Artificial-Intelligence 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.

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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.

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Artificial Intelligence's Ethical Dilemma

Vin Patel — Fri, 02 Dec 2022 07:53:33 +0000

Key takeaways

AI has changed both our personal and professional lives with its exciting features.
According to the World Economic Forum report, 85 million jobs will be displaced, while 97 million new jobs will be created worldwide by 2025 because of AI automation.
“Always merits are superior to demerits”, so ponder before quest while using AI or other technologies.

Why do we often negatively describe Artificial Intelligence’s Dilemma? Are we not using Artificial Intelligence (AI) to simplify tedious and time-consuming tasks? Are companies not using chatbots for lead generation? There are multiple similar examples, so why not discuss the positive (or white) side of AI’s Ethical Dilemmas?

Introduction #

Like any other technology, AI has its strength and weakness. AI has changed both our personal and professional lives with its exciting features. Although, there are concerns regarding the ethical values embedded in AI systems. In this article, I will discuss the positive side of the five major ethical concerns associated with artificial intelligence technology.

Artificial Intelligence’s Ethical Concerns and Their Positive Side #

Biased Outcomes #

Biased outcomes are considered to be the major AI dilemma. Let us first understand what biased outcomes are? AI bias is an output inconsistency generated by machine algorithms. The prejudiced assumptions during algorithm designing or while training the data are the main reasons behind this inconsistency. We mostly assume that AI bias an anomaly. For example, when we search keywords like “great leaders” or “powerful personalities” in search engines, we get a list of eminent males rather than females. The generated search result has been labelled as “AI bias”.

In other words, AI-based systems have been giving high priority to men as compared to women. It occurs because of the bias training in the system that uses predictive analysis and a powered algorithm. The training generates data based on historical data sets provided to the system. The search engine’s results are based on user clicks that depend on locations, preferences, and choices.

Where is the gap? The gap is due to certain images regarding woman’s presence in the digital world. The system is not prioritizing genders and generate search results for any query in a few seconds.

Looking at the positive side, we are getting search results in a few seconds because of an AI-based system. For instance, if we search “richest people” in any search engine, we get accurate output because AI developers created an algorithm that is based on real facts.

Instead of putting questions on biased outcomes, think that the outcome is the result of reality. Developers are putting effort while designing advanced algorithms, extracting critical information from historical data, and building those data sets. The developers are making patterns based on actual data. Earlier, some algorithm-trained data favoring male nominees. Although things have been changing, the developers are now creating a neutral data set. If we can ignore some specific gender bias output, we can focus on how fast we get search results because of AI systems.

Autonomous Vehicle / Carrier #

The autonomous vehicle or carrier is an excellent advancement of artificial intelligence. We know the concept of an autonomous environment; it senses the surroundings and makes decisions without human intervention.

For instance, autonomous mobile robots operate without any human administrator based on specific training. Companies have already adopted these robots to reduce laborious tasks in their factories. During the COVID period, various robots were developed to help patients while reducing direct human interaction; following social distancing protocol was a need, even for medical professionals.

The autonomous car senses the environment and moves robotically. The autonomous car reduces accidents, traffic congestion, and fuel consumption because of in-built sensors and other technologies. It further results in reduced CO2 emissions. The autonomous car offers various benefits, especially to elders and people with disabilities.

Developers collect a massive amount of data to understand the driving environment and build an autonomous driving system that processes them to make decisions while driving. The ethical dilemma occurs while making critical decisions in case of travel emergencies. Suppose a human being jumps in front of an autonomous car suddenly, leading to an accident. It would be assumed that the vehicle was at fault. Although, in the same scenario with a normal car, the man who jumped suddenly would be blamed for the accident.

The contraction between a car with driver and a driverless car causes an unneeded dilemma ahead of us. The autonomous carrier is enhancing lives and making things easier. The world is accepting autonomous vehicle-based transportation systems because of the great advantages they offer. We could conclude that the positive side of autonomous vehicles must have high weightage than the opposing side.

Unemployment #

AI tools, machine learning algorithms, and advanced robots have already replaced humans in the workplace. Automation in factories, automated scheduling systems, advanced software, marketing analytic, and chatbots have replaced time-consuming and complex business processes. As AI grew more sophisticated, it is now difficult to tell if you are chatting with a bot or human.

Though artificial intelligence offers numerous ways to make life easier, it causes concern about unemployment. “Is my job safe for the next few years considering the automation?”, this question bothers a lot of people. An increased rate of unemployment is one of the biggest ethical dilemmas associated with advanced technologies.

According to the World Economic Forum report, 85 million jobs will be displaced, while 97 million new jobs will be created worldwide by 2025 because of AI automation. AI will require businesses and governments to collaborate on massive deskilling and upskilling initiatives to help employees retrain and prepare for new and future jobs.

Culture-based Ethical Dilemma #

Breakthrough development in AI has brought another ethical dilemma in the Art industry. We can create a copy of the original artwork designed by a human being using AI tools and techniques. For this, Al algorithms analyze existing arts, upscaling and preparing an exclusive database. Ethical questions arise when we need to identify both arts and distinguish which one has been designed by humans and which one by machine. A similar incident happened in 2016 when a 3D printer and computer designed a Rembrandt painting named “The Next Rembrandt” after 351 years. If we think from a distinct perspective creating such an artwork would be an achievement for those artists, and through this, we can remember them.

Today, we are all curious to know about history, art, and culture. We can take the help of AI to understand and make it memorable. Whenever the machine is designing existing artworks, designers must have to take permission from respective artists.

The AI-based machine can only create similar copies; it can’t create a creative piece of art like a human artist. By designing the appropriate AI ethical rules while creating existing art, we can look back at our history and make the database for future generations.

AI in Judicial System #

We all know that AI has entered every human space; it has changed many things and working styles. We have explored the entry of autonomous vehicles as a good indicator of growth. We are getting shopping options in a personalized manner on Amazon and other similar E-commerce websites. It is possible because developers create training data and build AI recommendation systems to provide equivalent recommendations.

AI entered the judicial system, and we have a system that can evaluate legal cases to make more accurate administrative and legislative decisions. The main reason to introduce AI in this area is to encourage effective counseling sessions and judicial proceedings.

“Are AI-based judicial systems able to assess law cases and make decisions like a human being?” “Can these systems make better judgments?” These are the questions that provoke questions about the usage of AI in the Judicial system. Accepting machine-based judgment is a cause for concern.

AI-based judicial systems can collect massive data, analyze relevant factors, and evaluate these efficiently. For a human being, it is tough to read a plethora of legal cases and other relevant factors. Lawyers can utilize this system to extract meaningful data while processing any case.

By considering an AI-based judicial system as a supporting tool for lawyers, it is possible to overlook the dilemma. This support tool would help lawyers make better decisions after analyzing the AI-based systems’ decisions.

How can the human mindset ignore the black side of these dilemmas? #

After discussing various examples and cases, we can conclude that these dilemmas are assumptions. Overlooking these dilemmas is better compared to augmenting their black side. We, humans, are developing these technologies and putting all our efforts into providing the best output. Irrespective of these efforts, it might be possible that AI will never make moral decisions. However, developers can use more accurate training data sets in AI systems to provide ethical outputs. The development of universal ethics would also support AI system usage without having doubt. These small changes would aid in focusing on strengths rather than weaknesses.

End Note #

A human being’s common nature always gives preferences to the “Black Side of technologies” and then analyzes the “White Side”. However, every human being depends on technologies and tools for their personal and professional works. The ethical dilemmas of artificial intelligence are not created purposely. It is a flaw that could be ignored. On one side, people are demanding “drones”, “instant result list”, “autonomous car”, “medium for storing historical arts”. On the other side, they assume that these are unethical activities and raise unnecessary questions. So, rather than considering that these Artificial Intelligence dilemmas might harm us, we should think that these technologies are making our lives easy. “Always merits are superior to demerits”, so ponder before quest while using AI or other technologies.

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Questions and answers

How can the human mindset ignore the black side of these dilemmas? #

After discussing various examples and cases, we can conclude that these dilemmas are assumptions. Overlooking these dilemmas is better compared to augmenting their black side.

Let us first understand what biased outcomes are? #

AI bias is an output inconsistency generated by machine algorithms. The prejudiced assumptions during algorithm designing or while training the data are the main reasons behind this inconsistency.

The Future is Now; How AI is All Around Us

Vin Patel — Fri, 02 Dec 2022 07:51:41 +0000

Key takeaways

But, as they have become a part of our routine, we usually don’t really stop to think about how incredible those little pieces of technology actually are and how they work.
In fact, smart, self-driving cars like Tesla are already in use, and AI has already entered our kitchens, from the mechanisms that can generate recipes from photos to an actual robot that can cook.
But it seems that we ourselves tend to forget it somewhere in the process, or in between, of communicating with some of the forms of AI that surround us and consume our attention, time and life.

Today, in 2021, we live in a reality where it would be pretty hard to imagine a day without an increasingly sophisticated range of machines; for most of us, it is our phone, computer, tablet, or even a coffee machine without which our everyday routine would be almost unimaginable. But, as they have become a part of our routine, we usually don’t really stop to think about how incredible those little pieces of technology actually are and how they work.

Is AI The Breaking Point in The History of Humanity? #

So, even though these game changers quietly crawl under our skin and we don’t feel the rapid difference we have half-consciously embraced, it is a fact that we are witnessing a profound breakpoints in the history of humanity.

Our lives are undeniably saturated with all sorts of devices that hold immense potential, which arouses the biggest expectations and curiosities regarding the future. And let us not forget that all these devices we today perceive not just as a norm but as a necessity would have been (not that long ago) seen as robotic and extremely futuristic.

Well, all these devices use AI, or Artificial Intelligence, to manage complex data and in order to be able to learn and thus refine themselves through collecting information. When you think of it, that is amazing, and it actually resembles our learning patterns and the basic principle of what we call intelligence altogether, but we will get to that a little later.

The truth is that this brave new worldis so incredible because of the speed at which technology develops and the enormous gap between the picture of the individual everyday life of today and that same picture 50 or 100 years ago. It is all happening so fast, but how did we get to this in the first place?

Anticipations of the Technological Era #

Dystopian novels from the last century, like George Orwell’s 1984and Aldo’s Huxley’s Brave New Worlddepict a (not that far) future in which humans will be deprived of free will through drugs that navigate their satisfactory system in the brain and subdue them using short-term pleasure. On the other hand, as portrayed in 1984, we will face a reality in which humans suffer an inhibition of freedom, summed in the infamous line Big Brother is watching you. This line refers to totalitarian surveillance conducted through security cameras and other recognition and controlling pieces of advice.

Nevertheless, even though we are past the years in which these stories took place and we can’t say that their ‘prophecies’ came to life in the letter, a careful reader will catch on serious key philosophical points between the authors’ views of the future and modern life of today.

Many perceive this stream of thought as a warning that someone out there is out to take over the world, but the truth is that this might be too big of an endeavor for a man or even a group of people and that they would need technologically advanced mechanisms to implement their plans. Hence the development of AI is the most promising concept in the world of technology.

According to these theories, the fact that these mechanisms would be so widespread and get into the very pores of individual everyday life is what gives them the power to navigate values, politics, ethics, fashion, and many more aspects of people’s lives worldwide.

Why AI is also the reason you are reading this article, far more than your conscious choice #

In a sea of articles about artificial intelligence, you didn’t go to page 35 of a Google search and consciously chose this article to be your source of information about AI. According to a new behavioral user study, 50% of people who Google search click on one of the search results within 9 seconds, and 9% make it to the bottom of the first search page.

What do you think, who (or what) does the selection of the first five articles (beyond which you probably won’t explore) that will be conveniently thrown in your face and make a choice of your information sources instead of you?

Well, search engine optimization AI has a role in this, especially when it comes to suggested content, as it analyzes the patterns and links between sites and is able to follow the popularity and recognition of content. Apart from this, AI also recognizes focal points of your interests based on which it can feed you with similar content that you won’t be able to resist. Then you find yourself after five hours wondering what the hell it is you’re doing?! You know, story as old as time? or maybe not?

Where Can We Find AI? #

The truth is that what defines modern life as ‘modern’ is an almost overnight advance of technology, and the base of the future technology is AI, as the ‘intelligence’ part itself involves the potential autonomy of machines so that they will be able to solve tasks, function on their own, and even bring to life new, autonomous intelligent mechanisms.

Even though most people associate AI with humanoid robots, artificial intelligence is way more widespread in the area of robotics: AI is literally all around us!

In our hands (in the form of our phones, only Sir itself is an AI mechanism),
In our cars (navigation and Google maps use AI),
In all of our social media (Facebook uses AI for collecting and managing data, as well as for face recognition; for example, Facebook can recognize and tag your friends on your photos; Instagram and YouTube use AI for suggestions, and e-mail filters spam using AI)
In the world’s quickest, smartest and most available encyclopedia -Google,
In our health systems,
In our payment systems,
Even in some of our washing machines.

Experts are even more ambitious when it comes to the future of AI, as they are working on implementing AI in areas like the traffic, kitchen, sports, construction and fire department, along with other jobs that are life-endangering, etc. In fact, smart, self-driving cars like Tesla are already in use, and AI has already entered our kitchens, from the mechanisms that can generate recipes from photos to an actual robot that can cook.

Meet Alfred, a robot that can help out in the kitchen!

And when it comes to all AI devices, they’re still learning, and we are all helping them with that. For example, one of the purposes of the ‘prove you are not a robot’ tests on Google is AI learning.

Twitterati Unite to Speak up Against the Inconveniences caused by reCaptcha!

The Harvard Gazette released an article called ‘Imagine a world in which AI is in your home, at work, everywhere’, but the truth is that AI already is everywhere and anywhere you turn, only in let’s say baby forms.

Is AI Dangerous? #

The concept of AI itself possesses immense potential and possibilities. Many experts today suggest that once this kind of technology is improved to the point, it passes the line after which the intelligence can further create new patterns and solve new problems on its own - the behavior that was artificially created will not be predictable, comprehensible, or controllable anymore.

This is exactly what many are afraid of when it comes to the development of AI, both nonprofessionals and experts. The most sensitive questions that make the progress of AI a controversial topic are ethical problems and a possibility that AI will replace humans in some lines of work.

On the other hand, the majority of the field’s specialists argue that creating a truly human-like intelligence is as far as it is complex. To begin with, we don’t even fully understand how our own intelligence functions beyond the principles of neuronal communication.

Humanizing machines are all the rage amongst some of the most valued minds of today and it seems like mechanical and electrical engineering, hand in hand with computer science, might replace science as we know it altogether, as science fundamentally examines nature and its laws.

Perhaps we have scooped dry the essence of the nature around us (or at least our epistemological potential to grasp it and come to a point where it is time to make our new one.

After all, God is often described as a form of super-intelligent being that created such a complex and perfect organic structure as the world that we are a part of. In the light of the vast technological improvement, some argue that it wouldn’t be impossible that even the world we know isn’t the most complex known algorithm, a Matrix-like simulation.

Conclusion #

We are the generation that is witnessing the rise of technology at its full strength, and whatever happens in the future, it will more than partly be our responsibility and a direct consequence of our reactions to these rapid changes or a lack of those same reactions. And the only way to make a decision is to be conscious, awake, to think- which is exactly what we are now working so hard to teach these AI creations. But it seems that we ourselves tend to forget it somewhere in the process, or in between, of communicating with some of the forms of AI that surround us and consume our attention, time and life.

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Artificial Intelligence in Healthcare - Making Impactful

Vin Patel — Thu, 01 Dec 2022 04:26:14 +0000

Key takeaways

Imagine a world where you are lying on a bed in a hospital while a robot performs necessary surgery single-handedly.
These robots can work alongside people and are simple to teach to do specific jobs.
Building an intelligent health system will improve patient experiences while making professionals better at what they do rather than replacing them.

Imagine a world where you are lying on a bed in a hospital while a robot performs necessary surgery single-handedly. While the world may not have advanced to this hyper-realistic level of technology, artificial intelligence has made significant strides in healthcare. The following article contains detailed insights into the progress of artificial intelligence or AI in healthcare and talks about its impact on the future of our civilization.

AI in Healthcare #

Numerous technologies that have revolutionized medicine and healthcare have emerged due to the quick technological advancements in recent years. The development of software systems, the rapid rise of microprocessor technology, and the expanding accessibility of the internet all benefit the field of artificial intelligence. These developments have led to various widely used devices, such as tablets, mobile phones, laptops, and interactive virtual reality devices.

The McKinsey Global Institute (MGI) has investigated how automation and AI may change the nature of labor. It is concluded that while automation will touch most employment across industries, the degree of impact will vary greatly. Healthcare has one of the lowest overall automation potentials, with only 35% of time spent being automatable, depending on the vocation. The possibility of acceptance differs from the potential for automation. The analysis assumes a midway scenario that predicts that 15% of current healthcare labor hours would be automated.

The pandemic increased diseases linked to modern lifestyles, and an expanding global population has put enormous strain on the modern-day healthcare system. The best part about implementing artificial intelligence is that healthcare might become more affordable, efficient, individualized, and equitable to develop intelligent systems and workflows. According to some experts, the healthcare sector will likely be most impacted by the massive changes brought on by the fourth industrial revolution.

Impact on the Workforce and Beyond #

Biotech companies and healthcare employees presently use a range of AI technologies. The critical application categories are recommendations for diagnosis and treatment, patient involvement and adherence, and administrative chores. Implementation challenges will prevent the jobs of healthcare professionals from becoming immensely automated for a considerable amount of time, even though AI can perform healthcare tasks in many instances just as well as or better than humans.

Demographic aging, shifting patient expectations, a change in lifestyle preferences, and the never-ending innovation cycle are just a few of the inescapable drivers driving demand. The effects of an aging population stand out among these. One-fourth of the people in North America and Europe will be over 65 by 2050, meaning the healthcare systems will have to deal with more patients with complicated demands. It is expensive to manage these patients, and procedures must change from a philosophy of episodic care to a considerably more proactive and centered on long-term care management.

Changing clinical workflows is where AI in healthcare has the most potential. AI can provide value by automating or enhancing the job of staff members and clinicians. We can utilize AI to help health professionals perform better at their professions and improve patient outcomes, and many repetitive operations will eventually become entirely automated.

Healthcare organizations that can completely rethink and redesign their workflows and procedures and apply machine learning and AI to build an intelligent health system will be the most successful.

The Types of AI in Healthcare #

Machine learning #

Machine learning, or ML, is a subfield of artificial intelligence that analyzes past data, present knowledge, and interactions using an algorithm. Medical professionals can now predict the optimum course of treatment for patients thanks to this study. It is essential for streamlining and improving healthcare administration and procedures. One of the critical components of ML is data annotators, wherein tumor cells are more precisely marked using CT and MRI imaging.

Natural Language Processing (NLP) #

It is the process of a machine comprehending and interpreting text or language. NLP can assist in analyzing patient medical information and provide recommendations for enhancing the effectiveness of the techniques employed and improving patient outcomes. NLP is frequently used in AI to comprehend and categorize medical records. When a machine understands what a user is attempting to say, it responds appropriately.

