July 16, 2024 • 39 mins
Major breakthroughs in artificial intelligence research often reshape the design and utility of AI in both business and society. In this special rebroadcast 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.
When we first aired this episode last year, the concept of foundation models was just beginning to capture our attention. Since then, this technology has evolved and redefined the boundaries of what's possible. Businesses are becoming more savvy about selecting the right models and understanding how they can drive revenue and efficiency.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jacob Goldstein (00:02):
Hey, it's Jacob Goldstein for Smart Talks with IBM. Last
year I had the pleasure of sitting down with doctor
David Cox, VP of AI Models at IBM Research. We
explored the fascinating world of AI foundation models and their
revolutionary potential for business automation and innovation. When we first
aired this episode, the concept of foundation models was just
(00:25):
beginning to capture our attention. Since then, this technology has
evolved and redefined the boundaries of what's possible. Businesses are
becoming more savvy about selecting the right models and understanding
how they can drive revenue and efficiency. As I listened
back to the conversation, it was interesting to reflect on
some new developments and ideas that have emerged, and many
(00:47):
of these we will continue to explore throughout the season,
like how to play an active role in choosing the
best model for your needs. Whether you're a longtime listener
or tuning in for the first time, I'm certain you'll
find doctor Cox's in sites as thought provoking as ever.
Thanks as all ways for joining us. Now let's dive in.
Malcolm Gladwell (01:07):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. 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
(01:30):
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 simmering
ideas of a few academics and technologists into the industrial
boom we are experiencing today. Having dedicated his life to
(01:54):
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,
(02:17):
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
(02:38):
longtime host of the NPR program Planet Money. Okay, let's
get to the interview.
Jacob Goldstein (02:47):
Tell me about your job at IBM.
David Cox (02:49):
So, I wear two hats at IBM. So one, I'm
the IBM Doctor of the MIT IBM Watson They Lab.
So that's a joint lab between IBM and MIT where
we 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,
(03:12):
building these these big models, generative models that allow us
to have all kinds of new exciting capabilities in AI.
Jacob Goldstein (03:17):
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 IBM
MIT collaboration. Where did that partnership start, How did it originate?
David Cox (03:33):
Yeah? So, actually, it turns out that MIT 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
O one. So we've really been together in AI since
(03:55):
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'll be committing close
to a quarter billion dollars over ten years to have
this joint lab with MIT, and we we located ourselves
right on the campus and we've been developing very very
(04:18):
deep relationships where we can you know, 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, you know, very few entities like this exist
between academia industry. It's been really fun of the last
five years to be a part of it.
Jacob Goldstein (04:35):
And what do you think are some of the most
important outcomes of this collaboration between IBM and MIT.
David Cox (04:42):
Yeah, so we're really kind of the tip of the
sphere for for IBM's the I strategy, So we're really looking,
you know, what's coming ahead and you know in areas
like Foundation Models, you know, as the field changes, MIT
people are interested in working on you know, faculty, students
and staff are interesting working on what's the latest thing,
what's the next thing. We at IBM Research are very
(05:04):
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
(05:26):
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 what we might consider just looking at the product
portfolio of IBM, and that that gives us again a
breadth where we can see connections that we might not
have seen otherwise. We can you know, think things that
help out society and also help out our customers.
Jacob Goldstein (05:49):
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.
(06:11):
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.
David Cox (06:22):
Yeah. You know, I've been studying AI basically my entire
adult life. Before I came to IBM, 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
(06:44):
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 back propagation, which is sort of
key to training networks that's been around, you know, since
the eighties in wide use, and really what happened was
(07:06):
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 caught a lucky break
that there was something called the graphics processing unit, which
(07:27):
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 rebrand of neural networks that
I mentioned from from the eighties, and that was enabled
again by data because we digitalized the world, and compute
(07:50):
because because we kept building faster and faster and more
powerful computers, and then that allowed us to make this
this big breakthrough. And then you know, more recently, using
the same building blocks, that inexorable rise of more and
more and more data that the technology called self supervised learning.
