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 3: Hello, Hello, Welcome to Smart Talks with IBA, a podcast
Speaker 3: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gabwell. This
Speaker 3: season we're continuing our conversation with new creators visionaries who
Speaker 3: are creatively applying technology in business to drive change, but
Speaker 3: with a focus on the transformative power of artificial intelligence
Speaker 3: and what it means to leverage AI as a game
Speaker 3: changing multiplier for your business. Our guest today is doctor
Speaker 3: David Cox, VP of AI Models at IBM Research and
Speaker 3: IBM Director of the MIT IBM Watson AI Lab, a
Speaker 3: first of its kind industry academic collaboration between IBM and
Speaker 3: MIT focused on the fundamental research of artificial intelligence. Over
Speaker 3: the course of decades, David Cox watched as the AI
Speaker 3: revolution steadily grew from the simmering ideas of a few
Speaker 3: academics and technologists into the industrial boom we are experiencing today.
Speaker 3: Having dedicated his life to pushing the field of AI
Speaker 3: towards new horizons, David has both contributed to and presided
Speaker 3: over many of the major breakthroughs in artificial intelligence. In
Speaker 3: today's episode, you'll hear David explain some of the conceptual
Speaker 3: underpinnings of the current AI landscape, things like foundation models,
Speaker 3: in surprisingly comprehensible terms. I might add, we'll also get
Speaker 3: into some of the amazing practical applications for AI in business,
Speaker 3: as well as what implications AI will have for the
Speaker 3: future of work and design. David spoke with Jacob Goldstein,
Speaker 3: host of the Pushkin podcast What's Your Problem. A veteran
Speaker 3: business journalist, Jacob has reported for The Wall Street Journal,
Speaker 3: the Miami Herald, and was a longtime host of the
Speaker 3: NPR program Planet Money. Okay, let's get to the interview.
Speaker 4: Tell me about your job at IBM.
Speaker 2: So.
Speaker 5: I wear two hats at IBM. So one, I'm the
Speaker 5: IBM Director of the MIT, IBM Watson AI Lab. So
Speaker 5: that's a joint lab between IBM and MIT where we
Speaker 5: try and invent what's next in AI. It's been running
Speaker 5: for about five years, and then more recently I started
Speaker 5: as the vice president for AI Models, and I'm in
Speaker 5: charge of building IBM's foundation models, you know, building these
Speaker 5: these big models, generative models that allow us to have
Speaker 5: all kinds of new exciting capabilities in AI.
Speaker 4: So, so I want to talk to you a lot
Speaker 4: about foundation models, about genitive AI. But before we get
Speaker 4: to that, let's just spend a minute on the on
Speaker 4: the IBM MIT collaboration. Where did that partnership start, How
Speaker 4: did it originate?
Speaker 5: Yeah, So, actually it turns out that MIT and IBM
Speaker 5: have been collaborating for a very long time in the
Speaker 5: area of AI. In fact, the term artificial intelligence was
Speaker 5: coined in a nineteen fifty six workshop that was held
Speaker 5: at Dartmouth, but it was actually organized by an IBM
Speaker 5: or Nathaniel Rochester, who led the development of the IBM
Speaker 5: seven and one. So we've really been together in AIS
Speaker 5: since the beginning, and as AI kept accelerating more and
Speaker 5: more and more, I think there was a really interesting
Speaker 5: decision to say, let's make this a formal partnership, so
Speaker 5: IBM in twenty seventeen and also to be committing close
Speaker 5: to a quarter billion dollars over ten years to have
Speaker 5: this joint lab with MIT, and we located ourselves right
Speaker 5: on the campus and we've been developing very very deep
Speaker 5: relationships where we can really get to know each other,
Speaker 5: work shoulder to shoulder, conceiving what we should work on next,
Speaker 5: and then executing the projects. And it's really very few
Speaker 5: entities like this exist between academia industry. It's been really
Speaker 5: fun the last five years to be a part of it.
Speaker 4: And what do you think are some of the most
Speaker 4: important outcomes of this collaboration between IBM and MIT.
Speaker 5: Yeah, so we're really kind of the tip of the
Speaker 5: sphere for IBM the I strategy. So we're we're really
Speaker 5: looking what, you know, what's coming ahead, and you know,
Speaker 5: in areas like Foundation models, you know, as the field
Speaker 5: changes and I T people are interested in working on
Speaker 5: you know, faculty, students and staff are interested in working
Speaker 5: on what's the latest thing, what's the next thing. We
Speaker 5: at IBM Research are very much interested in the same.
Speaker 5: So we can kind of put out feelers, you know,
Speaker 5: interesting things that we're seeing in our research, interesting things
Speaker 5: we're hearing in the field. We can go and chase
Speaker 5: those opportunities. So when something big comes, like the big
Speaker 5: change that's been happening lately with Foundation Models, we're ready
Speaker 5: to jump on it. That's really the purpose, that's that's
Speaker 5: the lab functioning the way it should. We're also really
Speaker 5: interested in how do we advance you know AI that
Speaker 5: can help with climate change or you know, build better
Speaker 5: materials and all these kinds of things that are you know,
Speaker 5: a broader aperture sometimes than than what we might consider
Speaker 5: just looking at the product portfolio of IBM, and that
Speaker 5: that gives us again a breadth where we can see
Speaker 5: connections that we might not have seen otherwise. We can
Speaker 5: you know, think things that help out society and also
Speaker 5: help out our customers.