Physical Robots #

Physical robots are a common form of artificial intelligence in the medical industry. Scientists initially created robots to transport medical supplies. However, modern robots can be found in better conditions. Given the fact that more than 200,000 industrial robots are used each year worldwide, physical robots are now well-known. These robots can work alongside people and are simple to teach to do specific jobs. Due to the operating system’s integration of AI capabilities, they are more intelligent- intellectually. Since 2000, surgical robots have left their imprint on operating rooms. This field of AI is still undergoing potential advancements.

Robotic Process Automation #

This branch of AI was primarily created for administrative functions. RPA (Robotic Process Automation) is less expensive than other forms of AI. Contrary to its name, this technology does not employ robots; instead, it uses a computer program. RPA is used in healthcare to bill patients, update their medical data, and record early authorizations. It can even be combined with other technologies, like picture recognition, to extract data.

Most sizable healthcare firms are starting to embrace AI in some capacity. But the process of discovering how to use artificial intelligence to improve healthcare is still in its infancy.

Making predictions using machine learning and AI is one of the prominent use cases. Organizations are employing AI to forecast everything from emergency department volumes (to improve staffing and triage) to the medicines that may be most successful for breast cancer patients.

The Future of AI in Healthcare #

Every aspect of healthcare, from accounting to surgery, can benefit from using AI. Let’s look at some potential healthcare uses. Doctors can highlight pertinent information from patients’ medical records using the Natural Learning Process (NLP). This data aids them in making wise treatment decisions for the patient. AI can store a sizable amount of data, offering pertinent patient information and suggesting the best course of therapy.

Another vital contribution of artificial intelligence in healthcare is the introduction of chatbots. Even for minor health issues that people may handle on their own, people often arrange appointments with their doctors, which results in a busy schedule for doctors. Patients can use chatbots to cut costs associated with unnecessary healthcare visits. Chatbots are nothing but artificial intelligence applications that use algorithms to operate. These chatbots rapidly respond to any inquiries or concerns. Additionally, they offer solutions to patient issues and even advise people to self-treat minor health issues. Chatbot services around-the-clock allow doctors to work in a laid-back setting.

Robots with integrated AI technology can carry out medical procedures, including surgeries. Surgery performed by surgical robots is exceptionally accurate. These surgical robots can gather patient surgery histories from the past and make decisions based on those facts. AI-implemented robots can, at times, replace people as nursing aides. These machines are referred to as virtual nursing aides. They may take on responsibilities ranging from talking with patients to caring for patients. They can examine patients, respond to their inquiries, and offer appropriate remedies because they are available around the clock. Care Angel is the first virtual nurse’s aide in the world. Care management for vulnerable and at-risk populations was one of the most challenging and expensive aspects of healthcare that Care Angel was founded to address. Millions of high-cost, high-risk patients (particularly those with chronic diseases) can receive dependable, cheap remote monitoring from the comfort of their homes thanks to Care Angel’s SmartCare platform.

Bottom Line #

As AI is still in its early stages, its long-term impacts are not entirely clear. Future AI applications may profoundly change how healthcare is delivered, how innovation is done, and how each of us perceives our own health. We can see a moment in the future when population-level data from wearable and implants will transform our knowledge of human biology and the mechanisms underlying the action of medications, enabling individualized and real-time care for all. Building an intelligent health system will improve patient experiences while making professionals better at what they do rather than replacing them.

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The Data-Centric Approach to Artificial Intelligence

Vin Patel — Tue, 29 Nov 2022 14:53:33 +0000

Key takeaways

“Who has the data has the power.” In 2022, nobody disagrees with O’Reilly on the potential of data.
For example, suppose a data labelled is given an image of 2 iguanas and is instructed to indicate their position.
Unlocking the true potential of data must be our objective if we want programs to run with greater efficiency and accuracy.

“Who has the data has the power.” In 2022, nobody disagrees with O’Reilly on the potential of data. With technology reaching newer heights and exploring newer domains, data powers the process in every respect.

AI pioneer, Andrew Ng, co-founder of the popular e-learning platform Courser and former head of Google Brain, advocated that it’s high time we switch our focus from the performance of several data models to the quality of the data being made available to existing systems. Data-centric AI is precisely defined by Ng as “the discipline of systematically engineering the data required to develop a successful AI system.”

Data Vs Code: A closer look #

The model-centric approach targets the development and optimization of algorithms to drive the entire process. This, in turn, limits our scope to ameliorate the gross efficiency of most programs. Oversaturation will only lead it to transform into a program run under a very selective responsive mechanism. A vast section of the AI community believes that the hype over model architecture kills the overall productivity of any program to a large extent. Therefore, the significant implications of following such a strategy will definitely highlight how the quality of data might play a hindrance when fed to sophisticated programs.

And that’s why data needs to be the bedrock for any AI system to be more coherent and accurate. Quality over quantity is beyond doubt the absolute necessity prior to implementation. Refined and labelled data that is relevant to your project and is thoroughly consistent, when run through iteratively driven by proper error analysis, surely promises a more explicit outcome. Deploying an ML model is just the first few baby steps, but a collection of high-quality data periodically over time is what leads to accomplishment. Hence, neglecting the importance of data in AI is equivalent to boarding a sinking ship. Data is food for AI*.*

The Data-Centric Approach #

Going by the general pathway that most project follows; when we are upbeat about adopting a data-centric approach, it is important that we collect the right data for our project. Storing compromised data irrelevant in the bigger scheme of things will only have a detrimental step towards improving accuracy. Quantity is not always the solution!

Labelling - Perhaps the most crucial step in the overall process as this is where we obtain our required clean data by continual reduction of noise, if present. For the quality of our data to be more systematic and consistent, one needs to tally the differences in labels for a specific sample independently labelled. On revising the labelling dictum, we try to reduce the points of disagreement till the consistency is achieved.

For example, suppose a data labelled is given an image of 2 iguanas and is instructed to indicate their position. In that case, they might come up with any one of the labels given besides on the right-hand side. Although none of them are technically wrong, such inconsistencies will only puzzle the neural networks.

Error analysis-driven iteration is the next step that involves training the model as soon as sufficient data has been collected. In contrast to the model-centric approach, where one needs to wait till the entire data has been mustered, it is evident how data-centric options save a lot more time. In addition, as the process is continuous, it is easily detectable whether the right data is being acquired or not and thus allows appropriate changes to be made.

Nevertheless, timely supervision of the input data when the model goes under production is of paramount significance. Drifts are inevitable as data evolves, just like how we evolve in this real world. For instance, data drifts might occur when a model encounters situations for which it is not adequately trained as the distribution of the incoming data did not count such cases. Like if a program that is only trained on properties of integers is now fed fractional values as input, data drift can be observed.

Concept drift, unlike data drift, is not as abrupt but surely adversely affects the performance of any model. A model trained on a dataset of the last decade on costs of commodities will never be able to predict the actual pricing of a similar product in 2022. Similarly, one can never expect a dataset trained as per today’s standards will result in successful predictions as an outcome 10-20 years down the line.

Thus, to hold the code fixed and iteratively improve the data and not the vice-verse is what is actually proposed by the likes of Andrew Ng, who have called for the shift toward Data-centric AI.

Good Data #

All the discussions above have only paved the way to a specific objective for MLOps: corroborating the availability of premium quality data in each phase of an ML project lifecycle. The fact that good data over big data needs to be the approach for efficiency-oriented developments raises a very basic question - How do you define Good Data?

In conclusion, it is fair enough to say that it is high time that the AI community overcomes its bias toward complex model architectures and treats data as an asset. Unlocking the true potential of data must be our objective if we want programs to run with greater efficiency and accuracy.

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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.

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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.

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5 Different Ways Advertising Helps Your Business

Vin Patel — Thu, 24 Nov 2022 16:51:21 +0000

Key takeaways

Ranging from traditional advertising methods, including broadcasting and printed media, to modern advertising methods, including digital marketing and AI advertising.
These platforms provide a vast audience to display and promote your business.
If the results are not worth the amount spent, it is time to modify your strategy.

#

Advertising and businesses go hand in hand. Ever since businesses have been in the market, so has advertising. Since ancient times, advertising has been enormously used as a promotional tool to grow, expand, and spread awareness about one’s products and services.

Ranging from traditional advertising methods, including broadcasting and printed media, to modern advertising methods, including digital marketing and AI advertising. A modern, innovative AI advertising company analyzes one’s data, driving sales and revenue, maintaining market shares, and establishing a solid brand identity.

Here is a list of advertising strategies one can use to increase business growth exponentially.

Artificial Intelligence Advertising #

An AI advertising company can significantly help in driving the growth of one’s business. The implementation of AI in advertising has been an unprecedented factor until now. It has wholly evolved advertising strategies and results by detecting data patterns, making predictions based on previous patterns, and suggesting the best possible approaches.

The more and more data is provided to AI machines, the better they tend to work. Recently, every other platform has taken advantage of AI systems, including Google Ads and Mail. This method is widely known to enhance customer engagement and deliver improved customer experience.

Email Marketing #

Email marketing is yet another effective method to drive your business sales. Numerous surveys reveal that every time a marketer sends an email regarding their business to their customers, it has a good 22% possibility of engaging the customer.

Email campaigning can help keep your products and services at your customer’s fingertips. Depending on the target audience, email marketing can be an optimal option.

Most of the world’s population has shifted from watching television to using social media all day. Platforms like LinkedIn, Facebook, Instagram, and Twitter promote the feature of social media advertising. It can be rightfully given the status as the financial backbone of most social media platforms.

These platforms provide a vast audience to display and promote your business. Although most people tend to skip unwanted ads while using an app, they do get familiar with the product and service. Social media can be the best way to create brand identification and business promotion.

Influence Advertising #

One of the newest forms of advertising witnessed is influence marketing. People proficient in their specific niche with a loyal following count of a minimum of 10,000 people are considered to be influences.

These influences tend to have a solid and loyal customer base that has the great potential to reach thousands of people in a given time. The cost of hiring an influence for brand promotion hovers from $271 to $739 based on their follower count.

Influence marketing is one of the excellent solutions to drive business sales and expand the reach of the business.

Location-Based Offers And Advertising #

One of the most unknown and neglected aspects of advertising is Google’s impressive location-based offers. Google allows people to create a Google My Business Page for every business owner free of cost.

Surveys reveal that about 75% of people who find businesses around them on such a page tend to visit the physical stores in their location. Another way to increase the number of people visiting stores is to provide them with offers as incentives.

Conclusion #

Your target audience is the most important factor in choosing the optimal advertising method for your business. Knowing what kind of customer you are dealing with and how they like to consume information is essential.

Another factor essential for business growth is the return on investment. If one is paying for advertising, one must know the amount they are paying and the results one is getting from it. If the results are not worth the amount spent, it is time to modify your strategy.

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What Does AI Have In The Box For E-commerce?

Vin Patel — Tue, 22 Nov 2022 19:16:36 +0000

Key takeaways

Whether you are an entrepreneur aspiring to start an E-Commerce business or an employee presently working in an E-Commerce sector, AI packs countless surprises in the box for you.
· Cross-selling: Cross-selling encourages an existing customer to purchase an additional or complementary product.
The future brings advanced tools to integrate to enhance the experience at both individual and business levels.

Whether you are an entrepreneur aspiring to start an E-Commerce business or an employee presently working in an E-Commerce sector, AI packs countless surprises in the box for you. It’s AI that defines your business’s existence in the fast pacing and competitive marketplace. Without AI, it’ll be like a needle in a haystack.

Digital Marketing itself isn’t enough for your business. Of course, it’s essential for brand awareness, but when it comes to retaining the brand name, it means retaining customers by streamlining the buyer’s journey.

To sustain the saturated market and establish a fruitful customer relationship, AI brings automation for your E-Commerce business. Automation provides a unique feature of mufti-tasking by cutting down major time-consuming activities. It facilitates sufficient time for better customer service.

You will find e-commerce users at every corner of the world, but if you are to spend your career in the same, then here’s what you must know.

What exactly is E-Commerce Automation? #

E-Commerce automation is just one of the areas where automation has spread its roots. Automation technology is a versatile and convenient technology that uses collective tools to identify and automate repetitive and mundane tasks with dynamic strategies.

It plays a vital role from the start to the end of a customer’s buying journey. From a cold lead to making them loyal customers, automation does it all.

What’s all in the Box? #

1. Dynamic E-mail Content Strategy #

Digital Marketing campaigns will surely bring a significant number of visitors per day to your website, but converting a visitor to a hot lead can be a mundane task.

After a visitor visits the website, a dynamic form filling strategy is enabled. It facilitates them to fill in the details with an e-mail id.

Imagine you are in a store looking to buy a shirt, but the salesperson shows you with best denim jeans collection, you may have liked it, but that’s not on your tick-list. The salesperson continues showing you items other than shirts; won’t that trigger you to leave that store?

Similarly, bombarding customers with irrelevant e-mails and notifications can trigger a customer to unsubscribe from your e-mail list. Hence providing relevant content is crucial. To accomplish this, you can sync CRM with the e-mail list.

Here, automation studies the customer’s behavior, preference, interest, age, location, gender, etc., and prepares a demographic and behavioral report. A personalized e-mail is sent to the customer based on the report.

For Instance: If a customer has wish-listed an item, selected an out-of-stock product, or added a product to the cart but did not proceed with payment yet, an automated personalized e-mail is sent to the customer reminding them about the product status in their cart.

The ‘A/B’ testing feature allows marketers to use two different strategies to trigger a lead to take a Call-To-Action (CTA).

2. Customer Retention #

Artificial Intelligence with E-commerce has a lot more to offer than just selling. Only selling won’t benefit your business, but loyal customers influence the Return-on-Investment (ROI).

By integrating automation, you can establish a long-term fruitful relationship with a one-time customer, thereby converting them into a loyal one. So, how can this be achieved?

· Up-selling: This encourages your one-time customer to shop for a better version of the product/service. It is comparatively expensive than the primary purchase made.

· Cross-selling: Cross-selling encourages an existing customer to purchase an additional or complementary product.

· Cycle-based Selling: This type of selling can trigger an existing customer to purchase seasonal products.

3. Chatbot #

“All customer service representatives are busy right now. Please drop a message, and we’ll call back shortly.” Ever heard this? Here, chatbot comes into the picture.

It offers 24*7 availability, instant response, and detailed answer to your query. Chatbots thus assist in better customer service, increased interaction with visitors.

4. Automated Inventory Management #

With automation in inventory, it allows real-time tracking and managing stocks. It prevents under-stocking and over-stocking, thus reducing non-fulfillment and warehouse space wastage. An automated notification about stock status is sent to the supplier at a predetermined level if it is not as per the record (predictive analysis). For example, it predicts the order date of stock consumed 90% and signals for ordering the stock.

5. Customer Order Management #

One of the sole reasons for losing a potential customer is a delay in the order delivery. From order placement to the feedback stage, automation helps in serving the customer better. Accurate shipping labels avoid human error at the time of dispatch.

6. Analytic #

Tracking the buyer journey becomes simple with the analytical tools AI offers. By integrating it allows the marketers to target and re-target the audience.

It provides the real-time data of the customer. It ultimately tracks the sales and marketing funnel and analyzes ROI. It helps to measure-

Marketing campaign effectiveness.
Customer engagement.
Call-To-Action.
Website optimization required.
User-friendly navigation.
Demographics.
Inventory.

Conclusion #

AI uses the automation tool to ensure smooth operations flow into the system. Every step in the buyer journey is automated and results in an enhanced customer experience. Businesses are designing their online services to imitate the in-store experience. Additionally, AI reduces the susceptibility towards fraud by learning false attempt patterns.

The “ifs” and “buts” were the traditional approach to predicting business decisions. Today, AI’s prediction is far more reliable and has a great probability of success.

E-commerce is just one sector where AI has spread its roots. The future brings advanced tools to integrate to enhance the experience at both individual and business levels.

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Is Artificial Intelligence a Threat to Journalists

Vin Patel — Tue, 22 Nov 2022 19:12:21 +0000

Key takeaways

Remember the movie, WALL-E where every little thing was controlled by the machines – From household work to running the massive spaceship!
This is essential when most news organizations are fighting against their economic condition.
was this new shiny technology used by high teach companies, but now it’s actually becoming a necessity,".* So, it is probable that many more tools of journalism will require AI in the future.

Remember the movie, WALL-E where every little thing was controlled by the machines – From household work to running the massive spaceship! Well, guess what? With Artificial Intelligence’s (AI) development, we are relatively close to that!

But do we have to fear AI?

*‘Rob Journalism’*is a buzzword in the News Industry! Ever read an article written by a BOT? You may have without even realizing it. That’s because they are designed to imitate the writing style, language, and structure like a human. For now, the Rob Journalists write short articles. But are expected to assist with further journalistic tasks in the near future.

Yes, ‘assist’!

You need not fear that the AI will take away your jobs.

Why Do People Fear AI? #

Not only the news industry but almost all industries are using bots. The Robots assist in carrying out the laborious tasks! But the primary concern with this is the fear that it will lead to unemployment. The fear is genuine as we have watched many movies where robots are taking over the world!

However, in reality, these machines are helping the industries work faster. Although it is replacing a few employees, these machines also require someone to operate them! Companies are hiring people who have the expertise to run massive machines. Technological knowledge has thus become essential in today’s time.

People are afraid that if AI develops further in the future, it will replace more professions. This is possible only if Artificial General Intelligence (AGI) is developed. With AGI, the machines will think and work just like humans. However, as of today, it is possible only in Science Fiction!

But if you are a CEO, Social worker, Doctor, Teacher, or Psychiatrist, you need not fear the AI. The professions which require humane qualities are entirely safe from the bots.

Similarly, in the field of Journalism, the bots will only assist in producing rote articles. They won’t take over other journalistic works which require high-level understanding and interpretation. For example, Investigative and Interpretative Journalism will always require humans!

How Is AI Helping Journalists and Writers? #

A good journalist needs to have an excellent analytic mind, understanding of the affairs, curiosity, and creativity! The AI is not capable of handling these qualities as of now.

The Rob journalists are currently writing in the field of sports, weather, elections, and business. The news from these sectors is data-driven which is easy for AI to understand. Many large news outlets use automation in these beats to carry out the tasks. They leave the difficult work for the human journalists.

If you are a journalist or a writer, you must have already come in contact with AI! Things like Google Translate or any other translation tool are part of Artificial Intelligence.

Do you use Grammarly or Hemingway to sharpen your write-up?

Then you are already taking the help of the AI.

Do you use the Print to transcribe your interviews?

Well, that also works on the machine learning algorithms.

AI is helping journalists and writers by making their work easier. Moreover, it is also economical.

According to the Journalism AI report, AI frees the journalists to work on journalistic tasks that require in-depth analysis. This is essential when most news organizations are fighting against their economic condition.

Machine Learning Gadgets in Newsroom #

Large news outlets and agencies such as Reuters are experimenting with many new AI technologies. In 2018, Reuters came up with Lynx Insight and News Tracer. These are artificial intelligence tools that support Reuters in newsgathering.

