Hey everyone, it's Robert and Joe here. Today we've got something a little bit different to share with you. It is a new season of the Smart Talks with IBM podcast series.

Hey everyone, it's Robert and Joe here. Today we've got something a little bit different to share with you. It is a new season of the Smart Talks with IBM podcast series.

Today we are witnessed to one of those rare moments in history, the rise of an innovative technology with the potential to radically transform business and society forever. The technology, of course, is artificial intelligence, and it's the central focus for this new season of Smart Talks with IBM.

Today we are witnessed to one of those rare moments in history, the rise of an innovative technology with the potential to radically transform business and society forever. The technology, of course, is artificial intelligence, and it's the central focus for this new season of Smart Talks with IBM.

Join hosts from your favorite Pushkin podcasts as they talk with industry experts and leaders to explore how businesses can integrate AI into their workflows and help drive real change in this new era of AI. And of course, host Malcolm Gladwell will be there to guide you through the season and throw in his two cents as well.

Join hosts from your favorite Pushkin podcasts as they talk with industry experts and leaders to explore how businesses can integrate AI into their workflows and help drive real change in this new era of AI. And of course, host Malcolm Gladwell will be there to guide you through the season and throw in his two cents as well.

Look out for new episodes of Smart Talks with IBM every other week on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts, and learn more at IBM dot com slash smart Talks.

Look out for new episodes of Smart Talks with IBM every other week on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts, and learn more at IBM dot com slash smart Talks.

Hello, Hello, Welcome to Smart Talks with IBA, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gabwell. This season we're continuing our conversation with new creators visionaries who are creatively applying technology in business to drive change, but with a focus on the transformative power of artificial intelligence and what it means to leverage AI as a game changing multiplier for your business. Our guest today is doctor David Cox, VP of AI Models at IBM Research and IBM Director of the MIT IBM Watson AI Lab, a first of its kind industry academic collaboration between IBM and MIT focused on the fundamental research of artificial intelligence. Over the course of decades, David Cox watched as the AI revolution steadily grew from the simmering ideas of a few academics and technologists into the industrial boom we are experiencing today. Having dedicated his life to pushing the field of AI towards new horizons, David has both contributed to and presided over many of the major breakthroughs in artificial intelligence. In today's episode, you'll hear David explain some of the conceptual underpinnings of the current AI landscape, things like foundation models, in surprisingly comprehensible terms. I might add, we'll also get into some of the amazing practical applications for AI in business, as well as what implications AI will have for the future of work and design. David spoke with Jacob Goldstein, host of the Pushkin podcast What's Your Problem. A veteran business journalist, Jacob has reported for The Wall Street Journal, the Miami Herald, and was a longtime host of the NPR program Planet Money. Okay, let's get to the interview.

Hello, Hello, Welcome to Smart Talks with IBA, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gabwell. This season we're continuing our conversation with new creators visionaries who are creatively applying technology in business to drive change, but with a focus on the transformative power of artificial intelligence and what it means to leverage AI as a game changing multiplier for your business. Our guest today is doctor David Cox, VP of AI Models at IBM Research and IBM Director of the MIT IBM Watson AI Lab, a first of its kind industry academic collaboration between IBM and MIT focused on the fundamental research of artificial intelligence. Over the course of decades, David Cox watched as the AI revolution steadily grew from the simmering ideas of a few academics and technologists into the industrial boom we are experiencing today. Having dedicated his life to pushing the field of AI towards new horizons, David has both contributed to and presided over many of the major breakthroughs in artificial intelligence. In today's episode, you'll hear David explain some of the conceptual underpinnings of the current AI landscape, things like foundation models, in surprisingly comprehensible terms. I might add, we'll also get into some of the amazing practical applications for AI in business, as well as what implications AI will have for the future of work and design. David spoke with Jacob Goldstein, host of the Pushkin podcast What's Your Problem. A veteran business journalist, Jacob has reported for The Wall Street Journal, the Miami Herald, and was a longtime host of the NPR program Planet Money. Okay, let's get to the interview.

Tell me about your job at IBM.

Tell me about your job at IBM.

So.

So.

I wear two hats at IBM. So one, I'm the IBM Director of the MIT, IBM Watson AI Lab. So that's a joint lab between IBM and MIT where we try and invent what's next in AI. It's been running for about five years, and then more recently I started as the vice president for AI Models, and I'm in charge of building IBM's foundation models, you know, building these these big models, generative models that allow us to have all kinds of new exciting capabilities in AI.

I wear two hats at IBM. So one, I'm the IBM Director of the MIT, IBM Watson AI Lab. So that's a joint lab between IBM and MIT where we try and invent what's next in AI. It's been running for about five years, and then more recently I started as the vice president for AI Models, and I'm in charge of building IBM's foundation models, you know, building these these big models, generative models that allow us to have all kinds of new exciting capabilities in AI.

So, so I want to talk to you a lot about foundation models, about genitive AI. But before we get to that, let's just spend a minute on the on the IBM MIT collaboration. Where did that partnership start, How did it originate?

So, so I want to talk to you a lot about foundation models, about genitive AI. But before we get to that, let's just spend a minute on the on the IBM MIT collaboration. Where did that partnership start, How did it originate?

Yeah, So, actually it turns out that MIT and IBM have been collaborating for a very long time in the area of AI. In fact, the term artificial intelligence was coined in a nineteen fifty six workshop that was held at Dartmouth, but it was actually organized by an IBM or Nathaniel Rochester, who led the development of the IBM seven and one. So we've really been together in AIS since the beginning, and as AI kept accelerating more and more and more, I think there was a really interesting decision to say, let's make this a formal partnership, so IBM in twenty seventeen and also to be committing close to a quarter billion dollars over ten years to have this joint lab with MIT, and we located ourselves right on the campus and we've been developing very very deep relationships where we can really get to know each other, work shoulder to shoulder, conceiving what we should work on next, and then executing the projects. And it's really very few entities like this exist between academia industry. It's been really fun the last five years to be a part of it.

Yeah, So, actually it turns out that MIT and IBM have been collaborating for a very long time in the area of AI. In fact, the term artificial intelligence was coined in a nineteen fifty six workshop that was held at Dartmouth, but it was actually organized by an IBM or Nathaniel Rochester, who led the development of the IBM seven and one. So we've really been together in AIS since the beginning, and as AI kept accelerating more and more and more, I think there was a really interesting decision to say, let's make this a formal partnership, so IBM in twenty seventeen and also to be committing close to a quarter billion dollars over ten years to have this joint lab with MIT, and we located ourselves right on the campus and we've been developing very very deep relationships where we can really get to know each other, work shoulder to shoulder, conceiving what we should work on next, and then executing the projects. And it's really very few entities like this exist between academia industry. It's been really fun the last five years to be a part of it.

And what do you think are some of the most important outcomes of this collaboration between IBM and MIT.

And what do you think are some of the most important outcomes of this collaboration between IBM and MIT.

