Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4
 What difference between biological neural networks and artificial neural networks is most mysterious, captivating and profound for you? First of all, there's so much we don't know about biological neural networks, and that's very mysterious and captivating because maybe it holds the key to improving artificial neural networks. One of the things I studied recently is something that we don't know how biological neural networks do, but would be really useful for artificial ones, is the ability to do credit assignment through very long time spans. There are things that we can in principle do with artificial neural nets, but it's not very convenient and it's not biologically plausible. And this mismatch, I think, this kind of mismatch, may be an interesting thing to study to A, understand better how brains might do these things because we don't have good corresponding theories with artificial neural nets, and B, maybe provide new ideas that we could explore about things that brain do differently and that we could incorporate in artificial neural nets. So let's break credit assignment up a little bit. So what, it's a beautifully technical term, but it could incorporate so many things. So is it more on the RNN memory side, that thinking like that, or is it something about knowledge, building up common sense knowledge over time, or is it more in the reinforcement learning sense that you're picking up reward over time for a particular, to achieve a certain kind of goal? So I was thinking more about the first two meanings whereby we store all kinds of memories, episodic memories in our brain, which we can access later in order to help us both infer causes of things that we are observing now, and assign credit to decisions or interpretations we came up with a while ago when those memories were stored. And then we can change the way we would have reacted or interpreted things in the past, and now that's credit assignment used for learning. So in which way do you think artificial neural networks, the current LSTM, the current architectures are not able to capture the, presumably you're thinking of very long-term. Yes, so current nets are doing a fairly good jobs for sequences with dozens or say hundreds of time steps. And then it gets sort of harder and harder, and depending on what you have to remember and so on, as you consider longer durations. Whereas humans seem to be able to do credit assignment through essentially arbitrary times, like I could remember something I did last year. And then now because I see some new evidence, I'm gonna change my mind about the way I was thinking last year, and hopefully not do the same mistake again. I think a big part of that is probably forgetting. You're only remembering the really important things. So it's very efficient forgetting. Yes, so there's a selection of what we remember, and I think there are really cool connection to higher level cognition here regarding consciousness, deciding and emotions, like sort of deciding what comes to consciousness and what gets stored in memory, which are not trivial either. So you've been at the forefront there all along, showing some of the amazing things that neural networks, deep neural networks can do in the field of artificial intelligence is just broadly in all kinds of applications. But we can talk about that forever, but what in your view, because we're thinking towards the future, is the weakest aspect of the way deep neural networks represent the world? What is in your view is missing? So current state of the art neural nets trained on large quantities of images or texts have some level of understanding of, you know, what explains those data sets, but it's very basic. It's very low level, and it's not nearly as robust and abstract in general as our understanding. Okay, so that doesn't tell us how to fix things, but I think it encourages us to think about how we can maybe train our neural nets differently so that they would focus, for example, on causal explanation, something that we don't do currently with neural net training. Also, one thing I'll talk about in my talk this afternoon is instead of learning separately from images and videos on one hand and from texts on the other hand, we need to do a better job of jointly learning about language and about the world to which it refers, so that both sides can help each other. We need to have good world models in our neural nets for them to really understand sentences which talk about what's going on in the world, and I think we need language input to help provide clues about what high level concepts, like semantic concepts, should be represented at the top levels of these neural nets. In fact, there is evidence that the purely unsupervised learning of representations doesn't give rise to high level representations that are as powerful as the ones we're getting from supervised learning. And so the clues we're getting just with the labels, not even sentences, is already very powerful. Do you think that's an architecture challenge or is it a data set challenge? Neither. I'm tempted to just end it there. You elaborate slightly. Yes. Of course, data sets and architectures are something you wanna always play with, but I think the crucial thing is more the training objectives, the training frameworks. For example, going from passive observation of data to more active agents, which learn by intervening in the world the relationships between causes and effects, the sort of objective functions which could be important to allow the highest level explanations to rise from the learning, which I don't think we have now, the kinds of objective functions which could be used to reward exploration, the right kind of exploration. So these kinds of questions are neither in the data set nor in the architecture, but more in how we learn under what objectives and so on. Yeah, I've heard you mention in several contexts the idea of sort of the way children learn they interact with objects in the world. And it seems fascinating because in some sense, except with some cases in reinforcement learning, that idea is not part of the learning process in artificial neural networks. It's almost like, do you envision something like an objective function saying, you know what, if you poke this object in this kind of way, it would be really helpful for me to further learn. Right, right. Sort of almost guiding some aspect of the learning. Right, right, right. So I was talking to Rebecca Sachs just an hour ago and she was talking about lots and lots of evidence from infants seem to clearly pick what interests them in a directed way. And so they're not passive learners. They focus their attention on aspects of the world which are most interesting, surprising in a non-trivial way that makes them change their theories of the world. So that's a fascinating view of the future progress. But on a more maybe boring question, do you think going deeper and larger, so do you think just increasing the size of the things that have been increasing a lot in the past few years will also make significant progress? So some of the representational issues that you mentioned, they're kind of shallow in some sense. Oh, shallow, you mean in a sense of abstraction. In the sense of abstraction. They're not getting some. I don't think that having more depth in the network in the sense of instead of a hundred layers, we have 10,000 is going to solve our problem. You don't think so? No. Is that obvious to you? Yes. What is clear to me is that engineers and companies and labs and grad students will continue to tune architectures and explore all kinds of tweaks to make the current state of the art slightly ever slightly better. But I don't think that's gonna be nearly enough. I think we need some fairly drastic changes in the way that we're considering learning to achieve the goal that these learners actually understand in a deep way, the environment in which they are observing and acting. But I guess I was trying to ask a question that's more interesting than just more layers is basically once you figure out a way to learn through interacting, how many parameters does it take to store that information? So I think our brain is quite bigger than most neural networks. Right, right. Oh, I see what you mean. So I'm with you there. So I agree that in order to build neural nets with the kind of broad knowledge of the world that typical adult humans have, probably the kind of computing power we have now is gonna be insufficient. So well, the good news is there are hardware companies building neural net chips and so it's gonna get better. However, the good news in a way, which is also a bad news is that even our state of the art deep learning methods fail to learn models that understand even very simple environments like some grid worlds that we have built. Even these fairly simple environments, I mean, of course, if you train them with enough examples, eventually they get it. But it's just like, instead of what humans might need, just dozens of examples, these things