Tag Archive for: ML

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.

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.