Yeah, so we're really kind of the tip of the sphere for IBM the I strategy. So we're we're really looking what, you know, what's coming ahead, and you know, in areas like Foundation models, you know, as the field changes and I T people are interested in working on you know, faculty, students and staff are interested in working on what's the latest thing, what's the next thing. We at IBM Research are very much interested in the same. So we can kind of put out feelers, you know, interesting things that we're seeing in our research, interesting things we're hearing in the field. We can go and chase those opportunities. So when something big comes, like the big change that's been happening lately with Foundation Models, we're ready to jump on it. That's really the purpose, that's that's the lab functioning the way it should. We're also really interested in how do we advance you know AI that can help with climate change or you know, build better materials and all these kinds of things that are you know, a broader aperture sometimes than than what we might consider just looking at the product portfolio of IBM, and that that gives us again a breadth where we can see connections that we might not have seen otherwise. We can you know, think things that help out society and also help out our customers.

Yeah, so we're really kind of the tip of the sphere for IBM the I strategy. So we're we're really looking what, you know, what's coming ahead, and you know, in areas like Foundation models, you know, as the field changes and I T people are interested in working on you know, faculty, students and staff are interested in working on what's the latest thing, what's the next thing. We at IBM Research are very much interested in the same. So we can kind of put out feelers, you know, interesting things that we're seeing in our research, interesting things we're hearing in the field. We can go and chase those opportunities. So when something big comes, like the big change that's been happening lately with Foundation Models, we're ready to jump on it. That's really the purpose, that's that's the lab functioning the way it should. We're also really interested in how do we advance you know AI that can help with climate change or you know, build better materials and all these kinds of things that are you know, a broader aperture sometimes than than what we might consider just looking at the product portfolio of IBM, and that that gives us again a breadth where we can see connections that we might not have seen otherwise. We can you know, think things that help out society and also help out our customers.

So the last whatever six months, say, there has been this wild rise in the public's interest in AI, right clearly coming out of these generative AI models that are really accessible, you know, certainly chat GPT language models like that, as well as models that generate images like mid Journey. I mean, can you just sort of briefly talk about the breakthroughs in AI that have made this moment feel so exciting, so revolutionary for artificial intelligence.

So the last whatever six months, say, there has been this wild rise in the public's interest in AI, right clearly coming out of these generative AI models that are really accessible, you know, certainly chat GPT language models like that, as well as models that generate images like mid Journey. I mean, can you just sort of briefly talk about the breakthroughs in AI that have made this moment feel so exciting, so revolutionary for artificial intelligence.

Yeah. You know, I've been studying AI basically my entire adult life. Before I came to IBM, I was a professor at Harvard. I've been doing this a long time, and I've gotten used to being surprised. It sounds like a joke, but it's serious, Like I'm getting used to being surprised at the acceleration of the pace Again. It tracks actually a long way back. You know, there's lots of things where there was an idea that just simmered for a really long time. Some of the key math behind the stuff that we have today, which is amazing there's an algorithm called backpropagation, which is sort of key to training neural networks that's been around, you know, since the eighties in wide use. And really what happened was it simmered for a long time and then enough data and enough compute came. So we had enough data because you know, we all started carrying multiple cameras around with us. Our mobile phones have all, you know, all these cameras and this we put everything on the Internet, and there's all this data out there. We caught a lucky break that there was something called a graphics processing unit, which turns out to be really useful for doing these kinds of algorithms, maybe even more useful than it is for doing graphics. They're greater graphics too, And things just kept kind of adding to the snowball. So we had deep learning, which is sort of a rebrand of neural networks that I mentioned from from the eighties, and that was enable again by data because we digitalize the world and compute because because we kept building faster and faster and more powerful computers, and then that allowed us to make this this big breakthrough. And then you know, more recently, using the same building blocks that inexorable rise of more and more and more data met a technology called self supervised learning, where the key difference there in traditional deep learning, you know, for classifying images, you know, like is this a cat or is this a dog? And a picture, those technologies require super visions, so you have to take what you have and then you have to label it. So you have to take a picture of a cat, and then you label it as a cat. And it turns out that you know, that's very powerful, that it takes a lot of time to label gaps and to label dogs, and there's only so many labels that exist in the world. So what really changed more recently is that we have self supervised learning where you don't have to have the labels. We can just take unannotated data. And what that does is it lots you use even more data. And that's really what drove this latest sort of rage. And then and then all of a sudden we start getting these really powerful models. And then really this has been simmering technologies, right, this has been happening for a while and progressively getting more and more powerful. One of the things that really happened with CHATGBT and technologies like stable diffusion and mid journey was that they made it visible to the public. You know, you put it out there, the public can touch and feel and they're like, wow, not only is there palpable change and wow this you know, I could talk to this thing. Wow, this thing can generate an image. Not only that, but everyone can touch and feel and try. My kids can use some of these AI art generation technologies. And that's really just launched. You know. It's like a propelled slingshot at us into a different regime in terms of the public awareness of these technologies.

Yeah. You know, I've been studying AI basically my entire adult life. Before I came to IBM, I was a professor at Harvard. I've been doing this a long time, and I've gotten used to being surprised. It sounds like a joke, but it's serious, Like I'm getting used to being surprised at the acceleration of the pace Again. It tracks actually a long way back. You know, there's lots of things where there was an idea that just simmered for a really long time. Some of the key math behind the stuff that we have today, which is amazing there's an algorithm called backpropagation, which is sort of key to training neural networks that's been around, you know, since the eighties in wide use. And really what happened was it simmered for a long time and then enough data and enough compute came. So we had enough data because you know, we all started carrying multiple cameras around with us. Our mobile phones have all, you know, all these cameras and this we put everything on the Internet, and there's all this data out there. We caught a lucky break that there was something called a graphics processing unit, which turns out to be really useful for doing these kinds of algorithms, maybe even more useful than it is for doing graphics. They're greater graphics too, And things just kept kind of adding to the snowball. So we had deep learning, which is sort of a rebrand of neural networks that I mentioned from from the eighties, and that was enable again by data because we digitalize the world and compute because because we kept building faster and faster and more powerful computers, and then that allowed us to make this this big breakthrough. And then you know, more recently, using the same building blocks that inexorable rise of more and more and more data met a technology called self supervised learning, where the key difference there in traditional deep learning, you know, for classifying images, you know, like is this a cat or is this a dog? And a picture, those technologies require super visions, so you have to take what you have and then you have to label it. So you have to take a picture of a cat, and then you label it as a cat. And it turns out that you know, that's very powerful, that it takes a lot of time to label gaps and to label dogs, and there's only so many labels that exist in the world. So what really changed more recently is that we have self supervised learning where you don't have to have the labels. We can just take unannotated data. And what that does is it lots you use even more data. And that's really what drove this latest sort of rage. And then and then all of a sudden we start getting these really powerful models. And then really this has been simmering technologies, right, this has been happening for a while and progressively getting more and more powerful. One of the things that really happened with CHATGBT and technologies like stable diffusion and mid journey was that they made it visible to the public. You know, you put it out there, the public can touch and feel and they're like, wow, not only is there palpable change and wow this you know, I could talk to this thing. Wow, this thing can generate an image. Not only that, but everyone can touch and feel and try. My kids can use some of these AI art generation technologies. And that's really just launched. You know. It's like a propelled slingshot at us into a different regime in terms of the public awareness of these technologies.