will need millions, right, for very, very, very simple tasks. And so I think there's an opportunity for academics who don't have the kind of computing power that say Google has to do really important and exciting research to advance the state of the art in training frameworks, learning models, agent learning, in even simple environments that are synthetic, that seem trivial, but yet current machine learning fails on. We talked about priors and common sense knowledge. It seems like we humans take a lot of knowledge for granted. So what's your view of these priors of forming this broad view of the world, this accumulation of information and how we can teach neural networks or learning systems to pick that knowledge up? So knowledge, for a while, the artificial intelligence, what's maybe in the 80s, there's a time where knowledge representation, knowledge acquisition, expert systems, I mean, the symbolic AI was a view, was an interesting problem set to solve, and it was kind of put on hold a little bit, it seems like. Because it doesn't work. It doesn't work, that's right. But, that's right. But the goals of that remain important. Yes, remain important. And how do you think those goals can be addressed? Right, so first of all, I believe that one reason why the classical expert systems approach failed is because a lot of the knowledge we have, so you talked about common sense, intuition, there's a lot of knowledge like this, which is not consciously accessible. There are lots of decisions we're taking that we can't really explain, even if sometimes we make up a story. And that knowledge is also necessary for machines to take good decisions. And that knowledge is hard to codify in expert systems, rule-based systems, and classical AI formalism. And there are other issues, of course, with the old AI, like not really good ways of handling uncertainty, I would say something more subtle, which we understand better now, but I think still isn't enough in the minds of people. There's something really powerful that comes from distributed representations, the thing that really makes neural nets work so well. And it's hard to replicate that kind of power in a symbolic world. The knowledge in expert systems and so on is nicely decomposed into a bunch of rules. Whereas if you think about a neural net, it's the opposite. You have this big blob of parameters which work intensely together to represent everything the network knows. And it's not sufficiently factorized. And so I think this is one of the weaknesses of current neural nets, that we have to take lessons from classical AI in order to bring in another kind of compositionality, which is common in language, for example, and in these rules, but that isn't so native to neural nets. And on that line of thinking, disentangled representations. Yes. So let me connect with disentangled representations, if you don't mind. Yes, exactly. So for many years, I've thought, and I still believe, that it's really important that we come up with learning algorithms, either unsupervised or supervised, but, or reinforcement, whatever, that build representations in which the important factors, hopefully causal factors, are nicely separated and easy to pick up from the representation. So that's the idea of disentangled representations. It says, transform the data into a space where everything becomes easy. We can maybe just learn with linear models about the things we care about. And I still think this is important, but I think this is missing out on a very important ingredient, which classical AI systems can remind us of. So let's say we have these disentangled representations. You still need to learn about the relationships between the variables, those high-level semantic variables. They're not gonna be independent. I mean, this is like too much of an assumption. They're gonna have some interesting relationships that allow to predict things in the future, to explain what happened in the past. The kind of knowledge about those relationships in a classical AI system is encoded in the rules. Like a rule is just like a little piece of knowledge that says, oh, I have these two, three, four variables that are linked in this interesting way, then I can say something about one or two of them given a couple of others, right? In addition to disentangling the elements of the representation, which are like the variables in a rule-based system, you also need to disentangle the mechanisms that relate those variables to each other. So like the rules. So the rules are neatly separated. Each rule is living on its own. And when I change a rule because I'm learning, it doesn't need to break other rules. Whereas current neural nets, for example, are very sensitive to what's called catastrophic forgetting, where after I've learned some things and then I learn new things, they can destroy the old things that I had learned, right? If the knowledge was better factorized and separated, disentangled, then you would avoid a lot of that. Now you can't do this in the sensory domain, but- What do you mean by sensory domain? Like in pixel space. But my idea is that when you project the data in the right semantic space, it becomes possible to now represent this extra knowledge beyond the transformation from input to representations, which is how representations act on each other and predict the future and so on, in a way that can be neatly disentangled. So now it's the rules that are disentangled from each other and not just the variables that are disentangled from each other. And you draw a distinction between semantic space and pixel, like does there need to be an architectural difference or- Well, yeah, so there's the sensory space like pixels, where everything is entangled. The information, like the variables are completely interdependent in very complicated ways. And also computation, it's not just the variables, it's also how they are related to each other is all intertwined. But I'm hypothesizing that in the right high level representation space, both the variables and how they relate to each other can be disentangled and that will provide a lot of generalization power. Generalization power. Yes. Distribution of the test set is assumed to be the same as the distribution of the training set. Right, this is where current machine learning is too weak. It doesn't tell us anything, it's not able to tell us anything about how our neural nets, say, are gonna generalize to a new distribution. And people may think, well, but there's nothing we can say if we don't know what the new distribution will be. The truth is, humans are able to generalize to new distributions. How are we able to do that? Yeah, because there is something, these new distributions, even though they could look very different from the training distributions, they have things in common. So let me give you a concrete example. You read a science fiction novel. The science fiction novel maybe brings you in some other planet where things look very different on the surface, but it's still the same laws of physics. And so you can read the book and you understand what's going on. So the distribution is very different, but because you can transport a lot of the knowledge you had from Earth about the underlying cause and effect relationships and physical mechanisms and all that, and maybe even social interactions, you can now make sense of what is going on on this planet where like visually, for example, things are totally different. Taking that analogy further and distorting it, let's enter a science fiction world of say, Space Odyssey 2001 with HAL. Yeah. Or maybe, which is probably one of my favorite AI movies. And then- Me too. And then there's another one that a lot of people love that may be a little bit outside of the AI community is Ex Machina. I don't know if you've seen it. Yes, yes. By the way, what are your views on that movie? Are you able to enjoy it? So there are things I like and things I hate. So let me, you could talk about that in the context of a question I want to ask, which is there's quite a large community of people from different backgrounds, often outside of AI, who are concerned about existential threat of artificial intelligence. Right. You've seen this community develop over time. You've seen you have a perspective. So what do you think is the best way to talk about AI safety, to think about it, to have discourse about it within AI community and outside, and grounded in the fact that Ex Machina is one of the main sources of information for the general public about AI? So I think you're putting it right. There's a big difference between the sort of discussion we ought to have within the AI community and the sort of discussion that really matter in the general public. So I think that the picture of Terminator and AI loose and killing people and super intelligence that's gonna destroy us, whatever we try, isn't really so useful for the public discussion because for the public discussion, the things I believe really matter are the short term and medium term, very likely negative impacts of AI on society, whether