November 15, 2023 • 52 mins
In order to stay competitive in a rapidly changing marketplace, businesses need to adapt to the potential of generative AI. In this special live episode of Smart Talks with IBM, Malcolm Gladwell is joined onstage at iHeartMedia’s studio by Dr. Darío Gil, Senior Vice President and Director of Research at IBM. They chat about the evolution of AI, give examples of practical uses, and discuss how businesses can create value through cutting edge technology.
Watch the live conversation here: https://youtu.be/WOwM__St6aU
Hear more from Darío on generative AI for business: https://www.ibm.com/think/ai-academy/
Visit us at: ibm.com/smarttalks
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Episode Transcript
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Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It
is a new season of the Smart Talks with IBM
podcast series.
Speaker 2 (00:09):
Today we are witnessed to one of those rare moments
in history, the rise of an innovative technology with the
potential to radically transform business and society forever. The technology,
of course, is artificial intelligence, and it's the central focus
for this new season of Smart Talks with IBM.
Speaker 1 (00:25):
Join hosts from your favorite Pushkin podcasts as they talk
with industry experts and leaders to explore how businesses can
integrate AI into their workflows and help drive real change
in this new era of AI. And of course, host
Malcolm Gladwell will be there to guide you through the
season and throw in his two cents as well.
Speaker 2 (00:43):
Look out for new episodes of Smart Talks with IBM
every other week on the iHeartRadio app, Apple Podcasts, or
wherever you get your podcasts, and learn more at IBM
dot com slash smart Talks.
Speaker 1 (00:57):
All right, Welcome everybody, you guys excited, here we go.
Speaker 3 (01:04):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries. iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
we're continuing our conversations with new creators visionaries who are
creatively applying technology and business to drive change, but with
a focus on the transformative power of artificial intelligence and
(01:25):
what it means to leverage AI as a game changing
multiplier for your business. Today's episode is a bit different.
I was recently joined on stage by Dario Gill for
a conversation in front of a live audience at the
iHeartMedia headquarters in Manhattan. Dario is the senior vice president
and director of IBM Research, one of the world's largest
(01:47):
and most influential corporate research labs. We discussed the rise
of generative AI and what it means for business and society.
He also explained how organizations that leverage AI to create
value will dominate in the near future. Okay, let's get
on to the conversation. Hello everyone, welcome, and I'm here
(02:11):
with doctor Dario Gil and I wanted to say before
we get started. This is something I said backstage that
I feel very guilty today because you're the you know,
you know, arguably one of the most important figures in
AI research in the world, and we have taken you
away from your job for a morning. It's like, if
(02:33):
you know Oppenheimer's wife in nineteen forty four said let's
go and have a little getaway in the Bahamas. It's
that kind of thing. You know, what do you say
to your wife, I can't we have got to work
on this thing I can't tell you about. She's like
getting me out of Los Alamos. No, So I do
feel guilty. We've set back AI research by by about
(02:56):
four hours here. But I wanted to you've been up
with with ibo for twenty years, twenty years this summer.
So and how old were you when you Not to
give away your age, but you were how old when
you started?
Speaker 4 (03:08):
I was twenty eight?
Speaker 3 (03:09):
Okay, yeah, So I want to go back to your
twenty eight year old self. Now, if I asked you
about artificial intelligence, I asked twenty eight year old Dario,
what does the future hold for AI? How quickly will
this new technology transform our world? Et cetera, et cetera.
What would twenty eight year old Darigo said?
Speaker 4 (03:28):
Well, I think the first thing is that even though
AI as a feel has been with us for a
long time, since the mid nineteen fifties, at that time,
AI was not a very polite word to say, meaning
within the scientific community, people didn't use sort of that term.
They would have said things like, you know, maybe I
do things relate to machine learning, right, or statistical techniques
(03:50):
in terms of classifiers and so on. But AI had
a mixed reputation, right, it had gone through different cycles
of hype, and it's also if moments of you know,
a lot of negativity towards it because of lack of success.
And so I think that would be the first thing
we probably say, like AI is like what is that? Like,
(04:10):
you know, respectable scientists are not working on AI the
finest side and that really changed over the last fifteen years. Only, right,
I would say, with the advent of deep learning over
the last decade, is when that re enter again the
lexicon of saying AI and that that was a legitimate
thing to work on. So I would say that that's
the first thing I think we would have noticed a
contrast twenty years ago.
Speaker 3 (04:31):
Yeah, So what point in your twenty year tenure at
IBM would you say you kind of snapped into present
kind of wow mode.
Speaker 4 (04:42):
I would say in a late two thousands, when IBM
was working on the Jeopardy project and just seeing the
demonstrations of what could be done in question answering.
Speaker 3 (05:00):
It's literally Jeopardy. Is this crucial moment in the history
of YEA.
Speaker 4 (05:05):
You know, there had been a long and wonderful history
inside IBM on AI. So for example, like you know,
in terms of like these grand challenges at the very
beginning of the field founding, which is this famous Dartmouth
conference that actually IBM sponsored h to create. There was
an IBM and there called Nathaniel Rochester, and there were
(05:27):
a few others who right after that they started thinking
about demonstrations of this field. And then for example, they
created the first you know game to play checkers and
to demonstrate that you could do machine learning on that.
Obviously we saw later in the nineties like chess that
was a very famous example of that, Deep Blue with
Deep Blue right and playing with Caspar and then but
(05:49):
I think the moment that was really those other ones
felt like, you know, kind of like brute force anticipating,
sort of like moves ahead. But this aspect of dealing
with language and question answering felt different, and I think
for for us internally and many others, was when a
moment of saying like, wow, you know, what are the
possibilities here? And then soon after that connected to the
(06:09):
sort of advancements in computing and with deep learning. The
last decade has just been an all out, you know,
sort of like front of advancements and that, and I
just continue to be more and more impressed. And the
last few years have been remarkable too.
Speaker 3 (06:21):
Yeah. So I'll ask you three quick conceptual questions before
we dig into it, just so I sort of get
a we all get a feel for the shape of AI.
Question Number one is where are we in the evolution
of this? So you know the obvious question. We we're
all suddenly aware of it, we're talking about it. Can
(06:43):
you give us an analogy about where we are in
the kind of likely evolution of this is a technology.
Speaker 4 (06:50):
So I think we're on a significant inflection point that
it feels the equivalent of the first browsers when they
appear and people imagine the possibilities of the Internet or
more imagined experience the internet. The Internet had been around,
right for quite a few decades. AI has been around
for many decades. I think the moment we find ourselves
(07:12):
is that people can touch it and they can Before
they were a systems that were like behind the scenes,
like your search results or translation systems, but they didn't
have the experience of like, this is what it feels
like to interact with this thing. So that's what I mean.
I think maybe that analogy of the browser is appropriate
because it's all of a sudden, it's like whoa, you know,
these network of machines and content can be distributed and
(07:35):
everybody can self publish, and there was a moment that
we all remember that, and I think that that is
what the world has experience over the last nine months
or so on. So but fundamentally, also what is important
is that this moment is where the ease of the
number of people that can build and use AI has skyrocketed.
So over the last decade, you know, technology firms that
(07:58):
had large research teams could build AI that worked really well, honestly,
but when you went down into say hey can everybody
use it? Can a data science team in a bank,
you know, go and develop these applications, it was like
more complicated. Some could do it, but it was more
the barrier of entry was high. Now is very different
because of foundation models and the implications that that has.
Speaker 3 (08:20):
For at the moment where the technology is being democratized.
Speaker 4 (08:23):
In demarketized, frankly, it works better for classes of problems
like programming and other things. Is really incredibly impressive what
it can do. So the accuracy and the performance of
it is much better, and the ease of use and
the number of use cases we can pursue it much bigger,
So that democratization is a big difference.
Speaker 3 (08:40):
But when you say, when you make it an analogy
to the first browsers, if we do another one of
these time travel questions back at the beginning of the
first browsers, it's safe to say many of the potential
uses of the Internet and such we hadn't even begun.
We couldn't even anticipate, right, Right, So we're at the
point where the future direction is largely unpredictable.
Speaker 4 (09:03):
Yeah, I think that that is right. Because it's such
a horizontal technology that the intersection of the horizontal capability,
which is about expanding our productivity and tasks that we
wouldn't be able to do efficiently without it, has to
marry now the use cases that reflect the diversity of
human experience, our institutional diversity. So as more and more
(09:23):
institutions said, you know, I'm focused on agriculture, you know,
to be able to improve seeds. You know, in these
kinds of environments, they'll find their own context in which
that matters that the creators of a I did not
anticipate at the beginning. So I think that that is
then the fruit of surprises will be like why I
wouldn't even think that it could be used for that.
And also clever people will create new business models as
(09:44):
associated with that, like it happened with the Internet of
course as well, and that will be its own source
of transformation and change in its own right. So I
think all of that is yet to unfold. Right, what
we're seeing is this catalyst moment of technology that works
well enough and it can be democratized.
Speaker 3 (10:00):
Yeah, what next sort of conceptual question? You know, we
can loosely understand or categorize innovations in terms of their
impact on the kind of balance of power between haves
and have nots. Some innovations, you know, obviously favor those
who already have make the rich richer. Some some it's
(10:24):
arising to tie the lifts all boats, and some bias
in the other direction. They close the gap between is
it possible to say to predict which of those three
categories AI might fall into.
Speaker 4 (10:38):
It's a great question, you know. A first observation I
would make on your first two categories is that it
will be both likely be true that the use of
AI will be highly democratized, meaning the number of people
that have access to its power to make improvements in
terms of efficiency and so on will be fairly universal,
(10:59):
and that the ones who are able to create AI
uh may be quite concentrated. So if you look at
it from the lens of who creates wealth and value
over sustained periods of time, particularly it's saying a context
like business, I think just being a user of AI
technology is an insufficient strategy and UH. And the reason
(11:22):
for that is like, yes, you will get the immediate
productivity boost of like just making API calls, and you
know that would be a new baseline for everybody, but
you're not accruing value in terms of representing your data
inside the AI in way that gives you a sustainable
competitive advantage. So I always try to tell people is
don't just be an AI user. We an you know,
AI value creator. And I think that that will have
(11:45):
a lot of consequences in terms of the haves and
have nots as an example, and that will apply both
to institutions and regions and countries, et cetera. So I
think it would be kind of a mistake, right to
just develop strategies that are just about.
Speaker 3 (12:00):
Usage, but to to come back that question from them
to give you a specific suppose I'm a I'm an
industrial farmer in Iowa with ten million dollars of equipment
and move and I'm comparing it to a subsistence farmer
someone in the developing world who's got a cell phone.
(12:20):
Over the next five years, who's who's well being rises
by a greater amount.
Speaker 4 (12:28):
Yeah, I think, I mean, it's a it's a good question,
but it might be hard to do a one to
one sort of like attribution to just one variable in
this case, which is AI. But again, provided that you
have access to a phone, right and some kind to
you know, be able to be connected. I do think
so for example, in that context we've developed we don't
(12:48):
work with NASA as an example, to build your spatial
models using some of these new techniques, and I think
for example, or ability to do flood prediction, I'll tell
you an advantage of why would be a democratization force
that context. Before to build a flowed model based on
satellite imagery was actually so onerous and so complicated and
difficult that you would just target to very specific regions
(13:10):
and then obviously countries prioritize their own right. But what
we've demonstrated is actually you can extend the technique to
have like global coverage around that. So in that context,
I would say it's a four stores and markeratization that
everybody sort of would have access if you have some connectivity,
as today.
