Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Today we
Speaker 1: are witnessed to one of those rare moments in history,
Speaker 1: the rise of an innovative technology with the potential to
Speaker 1: radically transform business and society forever. That technology, of course,
Speaker 1: is artificial intelligence, and it's the central focus for this
Speaker 1: new season of Smart Talks with IBM. Join hosts from
Speaker 1: your favorite Pushkin podcasts as a talk with industry experts
Speaker 1: and leaders to explore how businesses can integrate AI into
Speaker 1: their workflows and help drive real change in this new
Speaker 1: era of AI. And of course, host Malcolm Gladwell will
Speaker 1: be there to guide you through the season and throw
Speaker 1: in his two cents as well. Look out for new
Speaker 1: episodes of Smart Talks with IBM every other week on
Speaker 1: the iHeartRadio app, Apple Podcasts, wherever you get your podcasts
Speaker 1: and learn more are at IBM dot com slash smart Talks.
Speaker 1: All Right, Welcome everybody, you guys excited, Here we go.
Speaker 2: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 2: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 2: we're continuing our conversations with new creators visionaries who are
Speaker 2: creatively applying technology and business to drive change, but with
Speaker 2: a focus on the transformative power of artificial intelligence and
Speaker 2: what it means to leverage AI as a game changing
Speaker 2: multiplier for your business. Today's episode is a bit different.
Speaker 2: I was recently joined on stage by Dario Gill for
Speaker 2: a conversation in front of a live audience at the
Speaker 2: iHeartMedia headquarters in Manhattan. Dario is the senior vice president
Speaker 2: and director of IBM Research, one of the world's largest
Speaker 2: and most influential corporate research labs. We discussed the rise
Speaker 2: of generative AI, what it means for business and society.
Speaker 2: He also explained how organizations that leverage AI to create
Speaker 2: value will dominate in the near future. Okay, let's get
Speaker 2: on to the conversation. Hello everyone, welcome, and I'm here
Speaker 2: with doctor Dario Gil and I wanted to say before
Speaker 2: we get started. This is something I said backstage that
Speaker 2: I feel very guilty today because you're the you know,
Speaker 2: you know, arguably one of the most important figures in
Speaker 2: AI research in the world, and we have taken you
Speaker 2: away from your job for a morning. It's like, if
Speaker 2: you know, Oppenheimer's wife in nineteen forty four said let's
Speaker 2: go and have a little getaway in the Bahamas. It's
Speaker 2: that kind of thing. You know, What do you say
Speaker 2: to your wife, I can't We have got to work
Speaker 2: on this thing I can't tell you about. She's like
Speaker 2: getting me out of Los Alamos. No, So I do
Speaker 2: feel guilty. We've set back AI research by by about
Speaker 2: four hours here. But I wanted to you've been up
Speaker 2: with with ibo for twenty years, twenty years this summer.
Speaker 2: So and how old were you when you Not to
Speaker 2: give away your age, but you were how old when
Speaker 2: you started?
Speaker 3: I was twenty eight?
Speaker 2: Okay, yeah, So I want to go back to your
Speaker 2: twenty eight year old self. Now, if I asked you
Speaker 2: about artificial intelligence, I asked twenty eight year old Dario,
Speaker 2: what does the future hold for AI? How quickly will
Speaker 2: this new technology transform our world? Et cetera, et cetera.
Speaker 2: What would twenty eight year old Darigo have said?
Speaker 4: Well, I think the first thing is that even though
Speaker 4: AI as a feel has been with us for a
Speaker 4: long time since the mid nineteen fifties, at that time,
Speaker 4: AI was not a very polite word to say, meaning
Speaker 4: within the scientific community, people didn't use sort of that
Speaker 4: term that would have said things like, you know, maybe
Speaker 4: I do things relate to machine learning, right, or statistical
Speaker 4: techniques in terms of classific fires and so on.
Speaker 3: But AI had a mixed.
Speaker 4: Reputation, right, it had gone through different cycles of hype,
Speaker 4: and it's also if moments of you know, a lot
Speaker 4: of negativity towards it because of lack of success. And
Speaker 4: so I think that would be the first thing we
Speaker 4: probably say, like AI is like what is that?
Speaker 3: Like, you know, respectable scientists are not working.
Speaker 4: On AI the fin as such, and that really changed
Speaker 4: over the last fifteen years. Only, right, I would say,
Speaker 4: with the advent of deep learning over the last decade
Speaker 4: is when that re enter again the lexicon of saying
Speaker 4: AI and that that was a legitimate thing to work on.
Speaker 4: So I would say that that's the first thing I
Speaker 4: think we would have noticed a contrast twenty years ago.
Speaker 2: Yeah, So what point in your twenty year tenure at
Speaker 2: IBM would you say you kind of snapped into present
Speaker 2: kind of wow mode.
Speaker 4: I would say in a late two thousands, when IBM
Speaker 4: was working on the Jeopardy project and just seeing the
Speaker 4: demonstrations of what could be done in question answering.
Speaker 2: It's literally Jeopardy is this crucial moment in the history
Speaker 2: of Yeah.
Speaker 4: You know, there had been a long and wonderful history
Speaker 4: inside IBM on AI. So so for example, like you know,
Speaker 4: in terms of like these grand challenges, at the very
Speaker 4: beginning of the field founding, which is this famous Dartmouth
Speaker 4: conference that actually IBM sponsored uh TO to create, there
Speaker 4: was an IBM and there called Nathaniel Rochester and there
Speaker 4: were a few others who right after that they started
Speaker 4: thinking about demonstrations of this field. And then for example,
Speaker 4: they created the first you know game to play checkers
Speaker 4: and to demonstrate that you could do machine learning on that.
Speaker 4: Obviously we saw later in the nineties like chess that
Speaker 4: was a very famous example of that, deep Blue with
Speaker 4: Deep Blue right and playing with Caspar and then but
Speaker 4: I think the moment that was really the ones felt like,
Speaker 4: you know, kind of like brute force anticipating, sort of
Speaker 4: like moves ahead. But this aspect of dealing with language
Speaker 4: and question answering felt different, and I think for for
Speaker 4: us internally and many others, was when a moment of
Speaker 4: saying like wow, you know, what are the possibilities here?
Speaker 4: And then soon after that connected to the sort of
Speaker 4: advancements in computing and with deep learning. The last decade
Speaker 4: has just been an all out, you know, sort of
Speaker 4: like front of advancements and that, and I just continued
Speaker 4: to be more and more impressed. And the last few
Speaker 4: years have been remarkable too.
Speaker 2: Yeah, So I ask you three quick conceptual questions before
Speaker 2: we dig into it, just so I sort of get
Speaker 2: a we all get a feel for the shape of AI.
Speaker 2: Question Number one is where are we in the evolution
Speaker 2: of this? So you know the obvious question. We we're
Speaker 2: all suddenly aware of it, we're talking about it. Can
Speaker 2: you give us an analogy about where we are in
Speaker 2: the kind of likely evolution of this is a technology?
Speaker 4: So I think we're on a significant inflection point that
Speaker 4: it feels the equivalent of the first browsers when they
Speaker 4: appear and people imagine the possibilities of the Internet or
Speaker 4: more imagined experience the internet. The Internet had been around,
Speaker 4: right for quite a few decades. AI has been around
Speaker 4: for many decades. I think the moment we find ourselves
Speaker 4: is that people can touch it, and they can. Before
Speaker 4: they were I systems that were like behind the scenes,
Speaker 4: like your search results or translation systems, but they didn't
Speaker 4: have the experience of like, this is what it feels
Speaker 4: like to interact with this thing.
