Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio.
Speaker 2: Today, we are witnessed.
Speaker 1: To one of those rare moments in history, the rise
Speaker 1: of an innovative technology with the potential to radically transform
Speaker 1: business in society forever. That technology, of course, is artificial intelligence,
Speaker 1: and it's the central focus for this new season of
Speaker 1: Smart Talks with IBM. Join hosts from your favorite Pushkin
Speaker 1: podcasts as they talk with industry experts and leaders to
Speaker 1: explore how businesses can integrate AI into their workflows and
Speaker 1: help drive real change in this new era of AI.
Speaker 1: And of course, host Malcolm Gladwell will be there to
Speaker 1: guide you through the season and throw in his two
Speaker 1: cents as well. Look out for new episodes of Smart
Speaker 1: Talks with IBM every other week on the iHeartRadio app,
Speaker 1: Apple Podcasts, or wherever you get your podcasts, and learn
Speaker 1: more at IBM dot com slash smart Talks.
Speaker 3: Hey, it's Jacob Goldstein for Smart Talks with IBM. Last year,
Speaker 3: I had the pleasure of sitting down with doctor David Cox,
Speaker 3: VP of AI Models at IBM Research. We explored the
Speaker 3: fascinating world of AI foundation models and their revolutionary potential
Speaker 3: for business automation and innovation. When we first aired this episode,
Speaker 3: the concept of foundation models was just beginning to capture
Speaker 3: our attention. Since then, this technology has evolved and redefined
Speaker 3: the boundaries of what's possible. Businesses are becoming more savvy
Speaker 3: about selecting the right models and understanding how they can
Speaker 3: drive revenue and efficiency. As I listened back to the conversation,
Speaker 3: it was interesting to reflect on some new developments and
Speaker 3: ideas that have emerged, and many of these we will
Speaker 3: continue to explore throughout the season, like how to play
Speaker 3: an active role in choosing the best model for your needs.
Speaker 3: Whether you're a longtime listener or two in for the
Speaker 3: first time, I'm certain you'll find doctor Cox's insights as
Speaker 3: thought provoking as ever. Thanks as always for joining us.
Speaker 3: 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 Glabwell. 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 5: Tell me about your job at IBM SO. I wear
Speaker 5: two hats at IBM SO one. I'm the IBM Doctor
Speaker 5: of the MIT IBM Watson the Eye Lab. That's a
Speaker 5: joint lab between IBM and MIT where we try and
Speaker 5: invent what's next in AI. It's been running for about
Speaker 5: five years, and then more recently I started as the
Speaker 5: vice president for AI Models, and I'm in charge of
Speaker 5: building IBM's foundation models, you know, building these these big models,
Speaker 5: generative models that allow us to have all kinds of
Speaker 5: 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.
Speaker 2: Where did that partnership start, How 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 AI 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'd 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 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, MiTV
Speaker 5: are interested in working on you know, faculty, students and
Speaker 5: 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. So we can kind of
Speaker 5: put out feelers, you know, interesting things that we're seeing
Speaker 5: in our research, interesting things we're hearing in the field.
Speaker 5: We can go and chase those opportunities. So when something
Speaker 5: big comes, like the big change that's been happening lately
Speaker 5: with foundation models, we're ready to jump on it. That's
Speaker 5: really the purpose, that's that's the lab functioning the way
Speaker 5: it should. We're also really interested in how do we
Speaker 5: advance you know, the AI that can help with climate
Speaker 5: change or you know, build better materials and all these
Speaker 5: kinds of things that are you know, a broader aperture
Speaker 5: sometimes than than what we might consider just looking at
Speaker 5: the product portfolio of IBM, and that that gives us
Speaker 5: again a breadth where we can see connections that we
Speaker 5: might not have seen otherwise. We can you know, think
Speaker 5: things that help out society 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 getting used to being
Speaker 5: surprised at the acceleration of the pace.
Speaker 2: Again.