it's from security, like big brother scenarios with face recognition or killer robots or the impact on the job market or concentration of power and discrimination, all kinds of social issues, which could actually, some of them could really threaten democracy, for example. Just to clarify, when you said killer robots, you mean autonomous weapon, weapon systems, so not Terminator. That's right. So I think these short and medium term concerns should be important parts of the public debate. Now, existential risk for me is a very unlikely consideration but still worth academic investigation in the same way that you could say, should we study what could happen if meteorite came to earth and destroyed it? So I think it's very unlikely that this is gonna happen or happen in a reasonable future. The sort of scenario of an AI getting loose goes against my understanding of at least current machine learning and current neural nets and so on. It's not plausible to me. But of course, I don't have a crystal ball and who knows what AI will be in 50 years from now. So I think it is worth that scientists study those problems. It's just not a pressing question as far as I'm concerned. So before I continue down that line, I have a few questions there, but what do you like and not like about Ex Machina as a movie? Because I actually watched it for the second time and enjoyed it. I hated it the first time and I enjoyed it quite a bit more the second time when I sort of learned to accept certain pieces of it, to see it as a concept movie. What was your experience? What were your thoughts? So the negative is the picture it paints of science is totally wrong. Science in general and AI in particular. Science is not happening in some hidden place by some really smart guy. One person. One person. This is totally unrealistic. This is not how it happens. Even a team of people in some isolated place will not make it. Science moves by small steps thanks to the collaboration and community of a large number of people interacting. And all the scientists who are expert in their field kind of know what is going on even in the industrial labs. It's information flows and leaks and so on. And the spirit of it is very different from the way science is painted in this movie. Yeah, let me ask on that point. It's been the case to this point that kind of even if the research happens inside Google or Facebook, inside companies, it still kind of comes out. Ideas come out. Absolutely. Do you think that will always be the case with AI? Is it possible to bottle ideas to the point where there's a set of breakthroughs that go completely undiscovered by the general research community? Do you think that's even possible? It's possible, but it's unlikely. Unlikely. It's not how it is done now. It's not how I can foresee it in the foreseeable future. But of course I don't have a crystal ball. And so who knows? This is science fiction after all. But usually science- I think it's ominous that the lights went off during that discussion. So the problem, again, there's a, one thing is the movie and you could imagine all kinds of science fiction. The problem with, for me, maybe similar to the question about existential risk, is that this kind of movie paints such a wrong picture of what is actual, the actual science and how it's going on, that it can have unfortunate effects on people's understanding of current science. And so that's kind of sad. There's an important principle in research, which is diversity. So in other words, research is exploration. Research is exploration in the space of ideas. And different people will focus on different directions. And this is not just good, it's essential. So I'm totally fine with people exploring directions that are contrary to mine or look orthogonal to mine. I am more than fine. I think it's important. I and my friends don't claim we have universal truth about what will, especially about what will happen in the future. Now, that being said, we have our intuitions and then we act accordingly, according to where we think we can be most useful and where society has the most to gain or to lose. So we should have those debates and not end up in a society where there's only one voice and one way of thinking and research money is spread out. So disagreement is a sign of good research, good science. So the idea of bias in the human sense of bias, how do you think about instilling in machine learning something that's aligned with human values in terms of bias? We intuitively as human beings have a concept of what bias means, of what fundamental respect for other human beings means, but how do we instill that into machine learning systems, do you think? So I think there are short-term things that are already happening and then there are long-term things that we need to do. In the short term, there are techniques that have been proposed and I think will continue to be improved and maybe alternatives will come up. To take data sets in which we know there is bias, we can measure it. Pretty much any data set where humans are being observed taking decisions will have some sort of bias, discrimination against particular groups and so on. And we can use machine learning techniques to try to build predictors, classifiers that are gonna be less biased. We can do it, for example, using adversarial methods to make our systems less sensitive to these variables we should not be sensitive to. So these are clear, well-defined ways of trying to address the problem. Maybe they have weaknesses and more research is needed and so on, but I think in fact, they're sufficiently mature that governments should start regulating companies where it matters, say like insurance companies, so that they use those techniques. Because those techniques will probably reduce the bias, but at a cost, for example, maybe their predictions will be less accurate and so companies will not do it until you force them. All right, so this is short-term. Long-term, I'm really interested in thinking of how we can instill moral values into computers. Obviously, this is not something we'll achieve in the next five or 10 years. How can we, there's already work in detecting emotions, for example, in images, in sounds, in texts, and also studying how different agents interacting in different ways may correspond to patterns of, say, injustice, which could trigger anger. So these are things we can do in the medium term, and eventually train computers to model, for example, how humans react emotionally. I would say the simplest thing is unfair situations, which trigger anger. This is one of the most basic emotions that we share with other animals. I think it's quite feasible within the next few years that we can build systems that can detect these kinds of things to the extent unfortunately that they understand enough about the world around us, which is a long time away, but maybe we can initially do this in virtual environments. So you can imagine like a video game where agents interact in some ways, and then some situations trigger an emotion. I think we could train machines to detect those situations and predict that the particular emotion will likely be felt if a human was playing one of the characters. You have shown excitement and done a lot of excellent work with unsupervised learning, but there's been a lot of success on the supervised learning side. Yes, yes. And one of the things I'm really passionate about is how humans and robots work together. And in the context of supervised learning, that means the process of annotation. Do you think about the problem of annotation of put in a more interesting way as humans teaching machines? Yes. Is there... Yes, I think it's an important subject. Reducing it to annotation may be useful for somebody building a system tomorrow, but longer term, the process of teaching, I think is something that deserves a lot more attention from the machine learning community. So there are people who've coined the term machine teaching. So what are good strategies for teaching a learning agent? And can we design, train a system that is gonna be a good teacher? So in my group, we have a project called a baby AI or baby AI game, where there is a game or scenario where there's a learning agent and a teaching agent. Presumably the teaching agent would eventually be a human, but we're not there yet. And the role of the teacher is to use its knowledge of the environment, which it can acquire using whatever way brute force to help the learner learn as quickly as possible. So the learner is gonna try to learn by itself, maybe be using some exploration and whatever, but the teacher can choose, can use the learning and the learning, but the teacher can choose, can have an influence on the interaction with the learner so as to guide the learner, maybe teach it the things that the learner has most trouble with, or just at the boundary between what it knows and doesn't know and so on. So there's a tradition of these kinds of ideas from other fields and like tutorial systems, for example, and AI, and of course people in the humanities have