user satisfaction and involvement in your company.

Chatbots are a game-changing invention in the realm of automation and machine learning. They assist not only business owners and e-commerce giants but also the employees and customers in various ways. As technology advances, the requirement for natural language processing (NLP) to increase chatbot functionality grows. Before we dive into the technicalities and importance of NLP for chatbots, let’s discuss the fundamentals of NLP and AI chatbots.

What is NLP? #

NLP or Natural Language Processing is a type of artificial intelligence technology that helps chatbots to communicate with humans through speech or text. Many a time, people carrying out conversations with bots wonder to themselves - ‘It doesn’t understand precisely what I am saying?. This is where NLP can actually help.

NLP, based on deep learning is an artificial intelligence (AI) tool that aids a chatbot in analyzing and comprehending natural human language. It evaluates the intention of user input and then generates responses depending on a conceptual framework in the same way that a human would.

The basic difference between traditional AI chatbots and NLP-based chatbots is that rule-based AI Chatbots only provide information on the basis of keywords. Whereas, NLP-based chatbots try to understand the intent behind the human user’s query or request and take action based on that.

AI Chatbots #

AI Chatbots or Artificial Intelligence chatbots are text or voice-based software applications intended to provide human users with service and information. Through simulation of the original scenario, they offer a perfect alternative to traditional human-to-human communication.

Chatbots are nothing but computer programs that you can communicate with via messaging apes, chat windows, or voice calling applications. These integrated virtual assistants respond to customers’ questions in a cost-effective, prompt, and systematic manner.

The working of AI chatbots is simple and effective. They work in two steps, mainly. The first is to identify the meaning of the question asked by the human user. They collect data and gather information about the question asked. After that, they give answers based on either previously recorded queries or a relatively custom answer based on what the user asked. This disparity depends entirely on the smartness or intelligence of the chatbots.

Challenges with AI Chatbots #

AI chatbots, for all the glitz and glamour of artificial intelligence technology, are not without their own limitations. Some of the various challenges we can face with AI chatbots are listed below.

A. Humans can improvise and try to come up with an answer to a unique problem on the spot, while AI has trouble doing so. AI chatbots can fall apart if faced with a completely fresh and unforeseen problem. It can adopt one of the two ways - either the AI will have to delegate the problem to a human. Or it will try to solve it, but because of a lack of moral judgement, it will most definitely fail.

B. To function, AI chatbots require a significant amount of time and effort. That is, it takes time for everything to be fully operational and produce tangible benefits. It can take a long time to set up, implement, and learn something new. In terms of their knowledge base and the way they are supposed to engage with consumers, bots require ongoing supervision, maintenance, and optimization. To break it down in simple words, you must provide new and relevant data (content) to the bot regularly in order for it to respond to client questions effectively.

C. Another most crucial disadvantage with AI chatbots is the enormous amount of data they may collect in the process of delivering assistance. This may put the customers or users at serious risk of identity theft. AI chatbots may collect addresses or geolocation of users. They can also collect and store information about your payment or savings account i.e. banking details. While using AI chatbots, you are often required to fill in your full legal names and other information that can be used to steal a person’s identity.

Why Does Your Chatbot Need NLP? #

Chatbots are designed for a variety of reasons, including FAQs, customer support, virtual assistance, and much more. Almost always, chatbots without natural language processing rely heavily on pre-programmed static data. They lack empathy and human-like judgement. Therefore, they are poorly equipped to deal with human languages, which vary in emotions, intent, and feelings to communicate each individual enquiry. NLP can distinguish between the many types of requests made by humans, significantly improving the customer experience.

A. Natural language processing (NLP) aids AI in determining the best answer by providing meaning and context to text-based user inputs. AI that relies on language inputs is largely useless without NLP. For example, NLP enables technology like Amazon’s Alex or Apple’s Sir to understand what you are saying and respond appropriately. Without Natural Language Processing, chatbots would be unable to give much assistance.

B. Chatbots that use natural language processing (NLP) are able to comprehend language principles, linguistic structure, and spoken syllables. As a result, it enables you to interpret a large volume of unstructured data and text.

C. NLP enables chatbots to grasp and decode dialect and acquire abbreviations in the same way that humans do, as well as comprehend different emotions through sentiment analysis.

D. Costing is a critical component of every business’s growth and profitability. On this front, NLP is something that you must deploy in order to manage your finances. NLP-based chatbots can help reduce expenses related to manual labor and other resources engaged in repetitive operations. They can also help with other costs associated with customer retention. This will help in increasing efficiency and optimizing the overall work performance.

E. When you use chatbots, you will see an improvement in customer retention. This is because using NLP based chatbots decreases the time and cost of gaining a new client by enhancing existing customer loyalty. Chatbots give clients the attention and time they desire, making them feel valued and appreciated. This is the case especially for millennial, who want instant responses and solutions for their inquiries.

How Can Natural Language Processing (NLP) Overcome Natural Communication Barriers? #

Advanced Natural Language Processing (NLP) is overcoming natural communication barriers each day. For example, chatbots with NLP skills can detect and analyze spelling and grammatical faults. It can then offer a number of suggestions or corrections for the human user to choose from. NLP allows the chatbot to interpret your actual meanings despite the inaccuracies.

This intent-driven feature will be able to make connections between customers and businesses. This will ensure that your chatbot is someone customers will want to talk to when contacting your business.

Worldwide affairs, political changes, government policy statements, and the fundamental economic environment in an area all influence financial market movements. To evaluate the climate, NLP-based systems may read news, press releases, and other financial reports. This capability improves the efficiency of computerized financial advisors.

Benefits of NLP Based Chatbots #

When it comes to Natural Language Processing, programmers can train the chatbot on a variety of interactions and conversations. The developer can also teach different examples of content that the chatbot might encounter while interacting with human users or customers. This tends to provide it with a much broader base from which to analyze and interpret questions more efficiently.

With personalization at the forefront, companies are striving to make an effort to teach their chatbots about the many default responses and how they might make customers’ life easier. As a result, the chatbot will be able to streamline more personalized, unique responses. The chatbots will be able to interpret and answer new queries or commands and improve the customer’s experience based on their demands. All of these credits go to natural language processing (NLP) technology.

Customers in the newer generations would rather text a brand or business than call and chat with a human customer service provider. In the age of instant gratification and a chance to interact with emerging technology, chatbots are what millennial prefer. Thus, if you want to cater to this niche market, you’ll need to design a chatbot with NLP.

Future Thoughts #

Experts in business, market intelligence, and research and consultancy businesses are optimistic about the future of NLP-based chatbots. Very soon, almost all enterprises across every industry, as well as governments, will begin deploying AI solutions. In the creation of intelligent real-world applications, natural language processing (NLP) will be very critical. Businesses must innovate and make use of developing technology to improve customer relationships, production performance, and cost structures.

Summing Up #

In conclusion, we must remember that NLP cannot fully guarantee that a chatbot will answer efficiently to all messages. Nonetheless, natural language processing (NLP) technology is important enough to decide a chatbot’s sustainability.

Chatbots that use natural language processing (NLP) can help you improve your business operations and elevate your client experience while also enhancing overall growth and profitability. It gives you technological benefits to sustain in the market by saving you time, effort, and money, which leads to higher user satisfaction and involvement in your company.

Related reading

Questions and answers

Why Does Your Chatbot Need NLP? #

How Can Natural Language Processing (NLP) Overcome Natural Communication Barriers? #

Advanced Natural Language Processing (NLP) is overcoming natural communication barriers each day. For example, chatbots with NLP skills can detect and analyze spelling and grammatical faults.

8 Most Innovative Chatbots On The Web

Vin Patel — Fri, 13 Aug 2021 19:18:22 +0000

Key takeaways

You all might have used the live chat service on various websites that give us the suspicion if the person we are chatting with is a robot.
This chatbot is specially designed for all the night owls out there.
Although, chatbots have round-the-clock availability, are cost-effective and scalable, and help boost customer engagement, one cannot totally depend on them as they have a limited range of responses and cannot work dynamically.

Introduction #

You all might have used the live chat service on various websites that give us the suspicion if the person we are chatting with is a robot. The expressionless talking, repeated responses, and lack of interaction might make you realize that you are actually talking to a robot.

Chatbots have grown in popularity in recent years, owing to significant advances in machine learning and other existing solutions like artificial intelligence. Chatbots are becoming smarter, more adaptive, and more beneficial, and we’ll certainly see more of them in the future years.

In this article we will discuss some of the most ingenious chatbots on the web to date but, before that let’s know a little more about chatbots.

What Are Chatbots? #

In 1966, MIT computer scientist Joseph Weizenbaum introduced the world to chatbots in the form of Eliza, a chatbot based on a restricted, pre-determined flow that could imitate a psychotherapist’s discussion using a script. Eliza conducted conversations by employing pattern matching and replacement techniques that gave users the impression of comprehension.

In place of direct interaction with a live human agent, a chatbot is a software program that conducts an online chat discussion using text or text-to-speech. In dialogue systems, chatbots are used for a variety of functions, including customer support, handling query, and data collection. While some chatbots make significant use of word categorization, language processing, and advanced AI, others merely scan for generic keywords and produce answers using common words from a collection or databases.

How Do Chatbots Work? #

Natural Language Processing, or NLP, is at the foundation of chatbot technology, and it’s the same technology that powers speech recognition algorithms in virtual assistants like Google Assistant, Apple’s Sir, and Microsoft’s Cortana. Chatbots parse the language provided to them by the user before replying using a sophisticated set of algorithms that understand and identify what the user said, deduce what they intend and/or desire, then select a sequence of acceptable answers based on this data. Some chatbots provide a very realistic conversational experience, making it impossible to tell if the operator is a bot or a person. Whereas others are much easier to recognize.

Innovative Chatbots On Web #

Roof Ai: Generating and Assigning Leads Automatically #

If you work in marketing, you’re probably aware of the significance of lead assignments. After all, not all prospects are equal, and putting the right leads in front of the right representatives at the right moment is far more difficult than it appears. Roof Ai is a chatbot that assists real estate marketers in automating the interaction with potential leads and the assignment of leads via social media. The bot uses Facebook to identify possible leads and then answers nearly instantly in a polite, supportive, and interactive tone that closely mimics a human.

Endurance: A Companion for Dementia Patients #

Endurance is a bot specially designed for Dementia patients, suffering from diseases like Alzheimer’s who have short-term memory loss. Endurance, the bot converses with the patients and records the pattern in their habit of forgetting things. In addition to this, the bot being cloud-based helps the physician and the family members keep a check on the conversation of the patient so as to identify the probable improvement in the condition of the patient. As for now the Russian version of the bot is available whereas the English version will be available soon.

A. L. I. C. E.: The Bot That Launched a Thousand? Other Bots #

Artificial Linguistic Internet Computer Entity (A. L. I. C. E.) is one of those chatbots which are performing really well in the market, launched almost 20 years ago. Despite the fact that ALICE is based on an outdated codebase, it provides a conversational experience that is astonishingly realistic. ALICE, like many sophisticated bots, struggles with the subtleties of some queries and responds with a combination of unintentionally postmodern responses and comments that imply ALICE has a higher level of self-awareness, for which we should give the agent credit.

Casper: Helping Insomniacs Get Through the Night #

This chatbot is specially designed for all the night owls out there. For the insomniacs who find it difficult to sleep at night, Casper can help. It not only stops you from scrolling the endless posts on social media but also converses with you in a way that might stop you from feeling lonely when the whole world is asleep. Casper is more kind of a conversational bot in comparison to the bots that are mainly informative and functional in nature. This is distinct from other bots since it is more natural and human-like, and its main goal is to be a companion.

UNICEF: Helping Marginalized Communities Be Heard #

The bot focuses on large-scale data collection using polls. The bot then sends out prepared polls on a variety of pressing social topics on a regular basis, and users can react with their thoughts. UNICEF then utilizes this information to formulate policy recommendations. This bot is basically more of an informative one.

MedWhat: Making Medical Diagnoses Faster #

This chatbot seeks to make a medical diagnosis for both people and doctors faster, simpler, and more transparent. MedWhat is driven by a powerful machine learning system that provides more correct information to user queries based on behaviors that it “learns” through interacting with humans. MedWhat not only answers a rising number of medical queries, but it also consults enormous amounts of scientific research and peer-reviewed scientific articles to contribute to its already impressive library of medical knowledge.

NBC: Helping Newshounds Navigate the Headlines #

Checking the news headlines daily for getting the latest updates is of paramount importance these days. And NBC can be of great help. Users may interact with the conversational agent via Facebook to identify breaking news items that would be of interest to the network’s diverse demographics. Following the initial contact, the bot delivered personalized news results depending on the user’s choices. The bot also assisted NBC in determining which material was most popular among consumers, which the network would utilize to further personalize and enhance its programming in the future.

Marvel: Guarding the Galaxy with Comic-Book Crossovers #

This bot is basically aid in selling the Marvel comic books. Marvel’s cinematic universe appears to be expanding faster than the observable world’s bounds, so it was only a matter of time until the company resorted to chatbots to further immerse fans in their favorite comic-book tales in real life. The bots may pursue a number of different conversational paths based on the user’s input, but the ape’s main purpose is to sell comic books and movie tickets. The fact that users may have in-depth talks with the bot is one part of the ape’s experience that it gets right.

Conclusion #

With the latest technological evolutions, Chatbots have become the new normal. In the next years, the usage of chatbots for business is anticipated to grow. In order to be competitive in the sector, businesses should keep a close eye out over chatbots. If your company talks with consumers regularly, installing a chatbot for your company might save you money, engage customers, and make you available 24 hours a day, 7 days a week, all year.

Although, chatbots have round-the-clock availability, are cost-effective and scalable, and help boost customer engagement, one cannot totally depend on them as they have a limited range of responses and cannot work dynamically.

Related reading

Questions and answers

What Are Chatbots? #

How Do Chatbots Work? #

Data Annotations: Backbone For ML Model Trainings

Vin Patel — Tue, 27 Jul 2021 19:56:11 +0000

Key takeaways

Artificial Intelligence (AI) and Machine Learning (ML) have changed the way we live.
Text annotation includes various interpretations such as emotions, intentions, and inquiries.
This will continue to grow as more modest datasets are required to develop some machine learning tools.

Artificial Intelligence (AI) and Machine Learning (ML) have changed the way we live. Starting with the product recommendation and search engine results to self-driving cars and drones everything is powered by artificial intelligence.

Today we are building a future where automation and autonomous power are everything. To build such automated applications and machines, datasets need to be properly trained. However, since the dataset is very large and the human training method will not help, artificial intelligence companies use data interpretation to label this material and train machine learning models

‘What Is Data Annotation? #

Data annotation is a process to facilitate access to machines. It is important for machine learning and processing labelled datasets. And it is especially important to learn from the input patterns to reach the desired output. Data comes in various formats such as images, text, videos, and documents, but these types cannot be fed into a machine learning model without any distinctive documentation. Using data annotation, companies can train their ML models with the right tools and techniques. Here are some uses of data annotation:

? Using the data described to train the machine learning model, the accuracy of this method will be higher. ? Trained machine learning models with noted data take advantage of a seamless experience for end-users. ? Even virtual assistants or chatbots use trained datasets to answer users questions. ? A machine learning model trained with machine data provides comprehensive results in search engine recommendation. ? In addition to helping on a large scale, data annotation can help with local labelling based on geography. It is labelled locally for information, images and wither content.

What Is Human Annotation Data? #

Humans spend a lot of time helping machines learn how the world works. Therefore, the data annotation hinders humans in the process of improving performance.

Types of Data Annotation #

##? Text Annotation:

Original diagram for Techfastly by Vin Patel.

Today, most companies are moving towards automation models, in particular, the power of their text-based working systems. Recently, text annotation has been the focus of attention for increasing adoption. Text annotation includes various interpretations such as emotions, intentions, and inquiries. For example, text annotation helps machines recognize keywords in sentences and make sentences more meaningful. Text annotation is highlighted with specific colors and shades so that the reading sentences are carefully trained in machine learning algorithms.

##? Video Annotation:

Original diagram for Techfastly by Vin Patel.

Humans are seen as a good source for datasets training when it comes to video annotation. For example, companies search engine results. They gather information from many people according to their preferences and promote similar content to others. Video annotation using autonomous vehicles things like cars, traffic lights, signboards, street lights and walking on the roads can be understood.

##? Image Annotation:

Original diagram for Techfastly by Vin Patel.

Image annotation is important for dataset training. Many advanced technologies such as computer vision, robotic vision, facial recognition, etc. Rely on image annotation to interpret pictorial forms. In order to train models with image data, metadata should be assigned to images in the form of identifiers, or keywords.

##? Audio Annotation:

Audio annotation is different from other types of annotation. Unlike others, audio annotation takes in-depth steps to copy speech data and stamp time, including a copy of specific pronunciation and publication. Each case is different, and some require a very specific approach: for example, tagging aggressive speech gestures and non-speech sounds like a broken glass for use in security and emergency hotline technology applications.

Benefits of Data Annotation #

Data annotation benefits a variety of AI and machine learning technologies and companies and their users: ? Chatbots and voice assistants have been trained to communicate with users in a more humane way. ? High-quality results are returned for search queries. ? Internal IoT devices can detect everything from the human voice to sudden movement in the home, which improves availability and home security. ? Users Online videos, photos and articles have become increasingly accessible to users with vision or hearing impairment. Speech recognition technology has also increased access to mobile and desktop devices. ? Facial and body recognition tools can be used for anything from enhancing biosecurity to AI-driven medical diagnosis. ? New technologies, such as self-driving cars, can read and process landscape-based data that replaces most human actions.

Conclusion #

Data annotation is essential for AI and machine learning, and both have added immense value to the world. To keep the AI industry growing, data annotations are needed. This will continue to grow as more modest datasets are required to develop some machine learning tools.

Related reading

Questions and answers

‘What Is Data Annotation? #

Data annotation is a process to facilitate access to machines. It is important for machine learning and processing labelled datasets.

What Is Human Annotation Data? #

Humans spend a lot of time helping machines learn how the world works. Therefore, the data annotation hinders humans in the process of improving performance.

How Intelligent is Alex with Conversational AI?

Vin Patel — Fri, 23 Jul 2021 22:49:30 +0000

Key takeaways

Scientists have been studying Natural Human Language for decades.
Natural language generating capabilities are becoming increasingly advanced, even though the human language is highly complex.
These are significant advancements over what was available only a few years ago in terms of a more conversational and sophisticated voice assistant.

Scientists have been studying Natural Human Language for decades. Alex is the all-knowing, interactive voice assistant from Amazon. Alex is the voice of Amazon’s Echo devices, such as the Echo Dot, and Echo Tap, as well as the Amazon Fire TV and other third-party products. People saw Alex’s capabilities develop dramatically, and Machine Learning is clearly to applaud.

But how does it work?

What technologies are being used?

Conversational AI Demystified: #

You’ve probably spoken with a lot of machines in your life, whether it’s a support agent when you call your internet provider, an online bot on a website when you ask how to reschedule your travel ticket, or Alex when you ask for a recommendation for a decent pizza nearby.

What differs between these technologies from each other is how intelligent they really are. That’s where Conversational AI comes in. Conversational AI is that the technology that ultimately enables machines to naturally interact with humans via language. It’s a subset of Artificial Intelligence that leverages concepts like:

Automatic Speech Recognition (ASR)
Natural Language Processing (NLP) or Natural Language Understanding (NLU)
Text-to-Speech (TTS) with voice synthesis

The audio waveform is transformed to text at the ASR step when you ask a query to an application. During the NLP stage, the device interprets the question and delivers a smart response. During the TTS step, the text is transformed into voice signals and audio is generated for the user. Several deep learning models are also connected to a pipeline to build a conversational AI application. A similar pipeline is used in Alex as well.

Who/what is Alex? #

Alex is Amazon’s equivalent of Apple’s Sir. Alex is Amazon’s cloud-based voice service, which may be found on more than 100 million Amazon and third-party devices. With Alex, you’ll be able to build natural voice experiences that offer customers a lot of intuitive ways to interact with the technology they use every day. The Alex Voice Service (AVS) was created by Amazon to replicate real-life conversations. Alex records your voice when you ask, “What’s the weather going to be like today?” The recording is then transferred over the Internet to Amazon’s Alex Voice Services, which parses it into commands it recognizes. The system then sends the appropriate output back to your device. When you ask Alex about the weather, an audio file is delivered back to you. Alex delivers you the weather prediction without you even realizing any communication between systems. Of course, this means that if you lose your internet connection, Alex will stop working.