You mentioned earlier in the conversation foundation models, and I want to talk a little bit about that. I mean, can you just tell me, you know, what are foundation models for AI and why are they a big deal?

You mentioned earlier in the conversation foundation models, and I want to talk a little bit about that. I mean, can you just tell me, you know, what are foundation models for AI and why are they a big deal?

Yeah, So this term foundation model was coined by a group at Stanford, and I think it's actually a really apt term because remember I said, you know, one of the big things that unlocked this latest excitement was the fact that we could use large amounts of unannotated data. We could train a model. We don't have to go through the painful effort of labeling each and every example. You still need to have your model do something you wanted to do you still need to tell it what you want to do. You can't just have a model that doesn't have any purpose, but what a foundation model that provides a foundation, like a literal foundation, you can sort of stand on the shoulders of giants. You can have one of these massively trained models and then do a little bit on top. You know, you could use just a few examples of what you're looking for and you can get what you want from the model. So just a little bit on top now gets to the results that a huge amount of effort used to have to put in, you know, to get from the ground up to that level.

Yeah, So this term foundation model was coined by a group at Stanford, and I think it's actually a really apt term because remember I said, you know, one of the big things that unlocked this latest excitement was the fact that we could use large amounts of unannotated data. We could train a model. We don't have to go through the painful effort of labeling each and every example. You still need to have your model do something you wanted to do you still need to tell it what you want to do. You can't just have a model that doesn't have any purpose, but what a foundation model that provides a foundation, like a literal foundation, you can sort of stand on the shoulders of giants. You can have one of these massively trained models and then do a little bit on top. You know, you could use just a few examples of what you're looking for and you can get what you want from the model. So just a little bit on top now gets to the results that a huge amount of effort used to have to put in, you know, to get from the ground up to that level.

I was trying to think of of an analogy for sort of foundation models versus what came before, and I don't know that I came up with a good one, but the best I could do was this. I want you to tell me if it's plausible. It's like before foundation models, it was like you had these sort of single use kitchen appliances. You could make a waffle iron if you wanted waffles, or you could make a toaster if you wanted to make toast. But a foundation model is like like an oven with a range on top. So it's like this machine, and you could just cook anything with this machine.

I was trying to think of of an analogy for sort of foundation models versus what came before, and I don't know that I came up with a good one, but the best I could do was this. I want you to tell me if it's plausible. It's like before foundation models, it was like you had these sort of single use kitchen appliances. You could make a waffle iron if you wanted waffles, or you could make a toaster if you wanted to make toast. But a foundation model is like like an oven with a range on top. So it's like this machine, and you could just cook anything with this machine.

Yeah, that's a great analogy. They're very versatile. The other piece of it, too, is that they dramatically lowered the effort that it takes to do something that you want to do. And I used to say about the old world of AI, would say, you know, the problem with automation is that it's too labor intensive, which sounds like I'm making a joke.

Yeah, that's a great analogy. They're very versatile. The other piece of it, too, is that they dramatically lowered the effort that it takes to do something that you want to do. And I used to say about the old world of AI, would say, you know, the problem with automation is that it's too labor intensive, which sounds like I'm making a joke.

Indeed, famously, if automation does one thing, it substitutes machines or computing power for labor, right, So what does that mean to say AI is or automation is too labor intensive.

Indeed, famously, if automation does one thing, it substitutes machines or computing power for labor, right, So what does that mean to say AI is or automation is too labor intensive.

It sounds like I'm making a joke, but I'm actually serious. What I mean is that the effort it took the old regime to automate something was very very high. So if I need to go and curate all this data, collect all this data, and then carefully label all these examples, that labeling itself might be incredibly expensive and time. And we estimate anywhere between eighty to ninety percent of the effort it takes to feel an AI solution actually is just spent on data, so that that has some consequences, which is the threshold for bothering. You know, if you're going to only get a little bit of value back from something, are you going to go through this huge effort to curate all this data and then when it comes time to train the model you need highly skilled people defensive or hard to find in the labor market. You know, are you really going to do something that's just a title incremental thing? Now you're going to do the only the highest value things that weren't right level because.

It sounds like I'm making a joke, but I'm actually serious. What I mean is that the effort it took the old regime to automate something was very very high. So if I need to go and curate all this data, collect all this data, and then carefully label all these examples, that labeling itself might be incredibly expensive and time. And we estimate anywhere between eighty to ninety percent of the effort it takes to feel an AI solution actually is just spent on data, so that that has some consequences, which is the threshold for bothering. You know, if you're going to only get a little bit of value back from something, are you going to go through this huge effort to curate all this data and then when it comes time to train the model you need highly skilled people defensive or hard to find in the labor market. You know, are you really going to do something that's just a title incremental thing? Now you're going to do the only the highest value things that weren't right level because.

You have to essentially build the whole machine from scratch, and there aren't many things where it's worth that much work to build a machine that's only going to do one narrow thing.

You have to essentially build the whole machine from scratch, and there aren't many things where it's worth that much work to build a machine that's only going to do one narrow thing.

That's right, and then you tackle the next problem and you basically have to start over. And you know, there are some nuances here, like for images, you can pre train a model on some other task and change it around. So there are some examples of this, like non recurring cost that we have in the old world too, But by and large, it's just a lot of effort. It's hard. It takes you know, a large level of skill to implement. One analogy that I like is, you know, think about it as you know, you have a river of data, you know, running through your company or your institution. Traditional AI solutions are kind of like building a dam on that river. You know, dams are very expensive things to build. They require highly specialized skills and lots of planning. And you know, you're only going to put a dam on a river that's big enough that you're gonna get enough energy out of it that it was worth your trouble. You're going to get a lot of value out of that dam. If you have a river like that, you know, a river of data, but it's actually the vast majority of the water you know in your kingdom, actually isn't in that river. It's in puddles and creeks and bable bricks, And you know, there's a lot of value left on the table because it's like, well, I can't there's nothing you can do about it. It's just that that's too low value, so it takes too much effort, so I'm just not going to do it. The return around investment just isn't there, so you just end up not automating things. It's too much of a pain. Now what foundation models do is they say, well, actually no, we can train a base model, a foundation that you can work on and we don't we don't care. We have specifying what the task is ahead of time. We just need to learn about the domain of data. So if we want to build something that can understand English language, there's a ton of English language text available out in the world. We can now train models on huge quantities of it, and then it learned the structure, learned how language you know, good part of how language works on all that unlabeled data, and then when you roll up with your task, you know, I want to solve this particular problem. You don't have to start from scratch. You're starting from a very very very high place. So that just gives you the ability to just you know, now all of a sudden, everything is accessible. All the puddles and greeks and babbling brooks and klipons, you know, those are all accessible now. And that's that's very exciting. But it just changes the equation on what kinds of problems you could use AI to solve.