Speaker 4: So the last whatever six months, say, there has been
Speaker 4: this wild rise in the public's interest in AI, right
Speaker 4: clearly coming out of these generative AI models that are
Speaker 4: really accessible, you know, certainly chat GPT language models like that,
Speaker 4: as well as models that generate images like mid Journey.
Speaker 4: I mean, can you just sort of briefly talk about
Speaker 4: the breakthroughs in AI that have made this moment feel
Speaker 4: so exciting, so revolutionary for artificial intelligence.
Speaker 5: Yeah. You know, I've been studying AI basically my entire
Speaker 5: adult life. Before I came to IBM, I was a
Speaker 5: professor at Harvard. I've been doing this a long time,
Speaker 5: and I've gotten used to being surprised. It sounds like
Speaker 5: a joke, but it's serious, Like I'm getting used to
Speaker 5: being surprised at the acceleration of the pace Again. It
Speaker 5: tracks actually a long way back. You know, there's lots
Speaker 5: of things where there was an idea that just simmered
Speaker 5: for a really long time. Some of the key math
Speaker 5: behind the stuff that we have today, which is amazing
Speaker 5: there's an algorithm called backpropagation, which is sort of key
Speaker 5: to training neural networks that's been around, you know, since
Speaker 5: the eighties in wide use. And really what happened was
Speaker 5: it simmered for a long time and then enough data
Speaker 5: and enough compute came. So we had enough data because
Speaker 5: you know, we all started carrying multiple cameras around with us.
Speaker 5: Our mobile phones have all, you know, all these cameras
Speaker 5: and this we put everything on the Internet, and there's
Speaker 5: all this data out there. We caught a lucky break
Speaker 5: that there was something called a graphics processing unit, which
Speaker 5: turns out to be really useful for doing these kinds
Speaker 5: of algorithms, maybe even more useful than it is for
Speaker 5: doing graphics. They're greater graphics too, And things just kept
Speaker 5: kind of adding to the snowball. So we had deep learning,
Speaker 5: which is sort of a rebrand of neural networks that
Speaker 5: I mentioned from from the eighties, and that was enable
Speaker 5: again by data because we digitalize the world and compute
Speaker 5: because because we kept building faster and faster and more
Speaker 5: powerful computers, and then that allowed us to make this
Speaker 5: this big breakthrough. And then you know, more recently, using
Speaker 5: the same building blocks that inexorable rise of more and
Speaker 5: more and more data met a technology called self supervised learning,
Speaker 5: where the key difference there in traditional deep learning, you know,
Speaker 5: for classifying images, you know, like is this a cat
Speaker 5: or is this a dog? And a picture, those technologies
Speaker 5: require super visions, so you have to take what you
Speaker 5: have and then you have to label it. So you
Speaker 5: have to take a picture of a cat, and then
Speaker 5: you label it as a cat. And it turns out
Speaker 5: that you know, that's very powerful, that it takes a
Speaker 5: lot of time to label gaps and to label dogs,
Speaker 5: and there's only so many labels that exist in the world.
Speaker 5: So what really changed more recently is that we have
Speaker 5: self supervised learning where you don't have to have the labels.
Speaker 5: We can just take unannotated data. And what that does
Speaker 5: is it lots you use even more data. And that's
Speaker 5: really what drove this latest sort of rage. And then
Speaker 5: and then all of a sudden we start getting these
Speaker 5: really powerful models. And then really this has been simmering technologies, right,
Speaker 5: this has been happening for a while and progressively getting
Speaker 5: more and more powerful. One of the things that really
Speaker 5: happened with CHATGBT and technologies like stable diffusion and mid
Speaker 5: journey was that they made it visible to the public.
Speaker 5: You know, you put it out there, the public can
Speaker 5: touch and feel and they're like, wow, not only is
Speaker 5: there palpable change and wow this you know, I could
Speaker 5: talk to this thing. Wow, this thing can generate an image.
Speaker 5: Not only that, but everyone can touch and feel and try.
Speaker 5: My kids can use some of these AI art generation technologies.
Speaker 5: And that's really just launched. You know. It's like a
Speaker 5: propelled slingshot at us into a different regime in terms
Speaker 5: of the public awareness of these technologies.
Speaker 4: You mentioned earlier in the conversation foundation models, and I
Speaker 4: want to talk a little bit about that. I mean,
Speaker 4: can you just tell me, you know, what are foundation
Speaker 4: models for AI and why are they a big deal?
Speaker 5: Yeah, So this term foundation model was coined by a
Speaker 5: group at Stanford, and I think it's actually a really
Speaker 5: apt term because remember I said, you know, one of
Speaker 5: the big things that unlocked this latest excitement was the
Speaker 5: fact that we could use large amounts of unannotated data.
Speaker 5: We could train a model. We don't have to go
Speaker 5: through the painful effort of labeling each and every example.