The word “Alex” is merely a “wake word” that tells the service to begin listening to your voice. To receive a response from most gadgets, simply pronounce the wake word. While Alex is Amazon’s official voice assistant, you may also change it and can use “Amazon,” “Computer,” or “Echo” as a wake word.

Learning From Human Data Continuously: #

Alex’s strength is data. Every time Alex misinterprets your request, the data is used to improve the system’s intelligence for the following time. Understanding natural human speech is a massive challenge, but we now have the processing capability to improve it as we use it more. Alex is always learning from human data. Natural language generating capabilities are becoming increasingly advanced, even though the human language is highly complex. Amazon continues to have an army of specialists working on improving Alex and Alex Voice Services, as well as a legion of machines. Their goal is to make the spoken language as natural as possible as talking to another human being.

How Does Amazon Alex Work? #

Natural language processing (NLP), a method of turning speech into words, sounds, and thoughts, is the foundation of Alex. It all starts with signal processing, which offers Alex many opportunities to decipher the audio by cleaning it up. One of the most challenging aspects of far-field audio is signal processing. Seven microphones are utilized to determine the signal so that the device can concentrate on it. That signal can be subtracted via acoustic echo cancellation, leaving only the relevant signal. “Wake Word Detection” is the following assignment. It determines whether the user speaks one of the required words for the gadget to turn on, such as “Alex.” If the wake word is identified, the signal is routed to cloud-based voice recognition software, which converts the audio to text format. Alex will evaluate features of the user’s speech such as frequency and pitch to provide you with feature values to convert the audio to text.

Analysis of an ‘Order?: #

Wake word, Invocation name, and Utterance are the three essential pieces of the above command.

*? **Wake Word:***When users speak “Alex,” the device wakes up. The wake phrase activated Alex’s listening mode, allowing it to take commands from users.

? Invocation Name: The keyword used to trigger a specific “skill” is called the invocation name. The invocation name can be combined with an action, command, or query.

? Utterance: When a user requests Alex, they will use utterances. From the given utterance, Alex deduces the user’s intent and answers accordingly. In other words, the utterance determines what Alex will do for the user.

After that, Alex-enabled devices send the user’s instructions to Alex Voice Service, a cloud-based service (AVS). Consider the Alex Voice Service to be the brain of Alex-enabled devices, doing all of the complicated tasks like Automatic Speech Understanding (NLU). The Alex Voice Service analyses the response and determines the user’s purpose before sending a web service request to a third-party server if necessary.

Machine Learning Has Facilitated Extensive Growth: #

Alex is getting smarter all the time. Alex has figured out how to continue on a conversation from one inquiry to the next in the same way that humans do. If you don’t know the precise name of a skill you want Alex to perform, it will most likely be able to summon it if you get near. Additionally, using Alex Hunches and smart home connected devices, the assistant will be able to notify you if a normal pattern has been broken, such as lights being left on or a door being left unlocked, and will offer to fix it for you. These are significant advancements over what was available only a few years ago in terms of a more conversational and sophisticated voice assistant.

These are significant advancements over what was available only a few years ago in terms of a more conversational and sophisticated voice assistant. You guessed correctly. Machine learning has enabled these advancements.

Related reading

Questions and answers

How Does Amazon Alex Work? #

Alex records your voice when you ask, “What’s the weather going to be like today? #

" The recording is then transferred over the Internet to Amazon’s Alex Voice Services, which parses it into commands it recognizes. The system then sends the appropriate output back to your device.

Use of Quantum Machine Learning in Data Analytic

Vin Patel — Thu, 22 Jul 2021 23:06:17 +0000

Key takeaways

Imagine sitting in front of your computer and finishing off a certain work by staying awake the whole night.
The phrase “data analytic” refers to the act of analyzing datasets to make conclusions about the information contained within them.
For the data science experts sitting out there, this was one such article that can make your lives a bit easier, all it will take is a pinch of quantum physics combined with machine learning.

Introduction #

Imagine sitting in front of your computer and finishing off a certain work by staying awake the whole night. The following day, when you wake up and open your teach news feed, you come across an article telling you how a particular new technology can help you do the same thing in half the time that you took to finish it off. You regret staying awake the whole night and not knowing about the new technology and its application in your life. The article you read might not be such a big thing, but it surely made you acquainted with something that can stay with you in the long run. Consider this as one such article which can make your life easier.

Today, our area of focus is quantum machine learning. QML is a new thing for the IT industry but a thing that can change the dimensions of the industry effectively. Let us first try to know what quantum machine learning is.

What is Quantum Machine Learning? #

Traditional computers struggle to handle massive computations when data points are projected in greater and higher dimensions. Even if a traditional computer could do it, it would take too long.

Simply said, traditional machine learning techniques might be too demanding for traditional computers at times. Quantum computers, fortunately, have the computing capacity to tackle these difficult algorithms. They solve problems quicker than their classical equivalents by utilizing strong principles such as superposition and entanglement.

Scientists can convert a conventional machine learning algorithm into a quantum circuit that can be performed effectively on a quantum computer using quantum machine learning.

So, in simple words, Quantum Machine Learning (Quantum ML) is an interdisciplinary field that brings together quantum physics and machine learning (ML). It’s a symbiotic relationship, with quantum computing being used to create quantum versions of ML algorithms and traditional ML algorithms being used to study quantum systems.

Now that we know what quantum machine learning is, let us try to look at its applications.

Applications of Quantum Machine Learning #

Quantum machine learning is a very new area with a lot of room for advancement. However, we can already begin to forecast how it will affect our future!

Here are some of the areas where QML will have an impact:

Understanding Nanoparticles
Molecular and atomic mapping is used to create novel materials
Medical research and molecular modeling to identify new medicines
Understanding the human body’s inner workings
Pattern recognition and categorization have been improved
Expanding the scope of space exploration
By combining IoT and blockchain, we can achieve total linked security.
In the analysis of data

QML will address more issues than we could have anticipated as more remarkable discoveries occur every day.

After looking at the applications, we can be assured of one thing, and i.e., if QML can help in such kinds of things in the early stages of its discovery, in a few years it can be the next big thing. In the applications above we came across a point that mentioned ‘data analysis?. Watching the things that Big Data can do and how fast the data science industry is growing, anyone will be curious about this very application. So let us dive deeper into the ocean of quantum machine learning and search for the desired fish, data analysis. For this, we must start with what data analytic is.

What is Data Analytic? #

The phrase “data analytic” refers to the act of analyzing datasets to make conclusions about the information contained within them. Data analysis techniques allow you to take raw data and derive important insights from it by uncovering patterns.

In their research, data scientists and analysts employ data analytic methodologies, and companies use them to influence their judgments. Data analysis may assist businesses in better understanding their consumers, evaluating their advertising efforts, personalizing content, developing content strategies, and developing new goods. Finally, firms may utilize data analytic to improve their bottom line and raise their performance.

Quantum Machine Learning in Data Analytic #

While solving real-world problems, quantum machine learning algorithms work with existing datasets and provide a simple and better understanding of them. It makes the work easier for a data scientist who spends hours analyzing data. The rise of QML will benefit the ones in the industry and will also make them shed a lot of load from their already crowded head.

Researchers examine the topic of quantum advantage in machine learning in a recent paper published in Nature Communications titled “Power of data in quantum machine learning.” Researchers attempted to quantify how the difficulty of an issue varies formally with the availability of data in the article, and how this might occasionally improve the competitiveness of classical learning models using quantum techniques. The data can raise classical models to rival quantum models, despite the fact that the quantum circuits generating the data are difficult to calculate classically.

To begin, they demonstrated how data-driven classical algorithms might match quantum output. They then established rigorous prediction error limits for training conventional and quantum machine learning algorithms to learn quantum mechanical models using kernel functions. Kernel techniques give verifiable guarantees and are also quite flexible in terms of the functions they can learn, therefore they took these steps.

Computational complexity classes are occasionally used to represent the idea of quantum advantage over a conventional computer. Bounded Quantum Polynomial (BQP) time tasks, such as factoring large numbers and modeling quantum systems, are predicted to be easier for quantum computers to handle than classical systems. Bounded probabilistic polynomial (BPP) problems, on the other hand, are simple to answer on conventional computers.

The researchers working on these showed that learning algorithms with input from a quantum process, such as fusion or chemical processes, generate a new class of problems known as BPP/Same, which can effectively execute tasks that standard algorithms without data can’t. It’s a type of problem that can be solved quickly with polynomial-sized advice. This shows that understanding the quantum advantage for different machine learning tasks needs a data analysis as well.

Going deeper into this, we will realize that all the applications of QML are somewhere related to data analytic. From analyzing human behavior to pattern recognition and space exploration, QML uses data analytic to function smoothly and vice verse.

Conclusion #

In the near future when many new quantum machine learning algorithms will come up with the exploration of this field, more and more applications of it will show up too. Data analytic with time will get easier and easier for an industry in so much demand.

For the data science experts sitting out there, this was one such article that can make your lives a bit easier, all it will take is a pinch of quantum physics combined with machine learning.

Related reading

Questions and answers

What is Quantum Machine Learning? #

Traditional computers struggle to handle massive computations when data points are projected in greater and higher dimensions. Even if a traditional computer could do it, it would take too long.

What is Data Analytic? #

DeepMind's AI: How it Predicts Google Map's ETA?

Vin Patel — Wed, 14 Jul 2021 22:57:19 +0000

Key takeaways

It is our ‘Go to’ app for searching destinations and directions.
Each group has its neural network which does traffic prediction.
So, don’t forget to use Google Maps the next time you are on the road.

If you own a smart device, you must know about Google Maps. It is our ‘Go to’ app for searching destinations and directions.

According to Google’s blog, people drive over 1 billion kilometers in over 220 countries every day. And they do it with Google Maps.

Google Maps are known for its accurate traffic predictions and expected time of arrival (ETA). Ever thought, how is that happening? Well, it is the magic of DeepMind’s AI tool.

It uses machine learning tools to study how much time it takes for a person to arrive at a destination. Then it stores the data to show it to a different person wishing to visit the same place.

Along with that, it uses an algorithm to predict the ETA and live traffic on a particular route. With DeepMind’s AI model, Google Maps made sure we get only the accurate data!

What is DeepMind AI Lab? #

DeepMind is an Alphabet research AI lab. Its research team tests and studies various AI programs. If you check their blog, you will find works on AI games, health, and more.

It makes one wonder how many things can Artificial Intelligence do!

The team of engineers, scientists, AI experts, and more believe that Artificial Intelligence could be humanity’s most useful invention. So, they study various things that can make people’s life easier.

Last year, Google got DeepMind inboard on Google Map project. The main goal was to make Google Map prediction top-notch and provide better service to users.

How does Google Map Predict Live Traffic? #

For traffic prediction, the AI collects data from various sources and feeds them to the algorithm. It thus stores the data and learns how to use it to predict the traffic flows.

It collects several data from our phones such as live location, past traffic memory, construction sites location, road size and more.

Additionally, it uses public information to find the location of road damage, size, and direction, road quality, and more.

For example, the AI algorithm predicts the chances of traffic jams if there is an accident. It receives data of any damaged road from the local Government. Then uses it to predict the traffic around it.

It collects all these data from various sources and feeds them into DeepMind’s neural networks. The AI studies and predicts the traffic. However, during the COVID-19 lockdown, there was a change in the traffic pattern.

The scientists added new data into the neural networks to predict traffic during lockdown periods.

Google Maps product manager Johann Lau said,?We saw up to a 50 percent decrease in worldwide traffic when lockdowns started in early 2020. To account for this sudden change, we’ve recently updated our models to become more agile? automatically prioritizing historical traffic patterns from the last two to four weeks and deprioritizing patterns from any time before that.?

The AI algorithm divides the maps into several segments. They are called “Supersegments”.

There is no distinct size of these supersegments. However, they change with the traffic. One segment collects about terabytes of data.

So, you may assume the size!

The supersegment is a group of adjacent streets that have the same traffic volume. Each group has its neural network which does traffic prediction.

It uses a specific kind of neural network called ‘Graph Neural Network?. Google considers this as the most suitable AI algorithm for traffic prediction.

How DeepMind’s AI Help in ETA Prediction? #

Before partnering with DeepMind, Google already had a 97% accuracy rate of the expected arrival time for a destination.

For ETA predictions, DeepMind uses the Graph Neural Networks. The accuracy rate of the expected arrival time was up by 50% in major cities around the world.

Using the traffic data from various segments (as mentioned above), it collects current traffic information. It won’t be able to show future traffic like condition of traffic after 20-50 minutes.

For showing future traffic conditions, it uses historical data from various sources. The AI algorithm combines the current traffic and the historical data to predict ETA to a destination.

Original diagram for Techfastly by Vin Patel.

For example, the algorithm has data of the rush hour of a route in Sydney for a random Monday. But it turns out that a Monday is a holiday but it will show it is a rush hour for future predictions. So, it will also show that the ETA is much longer than the reality.

However, the current traffic and the ETA will be accurate on-road, thanks to the Graph Neural Network. Moreover, data on road quality, accidents, and more are added to the model to raise the accuracy rates for ETA and traffic prediction.

How Does It Work For You? #

Let’s say you are heading towards the airport. When you leave your house, the traffic shown in Google Maps is normal and free-flowing. There is no indication of any disruption or bad roads.

However, as you are on the move, Google Maps will start predicting live traffic details. It will tell you the exact condition of traffic as you move through the routes.

So, 5 minutes into the drive, it predicts that you may get stuck in a traffic jam. It also tells how much time it might take to get out of it. Therefore, it starts suggesting routes that can help you reach the airport on time.

Thus it will help you avoid the traffic and also reach on time.

Ready To Try Out The New Features? #

Google Maps partnering with DeepMind’s AI lab has brought a revolutionary change in the application. It made the predictions 50% more accurate. Now anyone can learn about traffic jams and ETA without relying on Radio FM or assumption.

The Graph Neural Network delivers accurate data for the user. Moreover, it will also suggest roadways that can lessen the travel duration. So, don’t forget to use Google Maps the next time you are on the road.

Related reading

Questions and answers

How does Google Map Predict Live Traffic? #

For traffic prediction, the AI collects data from various sources and feeds them to the algorithm. It thus stores the data and learns how to use it to predict the traffic flows.

How DeepMind’s AI Help in ETA Prediction? #

Before partnering with DeepMind, Google already had a 97% accuracy rate of the expected arrival time for a destination. For ETA predictions, DeepMind uses the Graph Neural Networks.

How Does It Work For You? #

Let’s say you are heading towards the airport. When you leave your house, the traffic shown in Google Maps is normal and free-flowing.

Ready To Try Out The New Features? #

Google Maps partnering with DeepMind’s AI lab has brought a revolutionary change in the application. It made the predictions 50% more accurate.

How AI Filters Objectional Content on YouTube?

Vin Patel — Thu, 01 Jul 2021 23:20:22 +0000

Key takeaways

According to Statista, YouTube removed 41 percent of content that jeopardizes child safety in the fourth quarter of 2020.
The video classifier only scans the YouTube homepage and checks feedback from users to remove any explicit video.
So, YouTube must come up with AI that can remove such content.

According to Statista, YouTube removed 41 percent of content that jeopardizes child safety in the fourth quarter of 2020. Do you think a human can take on such a humungous task?

No, right?

That’s why this is done by Artificial Intelligence. Google uses machine learning systems to detect explicit content in all its platforms including YouTube. Scams, misleading information, adult content, violent content, and more are deleted after scanning.

These contents take over a massive space on the web and result in social problems. Often people make hate speech on social media first. Uploading a video on YouTube is also a way people use to make explicit content.

Recently, the threats of storming on U. S Capital were first made online.

So, if Artificial Intelligence can detect such threats in time, it can keep the human community safe from any riot. However, it is not as easy as it seems. AI faces several challenges in detecting such contents.

How Youtube Uses Artificial Intelligence to Remove Objectional Content? #

There are more than 2 billion active monthly users on YouTube. Every minute, users upload 300 hours of videos on the platform. This means a lot of objectional content too.

YouTube has to use man force to remove such content. However, with the arrival of Artificial Intelligence, things changed. Although, there is a human specialist along with the machine at all times to inspect the work.

The platform uses video classifiers to filter content on YouTube. Any video on this platform is only a stack of images but these images together convey a message. So, classifying the images in a video is not the best.

However, this is how video classifier works. Here are a few steps of classifying videos using a classifier:

The video file is set on a loop to detect all the frames.
Each frame passes through CNN or convolutional neural networks (feature extractors)
They are classified independently.
The Frames are classified and labeled.

For example, there is a boat in a frame, but the video is about swimming. Therefore, such videos require more than one label to identify the correct content in them. However, video-classifiers can also provide an idea about the whole content in the video.

This is exactly how YouTube identifies explicit videos in its database. As soon as a video is uploaded on its system, it is stored in the data set. The video classifiers then work to find out what is the content type.

If it matches with something that is labeled objectional, then the video is removed. However, this also causes errors. The classifiers can even remove news content with violent images.

So, the artificial intelligence community has called the YouTube video classifiers?trashy?.

Why AI Specialists Disapprove of the YouTube Video Classifiers? #

In 2018, YouTube removed 77% of videos from April to June even before they received a single view. However, some YouTubers are actively trying to fool the algorithm, which is not that difficult.

For example, instead of using a racist term like ‘African American’ people started using ‘Basketball Americans’ to further genocide conspiracies and more. Many even resorted to using spelling errors to avoid algorithms detecting their content.

The video classifier only scans the YouTube homepage and checks feedback from users to remove any explicit video. Even videos with misleading thumbnails or cuss words on them are removed.

Are Video Classifiers Helping YouTube? #

The AI video classifiers are not full proof. However, before it came into the scene, YouTube could only detect 8% of the content as violent or explicit. From 8% it has went up to 83% which is a great feat.

Moreover, Artificial Intelligence can flag the content even before 10 views are completed. Before 2017, YouTube could remove content only after it got 10 views.

After the new mechanism rolled out, it also took down some of the news from Bellingcat and Middle East Eye. Those videos consisted of images from the Syrian war.

However, YouTube later apologized for deleting the war crime videos and other news about the civil war in Syria. Not only that, but a few news outlets found that their channels were suspended including Bellingcat.

The incident occurred just after Google announced the use of AI on YouTube. The AI was unable to differentiate from news documents and videos and flagged them as “extremist”. This led to the removal of this content.

“With the massive volume of videos on our site, sometimes we make the wrong call. When it’s brought to our attention that a video or channel has been removed mistakenly, we act quickly to reinstate it? Youtube told the news outlet.

All the videos that got deleted were restored in the channel as before. However, it caused a lot of problems for the news outlets.

Why Removing Objectional Content is Important? #

There is constant pressure from Governments, brands, and the public to take down explicit videos from YouTube. It is because sometimes brand or Government ads show up on offensive videos. When such ads appear with such content, it creates a bad reputation for the brand.

For example, after ads started appearing in videos supporting racism and terrorism, brands such as Haves UK pulled their ad investments.

Sometimes, YouTube also recommends content that may be harmful to children. This led to a backlash from netizens. To counter this, YouTube came up with options that allows the creators to select whether a particular video is for children or not.

However, this is not enough as the user may select the video for children option and upload the offensive content. The only way to fix this when any user reports it to YouTube. However, by the time someone identifies such content, it may have got a few views.

Having objectional videos on the platform may cause aggression and riots. In the case of racist videos, things may turn worse. So, it’s best to take down such videos.

Should YouTube Upgrade its AI Technologies? #

From the various cases, it is evident that the video classifiers are not doing the trick. They act only on the home page. YouTube needs an algorithm that can deep search such contents, analyze them and remove them immediately.

Although the AI which recommends videos are top-notch. They work according to the data collected from the user. Google collects every little data from the user. As Google owns YouTube, so it is easier to use those data to recommend.

However, the recommendation of harmful content may cause more damage than good. So, YouTube must come up with AI that can remove such content.

Related reading

Questions and answers

Why AI Specialists Disapprove of the YouTube Video Classifiers? #

In 2018, YouTube removed 77% of videos from April to June even before they received a single view. However, some YouTubers are actively trying to fool the algorithm, which is not that difficult.

Why Removing Objectional Content is Important? #

There is constant pressure from Governments, brands, and the public to take down explicit videos from YouTube. It is because sometimes brand or Government ads show up on offensive videos.

Should YouTube Upgrade its AI Technologies? #

From the various cases, it is evident that the video classifiers are not doing the trick. YouTube needs an algorithm that can deep search such contents, analyze them and remove them immediately.