The Lynx Insight identifies the on-going trends and key facts in a huge database. On the other hand, the News Tracer uses Twitter to find out any breaking news. Usually, news outlets hire a journalist for this task. However, with News Tracer, this work is done by the machine accurately.

In the present times, social media has turned into the hot zone for breaking news. Therefore, having a bot look over helps a lot! It can detect the news easily and inform the newsroom about it.

Bloomberg News has the ‘Cyborg’ to help them with business reports. It can study large databases and find out key facts within a few seconds. This is then used to generate reports. Cyborg is one such automated technology that saves time for the company.

In 2014, the Associated Press got their hands on a software that can generate news stories. They tried it out with the sports news first. When everything went well, they started using this for business news too.

The Washington Post has an AI known as the ‘Heliograf’. According to the wired.com article, the ‘Heliograf’ detects the important data from a massive database and then adjusts it into templates and phrases. It uses various templates and replaces the blanks to make different copies that are shared on various platforms. The software can also alert the user if it finds any issues in the machine.

So, we can expect that soon more media organizations will employ AI to produce news stories on a large scale.

The primary task of AI in the news and writing industry is to assist journalists and writers. They are programmed to do repetitive work. But in recent experiments, it has been found that AI can help in a wide range of tasks such as newsgathering.

The creative aspects of the Journalistic work are for humans only. Although the AI algorithms can assist in this job. So, journalists and writers must learn how to use Artificial Intelligence to make their work easier. Google News Initiative is one such program that educates journalists and writers about machine learning in Journalism.

You may visit the Google News Initiative training section to do the free online courses. You will not only learn aboutmachine learning in Journalism but also about other Journalistic google tools. After completion, you will also receive an e-certificate free of cost.

So, Artificial Intelligence and Journalists can work together to generate high-quality information for the public. This will not only help in strengthening the economic condition of the media organizations but also help in building public trust.

According to Francisco Marconi, the head of research and development at The Journal,“Maybe a few years ago A. I. was this new shiny technology used by high teach companies, but now it’s actually becoming a necessity,”. So, it is probable that many more tools of journalism will require AI in the future.

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Questions and answers

Why Do People Fear AI? #

Not only the news industry but almost all industries are using bots. The Robots assist in carrying out the laborious tasks!.

How Is AI Helping Journalists and Writers? #

A good journalist needs to have an excellent analytic mind, understanding of the affairs, curiosity, and creativity!. The AI is not capable of handling these qualities as of now.

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

More in Artificial intelligence

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

More in Artificial intelligence

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

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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

More in Artificial intelligence

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.

Top 10 B2B Marketing Trends That You Cannot Miss

Vin Patel — Tue, 22 Nov 2022 15:55:59 +0000

Key takeaways

The key marketing strategy in business nowadays is to identify the emerging marketing trends that affect the customer buying pattern.
For B2B marketers, emails offer an engaging, consistent, and reliable medium of distribution.
Lastly, make sure your company has the right marketing tools to keep the customers in the spotlight.

The key marketing strategy in business nowadays is to identify the emerging marketing trends that affect the customer buying pattern. While some marketing strategies tend to remain constantly rewarding in terms of sales, new ones always pop up with every passing year.

Understanding the B2B sectors in terms of marketing strategy can keep you ahead in the competition. It can fetch you more sales and conversions. That is why we have taken a close look at the emerging trends of this year and as a result, come up with a few prominent ones.

The Top B2B Marketing Trends in 2021 have been highlighted below.

1. INCREASING DOMINANCE OF ARTIFICIAL INTELLIGENCE (AI) #

Artificial Intelligence or AI enables businesses to reveal the value of their data, scale up content for a more diverse outreach plan, and give a more comprehensive view of customers and how to serve them.

Artificial intelligence can be used in the corporate world to gain a competitive advantage and enhance efficiency. Automated interactions with customers, partners, and employees are becoming more common and can be hailed as one of the top B2B marketing trends in 2021.

Data analysis plays a very prominent role in AI and will help in facilitating B2B marketing. Moreover, by automating operations, you may multiply your productivity immensely.

Chatbots are an essential component of websites, apes, and any other business structure in today’s world. More customers are expecting and receiving faster responses, better services, and a 24-hour agent to meet their needs.

As the world is still stuck in a pandemonium of the COVID-19 virus, businesses are experiencing an upsurge in customers buying their products through social media.

Social commerce involves directly using social media platforms to sell products and services to consumers. However, it is not the same as e-commerce, contrary to the popular notion. There is a slight difference between the two.

E-commerce refers to a website or dedicated brand application shopping experience. By definition, social commerce enables customers to buy their products through their experience on social media.

Today’s prominent players in social commerce are Instagram, Facebook, TikTok, YouTube, and Interest.

3. FOCUS ON ORIGINAL CONTENT #

No one wants to read the same information repeatedly in a recycled manner and that is why customers always respond best to ‘original content’.

This may appear to be overstated, but it remains true to this day. Be it graphics, writing blog content, preparing ad copies, etc., your original creation will always work best with your target audience. Remember, they are waiting for a fresh experience, something unique that you have to offer along with your product.

You will get the highest ROIs (return on investments) when you use original content rather than some copy-pasted or slightly alternated versions.

Here are some tricks to help you do that:

Brainstorm content ideas.
Mix short-form and long-form content.
Add quirky graphics or high-quality images to support your content.
Add keywords research strategies while publishing content.
Sell it to your prospective customers, B2B leads, etc.
Invest in paid promotional strategies.

4. VIDEO CONTENT RULES THE DAY #

No matter what type of business you are in, video marketing will always reward you beyond expectations. Think about it - more than 70% of B2B buyers watch product videos alone as part of their market research. Videos are used by 86% of businesses as a marketing tool.

Video marketing incorporates an important aspect of the B2B buyer’s journey mainly because of its visual appeal.

More than 93% of marketers believe that video is an essential element in their B2B as well as B2C marketing strategies. Especially after the pandemic struck, market research shows that video content consumption has skyrocketed.

Here are some video ideas for your B2B marketing strategy:

Informative videos/product demo videos
Customer testimonial videos
Event teasers, event recap videos
Company news
Behind-the-scenes video

Videos will enable you to show off your products or your teamwork and educate your clients interestingly and effectively.

The importance of paid ads in B2B marketing is monumental, and it is only increasing day by day. Especially in 2021, digital marketing and investing in paid ads is one of the top B2B marketing trends.

Social media platforms are an excellent lead generation tool solely because more than 50% of B2B marketers use them for research. Investing in paid ads is a foolproof way to acquire those highly valued B2B leads.

Don’t just post ads on social media; create high-quality content and promote them. Nearly 97% of B2B marketers use LinkedIn, which is why it is a key tool for marketing strategies for your business. The same is for Facebook, which 91% of B2B marketers use worldwide for business purposes.

6. REFINE YOUR EMAIL MARKETING TACTICS #

Do you remember those days when emails were regarded as long and dull pieces of text that were immediately discarded from your inbox? They seem ancient today. For B2B marketers, emails offer an engaging, consistent, and reliable medium of distribution. Even 60% of B2B marketers believe in its effectiveness, believing it to be a viable revenue-generating platform.

Email marketing is, without a doubt, one of the top B2B marketing trends in 2021. A successful way to hone your email marketing strategy is described below.

Craft attractive subject lines.
Include a clear and concise call-to-action(CTA) per email, not more than one in quantity.
Make sure your email marketing list consists of only potential customers who fit in your target audience description.
Cold emails can increase your B2B marketing game by almost 16 times, according to research.
Personalize your emails. Wish your clients on special occasions like birthdays, anniversaries etc.

7. CUSTOMER RETENTION THROUGH TARGETED MARKETING #

New B2B customers are interestingly and effectively good, but a long-term customer retention strategy is needed to grow your business in terms of a good reputation. Building rapport with existing customers is the golden rule of retention marketing.

Existing B2B customers should always be specially treated in terms of marketing. That is because selling a product to a current customer is always easier, faster, and less exhausting than selling to a stranger.

How can you do that? #

Establish clear and open lines of communication.
Provide customer care & support and answer their queries in simple, actionable terms.
Gather feedback, take surveys to see where you need to improve.
Appreciate them frequently for showing customer loyalty.
Give special offers, coupons & discounts, etc.

According to a Harvard study, a 5% in customer retention can increase your profits by 95%. This type of marketing is especially effective when you are on a tight budget.

8. EXPLORE THE REALM OF AUGMENTED REALITY #

With the increase in technological advancements, the rise of Augmented reality or AR has benefitted the B2B marketing realm to a unique extent. It enables businesses to provide customers with one-of-a-kind experiences through the use of their mobile devices or tablets.

As augmented shopping experiences are becoming increasingly popular, brands are engaging AR to let consumers have a ’taste’ of things before they buy them.

B2B organizations are now using augmented reality to improve their sales presentations, product demos, and brand introductions. Without the need for an app, the technology is now available to organizations of all sizes. It’s incredibly effective, cool and is one of the top B2B marketing trends that you cannot miss.

9. UTILISE MICRO-INFLUENCERS TO YOUR ADVANTAGE #

Influence marketing is the fastest-growing method of acquiring new customers as part of your B2B marketing strategy.

Someone who has 1,000 to 100,000 followers on social media can be termed as a ‘micro-influence’. In the B2B buying cycle, an influence is someone who clients trust, someone who knows a particular niche and can positively influence buyer decisions.

According to one study, B2B companies can manage an 11x yearly ROI through micro-influence marketing compared to traditional techniques. Social media experts say that 94 % of B2B marketers who have attempted influence marketing believe it is extremely beneficial.

10. OPTIMIZE YOUR WEBSITE FOR VOICE SEARCHES #

Sir or Alex or Cortana, even, are being used daily in our lives for answering queries or performing simple actions like telling the weather forecast, taking down notes, calling or testing someone, or simply chatting.

Another great thing is when we are searching for something we don’t always have to type in the words manually. Instead, we can just use our voice to talk to these artificial assistants and they’ll provide the desired search results.

Optimizing the website for voice searches is something a lot of B2B marketers are doing nowadays, and lately has been one of the top B2B marketing trends.

Voice search is in high demand among today’s audience, with over one billion queries conducted by voice internationally, according to a study. Furthermore, this figure is predicted to reach 5 billion by the end of 2021 and 6.4 billion by 2022.

ENDING NOTE #

Companies grow and evolve in their marketing strategies each year. Technology is developing with each passing day and for some, it may seem daunting. However, if you learn some simple tips and tricks, you can turn the game in your favor any day. The main aim is to know your competition and stay on top of the latest B2B marketing trends. Some of the aforementioned B2B marketing trends will come in handy.

Lastly, make sure your company has the right marketing tools to keep the customers in the spotlight. Only then, they will return the favor.

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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.

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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.

OpenAI Releases An Artificial Intelligence Tool That Can

Vin Patel — Tue, 22 Nov 2022 10:47:34 +0000

Key takeaways

OpenAI is an artificial intelligence company affiliated with Microsoft.
Language may be utilized to educate a huge neural network to do a range of text production tasks, as demonstrated by GPT-3.
If you tell it to put “a red cube on top of a blue cube,” it will occasionally propose putting the red cube below the blue cube.

OpenAI is an artificial intelligence company affiliated with Microsoft. OpenAI has announced the creation of an AI system that can take a description of an object and construct an extremely accurate picture of it all by itself. Instead of requiring traditional Photoshop or digital art expertise, the method allows a person to effortlessly change the image using simple tools and text modifications.

“We hope that tools like these democratize people’s capacity to build whatever they want,” said Alex Nichols, an OpenAI researcher on the project. He believes the tool might be valuable for product designers, magazine cover designers, and artists, either as a source of inspiration or to generate final works. He also suggested that game developers would wish to utilize it to create scenes and characters, despite the fact that the software only creates still photos, not animation or movies.

Because the software could be used to more easily generate racist memos, fake images for propaganda or disinformation, or even pornography, OpenAI claims to have taken precautions to limit the software’s powers in this area, such as removing such photos from the AI’s training dataset and also adding rule-based filters and other human content evaluations to the images the AI generates.

Similarly, OpenAI is aiming to maintain strict control over the distribution of the new AI, which it refers to as a “research project” rather than a commercial service. According to the company, it is only distributing the software to a “limited and selected number of beta testers.” In the past, OpenAI’s natural-language processing advancements have routinely made their way into commercial products within 18 months.

OpenAI’s program is called DALL-E 2, and it is an upgraded version of DALL-E, which was first launched by OpenAI in early 2021. The name is confusing, but it is meant to represent a smashup of WALL-E, the cartoon robot from the Pixar film, and a play on words for Dali, as in Salvador, the surrealist artist, which makes sense given the system’s strange character.

The early DALL E version could only generate graphics in a cartoonish style, frequently on a basic background. In terms of photo quality, the new DALL-E 2 is capable of producing high-resolution images with rich backdrops, depth-of-field effects, realistic shadows, shading, and reflections.

Previously, extremely accurate representations were possible with computer-rendered graphics, but their development required a high level of creative expertise. Simply type “a Shiva Inu wearing a beret and a white shirt” and the AI will generate thousands of lifelike variants on what you just wrote.

DALLE is a 12-billion parameter GPT-3 version that was trained on a dataset of text-image pairs to generate images from text descriptions. We observed that it can, among other things, produce anthropomorphized representations of animals and objects, connect unrelated concepts in plausible ways, generate text, and make adjustments to existing images.

Language may be utilized to educate a huge neural network to do a range of text production tasks, as demonstrated by GPT-3. Image GPT demonstrated that the same type of neural network may be used to generate high-fidelity images. These findings are expanded upon to illustrate that controlling visual notions through language is now possible.

DALLE is a transformer language model, similar to GPT-3. It accepts both the text and the image as a single stream of data with up to 1280 tokens and is trained to create all of the tokens one by one using maximum likelihood. DALLE may use this training technique to not only create a picture from scratch but also to regenerate any rectangular portion of an existing image that extends to the bottom-right corner in a fashion that is consistent with the text prompt.

They understand that work using generative models has the potential to have far-reaching societal implications.

They intend to investigate how models like DALLE relate to social issues such as the economic impact on specific work processes and professions, the possibility for bias in model outputs, and the longer-term ethical challenges posed by this technology in the future.

It also makes image editing easier. What you have to do is simply draw a box around the image part that needs to be altered and enter natural-language instructions to explain the change. For example, You need to make some changes to Shiva In’s beret so all you have to do is draw a box around Shiva In’s beret and text “make the beret red,” the beret will turn red while the rest of the image stays the same. DALL-E 2 is also capable of generating the same image in a variety of styles that the user can specify in plain text.

DALL-E 2 was an important step toward OpenAI’s goal of creating artificial general intelligence (AGI). According to Ila Sutskever, confounder and chief scientist of OpenAI, it is a single piece of software capable of attaining human-level performance or better across a wide range of different jobs. Also that AGI would require “multimodal” conceptual understanding, or the ability to correlate a word with a visual or series of pictures and vice verse.

Natural-language processing has previously been used by OpenAI to achieve AGI. The company’s only commercial product is a programming interface that allows other companies to use GPT-3, a vast natural-language processing system that can construct long stretches of unique text and do a variety of other natural-language tasks, such as translation and summarization.

However, DALL-E 2 is not something one would call perfect. In complex settings, it is sometimes unable to render details. Imperfection is seen in the lighting and shadow effects. It also lacks the same understanding of “binding properties” as other multimodal A. I. products. If you tell it to put “a red cube on top of a blue cube,” it will occasionally propose putting the red cube below the blue cube.

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How Facebook Detects Fake Accounts Using AI?

Vin Patel — Tue, 22 Nov 2022 10:26:28 +0000

Key takeaways

But what if these communities have some fake people disguised as friends- it will make the community more vulnerable.
Facebook categorizes these accounts as misclassified accounts and converts them into pages.
And to lead, Facebook must keep coming up with upgraded defense systems using AI and ML coupled with human intelligence.

I think you can build things, and the world gets better, but with AI, especially, I am really optimistic?. In the next five to ten years, AI is going to deliver so many improvements in the quality of our lives.? -Mark Zuckerberg

Facebook is a platform used for connecting people and developing healthy communities. People here make friends and share things they love. But what if these communities have some fake people disguised as friends- it will make the community more vulnerable.

Original diagram for Techfastly by Vin Patel.

But before exploring the procedure, it is essential to understand why fake accounts are considered a threat.

Why Are Fake Accounts Considered a Threat to Users? #

Every day hundreds of thousands of fake accounts are tried to be created or are created on the Facebook platform by humans and bots. These accounts are credible technical spoofs created by using fake email addresses, synthesized pictures, and highly accurate data that make the accounts appear more authentic.

These accounts pose a significant threat to the platform and its users in most cases. Innocent users often fall prey to such stammers, and these accounts become an easy source of money. These accounts are used by fraudsters to run spam, incite violence, promote terrorism, influence elections, share phishing links, etc. Russian agencies’ influence on US midterm election results was one such famous example of the threats of fake accounts. Using technologies like Deepfakes, experts speculated that Facebook’s bogus accounts could also play a decisive role in upcoming US elections. And Facebook is using all means to prevent any interference.

How Fake Accounts Impact Facebook? #

Facebook generates a large part of its revenue from advertisements. The revenue growth may stay stable as long as the ads are watched, clicked, shared, or liked. Facebook doesn’t provide third-party access to calculate the engagement number for the advertisements. With fake accounts looming large, investors doubt whether the ad is reaching the correct audience. Fake accounts may lay inactive for extended periods, producing insufficient data to run generic consumer advertisements on the page. And then again, the fake accounts of bots are of no use to neither Facebook nor marketers. All these factors have affected revenue generation in several ways. Marketers will continue to associate with Facebook and its related products for advertising purposes as long as they get a good investment return. Having false accounts can cause a big concern to go forward for both Facebook and marketers. Facebook, in the year 2018, reported no new users sign-up from January till July from countries like the US and Canada, and the number of unique users joining from countries like Europe fell by one million. Although it didn’t cause any loss in revenue, its market value of shares had fallen by $120 billion. Such is the effect users can bring to social media platforms and their businesses.

How Facebook Detects Such Accounts? #

Abiding by the set of guidelines by CSER, Facebook identifies fake accounts using both human intelligence and machine learning. It uses hand-coded rules and machine-learning tools to determine whether accounts are genuine or not.

Fake accounts, once recognized, are blocked either at the time of creation or before they are live on the platform. But the real problem arises when these fake accounts pass under the nose of all these defenses and become active. Such accounts are identified by advanced AI tools when malicious activities done by these accounts come under the notice of Facebook’s detection system. These accounts are also sometimes reported by active users.

What Are The Tools used For The Procedure? #

One can easily deduce the severity of the problem by knowing that alone in the Q1, Q2, & Q3 2019 transparency report by Facebook, it has removed 5.4 billion fake profiles. Fake accounts are of two categories:

Some are accounts of children, pets, businesses, hobbies, etc., which users created instead of creating pages. Facebook categorizes these accounts as misclassified accounts and converts them into pages.
Other accounts are those created by stammers. These accounts are identified and taken down by Facebook.