Speaker 3 (13:26):
Iowa farmer might have a flood model. The guy in
the developing world definitely didn't, and now he's a shot
of getting one.
Speaker 4 (13:32):
Yeah, but now it has a shot of getting one.
So there's aspects of it that so long as we
provide connectivity and access to it, that they can be
democratization forces. But I'll give you another example that that
can be quite concerning, which is language. Right, So there's
so much language in the you know, in English, and
there is sort of like this reinforcement loop that happens
(13:53):
that the more you concentrate, because it has obvious benefits
for global communication and standardization, the more you can enrich
like base aim models based on that capability. If you
have very resource cars languages, you tend to develop less
powerful AI with those languages and so on. So one
has to actually worry and focus on the ability to
(14:14):
actually represent you know that in that case is language
as a piece of culture. Also in the AI sets
that everybody can benefit from it too. So there's a
lot of considerations in terms of equity about the data
and the data sets that we accrue and what problems
are we trying to solve. I mean, you mentioned agriculture
or healthcare and so on. If we only solve problems
(14:35):
that are related to marketing as an example, that would
be a less rich world in terms of opportunity that
if we incorporate many many other broad set of problems.
Speaker 3 (14:44):
Who do you think what do you think are the
biggest impediments to the adoption of AI as you would
like as you think AIR to be adopted. I mean,
if you look, what are the sticking points that you would.
Speaker 4 (14:57):
Look Indiana, I'm going to give a non time technological
answer as a first one has to do with workflow, right,
So even if the technology is very capable, the organizational
change inside a company to incorporate into the natural workflow
of people and how we work is it's a lesson
we have learned over the last decade is hugely important.
(15:18):
So there's a lot of design considerations. There's a lot
of how do people want to work right? How do
they work today? And what is the natural entry points
for AI? So that's like number one, and then the
second one is, you know, for the broad value creation
aspect of it is the understanding inside the companies of
how you have to curate and create data to combine
(15:42):
it with external data says that you can have powerful
AI models that actually fit your need. And that aspect
of what it takes to actually create and curate the
data for this modern AI, it's still working progress, right.
I think part of the problem that happens very often
when I talk to institutions is that they say yea yeah, yah, yeah,
(16:03):
I'm doing it. I've been doing it for a long time.
And the reality is that that answer can sometimes be
a little of our cop out, right, is like I
know you were doing machine learning, you were doing some
of these things, but actually the leader's version of AI,
what what's happening with foundation models? Not only is it
very new. It's very hard to do, and honestly, if
you haven't been you know, assembling very large teams and
(16:25):
spending hundreds of millions of dollars of compute, and so
you're probably not doing it right. You're doing something else
that is in the broad category. And I think the
lessons about what it means to make this transition to
this new wave is still in early phases of understanding.
Speaker 3 (16:39):
So what would you say? I want to give you
a couple of examples of people with kind of real
world in real world positions of responsibility. Imagine I'm sitting
right here, So imagine that I am the president of
a small liberal arts college and I come to you
and I say, Dario, I keep hearing about a AI
my college has you know I don't make it. You know,
I'm I'm not I'm making this much money. If that
(17:01):
every year enroments declining, I feel like this maybe is
an opportunity. What is the opportunity for me? What would
you say?
Speaker 4 (17:11):
So, it's probably in a couple of segments around that.
Right one has to do is well, what is the
implications of this technology inside the institution itself instead of
the college, And how we operate and can we improve
for example, efficiency, like if you have in very low
levels of sort of margin to be able to reinvest
(17:32):
is you know you run it, you run you know infrastructure,
you run many things inside the college. What are the
opportunities to increase the productivity or automate and drive savings
such that you can reinvest that money into the mission
of education?
Speaker 3 (17:46):
Right as an example, So number one is operational efficiency.
Speaker 4 (17:49):
Operational efficiency is a big one. I think the second
one is within the context of the college, there's implications
for the educational mission on its own, right, How will
you know how does a correct need to evolve or not?
What are acceptable use policies or of someone these ai
I think we've all read a lot about like what
can happen in terms of exams and so on and
cheating and not cheating, or what are the actually positive
(18:11):
elements of it in terms of how curriculum should be
developed and professions sustain around that. And then there's another
third dimension, which is the outdoor oriented element of it,
which is like prospect students right, so, which is frankly speaking,
a big use case that is happening right now, which
in the broader industry is called customer care or client
care or citizen care. So in this question will be education,
like you know, hey, are you reaching the right students
(18:34):
around that that may apply to the college. How can
you create them? For example, an environment to interact with
the college and answering questions that could be a chat
bought or something like that to learn about it. And personalization.
So I would say there's like at least three lenses
with which I would give advice, right.
Speaker 3 (18:49):
The positive seglee because it's really interesting. So I really
can't as sign an essay anymore? Can I?
Speaker 4 (18:58):
Can I sign an essay?
Speaker 3 (19:00):
Can I say? Rend me a research paper? And come
back to being three?
Speaker 4 (19:03):
We?
Speaker 3 (19:03):
Can I do that anymore?
Speaker 4 (19:05):
I think you can?
Speaker 3 (19:05):
How do I do that?
Speaker 4 (19:06):
And then you can that Look, there's there's two questions
around that. I think that if one goes and explains
in the context like what is it? Why are we here?
Why are in this class? What is the purpose of this?
And and one starts with assuming like an element of
like decency and people are people are there like to
learn and so on, and you just give it this disclaimer. Look,
(19:27):
I know that one option you have is like just
you know, put the essay question and click, go and
like and give an answer. You know, but that is
not why we're here, and that is not the intent
of what we're trying to do. So first I would
start with the sort of like the norms of intent
and decency and appeal to those as step number one.
Then we all know that there will be a distribution
(19:48):
of use cases of people like that will come in
one year and come out of the other and do that.
And so for a subset of that, you know, I
think the technology is going to have all in such
a way that we will have more and more of
the to discern, right, you know, when that has been
AI generated right and uncreated, it won't be perfect, right,
But there's some elements that you can imagine in putting
(20:09):
the essay and you say, hey, this is likely to
be generated right around that. And for example, one way
you can do that, just to give you an intuition,
you could just have an essay that you write with
pencil and paper. At the beginning, you get a baseline
of what you're writing is like, and then later when
you you know generate it, there will be obvious differences
around what kind of writing has been generating on the
(20:30):
other way.
Speaker 3 (20:30):
But you've turned it's everything you're describing makes sense put
it greatly in this respect, at least, it seems to
greatly complicate the life of the teacher, whereas the other
two use cases seem to kind of clarify and simplify
the role. Right suddenly, you know, reaching student perspective students,
(20:51):
sounds like I can do that much more kind of
efficient in a lite. Yeah, I can bring you administration costs,
but the teaching thing is tricky.
Speaker 4 (20:58):
Well, until we developed the new norms, right, I mean again,
I mean, I know it's not abuse analogy, but calculators
we deal. We've done with that too, right, And it says, well, calculator,
what is the purpose of math? How are we going
to do this?
Speaker 3 (21:10):
And so can I tell you my dad's calculator story?
Speaker 4 (21:14):
Yes? Please.
Speaker 3 (21:14):
My father was a mathematician, taught mathematics at University of Waterloo, Agada,
and in the seventies when people started to get pocket calculators,
his students demanded that they'd be able to use them,
and he said no, and they took him to the
administration and he lost. So he then changed completely throughout
all of his old exams and introduced new exams where
(21:37):
there was no calculation. It was all like deep think,
you know, figure out the problem on a conceptual level
and describe it to me. And they were all students
deeply unhappy that he'd made their lives for computation.
Speaker 4 (21:49):
But it's to other.
Speaker 3 (21:51):
Point, to your point, I mean, he probably the result
was probably a better education. He just removed the element
that they could gain with their pocket calculators. I suppose
it's a version of.
Speaker 4 (22:02):
I think it's a version of that, And so I
think they will develop the equivalent of what your father did.
And I think people say, you know what if like
these kinds of things, everybody's doing it generically and none
of us have any meaning because all you're doing is
pressing buttons, and like the intent of this was something
which was to teach you how to write or to
think or something. There may be a variant of how
we do all of this. I mean, obviously some version
of that that has happened is like Okay, we're all
(22:22):
going to sit down and doing with pencil on paper
and computers in their classroom. But there'll be other variants
of creativity that people will put forth to say, you
know what, you know, that's a way to solve that
problem too.
Speaker 3 (22:32):
But this is interesting because to stay on this analogy,
we're really talking about a profound rethinking. Just using college
as an example, a real profound rethinking of the way,
there's no part of this college it's unaffected by aia B.
In one case, I've made everyone's job easier. In one case,
(22:53):
I've made I'm asking us to really rethink from the
ground up what teaching means. In another case, I've automated
systems that I didn't think of it. I mean, it's like,
that's right, that's it's not that's a lot to ask
someone who got a PhD in medieval language literature, you know,
forty years ago.
Speaker 4 (23:11):
Yeah. But you know, I'll tell you a positive sort
of development that I'm seeing the sciences around this, which
is you're seeing as you see more and more examples
of applying AI technology within the context of like historians
to as an example, right, you have archival and you know,
and you have all these books and being able to
actually help you as an assistant right around that, but
(23:32):
not only with text now, but with diagrams, right, And
I've seen it in anthropology too, Rite and archaeology with
examples of engravings and translations and things that can happen.
So so as you see in diverse fields people applying
these techniques to advance and how to do physics or
how to do chemistry, they inspire each other, right, and
(23:53):
they said, you know, how does it apply actually to
my area? So once as that happens, it becomes less
of a chore of like my god, you know, how
do I have to deal with this? But actually it's
triggered by curiosity, is triggered by you know, they'll be like,
you know, faculty that will be like you know what,
you know, let me explore what this means for my area,
and they will adapt it to the local context, to
the local you know, language, and the professional itself. So
(24:16):
I see that as a positive vector that is not
all going to feel like homework, you know, it's not
going to feel like, oh my god, this is so overwhelming,
but rather to be very practical to see what works,
What have I seen others to do that is inspiring,
and what am I inspired to do? You know what?
What is how is this going to help my career?
I think that that's going to be an interesting question
for for you know, those faculty members for the.
Speaker 3 (24:37):
Students, the professional Sorry, I'm gonna stick with this example
along because it's really interesting. I'm curious, following up on
what you just said, that one of the most persistent
critiques of academic but also of many of many corporate
institutions in these years, has been siloing. Right, Different parts
of the of the organization are going off on their own,
(24:59):
and that's to each other. Is a potential is a
real potential benefit to AI the kind of breaking down
a simple tool for breaking down those kinds of barriers.
Is that a very Is that an elegant way of
sort of saying what.
Speaker 4 (25:15):
I really think? And I was actually just having a
conversation with Provos stuff and very much on this topic,
very recently, exactly on that which is all these this,
you know, this appetite right to collaborate across disciplines. There's
a lot of attempts stores a goal, right, creating interdisciplinary centers,
creating dual degree programs or dual appointment programs. But actually
(25:37):
in a lot of progress in academia happens by methodology too.