Speaker 5: It tracks actually a long way back. You know, there's
Speaker 5: lots of things where there was an idea that just
Speaker 5: simmered for a really long time. Some of the key
Speaker 5: math behind the stuff that we have today, which is amazing.
Speaker 5: There's an algorithm call 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 a 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 because we kept building faster and faster and
Speaker 5: more powerful computers, and then that allowed us to make
Speaker 5: this this big breakthrough. And then you know, more recently,
Speaker 5: using the same building blocks, that inexorable rise of more
Speaker 5: and more and more data that the technology called self
Speaker 5: supervised learning. Where the key difference there in traditional deep learning,
Speaker 5: you know, for classifying images, you know, like is this
Speaker 5: a cat or is this a dog? And a picture
Speaker 5: those technologies require supervision, so you have to take what
Speaker 5: you have and then you have to label it. So
Speaker 5: you have to take a picture of a cat and
Speaker 5: then you label it as a cat, and it turns
Speaker 5: out that you know, that's very powerful, but it takes
Speaker 5: a lot of time to label gats and to label dogs,
Speaker 5: and there's only so many labels that exist in the world.
Speaker 5: So what really changed more recently is that we have
Speaker 5: self supervised learning where you don't have to have the labels.
Speaker 5: We can just take unannotated data. And what that does
Speaker 5: is allows you to 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 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: Could we could train a model. We don't have to
Speaker 5: go 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.
Speaker 2: But what a.
Speaker 5: Foundation model is 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 one of these massively trained models and
Speaker 5: then do a little bit on top. You know, you
Speaker 5: could use just a few examples of what you're looking
Speaker 5: for 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: Trying to think of of an analogy for sort of
Speaker 3: foundation models versus what came before, and I don't know
Speaker 3: that I came up with a good one, but the
Speaker 3: best I could do was this. I want you to
Speaker 3: tell me if it's plausible. It's like before foundation models,
Speaker 3: it was like you had these sort of single use
Speaker 3: kitchen appliances. You could make a waffle iron if you
Speaker 3: wanted waffles, or you could make a.
Speaker 2: Toaster if you wanted to make toast.
Speaker 3: But a foundation model is like like an oven with
Speaker 3: a range on top. So it's like this machine and
Speaker 3: you could just cook 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 someone 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 It
Speaker 5: sounds 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: And what I mean is that the effort it took
Speaker 5: the old regime to automate something was very, very high.
Speaker 5: So 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 warn't right
Speaker 5: level because you.
Speaker 3: Have to essentially build the whole machine from scratch, and
Speaker 3: there aren't many things where it's worth that much work
Speaker 3: to build a machine that's only going to do one
Speaker 3: 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 going to get
Speaker 5: enough energy out of it that it was worth your trouble.
Speaker 5: You're 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 ballet bricks.
Speaker 5: And you know, there's a lot of value left on
Speaker 5: the table because it's like, well, I can't there's nothing
Speaker 5: you can do about it. It's just that that's too
Speaker 5: low value. So it takes too much effort, so I'm
Speaker 5: just not going to do it. The return on 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 the don't We don't care. We have to
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 on huge quantities of it,
Speaker 5: and then it learned the structure, learned how language you know,
Speaker 5: good part of how language works on all that unlabeled data,
Speaker 5: and then when you roll up with your task, you
Speaker 5: know I want to solve this particular problem. You don't
Speaker 5: have to start from scratch. You're starting from a very
Speaker 5: very very high place. So that just gives you the
Speaker 5: ability to just, you know, now, all of a sudden,
Speaker 5: everything is accessible. All the puddles and greeks and babbling
Speaker 5: brooks and kettlepons, you know, those are all accessible now.
Speaker 5: And that's that's very exciting. But it just changes the
Speaker 5: equation on what kinds of problems you could use AI
Speaker 5: 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 fits.
Speaker 2: All foundation model.
Speaker 3: So you know, what does that mean and why is
Speaker 3: that important to think about?