Speaker 3: So that's what I mean.
Speaker 4: I think maybe that analogy of the browser is appropriate
Speaker 4: because it's all of a sudden, it's like whoa, you know,
Speaker 4: these network of machines and content can be distributed and
Speaker 4: everybody can self publish, and there was a moment that
Speaker 4: we all remember that, and I think that that is
Speaker 4: what the world has experience over the last nine months
Speaker 4: or so on. So but fundamentally, also what is important
Speaker 4: is that this moment is where the ease of the
Speaker 4: number of people that can build and u use AI
Speaker 4: has skyrocketed. So over the last decade, you know, technology
Speaker 4: firms that had large research teams could build AI that
Speaker 4: worked really well, honestly, but when you went down into
Speaker 4: say hey, can everybody use it? Can a data science
Speaker 4: team in a bank, you know, go and develop these applications.
Speaker 3: It was like more complicated. Some could do it, but
Speaker 3: it was more the barrier of entry was high.
Speaker 4: Now it's very different because of foundation models and the
Speaker 4: implications that that has.
Speaker 2: For at the moment where the technology is being democratized.
Speaker 4: In demarketized, frankly, it works better for classes of problems
Speaker 4: like programming and other things, is really incredibly impressive what
Speaker 4: it can do. So the accuracy and the performance of
Speaker 4: it is much better, and the ease of use and
Speaker 4: the number of use cases we can pursue it much bigger,
Speaker 4: So that democratization is a big difference.
Speaker 2: But when you say, when you make it an analogy to
Speaker 2: the first browsers, if you if we do another one
Speaker 2: of these time travel questions back at the beginning of
Speaker 2: the first browsers, to say, many of the potential uses
Speaker 2: of the Internet and such we hadn't even begun. We
Speaker 2: couldn't even anticipate, right, right, So we're at the point
Speaker 2: where the future direction is largely unpredictable.
Speaker 4: Yeah, I think that that is right, because it's such
Speaker 4: a horizontal technology that the intersection of the horizontal capability,
Speaker 4: which is about expanding our productivity and tasks that we
Speaker 4: wouldn't be able to do efficiently without it, has to
Speaker 4: marry now the use cases that reflect the diversity of
Speaker 4: human experience, our institutional diversity. So as more and more
Speaker 4: institutions said, you know, I'm focused on agriculture, you know,
Speaker 4: to be able to improve seeds, you know, in these
Speaker 4: kinds of environments. They'll find their own context in which
Speaker 4: that matters that the creators of a I did not
Speaker 4: anticipate at the beginning. So I think that that is
Speaker 4: then the fruit of surprises will be like why I
Speaker 4: wouldn't even think that it could be.
Speaker 3: Used for that?
Speaker 5: And also clever people will create.
Speaker 4: New business models as associated with that, like it happened
Speaker 4: with the Internet of course as well, and that will
Speaker 4: be its own source of transformation, unchanged in its own right.
Speaker 5: So I think all of that is yet to unfold.
Speaker 2: Right.
Speaker 4: What we're seeing is this catalyst moment of technology that
Speaker 4: works well enough and it can be democratized.
Speaker 2: What next sort of conceptual question?
Speaker 3: You know?
Speaker 2: We can loosely understand or categorize innovations in terms of
Speaker 2: their impact on the kind of balance of power between
Speaker 2: haves and have nots. Some innovations, you know, obviously favor
Speaker 2: those who already have will make the rich richer. Some
Speaker 2: some it's a rising tie the lift cell boats and
Speaker 2: some bias in the other direction, they close the gap between.
Speaker 2: Is it possible to say to predict which of those
Speaker 2: three categories AI might fall into.
Speaker 3: It's a great question, you know.
Speaker 4: A first observation I would make on your first two categories.
Speaker 5: Is that it will be both likely be true that
Speaker 5: the use.
Speaker 4: Of AI will be highly demarketized, meaning the number of
Speaker 4: people that have access to its power to make improvements
Speaker 4: in terms of efficiency and so on will be fairly universal,
Speaker 4: and that the ones who are able to create AI
Speaker 4: UH may be quite concentrated. So if you look at
Speaker 4: it from the lens of who creates wealth and value
Speaker 4: over sustained periods of time, particularly it's saying a context
Speaker 4: like business, I think just being a user of AI
Speaker 4: technology is an insufficient strategy and UH. And the reason
Speaker 4: for that is, like, yes, you will get the immediate
Speaker 4: productivity boost of like just making API calls and you
Speaker 4: know that would be a new baseline for everybody, but
Speaker 4: you're not accruing value in terms of representing your data
Speaker 4: inside the AI in way that it.
Speaker 3: Gives you a sustainable competitive advantage.
Speaker 4: So I always try to tell people is don't just
Speaker 4: be an AI user, be an you know, AI value creator.
Speaker 4: And I think that that will have a lot of
Speaker 4: consequences in terms of the haves and have nots as
Speaker 4: an example, and that will apply both to institutions and
Speaker 4: regions and countries, et cetera. So I think it would
Speaker 4: be kind of a mistake, right to just develop strategies
Speaker 4: that are just about.
Speaker 2: Usage, but to come back to that question from them
Speaker 2: and to give you a specific suppose I'm a I'm
Speaker 2: an industrial farmer in Iowa with ten million dollars of
Speaker 2: equipment and move and I'm comparing it to a subsistence
Speaker 2: farmer somewhere in the developing world who's got a cell phone.
Speaker 2: Over the next five years, who's whose well being rises
Speaker 2: by a greater amount.
Speaker 4: Yeah, I think, I mean, it's a it's a good question,
Speaker 4: but it might be hard to do a one to
Speaker 4: one sort of like attribution to just one variable in
Speaker 4: this case, which is AI. But again, provided that you
Speaker 4: have access to a phone, right and some kind to
Speaker 4: you know, be able to be connected. I do think
Speaker 4: so for example, in that context we've developed, we don't
Speaker 4: work with NASA as an example, to build you spatial
Speaker 4: models using some of these new techniques, and I think
Speaker 4: for example, or ability to do flood prediction, I'll tell
Speaker 4: you an advantage of why it would be a democratization
Speaker 4: force in that context. Before to build a flowed model
Speaker 4: based on satellite imagery was actually so.
Speaker 3: Onerous and so complicated and difficult.
Speaker 4: That you would just target to very specific regions and
Speaker 4: then obviously countries prioritize their own right. But what we've
Speaker 4: demonstrated is actually you can extend that technique to have
Speaker 4: global coverage around that. So in that context, I would
Speaker 4: say it's a four stores democratization that everybody sort of
Speaker 4: would have access if you have some connectivity, as.
Speaker 2: Today, Iowa farmer might have a flood model. The guy
Speaker 2: in the developing world definitely didn't, and now he's a
Speaker 2: shot at getting one.
Speaker 3: Yeah, but now it has a shot of getting one.
Speaker 4: So there's aspects of it that so long as we
Speaker 4: provide connectivity and access to it, that they can be
Speaker 4: democratization forces.
Speaker 3: But I'll give you.
