Tag Archive for: Machine Learning

What Is Adaptive AI and What Could it Mean for AI Startups?

The next major evolutionary step in AI and machine learning will be the large-scale implementation of “adaptive AI.” What exactly is “adaptive AI,” and what will the leap to this new technology mean for fledgling AI companies and startups?

The power of AI is its ability to take in and interpret quite large volumes of data and then accurately generate insights and predictions that can lead to smarter decision-making by the humans leveraging the algorithms. As the name implies, adaptive AI systems take that ability to the next level by being able to “adapt” or continuously respond to new as it becomes available and modify its outputs accordingly.

Adaptive AI dynamically incorporates new data from its operating environment to generate more accurate insights on a real-time basis. It is increasingly regarded as artificial intelligence’s next evolutionary stage. By incorporating a more responsive learning methodology, such as agent-based modeling (ABM) and reinforcement learning (RL) techniques, adaptive AI systems are more reactive to the changing world around them and can thus more seamlessly adapt to new environments and circumstances that were not present during the earlier stages of the AI system’s development.

This kind of almost instantaneous adaptability is certain to prove critical over the coming years, during which the likes of the Internet of things (IoT) and autonomous vehicles are expected to expand greatly in popularity. Such applications must continuously consume massive quantities of data to reflect ongoing changes in the external environment in real time.

Well-known IT Analyst Erick Brethenoux observed in October 2022. “Adaptive AI systems aim to continuously retrain models or apply other mechanisms to adapt and learn within runtime and development environments—making them more adaptive and resilient to change.”

Advancements in adaptive AI will also greatly improve AI applications in healthcare and will likely save lives. The ability to consistently analyze data related to thousands, if not millions, of patient symptoms and vital signs can enable adaptive AI systems to optimize the clinical recommendations they produce.

Over the long term, adaptive AI delivers faster, more accurate outcomes, which should mean that more meaningful insights can be gleaned by any enterprise relying on AI for intuitive decision-making.

IT research and consulting group Gartner has predicted that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the time and the number of processes it takes to operationalize AI models by at least 25 percent.

All of this speaks volumes to the opportunities for AI startups that focus their R&D efforts on adaptive AI.

How BigRio Helps Bring Advanced AI Solutions to the Marketplace

Adaptive AI, indeed, will be one of the next big leaps forward in artificial intelligence and machine learning. At BigRio, we are at the leading edge of helping such advancements in AI get to market.

BigRio prides itself on being a facilitator and incubator for these kinds of revolutionary breakthroughs in AI.

In fact, we like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting out in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the Sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the expertise to not only weed out the companies that are not ready for the market, as the sharks on the TV show do, but also mentor and get those that we feel are readily noticed by the right people in the AI investment community.

You can read much more about how AI is redefining the world in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. While the book’s primary focus is on healthcare delivery, it also takes a deep dive into AI in general, with specific chapters on advances such as adaptive AI.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

CHAI Releases a “Blueprint” for Implementing Trustworthy AI for Healthcare

The Coalition for Health AI (CHAI) has recently released its long-awaited Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare. The “blueprint” outlines recommendations to increase trustworthiness and a roadmap to promote high-quality patient care and improved outcomes within the context of AI implementation in the healthcare environment.

The 24-page BlueprintBlueprint is the product of CHAI’s year-long effort to help health systems, AI and IT experts, and other healthcare stakeholders advance health AI while addressing important issues such as health equity and bias.

Brian Anderson, MD, a co-founder of the coalition and chief digital health physician at MITRE, said in a press release detailing the BlueprintBlueprint, “Transparency and trust in AI tools that will be influencing medical decisions is absolutely paramount for patients and clinicians. The CHAI Blueprint seeks to align health AI standards and reporting to enable patients and clinicians to better evaluate the algorithms that may be contributing to their care.”

