Speaker 1: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gabwell. This season,
Speaker 1: we're continuing our conversation with new creators visionaries who are
Speaker 1: creatively applying technology in business to drive change, but with
Speaker 1: a focus on the transformative power of artificial intelligence and
Speaker 1: what it means to leverage AI as a game changing
Speaker 1: multiplier for your business. Our guest today is doctor David Cox,
Speaker 1: VP of AI Models at IBM Research and IBM Director
Speaker 1: of the MIT IBM Watson AI Lab, a first of
Speaker 1: its kind industry academic collaboration between IBM and MIT focused
Speaker 1: on the fundamental research of artificial intelligence. Over the course
Speaker 1: of decades, David Cox watched as the AI revolution steadily
Speaker 1: grew from the sim ideas of a few academics and
Speaker 1: technologists into the industrial boom we are experiencing today. Having
Speaker 1: dedicated his life to pushing the field of AI towards
Speaker 1: new horizons, David has both contributed to and presided over
Speaker 1: many of the major breakthroughs in artificial intelligence. In today's episode,
Speaker 1: you'll hear David explain some of the conceptual underpinnings of
Speaker 1: the current AI landscape, things like foundation models in surprisingly
Speaker 1: comprehensible terms, I might add, we'll also get into some
Speaker 1: of the amazing practical applications for AI in business, as
Speaker 1: well as what implications AI will have for the future
Speaker 1: of work and design. David spoke with Jacob Goldstein, host
Speaker 1: of the Pushkin podcast What's Your Problem. A veteran business journalist,
Speaker 1: Jacob has reported for The Wall Street Journal, the Miami Herald,
Speaker 1: and was a longtime host of the NPR program Planet Money. Okay,
Speaker 1: let's get to the interview.
Speaker 2: Tell me about your job at IBM.
Speaker 3: SO. I wear two hats at IBM. SO one, I'm
Speaker 3: the IBM Doctor of the MI t IBM Watson AI Lab.
Speaker 3: So that's a joint lab between IBM and MIT where
Speaker 3: we try and invent what's next in AI. It's been
Speaker 3: running for about five years, and then more recently I
Speaker 3: started as the vice president for AI Models, and I'm
Speaker 3: in charge of building IBM's foundation models, you know, building
Speaker 3: these these big models, generative models that allow us to
Speaker 3: have all kinds of new exciting capabilities in AI.
Speaker 2: So, so I want to talk to you a lot
Speaker 2: about foundation models, about genitive AI. But before we get
Speaker 2: to that. Let's just spend a minute on the on
Speaker 2: the IBM MI T collaboration. Where where did that partnership start?
Speaker 2: How did it originate?
Speaker 3: Yeah, so, actually it turns out that MI T and
Speaker 3: IBM have been collaborating for a very long time in
Speaker 3: the area of AI. In fact, the term artificial intelligence
Speaker 3: was coined in a nineteen fifty six workshop that was
Speaker 3: held at Dartmouth. It was actually organized by an IBM
Speaker 3: or Nathaniel Rochester, who led the development of the IBM
Speaker 3: seven and one. So we've really been together in AIS
Speaker 3: since the beginning, and as AI kept accelerating more and
Speaker 3: more and more, I think there was a really interesting
Speaker 3: decision to say, let's make this a formal partnership. So
Speaker 3: IBM in twenty seventeen and now, so it'd be committing
Speaker 3: close to a quarter billion dollars over ten years to
Speaker 3: have this joint lab with MIT, and we located ourselves
Speaker 3: right on the campus and we've been developing very very
Speaker 3: deep relationships where we can really get to know each other,
Speaker 3: work shoulder to shoulder, conceiving what we should work on next,
Speaker 3: and then executing the projects. And it's really very few
Speaker 3: entities like this exist between academia industry. It's been really
Speaker 3: fun the last five years to be a part of it.
Speaker 2: And what do you think are some of the most
Speaker 2: important outcomes of this collaboration between IBM and MIT.
Speaker 3: Yeah, so we're really kind of the tip of the
Speaker 3: sphere for for IBM's AI strategy. So we're we're really
Speaker 3: looking what, you know, what's coming ahead, and you know,
Speaker 3: in areas like Foundation models, you know, as the field
Speaker 3: changes and I T people are interested in working on
Speaker 3: you know, faculty, students and staff are interested in working
Speaker 3: on what's the latest thing, what's the next thing. We
Speaker 3: at IBM Research very much interested in the same so
Speaker 3: we can kind of put out feelers, you know, interesting
Speaker 3: things that we're seeing in our research, interesting things we're
Speaker 3: hearing in the field. We can go and chase those opportunities.
Speaker 3: So when something big comes, like the big change that's
Speaker 3: been happening lately with Foundation Models, we're ready to jump
Speaker 3: on it. That's really the purpose, that's that's the lab
Speaker 3: functioning the way it should. We're also really interested in
Speaker 3: how do we advance you know, AI that can help
Speaker 3: with climate change or you know, build better materials and
Speaker 3: all these kinds of things. That are you know, a
Speaker 3: broader aperture sometimes than than what we might consider just
Speaker 3: looking at the product portfolio of IBM, and that that
Speaker 3: gives us again a breadth where we can connections that
Speaker 3: we might not have seen otherwise. We can, you know,
Speaker 3: think things that help out society and also help out
Speaker 3: our customers.
