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analysis dot com and now here's your host through Eric Kavanaugh. Yes, oh,
yes, indeed, folks, welcome to the future. The future is
here already. That's what William Gibson once wrote. It's just not evenly distributed.
I have to say I heard that line years ago and it just blew
my mind. I was like, what, that's pretty cool. The future
is here already. It's just not evenly distributed yet. But that's happening right
now. You see it with so for example, you see it with certain
kinds of construction, skyscrapers. You look at the new skyscrapers and places like
Dubai in the UAE and they're just amazing. And that is the forefront of
the industry, and it's primarily because they have a lot of money. You
want to build some cool buildings. And in the rest of the world you
have older buildings that are still standing obviously, but I bring in this metaphor
just to point out that we are at a very transformative phase or moment in
time as human beings, quite frankly, and with technology, of course,
it's always out there improving, usually improving our lives, doing cool things for
us well. Artificial intelligence not new. It's been around for decades, fifty
sixty years if you really push it all the way back even further depending upon
your definition. But in terms of widespread adoption and use, boom, we
are in the middle of that first major explosion. And you can thank the
folks that open AI for sure with their chat GPT, which just the doors
off. Now that even isn't all that new. GPT's been around for a
number of years now, but this version that they unleashed on the public really
opened eyeballs and it forced the game. It was a forcing function. But
I have someone on the show today who knows a whole lot about AI and
has known a lot about AI for a long long time, and he taught
me a lot about this stuff too, Osama Fayad. He's got a company,
Open Insights, but also he was the chief data officer of a company
called Yahoo some of you all may remember those guys. They were the center
of gravity in like the nineties and the early aughts. That was where you
wanted to be as an engineer, as an AI person. And then of
course it kind of shifted to companies like Cloud, Deerra and Google of course
and now open Ai. But they're still out there. Yeah, he's still
doing cool stuff and so is Osama Asama Fayad. He is now the executive
director for the Institute for Experiential AI at Northeastern University. Sama, welcome to
the show here. Thanks so much for your time and attention, and first
of all, tell us about the Northeastern University Institute for Experiential AI. Where
did this idea come from and what's your focus? Thanks? Thanks, Eric.
Idea came from the president of the university, Joseph and the provost David
Madigan, who's actually a well established statistician an AI expert in his own right,
and some donors who gave some large grants saying hey, AI is going
to be big. This was about This was five years ago, so around
late two thousand they started saying, Okay, we've got a lot of funding,
let's recruit for someone to head it up and define it the way we
I'm the inaugural executive director for this, as well as joining as a professor
in Computer Science College Cory College for Computer Sciences. So experiential AI, the
first thing you need to do is name it. And we use the term
experiential AI to meet AI with the human in the loop. Because the reality
of my exposure to AI through my work, whether it was with Microsoft,
NASA's GPL, my own startups, Yahoo, later with Barclays, and definitely
with Open Insights, working with many companies around the world including AI in the
healthcare space, etc. Has shown me one theme that is constant, which
is every working AI has a huge element of the human in the loop,
whether you're talking about the core of the Google Search Engine or we're talking about
open AI and chat GPT in any of these large language models, a lot
of feedback all the time from humans supplying what AI cannot supply. And like
you mentioned, we've spent over seven decades trying to figure out stuff like common
sense reasoning. One of the big learning lessons is you only can get that
from humans today. You cannot get a machine or an algorithm to provide you
that feedback, which is why there's a lot of human in the loop intervention
in the operation of AI the other and that became a theme because what it
means is you need to think about how AI help human intelligence and how human
intelligence helps machine intelligence or AI become a reality. So it's about kind of
the collaboration with algorithms and robots and so forth. So that's how we define
it. And then we had to go look for, Okay, what are
areas that need focus where as an institute we can make a large contribution,
we can become the leader in certain areas, and quickly identified responsible AI as
one big area of focus from a research perspective, defining what it is,
how do you do it, how do you do risk assessment, et cetera.
