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September 19, 2023 39 mins

Major breakthroughs in artificial intelligence research often reshape the design and utility of AI in both business and society. In this episode of Smart Talks with IBM, Malcolm Gladwell and Jacob Goldstein explore the conceptual underpinnings of modern AI with Dr. David Cox, VP of AI Models at IBM Research. They talk foundation models, self-supervised machine learning, and the practical applications of AI and data platforms like watsonx in business and technology.

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Episode Transcript

Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gabwell. This season,
we're continuing our conversation with new creators visionaries who are
creatively applying technology in business to drive change, but with
a focus on the transformative power of artificial intelligence and

what it means to leverage AI as a game changing
multiplier for your business. Our guest today is doctor David Cox,
VP of AI Models at IBM Research and IBM Director
of the MIT IBM Watson AI Lab, a first of
its kind industry academic collaboration between IBM and MIT focused

on the fundamental research of artificial intelligence. Over the course
of decades, David Cox watched as the AI revolution steadily
grew from the sim ideas of a few academics and
technologists into the industrial boom we are experiencing today. Having
dedicated his life to pushing the field of AI towards

new horizons, David has both contributed to and presided over
many of the major breakthroughs in artificial intelligence. In today's episode,
you'll hear David explain some of the conceptual underpinnings of
the current AI landscape, things like foundation models in surprisingly
comprehensible terms, I might add, we'll also get into some

of the amazing practical applications for AI in business, as
well as what implications AI will have for the future
of work and design. David spoke with Jacob Goldstein, host
of the Pushkin podcast What's Your Problem. A veteran business journalist,
Jacob has reported for The Wall Street Journal, the Miami Herald,
and was a longtime host of the NPR program Planet Money. Okay,

let's get to the interview.

Speaker 2 (02:05):
Tell me about your job at IBM.

Speaker 3 (02:08):
SO. I wear two hats at IBM. SO one, I'm
the IBM Doctor of the MI t IBM Watson AI Lab.
So that's a joint lab between IBM and MIT where
we try and invent what's next in AI. It's been
running for about five years, and then more recently I
started as the vice president for AI Models, and I'm
in charge of building IBM's foundation models, you know, building

these these big models, generative models that allow us to
have all kinds of new exciting capabilities in AI.

Speaker 2 (02:36):
So, so I want to talk to you a lot
about foundation models, about genitive AI. But before we get
to that. Let's just spend a minute on the on
the IBM MI T collaboration. Where where did that partnership start?
How did it originate?

Speaker 3 (02:51):
Yeah, so, actually it turns out that MI T and
IBM have been collaborating for a very long time in
the area of AI. In fact, the term artificial intelligence
was coined in a nineteen fifty six workshop that was
held at Dartmouth. It was actually organized by an IBM
or Nathaniel Rochester, who led the development of the IBM
seven and one. So we've really been together in AIS

since the beginning, and as AI kept accelerating more and
more and more, I think there was a really interesting
decision to say, let's make this a formal partnership. So
IBM in twenty seventeen and now, so it'd be committing
close to a quarter billion dollars over ten years to
have this joint lab with MIT, and we located ourselves

right on the campus and we've been developing very very
deep relationships where we can really get to know each other,
work shoulder to shoulder, conceiving what we should work on next,
and then executing the projects. And it's really very few
entities like this exist between academia industry. It's been really
fun the last five years to be a part of it.

Speaker 2 (03:53):
And what do you think are some of the most
important outcomes of this collaboration between IBM and MIT.