Speaker 2: So the last whatever six months, say, there has been
Speaker 2: this wild rise in the public's interest in AI, right
Speaker 2: clearly coming out of these generative AI models that are
Speaker 2: really accessible, you know, certainly chat GPT language models like that,
Speaker 2: as well as models that generate images like mid Journey.
Speaker 2: I mean, can you just sort of briefly talk about
Speaker 2: the breakthroughs in AI that have made this moment feel
Speaker 2: so exciting, so revolutionary for artificial intelligence.
Speaker 3: Yeah. You know, I've been studying AI basically my entire
Speaker 3: adult life. Before I came to IABM, I was a
Speaker 3: professor at Harvard. I've been doing this a long time,
Speaker 3: and I've gotten used to being surprised. It sounds like
Speaker 3: a joke, but it's serious, Like I'm getting used to
Speaker 3: being surprised at the acceleration of the pace. Again. It
Speaker 3: tracks actually a long way back. You know, there's lots
Speaker 3: of things where there was an idea that just simmered
Speaker 3: for a really long time. Some of the key math
Speaker 3: behind the stuff that we have today, which is amazing.
Speaker 3: There's an algorithm called backpropagation, which is sort of key
Speaker 3: to training neural networks that's been around, you know, since
Speaker 3: the eighties in wide use. And really what happened was
Speaker 3: it simmered for a long time, and then enough data
Speaker 3: and enough compute came. So we had enough data because
Speaker 3: you know, we all started carrying multiple cameras around with us.
Speaker 3: Our mobile phones have all, you know, all these cameras
Speaker 3: and this we put everything on the Internet, and there's
Speaker 3: all this data out there. We called a lucky break
Speaker 3: that there was something called a graphics processing unit, which
Speaker 3: you know, turns out to be really useful for doing
Speaker 3: these kinds of algorithms, maybe even more useful than it
Speaker 3: is for doing graphics. They're great graphics too. And things
Speaker 3: just kept kind of adding to the snowball. So we
Speaker 3: had deep learning, which is sort of a a rebrand
Speaker 3: of neural networks that I mentioned from the eighties, and
Speaker 3: that was enabled again by data because we digitalized the
Speaker 3: world and compute because because we kept building faster and
Speaker 3: faster and more powerful computers, and then that allowed us
Speaker 3: to make this big breakthrough. And then, you know, more recently,
Speaker 3: using the same building blocks, that inexorable rise of more
Speaker 3: and more and more data met the technology called self
Speaker 3: supervised learning, where the key difference there in traditional deep learning,
Speaker 3: you know, for classifying images, you know, like is this
Speaker 3: a cat or is this a dog? And a picture
Speaker 3: those technologies require supervision, so you have to take what
Speaker 3: you have and then you have to label it. So
Speaker 3: you have to take a picture of a cat, and
Speaker 3: then you label it as a cat, and it turns
Speaker 3: out that you know, that's very powerful, but it takes
Speaker 3: a lot of time to label gats and to label dogs,
Speaker 3: and there's only so many labels that us in the world.
Speaker 3: So what really changed more recently is that we have
Speaker 3: self supervised learning where you don't have to have the labels.
Speaker 3: We can just take unannotated data. And what that does
Speaker 3: is it lots you use even more data. And that's
Speaker 3: really what drove this latest sort of rage. And then
Speaker 3: and then all of a sudden we start getting these
Speaker 3: these really powerful models. And then really this has been
Speaker 3: simmering technologies, right, this has been happening for a while
Speaker 3: and progressively getting more and more powerful. One of the
Speaker 3: things that really happened with CHATGBT and technologies like stable
Speaker 3: Diffusion and mid Journey was that they made it visible
Speaker 3: to the public. You know, you put it out there.
Speaker 3: The public can touch and feel and they're like, wow,
Speaker 3: not only is there palpable change and wow this you know,
Speaker 3: I can talk to this thing. Wow, this thing can
Speaker 3: generate an image. Not only that, but everyone can touch
Speaker 3: and feel and try. My kids can use some of
Speaker 3: these AI art generation technologies. And that's really just launched.
Speaker 3: You know. It's like a propelled slingshot at a into
Speaker 3: a different regime. In terms of the public awareness of
Speaker 3: these technologies.
Speaker 2: You mentioned earlier in the conversation foundation models, and I
Speaker 2: want to talk a little bit about that. I mean,
Speaker 2: can you just tell me, you know, what are foundation
Speaker 2: models for AI and why are they a big deal?
Speaker 3: Yeah, So this term foundation model was coined by a
Speaker 3: group at Stanford, and I think it's actually a really
Speaker 3: apt term because remember I said, you know, one of
Speaker 3: the big things that unlocked this latest excitement was the
Speaker 3: fact that we could use large amounts of unannotated data.
Speaker 3: We could train a model. We don't have to go
Speaker 3: through the painful effort of labeling each and every example.