But disabling accounts means denying the users access to the platform, which is entirely against the norms of Facebook. So, how can weeding be done without harming others? Keeping all these in mind, the social giant came up with several layers of analysis to root out problem accounts. And the tool it uses for this purpose isDeep Entity Classification (DEC).

Facebook deploys the screening of accounts at three different stages: #

Stage of Creation: Fake accounts are recognized while they are being created by taking the help of exclusive detection technology. AI immediately blocks these accounts from being created. It processes high amounts of data and makes quick decisions. For example, the technology detects and reports about numerous accounts created from the same IP address, which is then blocked, obstructing their further visit to the social networking site. AI immediately removes these accounts, and hence no harm is caused to active users.

Stage of Activation: Once such accounts sign-up, the artificial learning algorithms lockup for any suspicious actions that can set on the alarm. These algorithms use the knowledge gained from the removal of previous fake accounts to detect such accounts. For example, DeepFace technology recognizes Deepfakes, and the system is alerted. AI traces signals associated with suspicious email addresses or already disabled accounts and blocks the user’s account even before appearing on the platform.

Stage of Blocking Registered Accounts: Deep Entity Classification plays a massive role in identifying challenging accounts that are active on Facebook. It beats those who have already learned to play with identification features like numbers of friends, location, age, etc. DEC’s AI recognizes such accounts after screening them thoroughly and, if faulty, blocks them.

How DEC AI Works? #

Since its implementation, Deep Entity Classification has helped pull down numerous convincing fake accounts. Instead of surface study, the AI scans in-depth features of the accounts, like the details of pages, groups, friends, etc., with which the account interacts. The algorithms screen Their friend’s ages, the gender distribution of friends, age groups they interact with, admits of the pages, members of groups, all. The technology takes into consideration as many as 20,000 deep features. The user cannot control these features, and hence a clear snapshot of how a profile behaves appears based on which decisions are taken by AI.

It uses numerous machine learning models established and developed on complex algorithms to identify the account’s authenticity. These models are trained on a vast set of simulated and real-time data for higher accuracy. High-precision human tags then scrutinize the segregated accounts that security experts generate. Language-agnostic AI trained in 93 different languages and 30 dialect patterns is also used to identify fake accounts. Accounts such as those that repeatedly send revenue-generating messages or are hacked accounts of real users, an incorrect account that doesn’t belong to the character ascribed, or accounts of manipulative stammers are then deemed fake by the algorithms. AI further investigates these and, at last, blocks them with an accuracy rate of 95%.

Facebook has claimed on its Transparency page that DEC has prevented millions of fake accounts from being made every single day in 2019.

Is The Problem Solved? #

No, with advancing AI, the intellectual capacity of spammers is mutating as well. According to an estimate by Facebook, irrespective of its efforts, around 5% (approximately 125 million) of the monthly active users are still fake accounts. So it won’t be wrong to infer that it’s an ever-ending race where technology on both sides competes to be ahead of each other. And to lead, Facebook must keep coming up with upgraded defense systems using AI and ML coupled with human intelligence.

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Questions and answers

How Fake Accounts Impact Facebook? #

Facebook generates a large part of its revenue from advertisements. The revenue growth may stay stable as long as the ads are watched, clicked, shared, or liked.

So, how can weeding be done without harming others? #

Keeping all these in mind, the social giant came up with several layers of analysis to root out problem accounts. And the tool it uses for this purpose isDeep Entity Classification ( DEC ).

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.

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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.

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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.

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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.

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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.

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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.

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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’.

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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.

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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.

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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.

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.

#

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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?

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Role of AI and 5G in Drone Technology

Vin Patel — Tue, 15 Nov 2022 12:37:09 +0000

Key takeaways

Drones are one of the amazing creations and instruments that technology has provided us.
Further developments in computer vision and risk awareness are necessary for many of the more sophisticated drone applications, which is why there has been a lot of research and development in this field.
Engineers that are proactive rely on these developments to largely increase the capabilities and applications of drones, enhancing their subtle intelligence, safety, and general capability.

Drones are one of the amazing creations and instruments that technology has provided us. Since their debut in 2007, drones have gained popularity, often referred to as Unmanned Aerial Vehicles (UAVs) in technical parlance. Initially, manual and remote controls were used to operate these devices. Artificial intelligence, which automates some or all activities, is increasingly routinely used in drones.

The ability to take photos and videos from the air while standing still made drones originally popular among photographers. But soon, they were being used to gather information to better comprehend crime scenes and weather trends.

As artificial intelligence (AI) capabilities and the availability of low-cost drones grow, it is more critical than ever to maintain ongoing communication and efficiency. A drone must also be able to fly outside the pilot’s line of sight and go longer distances without human assistance.

In this article, we will look at drone technology and how artificial intelligence is being utilized to improve drones.

What Are Drones? #

Civilians refer to drones as “unmanned aerial vehicles” and often consider them as a weapon or tools used by military people. They are, however, now available to the general public for ideography, surveillance, traffic monitoring, weather monitoring, agricultural, and other purposes. Drones do not have a vast storage capacity, but they do have adequate space to deliver important goods to humans.

Drones can travel long distances from a remote points. It may be in space, disaster areas, the arctic, or just outside your front door. As a consequence, the extremeness of the site is immaterial because they can reach almost everywhere. They are safe and function admirably. Drones are remote-controlled since they are robots.

What Role Does Artificial Intelligence Play in Drone Development? #

This fast-developing drone technology’s integration with advances in AI and autonomy, as well as 5G and long-range Wi-Fi communication, is a significant accomplishment that has gone unnoticed. The “Qualcomm Flight RB5 5G Platform,” the first drone platform in the world with 5G and AI capabilities, was developed by Qualcomm.

Drone Development using 5G #

5G is seen as a game changer in drone development. Networks like 4G LTE and 5G enable safe, trustworthy autonomous drone operation outside the visual line of sight due to wide-area, high-speed, secure wireless network coverage from major wireless service providers available practically wherever a drone may travel.

5G connection is not just the present and future of connectivity; it also allows for large-scale drone deployment for mission-critical drone applications such as search and rescue, air traffic control, and 360-degree virtual reality video recording. Drones with artificial intelligence have increased perceptual skills and can transmit data in real time, which is critical when seconds count. Knowing what is happening and when it will happen, whether a drone is dropping medical supplies or delivering food or presents, might save lives.

Where traditional ways have failed, AI will provide the spark to address major wireless challenges. AI is widely assumed to greatly impact a wide range of critical 5G use cases, including higher service quality, simpler deployment, increased network efficiency, and improved network security. 5G’s low latency and large capacity, on the other hand, will allow AI processing to be distributed across devices, edge cloud, and central cloud, providing flexible system solutions for a variety of new and improved experiences. This adaptive wireless edge architecture provides performance and cost trade-offs to determine workload distribution to suit a given application’s latency or computing demands.

How to Create a 5G Drone with Artificial Intelligence? #

The following things are required for the creation of a drone:

A virtual private network (VPN) is necessary for security and mufti-device networking.
Simultaneous Localization and Mapping (SLAM) aids in orienting the drone to its ever-changing environment, as make deep learning object identification and integrated depth sensing in building mission pathways and avoiding obstacles.
To determine device location, use visual-inertial iodometry (VIO).
To facilitate development, flexible software architecture is used.
Several image sensors are provided for simultaneous 4K video capture, streaming, and computer vision processing.
A lightweight, cost-effective, plug-and-play device with significant computational capacity on a single printed circuit board (PCB).

Drone Applications Using Artificial Intelligence #

Here are four important artificial intelligence-based initiatives that make use of drone technology for a variety of purposes:

Automatic Flying Machines: #

For obvious reasons, computer vision is a widely disputed artificial intelligence topic in the drone market. To begin, numerous manufacturers already use powerful computer vision to keep drone pilots safe: for example, certain DJI drones can recognize an item and automatically figure out how to avoid it.

Further developments in computer vision and risk awareness are necessary for many of the more sophisticated drone applications, which is why there has been a lot of research and development in this field.

AlphaPilot, a drone racing competition with a twist, is one noteworthy project showcasing accomplishments in this field. The Drone Racing League (DRL), which Lockheed Martin supports, is in charge of planning the event.

Easy Surveillance: #

A number of surveillance tools may be installed on drones to capture HD video and still images at all hours of the day and night. A wide variety of surveying tools, such as liar scanners, mufti- and hyperspectral sensors, and many more, may be used with little effort and expense seven days a week due to the high payload compatibility.

Drone surveillance allows you to gather information about a target from a safe distance or height while staying undiscovered. Drone surveillance enables the stealth collecting of information about a target from a safe distance or height.

In actuality, drone use in this large industry (or sector) extends much beyond these simple and straightforward boundaries. Drone technology is employed by government officials, police officers, and other security personnel. Automated monitoring will become increasingly common as companies and researchers develop new ways to use machine learning to assess live video footage.

A recent experiment was done by academics in the United Kingdom and India, for example, reveals one potential application for this technology: utilizing camera-equipped drones to identify violent behavior in crowds. It makes use of a low-cost Parrot AR quadcopter to transmit video footage for real-time analysis over a mobile internet connection. A deep learning algorithm estimates the stances of people in the film and connects them to “violent” postures determined by the researchers. The project only includes five positions: strangling, punching, kicking, shooting, and stabbing.

Heritage Protection: #

Many people face risks from looting, urbanization, mass tourism, violence, and climate change. As a result, their preservation has become a global priority. After all, they are the birthplaces of civilizations.

The purpose is to preserve and protect world history, but the approach is cutting-edge. Iconem combines drones, innovative modelling techniques, and cloud computing. As a result, historians, restorers, and the general public may now view very realistic and immersive depictions of significant historical locations. Among other things, Iconem has worked on restoration projects, displays, and virtual reality experiences.

Accurate Weather Forecast: #

Drones, admittedly, fall short of satellite images in terms of predicting severe weather events. Nonetheless, they are capable of providing critical assistance in the case of a disaster. Government agencies and insurers are becoming increasingly aware of the idea of using them to estimate post-disaster damages, particularly in areas where people have not been approved as safe to enter.

Weather drones may collect important data on temperature, moisture, air pressure, and wind speed and direction by flying through the whole vertical layer of the atmosphere’s boundary layer.

Whether drones can acquire this data in a variety of methods, one option is to attach the drone’s temperature, humidity, and air pressure sensors. Another method is to drop sensors known as dropsonde from a high altitude using a parachute. As the dropsonde fall through the vertical profile of the boundary layer, they collect data. Another important way for weather drones to collect data is through visual imaging, such as photos and video.

Conclusion #

In the technological sector, artificial intelligence (AI) essentially serves as a useful tool. When used in a subtle way, a 5G network is unquestionably beneficial for the advancement of drone technology. Engineers that are proactive rely on these developments to largely increase the capabilities and applications of drones, enhancing their subtle intelligence, safety, and general capability.

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Questions and answers

What Are Drones? #

What Role Does Artificial Intelligence Play in Drone Development? #

This fast-developing drone technology’s integration with advances in AI and autonomy, as well as 5G and long-range Wi-Fi communication, is a significant accomplishment that has gone unnoticed.

How to Create a 5G Drone with Artificial Intelligence? #

The following things are required for the creation of a drone: - A virtual private network (VPN) is necessary for security and mufti-device networking.

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.

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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: #

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

(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.

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How Does Conversational AI Benefit in Business Growth?

Vin Patel — Thu, 10 Nov 2022 11:14:02 +0000

Key takeaways

Imagine working day and night to take your business to enormous heights and yet not succeeding in doing so.
This is because they may obtain responses immediately and do not have to wait for a staff member to call them.
It was rightly said by someone when chatbots were introduced: > *“You all know that chatbots are a new technology altogether.

Imagine working day and night to take your business to enormous heights and yet not succeeding in doing so. You can’t figure out the reason behind it. You tried everything, from pamphlet printing to tying hoardings around the city, and from door-to-door advertisement campaigns to social media advertisements. Yet, you couldn’t drive enough engagements. You are quite surprised because the product is in demand and there are not many people in your business line. What could be the reason behind you not succeeding in making it work out?

One day, you meet your friend who tells you to make better use of technology and advises you to implement conversational AI. You sit down to learn what conversational AI is and how it can transform your business. Read on to find the discoveries.

What Is Conversational AI? #

Conversational AI refers to the set of technologies that underpin automated messaging and speech-enabled apes that enable computers and people to communicate in human-like ways. Conversational AI can interact like a person by detecting speech and text, comprehending intent, translating multiple languages, and replying in a manner similar to human conversation.

Applied conversational AI involves both science and art to build effective applications that combine context, personalization, and relevance within human-computer interaction. Conversational design, a science focused on creating natural-sounding processes, is essential in developing Conversational AI systems. Though chatbots have acquired popularity (and a bad reputation), conversational AI solutions may be delivered via text and voice modalities and the numerous channels and devices that enable these modalities - from SMS and online chat for text to phone calls to smart speakers for speech.

The greatest Conversational AI delivers results that are indistinguishable from those offered by a person. Consider the last time you communicated with a business and realized you could have performed the same duties with the same, if not less, effort than you could have with a human. That is Conversational AI at its finest.

Benefits of Conversational AI in Business #

Reduces the need for ccustomer** rrelations staff** One of the advantages of conversational AI for corporate management is that it may minimize the number of personnel required for some of the more repetitive jobs, such as answering simple inquiries. As a result, the company’s costs are reduced. Of course, it is necessary to retain certain customer relations professionals so that consumers may speak with humans when they have difficult inquiries or want to feel more connected to the organization.

On the other hand, the reduced requirement for workers in this area may inspire the remainder of the staff to enhance their management abilities in other areas. For example, becoming a member of the CMI and honing your leadership abilities.

Provides around-the-clock** aassistance** Another advantage is that if you have a worldwide organization, you will be able to give customer assistance for your company at all hours. This is especially important for firms who need to service their consumers at various hours of the day. An internet service provider is an example of a company. If a company is only situated in one nation, it can only give so many hours of customer service.

Customers receive prompt responses For their inquiries, many consumers choose to employ automatic response software. This is because they may obtain responses immediately and do not have to wait for a staff member to call them. A consumer would be better off using conversational AI software if the company is especially busy. The majority of chatbots and similar replies would deliver instant responses and be able to fix any minor concerns.

AI can** aassist eemployees with pprompts** Businesses frequently utilize a monitoring system for a lot of conversational AI that has been built for responding to more complicated client queries than basic questions and responses. This implies that the computer leads the conversation for the most part, and the person only intervenes when extra assistance is required. This is also demonstrated to be advantageous because a computer can store far more information than a human. It can then comprehend what the consumer says and choose from millions of possible responses.

For example, if someone asked a question regarding a company’s privacy policy and the employee was confused or would take a long time to find the answer, the computer would be able to deliver this information relatively immediately. This improves the number of jobs that can be completed in a day and hence the total productivity of the firm.

After you read all this, you sit for a while to process the information. After massive brainstorming, you come to the conclusion that conversational AI is really effective in making your business a success. If customer assistance gets prompt, you don’t need to worry about much. Word-of-mouth will spread, and more and more customers will reach out to you in the near future, and this will help your business grow effectively.

After reaching this conclusion, you decided to sum this up with an endnote in your business diary.

Conclusion #

Virtual customer assistants execute complicated jobs for consumers, and businesses will benefit from more creative AI solutions in the future. They can provide solutions to complicated issues and process all information presented to them to discover a solution.

With more and more scopes of improvement in the future, conversational AI proves to be the most amazing thing for both big and small business owners.

It was rightly said by someone when chatbots were introduced:

“You all know that chatbots are a new technology altogether. It’s like the early age of the Web. Things are still shaky yet growing at the speed of light.”

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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.

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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.

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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? #

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.

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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.

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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.

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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.

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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.

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Cognitive Agents and Reinforcement of User Experience

Vin Patel — Wed, 03 Aug 2022 11:53:15 +0000

Key takeaways

In a world where more than 2.5 billion bytes of data are created every day, it is essential to deliver information to users effectively.
And less time searching for information means shorter resolution times, which equates to lower costs.
We are at the crossroads of three momentum, application modernization, innovation through AI, and DevOps that will elevate the value of services for us and our customers.

In a world where more than 2.5 billion bytes of data are created every day, it is essential to deliver information to users effectively. Cognitive agents’ technology can understand human language to provide accurate information and quickly direct users to the optimal next step. Allowing cognitive agents to do the heavy lifting frees up human resources to tackle more extensive and more complex problems. This combination of technology helps humans produce a better user experience for everyone. As a result, advisors get the information they need to help clients resolve their inquiries faster.

How Does This Technology Work, And Who Makes It So Valuable? #

Cognitive Computing #

Cognitive agents are born from one of the main building blocks of artificial intelligence: cognitive computing. It is basically simulation of human thought processes in a computerized model. And it includes self-learning systems that harness data mining, pattern recognition, and natural language processing (NLP) to mimic patterns of the human brain. These applications are:

ADAPTIVE Cognitive solutions adapt to changing information, objectives, and requirements, thus making it possible to plan and resolve ambiguities. Most of the time, cognitive computing solutions can also process data in near real-time.

ITERATIVE AND STATEFULCognitive solutions help define a problem or find additional sources if a problem statement seems ambiguous or incomplete. Previous interactions may also be involved in determining or completing problem statements.

CONTEXTUAL Cognitive solutions can understand, identify, and extract contextual elements, including syntax, meaning, time, location, domain, regulation, user profile, process, task, and purpose. In some cases, they can also extract structured and unstructured information as well as sensory input.

While cognitive computer applications (like cognitive agents) can mimic the human brain, that doesn’t mean they are meant to replace human advisers. Instead, this technology can be seen as a powerful assistant capable of handling tedious tasks and freeing up human resources to tackle complex problems. And less time searching for information means shorter resolution times, which equates to lower costs.

Benefits Beyond Cost Savings #

Cognitive agents are an excellent money saver but they also have other benefits. For example, this technology can also improve data security, customer and employee experience, and visibility into business processes. At the same time, many organizations see benefits in simply using cognitive agents as “helpers” to living agents. à help provides information and recommends personalized offers to the customer more quickly; some use this technology to eliminate possible phishing before the interaction of natural agents to reduce the average processing time. Others have also benefited from combining cognitive agents with internal processes to speed up the onboarding process and reduce security issues and errors.

Success With Cognitive Agents #

Cognitive agents can be part of your digital transformation in many ways. Browse the stories below from real organizations to see how they implemented cognitive agent solutions to deliver valuable insights and a better user experience for clients and advisors.

Health insurance company #

Interactive cognitive agent, allowing providers to access member information quickly

CHALLENGE

A health insurance company with 13 million customers in the United States needed a better way to handle member inquiries from providers. Calls were driven by an outdated interactive voice response (IVR) system, and volumes exceeded 700,000 calls per month, with most callers opting out of the IVR system for expensive outsourced call centers.

