The Exchanges

Q If they're non-linguistic, like the majority, I'm sorry for the base questions, but then what are they and are, is that that we don't have able to be ingested by AI models and engines over time?

A Well, so first of all, there is no question that eventually AI systems will understand the world in similar ways that, that humans do, uh, perhaps better ways, uh, but there will not be autoregressive large language models as a type that we're now, uh, talking about. They will be different, uh, for a number of different, different reasons. But, but to answer your question more directly, Anything that has to do with sort of an intuition of the real world requires an experience of the real world or, or a simulated version of it, uh, which, uh, those large language models don't have. They're purely trained from text. So you can, you, there's a number of questions that, about the physical world that they'll be able to answer because there's a template for it in the, or something very similar in the data that they've been trained on. Same for planning. You can ask them to You know, plan a trip or something, and they will adapt a template that they've, they've been trained on. But they don't really have sort of a model of, a mental model of how the world works that allows them to plan complex action sequences or, or use tools or things like that.

AI assessment note: “Anything that has to do with sort of an intuition of the real world requires an experience”

Q Can I, can I ask that? How do we instill values within models where they don't have a desire to dominate?

A Right, so these objectives I was telling you about, so, okay, so let me describe the, the sort of architecture of future AI systems as I see it. We're going to have AI systems that basically are going to plan their actions, and actions can include sequences of words that you tell someone, but they're going to plan the sequence of actions or words so as to optimize a series of objectives that we set them. Okay, so one objective is, uh, Does this answer the question I just asked? Ok. Another objective might be, while you're talking to a thirteen-year-old, make that answer understandable by a thirteen-year-old. Another objective might be, you know, I asked you to answer a question about the world, so be factual. Or it's a question about, you know, yesterday's political event, you know, can you kind of be compatible with everything you've read in the press, uh, this morning? Uh, things like that, right? Uh, so you, you know, you'll have those systems that have, you know, a series of objectives and their output, their answer by construction is going to have to satisfy those objectives. And some of those objectives will be hardwired to make those systems safe. Like, if it's a domestic robot that can, you know, cook, uh, cook dinner and can wield, you know, kitchen knife in its, uh, in its arm, there's gonna be a term in there that says, like, stop moving your arm when there's people …

AI assessment note: “you design those objectives so that their actions are safe”

Q Depends on that cooking robot, you never know. Um, how do you determine the Who's able to set the objectives? Cause that could be right or wrong depending on who sets them.

A That's true. So that's gonna have to be, uh, there's gonna have to be a process by which, you know, we, we allow people to do this, like some vetting process, you know, the same way that, you know, there's a vetting process for, you know, people to take care of, take care of your health or cut your hair, fix your plumbing or your car. Right. Um, so there's some, you know, some vetting process, certainly some testing and You know, market deployment procedure with regulating agencies for things that have, you know, that are potentially dangerous, probably not for all applications, but for many applications, certainly in healthcare, transportation, and things like that. And then perhaps also it could be that, um, you know, let, let's take the example of, of intelligent assistants. So let's imagine a future where everyone can, you know, talk to, to their intelligent assistant. That system will have pretty close to human level intelligence, probably more accumulated knowledge than most, most humans, you know, they could translate in any language and probably, you know, give you a quick summary of, you know, yesterday's newspaper and things like that. Right. Explain mathematical concepts to you, things like that. So people are probably going to use this almost exclusively in the future for their interaction with the digital world. You know, you're not going to go to Google or Wikiped…

AI assessment note: “there's gonna have to be a process by which, you know, we allow people”

Q industrial revolution, Even the introduction of PCs into kind of, you know, working environments. These were multi-decade introductions. Bluntly, what AI feels like in some industries today, we use it at the media company, and it's cutting our employment. Like, the speed of transition is much, much more compressed in this timeline, which will lead to short-term significant high unemployment. Do you concede that, or do you not concede that?

A So this is something I used to be very worried about, that the, the speed of progress of technology was going to leave a certain number of people behind who, you know, cannot be basically retrained fast enough or be, or maybe they are too old to retrain themselves for the new, uh, the new world. I was worried about this. And then I talked to a bunch of economists and they say, oh, you know, not really, because the speed at which a technology disseminate dans l'économie est limité par la façon rapide que les gens peuvent apprendre à l'utiliser Um, so a good person to talk to about this is Eric Brynjolfsson at Stanford, and what he says is that when a new technology is introduced, let's say the, the PC, right, with, you know, a graphical user interface, the mouse, et cetera, right, in the mid-nineties, how long did it take to have a measurable effect on productivity, you know, which is the amount of wealth produced by per hour worked? Yeah. He says, you know, typically it's 1520 years, and the reason is that that's what it takes for people to learn to use that new technology, basically.