That's right, and then you tackle the next problem and you basically have to start over. And you know, there are some nuances here, like for images, you can pre train a model on some other task and change it around. So there are some examples of this, like non recurring cost that we have in the old world too, But by and large, it's just a lot of effort. It's hard. It takes you know, a large level of skill to implement. One analogy that I like is, you know, think about it as you know, you have a river of data, you know, running through your company or your institution. Traditional AI solutions are kind of like building a dam on that river. You know, dams are very expensive things to build. They require highly specialized skills and lots of planning. And you know, you're only going to put a dam on a river that's big enough that you're gonna get enough energy out of it that it was worth your trouble. You're going to get a lot of value out of that dam. If you have a river like that, you know, a river of data, but it's actually the vast majority of the water you know in your kingdom, actually isn't in that river. It's in puddles and creeks and bable bricks, And you know, there's a lot of value left on the table because it's like, well, I can't there's nothing you can do about it. It's just that that's too low value, so it takes too much effort, so I'm just not going to do it. The return around investment just isn't there, so you just end up not automating things. It's too much of a pain. Now what foundation models do is they say, well, actually no, we can train a base model, a foundation that you can work on and we don't we don't care. We have specifying what the task is ahead of time. We just need to learn about the domain of data. So if we want to build something that can understand English language, there's a ton of English language text available out in the world. We can now train models on huge quantities of it, and then it learned the structure, learned how language you know, good part of how language works on all that unlabeled data, and then when you roll up with your task, you know, I want to solve this particular problem. You don't have to start from scratch. You're starting from a very very very high place. So that just gives you the ability to just you know, now all of a sudden, everything is accessible. All the puddles and greeks and babbling brooks and klipons, you know, those are all accessible now. And that's that's very exciting. But it just changes the equation on what kinds of problems you could use AI to solve.

And so foundation models basically mean that automating some new task is much less labor intensive. The sort of marginal effort to do some new automation thing is much lower because you're building on top of the foundation model rather than starting from scratch. Absolutely, So that is that is like the exciting good news. I do feel like there's a little bit of a countervailing idea that's worth talking about here, and that is the idea that even though there are these foundation models that are really powerful, that are relatively easy to build on top of, it's still the case right that there is not some one size fits all foundation model. So you know, what does that mean and why is that important to think about in this context?

And so foundation models basically mean that automating some new task is much less labor intensive. The sort of marginal effort to do some new automation thing is much lower because you're building on top of the foundation model rather than starting from scratch. Absolutely, So that is that is like the exciting good news. I do feel like there's a little bit of a countervailing idea that's worth talking about here, and that is the idea that even though there are these foundation models that are really powerful, that are relatively easy to build on top of, it's still the case right that there is not some one size fits all foundation model. So you know, what does that mean and why is that important to think about in this context?

Yeah, so we believe very strongly that there isn't just one model to rule them all. There's a number of reasons why that could be true. One which I think is important and very relevant today is how much energy these models can consume. So these models, you know, can get very very large. So one thing that we're starting to see or starting to believe, is that you probably shouldn't use one giant sledgehammer model to solve every single problem, you know, like we should pick the right size model to solve the problem. We shouldn't necessarily assume that we need the biggest, baddest model for every little use case. And we're also seeing that, you know, small models that are trained like to specialize on particular domains can actually outperform much bigger models. So bigger isn't always even better.

Yeah, so we believe very strongly that there isn't just one model to rule them all. There's a number of reasons why that could be true. One which I think is important and very relevant today is how much energy these models can consume. So these models, you know, can get very very large. So one thing that we're starting to see or starting to believe, is that you probably shouldn't use one giant sledgehammer model to solve every single problem, you know, like we should pick the right size model to solve the problem. We shouldn't necessarily assume that we need the biggest, baddest model for every little use case. And we're also seeing that, you know, small models that are trained like to specialize on particular domains can actually outperform much bigger models. So bigger isn't always even better.

So they're more efficient and they do the thing you want them to do better as well.

So they're more efficient and they do the thing you want them to do better as well.

That's right. So Stanford, for instance, a group of Stanford trained a model. It is a two point seven billion parameter model, which isn't terribly big by today's standards. They trained it just on the biomedical literature, you know, this is the kind of thing that universities do. And what they showed was that this model was better at answering questions about the biomedical literature than some models that are one hundred billion parameters, you know, many times larger. So it's a little bit like you know, asking an expert for help on something versus asking the smartest person you know. Ye, the smartest person you know may be very smart, but they're not going to be expertise. And then as an added bonus, you know, this is now a much smaller model, it's much more efficient to run, we are you know, you know, it's cheaper, so there's lots of different advantages there. So I think we're going to see attention in the industry between vendors that say hey, this is the one, you know, big model, and then others that say, well, actually, you know, there's there's you know, lots of different tools we can use that all have this nice quality that we outlined at the beginning, and then we should really pick the one that makes the most sense for the task at hand.

That's right. So Stanford, for instance, a group of Stanford trained a model. It is a two point seven billion parameter model, which isn't terribly big by today's standards. They trained it just on the biomedical literature, you know, this is the kind of thing that universities do. And what they showed was that this model was better at answering questions about the biomedical literature than some models that are one hundred billion parameters, you know, many times larger. So it's a little bit like you know, asking an expert for help on something versus asking the smartest person you know. Ye, the smartest person you know may be very smart, but they're not going to be expertise. And then as an added bonus, you know, this is now a much smaller model, it's much more efficient to run, we are you know, you know, it's cheaper, so there's lots of different advantages there. So I think we're going to see attention in the industry between vendors that say hey, this is the one, you know, big model, and then others that say, well, actually, you know, there's there's you know, lots of different tools we can use that all have this nice quality that we outlined at the beginning, and then we should really pick the one that makes the most sense for the task at hand.

So there's sustainability basically efficiency. Another kind of set of issues that come up a lot with ai A are bias hallucination. Can you talk a little bit about bias and hallucination, what they are and how you're working to mitigate those problems.

So there's sustainability basically efficiency. Another kind of set of issues that come up a lot with ai A are bias hallucination. Can you talk a little bit about bias and hallucination, what they are and how you're working to mitigate those problems.

Yeah, so there are lots of issues still as amazing as these technologies are, and they are amazing, let's let's be very clear, lots of great things we're going to enable with these kinds of technologies. Bias isn't a new problem, so you know, basically we've seen this since the beginning of AI. If you train a model on data that has a bias in it, the model is going to recapitulate that bias when it provides its answers. So every time, you know, if all the text you have says, you know, it's more likely to refer to female nurses and male scientists. Then you're going to you know, get models that you know. For instance, there was an example where a machine learning based translation system translated from Hungarian to English. Hungarian doesn't have gendered pronouns. English does, and when you ask them to translate, it would translate they are a nurse to she is a nurse, would translate they are a scientist to he is a scientist. And that's not because the people who wrote the algorithm were building in bias and coding in like oh, it's got to be this way. It's because the data was like that. You know, we have biases in our society and they're reflected in in our data and our text and our images everywhere. And then the models they're just mapping from what they've what they've seen in their training data to to the result that you're trying to get them to do and to give, and then these biases come out. So there's a very active program of research, and you know, we we do quite a bit at IBM research and my T but also all over the community and industry and academia trying to figure out how do we explicitly remove these biases, how do we identify them, how do you know, how do we build tools that allow people to audit their systems to make sure they aren't biased. So this is a really important thing. And you know, again this was here since the beginning, you know of machine learning and AI, but foundation models and large language models and generative AI just bring it into sharper even sharper focus because there's just so much data and it's sort of building in baking and all these different biases we have, so that that's that's absolutely a problem that these model have. Another one that you mentioned was hallucinations. So even the most impressive of our models will often just make stuff up. You know, the technical term that the heels chosen as is hallucination. To give you an example, I asked chat tbt to create a biography of David Cox IBM, and you know, it started off really well. You know, they identified that I was the director of the mt IBM Watsonay and said a few words about that, and then it proceeded to create an authoritative but completely fake biography of me. Where I was British. I was born in the UK. I went to British university, you know universities in the UK. I was professorating the authority.