STRONG POINTS #

Development of a cognitive agent to converse naturally with suppliers and return correct information in good time and efficiently
Performed IBM Watson Dialogue, Watson Natural Language Classification, Watson Speech to Text, and Watson Text to Speech

MAIN ADVANTAGES #

Substantial reduction in the number of real agent requests, resulting in substantial savings.
Reduced average call duration from eight minutes to three minutes

Multinational Software Company #

Reshaping the Customer Service Infrastructure with the Cognitive Agent IBM Watson

CHALLENGE

A software company specializing in architecture, engineering, construction, manufacturing, media, and entertainment struggled with an unfriendly and inefficient user experience for customers calling with service requests and questions. The company intended to improve this crucial customer experience and reduce its time to resolve issues and requests.

HIGHLIGHTS OF THE SOLUTION #

Implementation of IBM Watson Dialog and Natural Language Classifier. transform, automate and improve interactions

Understands the intention of conversations and can act as a digital concierge

KEY BENEFITS #

Assisted management of around 60% of Group 1 requests from start to finish
90% decrease in support costs
99% reduction in resolution time

Multinational Conglomerate Company #

New cognitive shopping experience improves customer service and conversion rates

CHALLENGE

A Japanese and Korean conglomerate with functions such as retail, financial services, hotels, etc., seeks to optimize the shopping experience within their retail subsidiaries using cognitive technology to use their data better.

HIGHLIGHTS OF THE SOLUTION #

Creation of a virtual advisor for purchases with a recommendation engine to analyze structured and unstructured data
Text recognition, text-to-speech, and visual recognition capabilities
Creation of a “customer DNA” with personalized recommendations based on purchase history
Trend recommendations provided with social media keywords analyzed by AI

KEY ADVANTAGES #

New services concerning in-store purchases and advice via a cognitive chatbot
Personalized recommendations in real-time
Improved customer experience with higher conversion rates
Consolidation of disparate data sources into a unified “customer DNA.”

We all have scenarios in mind that use, for example, machine learning techniques that can automatically process ever-increasing volumes of data. This field has grown exponentially in terms of innovation in recent years. As a result, human-machine parity is getting closer to detecting and recognizing objects, speech, text comprehension, or translation.

The design of innovative services based on cognitive benefits allows companies to tell the right story by drawing on the user’s context as a differentiator. For example, it is possible to locate a user to generate engagement in a public place, manage employees’ safety on industrial sites, or imagine new residential uses of our daily lives. The field of possibilities is vast!

We are at the crossroads of three momentum, application modernization, innovation through AI, and DevOps that will elevate the value of services for us and our customers.

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Entry, Descent, And Landing Technologies for Perseverance

Vin Patel — Tue, 02 Aug 2022 09:45:26 +0000

Key takeaways

This initiative aims to find evidence of life on Mars and go even beyond that to have a better understanding of the continuously expanding universe.
It is the point where it travels nearly 12,100 miles per hour (19500 kilometers per hour).
Consequently, the Perseverance Rover operates with an accuracy of 40 meters or 130 feet.

In February 2021, Perseverance Rover landed on Mars and was set to explore the red planet and uncover traces of ancient life (microbial).

The Perseverance Rover happens to be the most expensive and upgraded mobile laboratory sent on a Mars mission ever. This initiative aims to find evidence of life on Mars and go even beyond that to have a better understanding of the continuously expanding universe. Also, the Rover is supposed to throw light on the future of both manned and unmanned missions to the red planet.

The Rover is expected to begin the drilling work during the summer months. It has been programmed to explore the delta first, move across the lakeshore, and then go for the crater’s edges.

Let us now explore the technological aspects of the Perseverance Rover.

Searching for Clues #

The first thing the Perseverance Rover is expected to do is search for microscopic life forms that may be millions of years old. For this purpose, scientists and engineers have fitted the Rover with a PIXL (Planetary Instrument for X-ray Lithochemistry). It is a precision x-ray device that runs on AI (Artificial Intelligence). The PIXL has a superior ability to scan rocks through a focussed x-ray beam. In addition, the device can detect chemicals along with their quantity and distribution.

Ingenuity Mars Helicopter #

It is attached to the belly of the Rover and has a lot of autonomy while flying through waypoints since it is not controlled from Earth. In fact, it happens to be the first helicopter that would be flying outside Earth. Commands are sent beforehand, and accordingly, the helicopter executes the flight. The most interesting aspect of the Ingenuity Mars Helicopter is accessing terrain off-limits for the Rover.

Data Collection for Future Flights and Landings #

Scientists have designed MEDLI (MSL Entry, Descent, and Landing Instrumentation) which helps to detect the pressure and temperature of the Rover.

The next-gen sensor suite MEDLI2 assists in collecting data from both the backshell and heat shield. It combines the pressure and temperature of the Rover along with the data collected from the Rover’s weather station to extract insights and help scientists decide entry, descent, and landing techniques in the future, thereby mitigating risks.

The AI-controlled terrain navigation system ensures that the Rover can safely descend on the surface, like Mars. The terrain relative navigation ensures that the Rover can calculate its position and location with respect to the ground. The accuracy limit is about 40 meters.

While descending, the Rover, with the help of LiDAR or similar technology, can create a map of the landing site. The map is then compared with the images of the Rover while approaching the surface, thereby allowing it to redirect if needed. That way, the Rover can figure out and land in the safest spot possible.

Fittings #

The other fittings of the Rover include MOXIE that can demonstrate how oxygen is produced from the Martian atmosphere. The MEDA analyzes and measures the dust with the help of sensors. RIMFAX uses radar waves to scan the surface below the Rover. SuperCam combines camera, laser, and spectrometers to analyze the soil and rocks.

How The Perseverance Rover Works #

The entry, descent, and landing (EDL) of the Rover took only seven minutes in February 2021. Then, it touched down on the surface of Mars, which is now termed as the ‘Seven Minutes Of Terror’ sequence. The entire process and steps involved precise and careful maneuvering and control.

The Mars 2020 mission, the Perseverance Rover, and The Ingenuity Mars Helicopter were given the green signal in July 2020. The objective was to attempt to land on the Jezero Crater, almost 45 kilometers (28 miles) wide. It took nearly six and a half months to reach Mars. The Rover has been programmed to search for evidence of previous microbes and, if possible liveable environments and conditions on the red planet. Samples would be collected from the planet that would be analyzed back on Earth for designing future mission campaigns.

The journey and operation of Perseverance Rover to Mars are more or less similar to what Curiosity Rover did in 2012. As the air vehicle cruised towards Mars, the Perseverance Rover, along with the descent stages, were well protected in a capsule called an ‘aeroshell’. It is attached to the ring structured cruise phase.

The cruise phase comprises eight thrusters that give power at specific times so that the spacecraft is on its designated course. In addition, the spacecraft is fitted with solar arrays that help to power the craft. Also, it has antennas for communicating with the control center on Earth.

Given below is an account of how the various components work together to assist in the landing of the craft on Mars:

The Over was 2.40 million miles (3.90 kilometers) away from Mars in February. At that time, it was buzzing through space at a speed of 49290 miles per hour (79330 kilometers per hour).
In the last three weeks before landing, the mission control focussed on the navigational aspects, like whether the craft was following the correct trajectory or not, along with activities related to the preparation of the vehicle for EDL.
During the cruise stage, solar arrays could be seen that help to provide extra power to the craft apart from the main power source. It is referred to as the Mufti-Mission Radioisotope Thermoelectric Generator.

#

Aeroshell Separation #

Almost 10 minutes before entering the Martian atmosphere in 2020, the aeroshell separated from the cruise stage. Thereafter, the aeroshell having the Perseverance Rover and descent stage would descent to the Martian soil on its own.

The aeroshell measures nearly 15 feet (4.50 meters) in diameter. It consists of two parts, namely the backshell and the heat shield. Both protect the craft from getting burned when extreme heat is generated at the initial descent through Mar’s atmosphere. The aerodynamic design and property of the heat shield also function as a stopping mechanism that helps slow the craft’s pace.

Entering The Martian Atmosphere #

The EDL phase starts when the craft reaches the top of the Martian atmosphere. It is the point where it travels nearly 12,100 miles per hour (19500 kilometers per hour). Then, roughly after 90 seconds after entering the atmosphere, the peak acceleration occurs. During this time, the spacecraft slows to less than 1000 miles per hour (1600 kilometers per hour).

While descending to the surface of Mars, the speed of the Rover decreases because of the drag generated by the motion in the Martian atmosphere. The aeroshell also releases small thrusters on the backshell. It helps in reorienting and ensures that the heat shield faces the Red Planet as the Rover descends through the atmosphere.

Seven Minutes of Nailbiting #

EDL will last for approximately seven minutes. Once the EDL phase starts, the craft uses a technique referred to as the guided entry. It takes around 14 minutes or so for radio signals to travel from Earth to the Rover and vice verse. It implies that the spacecraft will land on the red planet without any assistance or intervention from the ground team on Earth. It is why the landing of the craft has been referred to as the Seven Minutes Of Terror.

The spacecraft’s heat shield that faces Mars endures peak heating when it enters the Martian atmosphere. This is because the friction of the Mar’s atmosphere decreases the rate of descent, thereby increasing the temperature of the heat shield.

The Parachute #

After about four minutes post entering the Martian atmosphere, the craft deploys a supersonic parachute that comes out from the aeroshell. The diameter of the chute is 70.50 feet (21.50 meters), and it gets deployed at an altitude of 11 km or 7 miles above the surface of the red planet. The craft’s velocity at that point is close to 940 miles per hour, equivalent to 1512 kilometers per hour. The chute helps to slow down the craft’s motion to almost 200 miles per hour or 320 kilometers per hour.

The chute is deployed using the latest technology called Range Trigger. It helps to narrow down on the intended landing area. According to the engineers who designed the system, the Range Trigger technology can significantly reduce the size of the landing ellipse, which is an oval area around the target landing, by almost 50 per cent. As a result, it implies that it will take the Rover less time to get to its location than previous missions that have landed on Mars. In addition, the Range Trigger will autonomously adjust the timing of the parachute deployment with respect to the position of the craft and distance from the landing target.

Rover in the Backshell #

The Heat Shield separates and drops away from the backshell about 20 seconds post deployment of the parachute. The descent stage and Rover remain tucked in the backshell of the craft as the whole thing slows the dive towards the surface of Mars.

When the Rover is exposed to the Martian atmosphere for the first time. As part of the landing system of the craft, the Terrain Relative Navigation System, along with the landing radar of the craft, starts accessing the velocity, position, and altitude to help guide the Rover to a safe landing on Mars.

The navigation technology is a new approach and comes with a Lander Vision System. It is equipped with a down-facing camera that can take images when the craft descends. The inboard computers work on the images and accordingly calculate the approximate position of the craft with respect to the ground on a fine-scale as compared to previously available systems. Using this data, the new navigation system can adjust the touchdown of the Rover, thereby averting any unsafe landing.

The navigation technologies and the range trigger allow landing on the floor of the Jazero Crater possible. The area has a complicated terrain since it includes sand dunes, cliffs, boulder areas, and small impact craters.

Powered Decent Phase #

When the Rover is at the height of 2.10 kilometers (1.30 miles), the Rover and the descent stage separate from the backshell and parachute. The descent phase consists of eight throttleable retro-rockets, also called landing engines. They help to decrease the pace of the spacecraft while preparing for a touchdown on the Martian surface.

During the descent stage, the engine fires up at the final moment before Rover lands on Mars. At this point, the craft travels at almost 306 kilometers per hour or 190 miles per hour. The landing engines assist in guiding the craft to the designated landing spot. It first levels out and then decreases the vehicle’s speed to the final landing speed of 2.70 kilometers per hour or 1.70 miles per hour.

Touchdown #

At about 20 meters above the ground (65 feet) or about 12 seconds before touchdown, the Rover is safely lowered to the ground by a sky crane. Three nylon cables are extended almost 25 feet (7.60 meters) below the descent stage, bringing the Rover to the ground. In the meantime, the Rover stretches its legs, bringing the wheels for the final landing.

Once the Rover senses that it has touched the ground, the cables are released automatically, linking the craft to the descent stage. The cables then fly off safely from the Rover. If everything goes as per plan, nothing can stop the Rover from completing its two years mission of exploring the Jezero crater.

Interplanetary Helicopter (First of its Kind) #

The Ingenuity Mars Helicopter is attached to the belly of the Perseverance. It will detach after 30 - 90 post touchdown of the craft on the Red Planet. Thereafter, the helicopter will perform some test flights to demonstrate that robotic flights are no big deal on another planet.

Overall, to improve the entry, descent, and landing of the Rover, two key factors need to be taken into account. The first is to know where the craft is headed and, secondly, to divert to a safe place in case the terrain seems hazardous.

The most crucial component of the Rover happens to be the navigation system that allows the Rover to make accurate estimates of the position with respect to the ground below. The Rover can know its exact position while descending the Martian atmosphere. Consequently, the Perseverance Rover operates with an accuracy of 40 meters or 130 feet.

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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.

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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.

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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.

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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.

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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.

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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.

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How AI Is Helping Preserve Historic Smells?

Vin Patel — Fri, 07 Jan 2022 15:30:00 +0000

Key takeaways

Owing to the connection of smells to emotional satisfaction, scientists are exploring the usage of Artificial Intelligence in recognizing the smells prevalent to a certain culture or organization in history.
Training The first step towards developing the olfactory sense in artificial intelligence bots is to train them to detect and identify odors, smells, fragrances, and all kinds of scents.
This technology will give the future generation a true sniff of the past.

Ever had the experience where a particular scent reminded you of a place or flashed back a memory? Various scents and smells possess the power to take us to the past in seconds and relive the emotions associated with them. Owing to the connection of smells to emotional satisfaction, scientists are exploring the usage of Artificial Intelligence in recognizing the smells prevalent to a certain culture or organization in history. This will ensure that the historic smells are preserved and maintained for future generations, giving them the surreal experience of the past. While artificial intelligence techniques frequently brought back the structures of broken cities or revoked broken paintings in the past decade, its usage in tickling human olfactory nerves were limited. However, researchers and history scholars ensure that this crucial technology is being leveraged to its fullest potential by bringing AI into preserving historic smells.

What are Historic Smells? #

Historic smells comprise the aromas, scents, and odors prevalent to a certain culture, social group, or civilization during its era. For example, the industrial revolution in the west made the air shift towards more of a smoke-filled incense that lurked in the air for years. The aroma of rosemary during the disappearance of the plague is linked widely to its usage in the treatment of the disease.

Historic smells have garnered the attention of researchers in the past decade because the scents speak a lot about a culture’s mindset and economy. A great way to explain the connection is found in tobacco usage. The nineteenth-century witnessed the origin of this popular commodity wherein its smell was considered to be ’exotic? and luxurious among the masses. However, with its blatant usage over the years, its odor became too common, seemingly unnoticeable. By the beginning of the 21st century, the smell of tobacco started to make people in public spaces visibly uncomfortable and even nauseous. This goes on to show that scents and aromas hold a deeper meaning in history and can be used to track evolution and change the political and social environment of societies.

How Can AI Preserve Historic Smells? #

The algorithms used in AI programs are crafted to behave like a human. Humans have a complex olfactory system helping us identify the smells and distinguish different ones, and know when to register a new one on the encounter. AI bots and programs are wired to act like a ‘human nose? formed from a complex web of neural networks, hardwired to detect smells, put them in its database and recognize those smells with 100% accuracy in the future.

Here is a quick process to help you understand how an AI robot or software can detect the various smells it is subjected to with perfection. Training The first step towards developing the olfactory sense in artificial intelligence bots is to train them to detect and identify odors, smells, fragrances, and all kinds of scents. This requires the algorithm to act like a ‘computerized nose? that can catch smells by taking a whiff of old texts, objects and paintings, mimicking a mammalian’s olfactory organs.

Feeding data The next step involves feeding the AI machines and bots the crucial information regarding the characteristics of smells so it can accurately distinguish between the various odors in an instant. This will allow the machines to produce descriptions of odors and their history precisely.

Curating The team of researchers, scientists and scholars plan on working profusely to identify all kinds of smells and investigate the vast array of odors present throughout the historical references. The chemists, odor specialists, and top perfumer engineers will then evaluate this data for building and recreating different smells in compilations of an online encyclopedia. This online resource, composed of recreated scents, is then intended to be made available to all museums and heritage centers worldwide for assistance and reference.

A prominent group of experts called Odeuropa is attempting to put the science behind artificial intelligence into recreation and preservation of the smells of the past. The efforts aim to store the different scents and study their significance throughout history and make the information available to all the scholars, museum lovers, and anyone fascinated by history fragments. But this is not the first or the only group to have looked deeper into the smell of histories. We are now witnessing a number of countries giving odors their due importance, like Japan who created its list of top smelling sites in the country. Olfactory exhibitions also help people take a whiff of the old scents.

What It Means For History Lovers #

Many countries have attempted to bring the sensory elements of their heritage and ancient culture into the picture for history enthusiasts. History is always perceived by sight and how we ‘see? things. The other senses like smell, touch and taste are often neglected or paid little heed to while recreating a culture in museums or science centers. With the advent and advancements of AI-powered programs, historians can get a ‘view? and experience the odors and actually ‘smell? history. The ambience created by the integration of visual-olfactory senses is sure to induce more realism and authenticity in any piece of art.

Moreover, having an authentic knowledge of the historic smells, the changing societal status of smells and fragrances can be studied and analyzed; all of which can aid scholars and researchers in reaching a better understanding of the reformations in social and political conditions within the heritage. The wonders of Machine learning and Artificial Intelligence are too many and prominent to ignore and pull out the ignored elements of the past. This technology will give the future generation a true sniff of the past.

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Questions and answers

How Can AI Preserve Historic Smells? #

What It Means For History Lovers? #

Many countries have attempted to bring the sensory elements of their heritage and ancient culture into the picture for history enthusiasts.

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.

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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.

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5G and AI for Advanced Drone Development

Vin Patel — Fri, 17 Dec 2021 11:08:00 +0000

Key takeaways

Drones, one of the most fascinating and useful devices that technology could provide us.
A 5G AI-enabled drone can perform the following tasks: > Perform security surveillance in a smart city.
Many companies like Qualcomm and Modal AI are continuously working in designing and manufacturing AI-powered modules that communicate with other devices with the help of 5G cellular networks.

Drones, one of the most fascinating and useful devices that technology could provide us. In technical terms, drones are known as Unmanned Aerial Vehicles (UAVs) and have experienced a lot of growth since their first emergence in 2007. Drones primarily gained popularity amongst photographers as they could take photos and videos from the sky and enabled people to get aerial views while standing on the ground. Soon they were used for collecting data for understanding weather patterns or crime scenes.

