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