Write like a new you know, when when some methodology
gets adopted, I mean the most famous example of that
is a scientific method as an example of that. But
when you have a methodology that gets adopted, it also
provides a way to speak to your colleagues across different disciplines,
and I think what's happened in AI is linked to
(25:59):
that that within the context of the scientific method as
an example, the methodology about we about what we do discovery,
the role of data, the role of these neural networks,
of how we actually find proximity to concepts to one
another is actually fundamentally different than how we've traditionally applied it.
(26:19):
So as we see across more professions, people applying this
methodology is also going to give some element of common
language to each other. Right And in fact, you know,
in this very high dimensional representation of information that is
pressent to neural networks, we may find amazing adjacencies or
connections of themes and topics in ways that the individual
(26:41):
practitioners cannot describe, but yet will be latent in these
large cal neural networks. We are going to suffer a
little bit from causality, from the problem of like, hey,
what's the root cause of that? Because I think one
of the unsatisfying aspects that this methodology will provide is
they may give you answers for which they don't give
you good reasons for where the answers came from and uh,
(27:04):
and then there will be the traditional process of discovery
of saying, if that is the answer, what are the reasons?
So we're going to have to do this sort of
hybrid way of understanding the world. But I do think
that common layer of AI is a powerful new thing.
Speaker 3 (27:18):
Yeah. Well, a couple of random questions. I couldn't mind
as you talk. In the In the Writer's strike that
just ended in Hollywood, one of the sticking points was
how the studios and writers would treat AI generated content. Right,
good writers get credit if their material with somehow the
source for a but more broadly, did the writers need
(27:40):
protections against the use of I could go on. You
know what, probably we're familiar with all of this. Had
you been I don't know whether you were, but had
either side called you in for advice during that the writers,
had the writers called you and said, Daria, what should
we do about AI? And how should we that should
be ref how should that be reflected in our content
track negotiations? What would you've told.
Speaker 4 (28:01):
Them the way I think about that is that I died.
I would divide it into two pieces. First, is what's
technically possible, right, and anticipate scenarios like you know, what
can you do with voice cloning for example? You know, now,
for example it is possible there being dubbing right legist
take that topic right around the world. There was all
(28:22):
these folks that would dub people in other languages. Well,
now you can do these incredible rendering in some I mean,
I know if you've seen them, where you know you
match the lips is your original voice, but speaking any
language that you want. As an example, so busy that
has a set of implications around that. I mean, just
to give an example, So I would say, create a
taxonomy that describes technical capabilities that we know of today
(28:45):
and applications to the industry, and two examples of like, hey,
you know I could film you for five minutes and
I could generate two hours of content of you and
I don't have to you know, then if you get
paid by the hour, obviously I'm not paying you for
that other thing. So I would say technological capability and
then map with their expertise consequences of how it changes
the way they work or the way they interact or
(29:06):
the way they negotiate and so on. So that would
be one element of it, and then the other one
is like a non technology related matter, which is an
element of almost of distributed justice, is like who deserves
what right and who has the power to get what?
And then that's a completely different discussion. That is to say, well,
if this is the scenario of what's possible, you know,
what do we want and what are we able to get?
(29:29):
And I think that that's a different discussion, which is
which we all as.
Speaker 3 (29:32):
Life, which when do you do?
Speaker 4 (29:33):
First? I think it's very helpful to have an understanding
of what's possible and how it changes a landscape as
part of a broader discussion, right, and a broad negotiation
because you also have to see the opportunities because there
will be a lot of ground to say, actually, you know,
(29:53):
if we can do it in this way and we
can all be that much more efficient in getting this
piece work done on this but we have a reasonable
agreement about how we both sides benefit from it, right,
then that's a win win for everybody. Yeah, Right, So
that's I think that would be a golden triangle, right.
Speaker 3 (30:12):
Here's my reading, and I would like you to correct
me if I'm wrong, and I'm likely to be wrong.
When I looked at that strike, I said, if they're
worried about AI, the writers are worried about AI. That
seems silly. It should be the studios who are worried
about the economic impact of AI. Does it in the
long run AI put the studios out of business long
before it puts the writers out of business. I only
(30:34):
need the studio because the costs of production are as
high as the sky, and the cost of production are overwhelming.
And whereas if I don't, if I have a tool
which brings introduces massive technological efficiencies to the production of movies,
then I don't. Why don't need a studio? Why would
they the scared ones?
Speaker 4 (30:53):
Or maybe maybe you need like a different kind of
studio or a different kind of different kind of study.
Speaker 3 (30:57):
But I mean the in the but in in this strike,
the fright the frightened ones with the writers and the
you know, with the studios. Wasn't that backwards?
Speaker 4 (31:09):
I haven't thought about it. Uh, it can be about
the implications of it. It goes back to we're talking
before the implications because are so horizontal. It is right
to think about it, like what does it do to
the studios as well? Right?
Speaker 3 (31:19):
Yeah?
Speaker 4 (31:21):
But then you know, the reason why that happens is
that it's the order of either negotiations or who first
got concerned about it and did something about it, right,
which is in the context of the strike. You know,
I don't know what the equivalent conversations are going inside
the studio and whether they have a workroom saying what
this is going to mean to us? Right, but it
(31:41):
doesn't get exercise through a strike, but maybe through a
task force inside you know, the companies about what are
they going to do? Right?
Speaker 3 (31:47):
Well, And to go back to your thing you said,
the first thing you do is you make a list
of what technological capabilities are. But don't technological capabilities change
every I mean they do. You're raising ahead so fast,
so you can't. Can you you have a contract? I'm
sorry for getting into little weeds here, but this is interesting.
Can you you can't have a five year contract if
(32:07):
the contract is based on an assessment of technological capabilities
in twenty twenty three, because by the time get to
twenty eight, twenty three eight, it's totally.
Speaker 4 (32:18):
Different, right, yeah, But like you know, I mean where
I was going is like there are some abstractions around
that is like, you know, one can we do with
my image?
Speaker 3 (32:28):
Right?
Speaker 4 (32:28):
Like if I generally get the category that my image
can be reproduced, generated contents and so on, it's like,
let's talk about the abstract notion about who has rights
to that or do we both get to benefit from that?
If you get that straight, Yes, the nature of how
the image gets alter created as something will change underneath,
but the concept will stay the same. And so I
(32:48):
think it's what's important is to get the categories right.
Speaker 3 (32:51):
Yeah. Yeah, If you just think about the biggest technological
revolutions of the post war era last seventy five years,
you can all come up with a list. Actually, it's
really fun to come up with the list. I was
thinking about this when we were you know, containerized shipping
is my favorite, the green revolution, the internet is Where
(33:17):
is the I in that list?
Speaker 4 (33:21):
So I would put it first in that context that
you put forth over since World War Two, undoubtedly, like
computing as a category is one of those trajectories that
has reshaped right or world. And I think we think computing,
I would say the role that semiconductors have had has
(33:41):
been incrowdly defining. I would say AI is the second
example of that as a core architecture that is going
to have an equivalent level of impact. And then the
third leg I would put to that equation will be
quantum and quantum information. And that's sort of like I
like to summarize that the future of computing it's bits,
neural and cubits, and is that idea of high precision computation,
(34:04):
the world of neural networks and artificial intelligence, and the
world of quantum and the combination of those things is
going to be the defining force over the next hundred
years in that category of computing. But it makes a
list for sure.
Speaker 3 (34:15):
If it's that high up on the list. This is
a total hypothetical. Would you if you were starting over,
if you're starting IBM right now, would you say, oh,
our AI operations actually should be way bigger, Like how
many how many thousands of people working for you?
Speaker 4 (34:32):
So within the research division it's about like three thousand,
five hundred scientists.
Speaker 3 (34:37):
So in a perfect world, would you if it's that big,
isn't that too small as a group?
Speaker 4 (34:42):
Yeah, Well, that's like in the RICAR division. I mean
IBM overall, But I mean.
Speaker 3 (34:48):
Like, so starting from first, so you have a you
we've got a technology that you're ranking with Compute and
you know, up there with as in terms of a
world changer. Are we So what I'm basically asking is
are we underinvested in this huge you know?
Speaker 4 (35:07):
But so so yeah, it's a good question. So like
what I would say is that I think we should
segment how many people do you need on the creation
of the technology itself, and what is the right size
of research and engineers and compute to do that? And
how many people do you need in the sort of
application of the technology to create better products, to deliver
(35:29):
services and consulting and then ultimately to diffuse it through
you know, sort of all feheres of society. And the
numbers are very different, and that is not different than
anywhere else. I mean, I mean, if you give examples
of since you were talking about in context of World
War two, how many people does it take to create,
you know, an atomic weapon as an example, it's a
large number. I mean, it wasn't just Los animals. There's
(35:51):
a lot of people in Okay, it's a large number,
but it wasn't a million people, right, So you could
have highly concentrated teams of people that, with enough resources,
can do extraordinary scientific and technological achievements. And that's always,
by definition, is going to be a fraction of like
one percent compared to the total volume that is going
to require to then deal with it.
Speaker 3 (36:11):
Yeah, but the application side is infinite almost.
Speaker 4 (36:14):
That's exactly. So that is where like in the end
the bottleneck really is. So with you know, thousands of
scientists and engineers, you can create world class AI, right,
And so no, you don't need ten thousand to be
able to create the large language model and the generatic
model and some but you need thousands, and you need
you know, very significant amount of computer and data. You
(36:35):
need that The rest is Okay, I build software, I
build databases, or I build a software product that allows
you to do inventory management, or I build you a
photo editor and so on. Now that product incorporating the AI, modifying,
expanding it and so on. Well, now you're talking about
the entire software industries. So now you're talking about millions
(36:57):
of people right who are nested, you know, who are
required to bring AI into their product. Then you go
on a step beyond the technology creators in terms of software,
and you say, well, okay, now what the skills to
help organizations go undeployed in the department of you know,
the interior, right, And then I said, okay, well, now
you need like consultants and experts and people to work
(37:19):
they are to integer into the workflow. So now you're
talking into the many tens of millions of people around that.
So I see it as these concentric circles of it,
but to some degree in many of these core technology areas,
just saying like well, I need a team of like
one hundred thousand people to create like AI or a
or a new transistor or a new quantum computer. It's
actually a diminished in return right in the end, like
(37:40):
too many people connecting with each other's very difficult.
Speaker 3 (37:42):
But on the application side of just to go back
to our example of that college, just the task of
sitting down with a faculty and working with them to
reimagine what they do with these new set of tools
in mind, and with the understanding that the students coming
in are probably going to know more about it than
(38:03):
they do that a lot, I mean, that's a that
is a curricullion people problem.
Speaker 4 (38:09):
It's a people problem. Yeah, that's why I started in
terms of the barriers of adoption of that. I mean
the context of IBM an example, that's why we have
a consulting organization, IVAN Consulting that complements ib AND technology,
and the IVAN Consulting Organization has over one hundred and
fifty thousand employees because of this question, right, because you
have to sit down and you say, Okay, what problem
(38:30):
are you trying to solve, what is the methodology we're
going to do, and here's the technology options that we
have to be able to bring into the table. In
the end, the adoption across or society will be limited
by this part. The technology is going to make it easier,
more cost effective to implement those solutions, but you first
(38:51):
have to think about what you want to do, how
you're going to do it, and how are you going
to bring it into a life of this in this
context faculty member or you know, the administrator and so
on in these colleges.