Speaker 2: In this context.
Speaker 5: Yeah, so we believe very strongly that there isn't just
Speaker 5: one model to rule them all. There's a number of
Speaker 5: reasons why that could be true. One which I think
Speaker 5: is important and very relevant today is how much energy
Speaker 5: these models can consume. So these models, you know, can
Speaker 5: get very, very large. So one thing that we're starting
Speaker 5: to see or starting to believe, is that you probably
Speaker 5: shouldn't use one giant sledgehammer model to solve every single problem,
Speaker 5: you know, like we should pick the right size model
Speaker 5: to solve the problem. We shouldn't necessarily assume that we
Speaker 5: need the biggest, baddest model for every little use case.
Speaker 5: And we're also seeing that, you know, small models that
Speaker 5: are trained like to specialize on particular domains can actually
Speaker 5: outperform much bigger models. So bigger isn't always even better.
Speaker 3: So they're more efficient and they do the thing you
Speaker 3: want them to do better as well.
Speaker 2: That's right.
Speaker 5: So Stanford, for instance, a group of Stanford trained a model.
Speaker 5: It is a two point seven billion parameter model, which
Speaker 5: isn't terribly big by today's standards. They trained it just
Speaker 5: on the biomedical literature.
Speaker 2: You know.
Speaker 5: This is the kind of thing that universities do and
Speaker 5: what they showed was that this model was better at
Speaker 5: answering questions about the biomedical literature than some models that
Speaker 5: are one hundred billion parameters, you know, many times larger.
Speaker 5: So it's a little bit like you know, asking an
Speaker 5: expert for help on something versus asking the smartest person,
Speaker 5: you know, the smartest person you know, maybe very smart,
Speaker 5: but they're not going to be expertise. And then as
Speaker 5: an added bonus, you know, this is now a much
Speaker 5: smaller model, it's much more efficient to run, we are
Speaker 5: you know, you know, it's cheaper. So there's lots of
Speaker 5: different advantages there. So I think we're going to see
Speaker 5: attension in the industry between vendors that say, hey, this
Speaker 5: is the one, you know, big model, and then others
Speaker 5: that say, well, actually, you know, there's there's you know,
Speaker 5: lots of different tools we can use that all have
Speaker 5: this nice quality that we outligned at the beginning, and
Speaker 5: then we should really pick the one that makes the
Speaker 5: most sense for the 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 and it provides its answers. So every time,
Speaker 5: you know, if all the text you have says, you know,
Speaker 5: it's more likely to refer to female nurses and male scientists,
Speaker 5: then you're going to you know, get models that you know.
Speaker 5: For instance, there was an example where a machine learning
Speaker 5: based translation system translated from Hungarian to English. Hungarian doesn't
Speaker 5: have gender pronouns, English does, and when you ask them
Speaker 5: to translate, it would translate they are a nurse to
Speaker 5: she is a nurse, would translate they are a scientist
Speaker 5: to he is a scientist. And that's not because the
Speaker 5: people who wrote the algorithm were building in bias and
Speaker 5: coding in like oh, it's got to be this way.
Speaker 5: It's because the data was like that. You know, we
Speaker 5: have biases in our society and they're reflected in our
Speaker 5: data and our text and our images everywhere, and then
Speaker 5: the models they're just mapping from what they've seen in
Speaker 5: their training data to the result that you're trying to
Speaker 5: get them to do and to give, and then these
Speaker 5: biases come out. So there's a very active program of
Speaker 5: research and you know, we we do quite a bit
Speaker 5: at IBM research and i T but also all over
Speaker 5: the community and industry and academia trying to figure out
Speaker 5: how do we explicitly remove these biases, how do we
Speaker 5: identify them, how do you know, how do we build
Speaker 5: tools that allow people to audit their systems to make
Speaker 5: sure they aren't biased. So this is a really important thing.
