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: Hey, it's Jacob Goldstein for Smart Talks with IBM. Last
Speaker 3: year I had the pleasure of sitting down with doctor
Speaker 3: David Cox, VP of AI Models at IBM Research. We
Speaker 3: explored the fascinating world of AI foundation models and their
Speaker 3: revolutionary potential for business automation and innovation. When we first
Speaker 3: aired this episode, the concept of foundation models was just
Speaker 3: beginning to capture our attention. Since then, this technology has
Speaker 3: evolved and redefined the boundaries of what's possible. Businesses are
Speaker 3: becoming more savvy about selecting the right models and understanding
Speaker 3: how they can drive revenue and efficiency. As I listened
Speaker 3: back to the conversation, it was interesting to reflect on
Speaker 3: some new developments and ideas that have emerged, and many
Speaker 3: of these we will continue to explore throughout the season,
Speaker 3: like how to play an active role in choosing the
Speaker 3: best model for your needs. Whether you're a longtime listener
Speaker 3: or tuning in for the first time, I'm certain you'll
Speaker 3: find doctor Cox's insights as thought provoking as ever. Thanks
Speaker 3: as always for joining us. Now let's dive in.
Speaker 4: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 4: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. Our
Speaker 4: guest today is doctor David Cox, VP of AI Models
Speaker 4: at IBM Research and IBM Director of the MIT IBM
Speaker 4: Watson AI Lab, a first of its kind industry academic
Speaker 4: collaboration between IBM and MIT focused on the fundamental research
Speaker 4: of artificial intelligence. Over the course of decades, David Cox
Speaker 4: watched as the AI revolution steadily grew from the simmering
Speaker 4: ideas of a few academics and technologists into the industrial
Speaker 4: boom we are experiencing today. Having dedicated his life to
Speaker 4: pushing the field of AI towards new horizons, David has
Speaker 4: both contributed to and presided over many of the major
Speaker 4: breakthroughs in artificial intelligence. In today's episode, you'll hear David
Speaker 4: explain some of the conceptual underpinnings of the current AI landscape,
Speaker 4: things like foundation models, in surprisingly comprehensible terms, I might add,
Speaker 4: we'll also get into some of the amazing practical applications
Speaker 4: for AI in business, as well as what implications AI
Speaker 4: will have for the future of work and design. David
Speaker 4: spoke with Jacob Goldstein, host of the Pushkin podcast What's
Speaker 4: Your Problem. A veteran business journalist, Jacob has reported for
Speaker 4: The Wall Street Journal, the Miami Herald, and was a
Speaker 4: longtime host of the NPR program Planet Money. Okay, let's
Speaker 4: get to the interview.
Speaker 3: Tell me about your job at IBM.
Speaker 5: So I wear two hats at IBM. So one, I'm
Speaker 5: the IBM Doctor of the MIT IBM Watson AI Lab.
Speaker 5: So that's a joint lab between IBM and MIT where
Speaker 5: we try and invent what's next in AI. It's been
Speaker 5: running for about five years, and then more recently I
Speaker 5: started as 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 3: So, so I want to talk to you a lot
Speaker 3: about foundation models, about genitive AI. But before we get
Speaker 3: to that, let's just spend a minute on the on
Speaker 3: the IBM MIT collaboration. Where did that partnership start, How
Speaker 3: 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. It was actually organized by an IBM or
Speaker 5: Nathaniel Rochester, who led the development of the IBM seven
Speaker 5: and one. So we've really been together in AIS since
Speaker 5: the beginning and as AI kept accelerating more and more
Speaker 5: and more, I think there was a really interesting decision
Speaker 5: to say, let's make this a formal partnership. So IBM
Speaker 5: in twenty seventeen and now, so it'll be committing close
Speaker 5: to a quarter billion dollars over ten years to have
Speaker 5: this joint lab with MIT, and we we located ourselves
Speaker 5: right on the campus and we've been developing very very
Speaker 5: deep relationships where we can you know, really get to
Speaker 5: know each other, work shoulder to shoulder, conceiving what we
Speaker 5: should work on next, and then executing the projects. And
Speaker 5: it's really you know, very few entities like this exist
Speaker 5: between academia industry. It's been really fun of the last
Speaker 5: five years to be a part of it.