Qualcomm's Latest IoT Chipsets Will Boost The Performance

Vin Patel — Fri, 18 Jun 2021 23:40:02 +0000

Key takeaways

Qualcomm Technologies have added seven new solutions to the IoT ecosystem to aid next-generation IoT devices.
Qualcomm’s new solutions have led to its growth at an astonishing rate.
But, the Qualcomm QCS6490/ QCM6490 solutions are expected to be available in the second half of 2021.

Introduction #

Qualcomm Technologies have added seven new solutions to the IoT ecosystem to aid next-generation IoT devices. There are various chipsets, from the entry-level to the premium tier. A wide range of industrial and commercial applications can make use of them.

Mr. Nagaraju Nail, Qualcomm’s senior director of product development said, ‘We have two product variants. One belongs to the QCS family and is a Wi-Fi application processor. The other belongs to the QCM family and is a cellular application processor.?

This article will give you all of the necessary information regarding the new solutions.

The New Chipsets By Qualcomm #

Qualcomm QCS8250 | Target: connected healthcare, digital signage, retail, and video collaboration

With support for Qualcomm Wi-Fi 6 technologies and 5G connection, the Qualcomm QCS8250 is the premium tier product, aimed to ensure maximum efficiency at the most significant power consumption feasible. This chipset supports seven concurrent cameras with encoding at up to 4K definition at 120 frames per second, courtesy of the Qualcomm Kayo 585 CPU architecture, Qualcomm Artificial Intelligence (AI) Engine, and strong image signal processor (ISP).

Apart from this, it offers a new Neural Processing Unit (NPU) besides machine learning for IoT applications to enable smart cameras, video collaboration, connected healthcare, and intelligent retail. This platform is designed specifically for corporate and business applications and it provides clients with a variety of configurable alternatives.

2. Qualcomm QCS6490 / QCM6490 | Target: connected healthcare, logistics management, retail, transportation, and warehousing

The upgraded Qualcomm QCS6490 and Qualcomm QCM490 solutions provide premium quality features such as worldwide 5G connection and blistering-fast Wi-Fi 6E for slightly elevated IoT devices. These chipsets are designed for industrial and commercial IoT applications such as transportation, linked healthcare, warehousing, logistics management, and retail point of sale (POS) kiosks, and are powered by Qualcomm’s Kayo 670 CPU architecture. These chipsets allow the next generation of highly portable handheld and tablets, industrial scanners, and human-machine interface devices by supporting 5G wireless communication and Wi-Fi 6E.

This solution provides astounding performance at reduced power with dynamic triple ISPs and advanced Edge-AI. It computes based on the 6th generation Qualcomm AI Engine compared to the previous generation solutions.

3. Qualcomm QCS4290 / QCM4290 | Target: cameras, industrial handheld, and security panels

Qualcomm QCS4290 and Qualcomm QCM4290 aim at providing mid-tier benefits with even more outstanding performance. This solution offers robust performance, dynamic camera capabilities, and extensive connection choices (LTE Cat13, Wi-Fi 6-Ready), making it perfect for industrial and commercial IoT applications such as industrial handheld in logistics and warehousing, security panels, and cameras. For the increased intuitive on-device intelligence, devices based on this new solution will help enable productive and efficient work environments. For providing a high speed and robust on-device performance, it is based on the Kayo 260 CPU architecture.

4. Qualcomm QCS2290 / QCM2290 | Target: camera applications, industrial handheld, retail and tracking

The Qualcomm QCS2290 and Qualcomm QCM2290 are robust entry-level offerings, enabling reliable performance and power conservation for low power consumption, with LTE connectivity, improved functionality, and memory support. Being equipped with the Cortex A53 CPU architecture, these solutions are cost-effective, delivering outstanding performance, better image quality, higher graphics capabilities, and improved power performance. This platform is very well-suited for retail point-of-sale (POS), industrial handheld, tracking, and camera applications.

Also, The QCS2290/QCM2290 baseband chip is also pin-to-pin compatible with the QCS4290/QCM4290, giving more flexibility to users for reusing software and hardware over many IoT devices, consequently reducing costs.

Facilitating the IoT Ecosystem #

Qualcomm’s new solutions have led to its growth at an astonishing rate. Analysts expect that Qualcomm earnings per share will rise to 86% altogether of fiscal 2021 and a further 10% in 2022, according to Backs Investment Research. The company returned to growth in 2020 after several years of decline. Sales are seen escalating 37% in 2021 and a further 10% in 2022. With all this, it’s pretty easy to imagine that how useful these solutions might have proved for many corporations. With its newest family of IoT chipsets, Qualcomm aspires to empower its more than 13,000 IoT customers to accelerate their development of relevant products.

‘We are ideally positioned to push the IoT ecosystem forward with our systems-level approach,? said Mr. Jeff Lorbeck, general manager of linked innovative systems at Qualcomm.? These new IoT solutions, together with the recently announced Qualcomm 315 5G IoT modem, to make our way to drive growing network change and delivering a spectrum of solutions to reconfigure manufacturing and commercial IoT applications for advanced performance and flawless connection,? he added.

The new chipsets have helped many applications to provide better performance. These are some of the applications whose performance got enhanced with the help of the new chipsets.

Amtrak- Amtrak can provide fine-tuned audio and video processing products because of the Qualcomm QCS8250’s advancement.
Arrow- Arrow now offers a full spectrum of IoT solutions, from entry-level to premium-tier, by adding the Qualcomm QCS8250 to its expanding product range with Qualcomm Technologies.
AVer- Qualcomm QCS8250 will allow AVer to not only remain competitive but also to provide superior UC&C solutions to the video conferencing industry.
eInfochips- The Qualcomm QCS8250 solution will be used by eInfochips to allow collaboration devices, smart robotics, augmented reality/virtual reality devices, home/industrial automation, intelligent cameras, and other intelligent products.
Fibocom- These innovative technologies, when used in conjunction with Fibocom’s end-to-end IoT modules and wireless communications solutions, will allow powerful, smart, and innovative IoT solutions across a wide range of industries and vertical applications.
Honeywell- Qualcomm’s solutions will provide Honeywell clients with strong processing capabilities that will help them speed up and improve their employee and customer experiences, resulting in significant productivity gains.
Quectel- Qualcomm’s latest chipsets are fully compatible with Quectel’s existing 5G and LTE modules, allowing clients to speed up their time-to-market.

Summing-up #

The new solutions by Qualcomm have proved to be of more significant benefit to many industrial and commercial applications whose performance has improved with the help of these new chipsets. The solutions Qualcomm QCS8250, Qualcomm QCS4290/ QCM4290, and Qualcomm QCS2290/ QCM2290 are accessible now. But, the Qualcomm QCS6490/ QCM6490 solutions are expected to be available in the second half of 2021.

Related reading

How Conversational AI Works? 8 Specific Components

Vin Patel — Tue, 01 Jun 2021 22:39:45 +0000

Key takeaways

As new technologies develop, they make the life of humans easier and simpler.
So, it is very important to make policy and terms that users can trust while handling the input data, which could be stored to improve user experience.
The teach companies also assumes that Conversational AI can be a dynamic way to explore what more technology offers to mankind.

? As the world is progressing towards digitization, new technologies are being introduced every day. As new technologies develop, they make the life of humans easier and simpler. These technologies have been evolving to make them more user-friendly and lucrative among human beings.

? One such super technology is Conversational Artificial Intelligence. In 2016, the launch of Facebook’s chatbot using Conversational AI led the world to another superwave.

You may have come across Conversational AI in smart car applications, smart homes, smart assistants, or navigation devices apart from chatbots.

Let’s delve more into it.

##? What is Conversational AI

‘A Conversational AI is a sub-type of artificial intelligence. It is a set of technologies designed to offer human-like interactions between humans and computer systems. Conversational AI uses technologies that enable the machines to imitate like a human by recognizing speech and text, understanding intent, deciphering different languages, and responding to mimic human conversation.

In simple words, Conversational AI acts as a digital agent to provide rich, reliable, and improved communication between humans and machines by automated messaging and speech recognition.

It allows the computer systems to have natural interactions by understanding the users? queries. Conversational AI also offers solutions to enhance the interactions and communication between customers and businesses.

For example, an e-commerce customer is seeking information about delivery of an item. The Conversational AI will answer their needs and help solve their issues around the clock.

Some of commonly used Conversational AI includes Apple’s Sir, Microsoft’s Cortana, and Amazon’s Alex.

The main feature of conversational AI is that they don’t depend on fixed and boring back and forth patterns; instead, they emulate and improve with experience.

###? How Conversational AI Works

? Conversational AI uses technologies like Speech Recognition, Natural Language Processing (NLP), Machine Learning (ML), and other language technologies to process, contextualize, and respond to user input.

Conversational AI gathers input through voice. It allows the application to identify spoken language and translate it for a better user experience.

Natural Language Processing is a process to interpret the meaning behind the texts. With the help of NLP, Conversational AI allows users to interact with systems using voice. It reduces the background noise, and then the sound is split down into phonemes which use analytical models to interpret the words.

Machine Learning training models allow computers to recognize the combinations of words that typically indicate an intent, learn and improve from experience.

###? Let’s Understand How Conversational AI Performs

Original diagram for Techfastly by Vin Patel.

? The application receives the input from the user in the form of text or speech. After that, the Automatic Speech Recognition technology converts them into machine readable format.

With the help of NLP, it tries to understand the intent behind the user input. Using the dialog model and Natural Language Generation, which is a part of NLP, converts the response into user understandable format. Hence, it delivers the response in the text or speech format that the user understands.

At the end, it uses Machine Learning which works on algorithms for correction and learning of data from the experience to provide better delivery services.

###?8 Specific Components For an Efficient Conversational AI

To be able to meet all the requirements of users and companies, there are some specific components for a Conversational AI.

Privacy and Security - Using any technology, a user is always concerned about how the user information is used. Conversational data may contain sensitive information that requires proper protection and careful management. So, it is very important to make policy and terms that users can trust while handling the input data, which could be stored to improve user experience.
Machine learning capabilities - Apart from language technologies, you may need to leverage other machine learning capabilities for successful interactions and better user experience. Better implementations of various models of machine learning can produce instant results to business’s sales.
Language analysis - The technology that analyzes the language and generates the text-to-Speech and audio-to-text helps cater to users’ needs. There are always different sentiments, queries, topics, or intent states in the form of text and voice by users essential to understanding what the user is saying.
Data format - Use of demographic data and transaction history may help to personalize the response for better communication and interaction.
Feedback - The user’s feedback plays a vital role in improving the relationship between users and businesses. Use relevant FAQs for more clear and precise details from users. The complete feedback, such as seeking suitable products to purchase specific products, enables sites to improve the quality of their services.
Engaging and valuable - A user tends to stick to a chatbot if it is engaging. The users will keep visiting a website until the conversation is engaging and gives value to their problem. They can pause the conversation at any point and continue the same according to their access without dealing with any particular service desk centers.
Design - The design of a Conversational AI is essential for natural and seamless interaction. A design involves creating and developing engaging content, choosing suitable messaging platforms to trigger the right points while interacting.
Cost-effective - Conversational AI helps companies to be more accessible for their customers. This technology increases the self-service rate of customers and decreases the workload of contact center agents, minimizing businesses’ operational costs.

The Future Scope of Conversational AI #

According to the International Conference on Information Management (ICIM) survey, Conversational AI has already significantly impacted e-commerce, healthcare, education, entertainment, and other sectors by improving productivity in these fields. From providing financial suggestions, acting as assistant to education, and advising for medical, it’s covering all aspects across the globe.

Now, the research institutions, governments, and other businesses are drawn towards using this emerging technology. By leveraging Conversational AI applications, companies are trying to market themselves and reach a broader audience. It automates operational services and provides a benefit of better engagement and experience with the users. As a result, building trust and an image for the growth of the organizations/institutions.

However, there are several challenges and shortcomings associated with the fascinating technology. Sometimes, Automatic Speech Recognition technology fails to recognize the characters that sound similar, leading to a bad user experience and harming the company’s reputation.

Nonetheless, the abilities to engage in human-like conversations and personalized assistants are its greatest highlights. The teach companies also assumes that Conversational AI can be a dynamic way to explore what more technology offers to mankind.

Tell us what’s your opinion about Conversational AI and why?

Related reading

How Deep Learning Can Address Your Video Streaming

Vin Patel — Thu, 20 May 2021 16:28:25 +0000

Key takeaways

After an exhausting day at work, you are damn tired but still want to catch up on your favorite shows.
This is beneficial for - A stronger grasp of the content - Streaming of the best possible standard - Identifying redundancies in content.
So, very soon, all populations will be shifted to unrivalled QoE and amazing video consumption.

After an exhausting day at work, you are damn tired but still want to catch up on your favorite shows. And, exactly when you’re on the verge of an intriguing scene, your video has paused. It then suffered a buffer, then never resumed at all. After a few frustrating minutes later, you had to experience a video in its lowest resolution hardly showing any clarity.

How Did An Exhilarating Engagement Turn Into A Disastrous One? #

Say hello to the complexities of video streaming. But this is not how a video streaming experience should be. That’s when game-changing technologies like AI and Deep Learning come into play. They will guarantee high-definition, zero-delay, enhanced streaming OTT, improving the user experience and making it more fun and worthwhile.

Adaptive BitRate #

Every week, OTT (Over the Top) content creators and providers release new and exciting content. However, one major drawback of OTT platforms is that they have not been able to address adequately is quality of experience, also known as QoE.

The overall resolution of a video, startup time, stalls, and delays that occur are factors that affect the QoE. Video distribution is heavily reliant on internet speed, or bandwidth, which can have a significant impact on the user experience. This is where the Adaptive BitRate helps solve the problems with a unique algorithm.

With insufficient bandwidth, in earlier days, a video completely stopped streaming. But today, any type of unpredictable bandwidths are handled seamlessly with ABR. The output of your network has dynamically “adapted” the parameters of a video. As a result, the video’s quality has been compromised. But, it continues to play by reducing the resolution instead of stopping the relay. However, internet speed remains a factor. It’s 2021, and there’s a lot of chatter about Deep Learning that we need to focus on.

Intelligent Solutions #

Videos have become the latest form of data communication, serving as a natural extension of email or text messages. This has necessitated advancements in video streaming, as well as network and storage optimization.

The majority of what is going on these days is unperceptive to the material it contains. A scene in which the actors are simply conversing has a different nitrate than an action scene. Imagine being able to see Dumbledore’s wrinkles as he lifts his wand but being unable to see Harry battle Voldemort.

And to do so, you’ll need to know the answers to the following questions.

What if the substance of a scene could be deciphered?
What if streaming algorithms learned the quality of experience for each scene?
What if all frames are given relative importance in the encoding?
What if the streaming is device-agnostic?

Deep Learning is the only possible solution to address these problems of video streaming.

Deep Learning #

Deep Learning and Artificial Intelligence are developing technologies to increase video content and user quality of experience. Content-aware AI has the potential to significantly enhance the viewing experience by making it more personalized, interactive, and novel. Device-aware DNN (Deep Neutral Network) can be created by dynamic computation with strategic resources that can scale up the performance of streaming.

When the encoder looks at the whole video rather than the pixels in individual frames, it is a clever and noticeable way to improve QoE. This is beneficial for

A stronger grasp of the content
Streaming of the best possible standard
Identifying redundancies in content.

The Impact of Deep Learning #

Device Awareness #

The quality of the device has a significant impact on the entire decoding process. Making it device friendly ensures that people can watch it at any time, from any place, on any device. Even with a simple tap on the application of their smartphones as the smartphones are the latest computers, after all. Knowing which device the user is using will make his or her personal experience more engaging. A device’s innate power can be used as its soul potential to reshape the entire streaming experience.

Context-Awareness

Being device-aware isn’t the end of the story. Deep Learning as well as Artificial Intelligence can be trained to pay close attention to the video’s overall context, search preferences, and genre preferences. For example, a user can stream a specific series weekly. Deep learning will use this information to show the user their favorite show, related genres, and other works by the director and cast. This not only increases the user’s QoE but can also benefit OTT providers.

But First, Understand the Streaming Business #

Why does an OTT platform need to invest in such user-friendly technologies? A recent survey of 1000 customers in Urban India who use OTT streaming provides the answer to this query. When using the OTT, as many as 62% of people experience problems such as pausing, buffering, and the app hanging while using it. Also, the issues were faced on specific devices where the same OTT was flexible to stream on a laptop but nor adaptable to a smartphone. And the streaming was affected majorly during travel.

Customers are expressing their disappointment with OTT services more than ever by leaving immediate reviews on official and social media pages. This demonstrates a strong intolerance for lower QoE, which can harm sales. Long load times, pixelation, buffering, and stalled videos are no longer acceptable in this industry.

Emerging patterns, as well as the pandemic situation, have brought online streaming new dynamics. The boundaries between short films and fiction videos have widened to include gaming, live sports streaming, and the release of movies. As a result, more revenue is generated.

Playing on personal computers was a thing of the past. However, games like PUBG have attracted larger audiences simply by using a smartphone. Stadia, Google’s latest gaming platform, announced that a low bandwidth of 25maps is sufficient for exceptional streaming.

This demonstrates how cost-effective ideas for changing the phase of this sector using AI and deep learning are evolving. According to a new survey, 53.98% of viewers will be able to switch to premium subscriptions for interesting content with few streaming issues.

Conclusion #

Finally, the big picture of providing high-quality videos at lower subscription costs to produce more sales is visible. Many practical issues have been solved as a result of the significant benefits gained from the use of AI and the role of deep learning in creating a better streaming experience. DNN-based developments are the most significant advances in this method. So, very soon, all populations will be shifted to unrivalled QoE and amazing video consumption.

Related reading

Questions and answers

How Did An Exhilarating Engagement Turn Into A Disastrous One? #

Say hello to the complexities of video streaming. But this is not how a video streaming experience should be.

Why does an OTT platform need to invest in such user-friendly technologies? #

A recent survey of 1000 customers in Urban India who use OTT streaming provides the answer to this query. When using the OTT, as many as 62% of people experience problems such as pausing, buffering, and the app hanging while using it.

Deep Learning Techniques for Stronger Cybersecurity

Vin Patel — Sat, 17 Apr 2021 23:05:15 +0000

Key takeaways

With the ever-demanding innovation of the new age technology, cybersecurity has become the most utilitarian software weapon.
Emote Malware: Coming into buzz from 2014, Emote Trojan is the virus affecting banking and financial sectors mainly, infecting the systems and unlocking sensitive data that could cost a lot to the victim.
With this, we hope to get a peaceful world as we advance with no unresolvable caber threats.

Cybersecurity in Layperson Perception: #

‘Hold? to the world of Cybersecurity: #

With the ever-demanding innovation of the new age technology, cybersecurity has become the most utilitarian software weapon. Cybersecurity, a human-engineered weapon, is designed to battle against the giant treacherous caber threats that are unsealing the extremely private-cum-non-shareable information, hampering our privacy.

When the contemporary world is way too much dependent on the digital floor to execute most of its operations, securing online activities should be the foremost responsibility. No doubt, for a very long-time, tons of antivirus software and firewalls have helped us secure our devices, networks, programs, etc. As we see the current scenario, we hope for a grand evolution of digitization future, requiring even stiffer security.

Briefing The Significance of Cybersecurity: #

A shield to our very own privacy: #

When technology expands, bringing a lot more inventions, it comes up with certain opportunities and obstacles. We see many awful caber threats that cybersecurity could prevent, making online activities hassle-free and secured. When everything is running digital across the globe, sharing sensitive information like log-in passwords, card details, identity proofs, etc. could be extremely risky. When all of our information is stored in a single cloud, we barely can afford any data leakage. Right? Caber threats are putting our systems more into jeopardy with unexpected attacks by targeting devices, browsers, and sensitive content; cybersecurity has proved itself as a stroke of luck in the world of technologies.

Research says that there has been a very noticeable rise in caber threats from 2020 to 2021, as we have moved more towards the digital zones with the topical pandemic situation. Undoubtedly, this limitless exposure to the technology world is certainly inviting loads of cybercrimes, which is tough to ignore. With these illegal online hacking, many businesses are falling apart, individualities being held at risk, even it is provoking some controversial social topics in an ugly manner. Hence, with the effective use of cybersecurity, we can surely protect our privacy from online attackers 24/7.