The report closely aligns with The National Academy of Medicine’s (NAM’s) AI Code of Conduct. NAM’s goal was to align health, healthcare, and biomedical science around a broadly adopted “code of conduct” in AI to ensure responsible AI for the “equitable benefit of all.” The NAM effort will inform CHAI’s future efforts, which will provide robust best-practice technical guidance, including assurance labs and implementation guides to enable clinical systems to apply the Code of Conduct.

CHAI’s technical focus will help to inform and clarify areas that will need to be addressed in NAM’s Code of Conduct. The work and final deliverables of these projects are mutually reinforcing and coordinated to establish a code of conduct and technical framework for health AI assurance.

“We have a rare window of opportunity in this early phase of AI development and deployment to act in harmony—honoring, reinforcing, and aligning our efforts nationwide to assure responsible AI. The challenge is so formidable, and the potential so unprecedented. Nothing less will do,” said Laura L. Adams, senior advisor National Academy of Medicine.

The CHAI Blueprint also builds upon the White House OSTP “Blueprint for an AI Bill of Rights” and the “AI Risk Management Framework” from the U.S. Department of Commerce’s National Institute of Standards and Technology.

“The needs of all patients must be foremost in this effort. In a world with increasing adoption of artificial intelligence for healthcare, we need guidelines and guardrails to ensure ethical, unbiased, appropriate use of the technology. Combating algorithmic bias cannot be done by any one organization but rather by a diverse group. The BlueprintBlueprint will follow a patient-centered approach in collaboration with experienced federal agencies, academia, and industry,” said Dr. John Halamka, president Mayo Clinic Platform and a co-founder of the coalition.

How BigRio Helps Bring Advanced AI Solutions to Healthcare

The CHAI report has presented a detailed roadmap on the best case and most ethical practices for AI implementation in the medical or healthcare setting. For the past several years at BigRio, we have been dedicated to much the same thing.

BigRio prides itself on being a facilitator and incubator for emerging and innovative healthcare AI, as well as helping facilities adapt to and successfully implement such AI solutions seamlessly and effectively into their legacy operations.

In fact, we have launched an AI Studio specifically for US-based Healthcare startups with AI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

• Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
• Assistance in the development and testing of the MVP, Prototypes, and POCs.
• Professional services for implementation and support of Pilot projects
• Sales and Marketing support and potential client introductions.
• Access to private capital sources.

BigRio excels in overcoming such initial hurdles, which present nearly insurmountable obstacles to a startup operation.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint, and it discusses many of the same issues raised in the CHAI report.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

Artificial Intelligence Moves Heavily Into the NFT Space!

Investors take note, machine learning is beginning to have a powerful impact on generative art non-fungible tokens (NFTs).

NFTs are cryptographic assets on a blockchain with unique identification codes and metadata that distinguish them from each other. Most often, NFTs are used to represent real-world items like artwork and real estate. “Tokenizing” these real-world tangible assets makes buying, selling, and trading them more efficient while reducing the probability of fraud.

With that in mind, AI is becoming increasingly important in the non-fungible token space. “Generative art” – art that has been created by AI — has quickly emerged as one of the main categories of the NFT market, driving innovative projects and investment in astonishing collections. From the works of AI art legends such as Refik Anadol or Sofia Crespo to Tyler Hobbs’s new QQL project, NFTs have become one of the main vehicles to access AI-powered art.

The rise of generative AI has come as a surprise even to many of the early AI pioneers, who mostly saw this discipline as a relatively obscure area of machine learning. Its leap to dominance in the NFT market has largely been driven by gains in computational power and next-gen MI algorithms that can help models learn without requiring a lot of labeled datasets, which are incredibly limited and expensive to build.

One of the most significant of these advances has been “text to image” (TTI). AI-driven TTI programs such as DALL-E and GLIDE allow users to describe in text what they would like the program to render, and it then creates an interpretive image based on the text, with some profoundly remarkable results. Thus making TTI ideal for the creation of unique and marketable NFTs.

There are also similar generative art solutions such as “text-to-video” or “image-to-image,” but TTI, by far, is having the greatest impact on the NFT market because a disproportionate percentage of digital art collectibles are represented as static images.