And then we also identified kind of what I like to call new generation
large language models or new generation generative AI. So how do you reduce this
obsession with larger and larger parameters in the model. Larger models that can so
easily go out of control, start making errors hallucinating as Google calls it,
or basically are extremely expensive to train because the more parameters you have, the
more and of course capital equipment wise, that the training infrastructure, and the
inference is so rather than how can we make models bigger, the question is
how do I get away with the smallest possible large model and enhance it by
stuff that we know prior knowledge? Do I have a knowledge graph about the
domain? Do I understand who the people are, professional graph, a citation
graph, et cetera. It is silly to try to let the AI rediscover
from pure data, very very expensive data, what we already know. So
can we supplement this and we call this better together? And then we picked
certain areas and we are seeing better results indeed qualitatively and qualitatively when we apply
these two things together. Finally, we had to pick kind of areas of
focus for applications, which is critical for us. So applications, in my
opinion, have been the driver in the developments of AI for the past decade
or more. So. We looked at applied areas in AI plus health,
AI plus life sciences, drug discovery and molecule design and things like that,
antibody design. And then the last area we added was AI for climate and
sustainability. How do we leverage the new generations of data sets that are kind
of hyper local, very detailed, huge big data, if you will,
in the world of climate and then use it to model both the climate and
the environment and predict things like here are the impacts of a potential disaster or
a change or what have you. So these areas define where we work and
to drive the wheel forward. To start the flywheel going, we created the
AI Solutions Hub. One of the big funders of the institute is Dave Rue
who funded also the Real Institute up in Portland, Maine, in order to
change the economy in Maine, and AI in his vision, was a big
area. So we created this AI Solutions Hub, which is a team,
a delivery team of data scientists, data engineers, AI practitioners whose job is
to engage with our partners companies, organizations, governments and work on real challenges
in the real world in order to drive that activity, figure out what the
real research agenda should be and at the same time figure out how do we
train our students and the corporate learners on AI in the wild AI happening in
reality? Right? What are the real issues? How do you deal with
it? You know, we all know that dependence deep dependence of data and
generative AI, of generative AI on data, but The real issue here is
what do you do? And the data is not in idealized circumstances. It's
got quality problems, half of it is missing, a third of what's there
is wrong. But you got to make the solution work, and you've got
to make it work in the constraints of kind of real life and real businesses
and real regulations and so forth. So all of this coming together gives us,
if you like, the active lab that generates the research agenda and generates
the chance to train our students and learners from the corporations on how to make
AI solutions work. Yeah, that's I really love the vision here because you're
appreciating the importance of research, but also the importance of training, the importance
of understanding use cases, hence the solution portfolio or the solution hub. And
you know, I want to drive into one key issue here that you brought
up, because I think it's the most important one to make this stuff enterprise
ready and business ready and consumer friendly, which is the truth. Right.
We have one of our regular listeners, Alex Husky, super smart guy works
for rex On Mobile. He sent me an email. We did an webinar
a few weeks ago he said, we cannot allow these large language models to
pattern recognize their way to the truth. And that's kind of what they're doing
is it's basically a pattern recognition engine that predicts text based upon the prompt and
all the corpus of text that it has consumed. Well, you mentioned citations
for example, and this whole concept of embeddings comes into play. And that's
what you need, right, that's the ground truth our citations. This is
where I got the information, so you can verify that it's correct. That's
a cornerstone of making this stuff doable and workable for business, right absolutely.
And you know, citations are a beautiful example. You know, this autocomplete
technology that we call large language models, it's trying to you know, build
its output token at a time, and the token is part of a word,
part of a group of pixels, depending on what your output looks like.
Well, trying to recollect a precise citation based on an autocomplete is a
very dangerous exercise and rife with room for making an error. You can get
the year wrong, the author's wrong, you can make up, you can
come up with a title, you could change the title, all based on
your recollection well, why do all that? If you already have these citations
in very precise format, how can you use them? And then tying the
citations to the content to the meaning is the other big challenge because most of
these models, I mean we call them technically stochastic parrots because they really parrot
out. They don't know anything, they don't understand what they're saying. So
tying that to kind of the sources of truth, as you say, is
another huge challenge which doesn't exist today. Well, and also we have to
realize there is a workflow to these models, and what I'm learning from folks
is you can do one of two things. Basically, you can fine tune
your model by embedding your corporate data into the model, so training the model
itself on your corporate data. Or the other very common use case is to
use a vector database and that's where you store your embeddings, and so embeddings
would be your financial reports for your business, or your training modules or documents
related to some process in your business. You want to populate all that and
ideally just use the large language model for its text, generative and syntactical knowledge
essentially, so it's you're using the engine to spin up the words, but
the facts, if you will, come from the embeddings. Isn't that the
general idea? Yeah, and well, you're touching on some of the more
interesting aspects of large language models. So let me start by saying, first
of all, no one understands why they work. Of why a very large
model that traditional statistics will tell you should never work because it's over parameterized,
it's got too many degrees of freedom it can fit the data, and therefore
no chance of learning why it works. That's one interesting mystery, right that
we don't write. The other mystery is this whole embedding space. Why is
it that if I took a group of words, or a group of sentences,
or a group of paragraphs and map them into this very large vector space,
which is what's called embedding a bunch of numbers. Basically, why is
it that those embeddings capture something about the domain that allows you to generalize,
that allows you to build own knowledge. So that representation is another mystery eric
that we have people working on trying to demystify and understand what the heck is
going on. Those two aspects are some of the deeper aspects of the technology
that represent an academic and research challenge trying to understand what's going on and how
do these models work. The fact is, though they work, and therefore
how do you best leverage them? And then how do you add these embeddings
with the other generative stuff which is straightforward and well understood. The autocomplete technology,
if you like, that kind of takes an input a prompt and produces
the output. The other thing that I'll mentioned here, which is the third
big mystery in the field, is the prompt themselves. How do you come
up with the right prompt. We know there's a super high sensitivity between prompt
variations and output variations, so small disturbances of the prompt may produce a completely
different answer. Some people claim that, hey, these are the new hot
jobs. There was a New York Times and Financial Times articles on this is
the highest paid jobs his job. My colleague and a core member of the
faculty of the Institute for Experiential AI at Northeastern, Byron Wallace, summarized it
very nicely in our recent conference where he said, this is not from the
engineering, This is more like incantations and black magic, right, And you
know, you might stumble on the right incantation, and you might not.