Yeah, so there are lots of issues still as amazing as these technologies are, and they are amazing, let's let's be very clear, lots of great things we're going to enable with these kinds of technologies. Bias isn't a new problem, so you know, basically we've seen this since the beginning of AI. If you train a model on data that has a bias in it, the model is going to recapitulate that bias when it provides its answers. So every time, you know, if all the text you have says, you know, it's more likely to refer to female nurses and male scientists. Then you're going to you know, get models that you know. For instance, there was an example where a machine learning based translation system translated from Hungarian to English. Hungarian doesn't have gendered pronouns. English does, and when you ask them to translate, it would translate they are a nurse to she is a nurse, would translate they are a scientist to he is a scientist. And that's not because the people who wrote the algorithm were building in bias and coding in like oh, it's got to be this way. It's because the data was like that. You know, we have biases in our society and they're reflected in in our data and our text and our images everywhere. And then the models they're just mapping from what they've what they've seen in their training data to to the result that you're trying to get them to do and to give, and then these biases come out. So there's a very active program of research, and you know, we we do quite a bit at IBM research and my T but also all over the community and industry and academia trying to figure out how do we explicitly remove these biases, how do we identify them, how do you know, how do we build tools that allow people to audit their systems to make sure they aren't biased. So this is a really important thing. And you know, again this was here since the beginning, you know of machine learning and AI, but foundation models and large language models and generative AI just bring it into sharper even sharper focus because there's just so much data and it's sort of building in baking and all these different biases we have, so that that's that's absolutely a problem that these model have. Another one that you mentioned was hallucinations. So even the most impressive of our models will often just make stuff up. You know, the technical term that the heels chosen as is hallucination. To give you an example, I asked chat tbt to create a biography of David Cox IBM, and you know, it started off really well. You know, they identified that I was the director of the mt IBM Watsonay and said a few words about that, and then it proceeded to create an authoritative but completely fake biography of me. Where I was British. I was born in the UK. I went to British university, you know universities in the UK. I was professorating the authority.

Right, it's the certainty that that is weird about it, right, it's it's dead certain that you're from the UK, et cetera.

Right, it's the certainty that that is weird about it, right, it's it's dead certain that you're from the UK, et cetera.

Absolutely, yeah, as all kinds of flourishes like I want awards in the UK. So yeah, it's it's problematic because it kind of pokes a lot of weak spots in our humans psychology, where if something sounds coherent, we're likely to assume it's true. We're not used to interacting with people who eloquently and authoritatively you know, emit complete nonsense like yeah, you know we can debate about that, but.

Absolutely, yeah, as all kinds of flourishes like I want awards in the UK. So yeah, it's it's problematic because it kind of pokes a lot of weak spots in our humans psychology, where if something sounds coherent, we're likely to assume it's true. We're not used to interacting with people who eloquently and authoritatively you know, emit complete nonsense like yeah, you know we can debate about that, but.

Yeah, we could debate about that. But yes, the sort of blive confidence throws you off when you realize it's completely wrong.

Yeah, we could debate about that. But yes, the sort of blive confidence throws you off when you realize it's completely wrong.

Right, that's right. And and we do have a little bit of like a great and powerful AWS sort of vibe going sometimes where we're like, well, you know, the AI is all knowing and therefore whatever it says must be true. But but these things will make up stuff, you know, very aggressively, and you know, you everyone can try asking it for their their bio. You'll you'll get something that You'll always get something that's of the right form, that has the right tone. But you know, the facts just aren't necessarily there, So that's obviously a problem. We need to figure out how to close those gaps, fix those problems. There's lots of ways we can use them much more easily.

Right, that's right. And and we do have a little bit of like a great and powerful AWS sort of vibe going sometimes where we're like, well, you know, the AI is all knowing and therefore whatever it says must be true. But but these things will make up stuff, you know, very aggressively, and you know, you everyone can try asking it for their their bio. You'll you'll get something that You'll always get something that's of the right form, that has the right tone. But you know, the facts just aren't necessarily there, So that's obviously a problem. We need to figure out how to close those gaps, fix those problems. There's lots of ways we can use them much more easily.

I'd just like to say, faced with the awesome potential of what these technologies might do, it's a bit encouraging to hear that even chat GPT has a weakness for inventing flamboyant, if fictional versions of people's lives. And while entertaining ourselves with chat GPT and mid journey is important, the way lay people use consumer facing chatbots and generative AI is just fundamentally different from the way an enterprise business uses AI. How can we harness the abilities of artificial intelligence to help us solve the problems we face in business and technology. Let's listen on as David and Jacob continue their conversation.

I'd just like to say, faced with the awesome potential of what these technologies might do, it's a bit encouraging to hear that even chat GPT has a weakness for inventing flamboyant, if fictional versions of people's lives. And while entertaining ourselves with chat GPT and mid journey is important, the way lay people use consumer facing chatbots and generative AI is just fundamentally different from the way an enterprise business uses AI. How can we harness the abilities of artificial intelligence to help us solve the problems we face in business and technology. Let's listen on as David and Jacob continue their conversation.

We've been talking in a somewhat abstract way about AI in the ways it can be used. Let's talk in a little bit more of a specific way. Can you just talk about some examples of business challenges that can be solved with automation? With this kind of automation we're talking about.

We've been talking in a somewhat abstract way about AI in the ways it can be used. Let's talk in a little bit more of a specific way. Can you just talk about some examples of business challenges that can be solved with automation? With this kind of automation we're talking about.

Yeah, so really really, this guy's the limit. There's a whole set of different applications that these models are really good at. And basically it's a super set of everything we used to use ALI for in business. So you know, the simple kinds of things are like, hey, if I have text and I you know, if I have product reviews and I want to be able to tell if these are positive or negative. You know, like, let's look at all the negative reviews so we can have a human look through them and see what was up. Very common business use case. You can do it with traditional deep learning based AI. So so there's things like that that are you know, it's very prosaic sort that we were already doing that, We've been doing it for a long time. Then you get situations that are that were harder for.

Yeah, so really really, this guy's the limit. There's a whole set of different applications that these models are really good at. And basically it's a super set of everything we used to use ALI for in business. So you know, the simple kinds of things are like, hey, if I have text and I you know, if I have product reviews and I want to be able to tell if these are positive or negative. You know, like, let's look at all the negative reviews so we can have a human look through them and see what was up. Very common business use case. You can do it with traditional deep learning based AI. So so there's things like that that are you know, it's very prosaic sort that we were already doing that, We've been doing it for a long time. Then you get situations that are that were harder for.

The old day.