Speaker 3: You still need to have your model do something you
Speaker 3: wanted to do. You still need to tell it what
Speaker 3: you want to do. You can't just have a model
Speaker 3: that doesn't have any purpose. But what a foundation models
Speaker 3: that provides a foundation, Like a literal foundation, you can
Speaker 3: sort of stand on the shoulders of giants. You can
Speaker 3: have them these massively trained models, and then do a
Speaker 3: little bit on top. You know, you could use just
Speaker 3: a few examples of what you're looking for and you
Speaker 3: can get what you want from the model. So just
Speaker 3: a little bit on top. Now it gets to the
Speaker 3: results that a huge amount of effort used to have
Speaker 3: to put in, you know, to get from the ground
Speaker 3: up to that level.
Speaker 2: I was trying to think of of an analogy for
Speaker 2: sort of foundation models versus what came before, and I
Speaker 2: don't know that I came up with a good one,
Speaker 2: But the best I could do was this. I want
Speaker 2: you to tell me if it's plausible. It's like before
Speaker 2: foundation models, it was like you had these sort of
Speaker 2: single use kitchen appliances. You could make a waffle iron
Speaker 2: if you wanted waffles, or you could make a toaster
Speaker 2: if you wanted to make toast. But a foundation model
Speaker 2: is like like an oven with a range on top.
Speaker 2: So it's like this machine and you could just cook
Speaker 2: anything with this machine.
Speaker 3: Yeah, that's a great analogy. They're very versatile. The other
Speaker 3: piece of it, too, is that they dramatically lower the
Speaker 3: effort that it takes to do something that you want
Speaker 3: to do. And I used to say about the old
Speaker 3: world of AI, would say, you know, the problem with
Speaker 3: automation is that it's too labor intensive, which sounds like
Speaker 3: I'm making a joke.
Speaker 2: Indeed, famously, if automation does one thing, it substitutes machines
Speaker 2: or computing power for labor. Right, So what does that
Speaker 2: mean to say AI is or automation is too labor intensive.
Speaker 3: It sounds like I'm making a joke, but I'm actually serious.
Speaker 3: And what I mean is that the effort it took
Speaker 3: the old regime to automate something was very, very high.
Speaker 3: So if I need to go and curate all this data,
Speaker 3: collect all this data, and then carefully label all these examples,
Speaker 3: that labeling itself might be incredibly expensive and time, and
Speaker 3: we estimate anywhere between eighty to ninety percent of the
Speaker 3: effort it takes to feel an AI solution actually is
Speaker 3: just spent on data, so that that has some consequences,
Speaker 3: which is the threshold for bothering. You know, if you're
Speaker 3: going to only get a little bit of value back
Speaker 3: from something, are you going to go through this huge
Speaker 3: effort to curate all this data? And then when it
Speaker 3: comes time to train the model, you need highly skilled
Speaker 3: people defensive or hard to find in the labor market.
Speaker 3: You know, are you really going to do something that's
Speaker 3: just a tiny, little incremental thing. Now you're going to
Speaker 3: do the only the highest value things that warrn't right
Speaker 3: level because you.
Speaker 2: Have to essentially build the whole machine from scratch, and
Speaker 2: there aren't many things where it's worth that much work
Speaker 2: to build a machine that's only going to do one
Speaker 2: narrow thing that's.
Speaker 3: Right, and then you tackle the next problem and you
Speaker 3: basically have to start over. And you know, there are
Speaker 3: some nuances here, like for images, you can pre train
Speaker 3: a model on some other task and change it around.
Speaker 3: So there are some examples of this, like non recurring
Speaker 3: cost that we have in the old world too, but
Speaker 3: by and large, it's just a lot of effort. It's hard.
Speaker 3: It takes you know, a large level of skill to implement.
Speaker 3: One analogy that I like is, you know, think about
Speaker 3: it as you know, you have a river of data,
Speaker 3: you know, running through your company or your institution. Traditional
Speaker 3: AI solutions are kind of like building a dam on
Speaker 3: that river. You know, Dams are very expensive things to build.
Speaker 3: They require highly specialized skills and lots of planning. And
Speaker 3: you know, you're only going to put a dam on
Speaker 3: a river that's big enough that you're gonna get enough
Speaker 3: energy out of it that it was worth your trouble.
Speaker 3: You're gonna get a lot of value out of that dam.
Speaker 3: If you have a river like that, you know, a
Speaker 3: river of data, but it's actually the vast majority of
Speaker 3: the water you know, in your kingdom actually isn't in
Speaker 3: that river. It's in puddles and greeks and vallet bricks.
Speaker 3: And you know, there's a lot of value left on
Speaker 3: the table because it's like, well, I can't there's nothing
Speaker 3: you can do about it. It's just that that's too
Speaker 3: low value, so it takes too much effort, so I'm
Speaker 3: just not going to do it. The return on investment
Speaker 3: just isn't there so you just end up not automating things.
Speaker 3: It's too much of a pain. Now what foundation models
Speaker 3: do is they say, well, actually, no, we can train
Speaker 3: a base model a foundation they can work on, don't
Speaker 3: We don't care. We not specify what the task is
Speaker 3: ahead of time. We just need to learn about the
Speaker 3: domain of data. So if we want to build something
Speaker 3: that can understand English language, there's a ton of English
Speaker 3: language text available out in the world. We can now
Speaker 3: train models on huge quantities of it, and then it
Speaker 3: learned the structure, learned how language you know, good part
Speaker 3: of how language works on all that unlabeled data, and
Speaker 3: then when you roll up with your task, you know,
Speaker 3: I want to solve this particular problem. You don't have
Speaker 3: to start from scratch. You're starting from a very, very
Speaker 3: very high place. So that just gives you the ability
Speaker 3: to just you know, now, all of a sudden, everything
Speaker 3: is accessible. All the puddles and greeks and babbling books
Speaker 3: and gelipons, you know, those are all accessible now. And
Speaker 3: that's that's very exciting. But it just changes the equation
Speaker 3: on what kinds of problems you could use AI to solve.