Speaker 3 (04:00):
Yeah, so we're really kind of the tip of the
sphere for for IBM's AI strategy. So we're we're really
looking what, you know, what's coming ahead, and you know,
in areas like Foundation models, you know, as the field
changes and I T people are interested in working on
you know, faculty, students and staff are interested in working
on what's the latest thing, what's the next thing. We

at IBM Research very much interested in the same so
we can kind of put out feelers, you know, interesting
things that we're seeing in our research, interesting things we're
hearing in the field. We can go and chase those opportunities.
So when something big comes, like the big change that's
been happening lately with Foundation Models, we're ready to jump
on it. That's really the purpose, that's that's the lab
functioning the way it should. We're also really interested in

how do we advance you know, AI that can help
with climate change or you know, build better materials and
all these kinds of things. That are you know, a
broader aperture sometimes than than what we might consider just
looking at the product portfolio of IBM, and that that
gives us again a breadth where we can connections that
we might not have seen otherwise. We can, you know,
think things that help out society and also help out

our customers.

Speaker 2 (05:08):
So the last whatever six months, say, there has been
this wild rise in the public's interest in AI, right
clearly coming out of these generative AI models that are
really accessible, you know, certainly chat GPT language models like that,
as well as models that generate images like mid Journey.

I mean, can you just sort of briefly talk about
the breakthroughs in AI that have made this moment feel
so exciting, so revolutionary for artificial intelligence.

Speaker 3 (05:41):
Yeah. You know, I've been studying AI basically my entire
adult life. Before I came to IABM, I was a
professor at Harvard. I've been doing this a long time,
and I've gotten used to being surprised. It sounds like
a joke, but it's serious, Like I'm getting used to
being surprised at the acceleration of the pace. Again. It
tracks actually a long way back. You know, there's lots

of things where there was an idea that just simmered
for a really long time. Some of the key math
behind the stuff that we have today, which is amazing.
There's an algorithm called backpropagation, which is sort of key
to training neural networks that's been around, you know, since
the eighties in wide use. And really what happened was

it simmered for a long time, and then enough data
and enough compute came. So we had enough data because
you know, we all started carrying multiple cameras around with us.
Our mobile phones have all, you know, all these cameras
and this we put everything on the Internet, and there's
all this data out there. We called a lucky break
that there was something called a graphics processing unit, which

you know, turns out to be really useful for doing
these kinds of algorithms, maybe even more useful than it
is for doing graphics. They're great graphics too. And things
just kept kind of adding to the snowball. So we
had deep learning, which is sort of a a rebrand
of neural networks that I mentioned from the eighties, and
that was enabled again by data because we digitalized the

world and compute because because we kept building faster and
faster and more powerful computers, and then that allowed us
to make this big breakthrough. And then, you know, more recently,
using the same building blocks, that inexorable rise of more
and more and more data met the technology called self
supervised learning, where the key difference there in traditional deep learning,

you know, for classifying images, you know, like is this
a cat or is this a dog? And a picture
those technologies require supervision, so you have to take what
you have and then you have to label it. So
you have to take a picture of a cat, and
then you label it as a cat, and it turns
out that you know, that's very powerful, but it takes
a lot of time to label gats and to label dogs,

and there's only so many labels that us in the world.
So what really changed more recently is that we have
self supervised learning where you don't have to have the labels.
We can just take unannotated data. And what that does
is it lots you use even more data. And that's
really what drove this latest sort of rage. And then
and then all of a sudden we start getting these

these really powerful models. And then really this has been
simmering technologies, right, this has been happening for a while
and progressively getting more and more powerful. One of the
things that really happened with CHATGBT and technologies like stable
Diffusion and mid Journey was that they made it visible

to the public. You know, you put it out there.
The public can touch and feel and they're like, wow,
not only is there palpable change and wow this you know,
I can talk to this thing. Wow, this thing can
generate an image. Not only that, but everyone can touch
and feel and try. My kids can use some of
these AI art generation technologies. And that's really just launched.

You know. It's like a propelled slingshot at a into
a different regime. In terms of the public awareness of
these technologies.

Speaker 2 (09:04):
You mentioned earlier in the conversation foundation models, and I
want to talk a little bit about that. I mean,
can you just tell me, you know, what are foundation
models for AI and why are they a big deal?

Speaker 3 (09:17):
Yeah, So this term foundation model was coined by a
group at Stanford, and I think it's actually a really
apt term because remember I said, you know, one of
the big things that unlocked this latest excitement was the
fact that we could use large amounts of unannotated data.
We could train a model. We don't have to go
through the painful effort of labeling each and every example.