Speaker 3: And what do you think are some of the most
Speaker 3: 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 for IBM's the I strategy. So we're really looking,
Speaker 5: you know, what's coming ahead, and you know, in areas
Speaker 5: like foundation models, you know, as the field changes, MIT
Speaker 5: people are interested in working on you know, faculty, students
Speaker 5: and staff are interested in working on what's the latest thing,
Speaker 5: what's the next thing. We at IBM Research are very
Speaker 5: much interested in the same. We can kind of put
Speaker 5: out feelers, you know, interesting things that we're seeing in
Speaker 5: our research, interesting things we're hearing in the field. We
Speaker 5: can go and chase those opportunities. So when something big comes,
Speaker 5: like the big change that's been happening lately with foundation models,
Speaker 5: we're ready to jump on it. That's really the purpose,
Speaker 5: that's that's the lab functioning the way it should. We're
Speaker 5: also really interested in how do we advance you know,
Speaker 5: AI that can help with climate change or you know,
Speaker 5: build better materials and all these kinds of things that
Speaker 5: are you know, a broader aperture sometimes than what we
Speaker 5: might consider just looking at the product portfolio of IBM,
Speaker 5: and that that gives us again a breadth where we
Speaker 5: can see connections that we might not have seen otherwise.
Speaker 5: We can you know, think things that help out society
Speaker 5: and also help out our customers.
Speaker 3: So the last whatever six months, say, there has been
Speaker 3: this wild rise in the public's interest in AI right
Speaker 3: clearly coming out of these generative AI models that are
Speaker 3: really accessible you know, certainly chat GPT language models like that,
Speaker 3: as well as models that generate images like mid journey.
Speaker 3: I mean, can you just sort of briefly talk about
Speaker 3: the breakthroughs in AI that have made this moment feel
Speaker 3: 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 back propagation, which is sort of
Speaker 5: key to training neural networks that's been around, you know,
Speaker 5: since the eighties in wide use. And really what happened
Speaker 5: was it simmered for a long time, and then enough
Speaker 5: data and enough compute came so we had enough data
Speaker 5: because you know, we all started carrying multiple cameras around
Speaker 5: with us. Our mobile phones have all you know, all
Speaker 5: these cameras and this we put everything on the Internet,
Speaker 5: and there's all this data out there. We caught a
Speaker 5: lucky break that there was something called the graphics processing unit,
Speaker 5: which turns out to be really useful for doing these
Speaker 5: kinds of algorithms, maybe even more useful than it is
Speaker 5: for doing graphics. They're greater graphics too, And things just
Speaker 5: kept kind of adding to the snowball. So we had
Speaker 5: deep learning, which is sort of a rebrand of neural
Speaker 5: networks that I mentioned from the eighties, and that was
Speaker 5: enabled again by data because we digitalized the world and
Speaker 5: compute 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, that 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 supper 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 that,
Speaker 5: you know, that's very powerful, but it takes a lot
Speaker 5: of time to label gats and to label dogs, and
Speaker 5: 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 allows you use even more data. And that's
Speaker 5: really what drove this this latest sort of rage. And
Speaker 5: then and then all of a sudden we start getting
Speaker 5: these these really powerful models. And then really, this has
Speaker 5: been simmering technologies, right, this has been happening for a
Speaker 5: while and progressively getting more and more powerful. One of
Speaker 5: the things that really happened with CHATGBT and technologies like
Speaker 5: Stable Diffusion and mid Journey was that they made it
Speaker 5: visible to the public. You know, you put it out
Speaker 5: there the public can touch and feel and they're like, wow,
Speaker 5: not only is there palpable change, and wow this you know,
Speaker 5: I can talk to this thing. Wow, this thing can
Speaker 5: generate an image. Not only that, but everyone can touch
Speaker 5: and feel and try. My kids can use some of
Speaker 5: these AI art generation technologies, and that's really just launched.