As artificial intelligence (AI) capabilities and availability of low drones grow, it is more critical than ever to ensure continual communication and efficiency. The capacity of a drone to fly beyond the pilot’s visual range and travel longer distances without human interference is also important. Although all these conditions were being fulfilled, a significant breakthrough that has been overlooked is the integration of this rapidly evolving drone technology with AI and autonomous developments, as well as 5G and long-range Wi-Fi connection. Qualcomm has come up with the world’s first 5G and AI-enabled drone platform named ‘Qualcomm Flight RB5 5G Platform?. If you are new to the concept of 5G and Artificial Intelligence, let us learn more about them.

5G in Drone Development #

According to a blog post on the website of Modal AI, which collaborated with Qualcomm on the Flight RB5 5G platform,

?5G is a game-changer for autonomous drones.?

Networks like 4G LTE and 5G allow safe, dependable autonomous drone operation outside the visual line of sight, thanks to wide-area, high-speed, secure wireless network coverage from major wireless service providers available virtually wherever a drone may travel. 5G is not just the present and future of connectivity; it also allows for large-scale drone deployment for mission-critical drone use cases such as search and rescue operations, air traffic control, and 360-degree virtual reality film capture. Drones with artificial intelligence (AI) have enhanced perceptual skills and can provide data in real-time, which is critical when seconds count. The capacity to know what’s going on and when, whether a drone is dropping medical supplies or delivering food or gifts, may be lifesaving. 5G-enabled drones are now used for COVID tests to limit the amount of human contact.

Major corporations are increasing their investments in 5G and autonomous drones. User Eats, Amazon, and UPS have all invested in autonomous drone fleets. User Eats have successfully used AI-powered drones for food delivery in 2019 in a highly urban setup with tall buildings creating challenges. Verizon, AT&T, and T-Mobile are all marketing their own private 5G networks, which are being tested for drone compatibility. Drones driven by artificial intelligence and equipped with cellular connectivity are also used by the US military. The Defense Innovation Unit of the Department of Defense (DoD) is successfully executing U. S. Army operations that will save lives utilizing AI-enabled drones with 4G LTE technology.

Applications of AI-enabled Drones #

A 5G AI-enabled drone can perform the following tasks:

Perform security surveillance in a smart city.

Area mapping for urban planning.

Acting fast as an emergency response.

In farming and agriculture, crop monitoring and soil evaluation are essential.

Scanning of the terrain and placement of construction equipment

Inspection of the structure and the subterranean environment

Face recognition and human tracking

Detecting danger and keeping an eye on security in dangerous situations

Warehouse inventory management

Food and supplies are delivered.

Military and defense personnel are responsible for border patrol, security surveillance, storm tracking, food supply, and safety inspection.

As seen, drones have many applications. Drones are wonderful in disaster management and ensuring public safety as well. Deep learning algorithms combined with LIDAR/FLIR cameras provide accurate heat intensity, wind speed, and susceptible regions analytic. Surveillance footage may also be transmitted immediately to the firemen and management authorities for real-time monitoring and short reaction times, thanks to fast, dependable 5G connectivity.

Building A 5G Drone #

One might want to build a drone for any project. For that, one must have the following things:

For flying outside visual line of sight, 4G LTE and/or 5G cellular technologies are required.

For security and mufti-device networking, a virtual private network (VPN) is needed.

A. I. for movement and vision capabilities, such as Simultaneous Localization and Mapping (SLAM) to orient the drone to its ever-changing environment, and deep learning object identification and integrated depth sensing to design mission routes and ease obstacle avoidance.

For estimating device location, use visual-inertial iodometry (VIO).

For simpler development, flexible software architecture is used.

Multiple image sensors are supported for 4K video capture, streaming, and computer vision processing all at the same time.

A lightweight, cost-effective, plug-and-play system with a large computational capacity on a single printed circuit board (PCB).

To comply with the National Defense Authorization Act of 2020, military drone makers must employ components produced outside of China, preferably in the United States. According to the Act, the Department of Defense cannot use or renew a contract for a foreign-made UAS that was manufactured in, uses equipment such as cameras or flight controllers developed in, or uses a ground control system developed in, or uses a network or data storage administered by China or an entity domiciled in China. This regulation provides dependable benefits such as quality control, safety, documentation, faster development timeframes, state-of-the-art innovation, and trustworthy support, in addition to promoting American-made technology.

EndNote #

Artificial Intelligence is a very important tool in the field of technology. When combined with a 5G network, it acts as a boon for drone development. Engineers who are looking ahead rely on these developments to enhance drone capabilities and expand use cases on a daily basis, making them smarter, safer, and more capable. Many companies like Qualcomm and Modal AI are continuously working in designing and manufacturing AI-powered modules that communicate with other devices with the help of 5G cellular networks.

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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.

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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.

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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 #

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 ME:

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.

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How Artificial Intelligence is transforming the Retail

Vin Patel — Thu, 04 Nov 2021 19:25:00 +0000

Key takeaways

For years, the retail business has been undergoing a digital change.
In addition, AI can create rich metadata for any product, avoiding human fatigue and error.
Automation alone can increase efficiency, give retail units time to think creatively, and provide great online and offline experiences.

For years, the retail business has been undergoing a digital change. It has improved speed, efficiency, and accuracy across the board. The primary asset to run the business efficiently is analytic, and digital will provide the data, using which all retail businesses can draw innovative strategies.

Without artificial intelligence, none of these insights would be feasible. AI in retail has given firms access to high-level data and information, which they can use to improve retail operations and create new business prospects. In fact, over three years, AI in retail is predicted to have generated $40 billion in increased revenue. According to a KPMG report, 90 percent of retail industry leaders believe their personnel are ready for AI adoption and have the necessary capabilities. Furthermore, 53% of retail executives indicated the COVID-19 pandemic had accelerated their company’s implementation. Simultaneously, 49% say AI adoption in their industry is growing faster than it should. Let us know in brief how artificial intelligence transformed the retail sector.

Helps to make better business decisions: #

The data collected, labeled, and organized may now be analyzed to find trends and patterns to help retailers make better decisions. AI provides performance insights of their target audience to retailers and helps create personalized experiences for every customer. The simple thing that can be done with the data available to find trends is exploratory analytic. While this is straightforward, it cannot be used to automate decision-making processes.

Retailers can use AI to generate data models and, as a result, build prescriptive or predictive decision engines. This can aid demand forecasting and enable them to make more data-driven decisions. In addition, these predictive models can learn to make better decisions over time by incorporating more and more data, allowing them to make better conclusions.

Data structuring: #

Data structuring is a very hectic process. The bulk data received from the manufacturer should be analyzed, structured, and then transformed into valuable data, which requires enormous effort and a time-taking approach. For example, Brand A puts up a crop top on their platform with a festive sale label, and Brand B puts up another crop top for the Diwali sale. While searching for offers, only Brand A’s will appear, and Brand B will not get so much traction and visibility.

AI helps with structuring raw data into valuable insights. As a result, Algorithm helps in identifying potential customers. Moreover, AI helps in identifying patterns of target audience and boosts your platform accordingly. With AI, structuring data can be done easily.

Data creation: #

AI can solve inaccurate and inconsistent data problems. Creating and labeling AI-supported data can be done at the beginning of the digitization process. In addition, AI can create rich metadata for any product, avoiding human fatigue and error.

This can be done using a computer vision algorithm that can automatically identify and accurately label various attributes of the product. You can also use the same Algorithm

to generate product titles and descriptions, making it easy to search for rich SEO-enabled metadata.

SEO-ready tags, titles, and descriptions make finding products on search engines such as Google and Being. In addition, digitization of image recognition products improves search, browsing, and SEO, achieving 90? accuracy and over 20% conversion.

Boost visibility: #

Finding a product is the first and most crucial step in a buyer’s journey. A straightforward discovery process can help retailers earn the trust of their buyers. Shoppers who find exactly what they are looking for are much more likely to return to the same retail store for their next purchase. On a website, discovery begins with a site search. According to research, 58% of web visitors often use internal search engines to navigate their website.

Search engine users are aspiring buyers and an essential segment of e-commerce sites. Therefore, search results on your website should capture the intent of your search query and display products that match the items you searched for as closely as possible.

For accurate, consistent, and structured data, the relevance of each individual’s results is more important than the relevance of the results. In other words, are the search results tailored to each individual’s style preferences?

AI-powered personalized retail search helps adjust search results by increasing the specific affinity of each shopper. Each time you click, you can see the buyer’s preferences and go to the relevant search bar to understand your customers.

Increase in sales: #

Few people add their favorite items to the cart but never make a purchase. This is known as ‘Cart Abandonment.? To help recover this situation, emails are sent to the respective user to purchase as a reminder. According to research, the retailers have lost almost $4 trillion through abandoned sales. Cart recovery emails are the best way to cope up with the loss. More than 60% of retailers utilize it, and it’s shown to be the most effective approach to engage with customers and recoup their losses.

Since the shopper’s inbox is constantly bombarded with many emails, you need to drop mail creatively to stand out from the rest. AI-powered emails help recover loss and build customer relationships, which again helps in conversions and sales.

Conclusion #

Retailers today need automation to collect data; they also need to consolidate that data to speak to the buyer in a single voice. Shoppers should be able to switch from online to offline effortlessly and back online again. This means that the data collected on your website should be made available to store sellers, and your website’s recommendations should be improved with products purchased by your store’s customers. Once a complete story about each buyer has been created, the data cannot be separated.

Retail and e-commerce AI can completely change the way teams work. Automation alone can increase efficiency, give retail units time to think creatively, and provide great online and offline experiences.

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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.

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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? #

Is Artificial Intelligence Capable of Defending Against

Vin Patel — Wed, 20 Oct 2021 18:06:00 +0000

Key takeaways

The pandemic was the perfect time for everybody to actualize their remote working dreams.
A few of the most common methods are that the tools look for anomalies; they understand the context of a message or how users communicate.
You stand a much better chance at protecting yourself and your enterprise when you use artificial intelligence-based software to detect and prevent phishing attacks.

The pandemic was the perfect time for everybody to actualize their remote working dreams. But with that, many problems have arisen in the meantime. In 2021, cybercrime increased by 600%, and there’s no stopping it. Funnily enough, most of them were phishing attacks in which the stammers pose as health representatives from the Center for Disease Control (CDC) and the World Health Organization (WHO).

Phishing attacks are not uncommon and have been in the news for decades. But what is worrying about it is that hackers find new ways to exploit your network’s vulnerabilities with every new year. With 1.5 million phishing sites being created every month, how do we keep up?

42% of the US labor force was working from home in 2020, including managerial and technical roles. Now that you look at it, email is the best way to impersonate somebody and carry out such attacks. High-level professionals have been the easy target for such attacks. With more specialized attacks created with each passing day, it’s becoming humanly impossible to identify and thwart them.

So, how do we track it? Using artificial intelligence. It is seemingly the only option out there that can act and adapt as fast as we need it to. But, can we trust artificial intelligence? Let’s understand why it could be a paradoxical solution.

Types of Phishing Attacks #

Phishing scams are more commonly known as ‘Nigerian Prince? scams, where impersonators steal your confidential information for their benefit, albeit illegal. They’re focused on stealing any kind of sensitive information and don’t have a particular target in mind. With time, this has evolved into a host of attacks that go much beyond the usual impersonation and are highly targeted.

As of 2021, more than 12 types of phishing scams are popular in the market. They are email phishing, HTTPS phishing, spear phishing, whaling, fishing, smashing, angler phishing, charming, pop-up phishing, clone phishing, evil twin, watering hole phishing. Out of these, the most common ones are detailed below.

Email phishing: It’s done using email and downloads malware using links, PDFs, or any other files that can be downloaded.

HTTPS Phishing: Phishing attacks are made using websites that seem encrypted with HTTPS security but are riddled with malware.

Spear phishing: These are targeted email phishing attacks directed towards either an individual or an organization.

Whaling: Also known as CEO fraud, the stammers impersonate senior-level executives and ask the user to process a payment or review a document.

Fishing: It’s done over the phone, and the stammers tend to alert you over in such a way that you’re forced to give out your personal information.

Most of these attacks are currently being identified using our own wits. It’s also one of the reasons why artificial intelligence is being purported as the new cybersecurity warrior.

Artificial Intelligence and Cybersecurity — How Does It Work? #

Daily, at least 25% of phishing attacks get past the current security defenses that we have. Some of these defenses include phishing filters, spam filters, and the like. But, these filters cannot cope with the never-ending adaptive capabilities of phishing networks. That’s where artificial intelligence or AI, in short, comes in.

Artificial intelligence networks can adapt by learning constantly. It’s one of the reasons why it’s able to identify phishing attacks much quicker than the usual methods out there. It can handle a lot of data at one go, and it stores it simultaneously for future use. It pretty much learns on its own while constantly monitoring your systems.

AI-powered devices also have better endpoint protection? one of the most vulnerable parts of any network. Cybersecurity solutions such as antivirus software or VPNs tend to learn the phishing behavior over time. This results in the creation of signature patterns over time. If these signatures change, they can’t identify it, but artificial intelligence-based tools can. In this case, you have increased awareness and faster-alerting systems.

Artificial Intelligence Tools to Fight Phishing Attacks #

There are many ways in which artificial intelligence and cybersecurity go hand in hand. A few of the most common methods are that the tools look for anomalies; they understand the context of a message or how users communicate.

When it comes to anomalies, it tends to look for specific phrases that indicate a sense of urgency or are asking you to fill out sensitive information. In the same way, once these tools are installed, they tend to read the messages and understand their context. It’ll account for points such as whether or not the sender has contacted you previously or whether they’re asking you for untoward information and the like. Using this information, they set up a filter based on your response and eventual communication with them.

Many machine learning models prevent such attacks with a low false-positive rate. They are classified into three main categories: single classifier, hybrid models, and deep learning. Single classifiers are those which make use of one algorithm only. Hybrid models make users of two or more algorithms get the job done. But deep learning models mimic the complex networks of our brain and are more thorough in their approach.

Some artificial intelligence and machine learning algorithms that are used to identify phishing attacks are given below:

Support Vector Machine: It makes use of a two-way classification system in which each section is labelled as either safe or dangerous. Depending on the type of attack it identifies, it classifies it and takes the necessary action.

Decision Tree: This model makes use of a tree structure in which a feature and a possibility are included. When certain choices are made, either of the two results already input into the algorithm can be either.

Random Forest: A random forest is a much more complex model that uses multiple decision trees. It averages the output of all the decision trees together and produces one result. It gives a much more accurate result.

Bagging: It’s a bootstrap aggregating procedure where the original dataset is divided into multiple sets. It uses resampling techniques to generate a dataset of the original size and then runs its analysis. Once it’s done, the output is given out as the average of all the divided datasets combined.

Convoluted Neural Network: It’s a deep learning algorithm used for image classification. The network consists of multiple layers? the outer layer, hidden layers, and the input layer. It stores the data so that the information in each layer contributes to the next (forming a feature map) and so forth.

Can We Rely On Artificial Intelligence to Protect Us? #

Artificial intelligence and cybersecurity can be a killer combination, but not in the way that you think. On the one hand, artificial intelligence can help you get ahead of phishing attacks by implementing algorithms that detect them in milliseconds. On the other hand, you also have many phishing attacks that are particularly orchestrated using artificial intelligence.

By using AI, they can create malware that precisely consists of the harmful application within the files that are being sent. It also prevents the reverse engineering of such a scam, making it hard to figure out the culprit. All of these come under the?Smart Phishing? domain and use several whitehat techniques to carry out numerous illegal activities.

From the protection perspective, it’s hard to disregard the importance of implementing Artificial Intelligence and subsequent machine learning models. These algorithms work based on the purpose that they’re created for. In that case, it makes more sense to use AI-assisted software to simplify and accelerate the data security process.

With remote work becoming the go-to option, work is limited to a laptop/ desktop device. Mobile phone usage is becoming increasingly common, and AI-assisted software works perfectly with smaller devices too. It also uses predictive modeling algorithms that can warn you much beforehand, thereby increasing the user’s protection. The most significant advantage is that even if the phishing attacks are made using an AI-based system, the user’s software can identify them.

All in all, while it may seem like artificial intelligence and cybersecurity are a paradox of their own, we err on the side of its benefits rather than the negatives. You stand a much better chance at protecting yourself and your enterprise when you use artificial intelligence-based software to detect and prevent phishing attacks.

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Questions and answers

Artificial Intelligence and Cybersecurity — How Does It Work? #

Daily, at least 25% of phishing attacks get past the current security defenses that we have. Some of these defenses include phishing filters, spam filters, and the like.

Phishing scams are more commonly known as ‘Nigerian Prince? #

scams, where impersonators steal your confidential information for their benefit, albeit illegal. They’re focused on stealing any kind of sensitive information and don’t have a particular target in mind.

The network consists of multiple layers? #

the outer layer, hidden layers, and the input layer. It stores the data so that the information in each layer contributes to the next (forming a feature map) and so forth.

Artificial Intelligence of Things (AIoT)

Vin Patel — Tue, 19 Oct 2021 18:04:00 +0000

Key takeaways

If you are a technology enthusiast or have a vague idea of what’s going in the world of technology, then you must be familiar with the popularity of artificial intelligence (AI) and the internet of things (IoT).
AI-based algorithms are used to remove trash data and utilize any business strategy in order to organize this unstructured data into a meaningful chunk of data.
AIoT is a one-time investment that yields lifelong benefits.

Introduction: Convergence of Internet of Things and Artificial Intelligence #

If you are a technology enthusiast or have a vague idea of what’s going in the world of technology, then you must be familiar with the popularity of artificial intelligence (AI) and the internet of things (IoT). There is no doubt that these two are individually powerful entities that have not only shaken the industries with their capabilities but have been performing as technological boons ever since. But what happens when these two technologies are clubbed together and become an exciting collaboration named Artificial Intelligence of things (AIoT)?

Speaking of what AIoT is, it is a new generation process of converging the source of intelligence received from artificial intelligence and making inter-device connections with the help of the internet of things. It is paving its way to being one of the most popular technical solutions for data analytic and processing. It is in the nature of IoT to aggregate things, particularly devices, producing huge amounts of data. When AI comes into the mix, the data-hungry algorithms of AI churn the data and process it throughout, making the analysis and action parts of it highly efficient. Now that we have a basic idea of AIoT, it is time for us to know its benefits.

Benefits of AIoT #

All the praise of AIoT would make no sense if you do not have a clear vision of its benefits. So, we have listed out the major benefits of AIoT:

Increase in Efficiency:

There is a saying, ‘Time is Money,? and these are the two factors that can drastically increase or decrease your efficiency. The employers of any company need to use their employee’s time as effectively and efficiently as possible. This will not only yield positive outcomes but also result in increased revenue. This is possible with the help of AIoT. Let us discuss some examples to prove this point. Preventive maintenance is a benefit of AIoT, which helps the maintenance team to recognize parts of any machinery that is not working correctly or even predict malfunction in advance. This saves the expenditure of replacing the entire machine and keeps the activities running smoothly, hence increasing efficiency.