And he has a field of study that has to do with, Okay,
how do we reduce that dependence, how do we reduce that sensitivity, because
at the end of the day, to make it practical and useful, you
can't have this huge variation and kind of weird stuff. And he says incantations
because sometimes prefixes to prompts that you might you and I might think have nothing
to do with it end up being very useful to guide the model to the
right space. So it's people are adding weird stuff in the front and in
the end of a prompt to make it work right. That is not a
science, That is not engineering. We got to figure this out. That's
funny, you know. I'm gonna close this opening segment here with a fun
story and basically say that this is not as crazy and different than unique as
some people might think. So I had a business partner for a number of
years back in the year two thousand and I think earlier you were talking twenty
men twenty twenty, I think when they started this whole thing. So it's
five years ago. But back in two thousand, I was working for a
company. It came gentled to call him a couple Russians, and the good
Russian was named Nicola, like Nikol, let me, you gods. One
thing he said to me, The funny thing about the web is that nobody
know how it works. All we know is it works right, and it's
the same thing. So now what's interesting is if you look at Internet and
cloud and hybrid architecture and all this stuff that people are talking about today,
containerization, Kubernetes, all that stuff, Well, what happened is Google came
along with Kubernetes as this orchestration layer to sit on top of Docker as a
way to containerize applications make them really really small, which is like the vision
of service storrates at architecture, but a different bus, if you will.
And what's happening is now we have all this data. We have tons and
tons. It's called observability. So what's happening now is we're finally starting to
kind of figure out how the web actually works. So I'm not saying we
didn't know anything, but there are a lot of stuff that we didn't really
fully understand about how the web actually works, like which ports are important in
what particular process. I mean there's all this stuff going on. So my
point view is that this AI stuff, You're right, we don't know how
it works. It's not the first time we've found some engine that works and
we're like, well, we don't know exactly how it works, but it
sure does work. But folks, don't touch that dollar. We'll be right
back talking to Osama Faiad of the AI Institute from the Experiential AI Institute.
Will be right back. Expect Welcome back to Inside Analysis. Here's your host
Eric Tavanaugh. All right, folks, back here on Inside Analysis, your
host Eric Kavanaugh with doctor u Sama Fayad from Northeastern Universities, the Institute for
Experiential AI, which basically just means humans in the loop. Humans are really
important. Even with self driving cars, you have to give correction to the
vehicle. I've actually driven a couple of these now, and it's kind of
freaky when it starts taking the wheel from you and like, whoa, what
are you doing. I didn't even know it was self driving. It was
a rental car. I'm like, oh, what's going on here? Okay,
it's taking over for me and seeing you shouldn't be doing something. We're
getting there it's I mean, thanks to innovations, thanks to Elon Musk and
Tesla and all these innovations, those folks are doing pretty cool stuff. But
doctor Fayad, we wanted to dive into the critical success factors, and one
absolutely positively is the data. You know, I keep cautioning people and we're
going to probably dive into this more in our own business, this of consulting
with organizations. Be very careful how you train your models. You want to
train your model on your data. You want to give it curated, trusted
data. You don't want to just point it at your whatever information system you
have, because there's a bunch of junk in there. And anyone who's ever
done data quality initiative knows. If you get an executive who doesn't believe the
data is crap, just show them. Just open up any database and point
to any corner of it and you'll find some problem somewhere. I promise.
It's just everywhere, whether it's data in the wrong fields, data in the
wrong format, data that was truncated during some ETL script or something, and
it's just everywhere. There's bad data everywhere. So getting the right data is
really important, Doctor Fiab how do you do that? What do you recommend
of When people come to you and ask you what are the best practices,
what do you tell them? Yeah? Great, great question. So so
data is a whole ocean to open up. I mean, there's the collecting
the data at quality, that's collecting data at the right level of granularity.
So we have spent decades, as you will know, Eric, in the
whole area of data warehousing and kind of information systems management and so forth,
and data management thinking about data for use by humans. Now what the AI
haves. The AI first companies or the companies who really are using AI today,
they have a deeper understanding that, hey, machines needed data at a
different level. They need high granularity. Not necessarily stuff understandable by humans,
but every little detail of what happened, every log of every event, every
kind of intervention, correction by a human, et cetera. Captured. This
is not how most organizations or companies work today. So that is one big
shift that needs to happen. The second thing is, if you notice a
lot of the literature and lower in the world of data has to do with
structured data. Well, according to Gardner and many others, the majority of
data in any organization, in any company is actually unstructured data. If you
actually talk to the IT teams and the technology teams and so forth, they
only typically only know how to deal with structured data, and then they hit
unstructured data, they're stuck. It's like in the headlights. We have to
change this dynamic to understand that, hey, we got to embrace the unstructured
data, the text, the images, the video, the audio interactions in
the customer center, all the conversations and leverage that information properly. And that
needs that. There exists many good tools out there. Most of them also
fortunately are in the open source, which is something I'm very big fan of.