Speaker 5: You know, it's like a propelled slingshot at us into
Speaker 5: a different regime. In terms of the public awareness of
Speaker 5: these technologies.
Speaker 3: You mentioned earlier in the conversation foundation models, and I
Speaker 3: want to talk a little bit about that. I mean,
Speaker 3: can you just tell me, you know, what are foundation
Speaker 3: 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, you know, have any purpose. But what a
Speaker 5: foundation models that provides a foundation, like a literal foundation.
Speaker 5: You can sort of stand on the shoulders of giants.
Speaker 5: You can have them these massively trained models, and then
Speaker 5: do a little bit on top. You know, you could
Speaker 5: use just a few examples of what you're looking for
Speaker 5: and you can get what you want from the model,
Speaker 5: So just a little bit on top now gets to
Speaker 5: the results that a huge amount of effort used to
Speaker 5: have to put in, you know, to get from the
Speaker 5: ground up to that level.
Speaker 3: I was trying to think of of an analogy for
Speaker 3: sort of foundation models versus what came before, and I
Speaker 3: don't know that I came up with a good one,
Speaker 3: but the best I could do was this. I want
Speaker 3: you to tell me if it's plausible. It's like before
Speaker 3: foundation models, it was like you had these sort of
Speaker 3: single use kitchen appliances. You could make a waffle iron
Speaker 3: if you wanted waffles, or you could make a toaster
Speaker 3: if you wanted to make toast. But a foundation model
Speaker 3: is like like an oven with a range on top.
Speaker 3: So it's like this machine and you could just cook
Speaker 3: 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 lower the
Speaker 5: effort that it takes to do something that you want
Speaker 5: to do. And sometimes I used to say about the
Speaker 5: old world of AI, would say, you know, the problem
Speaker 5: with automation is that it's too labor intensive. H sounds
Speaker 5: like I'm making a joke.
Speaker 3: Indeed, famously, if automation does one thing, it substitutes machines
Speaker 3: or computing power for labor. Right, So what does that
Speaker 3: 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. So
Speaker 5: and we estimate anywhere between eighty to ninety percent of
Speaker 5: the effort it takes to feel an AI solution actually
Speaker 5: is 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 expensive 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 tiny, little incremental thing. Now, you're going to
Speaker 5: do the only the highest value things that weren't at
Speaker 5: level because.
Speaker 3: You have to essentially build the whole machine from scratch,
Speaker 3: and there aren't many things where it's worth that much
Speaker 3: work to build a machine that's only going to do
Speaker 3: 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 tasks 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 trouble. You're
Speaker 5: gonna get a lot of value out of that dam.
Speaker 5: If you have a river like that, you know, a
Speaker 5: river of data, but it's actually the vast majority of
Speaker 5: the water you know in your kingdom actually isn't in
Speaker 5: that river. It's in puddles and greeks and babid brooks,
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
Speaker 5: things because it's too much of a pain. Now what
Speaker 5: foundation models do is they say, well, actually, no, we
Speaker 5: can train a base model a foundation that you can
Speaker 5: work on that we don't we don't care. We don't
Speaker 5: specify what the task is ahead of time. We just
Speaker 5: need to learn about the domain of data. So if
Speaker 5: we want to build something that can understand English language,
Speaker 5: there's a ton of English language text available out in
Speaker 5: the world. We can now train models on huge quantities
Speaker 5: of it, and then it learned the structure. It learned
Speaker 5: how language, you know, good part of how language works
Speaker 5: on all that unlabeled data. And then when you roll
Speaker 5: up with your task, you know, I want to solve
Speaker 5: this particular problem, you don't have to start from scratch.