Where the key difference there in traditional deep learning, you know,
(08:13):
for classifying images, you know, like is this a cat
or is this a dog? And a picture those technologies
require super visions, 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
(08:34):
there's only so many labels that exist 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 lets you use even more data. And that's
really what drove this 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
(08:58):
been simmering techechnologies, 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
(09:18):
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,
(09:38):
you know. It's like a propelled slingshot at us into
a different regime in terms of the public awareness of
these technologies.
Jacob Goldstein (09:45):
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?
David Cox (09:58):
Yeah? So this termoundation 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. Could
we could train a model. We don't have to go
through the painful effort of labeling each and every example.
(10:22):
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, you know, 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 could have on 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
(10:45):
and you can get what you want from the model.
So just a little bit on top now 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.
Jacob Goldstein (10:56):
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
(11:17):
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.
David Cox (11:30):
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 sometimes 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. U sounds
like I'm making a joke.
Jacob Goldstein (11:50):
Indeed, famously, if automation does one thing, it substitutes machines
or computing power for labor. Right, So what does that
mean to say AI or automation is too labor intensive?
David Cox (12:03):
It sounds like I'm making a joke, but I'm actually serious.
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. So
(12:23):
and we estimate anywhere between eighty to ninety percent of
the effort it takes to feel then 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
(12:46):
comes time to train the model. You need highly skilled
people expensive 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 warrant level
because you have.
Jacob Goldstein (13:01):
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.
David Cox (13:12):
That's 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 tasks 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.
(13:36):
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
(13:57):
a river that's big enough that you're gonna get enough
energy out of it that it was worth 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 bablet bricks,
And you know, there's a lot of value left on
(14:20):
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 because 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 that you can
(14:41):
work on that we don't we don't care. We don't
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
(15:02):
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 brooks and kettlepons, you know, those
(15:25):
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.
Jacob Goldstein (15:33):
And so foundation models basically mean that automating some new
task is much less labor 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
(15:58):
a little bit of a countervailing idea that 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 in this context?
David Cox (16:20):
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
(16:45):
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
(17:05):
are trained like to specialize on particular domains can actually
outperform much bigger models. So bigger isn't always even better.
Jacob Goldstein (17:13):
So they're more efficient and they do the thing you
want them to do better. As well.
David Cox (17:18):
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 were
(17:39):
one hundred billion parameters, you know, many 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 aren't know, you know,
(18:00):
it's cheaper. So there's lots of different advantages there. So
I think we're going to see at tension 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
(18:21):
pick the one that makes the most sense for the
task at hand.
Jacob Goldstein (18:25):
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.
David Cox (18:40):
Yeah, so there are lots of issues still as amazing
as these technologies are, and they are amazing, let's 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
(19:01):
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 you know, 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
(19:23):
have gendered pronouns English does, and when you ask them
to translate, it would translate they are a nurse to
she is a nurse, 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
(19:46):
and our text and our images everywhere. And then the
models they're just mapping from what they've seen in their
training data 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 my T but also all over the
(20:10):
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, uh,
you know, of machine learning and AI, but foundation models
(20:31):
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 in all
these different biases we have, so that 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
(20:51):
models will often just make stuff up. And you know,
the technical term that the field has chosen is hallucination.
To give you an example, I asked chat tbt to
create a biography of David Cox's IBM, and you know,
it started off really well, you know, identifying that I
was the director of the mt IBM, Watson may and
said a few words about that, and then it proceeded
(21:14):
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 the authority.
Jacob Goldstein (21:27):
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.
David Cox (21:34):
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, emit
(21:57):
complete nonsense.
Jacob Goldstein (21:58):
Like yeah, you know, you know, we get debate about that,
but yeah, we could debate about that. But yes, the
sort of blive confidence throws you off when you realize
it's completely wrong.
David Cox (22:09):
Right, that's right. And we do have a little bit
of like a great and powerful oz 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 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
(22:32):
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 can use them much more easily.
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
(22:56):
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
(23:17):
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.
Jacob Goldstein (23:27):
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.