Speaker 2: And so foundation models basically mean that automating some new
Speaker 2: task is much less labor and intensive. The sort of
Speaker 2: marginal effort to do some new automation thing is much
Speaker 2: lower because you're building on top of the foundation model
Speaker 2: rather than starting from scratch. Absolutely, So that is that
Speaker 2: is like the exciting good news. I do feel like
Speaker 2: there's a little bit of a countervailing idea that's worth
Speaker 2: talking about here, and that is the idea that even
Speaker 2: though there are these foundation models that are really powerful,
Speaker 2: that are relatively easy to build on top of, it's
Speaker 2: still the case right that there is not some one
Speaker 2: size fits all foundation model. So you know, what does
Speaker 2: that mean and why is that important to think about.
Speaker 3: In this context? Yeah, so we believe very strongly that
Speaker 3: there isn't just one model to rule them all. There's
Speaker 3: a number of reasons why that could be true. One
Speaker 3: which I think is important and very relevant today is
Speaker 3: how much energy these models can consume. So these models,
Speaker 3: you know, can get very very large. So one thing
Speaker 3: that we're starting to see or starting to believe, is
Speaker 3: that you probably shouldn't use one giant sledgehammer model to
Speaker 3: solve every single problem, you know, like we should pick
Speaker 3: the right size model to solve the problem. We shouldn't
Speaker 3: necessarily assume that we need the biggest, baddest model for
Speaker 3: every little use case. And we're also seeing that, you know,
Speaker 3: small models that are trained like to specialize on particular
Speaker 3: domains can actually outperform much bigger models. So bigger isn't
Speaker 3: always even better.
Speaker 2: So they're more efficient and they do the thing you
Speaker 2: want them to do better as well.
Speaker 3: That's right. So Stanford, for instance, a group of Stanford
Speaker 3: trained a model. It is a two point seven billion
Speaker 3: parameter model, which isn't terribly big by today's standards. They
Speaker 3: trained it just on the biomedical literature, you know, this
Speaker 3: is the kind of thing that universities do. And what
Speaker 3: they showed was that this model was better at answering
Speaker 3: questions about the biomedical literature than some models that are
Speaker 3: one hundred billion parameters you any times larger. So it's
Speaker 3: a little bit like you know, asking an expert for
Speaker 3: help on something versus asking the smartest person you know,
Speaker 3: the smartest person you know, maybe very smart, but they're
Speaker 3: not going to be expertise. And then as an added bonus,
Speaker 3: you know, this is now a much smaller model, it's
Speaker 3: much more efficient to run. We are you know, you know,
Speaker 3: it's cheaper, so there's lots of different advantages there. So
Speaker 3: I think we're going to see attention in the industry
Speaker 3: between vendors that say, hey, this is the one, you know,
Speaker 3: big model, and then others that say, well, actually, you know,
Speaker 3: there's there's you know, lots of different tools we can
Speaker 3: use that all have this nice quality that we outligned
Speaker 3: at the beginning, and then we should really pick the
Speaker 3: one that makes the most sense for the task at hand.
Speaker 2: So there's sustainability basically efficiency. Another kind of set of
Speaker 2: issues that come up a lot with ai A are
Speaker 2: bias hallucination. Can you talk a little bit about bias
Speaker 2: and hallucination, what they are and how you're working to
Speaker 2: mitigate those problems.
Speaker 3: Yeah, so there are lots of issues still as amazing
Speaker 3: as these technologies are, and they are amazing, let's be
Speaker 3: very clear, lots of great things we're going to enable
Speaker 3: with these kinds of technologies. Bias isn't a new problem,
Speaker 3: so you know, basically we've seen this since the beginning
Speaker 3: of AI. If you train a model on data that
Speaker 3: has a bias in it, the model is going to
Speaker 3: recapitulate that bias when it provides its answers. So every time,
Speaker 3: you know, if all the text you have says, you know,
Speaker 3: it's more likely to refer to female nurses and male scientists,
Speaker 3: then you're going to get models that you know. For instance,
Speaker 3: there was an example where a machine learning based translation
Speaker 3: system translated from Hungarian to English. Hungarian doesn't have gendered pronouns.
Speaker 3: English does, and when you ask them to translate, it
Speaker 3: would translate they are a nurse to she is a nurse,
Speaker 3: would translate they are a scientist to he is a scientist.
Speaker 3: And that's not because the people who wrote the algorithm
Speaker 3: were building in bias and coding in like oh, it's
Speaker 3: got to be this way. It's because the data was
Speaker 3: like that. You know, we have biases in our society
Speaker 3: and they're reflected in our data and our text and
Speaker 3: our images everywhere. And then the models they're just mapping
Speaker 3: from what they've what they've seen in their training data
Speaker 3: to to the result that you're trying to get them
Speaker 3: to do and to give, and then these biases come out.