been thinking about these questions, but I think it's time that machine learning people look at this because in the future, we'll have more and more human machine interaction with the human in the loop. And I think understanding how to make this work better. Oh, the problems around that are very interesting and not sufficiently addressed. You've done a lot of work with language too, what aspect of the traditionally formulated Turing test, a test of natural language understanding and generation in your eyes is the most difficult of conversation. In your eyes is the hardest part of conversation to solve for machines. So I would say it's everything having to do with the non-linguistic knowledge, which implicitly you need in order to make sense of sentences, things like the Wiener-Gerand Schema. So these sentences that are semantically ambiguous. In other words, you need to understand enough about the world in order to really interpret properly those sentences. I think these are interesting challenges for machine learning because they point in the direction of building systems that both understand how the world works and this causal relationships in the world and associate that knowledge with how to express it in language either for reading or writing. You speak French? Yes, it's my mother tongue. It's one of the romance languages. Do you think passing the Turing test and all the underlying challenges we just mentioned depend on language? Do you think it might be easier in French than it is in English? No. Is independent of language? I think it's independent of language. I would like to build systems that can use the same principles, the same learning mechanisms to learn from human agents, whatever their language. Well, certainly us humans can talk more beautifully and smoothly in poetry. So I'm Russian originally. I know poetry in Russian is maybe easier to convey complex ideas than it is in English, but maybe I'm showing my bias and some people could say that about French. But of course, the goal ultimately is our human brain is able to utilize any kind of those languages to use them as tools to convey meaning. Yeah, of course there are differences between languages and maybe some are slightly better at some things, but in the grand scheme of things where we're trying to understand how the brain works and language and so on, I think these differences are minute. So you've lived perhaps through an AI winter of sorts? Yes. How did you stay warm and continue your research? Stay warm with friends. With friends, okay. So it's important to have friends and what have you learned from the experience? Listen to your inner voice. Don't be trying to just please the crowds and the fashion. And if you have a strong intuition about something that is not contradicted by actual evidence, go for it. I mean, it could be contradicted by people. But not your own instinct of based on everything you've learned. So of course you have to adapt your beliefs when your experiments contradict those beliefs. But you have to stick to your beliefs otherwise. It's what allowed me to go through those years. It's what allowed me to persist in directions that took time, whatever other people think, took time to mature and bring fruits. So history of AI is marked with these, of course it's marked with technical breakthroughs, but it's also marked with these seminal events that capture the imagination of the community. Most recent, I would say, AlphaGo beating the world champion human goal player was one of those moments. What do you think the next such moment might be? Okay, sir, first of all, I think that these so-called seminal events are overrated. As I said, science really moves by small steps. Now what happens is you make one more small step and it's like the drop that fills the bucket. And then you have drastic consequences because now you're able to do something you were not able to do before. Or now say the cost of building some device or solving a problem becomes cheaper than what existed and you have a new market that opens up. So especially in the world of commerce and applications, the impact of a small scientific progress could be huge. But in the science itself, I think it's very, very gradual. Where are these steps being taken now? So there's unsupervised learning. So if I look at one trend that I like in my community, so for example, at Miele and my Institute, what are the two hottest topics? GANs and reinforcement learning. Even though in Montreal in particular, like reinforcement learning was something pretty much absent just two or three years ago. So there's really a big interest from students and there's a big interest from people like me. So I would say this is something where we're gonna see more progress, even though it hasn't yet provided much in terms of actual industrial fallout. Like even though there's AlphaGo, there's no, like Google is not making money on this right now. But I think over the long term, this is really, really important for many reasons. So in other words, agent, I would say reinforcement learning may be more generally agent learning because it doesn't have to be with rewards. It could be in all kinds of ways that an agent is learning about its environment. Now, reinforcement learning you're excited about, do you think GANs could provide something? Yes. Some moment in... Well, GANs or other generative models, I believe, will be crucial ingredients in building agents that can understand the world. A lot of the successes in reinforcement learning in the past has been with policy gradient where you'll just learn a policy, you don't actually learn a model of the world. But there are lots of issues with that. And we don't know how to do model-based RL right now, but I think this is where we have to go in order to build models that can generalize faster and better like to new distributions that capture to some extent, at least the underlying causal mechanisms in the world. Last question. What made you fall in love with artificial intelligence? If you look back, what was the first moment in your life when you were fascinated by either the human mind or the artificial mind? You know, when I was an adolescent, I was reading a lot, and then I started reading science fiction. There you go. That's it. That's where I got hooked. And then I had one of the first personal computers and I got hooked in programming. And so it just, you know. Start with fiction and then make it a reality. That's right. Yosh, thank you so much for talking to me. My pleasure.
 What difference between biological neural networks
 and artificial neural networks is most mysterious,
 captivating and profound for you?
 First of all, there's so much we don't know
 about biological neural networks,
 and that's very mysterious and captivating
 because maybe it holds the key
 to improving artificial neural networks.
 One of the things I studied recently
 is something that we don't know
 how biological neural networks do,
 but would be really useful for artificial ones,
 is the ability to do credit assignment
 through very long time spans.
 There are things that we can in principle
 do with artificial neural nets,
 but it's not very convenient
 and it's not biologically plausible.
 And this mismatch, I think, this kind of mismatch,
 may be an interesting thing to study
 to A, understand better how brains might do these things
 because we don't have good corresponding theories
 with artificial neural nets,
 and B, maybe provide new ideas that we could explore
 about things that brain do differently
 and that we could incorporate in artificial neural nets.
 So let's break credit assignment up a little bit.
 So what, it's a beautifully technical term,
 but it could incorporate so many things.
 So is it more on the RNN memory side,
 that thinking like that,
 or is it something about knowledge,
 building up common sense knowledge over time,
 or is it more in the reinforcement learning sense
 that you're picking up reward over time
 for a particular, to achieve a certain kind of goal?
 So I was thinking more about the first two meanings
 whereby we store all kinds of memories,
 episodic memories in our brain,
 which we can access later
 in order to help us both infer causes
 of things that we are observing now,
 and assign credit to decisions or interpretations
 we came up with a while ago
 when those memories were stored.
 And then we can change the way we would have reacted
 or interpreted things in the past,
 and now that's credit assignment used for learning.
 So in which way do you think artificial neural networks,
 the current LSTM, the current architectures
 are not able to capture the,
 presumably you're thinking of very long-term.
 Yes, so current nets are doing a fairly good jobs
 for sequences with dozens or say hundreds of time steps.
 And then it gets sort of harder and harder,
 and depending on what you have to remember and so on,
 as you consider longer durations.
 Whereas humans seem to be able to do credit assignment
 through essentially arbitrary times,
 like I could remember something I did last year.