Throughout the history of technology, there have been many examples of seemingly disparate trends coming together to form a market symbiosis that benefits both. The most recent example is the social-mobile-cloud revolution, in which each one of those trends expanded the market of the other two.

Generative AI and NFTs are starting to exhibit a similar dynamic. Both trends have been able to bring complex technology to mainstream culture. NFTs complement generative AI with digital ownership and distribution models that would be nearly impossible to implement otherwise. Similarly, generative AI is likely to become one of the most important sources of NFT creation now and into the future.

How BigRio Helps Facilitate Investment in AI Startups

Like what is occurring with NFT and AI-generated art, BigRio looks for and helps to facilitate such market symbiosis.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Because we see so many potential AI innovators, we are also ideally suited to create the kind of synergy between concepts and applications such as is occurring with AI, ML and NFTs.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

IT Experts Predict The Top Five Advances in AI for 2023!

AI is known for its ability to make very accurate predictions. But often, human prognosticators are pretty good at it too! In this article, we take a look at what leading IT experts say they think will be the top five advances in AI and machine learning in 2023, as compiled by The Enterprisers Project.

1. There Will Be Continue Advancement of AI Applications in Healthcare

“AI will yield tremendous breakthroughs in treating medical conditions in the next few years. Just look at the 2021 Breakthrough Prize winner Dr. David Baker. Dr. Baker used AI to design completely new proteins. This ground-breaking technology will continue having huge ramifications in the life sciences, potentially developing life-saving medical treatments for diseases like Alzheimer’s and Parkinson’s.” — Michael Armstrong, Chief Technology Officer, Authenticx.

2. Continued Merging of AI and Quantum Computing

Phil Tee, Co-founder, and CEO, of Moogsoft, says to, “Watch the crossover from fundamental physics into informatics in the guise of quantum and quantum-inspired computing. While I’m not holding my breath for a practical quantum computer, we will see crossover. The mix of advanced mathematics and informatics will unleash a new generation of engineers uniquely placed to exploit the AI wave.”

3. AI Will Not Replace Humans

Despite dozens of sci-fi movies and novels to the contrary, the experts do not believe that AI will replace humans in 2023 and the years ahead, but instead, they expect to see increased interaction between human and artificial intelligence with an increased synergy between the two. “While there will be growing adoption of AI to enhance our collective user experience at scale, it will be balanced with appropriate human intervention. Humans applying the insights provided by AI will be a more effective combination overall than either one doing it alone. How and where this balance is struck will vary depending on the industry and the criticality of the function being performed. For example, radiologists assisted by an AI screen for breast cancer more successfully than they do when they work alone, according to new research. That same AI also produces more accurate results in the hands of a radiologist than it does when operating solo.” – E.G. Nadhan, Global Chief Architect Leader, Red Hat

4. A Move Towards More Ethical AI and an AI Bill of Rights

As we reported earlier this year, the Biden administration had launched a proposed “AI Bill of Rights” to help ensure the ethical use of AI. Not surprisingly, it is modeled after the sort of patient “bills of rights” people have come to expect as they interact with doctors, hospitals, and other healthcare professionals.
David Talby, CTO of John Snow Labs, says to see continued movement in this direction. “We can expect to see a few major AI trends in 2023, and two to watch are responsible AI and generative AI. Responsible or ethical AI has been a hot-button topic for some time, but we’ll see it move from concept to practice next year. Smarter technology and emerging legal frameworks around AI are also steps in the right direction. The AI Act, for example, is a proposed, first-of-its-kind European law set forth to govern the risk of AI use cases. Similar to GDPR for data usage, The AI Act could become a baseline standard for responsible AI and aims to become law next Spring. This will have an impact on companies using AI worldwide.”

5. AI Will Support Increased and “Smarter” Automation

“Everyone understands the value of automation, and, in our software-defined world, almost everything can be automated. The decision point or trigger for the automation, however, is still one of the trickier elements. This is where AI will increasingly come in: AI can make more intelligent, less brittle decisions than automation’s traditional ‘if-this-then-that’ rules.” – Richard Whitehead, CTO, and Chief Evangelist, Moogsoft.