AI assessment note: “I talked to a bunch of economists and they say, oh, you know, not really”

Q I have to ask, Joshua described kind of the, the hype cycles within, uh, AI and neural nets like deserts when you're not in them. And he asked the question, How did Jan not get discouraged when for a solid decade we were in a desert where no one really cared about neural nets? How did you keep the enthusiasm, bluntly, when, as Joshua said, no one really cared?

A Both Joshua, Jeff, and I had in the back of our minds that those methods would eventually come to the fore and that, you know, we would have to kind of snap people out of their preconceived ideas about, uh, about neural nets. So yes, there was Um, so Yoshua and I were actually working together at, uh, AT&T Bell Labs in the early nineties. And then the interest of the community for those methods started waning around 1995 or so. And there was indeed about 10 years when not only nobody was interested in neural nets, but people were even making fun of it, you know, talking about it in, uh, sort of disparaging terms. Now there, there is, uh, something though, in 1996, I kind of changed the job. I, I stayed in the same company. I was still working at AT&T in the research labs. But I became, uh, a department head, and this was the early days of the internet, and, uh, my group and I started working on something completely different that had nothing to do, or not much to do at least with machine learning, this, uh, image compression. I had this, this idea that, uh, with the internet coming up, we should have a way of scanning existing paper documents and then, you know, put them on the internet so that everybody could, could have access to them. And so I worked on this for five or six years together with Leon Boutou, who's had been a long, long-term collaborator. Joshua was also involv…

AI assessment note: “my group and I started working on something completely different”

Q Can I ask you, David asked this as well, how long did it take to get, in terms of like the major breakthroughs, how long did it take you to get to the major breakthroughs that you're at the origin of, when you look back over that time to get to those major breakthroughs?

A Well, so there's a few breakthroughs. So the first one was, uh, in, in the When I was still on undergrad, basically finishing my engineering studies, I figured out that the, the way forward to kind of lift the limitations of the old systems that were abandoned in the sixties was to find learning algorithms that could train multilayer neural nets, essentially. And people had all but abandoned this, uh, type of research, except for a handful of people in Japan. And One guy I heard, I heard about called Jeff Hinton, um, who had published a paper in 1983, so this was just, ah, the year I graduated on something called the Bolson machine, which, ah, was clearly a method to go beyond those, those limitations, and so I had on my side kind of developed a, a method for training multi-layer nets, which was very close to what we now call backpropagation, but not exactly the same. It was closer to what we call target prop, actually, nowadays, And then, you know, published a few papers in French, and eventually met Jeff at a meeting in France in 1985, and we realized we'd been working on the same thing, and we're thinking alike, and, but I was, you know, in the middle of my PhD, and he was a associate professor at Carnegie Mellon, so we, we started, you know, a discussion, and then, you know, visited him at Carnegie Mellon for a summer school he organized, and then I, when I finished my PhD,…

AI assessment note: “first one was, uh, in, in the When I was still on undergrad”

Q you not also think there's this core business model challenge there, which is, it's the classic innovators dilemma. Like why didn't Google do this? Because it would have killed that absolute cash cow of Google ads. The cost to service a query versus the costs of this is so significantly different. You'd be killing your core cash cow with this, with unknown upside. Versus retaining what is a great business?

A You don't have a choice. I mean, there's no question that, you know, within some time, you know, we could take a, it could take a while, but there is no question that people interact mostly with the digital world using, uh, AI assistance. And, you know, they may run into your, your augmented reality glasses, ok, so, uh, or, or something of that type, like, you know, you know, like in the Spike Jonze movie, Her, that's, uh, that's not, not a bad depiction of what, you know, the, the way things could develop. And so, if you, if you take the assumption, you make the assumption this is going to happen, you, you, you have to build it as quick, as quickly as you can. And it might enableize your, you know, your newsfeed algorithm or, or whatever, or the case of Google, your, your search engine. But you have to do it. You know, it's like, um, I mean, Meta has been known to make those choices, uh, in the past, like the move to, to mobile, for example, um, and the, the move to, uh, you know, short, uh, short form video, for example, you know, which, you know, obviously TikTok has been, uh, very successful at, uh, Meta has entered that, that, that business in kind of a, a big way, despite the fact that the amount of revenue it derives from it is lower than a traditional news feed. Because it's hard to put, you know, put ads in videos basically.