Speaker 3 (39:01):
With that Hollywood that that notion I thought, which was
absolutely I thought really interesting that in a Hollywood strike
you have to have this conversation about a distributive justice,
conversation about how do we that's it's a really hard conversation,
right to have. And uh so this brings me to
my nee, which is that you we were talking about
stage you have. You have two daughters, one in college,
(39:25):
one about to go to college. That's right, so they're
both science minded. So tell me about the conversations you
you have with your daughter. You have a unique conversation
with your daughters because your conversation, your advice to them
is is influenced by what you do for a living.
Speaker 4 (39:42):
Yes, it's true.
Speaker 3 (39:43):
So did you warn your daughters away from certain fields?
Did you say whatever you do, don't be you know, no.
Speaker 4 (39:52):
No, no, that's not my style. I mean for me, no,
I try not to be like you know, preachy about that.
So for me, it was just about by example of
things I love, right and things I care about, and
then you know, bringing them to the lab and seeing
things and then the natural conversations of things working on
or interesting people I meet. So to the extent that
(40:13):
they have chosen that and obviously this has an influence
on them. It has been through seeing it, you know,
perhaps through my eyes, right, and what do you see
me do? And that I like my profession, right.
Speaker 3 (40:22):
But one of your daughters. You said, is thinking that
she wants to be a doctor. But being a doctor
in a post AI world it's surely a very different
proposition than being a doctor in a PREAI world. Do
you think have you tried to prepare her for that difference?
Have you explained to her what you think will happen
to this profession she might enter.
Speaker 4 (40:42):
Yeah, I mean not in like, you know, incredible amount
of detail, but yes, at the level of understanding what
is changing, like this lens of the information, lens with
which you can look at the world and what is
possible and what it can do, Like what is our
role and what is all of the technology and how
that shapes At that level of abstraction, for sure, but
(41:04):
not at the level of like, don't be a radiologist,
you know, because this is what we want for you.
Speaker 3 (41:08):
I was gonna say, if you're not't happy with your
current job, you could do a podcast called Parenting Tips
with Dario, which is just an AI person gives you
advice on what your kids should do based on exactly this,
Like should I be a readiologist? Dario tell me, like
it seems to be a really important question. Yeah, let
me ask this question in a more I'm joking, but
in a more serious way. Surely it would if I
(41:32):
don't mean to use your daughter as an example. But
let's imagine we're giving advice to somebody who wants to
enter medicine. A really useful conversation to have is what
are the skills that are will be most prized in
that profession fifteen years from now, and are they different
from the skills that are prized now. How would you
answer that question?
Speaker 4 (41:53):
Yeah, I think for example, this goes back to how
is this scientific method in this context, like the practice
of medicine going to change? I think we will see
more changes on how we practice a scientific method and
so on as a consequence of what is happening with
the world of computing and information, how we represent information,
how we represent knowledge, how we extract meaning from knowledge
(42:17):
as a method than we have seen in the last
two hundred years. So therefore, what I would like strongly
encourage is not about like, hey, use this tool for
doing this or doing that, but in the curriculum itself,
in understanding how we do problems solving in the age
of like data and data representation and so on, that
needs to be embedded in the curriculum of everybody you
(42:39):
know that is I would say, actually quite horizontally, but
certainly in the context of medicine and scientists and so on,
for sure, And to the extent that that gets ingrained,
that will give us a lens that no matter what
specialty they go within medicine, they will say, actually, the
way I want to be able to tackle improving the
quality of care, the way to do that is in
(43:00):
addition to all the elements that we have practiced in
the field of medicine. Is this new lens? And are
we representing the data the right way? Do we have
the right tools to be able to represent that knowledge?
Am I incorporating that in my own so with my
own knowledge in a way that gives me better outcomes? Right?
Do I have the rigor of benchmarking too? And quality
(43:20):
of the results? So that is what needs to be incorporated.
Speaker 3 (43:23):
How in a perfect world, if I asked you your
team to rewrite curriculum for American medical schools, how dramatic
a revision is that? Are we tinkering with ten percent
of the curriculum or we tinkering with fifty percent of it?
Speaker 4 (43:41):
I think they would be a subset of classes. That
is about the method the methodology, what has changed, like
have these lens of it to understand and then within
each class that methodology will represent something that is embedded
in it. Right, Well, it will be substantive but not
(44:02):
but doesn't mean replacing the specialization and the context and
the knowledge of each domain. But I do think everybody
should have sort of a basic knowledge of the horizontal, right,
what is it, how does it work? What tools you have,
what is the technology, and like you know what are
the dos and don'ts around that? And then every area
you say, and you know that thing that you learn,
this is how it applies to anatomy, and this is
(44:24):
how you know how it applies to you know, radiology
if you studying that, or or this is how you
apply you know, in the context of discovery right of
self structure and this is how we can use it,
or protein folding and this is how it does so
that way you'll see a connecting tissue through throughout the
whole thing.
Speaker 3 (44:40):
Yeah, I mean I would add to that because I
was sticking it the sch that it's also this incredible
opportunity to do what doctors are supposed to do but
don't have time to do now, which is they're so
consumed with figuring out what's wrong with you that they
have little, little time to talk about the implications of
(45:01):
the diagnosisness and what we really want to if we
can freedom of some of the burden of what is
actually quite a prosaic question of what's wrong with you
and leave the hard human thing of let make should
you be scared or hopeful? Should you you know? What
do you need to do?
Speaker 4 (45:18):
Or what?
Speaker 3 (45:19):
Let me put this in the context of all the
patients I've seen, that conversation, which is the most important one,
is the one that seems to me so like, if
I had to, I would add, if we're reimagining the
curriculum of med school, I'd like, with whatever this, by
the way, very little time. Maybe we have to add
two more years to med school, but like.
Speaker 4 (45:38):
A whole.
Speaker 3 (45:40):
But the whole thing about bringing back the human side
of you know, now, if I can give you ten
more minutes, how do you use that ten more minutes in.
Speaker 4 (45:52):
That in that reconceptualization that you just did is what
we should be doing around that, Because I think the
debate as to like, well, I'm I need doctors or
not it's actually a not very useful debate. But rather
this other question is how is your time being spent?
What problems are you getting stuck? I mean I generalize
this by like the obvious observation that if you look
around in your professions, in our daily lives, we have
(46:14):
not run out of problems to solve. So as an
example of that is, hey, if I'm spending all my
time trying to do diagnosis, and I could do that
ten times faster, and it allows me actually to go,
you know, and take care of the patients and all
the next steps of what we have to do about it.
That's probably a trade off that a lot of doctors
would take, right. And then you say, well, you know,
to what degree that does it allow me to do that?
(46:35):
And I can do these other things and these other
things are critically important for my profession around that. So
when you actually become less abstract and like we get
past the futile conversation of like, oh, there's no more
jobs and I'm going to take it all of it,
which is kind of nonsense, is you go back to say,
in practice in your context, right, for you, what does
(46:56):
it mean? How do you work? What can you do
differently around that. Actually, that's a much richer conversation, and
very often we would find ourselves that there's a portion
of the work we do that we say I would
rather do less of that. This is this other part
I like a lot, And if it is possible that
technology could help us make that trade off, I'll take
it in a heartbeat. Now, poorly implemented technology can also
(47:17):
create another problem. You say, Hey, this was supposed to
solve me things, but the way it's being implemented is
not helping me, right, it's making my life more more miserable,
or so on, or I've lost connection in how I
used to work, et cetera. So that is why design
is so important. That is why I also workflow is
so important in being able to solve these problems. But
(47:39):
it begins by, you know, going from the intergalactic to
the reality of it, of that faculty member in the
Liberal Arts college or you know, or a you know,
a practitioner in medicine in a hospital and what it
means for them. Right.
Speaker 3 (47:53):
Yeah. What struck me AREIO throughout our conversation is how
much of this revolution and is non technical? Is to say,
you guys are doing the technical thing here, but the
real the revolution is going to require a whole range
of people doing things that have nothing to do with software,
that have to do with working out new new human arrangements.
(48:16):
Talking about that, I mean, does keep coming back to
the Hollywood strike thing that you have to have a
conversation about our values. Is creators of of of of movies?
How are we going to divide up the exactly credit
and the like. That's a that's a conversation about philosophy,
and you know it is.
Speaker 4 (48:37):
And is it's in the grand tradition of why you know,
a liberal education is so important in the broadest possible sense, Right,
there's no common conception of the good, right, that is
always a contested dialogue that happens within our society. And
technology is going to fit in that context too, right.
(48:57):
So that's why I personally, as a philosophy I'm not
a technological determinists, right, And I don't like when colleagues
in my profession right starts saying like, well, this is
the way the technology is going to be, and by consequence,
this is how society is going to be. I'm like,
that's a highly contested goal. And if you want to
enter into realm of politics or the real other ones,
go and stand up on a stool and discuss with it.
(49:19):
That's what society wants. You will find that it's a
huge diversity of opinions and perspective and that's what makes
you know, you know, in a democracy, the richness of
our society. And in the end, that is going to
be the centerpiece of the conversation what do we want?
You know, who gets what? And so on? And that
is actually I don't think it's anything negative. That's acid
(49:39):
should be because in the end is anchor of who
we want as humans, you know, you know, as friends, family, citizens,
and we have many overlapping sets of responsibilities, right and
as a technology creator, my only responsibility is not just
as a scientist and a technology creator. I'm also a
member of family. I'm a citizen, and I'm many other
things that I care about. And I think that that
sometimes the debate of the technological determinists they start now
(50:04):
budding into what is the realm of you know, justice
and you know, in society and philosophy and democracy, And
that's where they get the most uncomfortable because it's like
I'm just telling you, like you know what's possible, and
when there's pushback, it's like, yeah, but now we're talking
about how we live and how we work and how
(50:27):
much I get paid or not paid. So that technology
is important. Technology shapes that conversation, but we're going to
have the conversation with a different language, as it should be,
and technologies need to get accustomed to if they want
to participate in that world with the broad consequences. Hey,
get a custom to deal with the complexity of that
world of politics, society, institutions, unions, all that stuff. And
(50:51):
you know, you can be like whiny about it. It's
like they're not adopting my technology. That's what it takes
to bring technology into the world.
Speaker 3 (50:58):
Yeah, well said, thank you Dario for this wonderful conversation.
Thank you to all of you for coming and listening,
and thank you.
Speaker 4 (51:10):
Thank you.
Speaker 3 (51:14):
Dario gild transformed how I think about the future of AI.
He explained to me how huge of a leap it
was when we went from chess playing models to language
learning models, and he talked about how we still have
a lot of room to grow. That's why it's important
that we get things right. The future of AI is
impossible to predict, but the technology has so much potential
(51:38):
in every industry. Zooming into an academic or medical setting
showed just how close we are to the widespread adoption
of AI. Even Hollywood is being forced to figure this out.