But you can put these things together in solutions that work for businesses,
and this is something we're very passionate about. How do we partner to figure
out, how we help our partners figure out how to approach an area,
how to capture the data, etc. Now that leads me to another very
very important data that's information or aspect that's very related to data, which is
kind of many companies out there think that all we have to do is wait
for Microsoft or open ai or whoever to refine their models so that they can
meet our needs. What happens then, is you become users of the technology.
You don't have your own private language model. You don't have the ability
to capture all these corrections. You mentioned corrections when you're driving right. Well,
it's great to intervene and correct, but if you don't capture that correction,
the context for that correction, why you made that correction, your model
is never going to benefit from it unless you're willing to share it back with
the company who's providing this public utility, this large language model. Often this
information is sensitive, it's proprietary. I argue it's one of the most valuable
sources of IP that any company operating in any space has, which is kind
of the secret sauce. How do our processes work? Where do we intervene,
Why do we fix what is way of doing things today? Companies let
all of that go into the exhaust by never capturing it. In reality,
you need to be able to not just capture it, but feed it back.
And feeding it back leads us to another whole interesting area, which means
you really need to have your own private language model. That model can't be
too huge, like the likes of open AI or CHG, GPT or any
of these. It has to be as small as possible to maintain its stability,
to maintain its maintainability and its reliability and so forth. But it needs
to get feedback. So you need to be able to capture all that feedback,
all those corrections, and feed them right back and retrain that language model
in an affordable way to maintain your own IP, your own knowledge and so
forth. Now, many of the companies who offer the large language models will
offer you the chance, of course, for money to kind of fine tune
the model or create your own a tuned version of the model, and there
we can talk about technically what happens. All they do is they cut off
the output layer, they throw it out, and retrain an output layer they
call these adapters that is kind of fine tuned just to your problem and on
your feedback. But we know that that's less effective than retraining the model and
reintegrating that data into the whole stack. So coming up with our large language
model, your own private language model, is not impossible. It is very
doable. Many tools are available out there, whether you're talking Falcon which came
from Abu Dhabi or Lama or Lama too. I'm a big fan of at
Facebook or Meta because they actually not only did they make it open source,
they gave a commercial open source license a company I saw, Yeah, and
that opens up a whole other approach and a whole other mechanism. But to
do that and to take advantage of it, you need to have the talent
and then know how of how do I use that language model? When do
I find tune it? How large does it have to be? How small
can I make it? All of these questions are the kinds of questions we
partner with companies on kind of answering and figure out what is the right approach
here in this very very confusing, and I might add fast evolving space.
I mean I try to keep up and I am challenged. Right every week,
every month there's new innovations, new ways of doing things. Somebody's crapping
something old and saying, ah, it didn't work. Actually it was part
of the system, but now we've got to do something completely new. All
good, all healthy, but it's a challenge to keep up with the latest,
greatest and the right best practice of how to use it. Yah.
Start with, I just want to remind everybody that data that you're letting go
into the exhaust by not capturing it systematically. That is gold that is actually
more valuable than gold. It's not just oil. It's more valuable than gold,
and you're letting it go out in the exhaust and losing all that ip
that your teams, your employees are working on day in, day out and
keeping operations going. Yeah, well, you know, I just as you
were talking, I thought of an interesting metaphor here, and I'll throw it
at you. I think you'll you'll find it interesting. So think about language
and feeding a dictionary of English to something like one of these large language models.
You feed it. The dictionary knows all the different words, but people
don't talk the way they write, and people don't talk the way dictionaries are
written. Necessarily, there's a lot of slang. There are lots of expressions,
idioms, all these different things that don't fit neatly into the definitions of
words. But it's how you actually speak when you're talking to someone. And
that's the unstructured data, really, or that's the unstructured syntax almost of how
people actually interact with words on the phone in person, and certain cultures and
in certain regions, and so if you don't absorb that that contextual information,
your engine will never speak like a normal person does. Right, So this
is the nuance you have to appreciate sheet, is that what's on paper often
is very different in the spoken word. What do you think? Absolutely,
and it's critical in many applications, actually, I would argue in most of
them, if you're dealing with conversational AI, if you're dealing with dealing with
humans, whether you're doing it for marketing purposes, whether you're doing it for
customer service, whether you're doing it for predictive maintenance. And it's not only
kind of the idiom or kind of what the public how the public or the
customer speed. It's often the culture within the company. Your engineering team has
their own terminology where they use certain terms. If you come up in the
right, they mean something, but they're using the company to mean a certain
aspect of a process, a certain aspect of a system. Learning all of
that and making it work in the right context is a real interesting challenge,
and it's very possible. I mean, that's why the data is, That's
why I describe it as gold or more valuable than gold, because it contains
all these instances and gives you the keys to allowing these systems to have the
bigger and correct impact on how you do acceleration, how you do automation,
how do you leverage generative AI to kind of give you that unique advantage competitive
advantage out there. I've experienced it personally in healthcare. Right if you're in
an operating room or in an X ray room or in an imaging center,
you know, the language being used is completely different than what's being used in
you know, a pathology lab or a practice for physical therapy or what have
you. So all of these things, all of these aspects are important.