Speaker 4: Another example that that can be quite concerning, which is language. Right,
Speaker 4: So there's so much language in the you know, in English,
Speaker 4: and there is sort of like the reinforcement loop that
Speaker 4: happens that the more you concentrate because it has obvious
Speaker 4: benefits for global communication and standardization, the more you can
Speaker 4: enrich like base AI models based on that capability. If
Speaker 4: you have very resource cars languages, you tend to develop
Speaker 4: less powerful AI with those languages and so on, So
Speaker 4: one has to actually worry and focus on the ability
Speaker 4: to actually represent that in that case is language as
Speaker 4: a piece of culture. Also in the AI sets that
Speaker 4: everybody can benefit from it too. So there's a lot
Speaker 4: of considerations in terms of equity about the data and
Speaker 4: the data sets that we accrue and what problems are
Speaker 4: we trying to solve. I mean, you mentioned agriculture or
Speaker 4: healthcare and so on. If we only solve problems that
Speaker 4: are related to marketing as an example, that would be
Speaker 4: a less rich world in terms of opportunity that if
Speaker 4: we incorporate many many other broader server problems.
Speaker 2: Who do you think what do you think are the
Speaker 2: biggest impediments to the adoption of a as you would
Speaker 2: like as you think aire to be adopted. I mean,
Speaker 2: if you look, what are the sticking points that.
Speaker 4: You would look In the end, I'm going to give
Speaker 4: a non technological answer as a first one has to
Speaker 4: do with workflow, right, So even if the technology is
Speaker 4: very capable, the organizational change inside a company to incorporate
Speaker 4: into the natural workflow of people or how we work
Speaker 4: is It's a lesson we have learned over the last
Speaker 4: decade is hugely important.
Speaker 3: So there's a lot of design considerations.
Speaker 4: There's a lot of how do people want to work right,
Speaker 4: how do they work today?
Speaker 3: And what is the natural entry points for AI? So
Speaker 3: that's like number one.
Speaker 4: And then the second one is, you know, for the
Speaker 4: broad value creation aspect of it is the understanding inside
Speaker 4: the companies of how you have to curate and create
Speaker 4: data to combine it with external data says that you
Speaker 4: can have powerful AI models that actually fit your need.
Speaker 4: And that aspect of what it takes to actually and
Speaker 4: curate the data for this modern AI, it's still working progress, right.
Speaker 4: I think part of the problem that happens very often
Speaker 4: when I talk to institutions is that they say yah, yeah, yeah, yeah,
Speaker 4: I'm doing it. I've been doing it for a long time.
Speaker 4: And the reality is that that answer can sometimes be
Speaker 4: a little of a cop out, right, is like, I
Speaker 4: know you were doing machine learning, you were doing some
Speaker 4: of these things.
Speaker 5: But actually the leader's version of AI.
Speaker 4: What was happening with foundation models, Not only is it
Speaker 4: very new, it's very hard to do and honestly, if
Speaker 4: you haven't been you know, assembling very large teams and
Speaker 4: spending hundreds of millions of dollars of compute, and so
Speaker 4: you're probably not doing it right. You're doing something else
Speaker 4: that is in the broad category. And I think the
Speaker 4: lessons about what it means to make this transition to
Speaker 4: this new wave is still in early phases of understanding.
Speaker 2: So what would you say? I want to give you
Speaker 2: a couple of examples of people with kind of real
Speaker 2: work in real world positions of responsibility. Imagine I'm sitting
Speaker 2: right here, So imagine that I am the president of
Speaker 2: a small liberal arts college and I come to you
Speaker 2: and I say, Dario, I keep hearing about the AI
Speaker 2: my college has. You know, I don't make it. You know,
Speaker 2: I'm I'm I'm making this much money. If that every
Speaker 2: year my enrollments declining, I feel like this maybe is
Speaker 2: an opportunity. What is the opportunity for me? What would
Speaker 2: you say?
Speaker 3: So it's probably in a couple of segments around that.
Speaker 3: Right one has.
Speaker 4: To do is well, what is the implications of this
Speaker 4: technology inside the institution itself instead of the college, and
Speaker 4: how we operate and can we improve for example, efficiency,
Speaker 4: like if you have in very low levels of sort
Speaker 4: of margin to be able to reinvest, is you know
Speaker 4: you run it, You run you know, infrastructure, you run
Speaker 4: many things inside the college. What are the opportunities to
Speaker 4: increase the productivity or automate and drive savings such that
Speaker 4: you can reinvest that money so into the mission of
Speaker 4: education right as an example.
Speaker 2: So number one is operational efficiency.
Speaker 4: Operational efficiencyage is is a big one. I think the
Speaker 4: second one is within the context of the college, there's
Speaker 4: implications for the educational mission on its own, right, how
Speaker 4: will you know how.
Speaker 3: Does a curriculum need to evolve or not?
Speaker 4: What are acceptable use policies or of some of these
Speaker 4: AI I think we've all read a lot about like
Speaker 4: what can happen in terms of exams and so on,
Speaker 4: and cheating and not cheating, or what are the actually
Speaker 4: positive elements of it in terms of how curriculum should
Speaker 4: be developed and professions sustain around that. And then there's
Speaker 4: another third dimension, which is the outdoor oriented element of it,
Speaker 4: which is like prospect students right, so, which is frankly speaking,
Speaker 4: a big use case that is happening right now, which
Speaker 4: in the broader industry is called customer care or client
Speaker 4: care or citizen care. So in this question will be education,
Speaker 4: like you know, hey are you reaching the right students
Speaker 4: around that that may apply to the college. How can
Speaker 4: you create them, for example, an environment to interact with
Speaker 4: the college and answering questions that could be a chat
Speaker 4: bought or something like that to learn about it, and personalization.
Speaker 3: So I would say there.
Speaker 4: Is like at least three lenses with which I would
Speaker 4: give advice right the positive.
Speaker 2: Second, because it's really interesting. So I really can't as
Speaker 2: sign an essay anymore? Can I?
Speaker 3: Can I sign an essay?
Speaker 2: Can I say, rend me a research paper and come
Speaker 2: back to being three? Can I do that anymore?
Speaker 3: I think you can?
Speaker 2: How do I do that?
Speaker 3: And then you can that.
Speaker 4: Look, there's there's two questions around that. I think that
Speaker 4: if one goes.
Speaker 5: And explains in the context like what is it?
Speaker 3: Why are we here?
Speaker 5: Why are in this class? What is the purpose of this?
Speaker 4: And and one starts with assuming like an element of
Speaker 4: like decency and people are people are there like to
Speaker 4: learn and so on, and you just give it disclaimer. Look,
Speaker 4: I know that one option you have is like just
Speaker 4: you know, put the essay question and click go and
Speaker 4: like and give an answer. You know, but that is
Speaker 4: not why we're here, and that is not the intent
Speaker 4: of what we're trying to do. So first I would
Speaker 4: start with the sort of like the norms of intent
Speaker 4: and decency and appeal to those as step number one.
Speaker 4: Then we all know that there will be a distribution
Speaker 4: of use cases of people like that will come in
Speaker 4: one year and come one or the other and do that.
Speaker 4: And so for a subset of that, you know, I
Speaker 4: think the technology is going to have all in such
Speaker 4: a way that we will have more and more of
Speaker 4: the ability to discern right, you know, when that has
Speaker 4: been AI generated right and uncreated.
Speaker 3: It won't be perfect, right.