David Cox (23:45):
Yeah, so the 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 superset of everything
we used to use AI for in business. So you know,
the simple kinds of things are like, hey, if I
have text and I you know, I have like product reviews,
and I want to be able to tell if these
are positive or negative. You know, like let's look at
(24:07):
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 that. 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
(24:29):
impress something like I want to I have like say
I have a chat transcript, Like a customer called in
and they had a complaint, they call back, Okay, now
a new you know, a 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 quick little paragraph. You know, customer
(24:49):
called they we up said 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 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 a
(25:09):
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 it has access to our
HR policies, and I can actually have a you know,
a multi turn conversation where I can, you know, like
I would have with you know, somebody, you know, actual
(25:30):
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,
we're 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.
(25:52):
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.
Jacob Goldstein (26:03):
Out 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
(26:23):
standard user interface, just like the graphical user interface, did
you know over the past several decades.
David Cox (26:28):
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 have to learn how
to use a tool or I have to kind of
have awkward, you know, interactions from 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.
(26:53):
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
(27:15):
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
(27:35):
even write code to do new things, you know, and
generate software itself. Given how much software we have, how
much craving we have for software, like we'll never have
enough software in our world, uh, you know, the ability
to have AI systems as a helper in that, I
think we're going to see a lot of a lot
of value there.
Jacob Goldstein (27:54):
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.
David Cox (28:12):
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 are like you imagine bars and joints
and overs, you know, the things that.
Jacob Goldstein (28:31):
Are building a thing, building a physical machine of some kind.
David Cox (28:35):
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 build 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
(28:56):
want to create, and then a human together with computer program,
can build a five or six bar linkage and then
that's kind of where you top out it 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
(29:20):
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 carbone. But I think the world around us
has always been shaped by technology. If we look around,
you know, just think about how many steps and how
(29:41):
many people and how many designs went into the table
and the chair and the LAYMP. 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 to have been you know, helped in some
(30:02):
way to get to you by you know.
Jacob Goldstein (30:05):
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 employees.
David Cox (30:18):
Yeah, so I think for businesses, this is going to
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
(30:39):
not right? You know, and we're used to having things
we don't like automated away, you know, we we didn't
you know, if you didn't like walking many miles to work,
then you know, like you can have a car and
you can drive there. Or we used to have a
huge traction 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
(31:00):
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
(31:21):
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,
will be so good at that that maybe jobs will
be displaced, and that's a legitimate concern, But just like
(31:42):
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 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,
(32:04):
that doesn't necessarily mean you'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 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
(32:24):
like we've seen that before, and the technology a least
has the potential to make our lives a lot easier.
Jacob Goldstein (32:33):
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.
David Cox (32:44):
Yeah. So, so Watson X is obviously a bit of
a new branding on the Watson brand. T. J. 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
(33:09):
is to labor intensive to this new world of possibilities
where it's much easier to use AI. And what watsonex
does it brings together tools for businesses to harness that power.
So whatsonex dot AI foundation models that our customers can use.
It includes tools that make it easy to run, easy
(33:30):
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
(33:52):
that you have to keep an eye on in an
enterprise context.
Jacob Goldstein (33:56):
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.
David Cox (34:15):
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 guess you're doing.
Jacob Goldstein (34:30):
The managing so that they could do the creator.
David Cox (34:33):
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 industries,
it's usually grounded and a real problem that somebody is having.
(34:54):
You know, a customer wants to solve this problem that's
losing money or there will be a new opportunity. You
identify that problem and then you 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
(35:17):
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 the technology is close enough to solving
a problem, and doing that matchmaking between problems that are
now solvable, you know, and an AI where the field's
moving so fast, this is constantly growing horizon of things
(35:41):
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.
Jacob Goldstein (36:00):
Like in a movie, Like in a movie, yeah, straight
from special casting, the drawing on the window, righting 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.
David Cox (36:19):
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
(36:42):
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, or not only can
we have things we can build or eat, use or
burn or whatever. Now we have, you know, this ability
(37:03):
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, oh, that's going to
displace our jobs, because eventually the AI system is going
(37:24):
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, Like think
about all the new activities and go out all the
ways we could use that to enrich our lives. That's
(37:46):
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.
Jacob Goldstein (37:56):
Great, Thank you so much for your time.
David Cox (38:00):
That's been pleasure. Thanks for inviting me.
Malcolm Gladwell (38:03):
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
(38:23):
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.