Increase in Safety:

Safety is a very important factor while running any business, as any small mishap due to careless behavior can lead to huge losses. AIoT helps to boost safety in several ways. One example of this could be employing computer vision to monitor personnel on a production floor or using virtual or augmented reality in dangerous circumstances. This application of artificial vision can be used to get real-time alerts of drivers while driving any vehicle. It provides alerts to prevent accidents in cases like the driver falling asleep behind the wheel.

Better Customer Eexperience:****

It is very important for companies to look for ways to improve their customer service in any customer-centric business, and AIoT could be of great help in this area. For example, in any shopping mall or store, there are surveillance cameras used to understand their customers’ shopping patterns and other information like which aisle is getting more sales and which is not getting any customer visits. This way, they can plan accordingly and make the shopping experience more personalized for their targeted customers.

Smart Business Ddecisions:****

IoT devices collect huge amounts of data every day, making it difficult for personnel to differentiate between important and unimportant information. AI-based algorithms are used to remove trash data and utilize any business strategy in order to organize this unstructured data into a meaningful chunk of data. Chief technology officers (CTOs) and other decision-makers may now make sound business decisions based on the important insights gleaned from this data.

Uses of AIoT in Various Sectors: #

AIoT is used in many sectors in real life. Let us learn about a few of them.

Healthcare Devices:

Many companies are coming with smartwatches that monitor the users’ heart health, activities, blood pressure, etc. These are created with the concept of AIoT. These have revolutionized the healthcare industry and made people more aware of their health. IoT facilitates the connection of many device kinds; on the other hand, AI turns the collected data into insights that can play an essential part in making users healthier.

Fleet Management:

Fleet vehicles are clusters of motor vehicles owned or leased by the Government or any private company. AIoT is used for the tracking, management, and maintenance of fleet vehicles. This set of use cases was created by combining GPS, additional sensors, and an AI system. An example of this is autonomous vehicles, which utilize sonars, radars, cameras, and GPS to collect data on driving conditions and then use AI to make choices based on IoT’s data.

Use of Drones:

Drones are trendy devices used worldwide for many purposes. When used to monitor vast regions, drones may assist in monitoring traffic in real-time and make changes in the flow of traffic. AI may be used to evaluate data and then make choices on reducing traffic congestion by adjusting speed limits and traffic light timing - all without the need for human intervention. Drones are also used to deliver products to consumers, usually in the eCommerce and food delivery sectors. There are other fields that are ready for exploration for the use of drones as well.

Smart Homes:

It is no longer a fantasy to have houses that understand their owners and respond to their commands. With the help of AIoT, people are able to build smart homes with electronic devices that work with just one voice command. Infused with voice assistants and artificial intelligence that understands the user’s behavior and acts accordingly, like switching off lights after the user moves out of the room or automatically switching on electronic devices when required. Between 2020 and 2025, the AIoT-driven, smart home industry is expected to achieve a $246 billion valuation at a CAGR of more than 25%.

Conclusion #

So, it is fitting to say that the combination of artificial intelligence and IoT has been revolutionizing many industries and has proven itself beneficial till now. Engineers and scientists are still working on AIoT to make improve it further and make it more convenient for the users. AIoT is a one-time investment that yields lifelong benefits.

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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.

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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.

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How is AI Transforming Social Media Marketing?

Vin Patel — Tue, 24 Aug 2021 19:13:41 +0000

Key takeaways

Most marketers and business owners are scared that AI will replace people.
!Diagram: Always Available for Communication Original diagram for Techfastly by Vin Patel. So, if you haven’t started using your Facebook chatbots, it’s high time you should go ahead with it.
But it will surely help you save a lot of time and manpower for your company.

Do you know that AI in social media will be worth over $2 billion by 2023?

That’s huge, right?

But the fact is even to this day several businesses do not understand the worth of automation and AI in social media marketing.

Most marketers and business owners are scared that AI will replace people. That’s why they are skeptical about using the right tools. But the companies that are investing in AI are camping up their social media marketing games.

Social media is all about time and content and, AI can help in that. One of the common ways AI helps is by choosing hashtags in your post. If you have been using LinkedIn for your marketing, you must have come across this.

Artificial Intelligence recommends these hashtags.

Similarly, if you invest in the right tools, the AI will help you discover useful hashtags which can propel your social media posts.

AI is helping in suggesting emojis, finding the trends, and a lot more. I’ll tell you all about it in detail. So, read till the end.

Social media has become complicated these days. A marketer has to handle a lot of social media platforms for a brand. And sometimes, things can get a little heated when you are managing so many platforms.

But AI is a lifesaver. Here are the reasons why?

Improves Content Writing #

Texts are a big part of social media marketing. In the end, it’s the engaging content that helps in reaching more people on social media. Obviously, you require a creative content writer or copywriter to design perfect content. But AI can tell you which words, hashtags, and emojis to use that can help in reaching more people.

Original diagram for Techfastly by Vin Patel.

Many social media websites also suggest hashtags for your content. You can find this on Instagram, LinkedIn, and even Facebook. These help you select the trending ones. To top it all off, you also may have to invest time in hashtag and emoji research.

There are tools such as Google Trends, which uses AI to find the trending hashtags. But you can rely on the social media AI recommends. They utilize the historical data of your posts to suggest suitable hashtags.

Always Available for Communication #

Facebook, LinkedIn, Instagram, and more are some popular names in the social media sector. Almost every brand has an account in these. People are more likely to research products and goods even on social media to get an idea about their purchase.

So, it’s always useful to have your communication line open for the people. But you never know when a person might ask a question. It can even be at the dead of the night.

Your marketer or sales team member may not be available to connect with the prospect as it’s beyond working hours. In such a scenario, AI chatbots are a great help. Facebook, Instagram, LinkedIn, and more use such chatbot facilities so, you don’t have to answer every message.

Using this AI tool, you can design and create generic messages for your prospects. So, they feel like they are talking to a person. Your sales team can carry on from where the conversation is left.

Original diagram for Techfastly by Vin Patel.

So, if you haven’t started using your Facebook chatbots, it’s high time you should go ahead with it. But try to make the messages as friendly as possible. The target is to make the prospect feel that they are communicating with a person and not a bot!

Keep up with All the Trends #

As a social media marketer, you have to stay ahead by following the trends. Even before something blows up or goes viral, you have to get into it and create suitable content. You may have an employee keeping up with all the updates, but it may be a waste of manpower.

Instead, you can get help from an AI tool that can tell you about all the trends and updates that are happening at the moment on popular social media platforms. For example, if a particular dance move is getting viral on Tiktok, you might want to recreate the same for your brand.

So, the AI will tell you which ones are getting viral, so you have enough time to create the content.

At the same time, you need to stay updated with all the news. You have to know who is talking about you and respond instantly. AI can help you with that as well.

You simply need to have the right tools.

When you keep up with the trends, your prospects and customers will feel more connected to your brand.

Time management is an important thing social media marketers have to deal with daily. Even when they are off-duty, they have to keep up with the happenings of social media. Constant observation is vital.

If you are too late to a trend, it can impact your brand image. So, you don’t want to miss it. AI and machine learning can help you remain updated with all the news and happenings. Those companies who have used AI technologies for their social media marketing have benefited a lot.

It lessens the manpower and provides accurate results. AI is constantly evolving. You can expect AI to write social media posts in the future. So, you have to be ready to get the right tools at the right time.

Artificial Intelligence lessens the guesswork a marketer has to do for social media. It will come up with the facts with proper research. Therefore, you should try to use it. It’s not going to replace your marketers, but enhance the work!

By using AI, you’ll focus more on the jobs that require human creation and finesse. AI can take over all the time-taking jobs like observing social media 24/7. However, you must note that AI isn’t perfect.

You can’t solely rely on all the results AI provides you. So, you might want to have your social media marketer go through the results. But it will surely help you save a lot of time and manpower for your company.

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Questions and answers

Time management is an important thing social media marketers have to deal with daily. Even when they are off-duty, they have to keep up with the happenings of social media.

By using AI, you’ll focus more on the jobs that require human creation and finesse. AI can take over all the time-taking jobs like observing social media 24/7.

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.

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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.

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Questions and answers

What Are Chatbots? #

How Do Chatbots Work? #

Everything You Need to Know About "ASTRA" by ANAROCK

Vin Patel — Wed, 04 Aug 2021 22:49:57 +0000

Key takeaways

Anarock coupled with G-Square Solutions aims to propel real estate housing sales.
However, It will understand the micro-market dynamic, past sales trends, local prices, and other important local policies to provide a concrete customer prediction.
So, try the primary version of Anarock’s Astray to boost your house sales and see the difference it makes.

Anarock coupled with G-Square Solutions aims to propel real estate housing sales. For this, they took the help of artificial intelligence. Artificial Intelligence and real estate markets do not usually go together.

But “ASTRA”, an AI algorithm has made it possible.

According to a study, the real estate sector in India is estimated to reach around USD 650 million by 2025.

And ‘Astray? is going to make it easier.

The real estate market in India still relies on traditional methods. These are proving to slow down the market as a whole. There is a lack of research and interest.

In recent years, people have been using the internet to search for their houses and real estate. It has made several companies go online and find their customers.

However, a huge portion of the market still relies on word of mouth, offline ads, and more to find prospects.

“Astray” is a one-of-a-kind AI algorithm that is going to change the complete scenario of housing sales in India.

What Is ‘Astray? All About? #

Anarock started developing the artificial intelligence model “Astray” in 2019.

The model will accelerate housing sales by studying the customer behavior data. Customer behavior data will help it find potential leads.

Companies will be able to narrow down to the right prospects which will save them capital and time. It will boost the market efficiency and provide an engaging customer experience to the prospects.

Anarock chairman Anus Puri said,?we started configuring various models in different kinds of real estate projects in India. In the final development stage, the AI algorithm ‘Astray? is designed to provide a 20 fold return on investment.?

The AI model uses a predictive analysis system to find out all about customer behavior. It is fed with the information that will help it to identify potential prospects and check their behavior.

You won’t get random prospects but high-quality leads.

This will cut down the time and effort of the sales team and they will be able to make more focused work. When they will understand and learn which prospects to focus on, they will be able to generate better results.

When the AI-selected customer inquiries will come up, the sales team can help the customer find the best deals. So, this revolutionary AI model is not going to replace any sales team. But it will help the team to do better work.

How Does ‘Astray? Work? #

Astray uses Anarock’s 2.8 million data of leads and 74 different information models across various cities in India. It is going to be one of the most efficient prop-teach or property technology the country has ever seen.

The pandemic period has made it possible for people to accept and adopt digital tools. So, there is great hype around this model.

G-square Solutions, the developer of this model, provides expert teach support and various tools which enhance the model.

The developers will have complete control over the “leads” information. It will not simply use the contact details of the leads to analyze. However, It will understand the micro-market dynamic, past sales trends, local prices, and other important local policies to provide a concrete customer prediction.

The developing team expects the algorithm to increase customers or leads for the various real estate companies. For now, Anarock will use the primary version of the algorithm to make their sales drive.

However, there is a call for developers to invest in the advanced versions of the AI model.

Core Functions of ‘Astray? #

Anarock’s team has provided three main functions of this real estate marketing AI algorithm. They are:

Lead Quality Scoring The AI model can analyze the lead data and score them individually. This will help the sales team understand which prospects have a higher score and focus more on them.

The real estate sales team will have better control over the lead data and they don’t have to always use their predictions.

The AI predictions will be much more accurate as they work on research and analyzed data. Using the large datasets, the model will analyze the behavior of the prospects. It will then match with the data and provide a score.

The higher the score, the more chances of the lead buying a house. So, your sales team will only have interactions with the right customers.

They don’t have to waste their time on people who are only researching but do not have a wish to move forward with a purchase.

Integrated SAAS CRM #

When the sales team of the real estate companies have access to the accurate data of the prospects, they can improve the customer relationship. Before they approach the customer, they will have all the important data vital for friendly and proper interaction with the customers.

When there is an improved customer relationship with the company, it will help in high ratings and reviews for the company. If your sales team can impress the prospects they may even help you find more leads.

Revenue Maximization #

When the real estate companies will be able to narrow down their probable customers, it will help in a revenue increase in the long run. According to Anarock’s blog, ‘Astray? will help in 12-15% faster sales.

So, the real estate companies will be able to make more profit and even score high-quality leads.

Get Your Hands on This AI Model #

With the country’s faster development in the past years, there has been a drastic increase in the real estate business in India. However, the companies have been lagging due to the use of traditional marketing methods.

However, “Astray” is a game-changer in the industry. If you are into real estate marketing, you may take the help of this tool to identify high-quality leads for your company. You may invest in the advanced version of this model to get bigger results.

So, try the primary version of Anarock’s Astray to boost your house sales and see the difference it makes.

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How AI-Enabled Robots Are Reshaping The World

Vin Patel — Mon, 02 Aug 2021 22:01:18 +0000

Key takeaways

You select the movie, get pillows and blanket on your couch, and order your favorite food from an online food delivery app.
- The use of machines eliminates human error and provides total efficiency.
The changes that take place with the developing technology can have both positive and negative impacts, for which we should be prepared.

? The intertwining of Robotics and Artificial Intelligence will lead us to the next generation.?

Overview #

It’s a weekend, and you have planned a movie night home. You select the movie, get pillows and blanket on your couch, and order your favorite food from an online food delivery app.

But when you open the parcel, it turns out to be cold and stale. You reopen the app from which you had placed your order and talk to the customer executive, complaining about the quality of food delivered. You manage to get a refund or credits to your account.

When you talked thinking it was a customer executive, you might or might not have noticed that it was a virtual assistant. The AI-powered chatbot responded immediately to your issue and acted as your problem-solver for the inconvenient matter.

What we had only imagined twenty years ago has already taken shape into a new reality. AI Robots are used everywhere now, from grocery buying and self-driving cars to smart speakers like Alex and Sir.

What is Artificial Intelligence? #

In simple terms, Artificial intelligence (AI) is the imitation of human intelligence by computers. The machines with AI are capable of problem-solving, copy human actions, display cognitive behavior and emotions.

The use of AI has begun in almost every industry. The most common examples include Facebook’s recommendation engine and Google’s AI-powered search predictions.

What is Robotics? #

It is a branch of engineering and technology that aims at the construction and functioning of robots. Robots are artificial agents programmed to perform tasks in the real world. They are the first automated technology known to man. They were designed to perform fixed tasks, but now the scenario is different.

Linking of Robotics and AI #

Artificial Intelligence has empowered modern robotics. Together, they are a powerful combination for task automation.

Now, instead of programming robots to perform specific or repetitive tasks, they are stippled with AI algorithms. They now exhibit human characteristics like recognizing scenarios, grasping, and objects. They can perform multiple tasks and learn new things.

How AI-Enabled Robots Influence Different Sectors #

We cannot argue the statement that AI robots will have a significant impact on humanity soon. They have raised vital questions like what they can do, what we should do with them, how they can be dangerous, and how we can control them.

The adaptability of robotics and AI has made them ideal in defense, healthcare, and education to manufacturing, locomotives, and gaming. Together, they have made rigid tasks flexible.

Let’s have a look at how AI and robotics affect the different sectors.

Impact on Workplaces #

A robot-integrated workforce means no vacations, illness, conflicts, or personal time. They can work day and night that will prove fruitful for the employers. However, it creates fears for employees since it can impact their employment.

However, there are two sides to this. While AI and robots will take over several jobs, they will also create new professions. Emerging automation doesn’t aim to eliminate jobs and advance the processes and make them more efficient.

According to a report, only 5% of jobs can get fully automated. The rest of the jobs will require human supervision, involvement, and participation.

Developing technologies might seem frightening, but it does create new jobs. Since machines cannot function independently, new types of careers with new skill requirements will emerge. People will shift from repetitive tasks to creativity. It will create new possibilities and innovations.

Impact on Industries #

Robotics in the manufacturing industry stepped around a half-century ago. Now, thinking about this industry without them is almost impossible. Few benefits of AI robots in manufacturing are:

The AI robots enhance productivity. They produce more in a shorter time and without breaks. They can also be customized, flexibly switched between tasks, and made to fix issues.
The use of machines eliminates human error and provides total efficiency. It increases customer satisfaction since it reduces defective products.
Some work environments are hazardous for humans. Using robots will ensure the safety of the workers and minimize physical injuries.
Using robots reduces the cost of manufacturing.

Robots in Healthcare #

Robotics fuelled with AI have rapidly advanced medical care and continues to grow.

Robotic machines have become a surgeon’s friend. They improve the accuracy of the process, which helps the patient recover quicker.
AI-enabled chatbots in consultancy platforms improve the field of medicine.

Besides the above applications, there are new advancements like medical wearable, analyzing diagnostics and medical history, 3D printing, and developing new drugs in a lesser amount of time.

Although medicine and healthcare have surpassed spheres that we could only imagine, there is still room for advancement.

In Farming and Agriculture #

Surprisingly, robotics and AI have greatly impacted agriculture and farming. Automation of agricultural tasks has enhanced productivity and boosted crop yield.

Robots designed to perform agricultural tasks handle watering, spraying, and harvesting better than humans. They also detect and effectively remove unwanted weeds and pests. AI algorithms analyze weather, crop, soil, and other conditions to predict the yield. Since the population keeps growing, automation of agricultural processes combats the challenge of food provision for the globe.

In the Automotive Industry #

Original diagram for Techfastly by Vin Patel.

The automotive industry widely uses AI-enabled robots. Robots perform most of the tasks, from lining up vehicles to the assembly of the parts.

This significantly reduces the cost of production. Also, when they are trained to perform tasks, they do it with full efficiency and better precision.

AI-Robots in Gaming #

AI and Robots have brought a new revolution in the gaming sector. It has transformed the designing and playing techniques of games.

With the help of AI and robotics, game developers have got their hands on incredible graphics, creating realistic environments, and creating characters that mimic the players.

It also helps collect data about the gamers, helping the developers create games that meet their expectations.

Apart from the course of computer games, online gambling has benefited from AI algorithms. It helps in predictions and provides gratification to the gamblers.

Sophia: The World’s First Robot Citizen #

Yes, humanoids are hitting the headlines now, out of which one of them is particularly famous. Hanson Robotics created Sophia, the world’s first humanoid citizen who can express emotions and exhibits social behaviors like a living being.

Sophia was unveiled in 2016, and she immediately gained prominence. She has interacted with distinguished personalities across the world and attended several conferences. She appointed the first humanoid innovation champion of the United Nations. She also holds citizenship from the Kingdom of Saudi Arabia, making her the first in line.

Conclusion #

With major advancements using the combination of artificial intelligence and robotics, the future is already there. Automating processes will not only make our lives easier and comfortable, but create scopes for further advancements, inventiveness, and the creation of innovative careers.

The changes that take place with the developing technology can have both positive and negative impacts, for which we should be prepared. Few questions remain unresolved until technology reaches its peak- will we control the AI-powered robots or will they empower us?

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Questions and answers

What is Artificial Intelligence? #

What is Robotics? #

It is a branch of engineering and technology that aims at the construction and functioning of robots. Robots are artificial agents programmed to perform tasks in the real world.

How AI-Enabled Robots Influence Different Sectors? #

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.