How BigRio Helps Facilitate the Future of AI

At BigRio, we not only agree with these experts on these top five advances in AI that will likely occur in 2023, but we are also actively trying to facilitate them!
We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the growing AI investment community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

The Enterprisers Project is a community and online publication helping CIOs and IT leaders solve problems and drive business value. The Enterprisers Project, supported by Red Hat, also partners with Harvard Business Review.

Topic Modeling for Personalized Entertainment

NLP evolved to be an important way to track and categorize viewership in the age of cookie-less ad targeting. While users resist being identified by a single user ID, they are much less sensitive to and even welcome the chance for advertisers to personalize media content based on discovered preferences. This personalization comes from improvements made upon the original LDA algorithm and incorporate word2vec concepts.

The classic LDA algorithm developed at Columbia University raised industry-wide interest in computerized understanding of documents. It incidentally also launched variational inference as a major research direction in Bayesian modeling. The ability of LDA to process massive amounts of documents, extract their main theme based on a manageable set of topics and compute with relative high efficiency (compared to the more traditional Monte Carlo methods which sometimes run for months) made LDA the de facto standard in document classification.

However, the original LDA approach left the door open on certain desirable properties. It is, at the end, fundamentally just a word counting technique. Consider these two statements:

“His next idea will be the breakthrough the industry has been waiting for.”

“He is praying that his next idea will be the breakthrough the industry has been waiting for.”

After removal of common stop words, these two semantically opposite sentences have almost identical word count features. It would be unreasonable to expect a classifier to tell them apart if that’s all you provide it as inputs.

The latest advances in the field improve upon the original algorithm on several fronts. Many of them incorporate the word2vec concept where an embedded vector is used to represent each word in a way that reflects its semantic meaning. E.g. king – man + woman = queen

Autoencoder variational inference (AVITM) speeds up inference on new documents that are not part of the training set. It’s variant prodLDA uses product of experts to achieve higher topic coherence. Topic-based classification can potentially perform better as a result.

Doc2vec – generates semantically meaningful vectors to represent a paragraph or entire document in a word order preserving manner.

LDA2vec – derives embedded vectors for the entire document in the same semantic space as the word vectors.

Both Doc2vec and LDA2vec provide document vectors ideal for classification applications.

All these new techniques achieve scalability using either GPU or parallel computing. Although research results demonstrate a significant improvement in topic coherence, many investigators now choose to deemphasize topic distribution as the means of document interpretation. Instead, the unique numerical representation of the individual documents became the primary concern when it comes to classification accuracy. The derived topics are often treated as simply intermediate factors, not unlike the filtered partial image features in a convolutional neural network.

Revolutionary AI: How AI Is Helping Make Healthcare Easy

With all this talk of the bright future of Artificial Intelligence (AI), it’s no surprise that almost every industry is looking into how they will reap the benefits from the forthcoming (dare I say already existing?) AI technologies. For some, AI will merely enhance the technologies already being used. For others, AI is becoming a crucial component to keeping the industry alive. Healthcare is one such industry.

The Problem: Diminishing Labor Force

Part of the need for AI-based Healthcare stems from the concern that one-third of nurses are baby boomers, who will retire by 2030, taking their knowledge with them. This drastic shortage in healthcare workers poses the imminent need for replacements and, while the enrollment numbers in nursing school stay stable, the demand for experienced workers will continue to increase. This need for additional clinical support is one area where AI comes into play. In fact, these emerging technologies will not only help serve as a multiplier force for experienced nurses, but for doctors and clinical staff support as well.

Healthcare-AI Automation Applications to the Rescue

One of the most notable solutions for this shortage will be automating processes for determining whether or not a patient actually needs to visit a doctor in-person. Doctors’ offices are currently inundated with appointments and patients who’s lower-level questions and concerns could be addressed without a face-to-face consultation via mobile applications. Usually in the from of chatbots, these AI-powered applications can provide basic healthcare support by “bringing the doctor to the patient” and alleviating the need for the patient to leave the comfort of their home, let alone scheduling an appointment to go in-office and visit a doctor (saving time and resources for all parties involved).