Speaker 5: You still need to have your model do something you
Speaker 5: wanted to do you still need to tell it what
Speaker 5: you want to do. You can't just have a model
Speaker 5: that doesn't have any purpose, but what a foundation model
Speaker 5: that provides a foundation, like a literal foundation, you can
Speaker 5: sort of stand on the shoulders of giants. You can
Speaker 5: have one of these massively trained models and then do
Speaker 5: a little bit on top. You know, you could use
Speaker 5: just a few examples of what you're looking for and
Speaker 5: you can get what you want from the model. So
Speaker 5: just a little bit on top now gets to the
Speaker 5: results that a huge amount of effort used to have
Speaker 5: to put in, you know, to get from the ground
Speaker 5: up to that level.
Speaker 4: I was trying to think of of an analogy for
Speaker 4: sort of foundation models versus what came before, and I
Speaker 4: don't know that I came up with a good one,
Speaker 4: but the best I could do was this. I want
Speaker 4: you to tell me if it's plausible. It's like before
Speaker 4: foundation models, it was like you had these sort of
Speaker 4: single use kitchen appliances. You could make a waffle iron
Speaker 4: if you wanted waffles, or you could make a toaster
Speaker 4: if you wanted to make toast. But a foundation model
Speaker 4: is like like an oven with a range on top.
Speaker 4: So it's like this machine, and you could just cook
Speaker 4: anything with this machine.
Speaker 5: Yeah, that's a great analogy. They're very versatile. The other
Speaker 5: piece of it, too, is that they dramatically lowered the
Speaker 5: effort that it takes to do something that you want
Speaker 5: to do. And I used to say about the old
Speaker 5: world of AI, would say, you know, the problem with
Speaker 5: automation is that it's too labor intensive, which sounds like
Speaker 5: I'm making a joke.
Speaker 4: Indeed, famously, if automation does one thing, it substitutes machines
Speaker 4: or computing power for labor, right, So what does that
Speaker 4: mean to say AI is or automation is too labor intensive.
Speaker 5: It sounds like I'm making a joke, but I'm actually serious.
Speaker 5: What I mean is that the effort it took the
Speaker 5: old regime to automate something was very very high. So
Speaker 5: if I need to go and curate all this data,
Speaker 5: collect all this data, and then carefully label all these examples,
Speaker 5: that labeling itself might be incredibly expensive and time. And
Speaker 5: we estimate anywhere between eighty to ninety percent of the
Speaker 5: effort it takes to feel an AI solution actually is
Speaker 5: just spent on data, so that that has some consequences,
Speaker 5: which is the threshold for bothering. You know, if you're
Speaker 5: going to only get a little bit of value back
Speaker 5: from something, are you going to go through this huge
Speaker 5: effort to curate all this data and then when it
Speaker 5: comes time to train the model you need highly skilled
Speaker 5: people defensive or hard to find in the labor market.
Speaker 5: You know, are you really going to do something that's
Speaker 5: just a title incremental thing? Now you're going to do
Speaker 5: the only the highest value things that weren't right level because.
Speaker 4: You have to essentially build the whole machine from scratch,
Speaker 4: and there aren't many things where it's worth that much
Speaker 4: work to build a machine that's only going to do
Speaker 4: one narrow thing.
Speaker 5: That's right, and then you tackle the next problem and
Speaker 5: you basically have to start over. And you know, there
Speaker 5: are some nuances here, like for images, you can pre
Speaker 5: train a model on some other task and change it around.
Speaker 5: So there are some examples of this, like non recurring
Speaker 5: cost that we have in the old world too, But
Speaker 5: by and large, it's just a lot of effort. It's hard.
Speaker 5: It takes you know, a large level of skill to implement.
Speaker 5: One analogy that I like is, you know, think about
Speaker 5: it as you know, you have a river of data,
Speaker 5: you know, running through your company or your institution. Traditional
Speaker 5: AI solutions are kind of like building a dam on
Speaker 5: that river. You know, dams are very expensive things to build.
Speaker 5: They require highly specialized skills and lots of planning. And
Speaker 5: you know, you're only going to put a dam on
Speaker 5: a river that's big enough that you're gonna get enough
Speaker 5: energy out of it that it was worth your trouble.
Speaker 5: You're going to get a lot of value out of
Speaker 5: that dam. If you have a river like that, you know,
Speaker 5: a river of data, but it's actually the vast majority
Speaker 5: of the water you know in your kingdom, actually isn't
Speaker 5: in that river. It's in puddles and creeks and bable bricks,
Speaker 5: And you know, there's a lot of value left on
Speaker 5: the table because it's like, well, I can't there's nothing
Speaker 5: you can do about it. It's just that that's too
Speaker 5: low value, so it takes too much effort, so I'm
Speaker 5: just not going to do it. The return around investment
Speaker 5: just isn't there, so you just end up not automating things.
Speaker 5: It's too much of a pain. Now what foundation models
Speaker 5: do is they say, well, actually no, we can train
Speaker 5: a base model, a foundation that you can work on
Speaker 5: and we don't we don't care. We have specifying what
Speaker 5: the task is ahead of time. We just need to
Speaker 5: learn about the domain of data. So if we want
Speaker 5: to build something that can understand English language, there's a
Speaker 5: ton of English language text available out in the world.
