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