Speaker 4: But there's some elements that you can imagine in putting
Speaker 4: the essay and you say, hey, this is likely to
Speaker 4: be generated right around that. And for example, one way
Speaker 4: you can do that, just to give you an intuition,
Speaker 4: you could just have an essay that you write with
Speaker 4: pencil and paper. At the beginning, you get a baseline
Speaker 4: of what you're writing is like. And then later when
Speaker 4: you you know generated, there will be obvious differences around
Speaker 4: what kind of writing has been generated.
Speaker 2: On the other way, but you've turned it's everything you're
Speaker 2: describing makes sense. Put it greatly in this In this respect,
Speaker 2: at least it seems to greatly come complicate the life
Speaker 2: of the teacher, whereas the other two use cases seem
Speaker 2: to kind of clarify and simplify the role. Right suddenly,
Speaker 2: you know, reaching student perspective students, sounds like I can
Speaker 2: do that much more kind of efficienty a lot. Yeah,
Speaker 2: I can bring out administration costs, but the teaching thing
Speaker 2: is tricky.
Speaker 4: Well, until we developed the new norms, right, I mean again,
Speaker 4: I mean, I know it's not abuse analogy, but calculators
Speaker 4: we deal. We've done with that too, right, And it says, well, calculator,
Speaker 4: what is the purpose of math?
Speaker 3: How are we going to do this?
Speaker 2: And so can I tell you my dad's calculator story?
Speaker 3: Yes? Please.
Speaker 2: My father was a mathematician, taught mathematics at University of Waterloo, Agada,
Speaker 2: and in the seventies when people started to get pocket calculators,
Speaker 2: his students demanded that they'd be able to use them,
Speaker 2: and he said no, and they took him to the
Speaker 2: administration and he lost. So he then changed completely throughout
Speaker 2: all of his old exams and introduced new exams where
Speaker 2: there was no calculation. It was all like deep think,
Speaker 2: you know, figure out the problem on a conceptual level
Speaker 2: and describe it to me. And they were all students
Speaker 2: deeply unhappy that he'd made their lives for complicated. But
Speaker 2: to your point, I mean he probably the result was
Speaker 2: probably a better education. He just removed the element that
Speaker 2: they could game with their pocket calculators. I suppose it's
Speaker 2: a version.
Speaker 4: Of I think it's a version of that, And so
Speaker 4: I think they will develop the equivalent of what your
Speaker 4: father did. And I think people say, you know what
Speaker 4: if like these kinds of things, everybody's doing it generically
Speaker 4: and none of us have any meaning because all you're
Speaker 4: doing is pressing buttons, and like the intent of this
Speaker 4: was something which was to teach you how to write
Speaker 4: or to think or something.
Speaker 5: There may be a variant of how we do all
Speaker 5: of this. I mean, obviously some.
Speaker 4: Version of that that has happened is like Okay, we're
Speaker 4: all going to sit down and doing with pencil and
Speaker 4: paper and computers in their classroom. But there'll be other
Speaker 4: variants of creativity that people will put forth to say,
Speaker 4: you know what, you know, that's a way to solve
Speaker 4: that problem too.
Speaker 2: But this is interesting because to stay on this analogy,
Speaker 2: we're really talking about a profound rethinking just using a
Speaker 2: college as an example, a real profound rethinking of the
Speaker 2: way there's no part of this college it's unaffected by
Speaker 2: aia B. In one case, I've made everyone's job easier.
Speaker 2: In one case, I've made I'm asking us to really
Speaker 2: rethink from the ground up what teaching means. In another case,
Speaker 2: I've automated systems that I didn't think of it. I mean,
Speaker 2: it's like, that's right, that's it's not that's a lot
Speaker 2: to ask someone who got a pH d in medieval
Speaker 2: language literature, you know, forty years ago.
Speaker 4: Yeah, but you know, I'll tell you a positive sort
Speaker 4: of development that I'm seeing the sciences around this, which
Speaker 4: is you're seen as you see more and more examples
Speaker 4: of applying AI technology within the context of like historians
Speaker 4: to as an example, Right, you have archival and you know,
Speaker 4: and you have all these books and being able to
Speaker 4: actually help you as an assistant right around that, but
Speaker 4: not only with text now, but with diagrams, right. And
Speaker 4: I've seen it in anthropology too, write and archaeology with
Speaker 4: examples of engravings and translations and things that can happen.
Speaker 4: So so as you see in diverse fields people applying
Speaker 4: these techniques to advance and how to do physics or
Speaker 4: how to do chemistry. They inspire each other, right, and
Speaker 4: this said, you know, how does it apply actually to
Speaker 4: my area? So once as that happens, it becomes less
Speaker 4: of a chore of like, my god, you know, how
Speaker 4: do I have to deal with this? But actually it's
Speaker 4: triggered by Curiosity's triggered by you know, there'll be like,
Speaker 4: you know, faculty that will be like, you know what,
Speaker 4: you know, let me explore what this means for my area,
Speaker 4: and they will adapt it to the local context, to
Speaker 4: the local you know, language, and the professional itself. So
Speaker 4: I see that as a positive vector that is not
Speaker 4: all going to feel like homework.
Speaker 5: You know, it's not going to feel like, oh my god,
Speaker 5: this is so overwhelming.
Speaker 4: But rather to be very practical to see what works,
Speaker 4: What have I seen others to do that is inspiring?
Speaker 3: And what am I inspired to do? You know what?
Speaker 3: What is how is this going to help my career?
Speaker 4: I think that that's going to be an interesting question
Speaker 4: for you know, those faculty members, for the students.
Speaker 2: The professionals. Sorry, I'm gonna stick with this example along
Speaker 2: because it's really interesting. I'm curious following up on what
Speaker 2: you just said, that one of the most persistent critiques
Speaker 2: of academic but also of many of many corporate institutions
Speaker 2: in recent UITs, has been siloing. Right, different parts of
Speaker 2: the of the organization are going off on their own
Speaker 2: and not speaking to each other. Is a potential is
Speaker 2: a real potential benefit to AI the kind of breaking
Speaker 2: down a simple tool for breaking down those kinds of barriers,
Speaker 2: is that a very is that an elegant way of
Speaker 2: sort of saying what.
Speaker 3: I really think?
Speaker 4: And I was actually just having a conversation with Provos
Speaker 4: stuff and very much on this topic, very recently exactly
Speaker 4: on that, which is all these this you know, this
Speaker 4: appetite right to collaborate across disciplines. There's a lot of
Speaker 4: attempts stores a goal, right, creating interdisciplinary centers, creating dual
Speaker 4: degree programs or dual appointment programs. But actually in a
Speaker 4: lot of progress in academia happens by methodology too. Write
Speaker 4: like a new you know, when when some methodology gets adopted,
Speaker 4: I mean the most famous example of that is a
Speaker 4: scientific method as an example of that. But when you
Speaker 4: have a methodology that gets adopted, it also provides a
Speaker 4: way to speak to your colleagues across different disciplines. And
Speaker 4: I think what's happened in AI is linked to that
Speaker 4: that within the context of the scientific method as an example,
Speaker 4: the methodology about we about what we do discovery, the
Speaker 4: role of data, the role of these neural networks, of
Speaker 4: how we actually find proximity to concepts to one another
Speaker 4: is actually fundamentally different than how we've traditionally applied it.