Speaker 3: So there's a very active program of research in you know,
Speaker 3: we we do quite a bit at IBM research and I,
Speaker 3: but also all over the community and industry and academia
Speaker 3: trying to figure out how do we explicitly remove these biases,
Speaker 3: how do we identify them, how do you know, how
Speaker 3: do we build tools that allow people to audit their
Speaker 3: systems to make sure they aren't biased. So this is
Speaker 3: a really important thing. And you know, again this was
Speaker 3: here since the beginning, you know, of machine learning and AI,
Speaker 3: but foundation models and large language models and generative AI
Speaker 3: just bring it into sharper even sharper focus because there's
Speaker 3: just so much data and it's sort of building in
Speaker 3: baking and all these different biases we have. So that's
Speaker 3: that's absolutely a problem that these models have. Another one
Speaker 3: that you mentioned was hallucinations. So even the most impressive
Speaker 3: of our models will often just make stuff up. You know,
Speaker 3: the technical term that the heels chosen is hallucination. To
Speaker 3: give you an example, I asked chat tbt to create
Speaker 3: a biography of David Cox IBM, and you know, it
Speaker 3: started off really well, you know, the identifying that I
Speaker 3: was the director of the mt IBM Watsonday and said
Speaker 3: a few words about that, and then it proceeded to
Speaker 3: create an authoritative but completely fake biography of me where
Speaker 3: I was British. I was born in the UK. I
Speaker 3: went to British university, you know, universities in the UK.
Speaker 3: I was professor in the authority.
Speaker 2: Right, it's the certainty that that is weird about it, Right,
Speaker 2: it's it's dead certain that you're from the UK, et cetera.
Speaker 3: Absolutely, yeah, it has all kinds of flourishes, like I
Speaker 3: want awards in the UK. So yeah, it's it's problematic
Speaker 3: because it kind of pokes at a lot of weak
Speaker 3: spots in our human psychology, where if something sounds coherent,
Speaker 3: we're likely to assume it's true. We're not used to
Speaker 3: interacting with people who eloquently and authoritatively, you know, admit
Speaker 3: complete nonsense like yeah, you know, you know we can
Speaker 3: debate about that, but yeah, we.
Speaker 2: Can debate about that. But yes, the sort of blive
Speaker 2: confidence throws you off when you realize it's completely wrong.
Speaker 3: Right, that's right. And we do have a little bit
Speaker 3: of like a great and powerful aws sort of vibe
Speaker 3: going sometimes where we're like, well, you know, the AI
Speaker 3: is all knowing and therefore whatever it says must be true.
Speaker 3: But but these things will make up stuff, you know,
Speaker 3: very aggressively, and you know, you everyone can try asking
Speaker 3: it for their their bio. You'll you'll get something that
Speaker 3: You'll always get something that's of the right form, that
Speaker 3: has the right tone. But you know, the facts just
Speaker 3: aren't necessarily there. So that's obviously a problem. We need
Speaker 3: to figure out how to close those gaps, fix those problems.
Speaker 3: There's lots of ways we could use them more easily.
Speaker 1: I'd just like to say, faced with the awesome potential
Speaker 1: of what these technologies might do, it's a bit encouraging
Speaker 1: to hear that even chat GPT has a weakness for
Speaker 1: inventing flamboyant, if fictional versions of people's lives. And while
Speaker 1: entertaining ourselves with chat GPT and mid journey is important,
Speaker 1: the way lay people use consumer facing chatbots and generative
Speaker 1: AI is just fundamentally different from the way an enterprise
Speaker 1: business uses AI. How can we harness the abilities of
Speaker 1: artificial intelligence to help us solve the problems we face
Speaker 1: in business and technology. Let's listen on as David and
Speaker 1: Jacob continue their conversation.
Speaker 2: We've been talking in a somewhat abstract way about AI
Speaker 2: in the ways it can be used. Let's talk in
Speaker 2: a little bit more of a specific way. Can you
Speaker 2: just talk about some examples of business challenges that can
Speaker 2: be solved with automation. With this kind of automation we're
Speaker 2: talking about.
Speaker 3: Yeah, so there really really this guy's the limit. There's
Speaker 3: a whole set of different applications that these models are
Speaker 3: really good at. And basically it's a super set of
Speaker 3: everything we used to use ALI for in business. So,
Speaker 3: you know, the simple kinds of things are like, hey,
Speaker 3: if I have text and i'm you know, I have
Speaker 3: product reviews and I want to be able to tell
Speaker 3: if these are positive or negative. You know, like let's
Speaker 3: look at all the negative reviews so we can have
Speaker 3: a human look through them and see what was up.