 And then now because I see some new evidence,
 I'm gonna change my mind about the way
 I was thinking last year,
 and hopefully not do the same mistake again.
 I think a big part of that is probably forgetting.
 You're only remembering the really important things.
 So it's very efficient forgetting.
 Yes, so there's a selection of what we remember,
 and I think there are really cool connection
 to higher level cognition here regarding consciousness,
 deciding and emotions,
 like sort of deciding what comes to consciousness
 and what gets stored in memory,
 which are not trivial either.
 So you've been at the forefront there all along,
 showing some of the amazing things that neural networks,
 deep neural networks can do
 in the field of artificial intelligence is just broadly
 in all kinds of applications.
 But we can talk about that forever,
 but what in your view,
 because we're thinking towards the future,
 is the weakest aspect of the way deep neural networks
 represent the world?
 What is in your view is missing?
 So current state of the art neural nets
 trained on large quantities of images or texts
 have some level of understanding of, you know,
 what explains those data sets, but it's very basic.
 It's very low level,
 and it's not nearly as robust and abstract in general
 as our understanding.
 Okay, so that doesn't tell us how to fix things,
 but I think it encourages us to think about
 how we can maybe train our neural nets differently
 so that they would focus, for example,
 on causal explanation,
 something that we don't do currently
 with neural net training.
 Also, one thing I'll talk about in my talk this afternoon
 is instead of learning separately from images and videos
 on one hand and from texts on the other hand,
 we need to do a better job of jointly learning
 about language and about the world to which it refers,
 so that both sides can help each other.
 We need to have good world models in our neural nets
 for them to really understand sentences
 which talk about what's going on in the world,
 and I think we need language input
 to help provide clues about what high level concepts,
 like semantic concepts, should be represented
 at the top levels of these neural nets.
 In fact, there is evidence that the purely unsupervised
 learning of representations doesn't give rise
 to high level representations that are as powerful
 as the ones we're getting from supervised learning.
 And so the clues we're getting just with the labels,
 not even sentences, is already very powerful.
 Do you think that's an architecture challenge
 or is it a data set challenge?
 Neither.
 I'm tempted to just end it there.
 You elaborate slightly.
 Yes.
 Of course, data sets and architectures
 are something you wanna always play with,
 but I think the crucial thing is more
 the training objectives, the training frameworks.
 For example, going from passive observation of data
 to more active agents, which learn by intervening
 in the world the relationships between causes and effects,
 the sort of objective functions which could be important
 to allow the highest level explanations to rise
 from the learning, which I don't think we have now,
 the kinds of objective functions which could be used
 to reward exploration, the right kind of exploration.
 So these kinds of questions are neither in the data set
 nor in the architecture, but more in how we learn
 under what objectives and so on.
 Yeah, I've heard you mention in several contexts
 the idea of sort of the way children learn
 they interact with objects in the world.
 And it seems fascinating because in some sense,
 except with some cases in reinforcement learning,
 that idea is not part of the learning process
 in artificial neural networks.
 It's almost like, do you envision something
 like an objective function saying, you know what,
 if you poke this object in this kind of way,
 it would be really helpful for me to further learn.
 Right, right.
 Sort of almost guiding some aspect of the learning.
 Right, right, right.
 So I was talking to Rebecca Sachs just an hour ago
 and she was talking about lots and lots of evidence
 from infants seem to clearly pick what interests them
 in a directed way.
 And so they're not passive learners.
 They focus their attention on aspects of the world
 which are most interesting, surprising
 in a non-trivial way that makes them change
 their theories of the world.
 So that's a fascinating view of the future progress.
 But on a more maybe boring question,
 do you think going deeper and larger,
 so do you think just increasing the size
 of the things that have been increasing a lot
 in the past few years will also make significant progress?
 So some of the representational issues
 that you mentioned, they're kind of shallow in some sense.
 Oh, shallow, you mean in a sense of abstraction.
 In the sense of abstraction.
 They're not getting some.
 I don't think that having more depth in the network
 in the sense of instead of a hundred layers,
 we have 10,000 is going to solve our problem.
 You don't think so?
 No.
 Is that obvious to you?
 Yes.
 What is clear to me is that engineers and companies
 and labs and grad students will continue to tune architectures
 and explore all kinds of tweaks
 to make the current state of the art
 slightly ever slightly better.
 But I don't think that's gonna be nearly enough.
 I think we need some fairly drastic changes
 in the way that we're considering learning
 to achieve the goal that these learners
 actually understand in a deep way,
 the environment in which they are observing and acting.
 But I guess I was trying to ask a question
 that's more interesting than just more layers
 is basically once you figure out a way to learn
 through interacting, how many parameters does it take
 to store that information?
 So I think our brain is quite bigger
 than most neural networks.
 Right, right.
 Oh, I see what you mean.
 So I'm with you there.
 So I agree that in order to build neural nets
 with the kind of broad knowledge of the world
 that typical adult humans have,
 probably the kind of computing power we have now
 is gonna be insufficient.
 So well, the good news is there are hardware companies
 building neural net chips and so it's gonna get better.
 However, the good news in a way,
 which is also a bad news is that even our state of the art
 deep learning methods fail to learn models
 that understand even very simple environments
 like some grid worlds that we have built.
 Even these fairly simple environments,
 I mean, of course, if you train them with enough examples,
 eventually they get it.
 But it's just like, instead of what humans might need,
 just dozens of examples,
 these things will need millions, right,
 for very, very, very simple tasks.
 And so I think there's an opportunity for academics
 who don't have the kind of computing power
 that say Google has to do really important
 and exciting research to advance the state of the art
 in training frameworks, learning models, agent learning,
 in even simple environments that are synthetic,
 that seem trivial, but yet current machine learning fails on.
 We talked about priors and common sense knowledge.
 It seems like we humans take a lot of knowledge for granted.
 So what's your view of these priors
 of forming this broad view of the world,
 this accumulation of information
 and how we can teach neural networks or learning systems
 to pick that knowledge up?
 So knowledge, for a while, the artificial intelligence,
 what's maybe in the 80s,
 there's a time where knowledge representation,
 knowledge acquisition, expert systems,
 I mean, the symbolic AI was a view,
 was an interesting problem set to solve,
 and it was kind of put on hold a little bit, it seems like.
 Because it doesn't work.
 It doesn't work, that's right.
 But, that's right.
 But the goals of that remain important.
 Yes, remain important.
 And how do you think those goals can be addressed?