(38:45):
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
jen Kott. Our end engineers are Jason Gambrel, Sarah Bouguer,
and Ben Holliday. Theme song by Gramosco. Special thanks to
(39:07):
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.
(39:29):
Hi'm Malcolm Gladwell. This is a paid advertisement from IBM.
Hey, it's Jacob Goldstein for Smart Talks with IBM. Last
year I had the pleasure of sitting down with doctor
David Cox, VP of AI Models at IBM Research. We
explored the fascinating world of AI foundation models and their
revolutionary potential for business automation and innovation. When we first
aired this episode, the concept of foundation models was just
(00:25):
beginning to capture our attention. Since then, this technology has
evolved and redefined the boundaries of what's possible. Businesses are
becoming more savvy about selecting the right models and understanding
how they can drive revenue and efficiency. As I listened
back to the conversation, it was interesting to reflect on
some new developments and ideas that have emerged, and many
(00:47):
of these we will continue to explore throughout the season,
like how to play an active role in choosing the
best model for your needs. Whether you're a longtime listener
or tuning in for the first time, I'm certain you'll
find doctor Cox's in sites as thought provoking as ever.
Thanks as all ways for joining us. Now let's dive in.
Malcolm Gladwell (01:07):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. 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
(01:30):
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 simmering
ideas of a few academics and technologists into the industrial
boom we are experiencing today. Having dedicated his life to
(01:54):
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,
(02:17):
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
(02:38):
longtime host of the NPR program Planet Money. Okay, let's
get to the interview.
Jacob Goldstein (02:47):
Tell me about your job at IBM.
David Cox (02:49):
So, I wear two hats at IBM. So one, I'm
the IBM Doctor of the MIT IBM Watson They Lab.
So that's a joint lab between IBM and MIT where
we 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,
(03:12):
building these these big models, generative models that allow us
to have all kinds of new exciting capabilities in AI.
Jacob Goldstein (03:17):
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 IBM
MIT collaboration. Where did that partnership start, How did it originate?
David Cox (03:33):
Yeah? So, actually, it turns out that MIT 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
O one. So we've really been together in AI since
(03:55):
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'll be committing close
to a quarter billion dollars over ten years to have
this joint lab with MIT, and we we located ourselves
right on the campus and we've been developing very very
(04:18):
deep relationships where we can you know, 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, you know, very few entities like this exist
between academia industry. It's been really fun of the last
five years to be a part of it.
Jacob Goldstein (04:35):
And what do you think are some of the most
important outcomes of this collaboration between IBM and MIT.
David Cox (04:42):
Yeah, so we're really kind of the tip of the
sphere for for IBM's the I strategy, So we're really looking,
you know, what's coming ahead and you know in areas
like Foundation Models, you know, as the field changes, MIT
people are interested in working on you know, faculty, students
and staff are interesting working on what's the latest thing,
what's the next thing. We at IBM Research are very
(05:04):
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
(05:26):
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 what we might consider just looking at the product
portfolio of IBM, and that that gives us again a
breadth where we can see connections that we might not
have seen otherwise. We can you know, think things that
help out society and also help out our customers.
Jacob Goldstein (05:49):
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.
(06:11):
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.
David Cox (06:22):
Yeah. You know, I've been studying AI basically my entire
adult life. Before I came to IBM, 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
(06:44):
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 back propagation, which is sort of
key to training networks that's been around, you know, since
the eighties in wide use, and really what happened was
(07:06):
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 caught a lucky break
that there was something called the graphics processing unit, which
(07:27):
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 rebrand of neural networks that
I mentioned from from the eighties, and that was enabled
again by data because we digitalized the world, and compute
(07:50):
because because we kept building faster and faster and more
powerful computers, and then that allowed us to make this
this big breakthrough. And then you know, more recently, using
the same building blocks, that inexorable rise of more and
more and more data that the technology called self supervised learning.
Where the key difference there in traditional deep learning, you know,
(08:13):
for classifying images, you know, like is this a cat
or is this a dog? And a picture those technologies
require super visions, 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
(08:34):
there's only so many labels that exist 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 lets you use even more data. And that's
really what drove this 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
(08:58):
been simmering techechnologies, 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
(09:18):
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,
(09:38):
you know. It's like a propelled slingshot at us into
a different regime in terms of the public awareness of
these technologies.