Speaker 5: And you know, again this was here since the beginning,
Speaker 5: you know, of machine learning and AI, but foundation models
Speaker 5: and large language models and generative AI just bring it
Speaker 5: into sharper even sharper focus because there's just so much
Speaker 5: data and it's sort of building in, baking in all
Speaker 5: these different biases we have, so that that's that's absolutely
Speaker 5: a problem that these models have. Another one that you
Speaker 5: mentioned was hallucinations. So even the most impressive of our
Speaker 5: models will often just make stuff up, you know, the
Speaker 5: technical term that heel has chosen as a hallucination. To
Speaker 5: give you an example, I asked chat tbt to create
Speaker 5: a biography of David Cox IBM, and you know, it
Speaker 5: started off really well. You know, they identified that I
Speaker 5: was the director of the mt IBM Watsonay and some
Speaker 5: a few words about that, and then it proceeded to
Speaker 5: create an authoritative but completely fake biography of me where
Speaker 5: I was British, I was born in the UK. I
Speaker 5: went to British university, you know, universities in the UK.
Speaker 3: I was a professor, the authority, right, it's the certainty
Speaker 3: that that is weird about it, right, It's it's dead
Speaker 3: 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, you know, we get debate about that, but.
Speaker 3: Yeah, we can debate about that, but yes, the sort
Speaker 3: of blive confidence throws you off when you realize it's
Speaker 3: 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 but these things will make up stuff, you know,
Speaker 5: very aggressively, and you know, you everyone can try asking
Speaker 5: it for their their bio. You'll you'll get something that
Speaker 5: you always get, something that's of the right form, that
Speaker 5: has the right tone. But you know, the facts just
Speaker 5: aren't necessarily there. So that's obviously a problem. We need
Speaker 5: to figure out how to close those gaps, fix those problems.
Speaker 5: There's lots 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 buoyant fictional versions of people's lives, and while entertaining
Speaker 4: ourselves with chat GPT, and mid journey is important. The
Speaker 4: way lay people 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.
Speaker 2: Let's talk in a little bit more.
Speaker 3: Of a specific way. Can you just talk about some
Speaker 3: examples of business challenges that can be solved with automation,
Speaker 3: with this kind of automation we're talking about.
Speaker 5: Yeah, so the really really guy's the limit. There's a
Speaker 5: whole set of different applications that these models are really
Speaker 5: good at. And basically it's a superset of everything we
Speaker 5: 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'm you know, I have like product reviews,
Speaker 5: and I want to be able to tell if these
Speaker 5: are positive or negative. You know, like, let's look at
Speaker 5: all the negative reviews so we can have a human
Speaker 5: look through them and see what was up. Very common
Speaker 5: business use case. You can do it with traditional deep
Speaker 5: learning based AI. So so there's things like that that
Speaker 5: are you know, it's very prosaic sort that we were
Speaker 5: already doing it, We've been doing it for a long time.
Speaker 5: Then you get situations that are that were harder for
Speaker 5: the old day. I like, if I'm I want to
Speaker 5: impress something like I want to I have, like say
Speaker 5: I have a chat transcript, Like a customer called in
Speaker 5: and they had a complaint. They call back, Okay, now
Speaker 5: a new you know, a person on the line needs
Speaker 5: to go read the old transcript to catch up. Wouldn't
Speaker 5: it be better if we could just summarize that, just
Speaker 5: condense it all down a quick little paragraph. You know,
Speaker 5: customer called they were up said about this, rather than
Speaker 5: having to read the blow by blow. There's just lots
Speaker 5: of settings like that where summarization is really helpful. Hey,
Speaker 5: you haven't meeting, and I'd like to just automatically, you know,
Speaker 5: have have that meeting or that email or whatever. I'd
Speaker 5: like to just have a condensed down so I can
Speaker 5: really quickly get to the heart of the matter. These
Speaker 5: models are are really good at doing that. They're also
Speaker 5: really good at question answering. So if I want to
Speaker 5: find out what's how many vacation days do I have?