The old day.

I like, if i'm I want to compress something like I want to I have like say I have a chat transcript, like a customer called in and they had a complaint. They call back, Okay, now a new you know, a person on the line needs to go read the old transcript to catch up. Wouldn't it be better if we could just summariz that, just condense it all down quick little paragraph, you know, customer call they're up said about this, rather than having to read the blow by blow. There's just lots of settings like that where summarization is really helpful. Hey, you have a meeting and I'd like to just automatically, you know, have have that meeting or that email or whatever. I'd like to just have a condensed down so I can really quickly get to the heart of the matter. These models are are really good at doing that. They're also a really good at question answering. So if I want to find out what's how many vacation days do I have? I can now interact in natural language with a system that can go and that has access to our HR policies, and I can actually have a you know, a multi turn conversation where I can, you know, like I would have with you know, somebody, you know, actual HR professional or customer service representative. So a big part, you know, what this is doing is it's it's putting an interface. You know, when we think of computer interfaces, we're usually thinking about UI user interface elements where I click on menus and there's buts and all this stuff. Increasingly, now we can just talk you know, you just in words, you can describe what you want, you want to answer, ask a question, you want to sort of command the system to do something, rather than having to learn how to do that clicking buttons, which might be inefficient. Now we can just sort of spell it out.

I like, if i'm I want to compress something like I want to I have like say I have a chat transcript, like a customer called in and they had a complaint. They call back, Okay, now a new you know, a person on the line needs to go read the old transcript to catch up. Wouldn't it be better if we could just summariz that, just condense it all down quick little paragraph, you know, customer call they're up said about this, rather than having to read the blow by blow. There's just lots of settings like that where summarization is really helpful. Hey, you have a meeting and I'd like to just automatically, you know, have have that meeting or that email or whatever. I'd like to just have a condensed down so I can really quickly get to the heart of the matter. These models are are really good at doing that. They're also a really good at question answering. So if I want to find out what's how many vacation days do I have? I can now interact in natural language with a system that can go and that has access to our HR policies, and I can actually have a you know, a multi turn conversation where I can, you know, like I would have with you know, somebody, you know, actual HR professional or customer service representative. So a big part, you know, what this is doing is it's it's putting an interface. You know, when we think of computer interfaces, we're usually thinking about UI user interface elements where I click on menus and there's buts and all this stuff. Increasingly, now we can just talk you know, you just in words, you can describe what you want, you want to answer, ask a question, you want to sort of command the system to do something, rather than having to learn how to do that clicking buttons, which might be inefficient. Now we can just sort of spell it out.

Interesting, right, the graphical user interface that we all sort of default to, that's not like the state of nature, Right, that's a thing that was invented and just came to be the standard way that we interact with computers. And so you could imagine, as you're saying, like chat essentially chatting with the machine could could become a sort of standard user interface, just like the graphical user interface, did you know over the past several decades.

Interesting, right, the graphical user interface that we all sort of default to, that's not like the state of nature, Right, that's a thing that was invented and just came to be the standard way that we interact with computers. And so you could imagine, as you're saying, like chat essentially chatting with the machine could could become a sort of standard user interface, just like the graphical user interface, did you know over the past several decades.

Absolutely, And I think those kinds of conversational interfaces are going to be hugely important for increasing our productivity. It's just a lot easier if I if I have to learn how to use a tool or I don't have to kind of have awkward, you know, interactions from the computer. I can just tell it what I want and I can understand it, could you know potentially and you ask questions back to clarify and have those kinds of conversations that can be extremely powerful, and in fact, one area where that's going to I think be absolutely game changing is in code. When we write code. You know, programming languages are a way for us to sort of match between our very sloppy way of talking and the very exact way that you need to command a computer to do what you wanted to do. They're cumbersome to learn, they can you know, create very complex systems that are very hard to reason about. And we're already starting to see the ability to just write down what you want and the AI will generate the code for you. And I think we're just going to see a huge revolution of like we just converse you and we can have a conversation to say what we want, and then the computer can actually not only do fixed actions and do things for us, but it can actually even write code to do new things, you know, and generate software itself. Given how much software we have, how much craving we have for software, like we'll never have enough software in our world, uh, you know, the ability to have AI systems as a helper in that, I think we're going to see a lot of a lot of value there.

Absolutely, And I think those kinds of conversational interfaces are going to be hugely important for increasing our productivity. It's just a lot easier if I if I have to learn how to use a tool or I don't have to kind of have awkward, you know, interactions from the computer. I can just tell it what I want and I can understand it, could you know potentially and you ask questions back to clarify and have those kinds of conversations that can be extremely powerful, and in fact, one area where that's going to I think be absolutely game changing is in code. When we write code. You know, programming languages are a way for us to sort of match between our very sloppy way of talking and the very exact way that you need to command a computer to do what you wanted to do. They're cumbersome to learn, they can you know, create very complex systems that are very hard to reason about. And we're already starting to see the ability to just write down what you want and the AI will generate the code for you. And I think we're just going to see a huge revolution of like we just converse you and we can have a conversation to say what we want, and then the computer can actually not only do fixed actions and do things for us, but it can actually even write code to do new things, you know, and generate software itself. Given how much software we have, how much craving we have for software, like we'll never have enough software in our world, uh, you know, the ability to have AI systems as a helper in that, I think we're going to see a lot of a lot of value there.

So if you if you think about the different ways AI might be applied to business. I mean you've talked about a number of the sort of classic use cases. What are some of the more out there use cases. What are some you know, unique ways you could imagine AI being applied to business.

So if you if you think about the different ways AI might be applied to business. I mean you've talked about a number of the sort of classic use cases. What are some of the more out there use cases. What are some you know, unique ways you could imagine AI being applied to business.

Yeah, there's really disguised the limit. I mean, we have one project that I'm kind of a fan of where we actually were working with a mechanical engineering professor at MIT working on a classic problem, how do you build linkage systems which are like, can imagine bars and joints and ogres, you know the things that are.

Yeah, there's really disguised the limit. I mean, we have one project that I'm kind of a fan of where we actually were working with a mechanical engineering professor at MIT working on a classic problem, how do you build linkage systems which are like, can imagine bars and joints and ogres, you know the things that are.

Building a thing, building a physical machine of some kind of.

Building a thing, building a physical machine of some kind of.

Like real like metal and you know nineteenth century just old school Industrial revolution. Yeah yeah, yeah, but you know the little arm that's that's holding up my microphone in front of me. Cranes, get build your buildings, you know, parts of your engines. This is like classical stuff. It turns out that you know, humans, if you want to build an advanced system, you decide what like curve you want to create, and then a human together with a computer program, can build a five or six bar linkage. And then that's kind of where you top out it because it gets too complicated to work more than that. We built a generative AI system that can build twenty bar linkages like arbitrarily complex. So these are machines that are beyond the capability of a human to design themselves. Another example, we have an AI system that can generate electronic circuits. You know, we had a project where we're working where we were building better power converters which allow our computers and our devices to be more efficient, save energy, you know, less less carbonet But I think the world around us has always been shaped by technology. If you look around, you know, just think about how many steps and how many people, and how many designs went into the table and the chair and the vamp. It's it's really just astonishing. And that's already you know, the fruit of automation and computers and those kinds of tools. But we're going to see that increasingly be act also of AI. It's just going to be everywhere around us, everything we touch is going to have to you know, helped in some way to get get to you by you know.