Should a patient need to see a doctor,  these applications also contain schedulers capable of determining appointment type, length, urgency, and available dates/times, foregoing the need for constant human-based clinical support and interaction. With these AI schedulers also comes AI-based Physician’s Assistants that provide additional in-office support like scheduling follow-up appointments, taking comprehensive notes for doctors, ordering specific prescriptions and lab testing, providing drug interaction information for current prescriptions, etc. And this is just one high-level AI-based Healthcare solution (albeit with many components).

With these advancements, Healthcare stands to gain significant ground with the help of domain-specific AI capabilities that were historically powered by humans. As a result, the next generation of healthcare has already begun, and it’s being revolutionized by AI.

MicroAgents: That’s not a personalized AI, THIS is a personalized AI

Sometimes I get to thinking that Alexa isn’t really my friend. I mean sure, she’s always polite enough (well, usually, but it’s normal for friends to fight, right?). But she sure seems chummy with that pickle-head down the hall too. I just don’t see how she can connect with us both — we’re totally different!

So that’s the state of the art of conversational AI: a common shared agent that represents an organization. A spokesman. I guess she’s doing her job, but she’s not really representing me or M. Pickle, and she can’t connect with either of us as well as she might if she didn’t have to cater to both of us at the same time. I’m exaggerating a little bit – there are some personalization techniques (*cough* crude hacks *cough*) in place to help provide a custom experience:

  • There is a marketplace of skills. Recently, I can even ask her to install one for me.
  • I have a user profile. She knows my name and zip code.
  • Through her marketplace, she can access my account and run my purchase through a recommendation engine (the better to sell you with, my dear!)
  • I changed her name to “Echo” because who has time for a third syllable? (If only I were hamming this up for the post; sadly, a true story)
  • And if I may digress to my other good friend Siri, she speaks British to me now because duh.

It’s a start but, if we’re honest, none of these change the agent’s personality or capabilities to fit with all of my quirks, moods, and ever-changing context and situation. Ok, then. What’s on my wishlist?

  • I want my own agent with its own understanding of me, able to communicate and serve as an extension of myself.
  • I want it to learn everything about how I speak. That I occasionally slip into a Western accent and say “ruf” instead of “roof”. That I throw around a lot of software dev jargon; Python is neither a trip to the zoo nor dinner (well, once, and it wasn’t bad. A little chewy.) That Pickle Head means my colleague S… nevermind. You get the idea.
  • I want my agent to extract necessary information from me in a way that fits my mood and situation. Am I running late for a life-changing meeting on a busy street uphill in a snowstorm? Maybe I’m just goofing around at home on a Saturday.
  • I want my agent to learn from me. It doesn’t have to know how to do everything on this list out of the box – that would be pretty creepy – but as it gets to know me it should be able to pick up on my cues, not to mention direct instructions.

Great, sign me up! So how do I get one? The key is to embrace training (as opposed to coding, crafting, and other manual activities). As long as there is a human in the loop, it is simply impossible to scale an agent platform to this level of personalization. There would be a separate and ongoing development project for every single end user… great job security for developers, but it would have to sell an awful lot of stuff.

To embrace training, we need to dissect what goes into training. Let’s over-simplify the “brain” of a conversational AI for a moment: we have NLU (natural language understanding), DM (dialogue management), and NLG (natural language generation). Want an automatically-produced agent? You have to automate all three of these components.