What contents to use, the words in, what they mean, new meanings
that are not available in the dictionary. All of that is so important and
so critical technically, socially and marketing wides. Yeah. Well, and there's
also tone of voice. And I remember, going all the way back to
like third grade or something, our teacher asked us a question and it stumped
all of us, and I thought about it later, I was like,
Oh, tone of voice, that's what he was talking about. You can
say something and meet it honestly, or you can say it sarcastically, and
usually it's the tone of voice and the context that will tip off a human
being. But it's gonna be harder for a machine to understand that, but
not if it has the intonation that if you capture the audio and it can
kind of pick up on that sort of thing. But these are the sort
of deeply understood aspects of communication that we just know as grown ups, that
we've learned over time. I mean, I remember, this is funny,
but probably most people don't remember this, but I remember the first time I
really experienced sarcasm because I was like two or three or something and I'd spill
milk and my mother and I knew it was a problem. Was like,
oh, no, this is bad. And my mother looked at me.
She goes, well, that's just great, And I was like, hold
on, I know the great is good, but that tone is not good.
What's happening here? And I was really defuddled as a child. But
that was my first experience with sarcasm. Right, I'll give you here.
You're touching on one of the biggest challenges that, for example, search engines
today face, which are memes. Right, you hit a meme on the
Internet, the search engine thinks it means one thing. Reality is, it's
something completely different, and the only way you catch that, the only way
you catch that today is human intervention. Search editorial team saying, wait a
second, this shouldn't be in your search result. This is a meme.
It means something completely different. It has nothing to do with biology or whatever.
It's being used to refer to a group of people or a group of
practices or what have you. So those are those are real things that matter
a lot, and you have to have mechanisms for catching them. And you
know, in a way, you can argue that a lot of the internal
culture of a company's operations team, a company's engineering team, a company's business
team, a company's sales team, a company's marketing team is filled with these
memes. We don't call them memes, but they are there. That's funny.
There's actually a great Seinfeld episode where George Costanza, he's working for the
Yankees, and I think he was talking with it was like the Texas Rangers
or something, and as a baseball team, and the culture of the executives
for that team loved using curse words, so they were insulting each other all
the time, but as a compliment and then George's boss walks by as he's
on the phone using all these expletives, and the boss is like, oh
no, what are you doing. This is terrible. In reality, he's
bonding with these guys because the context was there. Classic dramatic irony, right,
the one guy didn't know the context. But this is the importance of
nuanced and understanding context. And to your point, just getting it back to
training AI your own private language model, which is what most businesses are going
to want to have. I'm pretty sure you have to bake in that kind
of stuff. You have to have that human in the loop that goes okay,
no, this is an example of sarcasm where these guys are just being
goofy or whatever in their context. Calling you a jerk or a so and
so is actually a compliment. That's the human invention part, right, absolutely
absolutely, and I remember that episode. But the thing I wanted to mention
is, you know, language, the language use is very different than kind
of the formal language defined. And that is why I believe, like you,
most companies will find out that achieve true competitive advantage. To have differentiation,
you're going to have your own private language models, which opens up a
whole new area. We are a company that's in the you know, quick
service restaurant business, or we are a company that's doing manufacturing, what do
we know about large language model AI, et cetera, and how do we
approach it? And part of this demystification and kind of working with you on
the AI journey is one thing our institute is very past it about because that
actually puts us right up front on the front lines of getting the applications to
work and helping our partners getting it to work. And through that we will
learn what the real research problems are, where the real challenges are, and
we will learn what should we be teaching our students and our corporate learners in
order to be effective in that world. So that whole area of the infrastructure,
the talent, the know how, you will need to build your own
private, private language models to capture your own IP and your own knowledge.
Right, it's going to be a whole huge new trend that no one out
there is paying attention to in the business. They're focused on being of these
right, No, that's the very very good point. Well, folks,
we're talking to doctor Usama Fai will be right back don't touch that dout.
You're listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host,
means Eric Tavanaugh. All right, folks, back here on Inside Analysis
talking all about experiential AI. That's humans in the Loop with doctor Usama Fayad
of the Northeastern University Institute for Experiential AI. Got to get the all clean
and straight in my head. The Institute for Experiential AI, it just means
humans in the loop basically. But we're talking about the power of these models,
which is amazing. We're talking about how a lot of people don't really
know how they work, including people who design them. We just see that
they work, and so we're coming up with strategies to help companies really harness
the technology. And I think that's the key, right is this is if
unbridled, this thing will go wacko and cause you all kind of trouble.