You still need to have your model do something you
wanted to do. You still need to tell it what
you want to do. You can't just have a model
that doesn't have any purpose. But what a foundation models
that provides a foundation, Like a literal foundation, you can
sort of stand on the shoulders of giants. You can
have them these massively trained models, and then do a
little bit on top. You know, you could use just

a few examples of what you're looking for and you
can get what you want from the model. So just
a little bit on top. Now it gets to the
results that a huge amount of effort used to have
to put in, you know, to get from the ground
up to that level.

Speaker 2 (10:15):
I was trying to think of of an analogy for
sort of foundation models versus what came before, and I
don't know that I came up with a good one,
But the best I could do was this. I want
you to tell me if it's plausible. It's like before
foundation models, it was like you had these sort of
single use kitchen appliances. You could make a waffle iron

if you wanted waffles, or you could make a toaster
if you wanted to make toast. But a foundation model
is like like an oven with a range on top.
So it's like this machine and you could just cook
anything with this machine.

Speaker 3 (10:48):
Yeah, that's a great analogy. They're very versatile. The other
piece of it, too, is that they dramatically lower the
effort that it takes to do something that you want
to do. And I used to say about the old
world of AI, would say, you know, the problem with
automation is that it's too labor intensive, which sounds like
I'm making a joke.

Speaker 2 (11:09):
Indeed, famously, if automation does one thing, it substitutes machines
or computing power for labor. Right, So what does that
mean to say AI is or automation is too labor intensive.

Speaker 3 (11:22):
It sounds like I'm making a joke, but I'm actually serious.
And what I mean is that the effort it took
the old regime to automate something was very, very high.
So if I need to go and curate all this data,
collect all this data, and then carefully label all these examples,
that labeling itself might be incredibly expensive and time, and

we estimate anywhere between eighty to ninety percent of the
effort it takes to feel an AI solution actually is
just spent on data, so that that has some consequences,
which is the threshold for bothering. You know, if you're
going to only get a little bit of value back
from something, are you going to go through this huge
effort to curate all this data? And then when it

comes time to train the model, you need highly skilled
people defensive or hard to find in the labor market.
You know, are you really going to do something that's
just a tiny, little incremental thing. Now you're going to
do the only the highest value things that warrn't right
level because you.

Speaker 2 (12:20):
Have to essentially build the whole machine from scratch, and
there aren't many things where it's worth that much work
to build a machine that's only going to do one
narrow thing that's.

Speaker 3 (12:31):
Right, and then you tackle the next problem and you
basically have to start over. And you know, there are
some nuances here, like for images, you can pre train
a model on some other task and change it around.
So there are some examples of this, like non recurring
cost that we have in the old world too, but
by and large, it's just a lot of effort. It's hard.
It takes you know, a large level of skill to implement.

One analogy that I like is, you know, think about
it as you know, you have a river of data,
you know, running through your company or your institution. Traditional
AI solutions are kind of like building a dam on
that river. You know, Dams are very expensive things to build.
They require highly specialized skills and lots of planning. And
you know, you're only going to put a dam on

a river that's big enough that you're gonna get enough
energy out of it that it was worth your trouble.
You're gonna get a lot of value out of that dam.
If you have a river like that, you know, a
river of data, but it's actually the vast majority of
the water you know, in your kingdom actually isn't in
that river. It's in puddles and greeks and vallet bricks.
And you know, there's a lot of value left on

the table because it's like, well, I can't there's nothing
you can do about it. It's just that that's too
low value, so it takes too much effort, so I'm
just not going to do it. The return on investment
just isn't there so you just end up not automating things.
It's too much of a pain. Now what foundation models
do is they say, well, actually, no, we can train
a base model a foundation they can work on, don't

We don't care. We not specify what the task is
ahead of time. We just need to learn about the
domain of data. So if we want to build something
that can understand English language, there's a ton of English
language text available out in the world. We can now
train models on huge quantities of it, and then it
learned the structure, learned how language you know, good part

of how language works on all that unlabeled data, and
then when you roll up with your task, you know,
I want to solve this particular problem. You don't have
to start from scratch. You're starting from a very, very
very high place. So that just gives you the ability
to just you know, now, all of a sudden, everything
is accessible. All the puddles and greeks and babbling books
and gelipons, you know, those are all accessible now. And

that's that's very exciting. But it just changes the equation
on what kinds of problems you could use AI to solve.

