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