Institutions of all sorts will have to be at the
forefront of integration in order to unlock the full power
of AI thoughtfully and responsibly. Humans have the power and
(52:01):
the responsibility to shape the tech for our world. I
for one, I'm excited to see how things play out.
Smart Talks with IBM is produced by Matt Romano, Joey Fishground,
David jaw and Jacob Goldstein. We're edited by Lydia Jane Kott.
Our engineers are Jason Gambrel, Sarah Bruguier, and Ben Holliday.
(52:24):
Theme song by Gramoscope. Special thanks to 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 Ruby Studio at iHeartMedia. To
find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
(52:46):
or wherever you listen to podcasts. I'm Malcolm Gladwell. This
is a paid advertisement from IBM.
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It
is a new season of the Smart Talks with IBM
podcast series.
Speaker 2 (00:09):
Today we are witnessed to one of those rare moments
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Speaker 1 (00:57):
All right, Welcome everybody, you guys excited, here we go.
Speaker 3 (01:04):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries. iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
we're continuing our conversations with new creators visionaries who are
creatively applying technology and business to drive change, but with
a focus on the transformative power of artificial intelligence and
(01:25):
what it means to leverage AI as a game changing
multiplier for your business. Today's episode is a bit different.
I was recently joined on stage by Dario Gill for
a conversation in front of a live audience at the
iHeartMedia headquarters in Manhattan. Dario is the senior vice president
and director of IBM Research, one of the world's largest
(01:47):
and most influential corporate research labs. We discussed the rise
of generative AI and what it means for business and society.
He also explained how organizations that leverage AI to create
value will dominate in the near future. Okay, let's get
on to the conversation. Hello everyone, welcome, and I'm here
(02:11):
with doctor Dario Gil and I wanted to say before
we get started. This is something I said backstage that
I feel very guilty today because you're the you know,
you know, arguably one of the most important figures in
AI research in the world, and we have taken you
away from your job for a morning. It's like, if
(02:33):
you know Oppenheimer's wife in nineteen forty four said let's
go and have a little getaway in the Bahamas. It's
that kind of thing. You know, what do you say
to your wife, I can't we have got to work
on this thing I can't tell you about. She's like
getting me out of Los Alamos. No, So I do
feel guilty. We've set back AI research by by about
(02:56):
four hours here. But I wanted to you've been up
with with ibo for twenty years, twenty years this summer.
So and how old were you when you Not to
give away your age, but you were how old when
you started?
Speaker 4 (03:08):
I was twenty eight?
Speaker 3 (03:09):
Okay, yeah, So I want to go back to your
twenty eight year old self. Now, if I asked you
about artificial intelligence, I asked twenty eight year old Dario,
what does the future hold for AI? How quickly will
this new technology transform our world? Et cetera, et cetera.
What would twenty eight year old Darigo said?
Speaker 4 (03:28):
Well, I think the first thing is that even though
AI as a feel has been with us for a
long time, since the mid nineteen fifties, at that time,
AI was not a very polite word to say, meaning
within the scientific community, people didn't use sort of that term.
They would have said things like, you know, maybe I
do things relate to machine learning, right, or statistical techniques
(03:50):
in terms of classifiers and so on. But AI had
a mixed reputation, right, it had gone through different cycles
of hype, and it's also if moments of you know,
a lot of negativity towards it because of lack of success.
And so I think that would be the first thing
we probably say, like AI is like what is that? Like,
(04:10):
you know, respectable scientists are not working on AI the
finest side and that really changed over the last fifteen years. Only, right,
I would say, with the advent of deep learning over
the last decade, is when that re enter again the
lexicon of saying AI and that that was a legitimate
thing to work on. So I would say that that's
the first thing I think we would have noticed a
contrast twenty years ago.
Speaker 3 (04:31):
Yeah, So what point in your twenty year tenure at
IBM would you say you kind of snapped into present
kind of wow mode.
Speaker 4 (04:42):
I would say in a late two thousands, when IBM
was working on the Jeopardy project and just seeing the
demonstrations of what could be done in question answering.
Speaker 3 (05:00):
It's literally Jeopardy. Is this crucial moment in the history
of YEA.
Speaker 4 (05:05):
You know, there had been a long and wonderful history
inside IBM on AI. So for example, like you know,
in terms of like these grand challenges at the very
beginning of the field founding, which is this famous Dartmouth
conference that actually IBM sponsored h to create. There was
an IBM and there called Nathaniel Rochester, and there were
(05:27):
a few others who right after that they started thinking
about demonstrations of this field. And then for example, they
created the first you know game to play checkers and
to demonstrate that you could do machine learning on that.
Obviously we saw later in the nineties like chess that
was a very famous example of that, Deep Blue with
Deep Blue right and playing with Caspar and then but
(05:49):
I think the moment that was really those other ones
felt like, you know, kind of like brute force anticipating,
sort of like moves ahead. But this aspect of dealing
with language and question answering felt different, and I think
for for us internally and many others, was when a
moment of saying like, wow, you know, what are the
possibilities here? And then soon after that connected to the
(06:09):
sort of advancements in computing and with deep learning. The
last decade has just been an all out, you know,
sort of like front of advancements and that, and I
just continue to be more and more impressed. And the
last few years have been remarkable too.
Speaker 3 (06:21):
Yeah. So I'll ask you three quick conceptual questions before
we dig into it, just so I sort of get
a we all get a feel for the shape of AI.
Question Number one is where are we in the evolution
of this? So you know the obvious question. We we're
all suddenly aware of it, we're talking about it. Can
(06:43):
you give us an analogy about where we are in
the kind of likely evolution of this is a technology.
Speaker 4 (06:50):
So I think we're on a significant inflection point that
it feels the equivalent of the first browsers when they
appear and people imagine the possibilities of the Internet or
more imagined experience the internet. The Internet had been around,
right for quite a few decades. AI has been around
for many decades. I think the moment we find ourselves
(07:12):
is that people can touch it and they can Before
they were a systems that were like behind the scenes,
like your search results or translation systems, but they didn't
have the experience of like, this is what it feels
like to interact with this thing. So that's what I mean.
I think maybe that analogy of the browser is appropriate
because it's all of a sudden, it's like whoa, you know,
these network of machines and content can be distributed and
(07:35):
everybody can self publish, and there was a moment that
we all remember that, and I think that that is
what the world has experience over the last nine months
or so on. So but fundamentally, also what is important
is that this moment is where the ease of the
number of people that can build and use AI has skyrocketed.
So over the last decade, you know, technology firms that
(07:58):
had large research teams could build AI that worked really well, honestly,
but when you went down into say hey can everybody
use it? Can a data science team in a bank,
you know, go and develop these applications, it was like
more complicated. Some could do it, but it was more
the barrier of entry was high. Now is very different
because of foundation models and the implications that that has.
Speaker 3 (08:20):
For at the moment where the technology is being democratized.
Speaker 4 (08:23):
In demarketized, frankly, it works better for classes of problems
like programming and other things. Is really incredibly impressive what
it can do. So the accuracy and the performance of
it is much better, and the ease of use and
the number of use cases we can pursue it much bigger,
So that democratization is a big difference.
Speaker 3 (08:40):
But when you say, when you make it an analogy
to the first browsers, if we do another one of
these time travel questions back at the beginning of the
first browsers, it's safe to say many of the potential
uses of the Internet and such we hadn't even begun.
We couldn't even anticipate, right, Right, So we're at the
point where the future direction is largely unpredictable.
Speaker 4 (09:03):
Yeah, I think that that is right. Because it's such
a horizontal technology that the intersection of the horizontal capability,
which is about expanding our productivity and tasks that we
wouldn't be able to do efficiently without it, has to
marry now the use cases that reflect the diversity of
human experience, our institutional diversity. So as more and more
(09:23):
institutions said, you know, I'm focused on agriculture, you know,
to be able to improve seeds. You know, in these
kinds of environments, they'll find their own context in which
that matters that the creators of a I did not
anticipate at the beginning. So I think that that is
then the fruit of surprises will be like why I
wouldn't even think that it could be used for that.
And also clever people will create new business models as
(09:44):
associated with that, like it happened with the Internet of
course as well, and that will be its own source
of transformation and change in its own right. So I
think all of that is yet to unfold. Right, what
we're seeing is this catalyst moment of technology that works
well enough and it can be democratized.
Speaker 3 (10:00):
Yeah, what next sort of conceptual question? You know, we
can loosely understand or categorize innovations in terms of their
impact on the kind of balance of power between haves
and have nots. Some innovations, you know, obviously favor those
who already have make the rich richer. Some some it's
(10:24):
arising to tie the lifts all boats, and some bias
in the other direction. They close the gap between is
it possible to say to predict which of those three
categories AI might fall into.
Speaker 4 (10:38):
It's a great question, you know. A first observation I
would make on your first two categories is that it
will be both likely be true that the use of
AI will be highly democratized, meaning the number of people
that have access to its power to make improvements in
terms of efficiency and so on will be fairly universal,
(10:59):
and that the ones who are able to create AI
uh may be quite concentrated. So if you look at
it from the lens of who creates wealth and value
over sustained periods of time, particularly it's saying a context
like business, I think just being a user of AI
technology is an insufficient strategy and UH. And the reason
(11:22):
for that is like, yes, you will get the immediate
productivity boost of like just making API calls, and you
know that would be a new baseline for everybody, but
you're not accruing value in terms of representing your data
inside the AI in way that gives you a sustainable
competitive advantage. So I always try to tell people is
don't just be an AI user. We an you know,
AI value creator. And I think that that will have
(11:45):
a lot of consequences in terms of the haves and
have nots as an example, and that will apply both
to institutions and regions and countries, et cetera. So I
think it would be kind of a mistake, right to
just develop strategies that are just about.
Speaker 3 (12:00):
Usage, but to to come back that question from them
to give you a specific suppose I'm a I'm an
industrial farmer in Iowa with ten million dollars of equipment
and move and I'm comparing it to a subsistence farmer
someone in the developing world who's got a cell phone.
(12:20):
Over the next five years, who's who's well being rises
by a greater amount.
Speaker 4 (12:28):
Yeah, I think, I mean, it's a it's a good question,
but it might be hard to do a one to
one sort of like attribution to just one variable in
this case, which is AI. But again, provided that you
have access to a phone, right and some kind to
you know, be able to be connected. I do think
so for example, in that context we've developed we don't
(12:48):
work with NASA as an example, to build your spatial
models using some of these new techniques, and I think
for example, or ability to do flood prediction, I'll tell
you an advantage of why would be a democratization force
that context. Before to build a flowed model based on
satellite imagery was actually so onerous and so complicated and
difficult that you would just target to very specific regions
(13:10):
and then obviously countries prioritize their own right. But what
we've demonstrated is actually you can extend the technique to
have like global coverage around that. So in that context,
I would say it's a four stores and markeratization that
everybody sort of would have access if you have some connectivity,
as today.
Speaker 3 (13:26):
Iowa farmer might have a flood model. The guy in
the developing world definitely didn't, and now he's a shot
of getting one.
Speaker 4 (13:32):
Yeah, but now it has a shot of getting one.