 Right, so first of all,
 I believe that one reason
 why the classical expert systems approach failed
 is because a lot of the knowledge we have,
 so you talked about common sense, intuition,
 there's a lot of knowledge like this,
 which is not consciously accessible.
 There are lots of decisions we're taking
 that we can't really explain,
 even if sometimes we make up a story.
 And that knowledge is also necessary for machines
 to take good decisions.
 And that knowledge is hard to codify in expert systems,
 rule-based systems, and classical AI formalism.
 And there are other issues, of course, with the old AI,
 like not really good ways of handling uncertainty,
 I would say something more subtle,
 which we understand better now,
 but I think still isn't enough in the minds of people.
 There's something really powerful
 that comes from distributed representations,
 the thing that really makes neural nets work so well.
 And it's hard to replicate that kind of power
 in a symbolic world.
 The knowledge in expert systems and so on
 is nicely decomposed into a bunch of rules.
 Whereas if you think about a neural net, it's the opposite.
 You have this big blob of parameters
 which work intensely together
 to represent everything the network knows.
 And it's not sufficiently factorized.
 And so I think this is one of the weaknesses
 of current neural nets,
 that we have to take lessons from classical AI
 in order to bring in another kind of compositionality,
 which is common in language, for example, and in these rules,
 but that isn't so native to neural nets.
 And on that line of thinking, disentangled representations.
 Yes.
 So let me connect with disentangled representations,
 if you don't mind. Yes, exactly.
 So for many years, I've thought, and I still believe,
 that it's really important that we come up
 with learning algorithms, either unsupervised or supervised,
 but, or reinforcement, whatever,
 that build representations in which the important factors,
 hopefully causal factors, are nicely separated
 and easy to pick up from the representation.
 So that's the idea of disentangled representations.
 It says, transform the data into a space
 where everything becomes easy.
 We can maybe just learn with linear models
 about the things we care about.
 And I still think this is important,
 but I think this is missing out
 on a very important ingredient,
 which classical AI systems can remind us of.
 So let's say we have these disentangled representations.
 You still need to learn about the relationships
 between the variables,
 those high-level semantic variables.
 They're not gonna be independent.
 I mean, this is like too much of an assumption.
 They're gonna have some interesting relationships
 that allow to predict things in the future,
 to explain what happened in the past.
 The kind of knowledge about those relationships
 in a classical AI system is encoded in the rules.
 Like a rule is just like a little piece of knowledge
 that says, oh, I have these two, three, four variables
 that are linked in this interesting way,
 then I can say something about one or two of them
 given a couple of others, right?
 In addition to disentangling the elements
 of the representation, which are like the variables
 in a rule-based system,
 you also need to disentangle the mechanisms
 that relate those variables to each other.
 So like the rules.
 So the rules are neatly separated.
 Each rule is living on its own.
 And when I change a rule because I'm learning,
 it doesn't need to break other rules.
 Whereas current neural nets, for example,
 are very sensitive to what's called catastrophic forgetting,
 where after I've learned some things
 and then I learn new things,
 they can destroy the old things that I had learned, right?
 If the knowledge was better factorized
 and separated, disentangled,
 then you would avoid a lot of that.
 Now you can't do this in the sensory domain, but-
 What do you mean by sensory domain?
 Like in pixel space.
 But my idea is that when you project the data
 in the right semantic space,
 it becomes possible to now represent this extra knowledge
 beyond the transformation from input to representations,
 which is how representations act on each other
 and predict the future and so on,
 in a way that can be neatly disentangled.
 So now it's the rules that are disentangled from each other
 and not just the variables
 that are disentangled from each other.
 And you draw a distinction between semantic space
 and pixel, like does there need to be
 an architectural difference or-
 Well, yeah, so there's the sensory space like pixels,
 where everything is entangled.
 The information, like the variables
 are completely interdependent in very complicated ways.
 And also computation, it's not just the variables,
 it's also how they are related to each other
 is all intertwined.
 But I'm hypothesizing that in the right high level
 representation space, both the variables
 and how they relate to each other can be disentangled
 and that will provide a lot of generalization power.
 Generalization power.
 Yes.
 Distribution of the test set is assumed to be the same
 as the distribution of the training set.
 Right, this is where current machine learning is too weak.
 It doesn't tell us anything,
 it's not able to tell us anything about how our neural nets,
 say, are gonna generalize to a new distribution.
 And people may think, well, but there's nothing we can say
 if we don't know what the new distribution will be.
 The truth is, humans are able to generalize
 to new distributions.
 How are we able to do that?
 Yeah, because there is something,
 these new distributions, even though they could look
 very different from the training distributions,
 they have things in common.
 So let me give you a concrete example.
 You read a science fiction novel.
 The science fiction novel maybe brings you
 in some other planet where things look very different
 on the surface, but it's still the same laws of physics.
 And so you can read the book
 and you understand what's going on.
 So the distribution is very different,
 but because you can transport a lot of the knowledge
 you had from Earth about the underlying cause
 and effect relationships and physical mechanisms
 and all that, and maybe even social interactions,
 you can now make sense of what is going on on this planet
 where like visually, for example,
 things are totally different.
 Taking that analogy further and distorting it,
 let's enter a science fiction world of say,
 Space Odyssey 2001 with HAL.
 Yeah.
 Or maybe, which is probably one of my favorite AI movies.
 And then- Me too.
 And then there's another one that a lot of people love
 that may be a little bit outside of the AI community
 is Ex Machina.
 I don't know if you've seen it.
 Yes, yes.
 By the way, what are your views on that movie?
 Are you able to enjoy it?
 So there are things I like and things I hate.
 So let me, you could talk about that
 in the context of a question I want to ask,
 which is there's quite a large community of people
 from different backgrounds, often outside of AI,
 who are concerned about existential threat
 of artificial intelligence.
 Right.
 You've seen this community develop over time.
 You've seen you have a perspective.
 So what do you think is the best way to talk about AI safety,
 to think about it, to have discourse about it
 within AI community and outside,
 and grounded in the fact that Ex Machina
 is one of the main sources of information
 for the general public about AI?
 So I think you're putting it right.
 There's a big difference between the sort of discussion
 we ought to have within the AI community
 and the sort of discussion that really matter
 in the general public.
 So I think that the picture of Terminator and AI loose
 and killing people and super intelligence
 that's gonna destroy us, whatever we try,
 isn't really so useful for the public discussion
 because for the public discussion,
 the things I believe really matter
 are the short term and medium term,
 very likely negative impacts of AI on society,
 whether it's from security,
 like big brother scenarios with face recognition
 or killer robots or the impact on the job market
 or concentration of power and discrimination,
 all kinds of social issues,
 which could actually,
 some of them could really threaten democracy, for example.