Speaker 5: I can now interact in natural language with a system
Speaker 5: that can go and that it has access to our
Speaker 5: HR policies, and I can actually have a you know,
Speaker 5: a multi turn conversation where I can, you know, like
Speaker 5: I would have with you know, somebody, you know, actual
Speaker 5: HR professional or customer service representative. So a big part,
Speaker 5: you know, what this is doing is it's it's putting
Speaker 5: an interface. You know, when we think of computer interfaces,
Speaker 5: we're usually thinking about UI user interface elements where I
Speaker 5: click on menus and there's buttons and all this stuff. Increasingly,
Speaker 5: now we can just talk, you know, you just in words.
Speaker 5: You can describe what you want, you want to answer
Speaker 5: ask a question you want to sort of command this
Speaker 5: system to do something, rather than having to learn how
Speaker 5: to do that clicking buttons, which might be inefficient. Now
Speaker 5: we can just sort of 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 have to learn how
Speaker 5: to use a tool or I have to kind of
Speaker 5: have awkward, you know, interactions from the computer. I can
Speaker 5: just tell it what I want and I can understand it.
Speaker 5: Could you know, potentially even ask questions back to clarify
Speaker 5: and have those kinds of conversations that can be extremely powerful.
Speaker 5: And in fact, one area where that's going to I
Speaker 5: think be absolutely game changing is in code. When we
Speaker 5: write code. You know, programming languages are a way for
Speaker 5: us to sort of match between our very sloppy way
Speaker 5: of talking and the very exact way that you need
Speaker 5: to command a computer to do what you wanted to do.
Speaker 5: They're cumbersome to learn, they can, you know, create very
Speaker 5: complex systems that are very hard to reason about. And
Speaker 5: we're already starting to see the ability to just write
Speaker 5: down what you want and AI will generate the code
Speaker 5: for you. And I think we're just going to see
Speaker 5: a huge revolution of like we just converse, you know,
Speaker 5: we can have a conversation to say what we want,
Speaker 5: and then the computer can actually not only do fixed
Speaker 5: actions and do things for us, but it can actually
Speaker 5: even write code to do new things, you know, and
Speaker 5: generate software itself. Given how much software we have, how
Speaker 5: much craving we have for software, like we'll never have
Speaker 5: enough software in our world. Uh, you know, the ability
Speaker 5: to have AI systems as a helper in that, I
Speaker 5: think we're going to see a lot of a lot
Speaker 5: 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: Yeah, there's really disguised the limit. I mean, we have
Speaker 5: one project that I'm kind of a fan of where
Speaker 5: we actually were working with a mechanical engineering professor at
Speaker 5: MIT working on a classic problem, how do you build
Speaker 5: linkage systems which are like you imagine bars and joints
Speaker 5: and overs you know the things that.
Speaker 3: Are building a thing, building a physical machine of some.
Speaker 5: Kinda like real like metal, and you know nineteenth century
Speaker 5: just old school industrial revolution. Yeah yeah, yeah, but you
Speaker 5: know the little arm that's that's holding up my microphone
Speaker 5: in front of me, Cranes that buld your buildings, you know,
Speaker 5: parts of your engines. This is like classical stuff. It
Speaker 5: turns out that you know, humans, if you want to
Speaker 5: build an advanced system, you decide what like curve you
Speaker 5: want to create, and then a human together with a
Speaker 5: computer program, can build a five or six bar linkage,
Speaker 5: and then that's kind of where you top out it
Speaker 5: because it gets too complicated to work more than that.
Speaker 5: We built a generative AI system that can build twenty
Speaker 5: bar linkages, like arbitrarily complex. So these are machines that
Speaker 5: are beyond the capability of a human to design themselves.