Inevitable Cybersecurity Threats: #

Are you an online freak? Must go ahead: #

Along with tons of blissful benefits, the digital world has bestowed us with varieties of cons like caber threats, and below are some described briefly:

Ransomware:

Ever noticed some suspicious notifications or messages or links or emails popping up on your device and redirecting to some unseen software installation process on clicking? It could be spyware or malware that supposedly can make the data in your hard drive vulnerable or can damage the device, or corrupt the network.

Phishing:

It’s tough to admit, but sometimes a few processes seem so genuine that even being aware of many technology facts, we tend to fall prey of pre-scripted fake communication via emails or messages. The attacker tries to trick the victim into following the instruction and sharing sensitive information like bank details or credit card number or log-in credential. The attacker’s main intention could be to financially extort the victim or trespass illegally to the victim’s device.

Password Hacking:

We all have distinctive passwords to access our social media profiles, devices, software, and many more. With social engineering, it has been easier to breach sensitive private data through password attacks. A report says that there has been comparatively higher password hacking caber threats because of excessive human interactions violating security guidelines. Without the consent of the user, the attacker easily gets into the user’s confidential data.

Emote Malware:

Coming into buzz from 2014, Emote Trojan is the virus affecting banking and financial sectors mainly, infecting the systems and unlocking sensitive data that could cost a lot to the victim. The Trojan virus’s mission is to trick the antivirus installed in your computer and spy on all of your activities stealthily.

SQL injection attack:

This is the way where victims get tricked with vulnerable databases, infecting the web security. In SQL injection, a website can get infected, and any information browsed or shared, or submitted over that browser can be easily accessed by the attacker.

Man in the middle (MITM) attack:

The name ‘Man in the middle? describes quite a lot about the type of caber threat, where the attacker perpetrates illegal activities exactly when the user is ready to connect to a web application. For example, two people have a secret transaction; when a third person gets into the deal illegally with an awful intention of taking advantage, then it becomes a MITM attack.

Denial of Service (DoS) attack:

There are many services like some high-profile companies, banks, finance industries, where we need an uninterrupted smooth website running all the time. Denial-of-Service (DoS) attacks to slow down those websites with sudden traffic flow creating buffers and sometimes result in unexpected website crashes. Though these attacks’ main motto might not be online money laundering or scamming, such uninvited interruptions could cost the victim a lot of time and money.

Deep Learning: The New Age Trendsetter in Technology: #

The next level of automation is so ‘on fire!? #

When the whole world is widening up with the evolution of Artificial Intelligence and Machine Learning (AI/ML), Deep Learning is an essential part of ML that can imitate human brains using certain statistics and algorithms. Yes, this element of AI can amazingly detect objects, recognize speech, translate languages, and make necessary decisions. When human brains are not engineered to solve a large stock of unresolved data, this invention of humans is making those undone jobs pretty much easy! Not only this but proven research states that Deep Learning assists a lot in securing sensitive data helping cybersecurity in a very potential manner.

Implementation of Deep Learning Techniques in Cybersecurity: #

Proportionate coordination of two heroic technologies: #

When the new age technologies have contributed a lot to humanity by making our lives easier and more automated, here we will see a few Deep Learning techniques used in strengthening cybersecurity measures in organizations:

Intrusion Detection and Prevention Systems (IDS/IPS): #

This is one of the applications of Deep Learning, where the systems are responsible to create a secured network for the users. With Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in Deep Learning, the user gets alerts about unwanted access to the system and the internal security team gets activated filtering out the right and wrong activities in the network.

Battling Malware Cyberthreats: #

Gone were the days when firewalls were supposed to understand the pattern with signature-based ID systems to deal with malware. Earlier, companies used to make a list of probable threats observed recently and circulate it among all. But with topical Deep Learning algorithms in action, the system is now ready to filter out the probable malicious activities easily and is not bound to memorize the pattern.

Natural Language Processing (NLP) is one part of Deep Learning which can decode malicious languages and can manage spam and social engineering caber-attacks with utmost accuracy.

Analysis of Network Traffic: #

With Deep Learning Artificial Neural Networks (ANNs), we can easily trace out malware activities in HTTPS networks and deal with SQL injections and DDoS attacks.

Tracking through User Behavior Analytic: #

User and Entity Behavior Analytic (UEBA) of deep Learning is supposed to analyze a regular user’s behavior to understand its day-to-day activities and detect if anything suspicious occurs. This tool helps to cleanse the system from insider threats which are sometimes tough to detect.

Bottom Line: #

Now that you got an overall idea of Cybersecurity, Deep Learning, and the result of the combination of the two, it is time to practice and explore more on this. With this, we hope to get a peaceful world as we advance with no unresolvable caber threats.

Related reading

Questions and answers

The next level of automation is so ‘on fire!? #

When all of our information is stored in a single cloud, we barely can afford any data leakage. Right? #

Caber threats are putting our systems more into jeopardy with unexpected attacks by targeting devices, browsers, and sensitive content; cybersecurity has proved itself as a stroke of luck in the world of technologies.

The name ‘Man in the middle? #

describes quite a lot about the type of caber threat, where the attacker perpetrates illegal activities exactly when the user is ready to connect to a web application.

Types of AI and Emerging Technologies

Vin Patel — Tue, 30 Mar 2021 00:07:53 +0000

Key takeaways

It has disrupted technology with a widening range of applications in everyday life.
The system uses large volumes of training data for deep learning.
The future of our race with cutting-edge AI technology is uncertain, but if appropriately implemented, these cogent systems will bring the dawn of progress for us.

Artificial Intelligence (AI) isn’t just a buzzword anymore. It has disrupted technology with a widening range of applications in everyday life. AI was acknowledged as a possible technology by John McCarthy in the year 1955. He believed machines could be made to think, behave and perform as humans. Renowned as the Father of AI, McCarthy created Lisp computer language in 1958. It is the programming language used even today to create standard AI applications.

AI is a multifaceted technology with a variety of types and categories. In this article, I have simplified the types of AI by dividing them into two broad categories:

Types of AI

The realm of AI includes machine learning, big data, cognitive intelligence, deep learning, natural language processing (NLP), and much more. Here is a broader categorization to help you easily navigate through the types of AI and analyze which fits your requirement.

Type 1- AI can be categorized into three subsets based on current utilization and future viability.

1. Narrow- Narrow AI is the simplest form of AI that is efficient at performing a single task. Weather forecast applications and speech-to-text converters are typical examples of Narrow AI. Though sophisticated, Narrow AI is referred to as ‘Weak AI? considering its capabilities are limited. Narrow AI-powered devices cannot perform functions beyond their scope. Autonomous cars, computer vision, Google Translate, and other NLP-enabled apes, which we use frequently, have reached only up to the stage of Narrow AI. It doesn’t perform on the standards of human intelligence but is considered adequate for routine jobs. Narrow AI can cut down human intervention from repetitive and monotonous jobs.

2. General- General AI is a more complex version of AI as it is predicted to work like the human brain. This human-level AI will be capable of understanding, reasoning, and reacting to its surroundings. It is known as ‘Strong AI?, and we are coming across several challenges as we strive to achieve it. Computers can process data and pull up relevant information from their memory. Unlike humans, it is difficult for them to react to unprecedented situations or innovate to create something that never existed. Teaching computer skills like planning, perception, and decision-making based on unrelated information is a difficult task. Though it isn’t unachievable, General AI is still distant from real-life applications.

3. Super- Artificial Super Intelligence (ASI) is a concept given by Nick Bostrom, a University of Oxford scholar and AI expert. He intends to create an AI better than the combined power of best human brains from every creative and scientific field. ASI would be a more capable form of General AI and is unachievable at the moment. It is predicted to be a fully capable form of AI. Scientists like Stephen Hawking believe it to be devastating for the human race, while others claim it will enable humans to do more.

Type 2- Another classification of types of AI can be done based on its cognitive capabilities and functionality. The four subsets of type 2 AI are:

1. Reactive- Reactive AI systems are the oldest form of artificial intelligence enabled machines with extremely limited capability. They do not have a memory and emulate the human mind’s ability to respond in the present situation only. They are programmed to perform a set of tasks in response to a stimulus. Reactive AI machines do not have the ability to ’learn? as they do not store experiences or refer to them when performing automatic actions. These machines can effectively respond to a complex combination of instructions. IBM’s Deep Blue is a reactive AI-based machine. It is renowned for being the only computer to beat a human at chess. The machine won chess matches against Grandmaster Garry Kasparov in 1997.

2. Limited Memory- Limited memory machines have the capability to learn from historical data and make quick decisions. It derives information from stored data fed to the system earlier. Besides, it builds experiential knowledge by observing actions from its environment. The system uses large volumes of training data for deep learning. It creates reference models and uses them along with information stored in its memory for solving problems. All present-day AI systems fall under this subset. These include virtual assistants, chatbots, and autonomous vehicles. The process to train them includes image recognition AI and a wide variety of related images to act as training data. It scans thousands of pictures to identify and store them for future use. When the machine is exposed to a real environment, it makes predictions based on its ’learning experience? and stores them. The learning process continues to reach higher levels of accuracy. For instance, an autonomous vehicle is trained using images of routes, landmarks, and other vehicles. It stores the data and when on the road, it reconciles the information to make a decision. The vehicle also detects patterns and driving behavior of human-driven cars. It makes real-time adjustments based on these changes.

3. Theory of Mind- Theory of Mind is a proposed form of AI aimed at creating machines with the emotional capabilities of a human. These systems are complex and go beyond the memory or decision-making capabilities. They are focused on exhibiting human-like emotions to drive conversations. We can consider them the advanced versions of our current voice assistants or chatbots. The concept is aimed at building a thought process of the machine to derive a behavioral output. These machines are being trained to identify, understand, remember, and respond to emotional inputs. The applicability of the ‘Theory of Mind? has been noticed in robots Kismet and Sophia. Kismet was created in 2000 and could imitate human emotions through facial expressions. Sophia, launched in 2016, is a humanoid robot with human-like physical features. This bot has the capability to respond to human interaction with thoughtful responses and appropriate facial expressions.

4. Self-awareness- Self-awareness AI is the most advanced stage of AI development. It is a theoretical concept that perceives AI to reach the self explanatorily stage. An evolved form of AI that may possess human consciousness. Creating this type of AI is challenging, but all AI research by scientists aims to achieve this stage. AI systems based on self-awareness will possess emotions, understand human needs, and realize their own feelings. The system will be an advanced stage of Theory of Mind enabled machines, as it will not replicate human beliefs and feelings. It will be self-regulated with the capability to think, make inferences, and react. According to experts, this type of AI can be potentially dangerous for our existence or may boost our progress as a species. The dream is farfetched, and we cannot yet comment upon the outcome of implementing self-awareness AI-powered systems.

Way Forward

The rapid developments in the field have empowered companies to come up with innovative solutions backed by AI. Among the emerging types of AI and associated technologies are Generative AI, Federated Learning, and Neural Network Compression. Generative AI is a breakthrough development in the AI industry. It aids in content development by enabling computers to create 3D and 2D images using text, audio, and pictures. Federated Learning is an emerging machine learning technique that assists in training and improving AI-based tools. Neural Network Compression is another upcoming technology to address Big Data storage issues and facilitating access to Deep Neural Networks (DNN). It compresses data for easy storage and deployment on the cloud.

As we progress towards developing more competent AI systems, we inch closer to the most advanced tier of both Type 1 and 2 of AI. The future of our race with cutting-edge AI technology is uncertain, but if appropriately implemented, these cogent systems will bring the dawn of progress for us.

Related reading

Questions and answers

Though sophisticated, Narrow AI is referred to as ‘Weak AI? #

considering its capabilities are limited. Narrow AI-powered devices cannot perform functions beyond their scope.

It is known as ‘Strong AI? #

, and we are coming across several challenges as we strive to achieve it. Computers can process data and pull up relevant information from their memory.

Reactive AI machines do not have the ability to ’learn? #

as they do not store experiences or refer to them when performing automatic actions. These machines can effectively respond to a complex combination of instructions.

The applicability of the ‘Theory of Mind? #

has been noticed in robots Kismet and Sophia. Kismet was created in 2000 and could imitate human emotions through facial expressions.

Can Artificial Intelligence Detect Fake News?

Vin Patel — Wed, 24 Mar 2021 22:54:23 +0000

Key takeaways

Artificial Intelligence (AI) has gone through several developments over the years.
The Grover algorithm can search any unique content, in this case, ’non-human’ content.
We will have to wait and see how things unfold in the future.

Artificial Intelligence (AI) has gone through several developments over the years. Various industries around the world are taking the help of AI to get things done faster and better! News Industry is one such industry that has taken baby steps towards using Artificial Intelligence in their newsroom. For example, the News Tracer at Reuters can scan through Twitter to find out breaking news. Similarly, many researchers are developing algorithms that can discriminate between true and false information.

The ‘Fake News? or False Information Issue #

The term ‘Fake News’ is popular in the Internet world. However, the correct word to describe unauthentic news is “False information”! Fake news is associated with political agendas. But anyway, both of these are a threat to the netizens.

False news getting viral can have an impact on society. It creates a wrong notion. Once spread, it is impossible to contain. Although, with time, people are developing skills to detect false information. But most of the netizens aren’t teach-savvy. So, they become the victim of fake news.

The best way to reduce such viral news is to filter them out before they spread. This is where AI comes in handy. AI algorithms use spam filtering technology for filtering fake news. The researchers are developing a mechanism that can differentiate between human and machine-generated content.

Grover: The Fake News Detector #

At present, the most reliable AI algorithm to fight against fake news generators is Grover. Its goal is to detect any “neural fake news” that is doing rounds on the internet. The Grover can not only detect but also generate fake news. The generator mechanism follows the same habits and traits as other fake news generators. Therefore, it identifies any AI fake news. However, the main concern is that the fake news generators replicate the exact style of human writing. With time, they are only getting better at generating humane copies. Therefore, even for an AI like Grover, it is difficult to discriminate between machine-generated and human drafted articles.

The Mechanism Behind Fake News Detection #

AI uses a similar technique that we employ to discriminate and judge false information. The algorithm compares two similar write-ups. It goes through the facts and matches it with a reliable source.

The reliable source is the one that contains the facts and information. So, Al uses virtual data libraries to learn all about human writing styles. RealNews, Haggle, and George McIntire are some of the datasets used for the Grover algorithm.

We also look at the headline, description, and feature image to identify whether a link is clickbait. The AI algorithm uses the same process to identify fake news.

However, the new algorithm, Grover, can detect only 92% of false information. The Grover algorithm can search any unique content, in this case, ’non-human’ content.

There are many websites like NotRealNews.net that use Artificial Intelligence to generate fake news stories. These fake news stories resemble the original stories.

To detect fake news and false information Artificial Intelligence algorithm learns how to write them first. However, OpenAI has announced that its AI writer is too dangerous to publish articles publicly. The ‘Grover’ creators, on the other hand, feel that it is safe for the AI to learn to write fake news first.

In an article on Techcrunch.com, Rowan Sellers, the Grover team lead, said, “These models are not capable, we think right now, of inflicting serious harm. Maybe in a few years, they will be, but not yet. I don’t think it’s too dangerous to release? really, we need to release it, specifically to researchers who are studying this problem, so we can build better defenses. We need all these communities, security, machine learning, natural language processing, to talk to each other? we can’t just hide the model, or delete it and pretend it never happened.”

Grover and the other Artificial Intelligence algorithms are becoming efficient by generating more fake news articles and then identifying them. If the generated articles are not believable, they keep developing content that can replicate the human writing styles. In this way, they learn how to detect fake news.

Deepfakes and Artificial Intelligence #

Another issue we are facing is the “Deepfakes”! Deepfakes are fake photos and videos created by AI. The AI uses several pictures of a real object and superimposes them to create a similar fake image. In this way, it also generates images of fake people. However, when a person watches such videos or pictures, he or she easily believes them.

The Deepfakes are being used for Propaganda these days. Doctored images and videos are often employed to create unrest. They are even used to tarnish the reputation of celebrities.

It is not easy to fight against deepfake content. However, researchers are trying to develop algorithms that can identify fake videos and pictures.

How Can You Help to Fight Against Fake News? #

Fake news doesn’t get viral on its own. While we surf the internet, we share a lot of these fake news stories knowingly or unknowingly.

According to a survey, 10% of the participants shared fake news knowingly. 49% of them shared content that they later learned that the news was fake. So, the only solution for us is to learn to identify fake news.

If we do not understand whether a particular news is true or false, we should avoid sharing it! It may take time to judge whether the content is authentic or not, but it’s worth the time.

So, the next time you find an interesting article, check a few more similar news stories. This would help identify fake news.

Are There Other Artificial Intelligence Tools to Fight Fake News? #

The Grover algorithm isn’t the only AI tool working against fake news. There are other tools as well. Some of them are Hoax, Slopes, and more. Hoax is a tool that identifies fake news websites. On the other hand, Slopes is a website that detects fake news stories on the internet. However, these are not as efficient as Grover.

Dealing with Fake News is constant trouble for the world. With Artificial Intelligence and Big Data, we are getting stronger in our combat against the fake news generators on the world wide web.

However, we are only at the surface. Artificial Intelligence tools can not only identify fake news but also replace them with the correct facts. The researchers are working on algorithms that can reduce the fake news menace. We will have to wait and see how things unfold in the future.

Related reading

Questions and answers

How Can You Help to Fight Against Fake News? #

While we surf the internet, we share a lot of these fake news stories knowingly or unknowingly. According to a survey, 10% of the participants shared fake news knowingly.

Are There Other Artificial Intelligence Tools to Fight Fake News? #

The Grover algorithm isn’t the only AI tool working against fake news. Some of them are Hoax, Slopes, and more.

Are Industry 4.0 and Environmental Sustainability

Vin Patel — Fri, 29 Jan 2021 19:41:00 +0000

Key takeaways

Numerous studies have been carried out regarding the sustainability aspects of Industry 4.0 and its impact on the environment.
While considering sustainability, we also need to reorient our manufacturing processes as opposed to the entropy law, which states it is natural for any system to transform from a state of a high order to a high disorder.
We need to find ways to expand our consciousness where systems related to factors of production work in tandem with all entities, both men and the machines, to devise ways that impact the sustainability aspect in a positive way.

Introduction #

Numerous studies have been carried out regarding the sustainability aspects of Industry 4.0 and its impact on the environment. We examine how classical technologies in the form of assembly line forms of production techniques employing automated robots to the latest technologies wrt additive manufacturing have impacted both the natural and the man-made environment. This impact results in a net positive impact or resulting in just the degradation of both the entities. We also explore in this blog post whether both, Industry 4.0 and environmental sustainability, can coexist or is it just an oxymoron. Both of these subjects seem to be poles apart while considering sustainability, but the truth is that looking from an optimistic viewpoint, environmental sustainability and 4IR need to be looked at with a fresh perspective, offering hope and a ray of light at the end of the tunnel. Industry 4.0 is currently underway and is taking shape in the form of extension of the world wide web to computing devices and smartphones, and connecting all the devices that we use in every possible application, ranging from cars to transport mechanisms to education and e-learning. Leveraging technologies like artificial intelligence, machine learning, and natural language processing, we are currently transforming the way of life on ‘mother-ship earth’ and creating a synergy with man and machine to catapult us to the next stage of evolution and growth. It is to be noted that this scenario creates a significant impact not only on the natural environment but also on the very social fabric that binds humanity together. Thus, the exploitative measures need to be re-fabricated to incorporate a sense of responsibility, a sense of belonging, and a sense of respect for all the kinds of resources that we use as factors of production, right from the human capital to the bounty of the earth. Environmental sustainability plays a huge role in Industry 4.0, and the main essence of this concept is increasingly adopted and practiced by people, both in work and other spheres. It is the need of the hour, considering the level of damage we have inflicted on our planet, and how to accelerate nature’s healing process. By using the tools and processes that Industry 4.0 provides us, we can create a perfect balance with these two seemingly paradoxical entities, inferences that we have picked up from the ‘great acceleration,’ discussed in the first article of this series.

Possibilities and Solutions that Industry 4.0 offers #

Concerning environmental sustainability, preserving mother nature, and helping our planet cope up and recover from nearly a quarter-century era of deep exploitation is the need of the hour. By contributing to our individual and collective efforts, we as human species, chiefly responsible for creating imbalances in the natural cycles and contributing to climate change, we can change climatic and societal transformations into a highly positive and profoundly transformation event of our history. So, what is the role of industry 4.0 in the context of the environment, its preservation, and healing? Industry 4.0 is giving us powerful and widely available tools so that we all can help spaceship earth to recover and restore its pristine beauty and glory. By realizing that we are not the only stakeholders of earth, but it also belongs to other species and living beings; we can end man-animal conflicts and create a web of new and balanced food webs and ecosystems, that take into account sensible use of resources and natural bounties.