Like real like metal and you know nineteenth century just old school Industrial revolution. Yeah yeah, yeah, but you know the little arm that's that's holding up my microphone in front of me. Cranes, get build your buildings, you know, parts of your engines. This is like classical stuff. It turns out that you know, humans, if you want to build an advanced system, you decide what like curve you want to create, and then a human together with a computer program, can build a five or six bar linkage. And then that's kind of where you top out it because it gets too complicated to work more than that. We built a generative AI system that can build twenty bar linkages like arbitrarily complex. So these are machines that are beyond the capability of a human to design themselves. Another example, we have an AI system that can generate electronic circuits. You know, we had a project where we're working where we were building better power converters which allow our computers and our devices to be more efficient, save energy, you know, less less carbonet But I think the world around us has always been shaped by technology. If you look around, you know, just think about how many steps and how many people, and how many designs went into the table and the chair and the vamp. It's it's really just astonishing. And that's already you know, the fruit of automation and computers and those kinds of tools. But we're going to see that increasingly be act also of AI. It's just going to be everywhere around us, everything we touch is going to have to you know, helped in some way to get get to you by you know.

That is a pretty profound transformation that you're talking about in business. How do you think about the implications of that both for the sort of you know, business itself, and also for for employees.

That is a pretty profound transformation that you're talking about in business. How do you think about the implications of that both for the sort of you know, business itself, and also for for employees.

Yeah, so I think for businesses this is gonna cut costs, make new opportunities to like customers, you know, like there's just you know, it's sort of all upside right like for the for the workers, I think the story is mostly good too. You know, like how many things do you do in your day that you'd really rather not right? You know, and we're used to having things we don't like automated away, you know, we we didn't you know, if we didn't like walk getting many miles to work, then you know, like you can have a car and you can drive there. Or we used to have a huge fraction over ninety percent of the US population engaged in agriculture, and then we mechanized it. Now very few people work in agriculture. A small number of people can do the work of a large number of people. And then you know, things like email, and you know, they've led to huge productivity enhancements because I don't need to be writing letters and sending them in the mail. I can just instantly communicate with people. We just become more effective, Like our jobs have transformed, whether it's a physical job like agriculture, or whether it's a knowledge worker job where you're sending emails and communicating with people and coordinating teams. We've just gotten better and you know, the technology has just made us more productive. And this is just another example. Now, you know, there are people who worry that you know, will be so good at that that maybe jobs will be displaced, and that's that's a legitimate concern. But just like how in agriculture, you know, it's not like suddenly we had ninety percent of the population and unemployed. You know, people transitioned to to other jobs. And the other thing that we've found, too, is that our appetite for doing more things is as humans is sort of insatiable. So even if we can dramatically increase how much you know, one human can do, that doesn't necessarily mean we're going to do a fixed amount of stuff. There's an appetite to have even more, so we're going to you can continue to grow grow the pie. So I think at least certainly in the near term, you know, we're going to see a lot of drudgery go away from work. We're going to see people to be able to be more effective at their jobs. You know, we will see some transformation in jobs and what like. But we've seen that before and the technology a least has the potential to make our lives a lot easier.

Yeah, so I think for businesses this is gonna cut costs, make new opportunities to like customers, you know, like there's just you know, it's sort of all upside right like for the for the workers, I think the story is mostly good too. You know, like how many things do you do in your day that you'd really rather not right? You know, and we're used to having things we don't like automated away, you know, we we didn't you know, if we didn't like walk getting many miles to work, then you know, like you can have a car and you can drive there. Or we used to have a huge fraction over ninety percent of the US population engaged in agriculture, and then we mechanized it. Now very few people work in agriculture. A small number of people can do the work of a large number of people. And then you know, things like email, and you know, they've led to huge productivity enhancements because I don't need to be writing letters and sending them in the mail. I can just instantly communicate with people. We just become more effective, Like our jobs have transformed, whether it's a physical job like agriculture, or whether it's a knowledge worker job where you're sending emails and communicating with people and coordinating teams. We've just gotten better and you know, the technology has just made us more productive. And this is just another example. Now, you know, there are people who worry that you know, will be so good at that that maybe jobs will be displaced, and that's that's a legitimate concern. But just like how in agriculture, you know, it's not like suddenly we had ninety percent of the population and unemployed. You know, people transitioned to to other jobs. And the other thing that we've found, too, is that our appetite for doing more things is as humans is sort of insatiable. So even if we can dramatically increase how much you know, one human can do, that doesn't necessarily mean we're going to do a fixed amount of stuff. There's an appetite to have even more, so we're going to you can continue to grow grow the pie. So I think at least certainly in the near term, you know, we're going to see a lot of drudgery go away from work. We're going to see people to be able to be more effective at their jobs. You know, we will see some transformation in jobs and what like. But we've seen that before and the technology a least has the potential to make our lives a lot easier.

So IBM recently launched Watson X, which includes Watson x dot AI. Tell me about that, tell me about you know what it is and the new possibilities that it opens up.

So IBM recently launched Watson X, which includes Watson x dot AI. Tell me about that, tell me about you know what it is and the new possibilities that it opens up.

Yeah, So so Wat's the next is obviously a bit of a new branding on the Watson brand. TJ. Watson that was the founder of IBM and our EI technologies have had the Watson brand. Watson X is a recognition that there's something new, there's something that actually has changed the game. We've gone from this old world of automation is to labor intensive to this new world of possibilities where it's much easier to use AI. And what Watson X does it brings together tools for businesses to harness that power. So whattsonex dot AI foundation models that our customers can use. It includes tools that make it easy to run, easy to deploy, easy to experiment. There's a watsonex dot Data component which allows you to sort of organize and access to your data. So what we're really trying to do is give our customers a cohesive set of tools to the value of these technologies and at the same time be able to manage the risks and other things that you have to keep an eye on in an enterprise context.

Yeah, So so Wat's the next is obviously a bit of a new branding on the Watson brand. TJ. Watson that was the founder of IBM and our EI technologies have had the Watson brand. Watson X is a recognition that there's something new, there's something that actually has changed the game. We've gone from this old world of automation is to labor intensive to this new world of possibilities where it's much easier to use AI. And what Watson X does it brings together tools for businesses to harness that power. So whattsonex dot AI foundation models that our customers can use. It includes tools that make it easy to run, easy to deploy, easy to experiment. There's a watsonex dot Data component which allows you to sort of organize and access to your data. So what we're really trying to do is give our customers a cohesive set of tools to the value of these technologies and at the same time be able to manage the risks and other things that you have to keep an eye on in an enterprise context.

So we talk about the guests on this show as new creators, by which we mean people who are creatively applying technology in business to drive change. And I'm curious how creativity plays a role in the research that you do.

So we talk about the guests on this show as new creators, by which we mean people who are creatively applying technology in business to drive change. And I'm curious how creativity plays a role in the research that you do.