Jacob Goldstein (09:45):
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?
David Cox (09:58):
Yeah? So this termoundation 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. Could
we could train a model. We don't have to go
through the painful effort of labeling each and every example.
(10:22):
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, you know, 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 could have on 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
(10:45):
and you can get what you want from the model.
So just a little bit on top now 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.
Jacob Goldstein (10:56):
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
(11:17):
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.
David Cox (11:30):
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 sometimes 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. U sounds
like I'm making a joke.
Jacob Goldstein (11:50):
Indeed, famously, if automation does one thing, it substitutes machines
or computing power for labor. Right, So what does that
mean to say AI or automation is too labor intensive?
David Cox (12:03):
It sounds like I'm making a joke, but I'm actually serious.
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. So
(12:23):
and we estimate anywhere between eighty to ninety percent of
the effort it takes to feel then 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
(12:46):
comes time to train the model. You need highly skilled
people expensive 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 warrant level
because you have.
Jacob Goldstein (13:01):
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.
David Cox (13:12):
That's 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 tasks 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.
(13:36):
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
(13:57):
a river that's big enough that you're gonna get enough
energy out of it that it was worth 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 bablet bricks,
And you know, there's a lot of value left on
(14:20):
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 because 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 that you can
(14:41):
work on that we don't we don't care. We don't
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
(15:02):
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 brooks and kettlepons, you know, those
(15:25):
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.
Jacob Goldstein (15:33):
And so foundation models basically mean that automating some new
task is much less labor 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
(15:58):
a little bit of a countervailing idea that 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 in this context?
David Cox (16:20):
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
(16:45):
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
(17:05):
are trained like to specialize on particular domains can actually
outperform much bigger models. So bigger isn't always even better.
Jacob Goldstein (17:13):
So they're more efficient and they do the thing you
want them to do better. As well.
David Cox (17:18):
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 were
(17:39):
one hundred billion parameters, you know, many 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 aren't know, you know,
(18:00):
it's cheaper. So there's lots of different advantages there. So
I think we're going to see at tension 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
(18:21):
pick the one that makes the most sense for the
task at hand.
Jacob Goldstein (18:25):
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.
David Cox (18:40):
Yeah, so there are lots of issues still as amazing
as these technologies are, and they are amazing, let's 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
(19:01):
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 you know, 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
(19:23):
have gendered pronouns English does, and when you ask them
to translate, it would translate they are a nurse to
she is a nurse, 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
(19:46):
and our text and our images everywhere. And then the
models they're just mapping from what they've seen in their
training data 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 my T but also all over the
(20:10):
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, uh,
you know, of machine learning and AI, but foundation models
(20:31):
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 in all
these different biases we have, so that 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
(20:51):
models will often just make stuff up. And you know,
the technical term that the field has chosen is hallucination.
To give you an example, I asked chat tbt to
create a biography of David Cox's IBM, and you know,
it started off really well, you know, identifying that I
was the director of the mt IBM, Watson may and
said a few words about that, and then it proceeded
(21:14):
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 the authority.
Jacob Goldstein (21:27):
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.
David Cox (21:34):
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, emit
(21:57):
complete nonsense.
Jacob Goldstein (21:58):
Like yeah, you know, you know, we get debate about that,
but yeah, we could debate about that. But yes, the
sort of blive confidence throws you off when you realize
it's completely wrong.
David Cox (22:09):
Right, that's right. And we do have a little bit
of like a great and powerful oz 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 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
(22:32):
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 can use them much more easily.
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
(22:56):
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
(23:17):
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.
Jacob Goldstein (23:27):
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.
David Cox (23:45):
Yeah, so the 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 superset of everything
we used to use AI for in business. So you know,
the simple kinds of things are like, hey, if I
have text and I you know, I have like product reviews,
and I want to be able to tell if these
are positive or negative. You know, like let's look at
(24:07):
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 that. 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
(24:29):
impress something like I want to I have like say
I have a chat transcript, Like a customer called in
and they had a complaint, they call back, Okay, now
a new you know, a 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 quick little paragraph. You know, customer
(24:49):
called they we up said 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 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 a
(25:09):
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 it has access to our
HR policies, and I can actually have a you know,
a multi turn conversation where I can, you know, like
I would have with you know, somebody, you know, actual
(25:30):
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,
we're 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.