Speaker 4: So as we see across more professions, people applying this
Speaker 4: methodology is also going to give some element of common
Speaker 4: language to each other. Right And in fact, you know,
Speaker 4: in this very high dimensional representation of information that is
Speaker 4: pressent to neural networks, we may find amazing adjacencies or
Speaker 4: connections of themes and topics in ways that the individual
Speaker 4: practitioners cannot describe, but yet will be latent.
Speaker 3: In these large calural networks.
Speaker 4: We are going to suffer a little bit from causality,
Speaker 4: from the problem of like, hey, what's the root cause
Speaker 4: of that? Because I think one of the unsatisfying aspects
Speaker 4: that this methodology will provide is they may give you
Speaker 4: answers for which they don't give you good reasons for
Speaker 4: where the answers came from, and then there will be
Speaker 4: the traditional process of discovery of saying, if that is
Speaker 4: the answer, what are the reasons? So we're going to
Speaker 4: have to do this sort of hybrid way of understanding
Speaker 4: the world. But I do think that common layer of
Speaker 4: AI is a powerful new thing.
Speaker 2: Yeah, well, a couple of random questions. I couldn't manage
Speaker 2: you talk in the In the Writer's strike that just
Speaker 2: ended in Hollywood, one of the sticking points was how
Speaker 2: the studios and writers would treat AI generated content. Good
Speaker 2: writers get credit if their material with somehow the source
Speaker 2: for A but more broadly, did the writers need protections
Speaker 2: against the use of I could go on? You know what,
Speaker 2: I'll be worth familiar with all of this. Had you been,
Speaker 2: I don't know whether you were, but had either side
Speaker 2: called you in for advice? During that the writers, the
Speaker 2: writers called you and said, Daria, what should we do
Speaker 2: about AI? And how should we that should be reflect
Speaker 2: should that be reflected in our contract negotiations? What would
Speaker 2: you have told them?
Speaker 5: The way I think about that is that I divided.
Speaker 3: I would divide it into two pieces.
Speaker 4: First, is what's technically possible right and anticipate scenarios like
Speaker 4: you know, what can you do with voice cloning for example?
Speaker 4: You know, now, for example, it is possible this being
Speaker 4: dubbing right legis take that topic right around the world.
Speaker 4: There was all these folks that would dub people in
Speaker 4: other languages. Well, now you can do these incredible renderings.
Speaker 4: I mean, I know if you've seen them, where you
Speaker 4: know you match the lips is your original voice, but
Speaker 4: speaking any language that you want. As an example, so
Speaker 4: busy that has a set of implications around that. I mean,
Speaker 4: just to give an example, So I would say, create
Speaker 4: a taxonomy that describes technical capabilities that we know of
Speaker 4: today and applications to the industry and to examples of
Speaker 4: like hey, you know I could film you for five
Speaker 4: minutes and I could generate two hours of content of
Speaker 4: you and I don't have to you know, then if
Speaker 4: you'll get paid by the hour, obviously I'm not paying
Speaker 4: you for that other thing. So I would say technological
Speaker 4: capability and then map with their expertise consequences of how
Speaker 4: it changes the way they work or the way they
Speaker 4: interact or the way they negotiate and so on.
Speaker 3: So that would be one element of it, and then the.
Speaker 4: Other one is like a non technology related matter, which
Speaker 4: is an element of almost distributed justices like who deserves
Speaker 4: what right and who has the power to get what?
Speaker 4: And then that's a completely different discussion. That is to say, well,
Speaker 4: if this is the scenario of what's possible, you know,
Speaker 4: what do we want and what are we able to get?
Speaker 4: And I think that that's a different discussion, which.
Speaker 2: Is which all that life? Which when do you do?
Speaker 3: First?
Speaker 4: I think it's very helpful to have an understanding of
Speaker 4: what's possible and how it changes a landscape.
Speaker 3: As part of a broader.
Speaker 4: Discussion, right, and a broad negotiation, because you also have
Speaker 4: to see the opportunities because there will be a lot
Speaker 4: of ground to say, actually, you know, if we can
Speaker 4: do it in this way and we can all be
Speaker 4: that much more efficient in getting these piece work done
Speaker 4: or these filming done, but we have a reasonable agreement
Speaker 4: about how we both sides benefit from it, right, then
Speaker 4: that's a.
Speaker 3: Win win for everybody.
Speaker 2: Yeah.
Speaker 5: Right, so that's I think that would be a golden triangle.
Speaker 3: Right.
Speaker 2: Here's my reading, and I would like you to correct
Speaker 2: me if I'm wrong, and I'm likely to be wrong.
Speaker 2: When I looked at that strike, I said if they're
Speaker 2: worried about AI. The writers are worried about AI. That
Speaker 2: seems silly. It should be the studios who are worried
Speaker 2: about the economic impact of AI. Does it? In the
Speaker 2: long run AI put the studios out of business long
Speaker 2: before it puts the writers out of business. I only
Speaker 2: need the studio because the costs of production are as
Speaker 2: high as the sky, and the cost of production are overwhelming.
Speaker 2: And whereas if I don't, if I have a tool
Speaker 2: which brings introduces massive technological efficiencies to the production of movies,
Speaker 2: then why don't studio? Why would they the scared ones?
Speaker 4: Or maybe maybe you need like a different kind of
Speaker 4: studio or a different kind of different kind of studies.
Speaker 2: But I mean the in the but in the in
Speaker 2: this strike the fright the frightened ones with the writers
Speaker 2: and the you know, with the studios. Wasn't that backwards?
Speaker 3: I haven't thought about it.
Speaker 5: Uh, it can be about the implications of it.
Speaker 4: It goes back to we're talking before the implications because
Speaker 4: are so horizontal, it is right to think about it,
Speaker 4: like what does.
Speaker 3: It do to the studios as well? Right?
Speaker 2: Yeah?
Speaker 4: But then you know the reason why that happens is
Speaker 4: that it's the order of either negotiations or or who
Speaker 4: first got concerned about it and did something about it, right,
Speaker 4: which is in the context of the strike. You know,
Speaker 4: I don't know what the equivalent conversations are going inside
Speaker 4: the studio and whether they have a war room saying
Speaker 4: what this is going to mean to us? Right, but
Speaker 4: it doesn't get exercise through a strike, but maybe through
Speaker 4: a task force inside you know, the companies about what
Speaker 4: are they going to do?
Speaker 3: Right?
Speaker 2: Well, and to go back to your thing you said,
Speaker 2: the first thing you do is you make a list
Speaker 2: of what techological capabilities are. But don't technological capabilities change
Speaker 2: every I mean they do. You're racing ahead so fast,
Speaker 2: so you can't Can you have a contract? I'm sorry
Speaker 2: for begetting in the little weeds here, but this is interesting.
Speaker 2: Can you You can't have a five year contract if
Speaker 2: the contract is based on an assessment of technological capabilities
Speaker 2: in twenty twenty three, because by the time we get
Speaker 2: to twenty eight twenty three eight, it's totally different. Right.
Speaker 4: Yeah, But like you know, I mean where I was
Speaker 4: going is like there are some abstractions around that is like,
Speaker 4: you know, whate can we do with my image? Right, Like,
Speaker 4: if I generally get the category that my image can
Speaker 4: be reproduced, generated contents and so on, it's like, let's
Speaker 4: talk about the abstract notion about who has rights to
Speaker 4: that or do we both get to benefit from that?
Speaker 4: If you get that straight, Yes, the nature of how
Speaker 4: the image gets alter created as something will change underneath,
Speaker 4: but the concept will stay the same. And so I
Speaker 4: think what's important is to get the categories right.