Speaker 5: We can now train models on huge quantities of it,
Speaker 5: and then it learned the structure, learned how language you know,
Speaker 5: good part of how language works on all that unlabeled data,
Speaker 5: and then when you roll up with your task, you know,
Speaker 5: I want to solve this particular problem. You don't have
Speaker 5: to start from scratch. You're starting from a very very
Speaker 5: very high place. So that just gives you the ability
Speaker 5: to just you know, now all of a sudden, everything
Speaker 5: is accessible. All the puddles and greeks and babbling brooks
Speaker 5: and klipons, you know, those are all accessible now. And
Speaker 5: that's that's very exciting. But it just changes the equation
Speaker 5: on what kinds of problems you could use AI to solve.
Speaker 4: And so foundation models basically mean that automating some new
Speaker 4: task is much less labor intensive. The sort of marginal
Speaker 4: effort to do some new automation thing is much lower
Speaker 4: because you're building on top of the foundation model rather
Speaker 4: than starting from scratch. Absolutely, So that is that is
Speaker 4: like the exciting good news. I do feel like there's
Speaker 4: a little bit of a countervailing idea that's worth talking
Speaker 4: about here, and that is the idea that even though
Speaker 4: there are these foundation models that are really powerful, that
Speaker 4: are relatively easy to build on top of, it's still
Speaker 4: the case right that there is not some one size
Speaker 4: fits all foundation model. So you know, what does that
Speaker 4: mean and why is that important to think about in
Speaker 4: this context?
Speaker 5: Yeah, so we believe very strongly that there isn't just
Speaker 5: one model to rule them all. There's a number of
Speaker 5: reasons why that could be true. One which I think
Speaker 5: is important and very relevant today is how much energy
Speaker 5: these models can consume. So these models, you know, can
Speaker 5: get very very large. So one thing that we're starting
Speaker 5: to see or starting to believe, is that you probably
Speaker 5: shouldn't use one giant sledgehammer model to solve every single problem,
Speaker 5: you know, like we should pick the right size model
Speaker 5: to solve the problem. We shouldn't necessarily assume that we
Speaker 5: need the biggest, baddest model for every little use case.
Speaker 5: And we're also seeing that, you know, small models that
Speaker 5: are trained like to specialize on particular domains can actually
Speaker 5: outperform much bigger models. So bigger isn't always even better.
Speaker 4: So they're more efficient and they do the thing you
Speaker 4: want them to do better as well.
Speaker 5: That's right. So Stanford, for instance, a group of Stanford
Speaker 5: trained a model. It is a two point seven billion
Speaker 5: parameter model, which isn't terribly big by today's standards. They
Speaker 5: trained it just on the biomedical literature, you know, this
Speaker 5: is the kind of thing that universities do. And what
Speaker 5: they showed was that this model was better at answering
Speaker 5: questions about the biomedical literature than some models that are
Speaker 5: one hundred billion parameters, you know, many times larger. So
Speaker 5: it's a little bit like you know, asking an expert
Speaker 5: for help on something versus asking the smartest person you know. Ye,
Speaker 5: the smartest person you know may be very smart, but
Speaker 5: they're not going to be expertise. And then as an
Speaker 5: added bonus, you know, this is now a much smaller model,
Speaker 5: it's much more efficient to run, we are you know,
Speaker 5: you know, it's cheaper, so there's lots of different advantages there.
Speaker 5: So I think we're going to see attention in the
Speaker 5: industry between vendors that say hey, this is the one,
Speaker 5: you know, big model, and then others that say, well, actually,
Speaker 5: you know, there's there's you know, lots of different tools
Speaker 5: we can use that all have this nice quality that
Speaker 5: we outlined at the beginning, and then we should really
Speaker 5: pick the one that makes the most sense for the
Speaker 5: task at hand.
Speaker 4: So there's sustainability basically efficiency. Another kind of set of
Speaker 4: issues that come up a lot with ai A are
Speaker 4: bias hallucination. Can you talk a little bit about bias
Speaker 4: and hallucination, what they are and how you're working to
Speaker 4: mitigate those problems.
Speaker 5: Yeah, so there are lots of issues still as amazing
Speaker 5: as these technologies are, and they are amazing, let's let's
Speaker 5: be very clear, lots of great things we're going to
Speaker 5: enable with these kinds of technologies. Bias isn't a new problem,
Speaker 5: so you know, basically we've seen this since the beginning
Speaker 5: of AI. If you train a model on data that
Speaker 5: has a bias in it, the model is going to
Speaker 5: recapitulate that bias when it provides its answers. So every time,
Speaker 5: you know, if all the text you have says, you know,
Speaker 5: it's more likely to refer to female nurses and male scientists.
Speaker 5: Then you're going to you know, get models that you know.
Speaker 5: For instance, there was an example where a machine learning
Speaker 5: based translation system translated from Hungarian to English. Hungarian doesn't
Speaker 5: have gendered pronouns. English does, and when you ask them
Speaker 5: to translate, it would translate they are a nurse to
Speaker 5: she is a nurse, would translate they are a scientist
Speaker 5: to he is a scientist. And that's not because the
Speaker 5: people who wrote the algorithm were building in bias and
Speaker 5: coding in like oh, it's got to be this way.