So there's aspects of it that so long as we
provide connectivity and access to it, that they can be
democratization forces. But I'll give you another example that that
can be quite concerning, which is language. Right, So there's
so much language in the you know, in English, and
there is sort of like this reinforcement loop that happens
(13:53):
that the more you concentrate, because it has obvious benefits
for global communication and standardization, the more you can enrich
like base aim models based on that capability. If you
have very resource cars languages, you tend to develop less
powerful AI with those languages and so on. So one
has to actually worry and focus on the ability to
(14:14):
actually represent you know that in that case is language
as a piece of culture. Also in the AI sets
that everybody can benefit from it too. So there's a
lot of considerations in terms of equity about the data
and the data sets that we accrue and what problems
are we trying to solve. I mean, you mentioned agriculture
or healthcare and so on. If we only solve problems
(14:35):
that are related to marketing as an example, that would
be a less rich world in terms of opportunity that
if we incorporate many many other broad set of problems.
Speaker 3 (14:44):
Who do you think what do you think are the
biggest impediments to the adoption of AI as you would
like as you think AIR to be adopted. I mean,
if you look, what are the sticking points that you would.
Speaker 4 (14:57):
Look Indiana, I'm going to give a non time technological
answer as a first one has to do with workflow, right,
So even if the technology is very capable, the organizational
change inside a company to incorporate into the natural workflow
of people and how we work is it's a lesson
we have learned over the last decade is hugely important.
(15:18):
So there's a lot of design considerations. There's a lot
of how do people want to work right? How do
they work today? And what is the natural entry points
for AI? So that's like number one, and then the
second one is, you know, for the broad value creation
aspect of it is the understanding inside the companies of
how you have to curate and create data to combine
(15:42):
it with external data says that you can have powerful
AI models that actually fit your need. And that aspect
of what it takes to actually create and curate the
data for this modern AI, it's still working progress, right.
I think part of the problem that happens very often
when I talk to institutions is that they say yea yeah, yah, yeah,
(16:03):
I'm doing it. I've been doing it for a long time.
And the reality is that that answer can sometimes be
a little of our cop out, right, is like I
know you were doing machine learning, you were doing some
of these things, but actually the leader's version of AI,
what what's happening with foundation models? Not only is it
very new. It's very hard to do, and honestly, if
you haven't been you know, assembling very large teams and
(16:25):
spending hundreds of millions of dollars of compute, and so
you're probably not doing it right. You're doing something else
that is in the broad category. And I think the
lessons about what it means to make this transition to
this new wave is still in early phases of understanding.
Speaker 3 (16:39):
So what would you say? I want to give you
a couple of examples of people with kind of real
world in real world positions of responsibility. Imagine I'm sitting
right here, So imagine that I am the president of
a small liberal arts college and I come to you
and I say, Dario, I keep hearing about a AI
my college has you know I don't make it. You know,
I'm I'm not I'm making this much money. If that
(17:01):
every year enroments declining, I feel like this maybe is
an opportunity. What is the opportunity for me? What would
you say?
Speaker 4 (17:11):
So, it's probably in a couple of segments around that.
Right one has to do is well, what is the
implications of this technology inside the institution itself instead of
the college, And how we operate and can we improve
for example, efficiency, like if you have in very low
levels of sort of margin to be able to reinvest
(17:32):
is you know you run it, you run you know infrastructure,
you run many things inside the college. What are the
opportunities to increase the productivity or automate and drive savings
such that you can reinvest that money into the mission
of education?
Speaker 3 (17:46):
Right as an example, So number one is operational efficiency.
Speaker 4 (17:49):
Operational efficiency is a big one. I think the second
one is within the context of the college, there's implications
for the educational mission on its own, right, How will
you know how does a correct need to evolve or not?
What are acceptable use policies or of someone these ai
I think we've all read a lot about like what
can happen in terms of exams and so on and
cheating and not cheating, or what are the actually positive
(18:11):
elements of it in terms of how curriculum should be
developed and professions sustain around that. And then there's another
third dimension, which is the outdoor oriented element of it,
which is like prospect students right, so, which is frankly speaking,
a big use case that is happening right now, which
in the broader industry is called customer care or client
care or citizen care. So in this question will be education,
like you know, hey, are you reaching the right students
(18:34):
around that that may apply to the college. How can
you create them? For example, an environment to interact with
the college and answering questions that could be a chat
bought or something like that to learn about it. And personalization.
So I would say there's like at least three lenses
with which I would give advice, right.
Speaker 3 (18:49):
The positive seglee because it's really interesting. So I really
can't as sign an essay anymore? Can I?
Speaker 4 (18:58):
Can I sign an essay?
Speaker 3 (19:00):
Can I say? Rend me a research paper? And come
back to being three?
Speaker 4 (19:03):
We?
Speaker 3 (19:03):
Can I do that anymore?
Speaker 4 (19:05):
I think you can?
Speaker 3 (19:05):
How do I do that?
Speaker 4 (19:06):
And then you can that Look, there's there's two questions
around that. I think that if one goes and explains
in the context like what is it? Why are we here?
Why are in this class? What is the purpose of this?
And and one starts with assuming like an element of
like decency and people are people are there like to
learn and so on, and you just give it this disclaimer. Look,
(19:27):
I know that one option you have is like just
you know, put the essay question and click, go and
like and give an answer. You know, but that is
not why we're here, and that is not the intent
of what we're trying to do. So first I would
start with the sort of like the norms of intent
and decency and appeal to those as step number one.
Then we all know that there will be a distribution
(19:48):
of use cases of people like that will come in
one year and come out of the other and do that.
And so for a subset of that, you know, I
think the technology is going to have all in such
a way that we will have more and more of
the to discern, right, you know, when that has been
AI generated right and uncreated, it won't be perfect, right,
But there's some elements that you can imagine in putting
(20:09):
the essay and you say, hey, this is likely to
be generated right around that. And for example, one way
you can do that, just to give you an intuition,
you could just have an essay that you write with
pencil and paper. At the beginning, you get a baseline
of what you're writing is like, and then later when
you you know generate it, there will be obvious differences
around what kind of writing has been generating on the
(20:30):
other way.
Speaker 3 (20:30):
But you've turned it's everything you're describing makes sense put
it greatly in this respect, at least, it seems to
greatly complicate the life of the teacher, whereas the other
two use cases seem to kind of clarify and simplify
the role. Right suddenly, you know, reaching student perspective students,
(20:51):
sounds like I can do that much more kind of
efficient in a lite. Yeah, I can bring you administration costs,
but the teaching thing is tricky.
Speaker 4 (20:58):
Well, until we developed the new norms, right, I mean again,
I mean, I know it's not abuse analogy, but calculators
we deal. We've done with that too, right, And it says, well, calculator,
what is the purpose of math? How are we going
to do this?
Speaker 3 (21:10):
And so can I tell you my dad's calculator story?
Speaker 4 (21:14):
Yes? Please.
Speaker 3 (21:14):
My father was a mathematician, taught mathematics at University of Waterloo, Agada,
and in the seventies when people started to get pocket calculators,
his students demanded that they'd be able to use them,
and he said no, and they took him to the
administration and he lost. So he then changed completely throughout
all of his old exams and introduced new exams where
(21:37):
there was no calculation. It was all like deep think,
you know, figure out the problem on a conceptual level
and describe it to me. And they were all students
deeply unhappy that he'd made their lives for computation.
Speaker 4 (21:49):
But it's to other.
Speaker 3 (21:51):
Point, to your point, I mean, he probably the result
was probably a better education. He just removed the element
that they could gain with their pocket calculators. I suppose
it's a version of.
Speaker 4 (22:02):
I think it's a version of that, And so I
think they will develop the equivalent of what your father did.
And I think people say, you know what if like
these kinds of things, everybody's doing it generically and none
of us have any meaning because all you're doing is
pressing buttons, and like the intent of this was something
which was to teach you how to write or to
think or something. There may be a variant of how
we do all of this. I mean, obviously some version
of that that has happened is like Okay, we're all
(22:22):
going to sit down and doing with pencil on paper
and computers in their classroom. But there'll be other variants
of creativity that people will put forth to say, you
know what, you know, that's a way to solve that
problem too.
Speaker 3 (22:32):
But this is interesting because to stay on this analogy,
we're really talking about a profound rethinking. Just using college
as an example, a real profound rethinking of the way,
there's no part of this college it's unaffected by aia B.
In one case, I've made everyone's job easier. In one case,
(22:53):
I've made I'm asking us to really rethink from the
ground up what teaching means. In another case, I've automated
systems that I didn't think of it. I mean, it's like,
that's right, that's it's not that's a lot to ask
someone who got a PhD in medieval language literature, you know,
forty years ago.
Speaker 4 (23:11):
Yeah. But you know, I'll tell you a positive sort
of development that I'm seeing the sciences around this, which
is you're seeing as you see more and more examples
of applying AI technology within the context of like historians
to as an example, right, you have archival and you know,
and you have all these books and being able to
actually help you as an assistant right around that, but
(23:32):
not only with text now, but with diagrams, right, And
I've seen it in anthropology too, Rite and archaeology with
examples of engravings and translations and things that can happen.
So so as you see in diverse fields people applying
these techniques to advance and how to do physics or
how to do chemistry, they inspire each other, right, and
(23:53):
they said, you know, how does it apply actually to
my area? So once as that happens, it becomes less
of a chore of like my god, you know, how
do I have to deal with this? But actually it's
triggered by curiosity, is triggered by you know, they'll be like,
you know, faculty that will be like you know what,
you know, let me explore what this means for my area,
and they will adapt it to the local context, to
the local you know, language, and the professional itself. So
(24:16):
I see that as a positive vector that is not
all going to feel like homework, you know, it's not
going to feel like, oh my god, this is so overwhelming,
but rather to be very practical to see what works,
What have I seen others to do that is inspiring,
and what am I inspired to do? You know what?
What is how is this going to help my career?
I think that that's going to be an interesting question
for for you know, those faculty members for the.
Speaker 3 (24:37):
Students, the professional Sorry, I'm gonna stick with this example
along because it's really interesting. I'm curious, following up on
what you just said, that one of the most persistent
critiques of academic but also of many of many corporate
institutions in these years, has been siloing. Right, Different parts
of the of the organization are going off on their own,
(24:59):
and that's to each other. Is a potential is a
real potential benefit to AI the kind of breaking down
a simple tool for breaking down those kinds of barriers.
Is that a very Is that an elegant way of
sort of saying what.
Speaker 4 (25:15):
I really think? And I was actually just having a
conversation with Provos stuff and very much on this topic,
very recently, exactly on that which is all these this,
you know, this appetite right to collaborate across disciplines. There's
a lot of attempts stores a goal, right, creating interdisciplinary centers,
creating dual degree programs or dual appointment programs. But actually
(25:37):
in a lot of progress in academia happens by methodology too.
Write like a new you know, when when some methodology
gets adopted, I mean the most famous example of that
is a scientific method as an example of that. But
when you have a methodology that gets adopted, it also
provides a way to speak to your colleagues across different disciplines,
and I think what's happened in AI is linked to
(25:59):
that that within the context of the scientific method as
an example, the methodology about we about what we do discovery,
the role of data, the role of these neural networks,
of how we actually find proximity to concepts to one
another is actually fundamentally different than how we've traditionally applied it.