Speaker 5: You're starting from a very very very high place. So
Speaker 5: that just gives you the ability to just you know, now,
Speaker 5: all of a sudden, everything is accessible. All the puddles
Speaker 5: and greeks and babbling brooks and kettlepons, you know, those
Speaker 5: are all accessible now. And that's that's very exciting. But
Speaker 5: it just changes the equation on what kinds of problems
Speaker 5: you could use AI to solve.
Speaker 3: And so foundation models basically mean that automating some new
Speaker 3: task is much less labor intensive, The sort of marginal
Speaker 3: effort to do some new automation thing is much lower
Speaker 3: because you're building on top of the foundation model rather
Speaker 3: than starting from scratch. Absolutely, so that is that is
Speaker 3: like the exciting good news. I do feel like there's
Speaker 3: a little bit of a countervailing idea that's worth talking
Speaker 3: about here, and that is the idea that even though
Speaker 3: there are these foundation models that are really powerful, that
Speaker 3: are relatively easy to build on top of, it's still
Speaker 3: the case, right that there is not some one size
Speaker 3: fits all foundation model. So you know, what does that
Speaker 3: mean and why is that important to think about in
Speaker 3: 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 to like to specialize on particular domains can
Speaker 5: actually outperform much bigger models. So bigger isn't always even better.
Speaker 3: So they're more efficient and they do the thing you
Speaker 3: 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 were
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,
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 3: So there's sustainability basically efficiency, another kind of set of
Speaker 3: issues that come up a lot with AI A are bias, hallucination.
Speaker 3: Can you talk a little bit about bias and hallucination,
Speaker 3: what they are and how you're working to 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 it's 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, translate they are a scientist. To
Speaker 5: he is a scientist. And that's not because the people
Speaker 5: who wrote the algorithm were building in bias and coding
Speaker 5: in like, oh, it's got to be this way. It's
Speaker 5: because the data was like that. You know, we have
Speaker 5: biases in our society and they're reflected in our data
Speaker 5: and our text and our images everywhere. And then the
Speaker 5: models they're just mapping from what they what they've seen
Speaker 5: in their training data to to the result that you're
Speaker 5: trying to get them to do and to give, and
Speaker 5: then these biases come out. So there's a very active
Speaker 5: program of research and you know, we do quite a
Speaker 5: bit at IBM research and my T but also all
Speaker 5: over the community and industry and academia trying to figure
Speaker 5: out how do we explicitly remove these biases, how do
Speaker 5: we identify them, how do you know, how do we
Speaker 5: build tools that allow people to audit their systems to
Speaker 5: make sure they aren't biased. So this is a really
Speaker 5: important thing. And you know, again this was here since
Speaker 5: the beginning, you know, of machine learning and AI, but
Speaker 5: foundation models and large language models and generative AI just
Speaker 5: bring it into sharper even sharper focus because there's just
Speaker 5: so much data and it's sort of building in baking
Speaker 5: in all these different biases we have, so that that's
Speaker 5: that's absolutely a problem that these models have. Another one
Speaker 5: that you mentioned was hallucinations. So even the most impressive
Speaker 5: of our models will often just make stuff up. You know,
Speaker 5: the technical term that the field has chosen is hallucination.
Speaker 5: To give you an example, I asked chat tbt to
Speaker 5: create a biography of David Cox at IBM, and you know,
Speaker 5: it started off really well. You know, the identified that
Speaker 5: I was the director of the MNT IBM Watson and
Speaker 5: said a few words about that, and then it proceeded
Speaker 5: to create an authoritative but completely fake biography of me
Speaker 5: where I was British, I was born in the UK,
Speaker 5: I went to British university, you know universities in the UK.
Speaker 5: I was professor the authority.
Speaker 3: Right, it's the certainty that that is weird about it, right,
Speaker 3: It's it's dead certain that you're from the UK, et cetera.