Speaker 5: It's because the data was like that. You know, we
Speaker 5: have biases in our society and they're reflected in in
Speaker 5: our data and our text and our images everywhere. And
Speaker 5: then the models they're just mapping from what they've what
Speaker 5: they've seen in their training data to to the result
Speaker 5: that you're trying to get them to do and to give,
Speaker 5: and then these biases come out. So there's a very
Speaker 5: active program of research, and you know, we we do
Speaker 5: quite a bit at IBM research and my T but
Speaker 5: also all over the community and industry and academia trying
Speaker 5: to figure out how do we explicitly remove these biases,
Speaker 5: how do we identify them, how do you know, how
Speaker 5: do we build tools that allow people to audit their
Speaker 5: systems to make sure they aren't biased. So this is
Speaker 5: a really important thing. And you know, again this was
Speaker 5: here since the beginning, you know of machine learning and AI,
Speaker 5: but foundation models and large language models and generative AI
Speaker 5: just bring it into sharper even sharper focus because there's
Speaker 5: just so much data and it's sort of building in
Speaker 5: baking and all these different biases we have, so that
Speaker 5: that's that's absolutely a problem that these model have. Another
Speaker 5: one that you mentioned was hallucinations. So even the most
Speaker 5: impressive of our models will often just make stuff up.
Speaker 5: You know, the technical term that the heels chosen as
Speaker 5: is hallucination. To give you an example, I asked chat
Speaker 5: tbt to create a biography of David Cox IBM, and
Speaker 5: you know, it started off really well. You know, they
Speaker 5: identified that I was the director of the mt IBM
Speaker 5: Watsonay and said a few words about that, and then
Speaker 5: it proceeded to create an authoritative but completely fake biography
Speaker 5: of me. Where I was British. I was born in
Speaker 5: the UK. I went to British university, you know universities
Speaker 5: in the UK. I was professorating the authority.
Speaker 4: Right, it's the certainty that that is weird about it, right,
Speaker 4: it's it's dead certain that you're from the UK, et cetera.
Speaker 5: Absolutely, yeah, as all kinds of flourishes like I want
Speaker 5: awards in the UK. So yeah, it's it's problematic because
Speaker 5: it kind of pokes a lot of weak spots in
Speaker 5: our humans psychology, where if something sounds coherent, we're likely
Speaker 5: to assume it's true. We're not used to interacting with
Speaker 5: people who eloquently and authoritatively you know, emit complete nonsense
Speaker 5: like yeah, you know we can debate about that, but.
Speaker 4: Yeah, we could debate about that. But yes, the sort
Speaker 4: of blive confidence throws you off when you realize it's
Speaker 4: completely wrong.
Speaker 5: Right, that's right. And and we do have a little
Speaker 5: bit of like a great and powerful AWS sort of
Speaker 5: vibe going sometimes where we're like, well, you know, the
Speaker 5: AI is all knowing and therefore whatever it says must
Speaker 5: be true. But but these things will make up stuff,
Speaker 5: you know, very aggressively, and you know, you everyone can
Speaker 5: try asking it for their their bio. You'll you'll get
Speaker 5: something that You'll always get something that's of the right form,
Speaker 5: that has the right tone. But you know, the facts
Speaker 5: just aren't necessarily there, So that's obviously a problem. We
Speaker 5: need to figure out how to close those gaps, fix
Speaker 5: those problems. There's lots of ways we can use them
Speaker 5: much more easily.
Speaker 3: I'd just like to say, faced with the awesome potential
Speaker 3: of what these technologies might do, it's a bit encouraging
Speaker 3: to hear that even chat GPT has a weakness for
Speaker 3: inventing flamboyant, if fictional versions of people's lives. And while
Speaker 3: entertaining ourselves with chat GPT and mid journey is important,
Speaker 3: the way lay people use consumer facing chatbots and generative
Speaker 3: AI is just fundamentally different from the way an enterprise
Speaker 3: business uses AI. How can we harness the abilities of
Speaker 3: artificial intelligence to help us solve the problems we face
Speaker 3: in business and technology. Let's listen on as David and
Speaker 3: Jacob continue their conversation.
Speaker 4: We've been talking in a somewhat abstract way about AI
Speaker 4: in the ways it can be used. Let's talk in
Speaker 4: a little bit more of a specific way. Can you
Speaker 4: just talk about some examples of business challenges that can
Speaker 4: be solved with automation? With this kind of automation we're
Speaker 4: talking about.
Speaker 5: Yeah, so really really, this guy's the limit. There's a
Speaker 5: whole set of different applications that these models are really
Speaker 5: good at. And basically it's a super set of everything
Speaker 5: we used to use ALI for in business. So you know,
Speaker 5: the simple kinds of things are like, hey, if I
Speaker 5: have text and I you know, if I have product
Speaker 5: reviews and I want to be able to tell if
Speaker 5: these are positive or negative. You know, like, let's look
Speaker 5: at all the negative reviews so we can have a
Speaker 5: human look through them and see what was up. Very
Speaker 5: common business use case. You can do it with traditional
Speaker 5: deep learning based AI. So so there's things like that
Speaker 5: that are you know, it's very prosaic sort that we
Speaker 5: were already doing that, We've been doing it for a
Speaker 5: long time. Then you get situations that are that were
Speaker 5: harder for.
Speaker 2: The old day.
Speaker 5: I like, if i'm I want to compress something like
Speaker 5: I want to I have like say I have a
Speaker 5: chat transcript, like a customer called in and they had
Speaker 5: a complaint. They call back, Okay, now a new you know,
Speaker 5: a person on the line needs to go read the
Speaker 5: old transcript to catch up. Wouldn't it be better if
Speaker 5: we could just summariz that, just condense it all down
Speaker 5: quick little paragraph, you know, customer call they're up said
Speaker 5: about this, rather than having to read the blow by blow.