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‘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.

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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.

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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.

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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.

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More in Artificial intelligence

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.

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?

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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

More in Artificial intelligence

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.

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

More in Artificial intelligence

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

More in Artificial intelligence

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.

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).

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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.

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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.

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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.

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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.

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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? #

AI in Journalism

Vin Patel — Wed, 26 Aug 2020 22:30:42 +0000

Key takeaways

AI is continually making waves in the news and journalism industry.
For example, the FDA data dump revealed breast and dental implant problems among hidden reports.
The videos can be your preferred journalist oriented primer for the free and excellent fast.ai course practical deep learning for coders, which will significantly help you.

AI is continually making waves in the news and journalism industry. The work of communicating everything that happens in the society seems like it’s something entirely human-dependent. Furthermore, what does a machine have to do with what happens in the news?

Journalism, as we know, has a large consumer base and is highly dependent on technology from the collection of data from different news sources to the publication of the content.

Despite journalism being one of the consumers of technology, technological advancements change the way the news and journalism are carried out. AI is continuously focused on improving news production, publishing, and sharing ideas in particular.

In automated or algorithmic or robot journalism, computer programs are used to generate news articles. Computers, rather than human reporters, automatically produce stories through AI (Artificial Intelligence) software. These programs organize, interpret, and also present the data in a readable manner.

Artificial intelligence in journalism involves algorithms that scan large amounts of given data, select from pre-programmed articles, order key points and insert information such as places, amounts, statistics, and names. There is also the potential to customize the output to fit a specific tone, style, and voice.

Common Worldwide Data Leaks #

In recent years, investigative journalists had a protective duty between the public sphere and their confidential information sources. However, this duty has been undergoing changes in the last two decades with the introduction of new media.

Some instances like Snowmen and WikiLeaks show how contemporary the media allows people to directly release data to the global audience, which raises the question of how the operations of journalists are affected by the recent leaks.

The Bahamian Files

In the Bahamas, some 400,000 documents were leaked from the register of legal entities on offshore companies, including Ukrainians. There was data of about 60 companies on the Ukrainian part of the Bahamian archive. Massive datasets include information on citizens of more than 100 countries who had applied to one of the globally famous offshore zones, the Caribbean jurisdiction for the registration of their businesses.

The Bahamian files were publicly made available on midday. The documents had information on different Germans’ offshore secrets. The DDoS (Distributed Denial of Secrets) published a full archive of the documents, unlike the ICIJ, who posted the Bahamas leaks, with no documents. The archive posted by the DDoS contained much larger data amounts than the Bahamas Leaks with some of it, namely until 2018, relating to a very recent period.

As mentioned earlier, the Ukrainian part of the Bahamian archive contained information of about 60 companies. The companies were mostly founded by Ukrainians, low profile businessmen who were not part of the government, but several names drew public attention.

It’s not a crime to register companies in offshore jurisdictions. It is a global exercise that occurs for various reasons, such as convenience in the structuring of one’s business and quality services in the offshore zones.

FDA Data Dump

You may have only heard of Panama Leaks or WikiLeaks. However, every few months, there is a leak of information that journalists cover. For example, the FDA data dump revealed breast and dental implant problems among hidden reports. The majority of the reports about the 5.8 medical device problems that the FDA accessed were breast and dental implants.

Many medical device complaints are kept at the FDA, where they are accessible to the general public. The 5.8 million complaints were the ‘alternative summary reports’ that were hidden for decades, and the FDA only could access the files; nurses and doctors couldn’t even see them.

The Luanda Leaks

Isabel dos Santos, a businesswoman, was accused of making a fortune at the Angola citizens’ expense, as revealed in the Luanda Leaks. She moved billions through her shell empire, which was made possible by consultants, lawyers, and accountants.

The businesswoman claimed that her success was self-made, but an investigation, Luanda Leaks, by 36 media partners and the ICIJ revealed the true source of her wealth. Isabel dos Santos in her defense, accused the Luanda authorities of using falsified documents to freeze her assets as presented in her evidence.

The Luanda authorities denied the claims of Isabel alleging false emails and a fake passport. They further stressed that the estimated damages in the proceedings against her are more than 5 billion dollars by the state’s estimates.

There have been massive data leaks in recent years from the offshore jurisdictions like the offshore leaks, Panama papers, Bahamas leaks, and paradise papers.

Incorporation of Machine Learning (ML) in Journalism #

In some instances, machine learning cannot do something you couldn’t; but what machine learning does is accomplish it faster than humans. Machine learning has the potential to find emails, the same as the ones you already have, assist you in finding frames of a given video with a senator, etc. The computer needs to be fed particular knowledge on the questions you are trying to find solutions.

This knowledge helps you solve several common problem patterns such as flagging matching things from given sets of documents, filtering complex data point spreadsheets, searching through picture piles, and sorting caches of reader tips.

It’s up to you to determine whether the images, data points, or the documents found via machine learning are newsworthy or exciting. Still, ML can get you from an unmanageable pile to a manageable one. You should also know that the computer can be mistaken, confused, and fail to understand your question like any other source, so continue with your journalism. ML is continuously being incorporated and advanced in the journalism industry so that it perfectly answers questions that reporters have concerning their data.

Machine learning techniques greatly help journalists in daily stories and accomplish some investigations that may involve going through vast piles of documents for months, which can be done in just a week. Journalists have been known to utilize machine learning tools and codes like in the following real-world examples:

finding assaults classified as minor but were serious ones
detecting sexual abuse complaints in disciplinary reports
detecting political advertising
finding the discussed topics by the members of congress
detecting toxic comments

Learning ML With Free Hands-on Videos #

As a journalist, you can learn how to how to incorporate ML in your investigation tasks through several online coding notebooks and videos. Get to try your hands in searching images, analyzing text with AI, and sorting images.

You can enter these pre-trained machine learning lessons by John Keef, an editor at Quartz Al Studio, and a member of the Mauritius leaks. There are 15 lessons which are 4-15 minutes. You’ll get through a series of project examples focused on journalism like sorting docs into piles, detecting objects in images, and also the extraction of individuals’ names from troves of texts.

The lessons utilize the fast.ai ML library for python, which makes Machine Learning easy for individuals who are not well versed in computers and math. The videos can be your preferred journalist oriented primer for the free and excellent fast.ai course practical deep learning for coders, which will significantly help you.

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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.

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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?

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How to Calculate the ROI of AI Investment

Vin Patel — Fri, 26 Jun 2020 14:30:00 +0000

Key takeaways

As it requires a good deal of investment to include AI in your business, it is necessary to analyze and estimate the Return of Investment (ROI) before making the decision.
Some key elements that will help you with a good estimation of ROI: The first and most important thing is the purpose of implementing AI for your business.
If you have a budget, then implement AI in your business, even just for the sake of facilitation.

AI and data science are now leading the business world. As it requires a good deal of investment to include AI in your business, it is necessary to analyze and estimate the Return of Investment (ROI) before making the decision.

Cost of AI #

Implementing Artificial Intelligence requires skilled professional and smart tools. You will need to buy special tools and software to set up an AI-based business. These tools require smart investment and are technical. Graphic Processing Unit (GPU), which is the base of an AI-based system, alone costs hundreds of dollars to the business.

Also, AI tools are not easier to handle for traditional staff. So, you have to hire a new team or work on upskilling your current employees for an AI-based system, which is again a huge cost.

Here the question arises that is this investment in AI is profitable? Will it give the desired benefits? What will be its ROI? It is better to learn from experiences then to experiment.

Success and Failure of AI in Business World #

Large businesses and big brands are using Artificial Intelligence for years. Some projects failed to achieve their desired results, and some had to be abandoned. But there are a good number of businesses that get a good ROI by implementing AI in their businesses.

. Approx. 85% of AI business plans have failed in the past. Even big names, like IBM, Amazon, and Apple, were unsuccessful in getting their desired results.

. Watson for Oncology is the biggest failure of AI. It was a joint venture of IBM and The University of Texas to treat cancer with the help of AI. This project turned out to be a failure, and the investors had to shut it down after whooping 62 million dollars of investment.

. The exploitation of AI Cabot by Microsoft and the malfunction of Face Recognition feature of iPhone are other examples of AI failure.

Successful AI Implementation in Business #

But, before you drop your plan of investing in AI, have a look at these success stories as well. Even smaller businesses, like pizza outlets and bakeries, have increased their efficiency by using AI for their business.

. Amazon Go is one of the best examples of AI success. It smartly predicts the customers’ move and shows them what they are probably looking for. It increased the customer flow at Amazon and decreased the turnover rate.

. Barclay`s is successfully using AI to improve its security. Its AI system can detect and inform about any illegal activity.

. Domino`s Pizza has remarkably increased the accuracy of its estimated delivery time from 75% to 95%, which helps it in better customer service.

AI for Business Yay or Nay? #

The failure rate of AI projects is alarming, but its benefits are worth the risk. You can launch a successful AI plan for your business by carefully weighing the pros and cons and accurately estimating the Return of Investment.

What is the ROI? #

ROI or the Return of Investment is the profit and cost ratio of an investment. ROI estimates whether the investment will be fruitful for the business, or it will only be a financial burden for the company.

Calculating ROI #

Return of investment is the major concern of all businesses. In most cases, the ROI of Artificial intelligence is unpredictable and is challenging to calculate.

Key Elements of Calculating ROI #

Some key elements that will help you with a good estimation of ROI:

Analyze Your Goals #

The first and most important thing is the purpose of implementing AI for your business. AI has its limitations. If your goal of the task is within the realm of AI, go for it. But if it’s an abstract idea, be prepared for the risk of failure.

Calculate the Costs #

The investment of purchasing an AI-based system is not the only cost of your project. You will also need smart tools to boost this system further, and most importantly, need technical staff to manage the AI operations.

For the correct estimation of ROI, it is essential to calculate all the costs, including training of the staff and the cost of deployment of the project.

Retaining Talent #

A company invests a good chunk of money, time, and efforts to recruit and maintain its team. With the implementation of AI, a good number of your current team might not be functional. Wasting the efforts of creating the old team and investing in a new technical team, is another major cost for the business. It’s not easy to let go of your loyal employees, and you want to retain the talent. This will need intensive training and, again, a good investment.

Evaluate the Benefits #

After calculating the cost, your next step is to evaluate the benefits. List down the likely outcomes of implementing AI in your business, and critically examine its impact on your business. Just the tag that you are an AI-based business is not enough justification for investing in this system. There should be a profitable outcome for this system.

Weighing Cost against Benefits #

Once you have a clear picture of estimated costs and likely benefits of the project, you can calculate ROI.

If the cost of implementing AI is more than the estimated returns of the investment, then the project will be just a financial burden on your business.

But if the returns of investment are higher than the investment, then the project will likely give good ROI.

ROI percentage #

ROI percentage indicates the profitability percentile of the project.

The difference between your earnings and cost is the net revenue or net return of investment. You can calculate the percentage of ROI, by dividing the net return of investment with the cost of the investment and then multiplying it by 100.

A higher percentage of ROI indicates a higher probability of the success of the project.

Is ROI Enough to Evaluate the Worth of AI? #

ROI gives a clear picture of the monetary outcome of the business, but not all benefits of investment are financial. Boosting the soft image of the brand and facilitating customers is as important as generating revenues.

If the ROI of your AI investment is not higher, but it will facilitate your employees and clients, then it’s not a failure. If you have a budget, then implement AI in your business, even just for the sake of facilitation. It will definitely benefit your business in the long run.

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Questions and answers

AI for Business Yay or Nay? #

What is the ROI? #

Is ROI Enough to Evaluate the Worth of AI? #

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.

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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.

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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.

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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.

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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.

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Unleashing the Power of Machine Learning & AI for the Retail

Vin Patel — Fri, 24 Apr 2020 17:49:08 +0000

Key takeaways

!Diagram: Diagram Original diagram for Techfastly by Vin Patel. The retail sector provides employment to 29 million people directly and supports 42 million indirectly.
The store uses Deep Learning to analyze store receipts and returns to evaluate purchases at each store.
IKEA has also use a similar concept where before you buy, you can see how the furniture may look in your home.

Original diagram for Techfastly by Vin Patel.

The retail sector provides employment to 29 million people directly and supports 42 million indirectly. These pandemic times have brought home how browsing the grocery aisles is part of our normal life that has been disrupted.

Organized retail touches several aspects of industry. From real estate operations to merchandising, to shipping & logistics, to customer loyalty programs to old school flyer programs to shopping list apes, the list goes on and on.

The grocery industry which is a significant chunk of the organized retail sector has a slim profit margin of 2.2%.Time has not been kind to this sector. In the UK, over a 10 year period, margins have halved, falling from 8.8% to 4.1%.

The industry has to look for efficiencies like never before.

Whether a retailer is an old school brick and mortar store or an online only store, from an infrastructure standpoint, there is a large overlap between the two:

Original diagram for Techfastly by Vin Patel.

The onset of Machine Learning and Artificial Intelligence has made waves at both kinds of retailers.

Here is a quick review of how some retailers are using technology to shave off costs.

Automation & Warehousing: #

Kroger, a leading grocer in the US has been partnering with a robotics company from the UK to automate a few warehouses to speed up picking of items from its warehouses. Speed is a major consideration for the online ordering segment and continues to push the industry to focus on the use or robots.

Walmart has been experimenting with similar robots named Alphabets in partnership with a different vendor. Produce and other fresh items continue to be picked by hand. Alphabets enable real time data sharing and over time this will help better stacking in the warehouses as patterns in items that are frequently ordered together will begin to surface.

Facial recognition & Loyalty Management #

Finally, retailers are no longer content to identify us via swipe of a loyalty card at the point of sale. They want to identify us as soon as we enter the store. The iPhone X has helped to normalize facial recognition and using this technology retailers aim to provide a customized experience. By using facial recognition systems retail staff will know what type of products you buy and what your average spend is, so they can give more love to higher spenders. They’ll be able to use software that reads emotion to identify when you are frustrated and notify staff to respond.

Kroger App & In-Store Recommendations #

Grocery chain Kroger, in collaboration with Microsoft, is testing out the idea of smart shelves. When a customer walks down an aisle and has their Kroger app open, sensors identify the shopper and highlight products they might be interested in. The app might highlight gluten-free products for a gluten-free shopper or kid-friendly snacks for a parent. The app can also provide personal pricing and alert shoppers if an item on their shopping list is on sale.

H&M Uses AI to Keep Popular Items Stocked Popular #

Clothing store H&M relies on staying on top of trends to be successful. The store uses Deep Learning to analyze store receipts and returns to evaluate purchases at each store. The algorithm helps the store know what items to promote and stock more of in certain locations. The data could find that floral skirts sell well at urban stores and change the inventory to match what customers want.

Coca Cola has connected a third of its vending machines to the Internet. The company also has its freestyle machines where a customer can combine different combinations of flavors to create their own creation. They have an integration with apes that remind customers when to get hydrated (obviously with a Coke product). Coca Cola also has a mobile wallet which enables customers to use facial id to buy a coke at select kiosks.

Facial Recognition to Reduce shoplifting #

Facial recognition is being used in multiple ways in retail stores. Stores in the UK are using it to detect if purchasers of alcohol meet the legal drinking age.

Stores that have identified shoplifters can share these profiles with other stores too. Shoplifting costs retailers roughly $35.5 billion each year. The initial database can be created by uploading videos of documented shoplifters, organized retail crime associates and disgruntled ex-employees. Of course the Privacy folks may have a field day with this approach.

According to the National Retail Foundation, 10.8% of returns made each year are fraudulent, costing the retail industry $9.6 billion a year. Facial recognition also has the potential to protect retailers from a specific kind of return fraud. By uploading images of customers that suspected of abusing generous product return policies, companies can be alerted when such customers come in or make a purchase.

Facial Recognition and AR used by Sephora #

Sephora, is a leading example of using AR (augmented Reality) to let the consumer experience a product that many first thought was not suitable for online retail. Sephora took their time and only when they were convinced that the technology had achieved a certain level of preciseness, did they opt-in. Over 20,000 products sold by the retailer were added to the program. By 2018, the Sephora Virtual Artist has seen 200 million shades tried on, and over 8.5 million visits to the feature, according to the company.

IKEA has also use a similar concept where before you buy, you can see how the furniture may look in your home.

With so much going on in the retail industry, can any one afford to stand still?

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Artificial-Intelligence on Vin Patel

Artificial Intelligence Against Money Laundering

Using AI for Anti-Money Laundering (AML) #

Challenges Involved in AML #

Using AI to Drive Efficiencies in Operational Hotspots #

Identity verification #

Monitoring the activities #

Monitoring transactions #

Conclusion #

Artificial Intelligence — Will It Outpace Humans?

The Science Behind Artificial Intelligence #

Abstract Thinking – Can AI Emulate It? #

Artificial Intelligence vs Our Cognitive Abilities #

The Future as It Stands #

Artificial Intelligence's Ethical Dilemma

Introduction #

Artificial Intelligence’s Ethical Concerns and Their Positive Side #

Biased Outcomes #

Autonomous Vehicle / Carrier #

Unemployment #

Culture-based Ethical Dilemma #

AI in Judicial System #

How can the human mindset ignore the black side of these dilemmas? #

End Note #

How can the human mindset ignore the black side of these dilemmas? #

Let us first understand what biased outcomes are? #

The Future is Now; How AI is All Around Us

Is AI The Breaking Point in The History of Humanity? #

Anticipations of the Technological Era #

Why AI is also the reason you are reading this article, far more than your conscious choice #

Where Can We Find AI? #

Is AI Dangerous? #

Conclusion #

Artificial Intelligence in Healthcare - Making Impactful

AI in Healthcare #

Impact on the Workforce and Beyond #

The Types of AI in Healthcare #

Machine learning #

Natural Language Processing (NLP) #

Physical Robots #

Robotic Process Automation #

The Future of AI in Healthcare #

Bottom Line #

The Data-Centric Approach to Artificial Intelligence

Data Vs Code: A closer look #

The Data-Centric Approach #

Good Data #

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

What Exactly is GPT-4? #

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

Closing Thoughts #

What Exactly is GPT-4? #

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

Intelligent Test Automation: Why Should You Use It?

How Does Intelligent Test Automation Work? #

What Are The Risks of Insufficient Software Testing? #

Why Should Teams Adapt To This New Method? #

Conclusion #

5 Different Ways Advertising Helps Your Business

#

Artificial Intelligence Advertising #

Email Marketing #

Social Media Advertising #

Influence Advertising #

Location-Based Offers And Advertising #

Conclusion #

What Does AI Have In The Box For E-commerce?

What exactly is E-Commerce Automation? #

What’s all in the Box? #

1. Dynamic E-mail Content Strategy #

2. Customer Retention #

3. Chatbot #

4. Automated Inventory Management #

5. Customer Order Management #

6. Analytic #

Conclusion #

Is Artificial Intelligence a Threat to Journalists

Why Do People Fear AI? #

How Is AI Helping Journalists and Writers? #

Machine Learning Gadgets in Newsroom #