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