But if you harness it effectively, and that's your governance structure, it's your
embeddings. It's using ontologies, and I mentioned my friends from Praxy Data who
are using ontologies to really narrow the focus of their work and thus be able
to deliver some tangible business value. And you know that's always been the case
with machine learning and AI is you have to have a very narrow focus and
you have to know what you're trying to accomplish. I mean, you can
use deep learning to discover things, but even then you're trying to discover things.
So you have to have a purpose and a mission and an objective upon
which you train these models to be able to understand if you're getting anywhere right.
Tell us a bit about that and why ontologies matter, Yosama, Well,
I mean, ontologies give you the basics of understanding domains. There are
many things you have to worry about. And by the way, not coincidentally,
a lot of the work in generative AI now is using what we call
the RAG framework, which stands for retrieval augmented generation, which is another form
of kind of enforcing the model to stay within certain guardrails and to say here's
what we mean. And it's aided by kind of retrieving the right references and
all that to help it from straying, both in training and in answering.
But anyway, you know, working in different domains, you need to figure
out ways of bringing in that domain knowledge that domain into the problem in order
to constrain the space in order to focus it. You know, I was
talking about the secrets of making AI work, and we talked about the humans
in the loop or must interventions are necessary? Data at the right level is
absolutely necessary. Most data is being lost. But I will I will add
the last bit is how do we bring in non domain knowledge and how do
we narrow the problem down to a very narrow, very specific problem. Because
trying to solve AI in general has proven to be an impossible task, a
very difficult task for the past seven decades, and it will continue to be
that, and our best prescription for fixing it is by narrowing the problem and
resorting to what we call narrow AI versus general AI. General AI is still
far out of reach. Narrow AI has worked and has been working for many
years and decades actually in different domains. And the key there is domain knowledge.
The key there is narrowing it enough so you know that not every possibility
is here, and you don't have to worry about every language, and you
don't have to worry about every variation and every meaning. No, restrict yourself
to this domain. Here's what things mean. Try to get complete knowledge around
that domain. And that's a secret of making it work that only comes by
figuring out what are the use cases, what are the domains of applications,
and how do I come up with a methodology that allows me to build solutions
for each specific use case, Which is why we kind of focused on things
like AI plus health. How do we get healthcare applications into AI? How
do we leverage digital health? You know, we live in a world today.
Think about at the highest level, you go see your doctor, hopefully
once a year, maybe twice a year, right only when something is wrong
other than the routine visits. And what happens between these routine visits is a
total mystery to the provider, to your physician, or because who knows what
happened to Eric during this one year. Now Eric needs to fill me in,
and who knows whether Eric can fill me in completely or skip a whole
bunch of important things. Well, guess what. We live in a world
now where there's a lot of instruments. We've got these watches, we've got
the mobile phones, we've got the you know, all sorts of devices,
cameras, other things in our homes. They can actually capture a lot of
the information that typically is only captured in a clinic. So how do we
leverage that to close the loop at the highest level. Now we call that
digital health. Another form of digital health is things like oncology, or actually
more generally pathology. How do we bring these digital techniques down to a pathology
lab where somebody is taking a sample, looking at it in a microscope and
kind of storing it in this glass slide. Well, guess what digitization can
help a lot. You can look at it with instruments and with algorithms instead
of just human eyes, instead of just relying on staff to do you can
strike capturing what's happening in the heads of these very talented pathologists who are looking
at an image and concluding a result. This happened in radiology in a big
way. Radiology has been digitized. It benefits from a lot of these things.
In pathology, it hasn't happened. Pathology is still practiced like it was,
you know, three or four decades ago. It can benefit from a
lot of digitization, which brings in a lot of AI and a lot of
use of these things. In life sciences, we have many use cases,
whether it's the acceleration of drug discovery, how do you design how do you
accelerate the design of the next antibody? Right, antibodies are very effective ways
of coming up with cures, vaccines, ways to mitigate disease spread. But
it typically takes about three years to come up with the right antibody for something.
So it wasn't a viable solution for COVID back in twenty twenty or twenty
twenty one because it took three years. But if I can get that three
years down to three months by accelerating it through compute and through modeling, amazing
things can happen. Right, take things like that happen in a hospital or
a physician when you're being taken care of. In one of the projects we
have is called heart, which expands to something involving AI. But what it
is one of the applications and working with a large hospital system, is how
do you determine when somebody should not be downgraded from critical care? They're in
the ICU and things look good, or we can downgrade them, but the
algorithms can tell you no, no, no, no, there's a huge
danger here. If you downgrade that issues would recur, which is horrible for
the patient. Horrible for the hospital, horrible for the physician, And you
want to make that decision when you're absolutely sure that no complications will happen.