AI assessment note: “You don't have a choice. I mean, there's no question”

Q to ask, but what do those jobs look like? Like, what are they? Are they, they're creative oriented, but what does that actually mean? Like, sorry, I know it's a really hard question, but I'm just trying to understand how How we actually spend our time and my children, which I don't have, by the way, Jan. But, what, what do they do? Like, sculpt or paint? I don't know.

A I don't know. That's a good question. Uh, but it's not because I don't know that it won't happen because I mean, look at like how many people exercise their creative juices, uh, today, right. With all the tools that are available that, you know, weren't available, uh, 10, 20 or 30 years ago, like three, the artists or something like this, you know, game designers, you know, all kinds of things that, you know, I think creative jobs are the other ones. So there are two types of job sets that, you know, have a bright future of creative jobs, whether they are scientific, technical, Educational or artistic. ACI has to do with communication, right? And communication of human emotions, which is, you know, intrinsically human, if you want. So that's one category. And then the other one is personal services. So where you need actual people to interact with you.

AI assessment note: “two types of job sets that, you know, have a bright future of creative jobs”

Q So we've got China, we've got Europe. What about the US? What could they do differently or improve?

A Well, so there's a lot that the U.S. does right in terms of research, which is, to a large extent, a bit of a, you know, partial explanation for the success of the technological industry, the tech industry in the U.S. I think, you know, partly because, you know, the U.S. devotes a kind of, you know, significant amount of resources to fundamental research through NSF and NIH and, you know, various other outfits, probably more than Europe. Universities pays their faculty pretty well, particularly in areas like computer science and AI. Now this comes with a downside, and the downside is that studying in the US is expensive. It's a trade-off, right? So can you do one without the other? Switzerland figured out how to pay academics pretty well while actually offering free education to their, to their students. So, you know, there is a way to do it. Canada also figured out a pretty good trade-off as well. So a lot of things the U.S. does right, but they, one thing that the U.S. system, or, or like they're off, does, does right also is the willingness to take risk and invest on ideas that seem, you know, a little crazy, but, but basically, you know, the, the sort of vibrant startup scene in, uh, Silicon Valley and other places, uh, in the U.S., in New York, and in the Boston area is, is, You know, leading, uh, leading the world. Now, you start seeing a similar thing in Europe now. Ther…

AI assessment note: “the downside is that studying in the US is expensive. It's a trade-off”

Q to ask, but what do those jobs look like? Like, what are they? Are they, they're creative oriented, but what does that actually mean? Like, sorry, I know it's a really hard question, but I'm just trying to understand how How we actually spend our time and my children, which I don't have, by the way, Jan. But, what, what do they do? Like, sculpt or paint? I don't know.

A I don't know. That's a good question. Uh, but it's not because I don't know that it won't happen because I mean, look at like how many people exercise their creative juices, uh, today, right. With all the tools that are available that, you know, weren't available, uh, 10, 20 or 30 years ago, like three, the artists or something like this, you know, game designers, you know, all kinds of things that, you know, I think creative jobs are the other ones. So there are two types of job sets that, you know, have a bright future of creative jobs, whether they are scientific, technical, Educational or artistic. ACI has to do with communication, right? And communication of human emotions, which is, you know, intrinsically human, if you want. So that's one category. And then the other one is personal services. So where you need actual people to interact with you.

AI assessment note: “game designers, you know, all kinds of things that, you know, I think creative jobs”

Q But whose existing team are you like? Yeah, they're good.

A Yeah, yeah. So, so, uh, but I think, like, in terms of the, the sort of basic competence and, and the people who are going to push the, the science forward, because what we need now is not to work on applications of LLM. There's a lot of people who are capable of doing this, and they're going to do a good job. What we need to do, uh, people like me who are, you know, really working on research, is kind of coming up with new concepts that will allow us to, you know, get machines that basically have common sense, have And the experience of the real world have, you know, basically human level intelligence, right? And, uh, you know, in my opinion, the, the outfits that are best positioned for this are Claire from on one side and the, the new DeepMind now, which is, you know, DeepMind plus Google brain.

AI assessment note: “the outfits that are best positioned for this are Claire from on one side and the, the new DeepMind”

Q Yeah. And if we do this again in 10 years time, where is Jan in 10 years time in 2033?