Is This Kind of Utopia Possible? #

Readers may raise questions about how to connect the ongoing industry 4.0 transformation and environmental sustainability so that both these aspects contribute to the well-being of the planet and ensure the fruits of both technology and nature are available to everyone. Simultaneously maintaining the natural balance and creating processes and techniques that help in optimal resource use lead to a better growth model that can replace the defunct economic model on the brink of a major upheaval.

Changing Mindsets About Production is the Key #

With technologies like additive manufacturing leading the way from exploiting natural resources to their sustainable use, the fourth industrial revolution has already opened up new possibilities for us. Industry 4.0 uses resources in a way that redefines the efficiencies with which we currently operate, chiefly pertinent to capital resource-rich economies and lands where natural resources are in abundance. While considering sustainability, we also need to reorient our manufacturing processes as opposed to the entropy law, which states it is natural for any system to transform from a state of a high order to a high disorder. The entropy law implies it is natural for a system to go from a high state of order to disorder. But as suggested by the famous physicist Stephen Hawking, if the thermodynamic arrow of time reverses its direction and the universe starts to contract instead of expanding, will it reverse the order of the systems as well, i.e., will the systems transform from a state of high disorder to order, and we consume things first, and they can be manifested after they have been consumed? Is this the kind of healing touch we are looking for to stop the destruction of our natural habitats and prompt environmental sustainability? The same is the case with 3D printing, which is currently in a nascent stage but can be used to reduce, reuse, and recycle materials. With the resources available on the Earth, 3D printing can be used to put them in a high order of sustainability and efficiency, leaving near zero-waste, thus protecting and nurturing our planet.

Environmental Sustainability #

Environmental sustainability should also be seen as an act of re-balancing and the trade-offs that need to be carried out to achieve a near-perfect system in the near future. This is the dilemma that humanity has grappled with for ages, where we need to transform materials from one form to another, natural as well as man-made so that we find our “justification of existence.” We need to assume the control of our resources in a way that deems it fit to transform any material, say, for example, plastic, into biodegradable plastic, and regulate the use of the resources in a manner that does not exploit nature. The same is the case with the tremendous amount of e-waste generated, chiefly produced by high income and middle-income countries. It is common to ship e-waste from low-income countries, where most of the e-waste is processed and set as a true example of reusing and recycling. Now, the time is ripe to achieve a high degree of sustainability, where such kind of e-waste is processed at the source of their use, rather than shipping it from somewhere else. Another pertinent example of this can be of satellite derbies that can threaten the existence of other in-use human-made satellites, in case-of a ‘space race’ and anti-sat warfare. This extends the reach of our sustainable practices to the realm of our immediate spatial neighborhood.

Looking Ahead #

In the context of environment and sustainability, humans have to find a way that leads to a positive impact on both the natural and human-made environments. We need to find ways to expand our consciousness where systems related to factors of production work in tandem with all entities, both men and the machines, to devise ways that impact the sustainability aspect in a positive way. By transforming our production capacities, transportation capacities, way of living, and our association with both the animated and inanimate world, we can create a better and brighter future, making human impact a constructive one, rather than which leans on the exploitative side.

Related reading

Is This Kind of Utopia Possible? #

So, what is the role of industry 4.0 in the context of the environment, its preservation, and healing? #

Industry 4.0 is giving us powerful and widely available tools so that we all can help spaceship earth to recover and restore its pristine beauty and glory.

Ways AI Can Be Used in Marketing

Vin Patel — Mon, 14 Dec 2020 20:37:28 +0000

Key takeaways

Marketing with AI (Artificial Intelligence) may sound fancy; the term is thrown around in marketing tool ad copy by the marketing gurus and hyped by the media.
It involves automating all or parts of the ad-buying process by using software-driven technology.
This will help marketers to achieve better results, possibly boosting their business’s ROI (Return-On-Investment).

Marketing with AI (Artificial Intelligence) may sound fancy; the term is thrown around in marketing tool ad copy by the marketing gurus and hyped by the media. Though the hype around what AI might do in the next few years is overstated, the reality of how it is used today in marketing is often under-recognized.

? Google uses deep learning to rank search results.

? Amazon uses natural language processing for Alex.

? Facebook uses facial recognition to suggest who to tag in photos.

? The Washington Post uses natural language generation to write data-driven articles.

? Netflix uses machine learning to personalize recommendations for its users.

So, our lives are already machine-assisted, and similarly, marketing can be, too.

5 Platforms that can be leveraged for Marketing using AI #

Your marketing team is losing the competitive edge as long as you postpone using innovative AI-powered marketing tools. The Artificially Intelligent systems continuously work on the background of popular products and services such as Amazon, Netflix, and Google.

In recent years, AI has entered into marketing by helping brands to improve and enhance every step of their customer’s journey. Also, tools that were previously available to enterprise-level companies have now become affordable and accessible to medium and small-sized businesses.

Here are five ways in which AI can be used for your marketing campaigns:

1. Chatbots #

Messaging apes such as WhatsApp, Vibe, and Facebook Messenger have made it easier for customers to reach out to companies and businesses for inquiries, buying, or complaining. These apes are free and easy to use. However, the sad reality about using these apes for businesses, especially the big brands, is that it can be tough to reply every time a customer sends a message. Moreover, it could get tedious when you have to respond to thousands of messages within your work hours, and most of them are asking you the same question!

A chatbot is an AI program that can simulate a conversation with a user in natural language. This helps businesses keep up with their customers and answer their queries. Now, big brands can set predetermined answers to the frequently asked questions of their customers. And also, chatbots can help your customers to find and buy a product they like. All this will significantly reduce the time required for human intervention and response, thus, save both your time and money.

2. Content Generation #

We all know how the manual generation of content is a tedious process. Now, imagine having a website that can generate its own content. You don’t have to hire writers or editors as you’ll have a self-sustaining site that can earn for you.

AI machine learning algorithms can make life easier for you as a marketer, and this is called content generation. Unfortunately, it can’t create long and eloquently written articles by industry leaders, CEOs, bloggers, and other talented writers who offer in-depth knowledge about any given topic.

A content generation or writing can only cough up simple financial reports, stock updates, sports news, etc. However, if you need help in generating personalized content for your website, use content intelligence. It provides creators with data-driven feedback and insights for effective content that will yield better results.

3. Programmatic Ad Targeting #

It involves automating all or parts of the ad-buying process by using software-driven technology. The traditional way of buying/selling ads is a long and tedious process that begins with talking to a salesperson, setting up the terms of a contract, and finally executing it. However, AI in digital marketing changes the conventional way to a simple automated process.

The AI uses the data from the cookies of websites and mobile applications used or visited by users to target specific customers that match the advertiser’s or businesses’ criteria. In the case of a match, the ad-buying system will automatically bid on the impression and show the user’s winning content.

Facebook ads allow marketers and advertisers to use data analytic in digital marketing to create custom profiles to target and retarget their ads. Note that this is the data you share on the social media platforms when you click on to agree on the end-user license agreement (EULA).

4. Dynamic Pricing #

It involves using a personalized pricing strategy, in which its demand and/or supply determines the price of a product or service. For instance, the prices of ride-sharing apes increase as the demand rises. A website or application bot can monitor your customer’s predictive analytic using their searches, history, cookies, and other activities to provide them with real-time pricing. However, this could mean they get higher prices or fewer discounts for the services or products they need at the moment. Though it sounds unfair, there are always two sides to a story.

Your customers can benefit from the dynamic pricing when the demand for a product or service is down. For example, when hotel rooms go unsold, dynamic pricing can offer competitive pricing to attract customers and help reduce vacancy chances.

5. Speech Recognition #

At present, we have AI’s with speech recognition capabilities that can recognize spoken words and convert them into text for executing the command. Alex, Sir, and Google Assistant are some of the most popular chatbots that use speech recognition technology. Moreover, speech recognition is used in apes such as Google Maps, Shazam, and other hands-free systems.

But how can marketers take advantage of speech recognition for their campaigns? It is estimated that around 55% of households will have a smart speaker by 2022, compared to 13% in 2018. Further, sales from voice shopping are expected to reach USD 40 billion in 2022, from USD 2 billion in 2018. Based on this data, marketers should not miss out on using speech recognition in their marketing campaigns.

Final Words #

Considering all the above benefits of AI Marketing, does it mean that AI will soon take over marketers and content creators’ jobs? Although machine learning is making a huge improvement in its algorithm, you can rest assured for the next few years, as AI’s are still far from perfect.

Nonetheless, AI in marketing is a powerful tool that enables companies to engage with every individual customer in a personalized and meaningful way. It is time to start applying AI products and marketing analytic techniques to deliver personalized experiences to your users. This will help marketers to achieve better results, possibly boosting their business’s ROI (Return-On-Investment).

Related reading

Melon Musk's Neuralink and its Functionalities

Vin Patel — Mon, 09 Nov 2020 20:18:32 +0000

Key takeaways

The Neuralink is a wireless implant, unlike any other neural implants.
BMI technology enables a computer or other digital device to communicate directly with the brain.
The challenge is to design and develop adaptive algorithms that maintain reliable and robust performance while improving over time to include addition of new capabilities.

Neuralink is Melon Musk’s neuroscience startup, which has developed a coin-sized computer chip that can be implanted in the human brain to cure neurological conditions like Alzheimer’s, dementia, and spinal cord injuries and ultimately fuse humankind with artificial intelligence. The Neuralink is a wireless implant, unlike any other neural implants.

Before understanding Neuralink further, let’s understand the nervous system, neurons, neurotransmitters, and functionality of neurons to further understand the functionality of neural implants and Neuralink.

Our nervous system is divided into three main parts. They are central nervous system (CNS), peripheral nervous system (PNS), and autonomic nervous system (ANS).

The CNS consists of the brain and spinal cord. The PNS consists of nerves and ganglia that connect to the CNS. The ANS consists of the nerves which connect the involuntary organs like the heart, stomach and lungs.

The CNS is responsible for the integration of sensory information and responding to it. The primary function of the PNS is to connect CNS to every part of the body. The main function of the ANS is to regulate the function of internal organs.

What are neurons and how do they work? #

The nervous system is made up of nerve cells called neurons. They communicate with each other by nerve endings called synapses. They use neurotransmitters to pass on the electric signal from one neuron to another neuron or one neuron to an effector cell. A myelin sheath protects the neurons.

What are neural implants? #

A neural implant is an electrode placed inside the brain and is connected to a neurostimulator implanted on the chest or abdomen. This neurostimulator generates electric impulses that are transmitted to the electrodes placed deep in the brain to activate the targeted area of brain cells.

What are the uses of neural implants? #

Neural implants are used to treat essential tremors of hands, legs, trunk, voice and head; multiple sclerosis, intractable pain, dystopia, and other psychiatric conditions like obsessive compulsive disorder, anxiety, and depression that does not respond to medicines.

Melon Musk’s Neuralink is also a neural implant, but it is advanced and computerized. It does not need external simulators. It is a coin-sized computer chip. Melon Musk’s Neuralink was implanted in a pig’s brain named Gertrude for two months. Neuralink proved to be efficient in curing human disease with conventional implants.

Neuralink helps to cure neurological conditions like Alzheimer’s disease, dementia, spinal cord injuries and fuse humans with artificial intelligence. The first prototype in a person may take some time, as getting federal approval is difficult. But it is being tested on a monkey and the monkey was able to control a computer with his brain.

Neuralink and Computer Control #

Threads that are finer and measuring in microns are inserted into areas of the brain that control movement. There are many electrodes in each thread that connect them to the Neuralink. These implantable devices process, stimulate, and transmit neural signals.

The Neuralink App #

The Neuralink app will help you control your iOS device, keyboard, and mouse directly with your brain’s activity, just by thinking about it. The Neuralink app would guide you through exercises that teach you to control your device. FDA does not yet approve these. So, they are not available in the market. You will be able to control devices with Bluetooth connectivity, such as any mouse or keyboard, and experience unmediated reality and high fidelity.

What is Neuralink Developing? #

Neuralink builds a fully integrated brain machine interface (BMI) system. It is also called a brain computer interface (BCI). BMI technology enables a computer or other digital device to communicate directly with the brain. This helps a person with paralysis control a computer mouse or keyboard from the information read out from the brain. It also helps to write information into the brain. This helps to restore brain functions such as a sense of touch. The goal is to develop a system with at least two orders of magnitude and more communication channels (electrodes) than current clinically-approved devices. The safety measurements have to be improved. It must have full wireless communication through the skin. It should be ready for the patients to take home and use it on their own. Such features are in development stage. It will take time to implement all these features.

What are the Challenges Involved in Making a Scalable BMI? #

The technology used in Neuralink is built on decades of BMI research, including several ongoing studies with human participants. The BMI systems used in these studies have only a few hundred electrodes with connectors that pass through the skin. Their use requires laboratory equipment and personnel. The challenge is to scale up the number of electrodes and build a safe and effective clinical system to take home and operate by themselves. Recent developments in engineering in this field and new technologies developed by Neuralink pave the way for progress.

What are the Key Technical Hurdles? #

These are the key technical hurdles:

Electrodes

To optimize the compatibility of electrodes in Neuralink, they are being microfabricated out of thin film metals and polymers to match the neighboring neurons’ size and as flexible as possible. But the challenge is that threads should resist corrosion from fluid in the tissue and should have sufficient surface area to allow stimulation. The new microfabrication processes and advances made in materials science will help to combat these challenges. It also includes the process of integrating corrosion-resistant adhesion layers to the threads and rough electrode materials that increase their effective surface area without increasing their size.

Chips

Developing chips to meet real-time neural information requirements is a challenge as Neuralink needs to convert the small electrical signals recorded by each electrode into real-time neural information. As the neural signals in the brain are in microvolt, Neuralink must have high-performance signal amplifiers and digitizers. As the number of electrodes increases, the raw digital signals become too much information to upload with low power devices. To improve Neuralink, we require on-chip, real-time identification and characterization of neural spikes.

Hermetic Packaging #

The Neuralink has to be protected from the fluid and salts that bathe the surrounding tissue. Developing a water-proof enclosure can be challenging. It is even harder to build an enclosure from biocompatible materials, replace the skull structurally, and allow over 1,000 electrical channels to pass through it. Developing innovative techniques to build and seal each major component of the package helps to meet this challenge.

Neurosurgery

The threads used in Neuralink are too fine to be handled by hand. To safely place it inside the brain, we have to develop a new kind of surgical robot. We are innovating on robot design, imaging systems, and software to build a robot that can precisely and efficiently insert many threads through a single 8-mm skull opening while avoiding blood vessels on the surface of the brain.

Neural Decoding #

The neural spikes have a lot of information. This information has to be decoded to control a computer. We have designed computer algorithms to control a virtual computer mouse from the activity of hundreds of neurons. Our devices will be able to connect to more neurons to provide more precise and naturalistic control. This will help to include additional virtual devices such as a keyboard and game controller. This can be accomplished with advances in statistics and algorithm design. The challenge is to design and develop adaptive algorithms that maintain reliable and robust performance while improving over time to include addition of new capabilities.

Related reading

Questions and answers

What are neurons and how do they work? #

The nervous system is made up of nerve cells called neurons. They communicate with each other by nerve endings called synapses.

What are neural implants? #

What are the uses of neural implants? #

What is Neuralink Developing? #

Neuralink builds a fully integrated brain machine interface (BMI) system. It is also called a brain computer interface (BCI).

The Use of Python in AI and ML

Vin Patel — Thu, 22 Oct 2020 20:22:04 +0000

Key takeaways

The open-source programming language Python has been around for a long time, but it continues to be a dominant force.
So when AI engineers and ML code with Python, they are sure about the product that’s built (and can move forward with confidence).
What’s more, the lack of a learning curve also makes Python a highly cost-effective solution.

The open-source programming language Python has been around for a long time, but it continues to be a dominant force. Its popularity in the digital age is attributed to the fact that you can build many cool things with Python (like basic QA Systems).

In recent years, Python’s dominance has also extended as a result of its ability to manage complex coding scenarios, especially in the domain of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

According to O’Reilly Media, Python’s popularity was fueled by ML development. In fact, Python was the most popular coding language on the platform, accounting for 10% of all usage. O’Reilly further states that Python’s popularity on its platform was driven by data scientists, AI engineers, and ML engineers.

For example, AI is leveraged to build prediction models to engage in data analytic, create robust strategies, boost productivity, enhance efficiency, and more. All this is achieved through programming in Python.

As oceans of data are generated every second of the day, AI and ML are used extensively for its processing and analysis capabilities (a feat that can’t be achieved by the human brain because of the volume of data it can absorb at any given moment).

What Are the Key Benefits of Using Python in AI and ML? #

When it comes to AI and ML, Python is like a super stack demanded by AI and ML-based experiments.

The key benefits of leveraging Python for your next AI and ML project are as follows:

Low barrier to entry
Extensive libraries
Enhanced flexibility
Consistency
Stability
Readability
Platform agnostic

For this post, let’s take a closer look at some of these benefits.

Low Barrier to Entry #

When working with lots of data, you have to find innovative ways to process it conveniently and effectively. As Python is easy to learn, most data scientists pick it up quickly and engage in AI development without external support.

This is because Python resembles simple English with a simple syntax that allows the seamless management of complex systems. The extensive documentation available within the Python community also helps accelerate this process.

Extensive Libraries #

It also helps that Python is supported by extensive libraries, structures, and thriving online communities. Python libraries are also one of the main reasons why the code has come into prominence within AI.

These libraries are collections of modules distributed across a variety of sources like PyPi. The latter is equipped with a pre-composed bit of code that enables clients to perform analytical activities. Code like this can form the foundation of AI-based projects, saving time and money.

For ML projects, Python libraries enable continuous information prepping and data processing. In this scenario, Python libraries allow you to access, manage, and transform the data seamlessly.

So when AI engineers and ML code with Python, they are sure about the product that’s built (and can move forward with confidence).

Some popular libraries used in AI and ML projects are as follows:

Caff (for DL that enables seamless switching between CPU and the GPU, while processing over 60 million images a day using a single NVIDIA K40 GPU).
Eras (for DL, enabling rapid calculations and prototyping)
Matplotlib (for 2D plots, charts, histograms, and more)
NLTK (for processing, computational linguistics, and natural language recognition)
Pandas (for high-level data structures and analysis)
PyBrain (for neural networks, reinforcement, and unsupervised learning)
PyTorch (developed by Facebook to support Computer Vision, NLP, and many other ML programs)
Scikit-image (for image processing)
Scikit-learn (for managing basic ML algorithms like classification, clustering, linear and logistic regressions, regression, and more)
Spark MLlib (developed by Apache, enables easy scaling of your computations quickly and provides seamless integration with other tools)
StatsModels (for data exploration and statistical algorithms)
TensorFlow (for working with DL by setting up, training, and utilizing artificial neural networks with enormous datasets)

Some libraries also make excellent visualization tools. This makes it easy to represent data in a human-readable format. As mentioned above, Matplotlib, for example, allows the creation of charts, histograms, and plots to enable simple comprehension. These libraries and visualization tools also make it much easier to create reports that are easily understood by a non-technical audience.

Enhanced Flexibility #

Python is highly adaptable. For example, you can use scripting or OOPs when coding. AI engineers can implement changes quickly without ever recompiling the source code. You can also combine it with other programming languages as needed (especially C and C++ code modules).

This creates a robust environment where developers choose coding styles they are comfortable with and can solve different problems in the best possible way.

For example, the commands made in the imperative style describe how a task is performed. As the program state changes, you can define the computations sequence that needs to happen with each change.

Or if ML engineers choose a functional or declarative style, the operations that need to be performed are ‘declared.? In this scenario, declared statements take the form of mathematical equations and don’t consider the program state.

One benefit that’s not mentioned often is Python’s ability to simplify the code and make it readable. This programming language focuses on cutting down the number of lines of code to execute a function. So when AI and ML projects are coded in Python, programmers won’t have to write a lot of code.

If you’re just starting your Python journey, you’ll soon realize that it’s a fantastic tool that is more than a general-purpose programming language. Its extensive libraries, community support, flexibility, and low barrier to entry makes it the go-to language for anyone thinking about a career in AI or data science.

Going forward, Python’s popularity also takes pressure off startups and recruiters. Whenever engineers and team members need to be replaced, it’ll be much easier to put warm bodies on seats.

It also easier for anyone in the company to quickly learn and engage in an AI or ML-based project. What’s more, the lack of a learning curve also makes Python a highly cost-effective solution.