Speaker 2: Yeah. Yeah, if you had to you to think about
Speaker 2: the biggest technological revolutions of the post war era last
Speaker 2: seventy five years, you can all come up with a list. Actually,
Speaker 2: it's really fun to come up with a list. I
Speaker 2: was thinking about this when we were you know, containerized
Speaker 2: shipping is my favorite, the green revolution, the internet is
Speaker 2: Where is the I in that list?
Speaker 4: So I would put it first in that context that
Speaker 4: you put forth over since World War Two, undoubtedly, like
Speaker 4: computing as a category is one of those trajectories that
Speaker 4: has reshaped.
Speaker 3: Right or world.
Speaker 4: And I think we think computing, I would say the
Speaker 4: role that semiconductors have had has been increwdly defining. I
Speaker 4: would say AI is the second example of that as
Speaker 4: a core architecture that is going to have an equivalent
Speaker 4: level of impact. And then the third leg I would
Speaker 4: put to that equation.
Speaker 3: Will be quantum and quantum information.
Speaker 4: And that's sort of like I like to summarize that
Speaker 4: the future of computing its bits, neurons, and cubits, and
Speaker 4: it is that idea of high precision computation, the world
Speaker 4: of neural networks and artificial intelligence, and the world of
Speaker 4: quantum and the combination of those things is going to
Speaker 4: be the defining force of the next hundred years in
Speaker 4: that category of computing.
Speaker 3: But it makes the list for sure.
Speaker 2: If it's that high up on the list. This is
Speaker 2: a total hypothetical. Would you if you were starting over
Speaker 2: if you're starting IBM right now, would you say, oh,
Speaker 2: our AI operations actually should be way bigger, Like how
Speaker 2: many how many thousands of people working for you?
Speaker 4: So within the research division it's about like three thousand,
Speaker 4: five hundred scientists.
Speaker 2: So in a perfect world, would you if it's that big,
Speaker 2: isn't that too small as a group?
Speaker 4: Yeah, Well that's like in the ricer division. I mean
Speaker 4: IBM overall, but I mean, like.
Speaker 2: So starting from first, so you have a we've got
Speaker 2: a technology that you're ranking with compute and you know,
Speaker 2: up there with as in terms of a world changer,
Speaker 2: are we So what I'm basically asking is are we
Speaker 2: underinvested in this huge you.
Speaker 4: Know, but so so yeah, it's a it's a good question.
Speaker 4: So like what I would say is that I think
Speaker 4: we should segment how many people do you need on
Speaker 4: the creation of the technology itself, and what is the
Speaker 4: right size of research and engineers and compute to do that,
Speaker 4: And how many people do you need in the sort
Speaker 4: of application of the technology to create better products, to
Speaker 4: deliver services and consulting and then ultimately to diffuse it
Speaker 4: through you know, sort of all spheres of society. And
Speaker 4: the numbers are very different, and that is not different
Speaker 4: than anywhere else. I mean, I mean, if you give
Speaker 4: examples of since you were talking about in context of
Speaker 4: World War two, how many people does it take to
Speaker 4: create you know, an atomic weapon as an example, it's
Speaker 4: a large number.
Speaker 3: I mean, it wasn't just loss animals.
Speaker 4: There was a lot of people in Okay, it's a
Speaker 4: large number, but it wasn't a million people, right, So
Speaker 4: you could have highly concentrated teams of people that, with
Speaker 4: enough resources, can do extraordinary scientific and technological achievements, and
Speaker 4: that's always by definition, is going to be a fraction
Speaker 4: of like one percent compared to the total volume that
Speaker 4: is going to require to then.
Speaker 3: Deal with it.
Speaker 2: But the application side is infinite almost.
Speaker 3: That's exactly.
Speaker 5: So that is where like in the end, the bottleneck
Speaker 5: really is.
Speaker 4: So with you know, thousands of scientists and engineers, you
Speaker 4: can create world class AI, right, And so no, you
Speaker 4: don't need ten thousand to be able to create the
Speaker 4: large language model and the generatic model and some but
Speaker 4: you need thousands, and you need you know, very significant
Speaker 4: amount of computer and data.
Speaker 3: You need that.
Speaker 4: The rest is okay, I build software, I build databases,
Speaker 4: or I build a software product that allows you to
Speaker 4: do inventory management, or I build you know, a photo
Speaker 4: editor and so on. Now that product incorporating the AI, modifying,
Speaker 4: expanding it and so on. Well, now you're talking about
Speaker 4: the entire software industries. So now you're talking about millions
Speaker 4: of people right who are neted, you know, who are
Speaker 4: required to bring AI into their product. Then you go
Speaker 4: on a step beyond the technology creators in terms of
Speaker 4: software and you say, well, okay, now what the skills
Speaker 4: to help organizations go undeployed in the department of you know,
Speaker 4: the interior, right, And then I said, okay, well, now
Speaker 4: you need like consultants and experts and people to work
Speaker 4: they are to integer into the workflow. So now you're
Speaker 4: talking into the many tens of millions of people around that.
Speaker 4: So I see it as these concentric circles of it.
Speaker 4: But to some degree in many of these core technology areas,
Speaker 4: just saying like, well, I need a team of like
Speaker 4: one hundred thousand people to create like AI or a
Speaker 4: or a new transistor or a new quantum computer. It's
Speaker 4: actually a diminished in return right in the end, like
Speaker 4: too many people connecting with each other.
Speaker 2: Is very difficult. But on the application side of just
Speaker 2: to go back to our our example of that college,
Speaker 2: just a task of sitting down with a faculty and
Speaker 2: working with them to reimagine what they do with these
Speaker 2: new set of tools in mind, with the understanding that
Speaker 2: the students coming in are probably going to know more
Speaker 2: about it than they do that a lot. I mean,
Speaker 2: that's that is a hurriculeion people problem.
Speaker 3: It's a people problem.
Speaker 4: Yeah, that's why I started in terms of the barriers
Speaker 4: of adoption of that, I mean the context of IBM,
Speaker 4: an example That's why we have a consulting organization, Ivan Consulting,
Speaker 4: that complements ib and technology, and the Ivan Consulting Organization
Speaker 4: has over one hundred and fifty thousand employees because of
Speaker 4: this question, right, because you have to sit down and
Speaker 4: you say, Okay, what problem are you trying to solve?
Speaker 4: What is a methodology we're going to do, and here's
Speaker 4: the technology options that we have to be able to
Speaker 4: bring into the table. In the end, the adoption across
Speaker 4: or society will be limited by this part. The technology
Speaker 4: is going to make it easier, more cost effective to
Speaker 4: implement those solutions. You first have to think about what
Speaker 4: you want to do, how you're going to do it,
Speaker 4: and how are you going to bring it into a
Speaker 4: life of this in this context faculty member or you know,
Speaker 4: the administrator and so on in the college.
Speaker 2: With that Hollywood that that notion I thought, which was
Speaker 2: absolutely I thought really interesting that in a Hollywood strike
Speaker 2: you have to have this conversation about a distributive justice,
Speaker 2: conversation about how do we That's it's a really hard
Speaker 2: conversation right to have and a So this brings me
Speaker 2: to my netflie, which is that you we were talking
Speaker 2: a backstage you have. You have two daughters, one in college,
Speaker 2: one about to go to college. That's right, so they're
Speaker 2: both scients minded. So tell me about the conversations you
Speaker 2: you have with your daughter. You have a unique conversation
Speaker 2: with your daughters because your conversation, your advice to them
Speaker 2: is is influenced by what you do for a living.