(25:52):
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.
Jacob Goldstein (26:03):
Out 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
(26:23):
standard user interface, just like the graphical user interface, did
you know over the past several decades.
David Cox (26:28):
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 have to learn how
to use a tool or I have to kind of
have awkward, you know, interactions from 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.
(26:53):
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
(27:15):
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
(27:35):
even write code to do new things, you know, and
generate software itself. Given how much software we have, how
much craving we have for software, like we'll never have
enough software in our world, uh, you know, the ability
to have AI systems as a helper in that, I
think we're going to see a lot of a lot
of value there.
Jacob Goldstein (27:54):
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.
David Cox (28:12):
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 are like you imagine bars and joints
and overs, you know, the things that.
Jacob Goldstein (28:31):
Are building a thing, building a physical machine of some kind.
David Cox (28:35):
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 build 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
(28:56):
want to create, and then a human together with computer program,
can build a five or six bar linkage and then
that's kind of where you top out it 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
(29:20):
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 carbone. But I think the world around us
has always been shaped by technology. If we look around,
you know, just think about how many steps and how
(29:41):
many people and how many designs went into the table
and the chair and the LAYMP. 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 to have been you know, helped in some
(30:02):
way to get to you by you know.
Jacob Goldstein (30:05):
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 employees.
David Cox (30:18):
Yeah, so I think for businesses, this is going to
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
(30:39):
not right? You know, and we're used to having things
we don't like automated away, you know, we we didn't
you know, if you didn't like walking many miles to work,
then you know, like you can have a car and
you can drive there. Or we used to have a
huge traction 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
(31:00):
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
(31:21):
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,
will be so good at that that maybe jobs will
be displaced, and that's a legitimate concern, But just like
(31:42):
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 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,
(32:04):
that doesn't necessarily mean you'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 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
(32:24):
like we've seen that before, and the technology a least
has the potential to make our lives a lot easier.
Jacob Goldstein (32:33):
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.
David Cox (32:44):
Yeah. So, so Watson X is obviously a bit of
a new branding on the Watson brand. T. J. 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
(33:09):
is to labor intensive to this new world of possibilities
where it's much easier to use AI. And what watsonex
does it brings together tools for businesses to harness that power.
So whatsonex dot AI foundation models that our customers can use.
It includes tools that make it easy to run, easy
(33:30):
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
(33:52):
that you have to keep an eye on in an
enterprise context.
Jacob Goldstein (33:56):
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.
David Cox (34:15):
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 guess you're doing.
Jacob Goldstein (34:30):
The managing so that they could do the creator.
David Cox (34:33):
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 industries,
it's usually grounded and a real problem that somebody is having.
(34:54):
You know, a customer wants to solve this problem that's
losing money or there will be a new opportunity. You
identify that problem and then you 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
(35:17):
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 the technology is close enough to solving
a problem, and doing that matchmaking between problems that are
now solvable, you know, and an AI where the field's
moving so fast, this is constantly growing horizon of things
(35:41):
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.
Jacob Goldstein (36:00):
Like in a movie, Like in a movie, yeah, straight
from special casting, the drawing on the window, righting 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.
David Cox (36:19):
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
(36:42):
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, or not only can
we have things we can build or eat, use or
burn or whatever. Now we have, you know, this ability
(37:03):
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, oh, that's going to
displace our jobs, because eventually the AI system is going
(37:24):
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, Like think
about all the new activities and go out all the
ways we could use that to enrich our lives. That's
(37:46):
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.
Jacob Goldstein (37:56):
Great, Thank you so much for your time.
David Cox (38:00):
That's been pleasure. Thanks for inviting me.
Malcolm Gladwell (38:03):
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
(38:23):
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.
(38:45):
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
jen Kott. Our end engineers are Jason Gambrel, Sarah Bouguer,
and Ben Holliday. Theme song by Gramosco. Special thanks to
(39:07):
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.
(39:29):
Hi'm Malcolm Gladwell. This is a paid advertisement from IBM.
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