Similarly, you don't want to keep them in ic you too long because that's
not great for the patient and it's a use of a very limited resource,
the ice you. So a lot of these problems come up in a lot
of places. And there with my colleague Ray Winslow, who's also a core
member of the faculty in the Experiential AI Institute, he leverages what's called computational
medicine. How do we model these things? How do we model processes,
how do we model patients? How do we model diseases and use those to
come up with predictions that are hard to get otherwise. So those are applications
in healthcare, in life sciences. And our newest area is the climate and
sustainability. How do we leverage kind of new data sources and models. Yeah,
and to drill real quick into this, the ICU example, which is
a fantastic one I'd like to point out for our audience. And let's get
some feedback on this. Se mean email info at Dmradio dot biz or info
in Inside Analysis dot com both come to me. You know, at any
point in time in a workflow, you can insert an AI suggestion, for
example, or you can have it sort of waiting to make a suggestion.
So anytime the clinician is there and they're typing information, they're doing stuff as
you go to the screen where you're going to think about maybe downgrading this person
from ICU, that's an excellent point at which to offer an opportunity for the
AI to give a suggestion. Right, so it'll come in and say,
I see you've just launched this screen, maybe you should reconsider because of X
y Z. And I think that's really where a lot of this AI value
is going to manifest is in suggestions at certain points in a workflow. Right,
absolutely, Eric, And you've touched on a very important area where my
colleague Professor Ray Winslow who I mentioned working also with Professor Jene Tunnik, who
runs our AI plus health area for the institute. They like to call it,
how do I make AI a valued member of the medical team. That
word you use, the recommendation is key, Right. AI doesn't know everything.
It may sometimes it's right, sometimes it's wrong. The feedback from the
physicians or the care team, a nurse could be anybody would be very beneficial
to both improve the AI and to allow that AI to contribute to the proper
care of that patient. So I love that analogy, which is, you
know, AI needs to be just another member of the healthcare team, making
its own recommendations and learning from corrections, interventions or getting it right and wrong.
That's right in learning. And here's the beautiful thing. You know,
doctor Faiad mentioned you have to capture all this information. When you correct the
engine or when you accept the recommendation, all of that should be captured as
well, because for time you can see, well, doctor Jones never accepts
the recommendation of the AI engine, and doctor Phillips always accepts the recommendation of
the AI engine. Somewhere in between is probably where we want to be.
But the point is, you know, as human beings, we are actually
very predictable. And you will know that if you start looking at your device
that giving you feedback and how often you are on this app or that app
or all these different things, you will see a reflection of your behavior and
you'll realize why I am pretty darn predictable. Every day at this time,
I do X. But hey, hang out for one second. Uh,
We'll be right back after this break. You are listening to Inside Analysis.
Okay, folks, time for the podcast bonus segment here on Inside Analysis is
always so much fun talking to doctor Fayad. And you made a point there
after in the break about the importance of trust and building trust in the system,
for the system, for the doctors, for the team. Go ahead
with that. Yeah, So, I mean one of the crit pieces of
making the AI a valued member of the medical team or the care team is
that ability to kind of make suggestions and get feedback those suggestions, those recommendations.
They get value, you know, they get evaluated by a physician or
a nurse or whatever. They may get corrected every once in a while,
which gives them a chance to contribute to the system and it's evolution and they
feel that their baby. But at the same time they get kind of this
incremental exposure to hey, this thing is right. It caught something I missed.
So it's useful and by the way, applies equally well whether you're dealing
with a medical team or a factory. A team of engineers in a factory,
a team of operators in a monitoring center, every application where the AI
can contribute, you need to gain the trust of the users and the domain
experts, because that's the only chance you're going to have of getting acceptance,
getting important data, getting feedback, and keeping the system healthy. Yeah,
that's right. And you know what I've noticed about the AI that I've seen
in practice, which I see in my email or my text messages and other
places, it's like, oh, you haven't followed up on this text,
you know, do you think there was three days ago? Basically that's a
perfect example of a reminder that is helpful because, let's face it, even
the best doctor in the world will forget sometimes to do one little process,
one little piece, you know, and depending upon the use case, that
could be a very serious thing or not so serious thing. But you know,
you forgot to wash your hands, for example, you forgot to put
the mask on, you forgot to put the scalpel back where it belonged,
these little teeny tiny things because guess what, People get distracted, Even very
focused people can get distracted. And so the AI is there as your little
helper watching you, making little suggestions along the way, not wrapping you on
the knuckles, getting you in trouble with your boss necessarily, but just giving
you a little bit of a hint and a nudge. And it reminds me
of one of my favorite expressions. That's an old cliche. I don't hear
much anymore. But uh, and of course it's it's posed on man,
but it means men and women. It said, man doesn't need to be
taught so much as reminded. Right, we already learned. We just have
to be reminded. Final thoughts from doctor Faytte go ahead, Yeah, and
absolutely, and you actually need both. You know. Learning in the system
is also critical because you know, no system is going to get it right.