Speaker 5: Absolutely, yeah, it has all kinds of flourishes like I
Speaker 5: want awards in the UK. So yeah, it's it's problematic
Speaker 5: because it kind of pokes a lot of weak spots
Speaker 5: in our human psychology where if something sounds coherent, We're
Speaker 5: likely to assume it's true. We're not used to interacting
Speaker 5: with people who eloquently and authoritatively, you know, emit complete nonsense,
Speaker 5: like yeah, you know, we can debate about that, but.
Speaker 3: Yeah, we can debate about that. But yes, it's the
Speaker 3: sort of blive confidence throws you off when you realize
Speaker 3: it's completely wrong.
Speaker 5: Right, that's right. And we do have a little bit
Speaker 5: of like a great and powerful oz sort of vibe
Speaker 5: going sometimes where we're like, well, you know, the AI
Speaker 5: is all knowing and therefore whatever it says must be true.
Speaker 5: But these things will make up stuff, you know, very aggressively,
Speaker 5: and you know, you everyone can try asking it for
Speaker 5: their their bio. You'll you'll get something that You'll always
Speaker 5: get something that's of the right form, that has the
Speaker 5: right tone. But you know, the facts just aren't necessarily there.
Speaker 5: So that's obviously a problem. We need to figure out
Speaker 5: how to close those gaps, fix those problems. There's lots
Speaker 5: of ways we can use them much more easily.
Speaker 4: I'd just like to say, faced with the awesome potential
Speaker 4: of what these technologies might do, it's a bit encouraging
Speaker 4: to hear that even chat GPT has a weakness for
Speaker 4: inventing flamboyant, if fictional versions of people's lives, and while
Speaker 4: entertaining ourselves with chat GPT and mid journey is important,
Speaker 4: the way lpeople use consumer facing chatbots and generative AI
Speaker 4: is just fundamentally different from the way an enterprise business
Speaker 4: uses AI. How can we harness the abilities of artificial
Speaker 4: intelligence to help us solve the problems we face in
Speaker 4: business and technology. Let's listen on as David and Jacob
Speaker 4: continue their conversation.
Speaker 3: We've been talking in a somewhat abstract way about AI
Speaker 3: in the ways it can be used. Let's talk in
Speaker 3: a little bit more of a specific way. Can you
Speaker 3: just talk about some examples of business challenges that can
Speaker 3: be solved with automation, with this kind of automation we're
Speaker 3: talking about.
Speaker 5: Yeah, so the really really, this guy's the limit. There's
Speaker 5: a whole set of different applications that these models are
Speaker 5: really good at. And basically it's a superset of everything
Speaker 5: we used to use AI for in business. So you know,
Speaker 5: the simple kinds of things are like, hey, if I
Speaker 5: have text and I have product reviews, and I want
Speaker 5: to be able to tell if these are positive or negative.
Speaker 5: You know, like let's look at all the negative reviews,
Speaker 5: so we can have a human look through them and
Speaker 5: see what was up. Very common business use case. You
Speaker 5: can do it with traditional deep learning based AI. So so
Speaker 5: there's things like that that are you know, it's very
Speaker 5: prosaic sort of we were already doing it. We've been
Speaker 5: doing it for a long time. Then you get situations
Speaker 5: that are that were harder for the old day. I like,
Speaker 5: if I'm I want to impress something like I want
Speaker 5: to I have like say I have a chat transcript,
Speaker 5: Like a customer called in and they had a complaint.
Speaker 5: They called back. Okay, now a new you know, a
Speaker 5: person on the line needs to go read the old
Speaker 5: transcript to catch up. Wouldn't it be better if we
Speaker 5: could just summarize that, just condense it all down a
Speaker 5: quick little paragraph. You know, customer called they we upset
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 had 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 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: it has access to our HR policies, and I can
Speaker 5: actually have a you know, a multi turn conversation where
Speaker 5: I can, 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, of what this is doing
Speaker 5: is it's it's putting an interface. You know, when we
Speaker 5: think of computer interfaces, we're usually thinking about UI user
Speaker 5: interface elements where I click on menus and there's buttons
Speaker 5: and all this stuff. Increasingly, now we can just talk,
Speaker 5: you know, you just in words. You can describe what
Speaker 5: you want, you want to answer, ask a question, you
Speaker 5: want to sort of command the system to do something,
Speaker 5: rather than having to learn how to do that clicking buttons,
Speaker 5: which might be inefficient, Now we can just sort of
Speaker 5: spell it out.