Speaker 5: Another example, we have an AI system that can generate
Speaker 5: electronic circuits. You know, we had a project where we're
Speaker 5: working where we'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 carbon ote. But I think the
Speaker 5: world around us has always been shaped by technology. If
Speaker 5: we look around you know, just think about how many
Speaker 5: steps and how many people and how many designs went
Speaker 5: into the table and the chair and the lamp. It's
Speaker 5: really just astonishing. And that's already you know, the fruit
Speaker 5: of automation and computers and those kinds of tools. But
Speaker 5: we're going to see that increasingly be 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 you know, helped in
Speaker 5: some way to get to you by.
Speaker 3: You know, that is a pretty profound transformation that you're
Speaker 3: talking about in business. How do you think about the
Speaker 3: 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 going to
Speaker 5: cut costs, make new opportunities to like customers, you know,
Speaker 5: like there's just you know, it's sort of all upside
Speaker 5: right like for the for the workers, I think the
Speaker 5: story is mostly good too. You know, like how many
Speaker 5: things do you do in your day that you'd really
Speaker 5: rather not right? You know, and we're used to having
Speaker 5: things we don't like automated away, you know, we we
Speaker 5: didn't you know, if you didn't like walking many miles
Speaker 5: to work, then you know, like you can have a
Speaker 5: car and you can drive there, or we used to
Speaker 5: have a huge traction over ninety percent of the US
Speaker 5: population engaged in agriculture. And then we mechanize how very
Speaker 5: few people work in agriculture, a small number of people
Speaker 5: can do the work of a large number of people.
Speaker 5: And then you know, things like email, and yeah, they've
Speaker 5: led to huge productivity enhancements because I don't need to
Speaker 5: be writing letters and sending them in the mail. I
Speaker 5: can just instantly communicate with people. We just become more effective,
Speaker 5: Like our jobs have transformed, whether it's a physical job
Speaker 5: like agriculture, or whether it's a knowledge worker job where
Speaker 5: you're sending emails and communicating with people and coordinating teams.
Speaker 5: We've just gotten better. And you know, the technology has
Speaker 5: just made us more productive. And this is just another example. Now,
Speaker 5: you know, there are people who worry that you know,
Speaker 5: will be so good at that that maybe jobs will
Speaker 5: be displaced, and that's 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've
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, you know, one human
Speaker 5: can do, that doesn't necessarily mean you're going to do
Speaker 5: a fixed amount of stuff. There's an appetite to have
Speaker 5: even more. So we're going to you can continue to
Speaker 5: grow grow the pie. So I think at least certainly
Speaker 5: in the near term, you know, we're going to see
Speaker 5: a lot of drudgery go away from work. We're going
Speaker 5: to see people be able to be more effective at
Speaker 5: their jobs. You know, we will see some transformation in
Speaker 5: jobs and like. But we've seen that before, and the
Speaker 5: technology a least has the potential to make our lives
Speaker 5: 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 2: Yeah.
Speaker 5: So, so Watson X is obviously a bit of a
Speaker 5: new branding on the Watson brand. You know TJ. Watson
Speaker 5: that was the founder of IBM and our eechnologies of
Speaker 5: the Watson brand. Watson X is a recognition that there's
Speaker 5: something new, there's something that actually has changed the game.
Speaker 5: We've 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 watson x does
Speaker 5: it brings together tools for businesses to harness that power.
Speaker 5: So whattsonex dot AI so foundation models that our customers
Speaker 5: can use. It includes tools that make it easy to run,
Speaker 5: easy to deploy, easy to experiment. There's a watsonex dot
Speaker 5: Data component which allows you to sort of organize and
Speaker 5: access to your data. So what we're really trying to
Speaker 5: do is give our customers a cohesive set of tools
Speaker 5: to harness the value of these technologies and at the
Speaker 5: same time be able to manage the risks and other
Speaker 5: things that you have to keep an eye on in
Speaker 5: an 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 business 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.
Speaker 3: Guess you're doing the managing so that they could do
Speaker 3: 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's having.
Speaker 5: You know, a customer wants to solve this problem that's
Speaker 5: losing money or there there would be a new opportunity.