Related reading

The Impact of Automation on the Future of Work And Beyond

Vin Patel — Tue, 06 Oct 2020 21:28:47 +0000

Key takeaways

We are witnessing a significant transformation in the way societies, countries, institutions, and economies function with the onset of the fourth industrial revolution.
AI and ML are evolving to read x-ray scans of patients, transforming healthcare and robots performing surgeries and telemedicine, allowing human doctors to perform remote surgeries.
What kind of world we leave for them depends on the choices that we make now, and it is better to make an informed choice for a world with unlimited possibilities.

The Great Acceleration #

We are witnessing a significant transformation in the way societies, countries, institutions, and economies function with the onset of the fourth industrial revolution. The rise of technologies like artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and others that make up the foundation of the fourth industrial revolution is leading to the increasing adoption of automation and changing the way we function as a society. They are significantly altering the way we work and giving rise to a paradigm shift in the manner societies are organized in tandem with other forces influencing the human culture and consciousness, shaping up new frontiers of the human evolution.

The onset of the fourth industrial revolution suddenly came into the limelight as the entire world, currently in the grip of an ongoing pandemic, realized the global economic order’s fragility and the drawbacks of globalization. A system that emphasizes maximizing the productivity and profitability rather than creating a bare-minimum buffer manufacturing capacity for any perceived emergency was considered an inefficient and unprofitable approach. This system faces severe criticism as advanced and rich economies face crippling shortages of things like ventilators and PPEs to fight the infection spread. Countries and politicians are now advocating a renewed emphasis on self-reliance and a significantly different economic order to ensure the availability and smooth supply of essential goods and services needed.

People now realize the follies of an economic system that encourages unlimited consumption of resources. It is worth exploring how technologies related to automation are shaping up the future of our planet, what impact the rise of automation will have on the way we work and how our planet will cope with the ill-effects of what has been termed ’the great acceleration.’ With its origins in the mid-twentieth century, the great acceleration has altered our planet’s ecosystems at a blinding pace. It has led to an extinction-level decline of the natural world. In this write-up, let us delve into the impact of automation on the future of work.

Impact of Automation On The Way We Work #

Looking at the history of the three industrial revolutions, it can be safely inferred that these transformations, even though creating temporary unemployment, led to the opening up of new areas of work and had a net positive impact on the number of jobs created. However, what sets apart the current fourth industrial revolution is the sheer scale of its impact and its potential to usher in very high levels of prosperity and human welfare. Machines are replacing humans in dangerous and labor-intensive jobs, which benefits society and gives rise to a more just and equitable system.

So, in the coming decades, automation will lead to autonomous vehicles becoming predominant, eliminating the need to drive. Consequently, jobs of truck-drivers, taxi-drivers, and the like are likely to become obsolete. On similar lines, robots are likely to take increasingly complex roles in sectors like defense (replacing human soldiers and saving the loss of life and limb) and agriculture (like taking up farming in barren soil conditions and further increasing the food production).

Such automation levels can be seen in our quest for ever-increasing productivity levels, which have stagnated and consequently stagnated the incomes of blue and white-collar workers in developed economies and gave rise to deeper economic inequalities a highly skewed distribution of wealth. From the perspective of the developing world, automation is perceived as a huge challenge. It gives rise to many unemployed youths, with major consequences on social stability and acting as a catalyst to upheavals and unrest in labor-intensive economies. On the other hand, automation and its large scale adoption leads to the emergence of new dilemmas and the need for a new ethical and moral code of conduct. For example, consider what may happen if any person is killed wrongfully by an autonomous law-enforcement robot, what kind of punishment can be meted out to such an entity, and will the people who have suffered will get a sense of justice merely by ‘switching off’ the erring machine? These are the new ethical and moral questions that we will be dealing with in the coming times.

Deskilling And Upskilling For The Jobs That Don’t Exist Presently #

As machines increasingly replace humans, it also gives rise to digitization, with systems like routine data-collection tasks, monitoring and tracking various parameters like air pollution levels and water contamination, or incident reporting and documentation. The impact of these changes will give rise to productive and efficient workflows and business processes, with humans having more time for tasks requiring fine thinking, collaboration, and creativity. Automation will also create new jobs, requiring people who can manage the system and processes required to develop and maintain such kind of automated workforce consisting of robots. Refinement of technologies like AI and ML leads to the rise of computers with capabilities to mimic human learning and decision-making patterns and automate jobs like surveying, accounting, office support, and the like.

Simultaneously, automation is creating jobs that currently don’t exist, and we cannot even imagine the kind of scope and skill-sets required for them. For example, when I was studying engineering in college, I never imagined that I would be working as a web content writer, indulging in content marketing and digital marketing. These terms didn’t even exist at that time.

We have an inkling of the things to come, the contours of the new economic order and the jobs it will create are slowly emerging. The future jobs, rather than emphasizing hardcore technical skills, will value skills concentrating on emotional intelligence, creativity, problem-solving, fostering collaboration, and value-creation. Partial automation of jobs in healthcare and warehousing is an indication of things to come. AI and ML are evolving to read x-ray scans of patients, transforming healthcare and robots performing surgeries and telemedicine, allowing human doctors to perform remote surgeries. Similarly, robots are already replacing humans in warehouses, with companies like Amazon deploying robots to stack goods and transport them speedily.

So it is clear that to stay relevant in the work sphere, a major overhaul needs to be done in our education sector. We need to prepare a workforce that can adapt to the rapid technological advances and can work and interact with machines and robots. We all need to undergo major deskilling and upskilling in how we handle the coming times to be dominated by an IoT based world, where everything is interconnected.

Wide-Scale Transformation of Our Cities #

We should also consider the impact of this major shift towards automation and AI on the type of jobs we will do and the related evolution of the workplace and the consequent shaping up of our cities and the related environment and sustainability aspects. As the workforce becomes increasingly mobile and flexible due to the nature of jobs changing, it will also dramatically change the concept of workspaces as it exists presently. Tomorrow’s office may be completely different, facilitating collaboration and interaction between people and serving as incubators of innovation and developing relationships.

This impact can already be felt with the sudden spurt in the work from home trend. This has resulted in vast and expensive office buildings lying vacant, businesses relying on the office workers going bust, and transportation networks operating way below their intended capacity, as less people commute to offices. Such a shift to a more flexible and mobile work culture will significantly alter our cities’ shape and design. The business district in cities will become outdated, as there would be no need for a large number of people to commute to a centralized location in the city.

The Thinking Machines #

The impact of automation and sophisticated AI-based machines doesn’t stop here. I was astonished to learn about the next-gen AI, which has led to the development of the Generative Pre-trained Transformer 3 (GPT -3) language model, developed by OpenAI research institute. This powerful and sophisticated language model can generate human-like text, ranging from commercial business writing to creative writing. Apart from performing routine and mundane tasks like business correspondences, the GPT-3 language model can also write poetry and perform technical tasks like coding. Based on billions of parameters, the development of this latest AI-based language model is astonishing and profound at the same time. I wonder what sort of consciousness the machine will possess, if any, and if it can feel the emotions capable of framing in words. The job of content writers, novelists, painters (remember the auction of the AI created portrait in 2018) are also on the line at par with drivers, accountants, and telecallers. Not to mention the impact of this technology on the menace of fake news, complicating matters further, as people would not be able to make out what is fake and propaganda dished out by vested interests from genuine and fact-based news is increasingly getting sidelined.

Final Words #

The writing on the wall is clear. Automation and AI-based technologies are here to stay. They are already shaping the future with a completely transformed work culture, leading to large-scale disruptions in employment patterns and the economic order. The world is at a crossroad. One way is leading to a ‘black mirror’ type of social credit system already being implemented in China. The State applies a suffocating and highly oppressive control system over its citizens’ lives, with every move and word of citizens being monitored continuously.

The other way leads to the creation of a world with equitable distribution of wealth and democratizing the tools and knowledge that would make our societies and people better educated and equipped for a bright and prosperous future. The choice is ours. By being aware of this profound shift taking place, we would be doing humanity a great service by making a conscious choice regarding how such technologies shape our world and culture, impacting not only us and our children but setting the stage for generations to come. What kind of world we leave for them depends on the choices that we make now, and it is better to make an informed choice for a world with unlimited possibilities.

Related reading

Dangers of Deep Fakes

Vin Patel — Mon, 21 Sep 2020 21:05:03 +0000

Key takeaways

We are sure you must have read some of the other forms of this quote, and there is no denying that this one is false!
The video was created in Bhojpuri, but it was deep faked in English to reach the English listening audience.
Many people even predict a cyberwar, and there is no denying of its possibility.

Privacy is a myth in the world of the internet! We are sure you must have read some of the other forms of this quote, and there is no denying that this one is false! With so many lawsuits being filed against either Google, Facebook, or other Internet companies, putting our data at risk has been a major concern.

What Is It? #

Going by the name, this is something deeply faked! Well, we won’t be joking with you on this, so let’s get into what exactly it is. Deep fake is a portmanteau of deep learning, a form of synthetic media in which part of the person in an existing image or video is replaced by someone else. Deep fake involves generative neural network architectures such as autoencoders or generative adversarial networks.

But, deep fakes are not just about deep learning, machine learning, or technology! They had existed for years before any of these technologies became part of our lives. What do you think made it possible for Fast and Furious to be completed after the death of Paul Walker? But, back in those days, it took days to achieve something like that. However, with the coming technology, this has become simpler than ever before.

How is it Done? #

Deep fakes are made possible because of machine learning. The creator first trains the neural network for long hours on the real video to give the machine a realistic understanding of what the video looks like from different angles and lighting. This trained network is then combined with computer-graphic techniques to super-impose a copy of the person onto a different person. Although many believe that GANs play an important role in deep fakes, mostly artificial intelligence algorithms and other non-AI algorithms are used for building deep fakes.

The Real Danger #

Pornography #

One of the deepest hit industries by deep fakes is the one we look around through shy eyes. With deep fake, it has become easier for revenge porn to float around. The creator only needs many photos of victims, which are often readily available in their social media account. It has been used to bully or harass individuals to spread hate and tarnish their image.

Politics #

If there is one thing we all know about politicians and politics, there are often lies in its web. Deep fakes have overblown the threat to democracy. Politicians across the world are using deep fakes to win new voters. Recently, an Indian politician’s video surfaced around the internet, where he is seen criticizing the opponent party in English. The video was created in Bhojpuri, but it was deep faked in English to reach the English listening audience. The more significant risk here is that people might get to the point where they stop trusting what they see or hear. It is not hard to imagine the corrosive impact that this might have on an already fragile political environment.

C-Level Fraud #

There is a rise in different types of C-level frauds, from early spear-fishing to the recent wailing attacks. A wailing attack is a phishing attack where the target is being made to the CEO or the CFO within the organization to steal information from them. This is usually done by making calls to them from those that hold higher positions than them. One recent incident happened with the anti-money laundering officers of the US Credit office. Cybercriminals tend to spend significant money when profiling their targets because of the potentially high returns.

The Measures #

With the world often feeling gripped by deep fake, there have been many measures taken across countries to control deep fakes. Although challenging to enforce, California has recently made a law that makes it illegal to create or distribute deep fakes. Newsom also signed a bill AB 602, which allows California residents to sue if their image is being tarnished or sexually explicit. February of 2020 brought us the unveiling Twitter’s plan to curb the spread of manipulated content, including “deep fake” videos. This was done as part of a move to fight information that might result in deep fakes or other harm.

Along with the DARPA, the Pentagon is currently working with the largest research centers to get ahead of deep fake. Computers are now being fed with real videos, training them to detect the deep fakes. The major challenge, however, is in detecting fake audios. Training computers to recognize visual inconsistencies is one way to see fake audio.

Conclusion #

We are already surrounded by fake news, with information creating deliberate falsehoods with deep fake worsening the situation. Many people even predict a cyberwar, and there is no denying of its possibility.

Deepfakes aren’t going away, posing the question of how to regulate them? With more advanced technology being developed everyday, governments will have a harder time creating laws and regulations.

Related reading

Questions and answers

What Is It? #

Going by the name, this is something deeply faked!. Well, we won’t be joking with you on this, so let’s get into what exactly it is.

How is it Done? #

A Guide to GANs (Generative Adversarial Networks)

Vin Patel — Mon, 10 Aug 2020 20:45:22 +0000

Key takeaways

Generative adversarial networks (GANS) are machine learning approaches that utilize two convolutional neural networks.
Christine’s became the first auction house to sell an AI portrait of Edmond Belay generated by a GAN for $432000 (almost 50 times its high estimate).
Game designers also use GANs to generate 3D backgrounds and avatars to develop realistic appearances.

Generative adversarial networks (GANS) are machine learning approaches that utilize two convolutional neural networks. These networks contest to generate new data instances similar to the training set. Deep learning experts, Ian Goodfellow, together with his colleagues, were the first to propose GANS in the 2014 NeurlPS paper.

GAN is structured into two: The generator that learns to produce plausible data and discriminator that distinguishes fake data from real data produced by the generator. The discriminator also penalizes the generator for generating implausible results.

Use cases of GANs #

Generative Adversarial Networks (GANS) has both positive and negative uses as they can learn to imitate any data distribution. They are widely used in video generation, image generation, and voice generation. In the real sense, GANS are robotic artists with impressive outputs that are usually taught to strangely create similar stuff in almost all domains, including images, speech, prose, and music. GANs can also be used in the generation of fake media content, and they are the technology supporting deepfakes.

Image to image translation. Generally, GANs are a perfect solution for the image to image translation problems. They usually learn the input to output image mapping with also the loss of functions for the mapping training. Conditional GANs are used in the generation of photorealistic images from inputs of sketches or semantic images. Many image-to-image tasks such as translation of photos from Google maps to satellite photographs, translation to color photographs from sketches, and translation of cityscape semantic images to photographs; use the pix2pix GANs? approach
Text to image generation. The automatic generation of real images from text is very useful and interesting. For instance, the stackGAN is used to produce accurate images relating to the textual description of objects such as flowers and birds.
Increasing the resolution of images. GAN can produce high pixel resolution output images with the Super-Resolution Generative Adversarial Network (SRGAN). This involves the creation of high-resolution images such as street scenes, human faces, and other objects.

4. Predicting the next video frame. Internal models designed with deep neural networks use a loss-based approach in predicting the future frames of synthetic video sequences with the use of CNN-LSTM’deCNN framework. This aspect is designed for the static elements of video scenes, which can be predicted up to a second.

Christine’s AI Artwork Selling At $432,000 #

Christine’s became the first auction house to sell an AI portrait of Edmond Belay generated by a GAN for $432000 (almost 50 times its high estimate). The GAN was open source-based, which was written by Robbie Barret of Stanford. What makes the artwork unique is that it was created through artificial intelligence by three French students who used a code from the 19-year old programmer, Barret. The AI community has had a major step through the development of this AI portrait. Image generation with GANs is a multistep process that involves the collection of training data (scraper) that enables your network to replicate, construction of the generative algorithm. Then you begin running the algorithm and sorting out the outputs to pick the best out of the many generated results. From the Belay story, in combination with their motto, we can truly attest that ‘creativity is not only for humans? with the creation of art through artificial intelligence.

Advantages and Disadvantages of Using GANs #

The Advantages of GANs include:

? GANs are learning methods that require less or no supervision; they usually don’t need data to be labeled as they are trained to use unlabeled data in the process of learning the internal data representation. This is more efficient when compared to the manual process of acquiring labeled data which is time-consuming — GANs make the modeling of data distribution better by the generation of clearer and sharper images.

? GANs are capable of training all forms of generator networks that are required by other frameworks to attain some specific forms of functionality.

? They can produce data similar to the real one, which is widely used in the real world in the generation of images, audio, videos, and texts. The images produced by the GANs are mostly used in marketing, advertisement, e-commerce, and games.

? They are also useful in machine learning approaches as they can easily interpret data into various versions by going into the details of the data. The combination of GANs and ML makes it easier to recognize people, cars, trees, and streets and can also determine the distance between different objects.

Disadvantages of GANs — GANs are hard to train; they usually need different types of data repeatedly to check if it functions accurately or not.

? The generation of discrete data in the form of a text or speech is a very complex process.

? Generally, GANs handle a single task at a time, which makes it difficult to accomplish tasks of guessing the values of a single-pixel given another pixel. This can be fixed by the use of BiGAN that enables you to guess the missing pixels by the use of Gibbs sampling.

Other Potential Uses of GANs: #

? GANs are used in image editing. For instance, changing the appearance of an aged individual by changing their hairstyle cannot be accomplished with just the normal image editing tools. Still, with the use of GANs, you will drastically reconstruct and change the appearance of the image.

? Application of GANs for security purposes. Caber threats are one of the major concerns revolving around the AI world, where even the deep neural networks are prone to hacking. GANs are in the front line to directly address the issues of adversarial attacks, which uses a variety of methods for fooling deep learning architectures. On the other hand, GANs increase the strength of the existing deep learning models against those methods through the creation of similar examples and training the models to detect them.

? GANs are used in cloth translation as they can produce photographs of similar clothes as they appear in online stores.

? They can also be used in photo inpainting (hole filling). This involves repairing and filling a missing section of an image that was reasonably removed with the use of created content.

? Generation of 3D objects such as cars, chairs, etc. with the provision of 2D pictures. Game designers also use GANs to generate 3D backgrounds and avatars to develop realistic appearances.

Related reading

Can Artificial Intelligence Predict Future Pandemics?

Vin Patel — Fri, 31 Jul 2020 17:39:02 +0000

Key takeaways

However, we have not seen that much about preventing future pandemics.
With the information generated, unique genetic sequencing algorithms can be created for each person in the world, being able to personalize treatments and overcome conventional medicine.
It is not possible to say with a hundred percent certainty that this idea is perfect, and in fact, it probably is not.

Do you know what telemedicine is and how it can help at predicting future pandemics like COVID-19? Since we got into the severe coronavirus outbreak, people have been talking about the development of technologies that can prevent it. However, we have not seen that much about preventing future pandemics.

In December of 2019, an alert was fired by health teach Blue Dot, warning about the risk of a pandemic in China, which we now know as the coronavirus.

The company explains that the alert could only be fired due to an algorithm that collects various information from different sources, and crosses it with its database, thus being able to report possible disease outbreaks, whether existing or not.

The Blue Dot algorithm uses techniques of natural language processing and machine learning to identify the symptoms present and the number of people who present it.

“We can pick up news of possible outbreaks, small murmurs, forums, or blogs with indications of some kind of unusual event happening.” according to the CEO of Blue Dot.

In fact, this is how the algorithm indicated the risk of contamination in Bangkok, Seoul, Taipei, and Tokyo - all confirmed and were some of the first places affected by the virus outside China.

A major factor responsible for the fast spread and slow prevention of the pandemic was the nine-day delay by the World Health Organization to put the problem in evidence and take the necessary measures. Sometimes, algorithms aren’t enough. You need health and medical professionals to take action as well.

According to Kai-Fu-Lee, ex-president of Google China and the current president of Sinovation Ventures (Chinese venture capital investment company), we can use these algorithms and improve them to help discover new pandemics in the future.

Lee talked a lot about the topic of his participation in Brazil at Silicon Valley and said that using telemedicine to create a large database of the health of the population and that it is possible to use artificial intelligence to predict and prevent pandemics.

“I think that in the long term, pandemic prevention will be possible with artificial intelligence,” said Lee. “We will use smart clothes, known as wearable, that will produce data and send it to the cloud where it will be possible to notice anomalies early. So I hope that the next pandemic can be predicted and prevented.”

The idea proposed by Lee and several experts on the subject, that through telemedicine - the use of technology to monitor the health of patients from different places - whether through applications or watches with health monitoring, we can generate enough data to create a large cloud with all the information needed to predict a new pandemic. But predicting a pandemic goes beyond AI. It would be best if you had health and medical professionals take action quickly and actively work against it early on. The nine-day delay by the WHO was catastrophic and led to a much faster spread of the coronavirus.

Original diagram for Techfastly by Vin Patel.

The importance of predicting the pandemic is directly linked to its prevention. The faster the diagnosis, the faster the treatment or isolation will be. Several researchers claim that this will be the last major global pandemic if we direct technology to this research.

With the information generated, unique genetic sequencing algorithms can be created for each person in the world, being able to personalize treatments and overcome conventional medicine.

“Doctors cannot digest complete genetic sequencing, but artificial intelligence can.” say the experts.

If you really can’t imagine this ideal world of algorithms, try to imagine the following:

You acquire a watch that monitors your heartbeat and body temperature. Every time it peaks or goes down drastically, the watch triggers an alert and redirects you to a diagnostic application.