Speaker 2 (14:52):
And so foundation models basically mean that automating some new
task is much less labor and intensive. The sort of
marginal effort to do some new automation thing is much
lower because you're building on top of the foundation model
rather than starting from scratch. Absolutely, So that is that
is like the exciting good news. I do feel like

there's a little bit of a countervailing idea that's worth
talking about here, and that is the idea that even
though there are these foundation models that are really powerful,
that are relatively easy to build on top of, it's
still the case right that there is not some one
size fits all foundation model. So you know, what does

that mean and why is that important to think about.

Speaker 3 (15:38):
In this context? Yeah, so we believe very strongly that
there isn't just one model to rule them all. There's
a number of reasons why that could be true. One
which I think is important and very relevant today is
how much energy these models can consume. So these models,
you know, can get very very large. So one thing

that we're starting to see or starting to believe, is
that you probably shouldn't use one giant sledgehammer model to
solve every single problem, you know, like we should pick
the right size model to solve the problem. We shouldn't
necessarily assume that we need the biggest, baddest model for
every little use case. And we're also seeing that, you know,

small models that are trained like to specialize on particular
domains can actually outperform much bigger models. So bigger isn't
always even better.

Speaker 2 (16:32):
So they're more efficient and they do the thing you
want them to do better as well.

Speaker 3 (16:37):
That's right. So Stanford, for instance, a group of Stanford
trained a model. It is a two point seven billion
parameter model, which isn't terribly big by today's standards. They
trained it just on the biomedical literature, you know, this
is the kind of thing that universities do. And what
they showed was that this model was better at answering
questions about the biomedical literature than some models that are

one hundred billion parameters you any times larger. So it's
a little bit like you know, asking an expert for
help on something versus asking the smartest person you know,
the smartest person you know, maybe very smart, but they're
not going to be expertise. And then as an added bonus,
you know, this is now a much smaller model, it's
much more efficient to run. We are you know, you know,

it's cheaper, so there's lots of different advantages there. So
I think we're going to see attention in the industry
between vendors that say, hey, this is the one, you know,
big model, and then others that say, well, actually, you know,
there's there's you know, lots of different tools we can
use that all have this nice quality that we outligned
at the beginning, and then we should really pick the

one that makes the most sense for the task at hand.

Speaker 2 (17:44):
So there's sustainability basically efficiency. Another kind of set of
issues that come up a lot with ai A are
bias hallucination. Can you talk a little bit about bias
and hallucination, what they are and how you're working to
mitigate those problems.

Speaker 3 (17:59):
Yeah, so there are lots of issues still as amazing
as these technologies are, and they are amazing, let's be
very clear, lots of great things we're going to enable
with these kinds of technologies. Bias isn't a new problem,
so you know, basically we've seen this since the beginning
of AI. If you train a model on data that

has a bias in it, the model is going to
recapitulate that bias when it provides its answers. So every time,
you know, if all the text you have says, you know,
it's more likely to refer to female nurses and male scientists,
then you're going to get models that you know. For instance,
there was an example where a machine learning based translation
system translated from Hungarian to English. Hungarian doesn't have gendered pronouns.