A Well, I'm 63. So, you know, 10 years from now, well, 12 years from now, I'll be, I'll be Geoff Hinton's age. Okay. Uh, and I don't know, uh, I think I'm excited, like, Like a teenager now because I, I see the opportunity of like the next step in AI and opportunity perhaps to, you know, get to the goal that I set myself so that I imagined for myself when I started working on AI many years ago, which of course I was very naive about at the time of, uh, understanding intelligence, first of all, and it's a scientific question. What, what is intelligence? What is human intelligence? And one good way, as an engineer, a good way to understand intelligence is to build a widget that actually, uh, reproduces it, right, to some extent. Uh, so I'm, I'm excited about this right now. Uh, I'll find the, you know, the, the, the, the substrate, the landscape, the, the location, the position where I can make the, the best, uh, Contributions to this. And currently that just happens to be, ah, to be fair, ah, at Meta. Ah, I keep a foot in academia because I think it's, ah, very complimentary and also important. There are projects of different types that you do in academia and industry that are complimentary. So I like the, the combination of the two. As long as my brain keeps working, that I think I can contribute, and that I've given, I'm given the means to contribute, I'll keep, um, I'll keep wo…

AI assessment note: “As long as my brain keeps working, that I think I can contribute, I'll keep working”

Q Can I ask, has there been any other surprising on the positive side developmental things you've seen over the last year or so? You said about self-supervised learning and the efficiencies there. Is there anything else where you're like, I didn't expect it to go as well as it has done in the last year?

A Yeah. So I already mentioned it. Uh, you know, the, the fact that mainly, uh, training a language model to predict the, the, the, the last word in the sequence of words, if you do it properly, you get a system that has capabilities that are somewhat unexpected and they, um, emerge as, as you make those systems bigger and you train them on larger amounts of data. That's, that's clearly, uh, clearly be the surprise for everyone. Uh, now the thing is, you know, as, uh, Researchers and scientists, we, we're always looking for the next, the next thing. So what I'm interested in at the moment is, you know, what goes beyond that? Like, you know, a lot of people are going to work on applications of autoregressive large language models, which is great. There's going to be a lot of, uh, you know, products and, and new ways for people to do things, and it's going to be wonderful. But I'm already, you know, I've already been thinking about the next, the next stage for the last, uh, three, three, four years, four, five years, even actually more. Um, which is like, what's missing from those systems?

AI assessment note: “what I'm interested in at the moment is, you know, what goes beyond that?”

Q Um, but how did you get that deal? Yeah. But no, seriously, I, I, many of my friends at Meta in Very high positions, as you know, with mutual friends. They don't have that deal.

A So they, there is, I mean, I mean, a particularly, uh, sweet spot because I have, uh, quite a bit of, uh, of, uh, following people who trust me or, or believe me or, or, or want to hear what I have to say, even if they don't trust me at all. Um, and at the same time, I'm not, I'm not an officer. So I, it's not like You know, there are things I can't say because of, uh, legal issues of, you know, financial blah, blah, blah. Right. I'm a vice president, but I'm just below the, the level where you had to be really, really careful and sort of control, control your message. And I think there is a cost benefit trade off here of, you know, AI is such a complicated, fast evolving, uh, issue that you basically, you need someone to be able to, you know, speak freely. And I, I think Jeff didn't feel like he had that option at Google, maybe, uh, you know, for, for various reasons. So I understand why, why he might, he might have wanted to, to leave, but I don't, I don't agree with him at all with the, uh, the whole sort of, you know, probability of, uh, human extinction or, or whatever.

AI assessment note: “I'm a vice president, but I'm just below the, the level where you had to”

Q But you, but do you buy that here? Like, people are pretty good at prompts. You know, social media content managers are using prompts very efficiently to produce content plans, to create content ideas, in under half an hour after watching a couple of TikToks.

A Yeah. But like, what is going to be the effect of this on, uh, first of all, on measurable productivity? Second of all, on the, the job market? Like, is it going to make people lose their job like right away? And no, it's going to take a while. It's going to take 1015 years, you know, possibly more. It depends when you start counting, right? Because the AI revolution maybe started 10 years ago. So if you start counting then, then it might only take, you know, another 10 years. But, you know, I mean, I don't think you want to underestimate the degree of conservative Ness of, of the business world, right? I mean, things tend to change, not that quickly.

AI assessment note: “what is going to be the effect of this on, uh, first of all”

Q Can I ask, has there been any other surprising on the positive side developmental things you've seen over the last year or so? You said about self-supervised learning and the efficiencies there. Is there anything else where you're like, I didn't expect it to go as well as it has done in the last year?