  • NLU – As of this writing, this is the most advanced component of the three. Today’s products often do incorporate at least some training automation, and that’s been a primary enabler that leads to the assistants that we have now. Improvements will need to include individualized NLU models that continually learn from each user, and the addition of (custom, rapid) language models that can expand upon the normal and ubiquitous day-to-day vocabulary to include trade-specific, hobby-specific, or even made-up terms. Yes, I want Alexa to speak my daughter’s imaginary language with her.
  • DM – Sorry developers, if we make plugin skills ala Mobile Apps 2.0 then we aren’t going to get anywhere. Dialogues are just too complex, and rules and logic are just too brittle. This cannot be a programming exercise. Agents must learn to establish goals and reason about using conversation to achieve those goals in an automated fashion.
  • NLG – Sorry marketing folks, there isn’t brilliant copy for you to write. The agent needs the flexibility to communicate to the user in the most effective way, and it can’t do that if it’s shackled by canned phrases that “reflect the brand”.

In my experience, most current offerings are focusing on the NLU component – and that’s awesome! But to realize the potential of MicroAgents (yeah, that’s right. MicroAgents. You heard it here first) we need to automate the entire agent, which is easier said than done. But that’s not to say that it’s not going to happen anytime soon – in fact, it might happen sooner than you think.  

Echo, I’m done writing. Post this sucker.

Doh!

 

Artificial Intelligence vs. Machine Learning: What’s the Difference?

In the 2011 Jeopardy! face-off between IBM’s Watson and Jeopardy! champions Ken Jennings and Brad Rutter, Jennings acknowledged his brutal takedown by Watson during the last double jeopardy in stating “I for one welcome our new computer overlords.” This display of computer “intelligence” sparked mass amounts of conversation amongst myriad groups of people, many of whom became concerned at what they perceived as Watson’s ability to think like a human. But, as BigR.io’s Director of Business Development Andy Horvitz points out in his blog “Watson’s Reckoning,” even the Artificial Intelligence technology with which Watson was produced is now obsolete.

The thing is, while Watson was once considered to be the cutting-edge technology of Artificial Intelligence, Artificial Intelligence itself isn’t even cutting-edge anymore. Now, before you start lecturing me about how AI is cutting-edge, let me explain.

Defining Artificial Intelligence

You see, as Bernard Marr points out, Artificial Intelligence is the overarching term for machines having the ability to carry out human tasks. In this regard, modern AI as we know it has already been around for decades – since the 1950s at least (especially thanks to the influence of Alan Turing). Moreso, some form of the concept of artificial intelligence dates back to ancient Greece when philosophers started describing human thought processes as a symbolic system. It’s not a new concept, and it’s a goal that scientists have been working towards for as long as there have been machines.

The problem is that the term “artificial intelligence” has become a colloquial term applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving.” But the thing is, AI isn’t necessarily synonymous with “human thought capable machines.” Any machine that can complete a task in a similar way that a human might can be considered AI. And in that regard, AI really isn’t cutting-edge.

What is cutting-edge are the modern approaches to Machine Learning, which have become the cusp of “human-like” AI technology (like Deep Learning, but that’s for another blog).

Though many people (scientists and common folk alike) use the terms AI and Machine Learning interchangeably, Machine Learning actually has the narrower focus of using the core ideas of AI to help solve real-world problems. For example, while Watson can perform the seemingly human task of critically processing and answering questions (AI), it lacks the ability to use these answers in a way that’s pragmatic to solve real-world problems, like synthesizing queried information to find a cure for cancer (Machine Learning).

Additionally, as I’m sure you already know, Machine Learning is based upon the premise that these machines train themselves with data rather than by being programmed, which is not necessarily a requirement of Artificial Intelligence overall.

https://xkcd.com/1838/

Why Know the Difference?

So why is it important to know the distinction between Artificial Intelligence and Machine Learning? Well, in many ways, it’s not as important now as it might be in the future. Since the two terms are used so interchangeably and Machine Learning is seen as the technology driving AI, hardly anyone would correct you if were you to use them incorrectly. But, as technology is progressing ever faster, it’s good practice to know some distinction between these terms for your personal and professional gains.

Artificial Intelligence, while a hot topic, is not yet widespread – but it might be someday. For now, when you want to inquire about AI for your business (or personal use), you probably mean Machine Learning instead. By the way, did you know we can help you with that? Find out more here.