Speaker 5: There's just lots of settings like that where summarization is
Speaker 5: really helpful. Hey, you have a meeting and I'd like
Speaker 5: to just automatically, you know, have have that meeting or
Speaker 5: that email or whatever. I'd like to just have a
Speaker 5: condensed down so I can really quickly get to the
Speaker 5: heart of the matter. These models are are really good
Speaker 5: at doing that. They're also a really good at question answering.
Speaker 5: So if I want to find out what's how many
Speaker 5: vacation days do I have? I can now interact in
Speaker 5: natural language with a system that can go and that
Speaker 5: has access to our HR policies, and I can actually
Speaker 5: have a you know, a multi turn conversation where I can,
Speaker 5: you know, like I would have with you know, somebody,
Speaker 5: you know, actual HR professional or customer service representative. So
Speaker 5: a big part, you know, what this is doing is
Speaker 5: it's it's putting an interface. You know, when we think
Speaker 5: of computer interfaces, we're usually thinking about UI user interface
Speaker 5: elements where I click on menus and there's buts and
Speaker 5: all this stuff. Increasingly, now we can just talk you know,
Speaker 5: you just in words, you can describe what you want,
Speaker 5: you want to answer, ask a question, you want to
Speaker 5: sort of command the system to do something, rather than
Speaker 5: having to learn how to do that clicking buttons, which
Speaker 5: might be inefficient. Now we can just sort of spell
Speaker 5: it out.
Speaker 4: Interesting, right, the graphical user interface that we all sort
Speaker 4: of default to, that's not like the state of nature, Right,
Speaker 4: that's a thing that was invented and just came to
Speaker 4: be the standard way that we interact with computers. And
Speaker 4: so you could imagine, as you're saying, like chat essentially
Speaker 4: chatting with the machine could could become a sort of
Speaker 4: standard user interface, just like the graphical user interface, did
Speaker 4: you know over the past several decades.
Speaker 5: Absolutely, And I think those kinds of conversational interfaces are
Speaker 5: going to be hugely important for increasing our productivity. It's
Speaker 5: just a lot easier if I if I have to
Speaker 5: learn how to use a tool or I don't have
Speaker 5: to kind of have awkward, you know, interactions from the computer.
Speaker 5: I can just tell it what I want and I
Speaker 5: can understand it, could you know potentially and you ask
Speaker 5: questions back to clarify and have those kinds of conversations
Speaker 5: that can be extremely powerful, and in fact, one area
Speaker 5: where that's going to I think be absolutely game changing
Speaker 5: is in code. When we write code. You know, programming
Speaker 5: languages are a way for us to sort of match
Speaker 5: between our very sloppy way of talking and the very
Speaker 5: exact way that you need to command a computer to
Speaker 5: do what you wanted to do. They're cumbersome to learn,
Speaker 5: they can you know, create very complex systems that are
Speaker 5: very hard to reason about. And we're already starting to
Speaker 5: see the ability to just write down what you want
Speaker 5: and the AI will generate the code for you. And
Speaker 5: I think we're just going to see a huge revolution
Speaker 5: of like we just converse you and we can have
Speaker 5: a conversation to say what we want, and then the
Speaker 5: computer can actually not only do fixed actions and do
Speaker 5: things for us, but it can actually even write code
Speaker 5: to do new things, you know, and generate software itself.
Speaker 5: Given how much software we have, how much craving we
Speaker 5: have for software, like we'll never have enough software in
Speaker 5: our world, uh, you know, the ability to have AI
Speaker 5: systems as a helper in that, I think we're going
Speaker 5: to see a lot of a lot of value there.
Speaker 4: So if you if you think about the different ways
Speaker 4: AI might be applied to business. I mean you've talked
Speaker 4: about a number of the sort of classic use cases.
Speaker 4: What are some of the more out there use cases.
Speaker 4: What are some you know, unique ways you could imagine
Speaker 4: AI being applied to business.
Speaker 5: Yeah, there's really disguised the limit. I mean, we have
Speaker 5: one project that I'm kind of a fan of where
Speaker 5: we actually were working with a mechanical engineering professor at
Speaker 5: MIT working on a classic problem, how do you build
Speaker 5: linkage systems which are like, can imagine bars and joints
Speaker 5: and ogres, you know the things that are.
Speaker 4: Building a thing, building a physical machine of some kind of.
Speaker 5: Like real like metal and you know nineteenth century just
Speaker 5: old school Industrial revolution. Yeah yeah, yeah, but you know
Speaker 5: the little arm that's that's holding up my microphone in
Speaker 5: front of me. Cranes, get build your buildings, you know,
Speaker 5: parts of your engines. This is like classical stuff. It
Speaker 5: turns out that you know, humans, if you want to
Speaker 5: build an advanced system, you decide what like curve you
Speaker 5: want to create, and then a human together with a
Speaker 5: computer program, can build a five or six bar linkage.
Speaker 5: And then that's kind of where you top out it
Speaker 5: because it gets too complicated to work more than that.
Speaker 5: We built a generative AI system that can build twenty
Speaker 5: bar linkages like arbitrarily complex. So these are machines that
Speaker 5: are beyond the capability of a human to design themselves.