Speaker 5: You identify that problem and then you you build something
Speaker 5: that's never been built before to do that. And I
Speaker 5: think that's honestly the adrenaline rush that keeps all of
Speaker 5: us in this field. How do you do something that
Speaker 5: nobody else on earth has has done before or tried before,
Speaker 5: So that that kind of creativity and there's also creativity
Speaker 5: as well and identifying what those problems are, being able
Speaker 5: to understand the places where you know the technology is
Speaker 5: close enough to solving a problem, and doing that matchmaking
Speaker 5: between problems that are now solvable, you know, and in
Speaker 5: AI where the fields moving so fast, this constantly growing
Speaker 5: horizon of things that we might be able to solve,
Speaker 5: So that matchmaking, I think, is also a really interesting
Speaker 5: creative problem. So I think I think that's that's that's
Speaker 5: why it's so much fun. And it's a fun environment
Speaker 5: we have here too. It's you know, people drawing on
Speaker 5: whiteboards and writing on pages of math and you.
Speaker 2: Know, like in a movie, like in a movie.
Speaker 5: Yes, straight from sexual casting.
Speaker 3: The drawing on the window, writing on the window in.
Speaker 2: Sharp absolutely so.
Speaker 3: So let's close with the really long view. How do
Speaker 3: you imagine AI and people working together twenty years from now?
Speaker 5: Yeah, it's really hard to make predictions.
Speaker 2: The vision that I.
Speaker 5: Like, actually this came from an MIT economist named David Ott,
Speaker 5: which was imagine AI almost as a natural resource. Yeah,
Speaker 5: we know how natural resources work, right, like this an
Speaker 5: or we can dig up out of the earth. It
Speaker 5: comes from springs from the earth. Or we usually think
Speaker 5: of that in terms of physical stuff. With AI, you
Speaker 5: can almost think of it as like there's a new
Speaker 5: kind of abundance potentially twenty years from now or not
Speaker 5: only can we have things we can build or eat
Speaker 5: or use or burn or whatever. Now we have you know,
Speaker 5: this ability to do things and understand things and do
Speaker 5: intellectual work, and I think we can get to a
Speaker 5: world where automating things is just seamless. We're surrounded by
Speaker 5: capability to augment ourselves to get things done. And you
Speaker 5: could think of that in terms of like, oh, that's
Speaker 5: going to displace our jobs, because eventually the AI system
Speaker 5: is going to do everything we can do. But you
Speaker 5: 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,
Speaker 5: think about all the new activities, and go out 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 2: 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 vision.
Speaker 4: David just described a world where natural conversation between mankind
Speaker 4: and machine can generate creative solutions to our most complex problems.
Speaker 4: A world where we view AI not as our replacements,
Speaker 4: but as a powerful resource we can tap into and
Speaker 4: exponentially boost our innovation and productivity. Thanks so much to
Speaker 4: doctor David Cox for joining us on smart Talks. We
Speaker 4: deeply appreciate him sharing his huge breadth of AI knowledge
Speaker 4: with us and for explaining the transformative potential of foundation
Speaker 4: models in a way that even I can understand. We
Speaker 4: eagerly await his next great breakthrough. Smart Talks with IBM
Speaker 4: is produced by Matt Romano, David jaw nishe Venkat and
Speaker 4: Royston Preserve with Jacob Goldstein. We're edited by Lydia Jane Kott.
Speaker 4: Our engineers are Jason Gambrel, Sarah Buguier and Ben Holliday.
Speaker 4: Theme song by Gramosco. Special thanks to Carli Megliori, Andy Kelly,
Speaker 4: Kathy Callahan and the eight Bar and IBM teams, as
Speaker 4: well as the Pushkin marketing team. Smart Talks with IBM
Speaker 4: is a production of Pushkin Industries and iHeartMedia. To find
Speaker 4: more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
Speaker 4: or wherever you listen to podcasts him, Malcolm Gladwell. This
Speaker 4: is a paid advertisement from IBM.