A Yeah. So I already mentioned it. Uh, you know, the, the fact that mainly, uh, training a language model to predict the, the, the, the last word in the sequence of words, if you do it properly, you get a system that has capabilities that are somewhat unexpected and they, um, emerge as, as you make those systems bigger and you train them on larger amounts of data. That's, that's clearly, uh, clearly be the surprise for everyone. Uh, now the thing is, you know, as, uh, Researchers and scientists, we, we're always looking for the next, the next thing. So what I'm interested in at the moment is, you know, what goes beyond that? Like, you know, a lot of people are going to work on applications of autoregressive large language models, which is great. There's going to be a lot of, uh, you know, products and, and new ways for people to do things, and it's going to be wonderful. But I'm already, you know, I've already been thinking about the next, the next stage for the last, uh, three, three, four years, four, five years, even actually more. Um, which is like, what's missing from those systems?

AI assessment note: “what I'm interested in at the moment is, you know, what goes beyond that?”

Q Um, but how did you get that deal? Yeah. But no, seriously, I, I, many of my friends at Meta in Very high positions, as you know, with mutual friends. They don't have that deal.

A So they, there is, I mean, I mean, a particularly, uh, sweet spot because I have, uh, quite a bit of, uh, of, uh, following people who trust me or, or believe me or, or, or want to hear what I have to say, even if they don't trust me at all. Um, and at the same time, I'm not, I'm not an officer. So I, it's not like You know, there are things I can't say because of, uh, legal issues of, you know, financial blah, blah, blah. Right. I'm a vice president, but I'm just below the, the level where you had to be really, really careful and sort of control, control your message. And I think there is a cost benefit trade off here of, you know, AI is such a complicated, fast evolving, uh, issue that you basically, you need someone to be able to, you know, speak freely. And I, I think Jeff didn't feel like he had that option at Google, maybe, uh, you know, for, for various reasons. So I understand why, why he might, he might have wanted to, to leave, but I don't, I don't agree with him at all with the, uh, the whole sort of, you know, probability of, uh, human extinction or, or whatever.

AI assessment note: “I'm a vice president, but I'm just below the, the level where you had to be”

Q Who's incumbent team do you most respect and admire when you look at Amazon, Facebook, Google, in terms of their approach and talent internally? Outside of matter, obviously.

A So this is changing a lot. And the reason it's changing is because a lot of people are leaving large companies and large labs. And the reason they're doing this is that until recently, a lot of AI research was very exploratory. And now there is a path towards commercialization for a lot of things. And so people think that they're better off just, you know, leaving large companies and doing this on their own, doing a startup and things like that. So you see a, you know, relatively large motion of applied research engineers, a few scientists, Um, basically leaving those labs to, to do startups. Um, and that's across the board, right? So you look at the original paper from Google about BERT or Transformers, right? The, the thing that revolutionized NLP, all of them have left. Okay. They're all in startups. Some of the people who produced Llama, the open source, uh, LLMs from, from Meta. So the key people have left already. Okay, to do startups.

AI assessment note: “So this is changing a lot. And the reason it's changing is because”

Q So we've got China, we've got Europe. What about the US? What could they do differently or improve?

A Well, so there's a lot that the U.S. does right in terms of research, which is, to a large extent, a bit of a, you know, partial explanation for the success of the technological industry, the tech industry in the U.S. I think, you know, partly because, you know, the U.S. devotes a kind of, you know, significant amount of resources to fundamental research through NSF and NIH and, you know, various other outfits, probably more than Europe. Universities pays their faculty pretty well, particularly in areas like computer science and AI. Now this comes with a downside, and the downside is that studying in the US is expensive. It's a trade-off, right? So can you do one without the other? Switzerland figured out how to pay academics pretty well while actually offering free education to their, to their students. So, you know, there is a way to do it. Canada also figured out a pretty good trade-off as well. So a lot of things the U.S. does right, but they, one thing that the U.S. system, or, or like they're off, does, does right also is the willingness to take risk and invest on ideas that seem, you know, a little crazy, but, but basically, you know, the, the sort of vibrant startup scene in, uh, Silicon Valley and other places, uh, in the U.S., in New York, and in the Boston area is, is, You know, leading, uh, leading the world. Now, you start seeing a similar thing in Europe now. Ther…

AI assessment note: “studying in the US is expensive. It's a trade-off, right?”