Speaker 3: Very common business use case. You can do it with
Speaker 3: traditional deep learning based AI. So so there's things like
Speaker 3: that that are you know, it's very prosaic sort that
Speaker 3: we were already doing it. We've been doing it for
Speaker 3: a long time. Then you get situations that are that
Speaker 3: were harder for the old day. I like, if i'm
Speaker 3: I want to compress something like I want to I
Speaker 3: have like they have a chat transcript, like a customer
Speaker 3: called in and they had a complaint, they called back. Okay,
Speaker 3: Now a new you know, person on the line needs
Speaker 3: to go read the old transcript to catch up. Wouldn't
Speaker 3: it be better if we could just summarize that, just
Speaker 3: condense it all down a quick little paragraph, you know,
Speaker 3: customer call they were upset about this, rather than having
Speaker 3: to read the blow by blow. There's just lots of
Speaker 3: settings like that where summarization is really helpful. Hey, you
Speaker 3: have a meeting and I'd like to just automatically, you know,
Speaker 3: have have that meeting or that email or whatever. I'd
Speaker 3: like to just have a condensed down so I can
Speaker 3: really quickly get to the heart of the matter. These
Speaker 3: models are are really good at doing that. They're also
Speaker 3: really good at question answering. So if I want to
Speaker 3: find out what's how many vacation days do I have?
Speaker 3: I can now interact in natural language with a system
Speaker 3: that can go and that has access to our HR policies,
Speaker 3: and I can actually have a you know, multi turn
Speaker 3: conversation where I can, you know, like I would have
Speaker 3: with you know, somebody, you know, actual HR professional or
Speaker 3: customer service representative. So a big part, you know, of
Speaker 3: what this is doing is it's it's putting an interface.
Speaker 3: You know, when we think of computer interfaces, usually thinking
Speaker 3: about UI user interface elements where I click on menus
Speaker 3: and there's buttons and all this stuff. Increasingly, now we
Speaker 3: can just talk, you know, you just in words. You
Speaker 3: can describe what you want, you want to answer, ask
Speaker 3: a question, you want to sort of command the system
Speaker 3: to do something, rather than having to learn how to
Speaker 3: do that clicking buttons, which might be inefficient. Now we
Speaker 3: can just sort of spell it out.
Speaker 2: Interesting, right, the graphical user interface that we all sort
Speaker 2: of default to, that's not like the state of nature, right,
Speaker 2: That's a thing that was invented and just came to
Speaker 2: be the standard way that we interact with computers. And
Speaker 2: so you could imagine, as you're saying, like chat essentially
Speaker 2: chatting with the machine could could become a sort of
Speaker 2: standard user interface, just like the graphical user interface, did
Speaker 2: you know over the past several decades.
Speaker 3: Absolutely, And I think those kinds of conversational interfaces are
Speaker 3: going to be hugely important for increasing our productivity. It's
Speaker 3: just a lot easier if I if I have to
Speaker 3: learn how to use a tool, or I don't have
Speaker 3: to kind of have awkward, you know, interactions for the computer.
Speaker 3: I can just tell it what I want, and I
Speaker 3: can understand it, could you know, potentially even ask questions
Speaker 3: back to clarify and have those kinds of conversations that
Speaker 3: can be extremely powerful. And in fact, one area where
Speaker 3: that's going to I think be absolutely game changing is
Speaker 3: in code. When we write code. You know, programming languages
Speaker 3: are a way for us to sort of match between
Speaker 3: our very sloppy way of talking and the very exact
Speaker 3: way that you need to command a computer to do
Speaker 3: what you wanted to do. They're cumbersome to learn, they
Speaker 3: can you know, create very complex systems that are very
Speaker 3: hard to reason about. And we're already starting to see
Speaker 3: the ability to just write down what you want and
Speaker 3: AI will generate the code for you. And I think
Speaker 3: we're just going to see a huge revolution of like
Speaker 3: we just converse you and we can have a conversation
Speaker 3: to say what we want, and then the computer can
Speaker 3: actually not only do fixed actions and do things for us,
Speaker 3: but it can actually even write code to do new things,
Speaker 3: you know, and generate software itself. Given how much software
Speaker 3: we have, of how much craving we have for software,
Speaker 3: like we'll never have enough software in our world, uh,
Speaker 3: you know, the ability to have a systems as a
Speaker 3: helper in that, I think we're going to see a
Speaker 3: lot of a lot of value there.
Speaker 2: So if you if you think about the different ways
Speaker 2: AI might be applied to business, I mean you've talked
Speaker 2: about a number of the sort of classic use cases.
Speaker 2: What are some of the more out there use cases.
Speaker 2: What are some you know, unique ways you could imagine
Speaker 2: AI being applied to business.
Speaker 3: Yeah, there's really disguised the limit. I mean, we have
Speaker 3: one project that I'm kind of a fan of where
Speaker 3: we actually were working with a mechanical engineering professor at
Speaker 3: MIT working on a classic problem, how do you build
Speaker 3: linkage systems which like you imagine bars and joints and ogres,
Speaker 3: you know, the things that are.
Speaker 2: Building a thing, building a physical machine of some.
Speaker 3: Kind of like real like metal and you know nineteenth
Speaker 3: century just old school industrial revolution. Yeah yeah, yeah, but
Speaker 3: you know the little arm that's that's holding up my
Speaker 3: microphone in front of me. Cranes get bold, your buildings,
Speaker 3: you know, parts of your engines. This is like classical stuff.
Speaker 3: It turns out that you know humans, if you want
Speaker 3: to build an advanced system, you decide what like curve
Speaker 3: you want to create, and then a human together with
Speaker 3: a computer program can build a five or six bar linkage,
Speaker 3: and then that's kind of where you top out. It is
Speaker 3: because it gets too complicated to work more than that.