English does, and when you ask them to translate, it
would translate they are a nurse to she is a nurse,
would translate they are a scientist to he is a scientist.
And that's not because the people who wrote the algorithm
were building in bias and coding in like oh, it's
got to be this way. It's because the data was
like that. You know, we have biases in our society
and they're reflected in our data and our text and

our images everywhere. And then the models they're just mapping
from what they've what they've seen in their training data
to to the result that you're trying to get them
to do and to give, and then these biases come out.
So there's a very active program of research in you know,
we we do quite a bit at IBM research and I,

but also all over the community and industry and academia
trying to figure out how do we explicitly remove these biases,
how do we identify them, how do you know, how
do we build tools that allow people to audit their
systems to make sure they aren't biased. So this is
a really important thing. And you know, again this was
here since the beginning, you know, of machine learning and AI,

but foundation models and large language models and generative AI
just bring it into sharper even sharper focus because there's
just so much data and it's sort of building in
baking and all these different biases we have. So that's
that's absolutely a problem that these models have. Another one
that you mentioned was hallucinations. So even the most impressive

of our models will often just make stuff up. You know,
the technical term that the heels chosen is hallucination. To
give you an example, I asked chat tbt to create
a biography of David Cox IBM, and you know, it
started off really well, you know, the identifying that I
was the director of the mt IBM Watsonday and said

a few words about that, and then it proceeded to
create an authoritative but completely fake biography of me where
I was British. I was born in the UK. I
went to British university, you know, universities in the UK.
I was professor in the authority.

Speaker 2 (20:46):
Right, it's the certainty that that is weird about it, Right,
it's it's dead certain that you're from the UK, et cetera.

Speaker 3 (20:53):
Absolutely, yeah, it has all kinds of flourishes, like I
want awards in the UK. So yeah, it's it's problematic
because it kind of pokes at a lot of weak
spots in our human psychology, where if something sounds coherent,
we're likely to assume it's true. We're not used to
interacting with people who eloquently and authoritatively, you know, admit

complete nonsense like yeah, you know, you know we can
debate about that, but yeah, we.

Speaker 2 (21:20):
Can debate about that. But yes, the sort of blive
confidence throws you off when you realize it's completely wrong.

Speaker 3 (21:28):
Right, that's right. And we do have a little bit
of like a great and powerful aws sort of vibe
going sometimes where we're like, well, you know, the AI
is all knowing and therefore whatever it says must be true.
But but these things will make up stuff, you know,
very aggressively, and you know, you everyone can try asking
it for their their bio. You'll you'll get something that

You'll always get something that's of the right form, that
has the right tone. But you know, the facts just
aren't necessarily there. So that's obviously a problem. We need
to figure out how to close those gaps, fix those problems.
There's lots of ways we could use them more easily.

Speaker 1 (22:05):
I'd just like to say, faced with the awesome potential
of what these technologies might do, it's a bit encouraging
to hear that even chat GPT has a weakness for
inventing flamboyant, if fictional versions of people's lives. And while
entertaining ourselves with chat GPT and mid journey is important,
the way lay people use consumer facing chatbots and generative

AI is just fundamentally different from the way an enterprise
business uses AI. How can we harness the abilities of
artificial intelligence to help us solve the problems we face
in business and technology. Let's listen on as David and
Jacob continue their conversation.

Speaker 2 (22:45):
We've been talking in a somewhat abstract way about AI
in the ways it can be used. Let's talk in
a little bit more of a specific way. Can you
just talk about some examples of business challenges that can
be solved with automation. With this kind of automation we're
talking about.

Speaker 3 (23:03):
Yeah, so there really really this guy's the limit. There's
a whole set of different applications that these models are
really good at. And basically it's a super set of
everything we used to use ALI for in business. So,
you know, the simple kinds of things are like, hey,
if I have text and i'm you know, I have
product reviews and I want to be able to tell
if these are positive or negative. You know, like let's

look at all the negative reviews so we can have
a human look through them and see what was up.
Very common business use case. You can do it with
traditional deep learning based AI. So so there's things like
that that are you know, it's very prosaic sort that
we were already doing it. We've been doing it for
a long time. Then you get situations that are that
were harder for the old day. I like, if i'm