Speaker 3: Yes, it's true.
Speaker 2: So did you warn your daughters away from certain fields?
Speaker 2: Did you say, whatever you do, don't be No, no.
Speaker 4: No, that's not my style. I mean, for me, no,
Speaker 4: I try not to be like you know, preachy about that.
Speaker 4: So for me, it was just about showing by example
Speaker 4: all things I love, right and things I care about,
Speaker 4: and then you know, bringing them to the lab and
Speaker 4: seeing things, and then the natural conversations of things working
Speaker 4: on or interesting people I meet, so to the extent
Speaker 4: that they have chosen that and obviously this has an
Speaker 4: influence on them. It has been through seeing it, you know,
Speaker 4: perhaps through my eyes, right.
Speaker 3: And what do you see me do? And that I
Speaker 3: like my profession? Right.
Speaker 2: But one of your daughters, you said, is thinking that
Speaker 2: she wants to be a doctor. But being a doctor
Speaker 2: in a post AI world is surely a very different
Speaker 2: proposition than being a doctor in a pre AI world.
Speaker 2: Do you think you have you tried to prepare her
Speaker 2: for that difference. Have you explained to her what you
Speaker 2: think will happen to this profession she might enter.
Speaker 4: Yeah, I mean not in like, you know, incredible amount
Speaker 4: of detail, but but but yes, at the level of
Speaker 4: understanding what is changing, like lens of the information, lens
Speaker 4: with which you can look at the world and what
Speaker 4: is possible and what it can do, Like what is
Speaker 4: our role and what is the role of the technology
Speaker 4: and how that shapes At that level of abstraction, for sure,
Speaker 4: but not at the level of like, don't be a radiologist,
Speaker 4: you know, because this is what we.
Speaker 2: Want for you. I was gonna say, if you'ren't happy
Speaker 2: with your current job, you could do a podcast called
Speaker 2: Parenting Tips with Dario, which is just an AI person
Speaker 2: gives you advice on what your kids should do based
Speaker 2: on exactly this, Like should I be a radiologist? Dario
Speaker 2: tell me, Like it seems to be a really important question. Yeah,
Speaker 2: let me ask this question in a more I'm joking,
Speaker 2: but in a more serious way. Surely it would If
Speaker 2: I don't mean to use your daughter as an example.
Speaker 2: But let's imagine we're giving advice to someone who wants
Speaker 2: to enter medicine. A really useful conversation to have is
Speaker 2: what are the skills that are will be most prized
Speaker 2: in that profession fifteen years from now, and are they
Speaker 2: different from the skills that are prized now. How would
Speaker 2: you answer that question?
Speaker 3: Yeah?
Speaker 4: I think for example, this goes back to how is
Speaker 4: the scientific method in this context, like the practice of
Speaker 4: medicine going to change. I think we will see more
Speaker 4: changes on how we practice a scientific method and so
Speaker 4: on as a consequence of what is happening with the
Speaker 4: world of computing and information. How we represent information, how
Speaker 4: we represent knowledge, how we extract meaning from knowledge as
Speaker 4: a method than we have seen in the last two
Speaker 4: hundred years. So therefore, what I would like strongly encourage
Speaker 4: is not about like, hey, use this tool for doing
Speaker 4: this or doing that, but in the curriculum itself, in
Speaker 4: understanding how we do problems solving in the age of
Speaker 4: like data and data representation and so on, that needs
Speaker 4: to be embedded in the curriculum of everybody you know.
Speaker 3: That is I would say, actually quite horizontally.
Speaker 4: But certainly in the context of medicine and scientists and
Speaker 4: so on for sure, And to the extent that that
Speaker 4: gets ingrained, that will give us a lens that no
Speaker 4: matter what what specialty they go within medicine, they will say, Actually,
Speaker 4: the way I want to be able to tackle improving
Speaker 4: the quality of care, the way to do that is
Speaker 4: in addition to all the elements that we have practiced
Speaker 4: in the field of medicine, is this new lens?
Speaker 3: And are we.
Speaker 4: Representing the data the right way? Do we have the
Speaker 4: right tools to be able to represent that knowledge? Am
Speaker 4: I incorporating that in my own so with my own
Speaker 4: knowledge in a way that gives me better outcomes?
Speaker 3: Right?
Speaker 4: Do I have the rigor of benchmarking too? And quality
Speaker 4: of the results? So that is what needs to be incorporated.
Speaker 2: How in a perfect world, if I asked you your
Speaker 2: team to rewrite curriculum for American medical schools, how dramatic
Speaker 2: a revision is that? Are we tinkering with ten percent
Speaker 2: of the curriculum or we tinkering with fifty percent of it?
Speaker 4: I think they would be a subset of classes that
Speaker 4: is about the method the methodology, what has changed? Like
Speaker 4: have these lens of it to understand? And then within
Speaker 4: each class that methodology will represent something that is embedded
Speaker 4: in it, right, so it will be substantive but not
Speaker 4: But but doesn't mean replacing the specialization and the context
Speaker 4: and the knowledge of each domain. But I do think
Speaker 4: everybody should have sort of a basic knowledge of the horizontal, right,
Speaker 4: what is it, how does it work, what tools you have,
Speaker 4: what is the technology, and like you know what are.
Speaker 3: The dos and don'ts around that?
Speaker 4: And then every area you say, and you know that
Speaker 4: thing that you learn, this is how it applies to anatomy,
Speaker 4: and this is how you know how it applies to
Speaker 4: you know, radiology if you study that, or or this
Speaker 4: is how you apply you know, in the context of discovery,
Speaker 4: right of self structure and this is how we can
Speaker 4: use it. Or protein folding and this is how it
Speaker 4: does so that way you'll see a connecting tissue through
Speaker 4: throughout the whole thing.
Speaker 2: Yeah, I mean I would add to that because I
Speaker 2: was thinking of the that it's also this incredible opportunity
Speaker 2: to do what doctors are supposed to do but don't
Speaker 2: have time to do now, which is they're so consumed
Speaker 2: with figuring out what's wrong with you that they have
Speaker 2: little time to talk about the implications of the diagnosisness
Speaker 2: and what we really want to if we can freedom
Speaker 2: of some of the burden of what is actually quite
Speaker 2: a prosaic question of what's wrong with you and leave
Speaker 2: the hard human thing of let make should you be
Speaker 2: scared or hopeful? Should you you know? What do you
Speaker 2: need to do? Or what? Let me put this in
Speaker 2: the context of all the patients I've seen. That conversation,
Speaker 2: which is the most important one, is the one that
Speaker 2: seems to me. So like, if I had to, I
Speaker 2: would add, if we're reimagining the curriculum of med school,
Speaker 2: I'd like with whatever, by the way, very little time.
Speaker 2: Maybe we have to add two more years to med school,
Speaker 2: but like a whole but the whole thing about bringing
Speaker 2: back the human side of you know, now, if I
Speaker 2: can give you ten more minutes, how do you use
Speaker 2: that ten more men?
Speaker 4: But in that, in that reconceptualization that you just did,
Speaker 4: is what we should be doing around that, because I
Speaker 4: think the debate as to like, well I'm not going
Speaker 4: to need doctors or not, it's actually a not very
Speaker 4: useful debate. But rather this other question is how is
Speaker 4: your time being spent? What problems are you getting stuck.