I honestly, I think the creative aspects of this job this is what makes this work exciting. You know, I should say, you know, the folks who work at my organization are doing the creating, and I.

I honestly, I think the creative aspects of this job this is what makes this work exciting. You know, I should say, you know, the folks who work at my organization are doing the creating, and I.

Guess you're doing the managing so that they could do the creator.

Guess you're doing the managing so that they could do the creator.

I'm helping them be their best and I still get to get involved in the weeds of the research as much as I can. But you know, there's something really exciting about inventing, you know, like nice things about doing invention and doing research on AI and industries. It's usually grounded and a real problem that somebody's having. You know, a customer wants to solve this problem. It's losing money or there there would be a new opportunity. You identify that problem and then you build something that's never been built before to do that. And I think that's honestly the adrenaline rush that keeps all of us in this field. How do you do something that nobody else on earth has done before or tried before, So that that kind of creativity, and there's also creativity as well, and identifying what those problems are, being able to understand the places where you know the technology is close enough to solving a problem, and doing that matchmaking between problems that are now solvable, you know, and an AI where the field is moving so fast, this is constantly growing horizon of things that we might be able to solve. So that matchmaking, I think is also a really interesting creative problem. So I think I think that's that's that's why it's so much fun. And it's a fun environment we have here too. It's you know, people drawing on whiteboards and writing on pages of math and you.

I'm helping them be their best and I still get to get involved in the weeds of the research as much as I can. But you know, there's something really exciting about inventing, you know, like nice things about doing invention and doing research on AI and industries. It's usually grounded and a real problem that somebody's having. You know, a customer wants to solve this problem. It's losing money or there there would be a new opportunity. You identify that problem and then you build something that's never been built before to do that. And I think that's honestly the adrenaline rush that keeps all of us in this field. How do you do something that nobody else on earth has done before or tried before, So that that kind of creativity, and there's also creativity as well, and identifying what those problems are, being able to understand the places where you know the technology is close enough to solving a problem, and doing that matchmaking between problems that are now solvable, you know, and an AI where the field is moving so fast, this is constantly growing horizon of things that we might be able to solve. So that matchmaking, I think is also a really interesting creative problem. So I think I think that's that's that's why it's so much fun. And it's a fun environment we have here too. It's you know, people drawing on whiteboards and writing on pages of math and you.

Know, like in a movie, like in a movie, yeah, straight from special casting drawing, the drawing on the window, writing on the window in sharp absolutely, So, so let's close with the really long view. How do you imagine AI and people working together twenty years from now?

Know, like in a movie, like in a movie, yeah, straight from special casting drawing, the drawing on the window, writing on the window in sharp absolutely, So, so let's close with the really long view. How do you imagine AI and people working together twenty years from now?

Yeah, it's really hard to make predictions. The vision that I like, actually this came from an MIT economist named David Ottur, which was imagine AI almost as a natural resource. You know, we have we know how natural resources work, right, Like there's an or we can dig up out of the earth, comes from kind of springs from the earth, or we usually think of that in terms of physical stuff. With AI, you can almost think of it as like there's a new kind of abundance potentially twenty years from now where not only can we have things we can build or eat or use or burn or whatever. Now we have, you know, this ability to do things and understand things and do intellectual work, and I think we can get to a world where automating things is just seamless. We're surrounded by capability to augment ourselves to get things done. And you could think of that in terms of like, oh, that's going to displace our jobs, because eventually the AI system is going to do everything we can do. But you could also think of it in terms of like, wow, that's just so much abundance that we now have, and really how we use that abundance is sort of up to us, you know, like you can writing software is super easy and fast, and anybody can do it. Just think about all the things you can do now, think about all the new activities, and go out all the ways we could use that to enrich our lives. That's where I'd like to see us in twenty years. You know we can. We can do just so much more than we were able to do before abundance.

Yeah, it's really hard to make predictions. The vision that I like, actually this came from an MIT economist named David Ottur, which was imagine AI almost as a natural resource. You know, we have we know how natural resources work, right, Like there's an or we can dig up out of the earth, comes from kind of springs from the earth, or we usually think of that in terms of physical stuff. With AI, you can almost think of it as like there's a new kind of abundance potentially twenty years from now where not only can we have things we can build or eat or use or burn or whatever. Now we have, you know, this ability to do things and understand things and do intellectual work, and I think we can get to a world where automating things is just seamless. We're surrounded by capability to augment ourselves to get things done. And you could think of that in terms of like, oh, that's going to displace our jobs, because eventually the AI system is going to do everything we can do. But you could also think of it in terms of like, wow, that's just so much abundance that we now have, and really how we use that abundance is sort of up to us, you know, like you can writing software is super easy and fast, and anybody can do it. Just think about all the things you can do now, think about all the new activities, and go out all the ways we could use that to enrich our lives. That's where I'd like to see us in twenty years. You know we can. We can do just so much more than we were able to do before abundance.

Great, thank you so much for your time.

Great, thank you so much for your time.

Yeah, it's been a pleasure. Thanks for inviting me.

Yeah, it's been a pleasure. Thanks for inviting me.

What a far ranging, deep conversation. I'm mesmerized by the vision David just described. A world where natural conversation between mankind and machine can generate creative solutions to our most complex problems. A world where we view AI not as our replacements, but as a powerful resource we can tap into and exponentially boost our innovation and productivity. Thanks so much to doctor David Cox for joining us on smart Talks. We deeply appreciate him sharing his huge breadth of AI knowledge with us and for explaining the transformative potential of foundation models in a way that even I can understand. We eagerly await his next great breakthrough. Smart Talks with IBM is produced by Matt Romano David jaw nishe Venkat and Royston Preserve with Jacob Goldstein. We're edited by Lydia Jean Kott. Our engineers are Jason Gambrel, Sarah Buguer and Ben Holliday. Theme song by Gramoscope. Special thanks to Carli Megliori, Andy Kelly, Kathy Callahan and the eight Bar and IBM teams, as well as the Pushkin marketing team. Smart Talks with IBM is a production of Pushkin Industries and iHeartMedia. To find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts. Hi'm Malcolm Gladwell. This is a paid advertisement from IBM.

What a far ranging, deep conversation. I'm mesmerized by the vision David just described. A world where natural conversation between mankind and machine can generate creative solutions to our most complex problems. A world where we view AI not as our replacements, but as a powerful resource we can tap into and exponentially boost our innovation and productivity. Thanks so much to doctor David Cox for joining us on smart Talks. We deeply appreciate him sharing his huge breadth of AI knowledge with us and for explaining the transformative potential of foundation models in a way that even I can understand. We eagerly await his next great breakthrough. Smart Talks with IBM is produced by Matt Romano David jaw nishe Venkat and Royston Preserve with Jacob Goldstein. We're edited by Lydia Jean Kott. Our engineers are Jason Gambrel, Sarah Buguer and Ben Holliday. Theme song by Gramoscope. Special thanks to Carli Megliori, Andy Kelly, Kathy Callahan and the eight Bar and IBM teams, as well as the Pushkin marketing team. Smart Talks with IBM is a production of Pushkin Industries and iHeartMedia. To find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts. Hi'm Malcolm Gladwell. This is a paid advertisement from IBM.