I want to compress something like I want to I
have like they have a chat transcript, like a customer
called in and they had a complaint, they called back. Okay,
Now a new you know, person on the line needs
to go read the old transcript to catch up. Wouldn't
it be better if we could just summarize that, just
condense it all down a quick little paragraph, you know,

customer call they were upset about this, rather than having
to read the blow by blow. There's just lots of
settings like that where summarization is really helpful. Hey, you
have a meeting and I'd like to just automatically, you know,
have have that meeting or that email or whatever. I'd
like to just have a condensed down so I can
really quickly get to the heart of the matter. These
models are are really good at doing that. They're also

really good at question answering. So if I want to
find out what's how many vacation days do I have?
I can now interact in natural language with a system
that can go and that has access to our HR policies,
and I can actually have a you know, multi turn
conversation where I can, you know, like I would have
with you know, somebody, you know, actual HR professional or

customer service representative. So a big part, you know, of
what this is doing is it's it's putting an interface.
You know, when we think of computer interfaces, usually thinking
about UI user interface elements where I click on menus
and there's buttons and all this stuff. Increasingly, now we
can just talk, you know, you just in words. You

can describe what you want, you want to answer, ask
a question, you want to sort of command the system
to do something, rather than having to learn how to
do that clicking buttons, which might be inefficient. Now we
can just sort of spell it out.

Speaker 2 (25:22):
Interesting, right, the graphical user interface that we all sort
of default to, that's not like the state of nature, right,
That's a thing that was invented and just came to
be the standard way that we interact with computers. And
so you could imagine, as you're saying, like chat essentially
chatting with the machine could could become a sort of
standard user interface, just like the graphical user interface, did

you know over the past several decades.

Speaker 3 (25:47):
Absolutely, And I think those kinds of conversational interfaces are
going to be hugely important for increasing our productivity. It's
just a lot easier if I if I have to
learn how to use a tool, or I don't have
to kind of have awkward, you know, interactions for the computer.
I can just tell it what I want, and I
can understand it, could you know, potentially even ask questions
back to clarify and have those kinds of conversations that

can be extremely powerful. And in fact, one area where
that's going to I think be absolutely game changing is
in code. When we write code. You know, programming languages
are a way for us to sort of match between
our very sloppy way of talking and the very exact
way that you need to command a computer to do

what you wanted to do. They're cumbersome to learn, they
can you know, create very complex systems that are very
hard to reason about. And we're already starting to see
the ability to just write down what you want and
AI will generate the code for you. And I think
we're just going to see a huge revolution of like
we just converse you and we can have a conversation
to say what we want, and then the computer can
actually not only do fixed actions and do things for us,

but it can actually even write code to do new things,
you know, and generate software itself. Given how much software
we have, of how much craving we have for software,
like we'll never have enough software in our world, uh,
you know, the ability to have a systems as a
helper in that, I think we're going to see a
lot of a lot of value there.

Speaker 2 (27:13):
So if you if you think about the different ways
AI might be applied to business, I mean you've talked
about a number of the sort of classic use cases.
What are some of the more out there use cases.
What are some you know, unique ways you could imagine
AI being applied to business.

Speaker 3 (27:31):
Yeah, there's really disguised the limit. I mean, we have
one project that I'm kind of a fan of where
we actually were working with a mechanical engineering professor at
MIT working on a classic problem, how do you build
linkage systems which like you imagine bars and joints and ogres,
you know, the things that are.

Speaker 2 (27:50):
Building a thing, building a physical machine of some.

Speaker 3 (27:53):
Kind of like real like metal and you know nineteenth
century just old school industrial revolution. Yeah yeah, yeah, but
you know the little arm that's that's holding up my
microphone in front of me. Cranes get bold, your buildings,
you know, parts of your engines. This is like classical stuff.
It turns out that you know humans, if you want
to build an advanced system, you decide what like curve

you want to create, and then a human together with
a computer program can build a five or six bar linkage,
and then that's kind of where you top out. It is
because it gets too complicated to work more than that.
We built a generative AI system that can build twenty
bar linkages, like arbitrarily complex. So these are machines that
are beyond the capability of a human to design themselves.