Speaker 3: Interesting, right, the graphical user interface that we all sort
Speaker 3: of default to, that's not like the state of nature, Right,
Speaker 3: that's a thing that was invented and just came to
Speaker 3: be the standard way that we interact with computers. And
Speaker 3: so you could imagine, as you're saying, like chat essentially
Speaker 3: chatting with the machine could could become a sort of
Speaker 3: standard user interface, just like the graphical user interface, did
Speaker 3: 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 even ask questions
Speaker 5: back to clarify and have those kinds of conversations that
Speaker 5: can be extremely powerful. And in fact, one area where
Speaker 5: that's going to I think be absolutely game changing is
Speaker 5: in code. When we write code. You know, programming languages
Speaker 5: are a way for us to sort of match between
Speaker 5: a very sloppy way of talking and the very exact
Speaker 5: way that you need to command a computer to do
Speaker 5: what you wanted to do. They're cumbersome to learn, they
Speaker 5: can you know, create very complex systems that are very
Speaker 5: hard to reason about. And we're already starting to see
Speaker 5: the ability to just write down what you want and
Speaker 5: AI will generate the code for you. And I think
Speaker 5: we're just going to see a huge revolution of like
Speaker 5: we just converse you and we can have a conversation
Speaker 5: to say what we want, and then the computer can
Speaker 5: actually not only do fixed actions and do things for us,
Speaker 5: but it can actually even write code to do new things,
Speaker 5: you know, and generate software itself. Given how much software
Speaker 5: we have, how much craving we have for software, like
Speaker 5: we'll never have enough software in our world. Uh, you know,
Speaker 5: the ability to have AI systems as a helper in that,
Speaker 5: I think we're going to see a lot of a
Speaker 5: lot of value there.
Speaker 3: So if you if you think about the different ways
Speaker 3: AI might be applied to business, I mean you've talked
Speaker 3: about a number of the sort of classic use cases.
Speaker 3: What are some of the more out there use cases.
Speaker 3: What are some you know, unique ways you could imagine
Speaker 3: AI being applied to business.
Speaker 5: You know, there's really disguised the limit. I mean, we
Speaker 5: have one project that I'm kind of a fan of
Speaker 5: where we actually were working with a mechanical engineering professor
Speaker 5: at MIT working on a classic problem, how do you
Speaker 5: build linkage systems which are like you imagine bars and
Speaker 5: joints and overs.
Speaker 3: You know, the things that are building a thing, building
Speaker 3: a physical machine of some kind.
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 comp replicated 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're building better power converters which allow our
Speaker 5: computers and our devices to be more efficient, save energy,
Speaker 5: you know, less less carbone. But I think the world
Speaker 5: around us has always been shaped by technology. If we
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 lamp. It's really
Speaker 5: just astonishing. And that's already you know, the fruit of
Speaker 5: automation and computers and those kinds of tools. But we're
Speaker 5: going to see that increasingly be product also of AI.
Speaker 5: It's just going to be everywhere around us. Everything we
Speaker 5: touch is going to have been helped in some way
Speaker 5: to get to you by.
Speaker 3: A you know, that is a pretty profound transformation that
Speaker 3: you're talking about in business. How do you think about
Speaker 3: the implications of that both for the sort of you know,
Speaker 3: business itself and also 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?
