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. Yes. 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 rewards 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, the current nets are doing a fairly good jobs for sequences with dozens or say hundreds of time steps. And then it gets 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 going to 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. 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, so 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 that? 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 it's very low level. And it's not nearly as robust and abstract and 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 the fact that 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, you know, 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 our 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, very high level. And I think that's a very important thing to keep in mind. It's 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. Can you elaborate slightly? Of course, data sets and architectures are something you want to 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. So 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 Sacks just a few minutes 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, is going to be a big thing? I think 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 in the 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 100 layers, you're going to have more layers. I don't think so. 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 going to be nearly enough. I think we need 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, you know, observing and acting. But I guess I was trying to ask a question that's more interesting than just more layers. It's basically, once you figure out a way to learn through interacting, how many parameters it takes to store that information. So I think our brain is quite bigger than most neural networks. Right, right. Oh, I see what you mean. Oh, 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 going to be insufficient. So the good news is there are hardware companies building neural net chips. And so it's going to 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 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 was maybe in the 80s, like 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 like 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. 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 might, if you don't mind. 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 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 going to be independent. I mean, this is like too much of an assumption. They're going to 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. Like each rule is, you know, 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. 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 inputs 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? Well, yeah. So there's the sensory space like pixels, which where everything is entangled. The information, like the variables are completely interdependent in very complicated ways. And also computation, like 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, is not able to tell us anything about how our neural nets, say, are going to generalize to a new distribution. And, you know, 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. Yeah. 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, you know, 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. Or maybe, which is probably one of my favorite AI movies. 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? Are there things I like and things I hate? So 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. 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 the picture of Terminator and AI loose and killing people and super intelligence that's going to 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, you know, 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. Yes, I don't mean 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, you know, came to earth and destroyed it. So I think it's very unlikely that this is going to happen in 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, 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, you know, really smart guy, 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. 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. 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 science is a crystal ball. And so who knows? This is science fiction after all. I think it's ominous that the lights went off during that discussion. So the problem, again, there's one thing is the movie and you could imagine all kinds of science fiction. The problem for me, maybe similar to the question about existential risk, is that this kind of movie paints such a wrong picture of what is 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. 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. Yes. 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 going to 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 are 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 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, you know, 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 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 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 have coined the term machine teaching. So what are good strategies for teaching a learning agent? And can we design and train a system that is going to be a good teacher? So in my group, we have a project called BBI or BBI 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 going to try to learn by itself, maybe using some exploration and whatever. 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 add the boundary between what it knows and doesn't know, and so on. So there's a tradition of these kind 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, all 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? What 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 Winograd 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, or 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, some 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, you know, 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. Not your own instinct of based on everything you've learned? 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, you know, 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 Go player was one of those moments. What do you think the next such moment might be? Okay, so 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, you know, 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, right? 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. And 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 Milan, my institute, what are the two hardest topics? GANs and reinforcement learning. Even though in Montreal in particular, 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 going to 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, 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, at the moment? 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 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, you know, 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. Yoshua, thank you so much for talking to me. My pleasure.

Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4


The following is a conversation with Vladimir Vapnik. He's the co inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union and worked at the Institute of Control Sciences in Moscow. Then in the United States, he worked at AT&T, NEC Labs, Facebook Research, and now is a professor at Columbia University. His work has been cited over 170,000 times. He has some very interesting ideas about artificial intelligence and the nature of learning, especially on the limits of our current approaches and the open problems in the field. This conversation is part of MIT course on artificial general intelligence and the artificial intelligence podcast. If you enjoy it, please subscribe on YouTube or rate it on iTunes or your podcast provider of choice, or simply connect with me on Twitter or other social networks at Lex Friedman spelled F R I D. And now here's my conversation with Vladimir Vapnik. Einstein famously said that God doesn't play dice. Yeah. You have studied the world through the eyes of statistics. So let me ask you in terms of the nature of reality, fundamental nature of reality, does God play dice? We don't know some factors. And because we don't know some factors, which could be important, it looks like God plays dice. But we should describe it. In philosophy, they distinguish between two positions, positions of instrumentalism, where you're creating theory for prediction and position of realism, where you're trying to understand what God did. Can you describe instrumentalism and realism a little bit? For example, if you have some mechanical laws, what is that? Is it law which is true always and everywhere? Or it is law which allow you to predict position of moving element? What you believe. You believe that it is God's law, that God created the world, which obey to this physical law. Or it is just law for predictions. And which one is instrumentalism? For predictions. If you believe that this is law of God, and it's always true everywhere, that means that you're realist. So you're trying to really understand God's thought. So the way you see the world is as an instrumentalist? You know, I'm working for some models, model of machine learning. So in this model, we can see setting, and we try to solve, resolve the setting to solve the problem. And you can do in two different way. From the point of view of instrumentalist, and that's what everybody does now. Because they say that goal of machine learning is to find the rule for classification. That is true. But it is instrument for prediction. But I can say the goal of machine learning is to learn about conditional probability. So how God played use, and if he play, what is probability for one, what is probability for another, given situation. But for prediction, I don't need this. I need the rule. But for understanding, I need conditional probability. So let me just step back a little bit first to talk about, you mentioned, which I read last night, the parts of the 1960 paper by Eugene Wigner, Unreasonable Effectiveness of Mathematics and Natural Sciences. Such a beautiful paper, by the way. Made me feel, to be honest, to confess my own work in the past few years on deep learning, heavily applied. Made me feel that I was missing out on some of the beauty of nature in the way that math can uncover. So let me just step away from the poetry of that for a second. How do you see the role of math in your life? Is it a tool, is it poetry? Where does it sit? And does math for you have limits of what it can describe? Some people say that math is language which use God. Use God. So I believe that... Speak to God or use God or... Use God. Use God. Yeah. So I believe that this article about effectiveness, unreasonable effectiveness of math, is that if you're looking at mathematical structures, they know something about reality. And the most scientists from Natural Science, they're looking on equation and trying to understand reality. So the same in machine learning. If you try very carefully look on all equations which define conditional probability, you can understand something about reality more than from your fantasy. So math can reveal the simple underlying principles of reality perhaps. You know what means simple? It is very hard to discover them. But then when you discover them and look at them, you see how beautiful they are. And it is surprising why people did not see that before. You're looking on equation and derive it from equations. For example, I talked yesterday about least square method. And people had a lot of fantasy how to improve least square method. But if you're going step by step by solving some equations, you suddenly will get some term which after thinking, you understand that it describes position of observation point. In least square method, we throw out a lot of information. We don't look in composition of point of observations, we're looking only on residuals. But when you understood that, that's very simple idea, but it's not too simple to understand. And you can derive this just from equations. So some simple algebra, a few steps will take you to something surprising that when you think about, you understand. And that is proof that human intuition is not too rich and very primitive. And it does not see very simple situations. So let me take a step back. In general, yes. But what about human, as opposed to intuition, ingenuity? Moments of brilliance. Do you have to be so hard on human intuition? Are there moments of brilliance in human intuition? They can leap ahead of math and then the math will catch up? I don't think so. I think that the best human intuition, it is putting in axioms. And then it is technical. See where the axioms take you. But if they correctly take axioms. But it axiom polished during generations of scientists. And