Within the application, you should answer a simple questionnaire about your symptoms. If the algorithm diagnoses some risk, it asks you to redirect to a health center. Otherwise, the application understands that it may have been a common change due to some other factor. This is already somewhat a reality, think about the new Apple Watch. It can monitor heart rate fairly well in older people. However, further research and development needs to be done to perfect this.

The system would have two main benefits:

Diagnose possible health problems, and redirect you to service as soon as possible.
Create a database with the symptoms and health monitoring of the world population, so that it identifies patterns and indicates epicenters of diseases.

As we can see, Artificial Intelligence plays a vital role in our daily lives, and even more so in matters of public health. We can and must use it for our well-being.

The current pandemic of COVID-19 has led us to global economic catastrophes, and the deaths of thousands of people in all countries. It is difficult to say whether our planet Earth could withstand yet another great outbreak like this.

Several researchers have developed studies of algorithms in disease forecast and prevention, so this can be the last pandemic experienced by us. In addition to it, companies around the world have invested high capital to finance this idea.

It is not possible to say with a hundred percent certainty that this idea is perfect, and in fact, it probably is not. However, the big question raised in this article is: how we must use technology and improve it to our advantage? As much as it does not work exactly as we expect, we will undoubtedly have a better scenario than the previous one.

If Artificial Intelligence can save us from losing 400,000 lives, do you think it’s worth a try?

Related reading

Data Engineers vs. Data Scientists

Vin Patel — Sat, 11 Jul 2020 17:30:00 +0000

Key takeaways

Original diagram for Techfastly by Vin Patel. Data Science is an emerging technology in this 21st century.
Data Scientists cannot directly use raw data with those errors.
After that, you can consider hiring a data scientist to find the hidden insights in your data.

The two positions are not interchangeable. #

Original diagram for Techfastly by Vin Patel.

What is the difference between Data Engineers and Data Scientists? #

Data Science is an emerging technology in this 21st century. Data Science involves all the processes which bring data into actionable insights. These processes include getting data, cleaning data, performing descriptive statistics,and building a model on data, implementing the insights to solve the given problem. Data Engineering is an aspect of Data Science that mainly focuses on how data is processed and analyzed practically.

There are several ways to define what a Data Scientist is. Let’s look at one of those definitions. A Data Scientist is a person who has a broad range of knowledge in multiple disciplines while specialized in one or two. He or She understands the domain knowledge of a particular field. For example, a Data Scientist needs to understand the business processes of a company, including marketing, strategy, and sales before doing any data science. A data scientist should have a good knowledge of Machine Learning and Statistics.

In contrast to a Data Scientist, a Data Engineer is a person who develops, constructs, tests, and maintains architectures, which are very useful for every step of the data science project lifecycle. For example, a Data Engineer practically develops, constructs, tests, and maintains databases and large-scale systems to store and manage data that are useful for Data Scientists.

Although Data Engineers and Data Scientists work together to get insights from large-scale data, their skills may vary, and the two positions are becoming more and more distinct. This article mainly focuses on those distinctions in detail between the two positions. Without further delay, welcome to ? Data Engineers vs. Data Scientists?.

What do Data Engineers and Data Scientists do? #

A Data Scientist performs actions on each step of the following Data Science project lifecycle.

Original diagram for Techfastly by Vin Patel.

At the start, a Data Scientist understands the business process. This understanding is often called getting domain knowledge. Then he or she defines the problem and sets up a goal and objectives to solve it.
Then, a Data Scientist acquires the data, performs some fundamental data analysis, and some visualizations to understand the nature of the data. If the data is not in the correct shape, he or she applies standard techniques such as data cleaning, dimensionality reduction, feature engineering to get data into shape.
After that, a Data Scientist decides which model gives the best output for the problem. In some situations, a traditional approach will not be able to solve the problem. Therefore, he or she needs to apply data-driven machine learning techniques for the modeling task.
Then, a Data Scientist will validate the model with assumptions and implement the model with the given problem.
Then, he or she sends the findings to the customer. Customer acceptance is required.
Finally, he or she writes a report about the entire process. This report should be clear and straightforward enough to understand the whole process and findings for a non-technical person.
A Data Scientist uses R or Python for data analysis.

Note that the data analysis process is not a one-time process. It often needs to go back to the previous steps if there is an error or change occurred.

A Data Engineer, contrary to a Data Scientist, performs the following tasks:

A Data Engineer deals with raw data that contains human or machine errors. Data Scientists cannot directly use raw data with those errors.
A Data Engineer discovers various opportunities to acquire relevant data. Data is everywhere, but a Data Engineer should use different techniques to collect data from various sources, for example, web data, sales records, etc.
A Data Engineer employes a variety of languages and tools (SQL, Hadoop) to process data and connects databases between different platforms. This process provides an opportunity for data scientists to do parallel computing, which speeds up data analysis tasks.
A Data Engineer recommends ways to improve data reliability, efficiency, and quality. Those data engineered by data engineers will give accurate results if the data scientists apply the correct techniques in their analysis.

What do Data Engineer and Data Scientists NOT do? #

Data Scientist and Data Engineers do NOT do the following things.

A Data Scientist does NOT do a data analysis task without defining the problem clearly:?A problem well defined is a problem half solved?. A Data Scientist often finds the domain knowledge to define the problem well and set up the goal.
A Data Scientist does NOT work alone even if he or she has all the necessary skills to complete a project: Data Scientists often prefer teamwork. Other team members are specialized in different fields. So, Data Scientists usually prefer to take advantage of those specializations.
A Data Engineer does NOT focus on the mathematical part of the model. Instead, he or she considers the efficiency, reliability of the model by improving the quality of the data.
A Data Scientist need NOT understand the inner working of a machine learning algorithm in terms of mathematics. Instead, a Data Scientist should know how to use which algorithm correctly for the given problem.
Data Scientists and Data Engineers do NOT do full work at once. Instead, they prefer to breakdown the big tasks into small pieces and make a Work Breakdown Structure (WBS).?You can not eat an apple at once, but you can eat the entire apple if you cut it into small pieces!? The same applies to the data science process.
Data Engineers and Data Scientists do NOT work separately. They should work together to deliver high-quality results for a real-life data science project.

How do I know when I need a Data Engineer or a Data Scientist? #

This question is a difficult one to give an exact answer since this depends on various factors. But I can provide some helpful facts to study for yourself and make the decision by yourself.

A Data Scientist is an expert in the following specified fields:

Advanced Statistics
Machine Learning
Python or R Programming
Big Data
Storytelling

In contrast to Data Scientist, a Data Engineer is an expert in the following specified fields and technologies:

Database Management
NoSQL
Hadoop
MongoDB
Web Scraping
Big Data

The question of ‘how do I know when I need a Data Engineer or a Data Scientist?’ should be answered from the perspective of the current company status. Suppose that you have a company that has clean, structured data in the company databases. If so, you can consider going straight to a data scientist with the above skills. The data scientist can do the rest of the work.

But what about if you have unstructured, messy data with missing values? What about if you need data from other external sources such as the web data? You should first hire a Data Engineer to get your data into shape and collect other necessary data. The Data Engineer will handle your unstructured data, which otherwise can’t be done in a traditional way like using SQL because the data is unstructured. The Data Engineer will use different technologies such as Hadoop, NoSQL, to handle unstructured data. The Data Engineer will also do web scrapping to get the necessary data from the web. After that, you can consider hiring a data scientist to find the hidden insights in your data.

Related reading

Questions and answers

What do Data Engineers and Data Scientists do? #

A Data Scientist performs actions on each step of the following Data Science project lifecycle. - At the start, a Data Scientist understands the business process.

The question of ‘***how do I know when I need a Data Engineer or a Data Scientist? #

’ should be answered from the perspective of the current company status. Suppose that you have a company that has clean, structured data in the company databases.

Machine Learning and Artificial Intelligence coming

Vin Patel — Sun, 31 May 2020 17:10:00 +0000

Key takeaways

The United States of America allocates a large part of its Federal budget to its healthcare system.
Studies have shown that SVM can help in distinguishing early morphological changes in the mind and can deduce significant initial findings for dementia with the help of MRI and other AI tools.
By far, it’s clear that AI and ML are not going to replace medical professionals but instead will enhance their efforts in patients’ care.

Revolutionizing Modern Healthcare #

The United States of America allocates a large part of its Federal budget to its healthcare system. Mandatory spending in 2019 included $1.2 trillion on primary health care programs. The Organization for Economic Co-operation and Development (OECD) health care and expenditure report 2019, the US had spent 16.9% of its Gross Domestic Product (GDP) on the national healthcare system, highest among OECD’s 36 members. In the same year, an estimated 30 million Americans were uninsured. Expensive healthcare services make healthcare inaccessible to many Americans.

What can be changed? #

Healthcare now needs smarter investment, where research on how existing human knowledge of medicine can be combined with technology, requires more resources. Artificial intelligence (AI) can be the answer. The targeted use of AI and Machine Learning (ML) can cut down the expenditure by a significant amount by reducing human efforts and bringing automation. Cutting the cost is the top priority of the US healthcare system. The United States is well aware of it, and hence the President’s Financial Year 2021 budget includes a significant increase in non-defense AI Research and Development (R&D). It also states there that the National Institutes for Health will invest $50 million for new research on chronic diseases using AI and ML.

What roles can AI play here? #

AI can play an essential role in the healthcare offerings of the future. Many features of patient care, fields of pharmaceutical organizations, administration of healthcare service providers, etc., can successfully use AI.

Genetic algorithms are capable of carrying out actions performed by humans, primarily when repetitive and trained movements are involved. Researches have shown that AI can diagnose diseases better, if not as accurately as humans. Algorithms, by far, have been able to diagnose malignant cancer more successfully than radiologists. Machine learning is already being used by healthcare, like predicting what treatment protocols will work best for the patient based on various patient’s attributes and treatment context. It has also helped in predicting a particular disease that a patient might acquire.

Original diagram for Techfastly by Vin Patel.

Healthcare is quite susceptible to human errors, and AI tools have helped in preventing many by now. More focused development of ML in fields like keeping and maintaining all past histories of patients will help avoid the occurrence of such errors. Surgical robots, for example, have provided superpower to surgeons in head and neck surgeries, gynecologic surgeries, prostate surgeries, etc. These robots have improved the surgeon’s ability to see, create precise and minimally invasive incisions, and stitch wounds. Many researchers have sought solutions to automate daily actions within medicine, like diagnosing patients and prescribing medications.

But AI and ML have their limitations: #

High potential legal costs related to any errors in medicine have slowed the adoption of innovation in many cases. What is needed is more accurate, controlled, and calculated research and development.

Research done by the National Center for Biotechnology Information (NCBI) shows that the application of machine learning in medicine is a premise with a promising future. The numbers point to a 95% improvement in the area if all studies are successful and implemented. Also, several studies performed at universities such as MIT, suggest the creation of algorithms aimed mainly at the detection of diseases. Such algorithms may remain active along with enormous improvement capacity. The demand for machine learning algorithms has been increasingly high, which is why different forms and different algorithms have emerged.

SVM- The most popular algorithm in medicine: #

Support Vector Machine (SVM) has been one of the most widely used algorithms in medicine for a long time to solve data issues with classification and regression, using repetition as input data. Studies have shown that SVM can help in distinguishing early morphological changes in the mind and can deduce significant initial findings for dementia with the help of MRI and other AI tools. Similarly, a research journal has also published that SVM can also classify the right clustered occurrence of the early phase of Diabetes.

The Support Vector Machine works as follows:

Each data is plotted as an n point in n-dimensional, n being a coordinate in dimensional space.

So, the classification is done by finding the hyperplane that differentiates the two classes very well. With the execution of the algorithm and study of the data generated on the hyperplane, SVM can detect the diseases themselves. This algorithm has been very successful in creating medicines and catching diseases.

Original diagram for Techfastly by Vin Patel.

Zilic- the promising 2020 algorithm: #

Zilic helps in the early detection of diseases in a single test through medical images. Zilic uses Generative Adversarial Network (GAN) and autoencoder. These tools, for example, are trained only with healthy images of lungs. When the systems get an unhealthy lung’s picture, the autoencoder reconstructs the image, where the diseased part looks different from the original one. GAN, using discriminator, gives slightly higher scores to the image of the lungs with diseases. And Zilic can highlight the affected area of the lungs and hence can detect the rarest disease of lungs in X-ray images.

Zilic has inspiration in GAN using two neural networks that are trained against each other. The first network is the Generator, which works as an algorithm trainer, that is, sending false images that are exactly like the original. The second neural network is the one who identifies whether the images generated by the first are real, identifying patterns.

Experts use this algorithm to identify rare diseases.

Original diagram for Techfastly by Vin Patel.

Artificial Intelligence’s future in healthcare: #

In Atrial Septal Defect (ASD), the algorithm has been very successful. With testing on 1000 children, it has 95% effectiveness. Many scientists see the potential for adaptation and development for this algorithm since it has had a high success rate.

But in cases of algorithms for Alzheimer’s, the rate of effectiveness has been 50%, which is quite low. It is not very easy to say that it is an excellent tool, as it is in a tie. However, this does not prevent the development of the algorithm. There are still many improvements to be made in the algorithms. However, there is much space for its development. We can and should invest in algorithmic-based healthcare businesses, as this is a branch that has brought many results to the population.

Conclusion:

There is no doubt that AI and ML perform better than humans in processing the vast amount of data of drugs, patients, diseases, historical data, and predicting the results. DNA and RNA analysis, for example, of various types of microbes, viruses, and bacteria, are helping researchers in gaining valuable insights into their biological behaviors. Biologists and pharmacologists in big companies often use AI and ML in their routine drug research and testing activities.

The use of AI and ML has helped in reducing the time and effort taken in bringing new medicines and vaccines for some of the epidemics like Ebola. Researchers are making use of available AI and ML techniques for finding the vaccine for the current Covid19 pandemic as well.

We have relied on computer scientists and health experts to develop these points further. And we firmly believe that entrepreneurs who invest in this field will lead to innovation and gain great popularity and financial return. By far, it’s clear that AI and ML are not going to replace medical professionals but instead will enhance their efforts in patients’ care.

Related reading

Questions and answers

What can be changed? #

What roles can AI play here? #

Ditch Your Rule-Based Chatbot Strategy for Conversational AI

Vin Patel — Tue, 26 May 2020 08:23:00 +0000

Key takeaways

Marketing Executives around the globe have a view to automating customer service using chatbots.
Think like you are running an image recognization like algorithm on natural conversations on chat to map the primary intent of the customer.
The challenges with rule-based bots get addressed by engaging the customers with an intent oriented conversation and engaging humans to perform validated tasks.

Marketing Executives around the globe have a view to automating customer service using chatbots. 91% of CEOs think about automating customer service, but only 52% can implement, and chatbot effectiveness is still questionable. Chatbots as a trend picked up in 2017 and 2018. 2017 had 84 tech startups whose primary focus area was chatbots. In 2018 the number decreased to 42 and in 2019 to 12. Gartner had predicted that 80% of enterprises would use chatbots in their applications by 2020. The fact in 2020 is that chatbots, in general, have failed to impress executives because of low Return of Investment (ROI) and the end-users on lack of providing relevant service. Most of the available chatbot engines have some rule-based engine combined with Artificial Intelligence (AI) working on the customer data of the enterprise. With the current trend, the best practice for Rules-based chatbot implementation is:

The CEO/CTO has to cultivate a data engineering culture first, meaning the enterprise should be ready to mine insights from the data assets they have collected over several years. The process involves collecting, consolidating, cleaning, and making the data available for analytic. The data that is capable of generating insights is the data that you need to input in the chatbots.
Start with a small proof of concept or a trial with long term strategic vision or business objective in mind. Making the objective clear makes you win half the battle. Example: " We need to increase our cross-sells by x% by reaching our customers on mobile apes by a chatbot in 3 months."
Embed the chatbot on the right delivery channel in your organization. The right digital channel is where your customers are engaged the most. Depending on your business, it can we web, mobile, or both.
Integration of proper authorization and data security. For example, you will like to authorize your existing customer by capturing his account number or his unique identifier and also encrypt his personally identifiable information so that it is not visible to all.

Original diagram for Techfastly by Vin Patel.

Most of the chatbot companies that currently provide a solution have a rule-based AI Mechanism to handle the responses, and they become efficient when customer data is leveraged. This process is quite time consuming and the reason why chatbots become a controversial topic in executive meetings. With the progress of deep machine learning technologies in the past two years, automated conversational chatbots are leading over rule-based chatbots. These chatbots, instead of being rule based on consumer data, they use the conversational data of entire social media to find the intent of the consumer. Matching with customer data is not required, as in the case with the rule-based chatbot. Think like you are running an image recognization like algorithm on natural conversations on chat to map the primary intent of the customer. The image recognition algorithms match an image rapidly with billions of mapped images to find the type of image. Similarly, a conversational chatbot map outlines a natural conversation to make it contextual based on conversations sets in social media.

Facebook is a pioneer of discussions on social media, and they have recently open-sourced their deep conversational machine learning intelligence. Facebook has pre-trained 9.4 billion neural networks on massive conversational data. It uses 1.5 billion existing open domain natural conversations to train conversations. The conversation seems like human interactions in a very contextual fashion. The algorithm( ) can handle a terabyte of conversation data sets quite quickly. The approach to bring your organization’s data to work will be to do speech to text conversation of your entire call center historical data set and include that as a training set on top of already available intelligent pre-trained neural network. The algorithm uses skills like knowledge, empathy & personality to engage a consumer in a natural conversation based on billions of trained conversations. It then blends all three skillets to keep the consumer more engaged in the conversation. This blending is known as blended skill talk. An example of a blended skill talk is as below:

Original diagram for Techfastly by Vin Patel.

The conversational chatbots can handle conversations like negotiations, chitchats, question and answer, empathetic dialogs, booking reservations, quizzes, and so on. This powerful conversational AI can be easily integrated into Facebook messenger to acquire customers, perform transactions, brand awareness, and excellent customer service.

This technology is easy to implement as compared to the enormous time, effort, and money required for rule-based custom chatbots. The key is to use powerful, open-sourced AI with an already available and integrable chat platform like Facebook messenger. The difference between rule-based chatbots and conversational chatbots is a faster time to market with better customer experience and engaging conversations. Also, Python is the language for this open-sourced framework, which is the preferred language for machine learning, and the skillet is easy to cultivate in your enterprise, large or small.

The library also provides seamless integration with Amazon Mechanical Turk so that human workers can do data collection, training, and evaluation. The human Turks and chatbots can all communicate in the same conversation. The group chat can have humans and bots talking to each other in a single chat thread. The challenges with rule-based bots get addressed by engaging the customers with an intent oriented conversation and engaging humans to perform validated tasks.

Original diagram for Techfastly by Vin Patel.

If conversational chatbots when used effectively, can

Provide detailed and expert level knowledgeable answers to complex questions
Enable 24x7 always-on customer service
Get your customer added to an email mailing list or subscribe to a newsletter
Make your customers agree on making a purchase or doing a cross-sell
Make and manage reservations
Resolve customer complaints with empathy

and many more use cases based on your industry.

Tweet to @parlay_parley

Related reading

AI in Digital Marketing

Vin Patel — Tue, 26 May 2020 05:03:34 +0000

Key takeaways

It has impacted all the spheres of life, and digital marketing is no exception.
You can generate your next sales proposal here and see the difference.
!Diagram: How the pieces fit Original diagram for Techfastly by Vin Patel..

Artificial Intelligence (AI) is the future of the world. It has impacted all the spheres of life, and digital marketing is no exception.

Digital marketing has replaced a good part of traditional marketing. And now it’s time for AI-based digital marketing to mark the future. But what exactly is AI?

What is AI? #

Artificial Intelligence is human-like intelligence to think and analyze, but it is artificially created. It mimics the human mind and assists in performing the tasks faster and with more precision.

AI machines are smart machines that can not only analyze data but can also take decisions and perform tasks accordingly. These machines are fed with relevant data and are programmed in a specific way to develop an artificial intelligence of their own.

But if AI tools are created and programmed by humans, then why do we need it? Can it replace human minds?

Why AI is Needed for Digital Marketing #

AI is not created to replace humans. It is an assistant that reduces the chances of errors and help in performing tasks faster and smarter.

Collecting and Analyzing Data #

Digital marketing involves collecting and analyzing large chunks of data. For humans, it is next to impossible to examine the data of all online users and find the relevant people, but AI has resolved this issue. It is smarter and can work on large data with precision.