Speaker 2: 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 you didn't like walking many miles to work, then
Speaker 5: you know, like you can have a car and you
Speaker 5: can drive there. Or we used to have a huge
Speaker 5: fraction over ninety percent of the US population engaged in agriculture,
Speaker 5: and then we mechanized it. How very few people work
Speaker 5: in agriculture, a small number of people can do the
Speaker 5: work of a large number of people. And then you know,
Speaker 5: things like email, and yeah, they've led to huge productor
Speaker 5: the enhancements because I don't need to be writing letters
Speaker 5: and sending them in the mail. I can just instantly
Speaker 5: communicate with people. We just become more effective, Like our
Speaker 5: jobs have transformed, whether it's a physical job like agriculture
Speaker 5: or whether it's a knowledge worker job where you're sending
Speaker 5: emails and communicating with people and coordinating teams. We've just
Speaker 5: gotten better. And you know, the technology has just made
Speaker 5: 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 a legitimate concern. But just like
Speaker 5: how in agriculture, you know, it's not like suddenly we
Speaker 5: had ninety percent of the population unemployed. You know, people
Speaker 5: transitioned to other jobs. And the other thing that we
Speaker 5: found too, is that our appetite for doing more things
Speaker 5: is as humans is sort of insatiable. So even if
Speaker 5: we can dramatically increase how much one human can do,
Speaker 5: that doesn't necessarily mean we're going to do fixed amount
Speaker 5: of stuff. There's an appetite to have even more, so
Speaker 5: we're going to you can continue to grow, grow the pie.
Speaker 5: So I think at least certainly in the near term,
Speaker 5: you know, we're going to see a lot of drudgery
Speaker 5: go away from work. We're going to see people be
Speaker 5: able to be more effective at their jobs. You know,
Speaker 5: we will see some transformation in jobs and like we've
Speaker 5: seen that before, and the technology least has the potential
Speaker 5: to make our lives a lot easier.
Speaker 3: So IBM recently launched Watson X, which includes Watson X
Speaker 3: dot AI. Tell me about that, Tell me about you
Speaker 3: know what it is and the new possibilities that it
Speaker 3: opens up.
Speaker 5: Yeah. So so Watson X is obviously a bit of
Speaker 5: a new branding on the Watson brand.
Speaker 2: T J.
Speaker 5: Watson that was the founder of IBM and our EI
Speaker 5: technologies have had the Watson brand. Watson X is a
Speaker 5: recognition that that there's something new, there's something that actually
Speaker 5: has changed the game.
Speaker 3: You know.
Speaker 5: We gone from this old world of automation is to
Speaker 5: labor intensive to this new world of possibilities where it's
Speaker 5: much easier to use AI. And what watsonex does it
Speaker 5: brings together tools for businesses to harness that power. So
Speaker 5: whatsonex dot AI foundation models that our customers can use.
Speaker 5: It includes tools that make it easy to run, easy
Speaker 5: to deploy, easy to experiment. There's a watsonex dot Data
Speaker 5: component which allows you to sort of organize and access
Speaker 5: to your data. So what we're really trying to do
Speaker 5: is give our customers a cohesive set of tools to
Speaker 5: harness the value of these technologies and at the same
Speaker 5: time be able to manage the risks and other things
Speaker 5: that you have to keep an eye on in an
Speaker 5: enterprise context.
Speaker 3: So we talk about the guests on this show as
Speaker 3: new creators, by which we mean people who are creatively
Speaker 3: applying technology in businesiness to drive change. And I'm curious
Speaker 3: 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 in my
Speaker 5: organization are doing the creating, and I guess you're.
Speaker 3: Doing the managing so that they could do 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 one of the nice
Speaker 5: things about doing invention and doing research on AI. In industries,
Speaker 5: it's usually grounded and a real problem that somebody is having.