 Just to clarify, when you said killer robots,
 you mean autonomous weapon, weapon systems,
 so not Terminator.
 That's right.
 So I think these short and medium term concerns
 should be important parts of the public debate.
 Now, existential risk for me is a very unlikely consideration
 but still worth academic investigation
 in the same way that you could say,
 should we study what could happen if meteorite
 came to earth and destroyed it?
 So I think it's very unlikely that this is gonna happen
 or happen in a reasonable future.
 The sort of scenario of an AI getting loose
 goes against my understanding
 of at least current machine learning
 and current neural nets and so on.
 It's not plausible to me.
 But of course, I don't have a crystal ball
 and who knows what AI will be in 50 years from now.
 So I think it is worth that scientists
 study those problems.
 It's just not a pressing question as far as I'm concerned.
 So before I continue down that line,
 I have a few questions there,
 but what do you like and not like about Ex Machina
 as a movie?
 Because I actually watched it for the second time
 and enjoyed it.
 I hated it the first time
 and I enjoyed it quite a bit more the second time
 when I sort of learned to accept certain pieces of it,
 to see it as a concept movie.
 What was your experience?
 What were your thoughts?
 So the negative is the picture it paints of science
 is totally wrong.
 Science in general and AI in particular.
 Science is not happening in some hidden place
 by some really smart guy.
 One person.
 One person.
 This is totally unrealistic.
 This is not how it happens.
 Even a team of people in some isolated place
 will not make it.
 Science moves by small steps
 thanks to the collaboration and community
 of a large number of people interacting.
 And all the scientists who are expert in their field
 kind of know what is going on even in the industrial labs.
 It's information flows and leaks and so on.
 And the spirit of it is very different
 from the way science is painted in this movie.
 Yeah, let me ask on that point.
 It's been the case to this point
 that kind of even if the research happens inside Google
 or Facebook, inside companies,
 it still kind of comes out.
 Ideas come out. Absolutely.
 Do you think that will always be the case with AI?
 Is it possible to bottle ideas to the point
 where there's a set of breakthroughs
 that go completely undiscovered
 by the general research community?
 Do you think that's even possible?
 It's possible, but it's unlikely.
 Unlikely.
 It's not how it is done now.
 It's not how I can foresee it in the foreseeable future.
 But of course I don't have a crystal ball.
 And so who knows?
 This is science fiction after all.
 But usually science-
 I think it's ominous that the lights went off
 during that discussion.
 So the problem, again, there's a,
 one thing is the movie
 and you could imagine all kinds of science fiction.
 The problem with, for me,
 maybe similar to the question about existential risk,
 is that this kind of movie paints such a wrong picture
 of what is actual, the actual science
 and how it's going on,
 that it can have unfortunate effects
 on people's understanding of current science.
 And so that's kind of sad.
 There's an important principle in research,
 which is diversity.
 So in other words, research is exploration.
 Research is exploration in the space of ideas.
 And different people will focus on different directions.
 And this is not just good, it's essential.
 So I'm totally fine with people exploring directions
 that are contrary to mine or look orthogonal to mine.
 I am more than fine.
 I think it's important.
 I and my friends don't claim we have universal truth
 about what will,
 especially about what will happen in the future.
 Now, that being said, we have our intuitions
 and then we act accordingly,
 according to where we think we can be most useful
 and where society has the most to gain or to lose.
 So we should have those debates
 and not end up in a society where there's only one voice
 and one way of thinking and research money is spread out.
 So disagreement is a sign of good research, good science.
 So the idea of bias in the human sense of bias,
 how do you think about instilling in machine learning
 something that's aligned with human values in terms of bias?
 We intuitively as human beings have a concept
 of what bias means, of what fundamental respect
 for other human beings means,
 but how do we instill that
 into machine learning systems, do you think?
 So I think there are short-term things
 that are already happening
 and then there are long-term things that we need to do.
 In the short term, there are techniques
 that have been proposed
 and I think will continue to be improved
 and maybe alternatives will come up.
 To take data sets in which we know there is bias,
 we can measure it.
 Pretty much any data set where humans are being observed
 taking decisions will have some sort of bias,
 discrimination against particular groups and so on.
 And we can use machine learning techniques
 to try to build predictors, classifiers
 that are gonna be less biased.
 We can do it, for example, using adversarial methods
 to make our systems less sensitive to these variables
 we should not be sensitive to.
 So these are clear, well-defined ways
 of trying to address the problem.
 Maybe they have weaknesses and more research is needed
 and so on, but I think in fact, they're sufficiently mature
 that governments should start regulating companies
 where it matters, say like insurance companies,
 so that they use those techniques.
 Because those techniques will probably reduce the bias,
 but at a cost, for example,
 maybe their predictions will be less accurate
 and so companies will not do it until you force them.
 All right, so this is short-term.
 Long-term, I'm really interested in thinking
 of how we can instill moral values into computers.
 Obviously, this is not something we'll achieve
 in the next five or 10 years.
 How can we, there's already work in detecting emotions,
 for example, in images, in sounds, in texts,
 and also studying how different agents interacting
 in different ways may correspond to patterns of, say,
 injustice, which could trigger anger.
 So these are things we can do in the medium term,
 and eventually train computers to model, for example,
 how humans react emotionally.
 I would say the simplest thing is unfair situations,
 which trigger anger.
 This is one of the most basic emotions
 that we share with other animals.
 I think it's quite feasible within the next few years
 that we can build systems that can detect
 these kinds of things to the extent
 unfortunately that they understand enough
 about the world around us, which is a long time away,
 but maybe we can initially do this
 in virtual environments.
 So you can imagine like a video game
 where agents interact in some ways,
 and then some situations trigger an emotion.
 I think we could train machines to detect those situations
 and predict that the particular emotion
 will likely be felt if a human was playing
 one of the characters.
 You have shown excitement and done a lot of excellent work
 with unsupervised learning,
 but there's been a lot of success
 on the supervised learning side.
 Yes, yes.
 And one of the things I'm really passionate about
 is how humans and robots work together.
 And in the context of supervised learning,
 that means the process of annotation.
 Do you think about the problem of annotation
 of put in a more interesting way
 as humans teaching machines?
 Yes.
 Is there...
 Yes, I think it's an important subject.
 Reducing it to annotation may be useful
 for somebody building a system tomorrow,
 but longer term, the process of teaching,
 I think is something that deserves a lot more attention
 from the machine learning community.