(26:19):
So as we see across more professions, people applying this
methodology is also going to give some element of common
language to each other. Right And in fact, you know,
in this very high dimensional representation of information that is
pressent to neural networks, we may find amazing adjacencies or
connections of themes and topics in ways that the individual
(26:41):
practitioners cannot describe, but yet will be latent in these
large cal neural networks. We are going to suffer a
little bit from causality, from the problem of like, hey,
what's the root cause of that? Because I think one
of the unsatisfying aspects that this methodology will provide is
they may give you answers for which they don't give
you good reasons for where the answers came from and uh,
(27:04):
and then there will be the traditional process of discovery
of saying, if that is the answer, what are the reasons?
So we're going to have to do this sort of
hybrid way of understanding the world. But I do think
that common layer of AI is a powerful new thing.
Speaker 3 (27:18):
Yeah. Well, a couple of random questions. I couldn't mind
as you talk. In the In the Writer's strike that
just ended in Hollywood, one of the sticking points was
how the studios and writers would treat AI generated content. Right,
good writers get credit if their material with somehow the
source for a but more broadly, did the writers need
(27:40):
protections against the use of I could go on. You
know what, probably we're familiar with all of this. Had
you been I don't know whether you were, but had
either side called you in for advice during that the writers,
had the writers called you and said, Daria, what should
we do about AI? And how should we that should
be ref how should that be reflected in our content
track negotiations? What would you've told.
Speaker 4 (28:01):
Them the way I think about that is that I died.
I would divide it into two pieces. First, is what's
technically possible, right, and anticipate scenarios like you know, what
can you do with voice cloning for example? You know, now,
for example it is possible there being dubbing right legist
take that topic right around the world. There was all
(28:22):
these folks that would dub people in other languages. Well,
now you can do these incredible rendering in some I mean,
I know if you've seen them, where you know you
match the lips is your original voice, but speaking any
language that you want. As an example, so busy that
has a set of implications around that. I mean, just
to give an example, So I would say, create a
taxonomy that describes technical capabilities that we know of today
(28:45):
and applications to the industry, and two examples of like, hey,
you know I could film you for five minutes and
I could generate two hours of content of you and
I don't have to you know, then if you get
paid by the hour, obviously I'm not paying you for
that other thing. So I would say technological capability and
then map with their expertise consequences of how it changes
the way they work or the way they interact or
(29:06):
the way they negotiate and so on. So that would
be one element of it, and then the other one
is like a non technology related matter, which is an
element of almost of distributed justice, is like who deserves
what right and who has the power to get what?
And then that's a completely different discussion. That is to say, well,
if this is the scenario of what's possible, you know,
what do we want and what are we able to get?
(29:29):
And I think that that's a different discussion, which is
which we all as.
Speaker 3 (29:32):
Life, which when do you do?
Speaker 4 (29:33):
First? I think it's very helpful to have an understanding
of what's possible and how it changes a landscape as
part of a broader discussion, right, and a broad negotiation
because you also have to see the opportunities because there
will be a lot of ground to say, actually, you know,
(29:53):
if we can do it in this way and we
can all be that much more efficient in getting this
piece work done on this but we have a reasonable
agreement about how we both sides benefit from it, right,
then that's a win win for everybody. Yeah, Right, So
that's I think that would be a golden triangle, right.
Speaker 3 (30:12):
Here's my reading, and I would like you to correct
me if I'm wrong, and I'm likely to be wrong.
When I looked at that strike, I said, if they're
worried about AI, the writers are worried about AI. That
seems silly. It should be the studios who are worried
about the economic impact of AI. Does it in the
long run AI put the studios out of business long
before it puts the writers out of business. I only
(30:34):
need the studio because the costs of production are as
high as the sky, and the cost of production are overwhelming.
And whereas if I don't, if I have a tool
which brings introduces massive technological efficiencies to the production of movies,
then I don't. Why don't need a studio? Why would
they the scared ones?
Speaker 4 (30:53):
Or maybe maybe you need like a different kind of
studio or a different kind of different kind of study.
Speaker 3 (30:57):
But I mean the in the but in in this strike,
the fright the frightened ones with the writers and the
you know, with the studios. Wasn't that backwards?
Speaker 4 (31:09):
I haven't thought about it. Uh, it can be about
the implications of it. It goes back to we're talking
before the implications because are so horizontal. It is right
to think about it, like what does it do to
the studios as well? Right?
Speaker 3 (31:19):
Yeah?
Speaker 4 (31:21):
But then you know, the reason why that happens is
that it's the order of either negotiations or who first
got concerned about it and did something about it, right,
which is in the context of the strike. You know,
I don't know what the equivalent conversations are going inside
the studio and whether they have a workroom saying what
this is going to mean to us? Right, but it
(31:41):
doesn't get exercise through a strike, but maybe through a
task force inside you know, the companies about what are
they going to do? Right?
Speaker 3 (31:47):
Well, And to go back to your thing you said,
the first thing you do is you make a list
of what technological capabilities are. But don't technological capabilities change
every I mean they do. You're raising ahead so fast,
so you can't. Can you you have a contract? I'm
sorry for getting into little weeds here, but this is interesting.
Can you you can't have a five year contract if
(32:07):
the contract is based on an assessment of technological capabilities
in twenty twenty three, because by the time get to
twenty eight, twenty three eight, it's totally.
Speaker 4 (32:18):
Different, right, yeah, But like you know, I mean where
I was going is like there are some abstractions around
that is like, you know, one can we do with
my image?
Speaker 3 (32:28):
Right?
Speaker 4 (32:28):
Like if I generally get the category that my image
can be reproduced, generated contents and so on, it's like,
let's talk about the abstract notion about who has rights
to that or do we both get to benefit from that?
If you get that straight, Yes, the nature of how
the image gets alter created as something will change underneath,
but the concept will stay the same. And so I
(32:48):
think it's what's important is to get the categories right.
Speaker 3 (32:51):
Yeah. Yeah, If you just think about the biggest technological
revolutions of the post war era last seventy five years,
you can all come up with a list. Actually, it's
really fun to come up with the list. I was
thinking about this when we were you know, containerized shipping
is my favorite, the green revolution, the internet is Where
(33:17):
is the I in that list?
Speaker 4 (33:21):
So I would put it first in that context that
you put forth over since World War Two, undoubtedly, like
computing as a category is one of those trajectories that
has reshaped right or world. And I think we think computing,
I would say the role that semiconductors have had has
(33:41):
been incrowdly defining. I would say AI is the second
example of that as a core architecture that is going
to have an equivalent level of impact. And then the
third leg I would put to that equation will be
quantum and quantum information. And that's sort of like I
like to summarize that the future of computing it's bits,
neural and cubits, and is that idea of high precision computation,
(34:04):
the world of neural networks and artificial intelligence, and the
world of quantum and the combination of those things is
going to be the defining force over the next hundred
years in that category of computing. But it makes a
list for sure.
Speaker 3 (34:15):
If it's that high up on the list. This is
a total hypothetical. Would you if you were starting over,
if you're starting IBM right now, would you say, oh,
our AI operations actually should be way bigger, Like how
many how many thousands of people working for you?
Speaker 4 (34:32):
So within the research division it's about like three thousand,
five hundred scientists.
Speaker 3 (34:37):
So in a perfect world, would you if it's that big,
isn't that too small as a group?
Speaker 4 (34:42):
Yeah, Well, that's like in the RICAR division. I mean
IBM overall, But I mean.
Speaker 3 (34:48):
Like, so starting from first, so you have a you
we've got a technology that you're ranking with Compute and
you know, up there with as in terms of a
world changer. Are we So what I'm basically asking is
are we underinvested in this huge you know?
Speaker 4 (35:07):
But so so yeah, it's a good question. So like
what I would say is that I think we should
segment how many people do you need on the creation
of the technology itself, and what is the right size
of research and engineers and compute to do that? And
how many people do you need in the sort of
application of the technology to create better products, to deliver
(35:29):
services and consulting and then ultimately to diffuse it through
you know, sort of all feheres of society. And the
numbers are very different, and that is not different than
anywhere else. I mean, I mean, if you give examples
of since you were talking about in context of World
War two, how many people does it take to create,
you know, an atomic weapon as an example, it's a
large number. I mean, it wasn't just Los animals. There's
(35:51):
a lot of people in Okay, it's a large number,
but it wasn't a million people, right, So you could
have highly concentrated teams of people that, with enough resources,
can do extraordinary scientific and technological achievements. And that's always,
by definition, is going to be a fraction of like
one percent compared to the total volume that is going
to require to then deal with it.
Speaker 3 (36:11):
Yeah, but the application side is infinite almost.
Speaker 4 (36:14):
That's exactly. So that is where like in the end
the bottleneck really is. So with you know, thousands of
scientists and engineers, you can create world class AI, right,
And so no, you don't need ten thousand to be
able to create the large language model and the generatic
model and some but you need thousands, and you need
you know, very significant amount of computer and data. You
(36:35):
need that The rest is Okay, I build software, I
build databases, or I build a software product that allows
you to do inventory management, or I build you a
photo editor and so on. Now that product incorporating the AI, modifying,
expanding it and so on. Well, now you're talking about
the entire software industries. So now you're talking about millions
(36:57):
of people right who are nested, you know, who are
required to bring AI into their product. Then you go
on a step beyond the technology creators in terms of software,
and you say, well, okay, now what the skills to
help organizations go undeployed in the department of you know,
the interior, right, And then I said, okay, well, now
you need like consultants and experts and people to work
(37:19):
they are to integer into the workflow. So now you're
talking into the many tens of millions of people around that.
So I see it as these concentric circles of it,
but to some degree in many of these core technology areas,
just saying like well, I need a team of like
one hundred thousand people to create like AI or a
or a new transistor or a new quantum computer. It's
actually a diminished in return right in the end, like
(37:40):
too many people connecting with each other's very difficult.
Speaker 3 (37:42):
But on the application side of just to go back
to our example of that college, just the task of
sitting down with a faculty and working with them to
reimagine what they do with these new set of tools
in mind, and with the understanding that the students coming
in are probably going to know more about it than
(38:03):
they do that a lot, I mean, that's a that
is a curricullion people problem.
Speaker 4 (38:09):
It's a people problem. Yeah, that's why I started in
terms of the barriers of adoption of that. I mean
the context of IBM an example, that's why we have
a consulting organization, IVAN Consulting that complements ib AND technology,
and the IVAN Consulting Organization has over one hundred and
fifty thousand employees because of this question, right, because you
have to sit down and you say, Okay, what problem
(38:30):
are you trying to solve, what is the methodology we're
going to do, and here's the technology options that we
have to be able to bring into the table. In
the end, the adoption across or society will be limited
by this part. The technology is going to make it easier,
more cost effective to implement those solutions, but you first
(38:51):
have to think about what you want to do, how
you're going to do it, and how are you going
to bring it into a life of this in this
context faculty member or you know, the administrator and so
on in these colleges.
Speaker 3 (39:01):
With that Hollywood that that notion I thought, which was
absolutely I thought really interesting that in a Hollywood strike
you have to have this conversation about a distributive justice,
conversation about how do we that's it's a really hard conversation,
right to have. And uh so this brings me to
my nee, which is that you we were talking about
stage you have. You have two daughters, one in college,
(39:25):
one about to go to college. That's right, so they're
both science minded. So tell me about the conversations you
you have with your daughter. You have a unique conversation
with your daughters because your conversation, your advice to them
is is influenced by what you do for a living.