Speaker 5: You know, a customer wants to solve this problem. It's
Speaker 5: losing money, or there wuld be a new opportunity. You
Speaker 5: identify that problem and then you build something that's never
Speaker 5: been built before to do that. And I think that's
Speaker 5: honestly the adrenaline rush that keeps all of us in
Speaker 5: this field. How do you do something that nobody else
Speaker 5: on earth has has done before or tried before, so
Speaker 5: that that kind of creativity and there's also creativity as
Speaker 5: well and identifying what those problems are, being able to
Speaker 5: understand the places where the technology is close enough to
Speaker 5: solving a problem and doing that matchmaking between problems that
Speaker 5: are now solvable, you know, and an AI where the
Speaker 5: field is moving so fast, this is constantly growing horizon
Speaker 5: of things that we might be able to solve. So
Speaker 5: that matchmaking, I think, is also a really interesting creative problem.
Speaker 5: So I think I think that's that's that's why it's
Speaker 5: so much fun. And it's a fun environment we have
Speaker 5: here too. It's you know, people drawing on whiteboards and
Speaker 5: writing on pages of math and like in a movie,
Speaker 5: like in a movie, yes, straight from sexual casting.
Speaker 3: Drawing, the drawing on the window, writing on the window,
Speaker 3: and sharp absolutely so, so let's close with the really
Speaker 3: long view. How do you imagine AI and people working
Speaker 3: together twenty years from now?
Speaker 5: Yeah, it's really hard to make predictions. The vision that
Speaker 5: I I like, actually this came from an MIT economist
Speaker 5: named David Ottur, which was imagine AI almost as a
Speaker 5: natural resource. You know, we know how natural resources work, right,
Speaker 5: Like there's an or we can dig up out of
Speaker 5: the earth that 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 now,
Speaker 5: where not only can we have things we can build
Speaker 5: or eat or use or burn or whatever. Now we have,
Speaker 5: you know, this ability to do things and understand things
Speaker 5: and do intellectual work, and I think we can get
Speaker 5: to a world where automating things is just seamless. We're
Speaker 5: 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 when you can writing software
Speaker 5: is super easy and fast and anybody can do it.
Speaker 5: Just think about all the things you can do now, like,
Speaker 5: think about all the new activities and go about all
Speaker 5: the ways we could use that to enrich our lives.
Speaker 5: That's where I'd like to see us in twenty years.
Speaker 5: You know, we can we can do just so much
Speaker 5: more than we were able to do before abundance.
Speaker 3: Great, thank you so much for your time.
Speaker 5: Yeah, It's been a pleasure. Thanks for inviting me.
Speaker 4: What a far ranging, deep conversation. I'm mesmerized by the
Speaker 4: vision David just described. A world where natural conversation between
Speaker 4: mankind and machine can generate creative solutions to our most
Speaker 4: complex problems. A world where we view AI not as
Speaker 4: our replacements, but as a powerful resource we can tap
Speaker 4: into and exponentially boost our innovation and productivity. Thanks so
Speaker 4: much to doctor David Cox for joining us on smart Talks.
Speaker 4: We deeply appreciate him sharing his huge breadth of AI
Speaker 4: knowledge with us and for explaining the transformative potential of
Speaker 4: foundation models in a way that even I can understand.
Speaker 4: We eagerly await his next great breakthrough. Smart Talks at
Speaker 4: IBM is produced by Matt Romano, David jaw nishe Venkat
Speaker 4: and Royston Preserve with Jacob Goldstein. We're edited by Lydia
Speaker 4: Jean Kott. Our engineers are Jason Gambrel, Sarah Bouguer and
Speaker 4: Ben Holliday. Theme song by Gramma's Scope. Special thanks to
Speaker 4: Carli Megliori, Andy Kelly, Kathy Callahan and the Eight Bar
Speaker 4: and IBM teams, as well as the Pushkin marketing team.
Speaker 4: Smart Talks with IBM is a production of Pushkin Industries
Speaker 4: and iHeartMedia. To find more Pushkin podcasts, listen on the
Speaker 4: iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts.
Speaker 4: Him Malcolm Gladwell. This is a paid advertisement from IBM.