So every once in a while it's going to guess the wrong thing and
it becomes annoying. It's reminding me of something I already know. Right,
well, I need to give it that feedback, hey, in this context,
don't remind me. And then if it adapts to that, Now you
have intelligence. Now you have something you value and it's personalized. So now
you want to keep it because now it's beginning to get to know you and
your preferences versus me and my preferences, which could be completely different. So
bringing machine learning into the loop and making sure these systems learn and adapt to
the feedback they're getting is very critive. Yeah, this is wonderful stuff.
Well, look this, gentleman up online and look up Northeastern University, the
Institute for Experiential AI. That just means humans in the loop. Doctor Fiduction
here, and you folks who put together a great program, and you understand
the interplay between training and research and development and solution designed. All these things
feed each other, and we'll feed each other over time. So you know
how to train people, know how to get people excited about stuff and really
nudge them and get them involved. Because the humans in the loop, they're
the ones who are going to make all this stuff successful and worthwhile. And
it's going to save lives in the healthcare space. I promise you it's going
to save lives. It's going to save time and effort. It's going to
do the most important thing I think for any business that has improved morale.
When morele goes up, good things happen. When morel is down, good
things do not happen. So just that's the Dallas Cowboys. Thank you,
thank you, Eric, and thank you for getting our mission and vision for
the institute. You got it perfectly. I love it. Well, we'll
talk to these folks again. I'm sure folks don't touch that boy. Actually,
we'll touch you next time. Info and Inside Analysis dot Com Bye bye.
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The Farando Zier Show on KCAA Radio. Last July, several GOP senators combined
their what intellects to charge that inflation was rising because of quote insane tax and
spending spree of President Biden and the Democrats. Never mind that the insane spending
is for such sensible, productive, and enormously popular national needs as childcare and
childless benefits. Mitch McConnell's rapidly partisan flock saw the chance to politicize the public's
legitimate worries about rising prices. You, poor consumers are made to pay more
for basics, they squawked because of socialist Joe's investment, and grassroots people follow
the ricocheting pinball of the GOP's logic. One, they say that helping hard
hit families induces them to refuse to go to work. Two, This creates
blockages in the global supply chain. Three, this causes shortages of everything.
Four This forces corporate bosses to raise all prices, which five slams the middle
class and poor. So six lazy workers cause inflation. Who rube Goldberg couldn't
have dreamed up a more fantastical diagram to deflect attention from what's really happening,
namely that instead of an inflation problem, we have a corporate greed problem.
Of course, the greed moisters insist that their pursuit of excess corporate profits has
not driven any price surges in our economy or free market competition. They snap.
Prices are established by the law of supply and demand. It's the magic
of the marketplace, they explain. But magicians don't do magic, they perform
illusions, and the illusion of free market competition implodes when it hits the reality
that our economy doesn't remotely resemble a competitive marketplace. This is Jim Hiitwer saying,
for some forty years, corporate directed government policies have been transforming America into
monopoly nation, letting the feud gouge the minute. That's where inflation comes from.
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F Brito Valley, NBC News Radio, I'm Chris Gragio. The GOP race
for the presidential nominee is now down to a two person race. Florida Governorn
de Santis dropped out and endorsed former President Trump today. DeSantis was considered a
front runner early on, but finished a distant second in the Iowa caucus.
That leaves former South Carolina Governor Niki Haley is the only remaining candidate against Trump.
With the New Hampshire primary just over twenty four hours away. New Hampshire
Governor and Nikki Haley surrogate Chris Sanunu was already calling the GOP primary a two
person race hours before DeSantis dropped out. Speaking on NBC's Meet the Press Today,
Sanunu said Haley knocked everybody out, even Ron. While he said Tuesday's
New Hampshire primary wasn't a make or break for Haley, he did say that
she'll have to start winning states. Michigan Governor Gretchen Whitmer says abortion is a
major issue in the presidential election. It is very clear that abortion is on
this ballot. Speaking on CBS's Faced the Nation, the two term Democrats said
she believes a Republican win in November in the race for the White House means
a national abortion ban, no matter who's the GOP candidate. Whitmer noted that
although former President Trump hasn't committed to a national ban on abortion, he's taking
credit for appointing three conservative judges to a US Supreme Court that overturned Roe v.
Wade. In twenty twenty two. Whitmer ie re election in Michigan,
largely campaigning on the issue of abortion. A crash of an air Vac helicopter
in Oklahoma's killed three crew members. The crew was returning to whether Ford,
Oklahoma, after transporting a patient to a hospital in Oklahoma City when the helicopter
craw late last night. Official say law enforcement and members of the Air Evac
Life Team work together to find the crash site. The NTSB will be investigating.
Police are looking for suspects after four people were injured in a shooting at
a Florida best Buy. It happened in Plantations shortly after eight pm Saturday,
when the victims were approached by another group while leaving the store. One person
was listed in critical condition. Police say the suspects fled the scene and investigators
are asking anybody with information to come forward. I'm Chris Caragio, NBC News
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