Another example, we have an AI system that can generate
electronic circuits. You know, we had a project where we're
working where we were building better power converters which allow
our computers and our devices to be more efficient, save energy,
you know, less less carbon ote. But I think the
world around us has always been shaped by technology. If
you look around, you know, just think about how many

steps and how people and how many designs went into
the table and the chair and the lamp. It's it's
really just astonishing. And that's already you know, the fruit
of automation and computers and those kinds of tools. But
we're going to see that increasingly be product also of AI.
It's just going to be everywhere around us. Everything we
touch is going to have to you know, helped in

some way to get get to you by a.

Speaker 2 (29:23):
You know, that is a pretty profound transformation that you're
talking about in business. How do you think about the
implications of that, both for the sort of you know,
business itself and also for for employees.

Speaker 3 (29:37):
Yeah, so I think for businesses this is gonna cut costs,
make new opportunities to like customers, you know, like there's
just you know, it's sort of all upside right, like
for the for the workers, I think the story is
mostly good too. You know, like how many things do
you do in your day that you'd really rather not right?

You know, and we're used to have I think, things
we don't like automated away, you know, we didn't you know,
if you didn't like walking many miles to work, then
you know, like you have a car and you can
drive there. Or we used to have a huge fraction
over ninety percent of the US population engaged in agriculture,
and then we mechanized it how very few people work
in agriculture. A small number of people can do the

work of a large number of people. And then you know,
things like email, and you know, they've led to huge
productivity enhancements because I don't need to be writing letters
and sending them in the mail. I can just instantly
communicate with people. We just become more effective. Like our
jobs have transformed, whether it's a physical job like agriculture
or whether it's a knowledge worker job where you're sending

emails and communicating with people and coordinating teams, we've just
gotten better. And you know, the technology has just made
us more productive. And this is just another example. Now,
you know, there are people who worry that, you know,
we'll be so good at that that maybe jobs will
be displaced, and that's that's a legitimate concern. But just
like how in agriculture, you know, it's not like suddenly

we had ninety percent of the population unemployed. You know,
people transitioned to other jobs. And the other thing that
we've found, too, is that our appetite for doing more
things is as humans is sort of insatiable. So even
if we can dramatically increase how much you know, one
human can do, that doesn't necessarily mean we're going to

do a fixed amount of stuff. There's an appetite to
have even more, so we're going to you can continue
to grow the pie. So I think at least certainly
in the near term, you know, we're going to see
a lot of drudgery go away from work. We're going
to see people be able to be more effective at
their jobs. You know, we will see some transformation in
jobs and what look like. But we've seen that before

and the technology a least has the potential to make
our lives a lot easier.

Speaker 2 (31:52):
So IBM recently launched Watson X, which includes Watson x
dot AI. Tell me about that, Tell me about you
know what it is and the new possibilities that it
opens up.

Speaker 3 (32:03):
Yeah, So Watson next is obviously a bit of a
new branding on the Watson brand. TJ. Watson that was
the founder of IBM and our EI technologies have had
the Watson brand. Watson X is a recognition that there's
something new, there's something that actually has changed the game.

We've gone from this old world of automation is to
labor intensive to this new world of possibilities where it's
much easier to use AI. And what Watson X does
it brings together tools for businesses to harness that power.
So whattsonex dot AI foundation models that our customers can use.

It includes tools that make it easy to run, easy
to deploy, easy to experiment. There's a watsonex dot Data
component which allows you to sort of organize and access
to your data. So what we're really trying to do
is give our customers a cohesive set of tools to
harness the value of these technologies and at the same

time be able to manage the risks and other things
that you have to keep an eye on in an
enterprise context.

Speaker 2 (33:15):
So we talk about the guests on this show as
new creators, by which we mean people who are creatively
applying technology in business to drive change. And I'm curious
how creativity plays a role in the research that you do.

Speaker 3 (33:33):
I honestly, I think the creative aspects of this job,
this is what makes this work exciting. You know, I
should say, you know, the folks who work in my
organization are doing the creating, and I.