Speaker 5: Another example, we have an AI system that can generate
Speaker 5: electronic circuits. You know, we had a project where we're
Speaker 5: working where we were building better power converters which allow
Speaker 5: our computers and our devices to be more efficient, save energy,
Speaker 5: you know, less less carbonet But I think the world
Speaker 5: around us has always been shaped by technology. If you
Speaker 5: look around, you know, just think about how many steps
Speaker 5: and how many people, and how many designs went into
Speaker 5: the table and the chair and the vamp. It's it's
Speaker 5: really just astonishing. And that's already you know, the fruit
Speaker 5: of automation and computers and those kinds of tools. But
Speaker 5: we're going to see that increasingly be act also of AI.
Speaker 5: It's just going to be everywhere around us, everything we
Speaker 5: touch is going to have to you know, helped in
Speaker 5: some way to get get to you by you know.
Speaker 4: That is a pretty profound transformation that you're talking about
Speaker 4: in business. How do you think about the implications of
Speaker 4: that both for the sort of you know, business itself,
Speaker 4: and also for for employees.
Speaker 5: Yeah, so I think for businesses this is gonna cut costs,
Speaker 5: make new opportunities to like customers, you know, like there's
Speaker 5: just you know, it's sort of all upside right like
Speaker 5: for the for the workers, I think the story is
Speaker 5: mostly good too. You know, like how many things do
Speaker 5: you do in your day that you'd really rather not right?
Speaker 5: You know, and we're used to having things we don't
Speaker 5: like automated away, you know, we we didn't you know,
Speaker 5: if we didn't like walk getting many miles to work,
Speaker 5: then you know, like you can have a car and
Speaker 5: you can drive there. Or we used to have a
Speaker 5: huge fraction over ninety percent of the US population engaged
Speaker 5: in agriculture, and then we mechanized it. Now very few
Speaker 5: people work in agriculture. A small number of people can
Speaker 5: do the work of a large number of people. And
Speaker 5: then you know, things like email, and you know, they've
Speaker 5: led to huge productivity enhancements because I don't need to
Speaker 5: be writing letters and sending them in the mail. I
Speaker 5: can just instantly communicate with people. We just become more effective,
Speaker 5: Like our jobs have transformed, whether it's a physical job
Speaker 5: like agriculture, or whether it's a knowledge worker job where
Speaker 5: you're sending emails and communicating with people and coordinating teams.
Speaker 5: We've just gotten better and you know, the technology has
Speaker 5: just made us more productive. And this is just another example. Now,
Speaker 5: you know, there are people who worry that you know,
Speaker 5: will be so good at that that maybe jobs will
Speaker 5: be displaced, and that's that's a legitimate concern. But just
Speaker 5: like how in agriculture, you know, it's not like suddenly
Speaker 5: we had ninety percent of the population and unemployed. You know,
Speaker 5: people transitioned to to other jobs. And the other thing
Speaker 5: that we've found, too, is that our appetite for doing
Speaker 5: more things is as humans is sort of insatiable. So
Speaker 5: even if we can dramatically increase how much you know,
Speaker 5: one human can do, that doesn't necessarily mean we're going
Speaker 5: to do a fixed amount of stuff. There's an appetite
Speaker 5: to have even more, so we're going to you can
Speaker 5: continue to grow grow the pie. So I think at
Speaker 5: least certainly in the near term, you know, we're going
Speaker 5: to see a lot of drudgery go away from work.
Speaker 5: We're going to see people to be able to be
Speaker 5: more effective at their jobs. You know, we will see
Speaker 5: some transformation in jobs and what like. But we've seen
Speaker 5: that before and the technology a least has the potential
Speaker 5: to make our lives a lot easier.
Speaker 4: So IBM recently launched Watson X, which includes Watson x
Speaker 4: dot AI. Tell me about that, tell me about you
Speaker 4: know what it is and the new possibilities that it
Speaker 4: opens up.
Speaker 5: Yeah, So so Wat's the next is obviously a bit
Speaker 5: of a new branding on the Watson brand. TJ. Watson
Speaker 5: that was the founder of IBM and our EI technologies
Speaker 5: have had the Watson brand. Watson X is a recognition
Speaker 5: that there's something new, there's something that actually has changed
Speaker 5: the game. We've gone from this old world of automation
Speaker 5: is to labor intensive to this new world of possibilities
Speaker 5: where it's much easier to use AI. And what Watson
Speaker 5: X does it brings together tools for businesses to harness
Speaker 5: that power. So whattsonex dot AI foundation models that our
Speaker 5: customers can use. It includes tools that make it easy
Speaker 5: to run, easy to deploy, easy to experiment. There's a
Speaker 5: watsonex dot Data component which allows you to sort of
Speaker 5: organize and access to your data. So what we're really
Speaker 5: trying to do is give our customers a cohesive set
Speaker 5: of tools to the value of these technologies and at
Speaker 5: the same time be able to manage the risks and
Speaker 5: other things that you have to keep an eye on
Speaker 5: in an enterprise context.
Speaker 4: So we talk about the guests on this show as
Speaker 4: new creators, by which we mean people who are creatively
Speaker 4: applying technology in business to drive change. And I'm curious
Speaker 4: how creativity plays a role in the research that you do.