 So there are people who've coined the term machine teaching.
 So what are good strategies for teaching a learning agent?
 And can we design, train a system
 that is gonna be a good teacher?
 So in my group, we have a project called a baby AI
 or baby AI game, where there is a game or scenario
 where there's a learning agent and a teaching agent.
 Presumably the teaching agent would eventually be a human,
 but we're not there yet.
 And the role of the teacher is to use its knowledge
 of the environment, which it can acquire using
 whatever way brute force to help the learner
 learn as quickly as possible.
 So the learner is gonna try to learn by itself,
 maybe be using some exploration and whatever,
 but the teacher can choose, can use the learning
 and the learning, but the teacher can choose,
 can have an influence on the interaction with the learner
 so as to guide the learner, maybe teach it the things
 that the learner has most trouble with,
 or just at the boundary between what it knows
 and doesn't know and so on.
 So there's a tradition of these kinds of ideas
 from other fields and like tutorial systems, for example,
 and AI, and of course people in the humanities
 have been thinking about these questions,
 but I think it's time that machine learning people
 look at this because in the future,
 we'll have more and more human machine interaction
 with the human in the loop.
 And I think understanding how to make this work better.
 Oh, the problems around that are very interesting
 and not sufficiently addressed.
 You've done a lot of work with language too,
 what aspect of the traditionally formulated Turing test,
 a test of natural language understanding and generation
 in your eyes is the most difficult of conversation.
 In your eyes is the hardest part of conversation
 to solve for machines.
 So I would say it's everything having to do
 with the non-linguistic knowledge,
 which implicitly you need in order to make sense
 of sentences, things like the Wiener-Gerand Schema.
 So these sentences that are semantically ambiguous.
 In other words, you need to understand enough
 about the world in order to really interpret properly
 those sentences.
 I think these are interesting challenges
 for machine learning because they point in the direction
 of building systems that both understand how the world works
 and this causal relationships in the world
 and associate that knowledge with how to express it
 in language either for reading or writing.
 You speak French?
 Yes, it's my mother tongue.
 It's one of the romance languages.
 Do you think passing the Turing test
 and all the underlying challenges we just mentioned
 depend on language?
 Do you think it might be easier in French
 than it is in English?
 No.
 Is independent of language?
 I think it's independent of language.
 I would like to build systems that can use
 the same principles, the same learning mechanisms
 to learn from human agents, whatever their language.
 Well, certainly us humans can talk more beautifully
 and smoothly in poetry.
 So I'm Russian originally.
 I know poetry in Russian is maybe easier
 to convey complex ideas than it is in English,
 but maybe I'm showing my bias
 and some people could say that about French.
 But of course, the goal ultimately is our human brain
 is able to utilize any kind of those languages
 to use them as tools to convey meaning.
 Yeah, of course there are differences between languages
 and maybe some are slightly better at some things,
 but in the grand scheme of things
 where we're trying to understand how the brain works
 and language and so on,
 I think these differences are minute.
 So you've lived perhaps through an AI winter of sorts?
 Yes.
 How did you stay warm and continue your research?
 Stay warm with friends.
 With friends, okay.
 So it's important to have friends
 and what have you learned from the experience?
 Listen to your inner voice.
 Don't be trying to just please the crowds and the fashion.
 And if you have a strong intuition about something
 that is not contradicted by actual evidence, go for it.
 I mean, it could be contradicted by people.
 But not your own instinct of based on everything
 you've learned.
 So of course you have to adapt your beliefs
 when your experiments contradict those beliefs.
 But you have to stick to your beliefs otherwise.
 It's what allowed me to go through those years.
 It's what allowed me to persist in directions that
 took time, whatever other people think,
 took time to mature and bring fruits.
 So history of AI is marked with these,
 of course it's marked with technical breakthroughs,
 but it's also marked with these seminal events
 that capture the imagination of the community.
 Most recent, I would say, AlphaGo beating the world champion
 human goal player was one of those moments.
 What do you think the next such moment might be?
 Okay, sir, first of all, I think that these so-called
 seminal events are overrated. As I said, science really moves
 by small steps.
 Now what happens is you make one more small step
 and it's like the drop that fills the bucket.
 And then you have drastic consequences
 because now you're able to do something
 you were not able to do before.
 Or now say the cost of building some device
 or solving a problem becomes cheaper than what existed
 and you have a new market that opens up.
 So especially in the world of commerce and applications,
 the impact of a small scientific progress could be huge.
 But in the science itself, I think it's very, very gradual.
 Where are these steps being taken now?
 So there's unsupervised learning.
 So if I look at one trend that I like in my community,
 so for example, at Miele and my Institute,
 what are the two hottest topics?
 GANs and reinforcement learning.
 Even though in Montreal in particular,
 like reinforcement learning was something pretty much absent
 just two or three years ago.
 So there's really a big interest from students
 and there's a big interest from people like me.
 So I would say this is something
 where we're gonna see more progress,
 even though it hasn't yet provided much
 in terms of actual industrial fallout.
 Like even though there's AlphaGo,
 there's no, like Google is not making money
 on this right now.
 But I think over the long term,
 this is really, really important for many reasons.
 So in other words, agent, I would say reinforcement learning
 may be more generally agent learning
 because it doesn't have to be with rewards.
 It could be in all kinds of ways
 that an agent is learning about its environment.
 Now, reinforcement learning you're excited about,
 do you think GANs could provide something?
 Yes.
 Some moment in...
 Well, GANs or other generative models, I believe,
 will be crucial ingredients in building agents
 that can understand the world.
 A lot of the successes in reinforcement learning
 in the past has been with policy gradient
 where you'll just learn a policy,
 you don't actually learn a model of the world.
 But there are lots of issues with that.
 And we don't know how to do model-based RL right now,
 but I think this is where we have to go
 in order to build models that can generalize faster
 and better like to new distributions
 that capture to some extent,
 at least the underlying causal mechanisms in the world.
 Last question.
 What made you fall in love with artificial intelligence?
 If you look back, what was the first moment in your life
 when you were fascinated by either the human mind
 or the artificial mind?
 You know, when I was an adolescent, I was reading a lot,
 and then I started reading science fiction.
 There you go.
 That's it.
 That's where I got hooked.
 And then I had one of the first personal computers
 and I got hooked in programming.
 And so it just, you know.
 Start with fiction and then make it a reality.
 That's right.
 Yosh, thank you so much for talking to me.
 My pleasure.