Speaker 3: We built a generative AI system that can build twenty
Speaker 3: bar linkages, like arbitrarily complex. So these are machines that
Speaker 3: are beyond the capability of a human to design themselves.
Speaker 3: Another example, we have an AI system that can generate
Speaker 3: electronic circuits. You know, we had a project where we're
Speaker 3: working where we were building better power converters which allow
Speaker 3: our computers and our devices to be more efficient, save energy,
Speaker 3: you know, less less carbon ote. But I think the
Speaker 3: world around us has always been shaped by technology. If
Speaker 3: you look around, you know, just think about how many
Speaker 3: steps and how people and how many designs went into
Speaker 3: the table and the chair and the lamp. It's it's
Speaker 3: really just astonishing. And that's already you know, the fruit
Speaker 3: of automation and computers and those kinds of tools. But
Speaker 3: we're going to see that increasingly be product also of AI.
Speaker 3: It's just going to be everywhere around us. Everything we
Speaker 3: touch is going to have to you know, helped in
Speaker 3: some way to get get to you by a.
Speaker 2: You know, that is a pretty profound transformation that you're
Speaker 2: talking about in business. How do you think about the
Speaker 2: implications of that, both for the sort of you know,
Speaker 2: business itself and also for for employees.
Speaker 3: Yeah, so I think for businesses this is gonna cut costs,
Speaker 3: make new opportunities to like customers, you know, like there's
Speaker 3: just you know, it's sort of all upside right, like
Speaker 3: for the for the workers, I think the story is
Speaker 3: mostly good too. You know, like how many things do
Speaker 3: you do in your day that you'd really rather not right?
Speaker 3: You know, and we're used to have I think, things
Speaker 3: we don't like automated away, you know, we didn't you know,
Speaker 3: if you didn't like walking many miles to work, then
Speaker 3: you know, like you have a car and you can
Speaker 3: drive there. Or we used to have a huge fraction
Speaker 3: over ninety percent of the US population engaged in agriculture,
Speaker 3: and then we mechanized it how very few people work
Speaker 3: in agriculture. A small number of people can do the
Speaker 3: work of a large number of people. And then you know,
Speaker 3: things like email, and you know, they've led to huge
Speaker 3: productivity enhancements because I don't need to be writing letters
Speaker 3: and sending them in the mail. I can just instantly
Speaker 3: communicate with people. We just become more effective. Like our
Speaker 3: jobs have transformed, whether it's a physical job like agriculture
Speaker 3: or whether it's a knowledge worker job where you're sending
Speaker 3: emails and communicating with people and coordinating teams, we've just
Speaker 3: gotten better. And you know, the technology has just made
Speaker 3: us more productive. And this is just another example. Now,
Speaker 3: you know, there are people who worry that, you know,
Speaker 3: we'll be so good at that that maybe jobs will
Speaker 3: be displaced, and that's that's a legitimate concern. But just
Speaker 3: like how in agriculture, you know, it's not like suddenly
Speaker 3: we had ninety percent of the population unemployed. You know,
Speaker 3: people transitioned to other jobs. And the other thing that
Speaker 3: we've found, too, is that our appetite for doing more
Speaker 3: things is as humans is sort of insatiable. So even
Speaker 3: if we can dramatically increase how much you know, one
Speaker 3: human can do, that doesn't necessarily mean we're going to
Speaker 3: do a fixed amount of stuff. There's an appetite to
Speaker 3: have even more, so we're going to you can continue
Speaker 3: to grow the pie. So I think at least certainly
Speaker 3: in the near term, you know, we're going to see
Speaker 3: a lot of drudgery go away from work. We're going
Speaker 3: to see people be able to be more effective at
Speaker 3: their jobs. You know, we will see some transformation in
Speaker 3: jobs and what look like. But we've seen that before
Speaker 3: and the technology a least has the potential to make
Speaker 3: our lives a lot easier.
Speaker 2: So IBM recently launched Watson X, which includes Watson x
Speaker 2: dot AI. Tell me about that, Tell me about you
Speaker 2: know what it is and the new possibilities that it
Speaker 2: opens up.
Speaker 3: Yeah, So Watson next is obviously a bit of a
Speaker 3: new branding on the Watson brand. TJ. Watson that was
Speaker 3: the founder of IBM and our EI technologies have had
Speaker 3: the Watson brand. Watson X is a recognition that there's
Speaker 3: something new, there's something that actually has changed the game.
Speaker 3: We've gone from this old world of automation is to
Speaker 3: labor intensive to this new world of possibilities where it's
Speaker 3: much easier to use AI. And what Watson X does
Speaker 3: it brings together tools for businesses to harness that power.
Speaker 3: So whattsonex dot AI foundation models that our customers can use.
Speaker 3: It includes tools that make it easy to run, easy
Speaker 3: to deploy, easy to experiment. There's a watsonex dot Data
Speaker 3: component which allows you to sort of organize and access
Speaker 3: to your data. So what we're really trying to do
Speaker 3: is give our customers a cohesive set of tools to
Speaker 3: harness the value of these technologies and at the same
Speaker 3: time be able to manage the risks and other things
Speaker 3: that you have to keep an eye on in an
Speaker 3: enterprise context.