Digital marketers can use AI to filter out people that are relevant to the business and help in increasing the reach to the right audience.

H2O is a great AI tool for analyzing data and is good even for those who have no experience in machine learning and AI.

Customers Interest #

Ever wonder, how Facebook knows your interests and shows you products that you are already looking for? Well AI is behind it.

AI can be programmed to store cues that will detect the interest of people. It helps in targeted marketing and will show the relevant products to probable customers. When people find customized promotions, they are more likely to get attracted to the business.

Adept is an AI-based tool for marketers. Its smart programming will help you in finding the right audience.

Personalized Contents #

AI is versatile. It is how you program it. It can even write ads and email content for you, and that too personalized according to your client. Isn’t it amazing?

Now, with AI, you don’t need to send boring and monotonous messages to your client. Send tailored emails and posts to your clients. This will help you in strengthening your bond with the clients and retain them for longer. Custom made advertisements also help in attracting more customers to the business.

Wordsmith, Articoolo, and Quill are great tools for auto-generating the content. You can generate your next sales proposal here and see the difference.

Automated Customer Service and Care #

Customer service and care is a big industry. Traditionally you need competent and professional people to handle your customers. This obviously is a great cost for your business and is a very time taking task. But AI has a solution to this problem as well.

AI-based robots can handle all your customers` queries, purchases, and can even record their feedback. This will not only save you from the hassle but also reduce your marketing cost. You just need to program it correctly, and AI tools will be your customer service representative, salesman, and front desk HR.

Phrase and Boomtrain are AI tool for generating quick automated responses to the queries. Your customers now don’t need to wait for you to get back to them.

Chatbots #

AI Chatbots can automatically handle all your online customer queries and can even assist them in selecting the right product. Big brands like Sephora use chatbots for facilitating their customers.

Shopify is successfully using Kik bot to engage with its customers.

Future Suggestions #

AI is amazing. It can even become your best business partner. Yes, you can make it your friend and business advisor. AI tools understand the algorithm of customers` interests and purchases. It can give you suggestions for improvement and help you in developing future strategies for your business.

Simzek is a great marketing tool that helps in developing future marketing strategies.

AI has revolutionized digital marketing and is the demand of the time. But if you are still not convinced to include AI in your business marketing, then have a look at what big businesses are doing.

The Big Duopoly in Digital Marketing #

Facebook and Google are a powerful duo of digital marketing. They enjoy the big chunk of all online ads and have an edge over all other platforms.

According to local solutions, this powerful duo succeeded to grab half of all digital ad revenue worldwide and more than 60 per cent in the U. S last year.

Facebook and Google have maintained this supreme position for years. Wondering how? A big credit goes to AI. These two companies are smart and have identified the need for the future. They have been using AI tools for years. The superfast and relevant searches of Google is a result of AI, and grabbing the customers with relevant ads and personalized marketing by Facebook is again possible with the help of AI.

Will this biggest duopoly of digital marketing retain their position in the coming years? The answer could be yes and no, as other powerful entrants are coming to the competition.

Amazon #

Amazon seems to be the new giant of digital marketing. According to AdAge, Amazon has grown more than 50% in 2019 to occupy almost 9 per cent of the market. Though it is not a larger share in global marketing, it is a powerful entry. Amazon is expected to be a tougher competition to the biggest duopoly of digital marketing, it has more virtual resources and versatile data as compared with the traditional duos.

Snap chat, TikTok, and Instagram also seem to be challenging Facebook supremacy in social media marketing.

It’s time to accept the changing world and make the most of Artificial Intelligence.

Original diagram for Techfastly by Vin Patel.

Related reading

Questions and answers

What is AI? #

Artificial Intelligence is human-like intelligence to think and analyze, but it is artificially created. It mimics the human mind and assists in performing the tasks faster and with more precision.

Why AI is Needed for Digital Marketing? #

It is an assistant that reduces the chances of errors and help in performing tasks faster and smarter.

Machine Learning 101-II- Iris Flower Data Set? The 'Hello

Vin Patel — Thu, 14 May 2020 12:56:15 +0000

Key takeaways

In the first part of this series of posts, we leveraged a 2016 YouTube video by Google to create an algorithm with six lines of code.
!Diagram: Diagram Original diagram for Techfastly by Vin Patel. For the program to output the actual field values, we need to name the columns of the file and ensure that they and the values are read by it.
!Diagram: Diagram Original diagram for Techfastly by Vin Patel. Congratulations!

Preparing for a Real-Life Machine Learning Project #

In the first part of this series of posts, we leveraged a 2016 YouTube video by Google to create an algorithm with six lines of code. The purpose of the exercise was to show that with just a few lines of code, a non-programmer like me was able to create a Machine Learning Algorithm.

Now, let’s take this process a bit further. Whether you deal with a small project or a large project in Machine Learning, the overall process is the same. I have now relied on a combination of this post by Jason Brownlee, and this YouTube video from Google.

In the following steps, I have summarized the series of steps in Machine Learning:

Setting Up the Environment
Loading the data set
Verifying the data set
Choosing a Machine Learning Model & Training It
Verifying the Accuracy of the Model

A. Setting Up the Environment #

You can view ready-made data sets in sci-kit (a machine learning library) here-. You can choose any of the seven data sets. In this post, I picked the Iris flower data set that contains a collection of 150 records under five attributes - petal length, petal width, sepal length, sepal width, and the name of species.

In case you are wondering what the difference between a sepal and a petal is, the following image should help clarify that.

Original diagram for Techfastly by Vin Patel.

The database of 150 petal measurements covers three different flowers (labels) and has four dimensions (features). The four features are- sepal length in cm, sepal width in cm, petal length in cm, and petal width in cm. The three labels (flowers) are Iris Serosa, Iris Versicolor, and Iris Virginia.

To pick a data set, the first thing we do is- import SciPy. SciPy is a free and open-source Python library used for scientific computing and technical computing.

Original diagram for Techfastly by Vin Patel.

As the data set is in a CSV format, we need to import a module that can read a CSV file. For that, we import pandas. In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis.

As all the values in this dataset are numerical, we also import NumPy. NumPy is a library for the Python programming language, adding support for large, mufti-dimensional arrays and matrices, along with a comprehensive collection of high-level mathematical functions to operate on these arrays.

Original diagram for Techfastly by Vin Patel.

And lastly, as we need to import a Machine Learning Library, we import sklearn. Sklearn is used to build machine learning models.

Original diagram for Techfastly by Vin Patel.

Now we need to import the specific components we need from each of these modules for use:

From NumPy, we import the ability to read a CSV file by typing in- from pandas import read_csv.

Original diagram for Techfastly by Vin Patel.

From sklearn, we import the ability to select a specific machine learning model and the ability to split our data and examine it for training before being able to test it.

Original diagram for Techfastly by Vin Patel.

We then add in two more lines of code- lines 7 and 8 to ensure that we can get a read on the accuracy of our algorithm and to be able to use the Decision Tree approach on which to train our classifier.

Original diagram for Techfastly by Vin Patel.

At this point, we have all the libraries we think we need. If we find out at a later point in time that we need additional libraries, we can always go back and add them.

B. Loading the Dataset #

We can access the data set in any one of the following three ways:

Some test data sets for beginners are available inside packages like SciPy
Same data sets are available online URLs
Some data sets are proprietary and are hence available internally on corporate systems

In our example here, the data set is on a CSV file, and we have called in the read_csv function from pandas. All we now need to do is to specify where the dataset resides.

We do that by specifying the URL by typing in URL = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv".

Original diagram for Techfastly by Vin Patel.

After each line of code, it is a good idea to use the print statement so that we can see if any errors have occurred. So, in this case, we execute print(URL).

We see that the program did execute the command and printed the URL itself. Our goal, though, is to have it print the contents of the file. We, therefore, did not get the validation for which we were looking.

Original diagram for Techfastly by Vin Patel.

For the program to output the actual field values, we need to name the columns of the file and ensure that they and the values are read by it.

Let’s first examine if the CSV file has headers.

A screenshot of the file from the URL confirms that the file has no headers.

Original diagram for Techfastly by Vin Patel.

So we type in the following to our program to name the columns and have the program read the values in:

names = [‘sepal_length’, ‘sepal_width’, ‘petal_length’, ‘petal_width’, ‘class’]

dataset = read_csv(URL, names=names)

To confirm that our algorithm can see the named values in the data sets appropriately, we use the print statement again. Your program and the output should now look like this:

Original diagram for Techfastly by Vin Patel.

C. Verifying the Data set #

To confirm that the program has identified the rows and columns of the data set correctly, we can use the following line of code:

print(dataset.shape)

Original diagram for Techfastly by Vin Patel.

The output of 150, five refers to 150 rows of data and five columns of data.

If you wanted to view the first ten records of the data, you would use the following line of code:

print(dataset.head(10))

Original diagram for Techfastly by Vin Patel.

If you wanted to see the count of records for each label (flower type), you would use the following line of code:

print(dataset.groupby(‘class’).size())

All of these steps are a great way of validating that your program has fully read the data.

Original diagram for Techfastly by Vin Patel.

At this point, you can delete or comment out the print command by adding a # as the first character in the line of code. Comments using # are a great way to make notes for yourself as you start your journey with Python.

D. Choosing a Machine Learning Model & Training It #

Choosing a Machine Learning Model #

Now that the program has been able to read our data, and we have verified that it is successfully reading all the elements of the data, it is time to choose a Machine Learning Model.

The Iris data follows a logical approach, where we can measure the length and width of a petal and a sepal, and based on that, we can tell what kind of an iris the flower is. As we can arrive at the answer by following a series of decisions, a Decision Tree model seems like the right choice for this algorithm.

We have already imported that library with the following line of code:

from sklearn import tree

Training a Machine Learning Model #

It is time now to split our data into Training data and Testing data. Let’s assume we wish to proceed with the standard approach of using 80% of our data as Training data.

As a first step, we need to specify the X values (Features) and Y values (Labels) for our program. We achieve this goal with the following lines of code:

array = dataset.values

X = array[:,0:4]

Y = array[:,4]

The ‘:’ used in the two lines above refers to using the slicing approach. A slice is a way for us to specify the starting index and the ending index in an array.

An array is a reference to a collection of items stored in the program’s memory. All the elements of an array must be of the same type. We already took care of the uniformity of data in our data set by maintaining all features in cm.

The line X = array[:,0:4] specifies the first three columns (0, 1,2, and 3) as the input variables assigned to X. Python starts counting with a 0. The last number, number 4, is only examined with an ‘up to? viewpoint but not taken into account for the variable X.

For Y, the label, we specify only column 4 in the array command.

Our code now should look like this:

Original diagram for Techfastly by Vin Patel.

As before, we can now validate if X and Y are picking up the correct values by using the print command.

When I use the command- print (X), I can see all of the numerical dimensions, and so should you.

Original diagram for Techfastly by Vin Patel.

When I print (Y), I can see all the labels, and so should you.

Original diagram for Techfastly by Vin Patel.

As before, you can now delete or comment out the print statement.

Our next goal is to split the data into 80% for Training and keep the remaining 20% for testing.

To do that we type in:

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.8)

At this point, we run our program to check if it generates any errors. As you can see in the screenshot below, it reported no errors.

Original diagram for Techfastly by Vin Patel.

By adding ‘my_classifier = tree. DecisionTreeClassifier()? we have named our classifier. Once again, we run our program, and we can see that it did not generate any errors.

Original diagram for Techfastly by Vin Patel.

It is now time to train our algorithm on the test data.

We do this by invoking the fit command and typing in:

my_classifier.fit (X_train, Y_train)

This line feeds in the training data to the algorithm. Once again, we run the program and do not see any errors:

Original diagram for Techfastly by Vin Patel.

Now that we have a trained algorithm, it is time to test it. To trigger the testing, we invoke the predictions command by typing in the following:

predictions = my_classifier.predict(X_test)

Once again, we run the program to check for any errors and use the print command to see if we get any output.

Original diagram for Techfastly by Vin Patel.

As expected of the algorithm, we did not get errors, and all of our predictions are the names of flowers.

E. Verifying the Accuracy of Our Algorithm #

Scikit has a measurement module that provides an accuracy score of the algorithm that we created and tested. To output the accuracy score, we type in the following:

from sklearn.metrics import accuracy_score

print accuracy_score(Y_test, predictions)

When these lines get added to the program and run it, you can see that the algorithm had an accuracy score of over 96%.

Original diagram for Techfastly by Vin Patel.

Congratulations!

Where do we go from here? #

We still have some unanswered questions like:

A. What if a decision tree is not the best choice for an algorithm? What do we do then?

B. How do we know that this model had the highest possible accuracy score? How would we test the other machine algorithm approaches?

C. What happens when our data set includes data or different types?

D. How can I tell whether some variables have a more direct relationship on the outcome than others?

These are some of the questions we will try to solve in the next post.

Related reading

Machine Learning 101 For Non-Programmers- I

Vin Patel — Mon, 11 May 2020 15:46:04 +0000

Key takeaways

This post is part-1 of a series of multiple posts that will help us get somewhat familiar with Machine Learning. In our work lives, many of our projects touch Machine Learning directly or indirectly.
The Training Classifier ingests this Training Data and detects patterns in the data.
Only when we feed in thousands of records, we will see our algorithm properly train itself and be able to provide reliable outcomes.

Writing an Algorithm with just six lines of Code- Inspired by a Google Developers’ YouTube Video From 2016 #

This post is part-1 of a series of multiple posts that will help us get somewhat familiar with Machine Learning.

In our work lives, many of our projects touch Machine Learning directly or indirectly. Apart from a few folks on the team, seldom do others get a chance to work on an algorithm. The very word, algorithm, brings up images of pages and pages of complicated code. The reality, however, is much different.

The kind of a Machine Learning model we will code today will be a Supervised Learning Machine Algorithm. What this means is that we will feed some sample data (referred to as ’training data’) to our 6 line algorithm and get the desired prediction.

As an example, if we were writing an algorithm that was going to predict if an email is a spam or not based on some rules we provide, the entire process would look like the image below.

Original diagram for Techfastly by Vin Patel.

The different emails coming in would be the input. The classifier or our algorithm would then classify emails as spam or not spam. The resulting segregation would be the output of the program.

In this transcription of a Google Video, I will walk you through all the steps I took to set up the right development environment and then understanding the six lines of the actual algorithm. The use case I have taken here is that of an algorithm that will determine if a new car insurance applicant is likely to be a Great/Average/Bad driver. From a business standpoint, of course, based on the prediction of how good a driver is, a different price can be provided to different applicants.

However, at the end of the exercise, you can save the algorithm and reuse it for any other use cases.

The steps I am describing below have been broken further into major categories:

Step 1? Setting up the Development/Coding Environment

Step 2? Writing the Algorithm

Step 3? Running the Algorithm

Let’s get started.

Step 1? Setting up the Development Environment #

We need to install two things:

A. A programming language to write the Machine Learning Code in and

B. A code editor that helps us detect errors easily as we write this code

You can achieve both objectives by visiting. Anaconda is an open-source data science platform that provides access to Python and R, two programming languages, and their editors in a convenient package.

Steps to download Anaconda and getting ready to write code in the Code Editor:

Go to the individual version of Anaconda page -
Choose the right version of Anaconda to download.

Original diagram for Techfastly by Vin Patel.

Suppose you are on a Windows machine, you can check whether you are on a 32 bit or 64-bit machine by typing in ‘system information’ in the Windows search bar at the bottom left.

Original diagram for Techfastly by Vin Patel.

Double click on the Anaconda software once you have downloaded it. Choose all the ‘recommended options’ as you install Anaconda.

Original diagram for Techfastly by Vin Patel.

Access the Anaconda Navigator by using the windows search bar.

Original diagram for Techfastly by Vin Patel.

Click to launch Spider- the code editor that you will be using to write the code

Original diagram for Techfastly by Vin Patel.

At this point, this is what your screen will look like

Original diagram for Techfastly by Vin Patel.

The Code Editor

As you write code in the left-hand panel, Spider will let you know if you have entered any wrong syntax. You are now ready to write code for your algorithm.

Step 2

Writing the Algorithm #

There are three key steps at a macro level to write the algorithm that we need to undertake:

Original diagram for Techfastly by Vin Patel.

The Training data collection exercise is where the algorithm gets the data from which it can learn. The Training Classifier ingests this Training Data and detects patterns in the data. At this point, the algorithm is ready to make predictions on any input data that it may get.

As a general rule, an algorithm usually gets 80% of all data. We use the remaining 20% of the data to test the algorithm. But more on this will be discussed much later in this series of posts.

Line 1

At this point, we need to import scikit. Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms.

In-Line 1, all you have to write is ‘import sklearn’. Don’t worry about the ‘alert’ that the code editor displays in orange. Any time you wish to run your program, click on the green arrow circled below.

Training Data #

Original diagram for Techfastly by Vin Patel.

To train the algorithm, we need to provide it with some relevant data. Here is an example of what this input data may consist of in our insurance example:

Original diagram for Techfastly by Vin Patel.

For this small example, we will stick with the four input variables of Zip Code, Age, Gender, and Car Type. On a live project, of course, there may be many other variables that can be relevant and required.

In terms of the terminology used in the field of Machine Learning, the four input variables of Zip Code, Age, Gender, and Car Type are called ‘Features’. The desired output of a Good/Average/Bad driver is called a ‘Label.

Let’s take a very small sample of 3 records:

Instead of filling in actual Zip codes, Age, Gender, and Car Type with actual values, using numeric values make it easy to write the algorithm. If that is not the case, we would have to deal with integers versus strings, etc. So let’s avoid that by using numeric codes for each feature field.

Original diagram for Techfastly by Vin Patel.

In our simple example, we will input the numeric codes above as a representative value for each feature.

Step 1? As we are getting ready to enter now important data, it is a good time to save this file with a real name. Use the file’s ‘Save As’ command and save it as ‘car-insurance’.

Original diagram for Techfastly by Vin Patel.

Please make sure that you change the file type to be as ‘Python’ when you are saving the file.

Line 2

Add in the features as line 2 into the algorithm.

features = [[1, 2, 1, 2],[1, 5, 1, 4],[3, 3, 2, 6]]

Your screen should now look like the following screenshot:

Original diagram for Techfastly by Vin Patel.

Line 3

Add in-line three, which is the ’labels’ for the algorithm to learn.

labels = [0,1,2]

Based on the code above, your excel file/Google Sheet of data should look like as follows:

Original diagram for Techfastly by Vin Patel.

The program now has the input data that it needs. We now need to train the algorithm.

Line 4

At this point, we need to import the Decision Tree module from scikit for data to be analyzed using Decision Trees.

A single line of code can easily achieve this-

clf = tree. DecisionTreeClassifier()

The word ‘clf’ refers to the ‘classifier’. Your screen should now look like this:

Original diagram for Techfastly by Vin Patel.

The red X on line 4 suggests an error. If you hover your mouse over the red X you see the code editor being helpful and providing you the reason for the error:

Original diagram for Techfastly by Vin Patel.

The error relates to ’tree’ not being a defined element. Line 1 needs to be revised from the sklearn to ‘import tree’ to resolve the error. As you can see from the screen below, the error has now gone away.

Original diagram for Techfastly by Vin Patel.

Line 5

At this point, we are ready to trigger the Training Classifier to ingest the rules and detect the patterns from the sample data. To do so, we need to add the following line of code-

clf = clf.fit(features, labels)

The ‘fit’ here refers to the command which instructs the algorithm to find patterns in the data.

Original diagram for Techfastly by Vin Patel.

The Prediction #

Your algorithm is now ready to accept input data for a sample case and provide a prediction of the kind of driver a prospect is likely to be, based on what it has learned.

Let’s assume that a new prospect has the following variables for their Zip Code, Age, Gender, Car Type - 3, 2, 3, 4

To output the prediction for the values above, please input the following line of code-

print(clf.predict([[3, 2, 3, 4]]))

At this point, your screen should look like this:

Original diagram for Techfastly by Vin Patel.

To run the program, click on the green arrow and see the output on the right side of the pane to view the prediction.

Original diagram for Techfastly by Vin Patel.

Congratulations! Your algorithm predicts that based on the values inputted, this new prospect driver will be a bad driver.

Based on this information, we can show this prospect as a higher car insurance quote.

This algorithm can now be saved and reused for other use-cases with modification. In the next post, we will examine how you can analyze results for thousands and tens of thousands of records. Only when we feed in thousands of records, we will see our algorithm properly train itself and be able to provide reliable outcomes.

Related reading