Speaker 4: I mean I generalize this by like the obvious observation
Speaker 4: that if you look around in your professions, in our
Speaker 4: daily lives, we have not run out of problems to solve.
Speaker 4: So as an example of that is, hey, if I'm
Speaker 4: spending all my time trying to do diagnosis, and I
Speaker 4: could do that ten times faster, and it allows me
Speaker 4: actually to go, you know, and take care of the
Speaker 4: patients and all the next steps of what we have
Speaker 4: to do about it. That's probably a trade off that
Speaker 4: a lot of doctors would take, right, And then you say, well,
Speaker 4: you know, to what degree does it allow me to
Speaker 4: do that? And I can do these other things and
Speaker 4: these other things are critically important for my profession around that.
Speaker 4: So when you actually become less abstract and like we
Speaker 4: get past the futile conversation of like, oh, there's no
Speaker 4: more jobs and I's going to take it all of it,
Speaker 4: which is kind of nonsense, is you go back to say,
Speaker 4: in practice in your context, right, for you, what does
Speaker 4: it mean, how do you work?
Speaker 3: What can you do differently around that?
Speaker 4: Actually that's a much richer conversation, and very often we
Speaker 4: would find ourselves that there's a portion of the work
Speaker 4: we do that we say I would rather do less
Speaker 4: of that. This is this other part I like a lot,
Speaker 4: And if it is possible that technology could help us
Speaker 4: make that trade off, I'll.
Speaker 3: Take it in a heartbeat.
Speaker 4: Now, poorly implemented technology can also create another problem.
Speaker 3: You say, Hey, this was supposed to solve.
Speaker 4: Me things, but the way it's being implemented is not
Speaker 4: helping me, right, it's making my life more more miserable,
Speaker 4: or so on, or I've lost connection in how I
Speaker 4: used to work, et cetera. So that is why design
Speaker 4: is so important. That is why I also workflow is
Speaker 4: so important in being able to solve these problems. But
Speaker 4: it begins by, you know, going from the intergalactic to
Speaker 4: the reality of it, of that faculty member in the
Speaker 4: liberal Arts college or you know, or a you know,
Speaker 4: a practitioner in medicine in a hospital and what it
Speaker 4: means for them.
Speaker 3: Right.
Speaker 2: Yeah. What struck me Daria throughout our conversation is how
Speaker 2: much of this revolution is non technical, as to say,
Speaker 2: you guys are doing the technical thing here, but the
Speaker 2: real the revolution is going to require a whole range
Speaker 2: of people doing things that have nothing to do with software,
Speaker 2: that have to do with working out new new human arrangements.
Speaker 2: Talking about that, I mean, does keep coming back to
Speaker 2: the Hollywood strike thing that you have to have a
Speaker 2: conversation about our values is creators of of of of movies?
Speaker 2: How are we going to divide up the exactly credit
Speaker 2: and the like. That's a that's a conversation about philosophy,
Speaker 2: and you know, you know it's it is.
Speaker 5: And is it's in the grand tradition of why you know,
Speaker 5: a liberal.
Speaker 4: Education is so important in the broadest possible sense. Right,
Speaker 4: there's no common conception of the good right that is
Speaker 4: always tested a dialogue that happens within our society, and
Speaker 4: technology is going to fit in that context too, right.
Speaker 4: So that's why I personally, as a philosophy I'm not
Speaker 4: a technological determinists, right, And I don't like when colleagues
Speaker 4: in my profession right starts saying like, well, this is
Speaker 4: the way the technology is going to be, and by consequence,
Speaker 4: this is how society is going to be. I'm like,
Speaker 4: that's a highly contested goal. And if you want to
Speaker 4: enter into realm of politics or the real other ones,
Speaker 4: go and stand up on a stool and discuss whether
Speaker 4: that's what society wants. You will find that it's a
Speaker 4: huge diversity of opinions and perspective and that's what makes
Speaker 4: you know, you know, in a democracy, the richness of
Speaker 4: our society, and in the end that is going to
Speaker 4: be the centerpiece of the conversation what do we want?
Speaker 4: You know, who gets what? And so on? And that
Speaker 4: is actually, I don't think it's anything negative. That's acid
Speaker 4: should be because in the end is anchored of who
Speaker 4: we want as humans, you know, you know, as friends, family, citizens,
Speaker 4: and we have many overlapping sets of responsibilities, right and
Speaker 4: as a technology creator my only version ponsibilities not just
Speaker 4: as a scientist and a technology creator.
Speaker 3: I'm also a member.
Speaker 4: Of a family, I'm a citizen, and I'm many other
Speaker 4: things that I care about. And I think that that
Speaker 4: sometimes in the debate of the technological determinists, they start
Speaker 4: now budding into what is the realm of you know,
Speaker 4: justice and you know, in society and philosophy and democracy,
Speaker 4: And that's where they get the most uncomfortable because it's
Speaker 4: like I'm just telling you, like, you know, what's possible,
Speaker 4: and when there's pushback, it's like, yeah, but now we're
Speaker 4: talking about how we live and how we work and
Speaker 4: how much I.
Speaker 3: Get paid or not paid.
Speaker 4: So that technology is important. Technology shapes that conversation, but
Speaker 4: we're going to have the conversation with a different language,
Speaker 4: as it should be, and technologists need to get accustomed
Speaker 4: to if they want to participate in that world with
Speaker 4: the broad consequences. Hey, get a custom to deal with
Speaker 4: the complexity of that world of politics, society, institutions, unions,
Speaker 4: all that stuff. And you know, you can be like
Speaker 4: whiny about it. It's like they're not adopting my technology.
Speaker 4: That's what it takes to bring technology into the world.
Speaker 2: Yeah, well, said thank you Dario for this wonderful conversation.
Speaker 2: Thank you to all of you for coming and listening,
Speaker 2: and thank you.
Speaker 1: Thank you.
Speaker 2: Dario gild transformed how I think about the future of AI.
Speaker 2: He explained to me how huge of a leap it
Speaker 2: was when we went from chess playing models to language
Speaker 2: learning models, and he talked about how we still have
Speaker 2: a lot of room to grow. That's why it's important
Speaker 2: that we get things right. The future of AI is
Speaker 2: impossible to predict, but the technology has so much potential
Speaker 2: in every industry. Zooming into an academic or medical setting,
Speaker 2: showed just how close we are to the widespread adoption
Speaker 2: of AI. Even Hollywood is being forced to figure this out.
Speaker 2: Institutions have all Soortz will have to be at the
Speaker 2: forefront of integration in order to unlock the full power
Speaker 2: of AI thoughtfully and responsibly. Humans have the power and
Speaker 2: the responsibility to shape the tech for our world. I
Speaker 2: for one, I'm excited to see how things play out.
Speaker 2: Smart Talks with IBM is produced by Matt Romano, Joey Fishground,
Speaker 2: David jaw and Jacob Goldstein. We're edited by Lydia Jane Kott.
Speaker 2: Our engineers are Jason Gambrel, Sarah Bruguier, and Ben Holliday.
Speaker 2: Theme song by Gramoscope. Special thanks to Andy Kelly, Kathy Callahan,
Speaker 2: and the eight Bar and IBM teams, as well as
Speaker 2: the Pushkin marketing team. Smart Talks with IBM is a
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