Speaker 2: So we talk about the guests on this show as
Speaker 2: new creators, by which we mean people who are creatively
Speaker 2: applying technology in business to drive change. And I'm curious
Speaker 2: how creativity plays a role in the research that you do.
Speaker 3: I honestly, I think the creative aspects of this job,
Speaker 3: this is what makes this work exciting. You know, I
Speaker 3: should say, you know, the folks who work in my
Speaker 3: organization are doing the creating, and I.
Speaker 2: Guess you're doing the managing so that they could do
Speaker 2: the creator.
Speaker 3: I'm helping them be their best and I still get
Speaker 3: to get involved in the weeds of the research as
Speaker 3: much as I can. But you know, there's something really
Speaker 3: exciting about inventing, you know, Like one of the nice
Speaker 3: things about doing invention and doing research on AI in
Speaker 3: industry is it's usually grounded and a real problem that
Speaker 3: somebody's having. You know, a customer wants to solve this
Speaker 3: problem it's losing money or there there would be a
Speaker 3: new opportunity. You identify that problem and then you build
Speaker 3: something that's never been built before to do that. And
Speaker 3: I think that's honestly the adrenaline rush that keeps all
Speaker 3: of us in this field. How do you do something
Speaker 3: that nobody else on earth has done before or tried before,
Speaker 3: So that that kind of creativity, and there's also creativity
Speaker 3: as well, and identifying what those problems are, being able
Speaker 3: to understand the places where you know the technology is
Speaker 3: close enough to solving a problem, and doing that matchmaking
Speaker 3: between problems that are now solvable, you know, and in AI,
Speaker 3: where the field is moving so fast, this is constantly
Speaker 3: growing horizon of things that we might be able to solve.
Speaker 3: So that matchmaking, I think is also a really interesting
Speaker 3: creative problem. So I think I think that's that's that's
Speaker 3: why it's so much fun. And it's a fun environment
Speaker 3: we have here too. It's you know, people drawing on
Speaker 3: whiteboards and writing on pages of math and.
Speaker 2: You know, like in a movie, like in a movie.
Speaker 3: Yeah, straight from special casting.
Speaker 2: The drawing on the window, writing on the window in sharp.
Speaker 2: Absolutely so, so let's close with the really long view.
Speaker 2: How do you imagine AI and people working together twenty.
Speaker 3: Years from now? Yeah, it's really hard to make predictions.
Speaker 3: The vision that I like, actually this came from an
Speaker 3: MIT economist named David Ottur, which was imagine AI almost
Speaker 3: as a natural resource. You know, we know how natural
Speaker 3: resources work, right, Like there's an ore we can dig
Speaker 3: up out of the earth that comes from kind of
Speaker 3: springs from the earth, or we usually think of that
Speaker 3: in terms of physical stuff. With AI, you can almost
Speaker 3: think of it as like there's a new kind of
Speaker 3: abundance potentially twenty years from now where not only can
Speaker 3: we have things we can build or eat or use
Speaker 3: or burn or whatever, now we have, you know, this
Speaker 3: ability to do things and understand things and do intellectual work.
Speaker 3: And I think we can get to a world where
Speaker 3: automating things is just seamless. We're surrounded by capability to
Speaker 3: augment ourselves to get things done. And you could think
Speaker 3: of that in terms of like, well, that's going to
Speaker 3: displace our jobs, because eventually the AI system is going
Speaker 3: to do everything we can do. But you could also
Speaker 3: think of it in terms of like, wow, that's just
Speaker 3: so much abundance that we now have, and really how
Speaker 3: we use that abundance is sort of up to us,
Speaker 3: you know, like when you can writing software is super
Speaker 3: easy and fast and anybody can do it. Just think
Speaker 3: about all the things you can do now, think about
Speaker 3: all the new activities, and go out all the ways
Speaker 3: we could use that to enrich our lives. That's where
Speaker 3: I'd like to see us in twenty years. You know,
Speaker 3: we can we can do just so much more than
Speaker 3: we were able to do before abundance.
Speaker 2: Great, thank you so much for your time.
Speaker 3: Yeah, it's been a pleasure. Thanks for inviting me.
Speaker 1: What a far ranging, deep conversation. I'm mesmerized by the
Speaker 1: vision David just described. A world where natural conversation between
Speaker 1: mankind and machine can generate creative solutions to our most
Speaker 1: complex problems. A world where we view AI not as
Speaker 1: our replacements, but as a powerful resource we can tap
Speaker 1: into and exponentially boost our innovation and productivity. Thanks so
Speaker 1: much to doctor David Cox for joining us on smart Talks.
Speaker 1: We deeply appreciate him sharing his huge breadth of AI
Speaker 1: knowledge with us and for explaining the transformative potential of
Speaker 1: foundation models in a way that even I can understand.
Speaker 1: We eagerly await his next great breakthrough. Smart Talks with
Speaker 1: IBM is produced by Matt Romano, David jaw nishe Venkat
Speaker 1: and Royston Preserve with Jacob Goldstein. We're edited by Lydia
Speaker 1: Jane Kott. Our engineers are Jason Gambrel, Sarah Buguier and
Speaker 1: Ben Holliday. Theme song by Gramosco. Special thanks to Carli Megliori,
Speaker 1: Andy Kelly, Kathy Callahan and the eight Bar and IBM teams,
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