Speaker 5: I honestly, I think the creative aspects of this job
Speaker 5: this is what makes this work exciting. You know, I
Speaker 5: should say, you know, the folks who work at my
Speaker 5: organization are doing the creating, and I.
Speaker 4: Guess you're doing the managing so that they could do
Speaker 4: the creator.
Speaker 5: I'm helping them be their best and I still get
Speaker 5: to get involved in the weeds of the research as
Speaker 5: much as I can. But you know, there's something really
Speaker 5: exciting about inventing, you know, like nice things about doing
Speaker 5: invention and doing research on AI and industries. It's usually
Speaker 5: grounded and a real problem that somebody's having. You know,
Speaker 5: a customer wants to solve this problem. It's losing money
Speaker 5: or there there would be a new opportunity. You identify
Speaker 5: that problem and then you build something that's never been
Speaker 5: built before to do that. And I think that's honestly
Speaker 5: the adrenaline rush that keeps all of us in this field.
Speaker 5: How do you do something that nobody else on earth
Speaker 5: has done before or tried before, So that that kind
Speaker 5: of creativity, and there's also creativity as well, and identifying
Speaker 5: what those problems are, being able to understand the places
Speaker 5: where you know the technology is close enough to solving
Speaker 5: a problem, and doing that matchmaking between problems that are
Speaker 5: now solvable, you know, and an AI where the field
Speaker 5: is moving so fast, this is constantly growing horizon of
Speaker 5: things that we might be able to solve. So that matchmaking,
Speaker 5: I think is also a really interesting creative problem. So
Speaker 5: I think I think that's that's that's why it's so
Speaker 5: much fun. And it's a fun environment we have here too.
Speaker 5: It's you know, people drawing on whiteboards and writing on
Speaker 5: pages of math and you.
Speaker 4: Know, like in a movie, like in a movie, yeah,
Speaker 4: straight from special casting drawing, the drawing on the window,
Speaker 4: writing on the window in sharp absolutely, So, so let's
Speaker 4: close with the really long view. How do you imagine
Speaker 4: AI and people working together twenty years from now?
Speaker 5: Yeah, it's really hard to make predictions. The vision that
Speaker 5: I like, actually this came from an MIT economist named
Speaker 5: David Ottur, which was imagine AI almost as a natural resource.
Speaker 5: You know, we have we know how natural resources work, right,
Speaker 5: Like there's an or we can dig up out of
Speaker 5: the earth, comes from kind of springs from the earth,
Speaker 5: or we usually think of that in terms of physical stuff.
Speaker 5: With AI, you can almost think of it as like
Speaker 5: there's a new kind of abundance potentially twenty years from
Speaker 5: now where not only can we have things we can
Speaker 5: build or eat or use or burn or whatever. Now
Speaker 5: we have, you know, this ability to do things and
Speaker 5: understand things and do intellectual work, and I think we
Speaker 5: can get to a world where automating things is just seamless.
Speaker 5: We're surrounded by capability to augment ourselves to get things done.
Speaker 5: And you could think of that in terms of like, oh,
Speaker 5: that's going to displace our jobs, because eventually the AI
Speaker 5: system is going to do everything we can do. But
Speaker 5: you could also think of it in terms of like, wow,
Speaker 5: that's just so much abundance that we now have, and
Speaker 5: really how we use that abundance is sort of up
Speaker 5: to us, you know, like you can writing software is
Speaker 5: super easy and fast, and anybody can do it. Just
Speaker 5: think about all the things you can do now, think
Speaker 5: about all the new activities, and go out all the
Speaker 5: ways we could use that to enrich our lives. That's
Speaker 5: where I'd like to see us in twenty years. You
Speaker 5: know we can. We can do just so much more
Speaker 5: than we were able to do before abundance.
Speaker 4: Great, thank you so much for your time.
Speaker 5: Yeah, it's been a pleasure. Thanks for inviting me.
Speaker 3: What a far ranging, deep conversation. I'm mesmerized by the
Speaker 3: vision David just described. A world where natural conversation between
Speaker 3: mankind and machine can generate creative solutions to our most
Speaker 3: complex problems. A world where we view AI not as
Speaker 3: our replacements, but as a powerful resource we can tap
Speaker 3: into and exponentially boost our innovation and productivity. Thanks so
Speaker 3: much to doctor David Cox for joining us on smart Talks.
Speaker 3: We deeply appreciate him sharing his huge breadth of AI
Speaker 3: knowledge with us and for explaining the transformative potential of
Speaker 3: foundation models in a way that even I can understand.
Speaker 3: We eagerly await his next great breakthrough. Smart Talks with
Speaker 3: IBM is produced by Matt Romano David jaw nishe Venkat
Speaker 3: and Royston Preserve with Jacob Goldstein. We're edited by Lydia
Speaker 3: Jean Kott. Our engineers are Jason Gambrel, Sarah Buguer and
Speaker 3: Ben Holliday. Theme song by Gramoscope. Special thanks to Carli Megliori,
Speaker 3: Andy Kelly, Kathy Callahan and the eight Bar and IBM teams,
Speaker 3: as well as the Pushkin marketing team. Smart Talks with
Speaker 3: IBM is a production of Pushkin Industries and iHeartMedia. To
Speaker 3: find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
Speaker 3: or wherever you listen to podcasts. Hi'm Malcolm Gladwell. This
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