Anomaly Detection in Video & Image Classification

We’re seeing and doing all sorts of interesting work in the Image domain. Recent blog posts, white papers, and roundtables capture some of this work, such as image segmentation and classification to video highlights. But an Image area of broad interest that, to this point, we’ve but scratched the surface of is Video-based Anomaly Detection. It’s a challenging data science problem, in part due to the velocity of data streams and missing data, but has wide-ranging solution applicability.

In-store monitoring of customer movements and behavior.

Motion sensing, the antecedent to Video-based Anomaly Detection, isn’t new and there are a multitude of commercial solutions in that area. Anomaly Detection is something different and it opens the door to new, more advanced applications and more robust deployments. Part of the distinction between the two stems from “sensing” what’s usual behavior and what’s different.

Anomaly Detection

Walkers in the park look “normal”. The bicyclist is the anomaly. 

Anomaly detection requires the ability to understand a motion “baseline” and to trigger notifications based on deviations from that baseline. Having this ability offers the opportunity to deploy AI-monitored cameras in many more real-world situations across a wide range of security use cases, smart city monitoring, and more, wherein movements and behaviors can be tracked and measured with higher accuracy and at a much larger scale than ever before.

With 500 million video cameras in the world tracking these movements, a new approach is required to deal with this mountain of data. For this reason, Deep Learning and advances in edge computing are enabling a paradigm shift from video recording and human watchers toward AI monitoring. Many systems will have humans “in the loop,” with people being alerted to anomalies. But others won’t. For example, in the near future, smart cities will automatically respond to heavy traffic conditions with adjustments to the timing of stoplights, and they’ll do so routinely without human intervention.

Human in the Loop

Human in the loop.

As on many AI fronts, this is an exciting time and the opportunities are numerous. Stay tuned for more from BigR.io, and let’s talk about your ideas on Video-based Anomaly Detection or AI more broadly.

Artificial Intelligence Will Take Jobs

A few months back, Treasury Secretary Steve Mnuchin said that AI wasn’t on his radar as a concern for taking over the American labor force and went on to say that such a concern might be warranted in “50 to 100 more years.” If you’re reading this, odds are you also think this is a naive, ill-informed view.

An array of experts, including Mnuchin’s former employer, Goldman Sachs, disagree with this viewpoint. As PwC states, 38% of US jobs will be gone by 2030. On the surface, that’s terrifying, and not terribly far into the future. It’s also a reasonable, thoughtful view, and a future reality for which we should prepare.

Naysayers maintain that the same was said of the industrial and technological revolutions and pessimistic views of the future labor market were proved wrong. This is true. Those predicting doom in those times were dead wrong. In both cases, technological advances drove massive economic growth and created huge numbers of new jobs.

Is this time different?

It is. Markedly so.

The industrial revolution delegated our labor to machines. Technology has tackled the mundane and repetitive, connected our world, and, more, has substantially enhanced individual productivity. These innovations replaced our muscle and boosted the output of our minds. They didn’t perform human-level functions. The coming wave of AI will.

Truckers, taxi and delivery drivers, they are the obvious, low-hanging fruit, ripe for AI replacement. But the job losses will be much wider, cutting deeply into retail and customer service, impacting professional services like accounting, legal, and much more. AI won’t just take jobs. Its impacts on all industries will create new opportunities for software engineers and data scientists. The rate of job creation, however, will lag far behind that of job erosion.

But it’s not all bad! AI is a massive economic catalyst. The economy will grow and goods will be affordable. We’re going to have to adjust to a fundamental disconnect between labor and economic output. This won’t be easy. The equitable distribution of the fruits of this paradigm shift will dominate the social and political conversation of the next 5-15 years. And if I’m right more than wrong in this post, basic income will happen (if only after much kicking and screaming by many). We’ll be able to afford it. Not just that — most will enjoy a better standard of living than today while also working less.

I might be wrong. The experts might be wrong. You might think I’m crazy (let’s discuss in the comments). But independent of specific outcomes, I hope we can agree that we’re on the precipice of another technological revolution and these are exciting times!