Speaker 4 (39:42):
Yes, it's true.
Speaker 3 (39:43):
So did you warn your daughters away from certain fields?
Did you say whatever you do, don't be you know, no.
Speaker 4 (39:52):
No, no, that's not my style. I mean for me, no,
I try not to be like you know, preachy about that.
So for me, it was just about by example of
things I love, right and things I care about, and
then you know, bringing them to the lab and seeing
things and then the natural conversations of things working on
or interesting people I meet. So to the extent that
(40:13):
they have chosen that and obviously this has an influence
on them. It has been through seeing it, you know,
perhaps through my eyes, right, and what do you see
me do? And that I like my profession, right.
Speaker 3 (40:22):
But one of your daughters. You said, is thinking that
she wants to be a doctor. But being a doctor
in a post AI world it's surely a very different
proposition than being a doctor in a PREAI world. Do
you think have you tried to prepare her for that difference?
Have you explained to her what you think will happen
to this profession she might enter.
Speaker 4 (40:42):
Yeah, I mean not in like, you know, incredible amount
of detail, but yes, at the level of understanding what
is changing, like this lens of the information, lens with
which you can look at the world and what is
possible and what it can do, Like what is our
role and what is all of the technology and how
that shapes At that level of abstraction, for sure, but
(41:04):
not at the level of like, don't be a radiologist,
you know, because this is what we want for you.
Speaker 3 (41:08):
I was gonna say, if you're not't happy with your
current job, you could do a podcast called Parenting Tips
with Dario, which is just an AI person gives you
advice on what your kids should do based on exactly this,
Like should I be a readiologist? Dario tell me, like
it seems to be a really important question. Yeah, let
me ask this question in a more I'm joking, but
in a more serious way. Surely it would if I
(41:32):
don't mean to use your daughter as an example. But
let's imagine we're giving advice to somebody who wants to
enter medicine. A really useful conversation to have is what
are the skills that are will be most prized in
that profession fifteen years from now, and are they different
from the skills that are prized now. How would you
answer that question?
Speaker 4 (41:53):
Yeah, I think for example, this goes back to how
is this scientific method in this context, like the practice
of medicine going to change? I think we will see
more changes on how we practice a scientific method and
so on as a consequence of what is happening with
the world of computing and information, how we represent information,
how we represent knowledge, how we extract meaning from knowledge
(42:17):
as a method than we have seen in the last
two hundred years. So therefore, what I would like strongly
encourage is not about like, hey, use this tool for
doing this or doing that, but in the curriculum itself,
in understanding how we do problems solving in the age
of like data and data representation and so on, that
needs to be embedded in the curriculum of everybody you
(42:39):
know that is I would say, actually quite horizontally, but
certainly in the context of medicine and scientists and so on,
for sure, And to the extent that that gets ingrained,
that will give us a lens that no matter what
specialty they go within medicine, they will say, actually, the
way I want to be able to tackle improving the
quality of care, the way to do that is in
(43:00):
addition to all the elements that we have practiced in
the field of medicine. Is this new lens? And are
we representing the data the right way? Do we have
the right tools to be able to represent that knowledge?
Am I incorporating that in my own so with my
own knowledge in a way that gives me better outcomes? Right?
Do I have the rigor of benchmarking too? And quality
(43:20):
of the results? So that is what needs to be incorporated.
Speaker 3 (43:23):
How in a perfect world, if I asked you your
team to rewrite curriculum for American medical schools, how dramatic
a revision is that? Are we tinkering with ten percent
of the curriculum or we tinkering with fifty percent of it?
Speaker 4 (43:41):
I think they would be a subset of classes. That
is about the method the methodology, what has changed, like
have these lens of it to understand and then within
each class that methodology will represent something that is embedded
in it. Right, Well, it will be substantive but not
(44:02):
but doesn't mean replacing the specialization and the context and
the knowledge of each domain. But I do think everybody
should have sort of a basic knowledge of the horizontal, right,
what is it, how does it work? What tools you have,
what is the technology, and like you know what are
the dos and don'ts around that? And then every area
you say, and you know that thing that you learn,
this is how it applies to anatomy, and this is
(44:24):
how you know how it applies to you know, radiology
if you studying that, or or this is how you
apply you know, in the context of discovery right of
self structure and this is how we can use it,
or protein folding and this is how it does so
that way you'll see a connecting tissue through throughout the
whole thing.
Speaker 3 (44:40):
Yeah, I mean I would add to that because I
was sticking it the sch that it's also this incredible
opportunity to do what doctors are supposed to do but
don't have time to do now, which is they're so
consumed with figuring out what's wrong with you that they
have little, little time to talk about the implications of
(45:01):
the diagnosisness and what we really want to if we
can freedom of some of the burden of what is
actually quite a prosaic question of what's wrong with you
and leave the hard human thing of let make should
you be scared or hopeful? Should you you know? What
do you need to do?
Speaker 4 (45:18):
Or what?
Speaker 3 (45:19):
Let me put this in the context of all the
patients I've seen, that conversation, which is the most important one,
is the one that seems to me so like, if
I had to, I would add, if we're reimagining the
curriculum of med school, I'd like, with whatever this, by
the way, very little time. Maybe we have to add
two more years to med school, but like.
Speaker 4 (45:38):
A whole.
Speaker 3 (45:40):
But the whole thing about bringing back the human side
of you know, now, if I can give you ten
more minutes, how do you use that ten more minutes in.
Speaker 4 (45:52):
That in that reconceptualization that you just did is what
we should be doing around that, Because I think the
debate as to like, well, I'm I need doctors or
not it's actually a not very useful debate. But rather
this other question is how is your time being spent?
What problems are you getting stuck? I mean I generalize
this by like the obvious observation that if you look
around in your professions, in our daily lives, we have
(46:14):
not run out of problems to solve. So as an
example of that is, hey, if I'm spending all my
time trying to do diagnosis, and I could do that
ten times faster, and it allows me actually to go,
you know, and take care of the patients and all
the next steps of what we have to do about it.
That's probably a trade off that a lot of doctors
would take, right. And then you say, well, you know,
to what degree that does it allow me to do that?
(46:35):
And I can do these other things and these other
things are critically important for my profession around that. So
when you actually become less abstract and like we get
past the futile conversation of like, oh, there's no more
jobs and I'm going to take it all of it,
which is kind of nonsense, is you go back to say,
in practice in your context, right, for you, what does
(46:56):
it mean? How do you work? What can you do
differently around that. Actually, that's a much richer conversation, and
very often we would find ourselves that there's a portion
of the work we do that we say I would
rather do less of that. This is this other part
I like a lot, And if it is possible that
technology could help us make that trade off, I'll take
it in a heartbeat. Now, poorly implemented technology can also
(47:17):
create another problem. You say, Hey, this was supposed to
solve me things, but the way it's being implemented is
not helping me, right, it's making my life more more miserable,
or so on, or I've lost connection in how I
used to work, et cetera. So that is why design
is so important. That is why I also workflow is
so important in being able to solve these problems. But
(47:39):
it begins by, you know, going from the intergalactic to
the reality of it, of that faculty member in the
Liberal Arts college or you know, or a you know,
a practitioner in medicine in a hospital and what it
means for them. Right.
Speaker 3 (47:53):
Yeah. What struck me AREIO throughout our conversation is how
much of this revolution and is non technical? Is to say,
you guys are doing the technical thing here, but the
real the revolution is going to require a whole range
of people doing things that have nothing to do with software,
that have to do with working out new new human arrangements.
(48:16):
Talking about that, I mean, does keep coming back to
the Hollywood strike thing that you have to have a
conversation about our values. Is creators of of of of movies?
How are we going to divide up the exactly credit
and the like. That's a that's a conversation about philosophy,
and you know it is.
Speaker 4 (48:37):
And is it's in the grand tradition of why you know,
a liberal education is so important in the broadest possible sense, Right,
there's no common conception of the good, right, that is
always a contested dialogue that happens within our society. And
technology is going to fit in that context too, right.
(48:57):
So that's why I personally, as a philosophy I'm not
a technological determinists, right, And I don't like when colleagues
in my profession right starts saying like, well, this is
the way the technology is going to be, and by consequence,
this is how society is going to be. I'm like,
that's a highly contested goal. And if you want to
enter into realm of politics or the real other ones,
go and stand up on a stool and discuss with it.
(49:19):
That's what society wants. You will find that it's a
huge diversity of opinions and perspective and that's what makes
you know, you know, in a democracy, the richness of
our society. And in the end, that is going to
be the centerpiece of the conversation what do we want?
You know, who gets what? And so on? And that
is actually I don't think it's anything negative. That's acid
(49:39):
should be because in the end is anchor of who
we want as humans, you know, you know, as friends, family, citizens,
and we have many overlapping sets of responsibilities, right and
as a technology creator, my only responsibility is not just
as a scientist and a technology creator. I'm also a
member of family. I'm a citizen, and I'm many other
things that I care about. And I think that that
sometimes the debate of the technological determinists they start now
(50:04):
budding into what is the realm of you know, justice
and you know, in society and philosophy and democracy, And
that's where they get the most uncomfortable because it's like
I'm just telling you, like you know what's possible, and
when there's pushback, it's like, yeah, but now we're talking
about how we live and how we work and how
(50:27):
much I get paid or not paid. So that technology
is important. Technology shapes that conversation, but we're going to
have the conversation with a different language, as it should be,
and technologies need to get accustomed to if they want
to participate in that world with the broad consequences. Hey,
get a custom to deal with the complexity of that
world of politics, society, institutions, unions, all that stuff. And
(50:51):
you know, you can be like whiny about it. It's
like they're not adopting my technology. That's what it takes
to bring technology into the world.
Speaker 3 (50:58):
Yeah, well said, thank you Dario for this wonderful conversation.
Thank you to all of you for coming and listening,
and thank you.
Speaker 4 (51:10):
Thank you.
Speaker 3 (51:14):
Dario gild transformed how I think about the future of AI.
He explained to me how huge of a leap it
was when we went from chess playing models to language
learning models, and he talked about how we still have
a lot of room to grow. That's why it's important
that we get things right. The future of AI is
impossible to predict, but the technology has so much potential
(51:38):
in every industry. Zooming into an academic or medical setting
showed just how close we are to the widespread adoption
of AI. Even Hollywood is being forced to figure this out.
Institutions of all sorts will have to be at the
forefront of integration in order to unlock the full power
of AI thoughtfully and responsibly. Humans have the power and
(52:01):
the responsibility to shape the tech for our world. I
for one, I'm excited to see how things play out.
Smart Talks with IBM is produced by Matt Romano, Joey Fishground,
David jaw and Jacob Goldstein. We're edited by Lydia Jane Kott.
Our engineers are Jason Gambrel, Sarah Bruguier, and Ben Holliday.
(52:24):
Theme song by Gramoscope. Special thanks to 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 Ruby Studio at iHeartMedia. To
find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
(52:46):
or wherever you listen to podcasts. I'm Malcolm Gladwell. This
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