Speaker 2 (33:47):
Guess you're doing the managing so that they could do
the creator.

Speaker 3 (33:52):
I'm helping them be their best and I still get
to get involved in the weeds of the research as
much as I can. But you know, there's something really
exciting about inventing, you know, Like one of the nice
things about doing invention and doing research on AI in
industry is it's usually grounded and a real problem that
somebody's having. You know, a customer wants to solve this

problem it's losing money or there there would be a
new opportunity. You identify that problem and then you build
something that's never been built before to do that. And
I think that's honestly the adrenaline rush that keeps all
of us in this field. How do you do something
that nobody else on earth has done before or tried before,

So that that kind of creativity, and there's also creativity
as well, and identifying what those problems are, being able
to understand the places where you know the technology is
close enough to solving a problem, and doing that matchmaking
between problems that are now solvable, you know, and in AI,
where the field is moving so fast, this is constantly

growing horizon of things that we might be able to solve.
So that matchmaking, I think is also a really interesting
creative problem. So I think I think that's that's that's
why it's so much fun. And it's a fun environment
we have here too. It's you know, people drawing on
whiteboards and writing on pages of math and.

Speaker 2 (35:18):
You know, like in a movie, like in a movie.

Speaker 3 (35:21):
Yeah, straight from special casting.

Speaker 2 (35:23):
The drawing on the window, writing on the window in sharp.
Absolutely so, so let's close with the really long view.
How do you imagine AI and people working together twenty.

Speaker 3 (35:38):
Years from now? Yeah, it's really hard to make predictions.
The vision that I like, actually this came from an
MIT economist named David Ottur, which was imagine AI almost
as a natural resource. You know, we know how natural

resources work, right, Like there's an ore we can dig
up out of the earth that comes from kind of
springs from the earth, or we usually think of that
in terms of physical stuff. With AI, you can almost
think of it as like there's a new kind of
abundance potentially twenty years from now where not only can
we have things we can build or eat or use
or burn or whatever, now we have, you know, this

ability to do things and understand things and do intellectual work.
And I think we can get to a world where
automating things is just seamless. We're surrounded by capability to
augment ourselves to get things done. And you could think
of that in terms of like, well, that's going to
displace our jobs, because eventually the AI system is going

to do everything we can do. But you could also
think of it in terms of like, wow, that's just
so much abundance that we now have, and really how
we use that abundance is sort of up to us,
you know, like when you can writing software is super
easy and fast and anybody can do it. Just think
about all the things you can do now, think about
all the new activities, and go out all the ways
we could use that to enrich our lives. That's where

I'd like to see us in twenty years. You know,
we can we can do just so much more than
we were able to do before abundance.

Speaker 2 (37:14):
Great, thank you so much for your time.

Speaker 3 (37:18):
Yeah, it's been a pleasure. Thanks for inviting me.

Speaker 1 (37:22):
What a far ranging, deep conversation. I'm mesmerized by the
vision David just described. A world where natural conversation between
mankind and machine can generate creative solutions to our most
complex problems. A world where we view AI not as
our replacements, but as a powerful resource we can tap

into and exponentially boost our innovation and productivity. Thanks so
much to doctor David Cox for joining us on smart Talks.
We deeply appreciate him sharing his huge breadth of AI
knowledge with us and for explaining the transformative potential of
foundation models in a way that even I can understand.

We eagerly await his next great breakthrough. Smart Talks with
IBM is produced by Matt Romano, David jaw nishe Venkat
and Royston Preserve with Jacob Goldstein. We're edited by Lydia
Jane Kott. Our engineers are Jason Gambrel, Sarah Buguier and
Ben Holliday. Theme song by Gramosco. Special thanks to Carli Megliori,

Andy Kelly, Kathy Callahan and the eight Bar and IBM teams,
as well as the Pushkin marketing team. Smart Talks with
IBM is a production of Pushkin Industries and iHeartMedia. To
find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
or wherever you listen to podcasts. Him Malcolm Gladwell. This

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