this is integral wisdom. That is beautifully put. But if you maybe look at, when you think of Einstein and special relativity, what is the role of imagination coming first there in the moment of discovery of an idea? So there is obviously a mix of math and out of the box imagination there. That I don't know. Whatever I did, I exclude any imagination. Because whatever I saw in machine learning that comes from imagination, like features, like deep learning, they are not relevant to the problem. When you are looking very carefully from mathematical equations, you are deriving very simple theory, which goes far beyond theoretically than whatever people can imagine. Because it is not good fantasy. It is just interpretation. It is just fantasy. But it is not what you need. You don't need any imagination to derive the main principle of machine learning. When you think about learning and intelligence, maybe thinking about the human brain and trying to describe mathematically the process of learning, that is something like what happens in the human brain. Do you think we have the tools currently? Do you think we will ever have the tools to try to describe that process of learning? It is not description what is going on. It is interpretation. It is your interpretation. Your vision can be wrong. You know, one guy invented microscope, Levenhuk, for the first time. Only he got this instrument and he kept secret about microscope. But he wrote a report in London Academy of Science. In his report, when he was looking at the blood, he looked everywhere, on the water, on the blood, on the sperm. But he described blood like fight between queen and king. So, he saw blood cells, red cells, and he imagined that it is army fighting each other. And it was his interpretation of situation. And he sent this report in Academy of Science. They very carefully looked because they believed that he is right. He saw something. Yes. But he gave wrong interpretation. And I believe the same can happen with brain. With brain, yeah. The most important part. You know, I believe in human language. In some proverbs, there is so much wisdom. For example, people say that it is better than thousand days of diligent studies one day with great teacher. But if I will ask you what teacher does, nobody knows. And that is intelligence. But we know from history and now from math and machine learning that teacher can do a lot. So, what from a mathematical point of view is the great teacher? I don't know. That's an open question. No, but we can say what teacher can do. He can introduce some invariants, some predicate for creating invariants. How he doing it? I don't know because teacher knows reality and can describe from this reality a predicate, invariants. But he knows that when you're using invariant, you can decrease number of observations hundred times. So, but maybe try to pull that apart a little bit. I think you mentioned like a piano teacher saying to the student, play like a butterfly. Yeah. I play piano. I play guitar for a long time. Yeah, maybe it's romantic, poetic, but it feels like there's a lot of truth in that statement. Like there is a lot of instruction in that statement. And so, can you pull that apart? What is that? The language itself may not contain this information. It is not blah, blah, blah. It is not blah, blah, blah. It affects you. It's what? It affects you. It affects your playing. Yes, it does, but it's not the laying. It feels like what is the information being exchanged there? What is the nature of information? What is the representation of that information? I believe that it is sort of predicate, but I don't know. That is exactly what intelligence and machine learning should be. Yes. Because the rest is just mathematical technique. I think that what was discovered recently is that there is two mechanism of learning. One called strong convergence mechanism and weak convergence mechanism. Before, people use only one convergence. In weak convergence mechanism, you can use predicate. That's what play like butterfly and it will immediately affect your playing. You know, there is English proverb, great. If it looks like a duck, swims like a duck, and quack like a duck, then it is probably duck. Yes. But this is exact about predicate. Looks like a duck, what it means. You saw many ducks that you're training data. So, you have description of how looks integral looks ducks. Yeah. The visual characteristics of a duck. Yeah. But you want and you have model for recognition. So, you would like so that theoretical description from model coincide with empirical description, which you saw on territory. So, about looks like a duck, it is general. But what about swims like a duck? You should know that duck swims. You can say it play chess like a duck. Okay. Duck doesn't play chess. And it is completely legal predicate, but it is useless. So, half teacher can recognize not useless predicate. So, up to now, we don't use this predicate in existing machine learning. So, why we need zillions of data. But in this English proverb, they use only three predicate. Looks like a duck, swims like a duck, and quack like a duck. So, you can't deny the fact that swims like a duck and quacks like a duck has humor in it, has ambiguity. Let's talk about swim like a duck. It doesn't say jump like a duck. Why? Because... It's not relevant. But that means that you know ducks, you know different birds, you know animals. And you derive from this that it is relevant to say swim like a duck. So, underneath, in order for us to understand swims like a duck, it feels like we need to know millions of other little pieces of information. Which we pick up along the way. You don't think so. There doesn't need to be this knowledge base in those statements carries some rich information that helps us understand the essence of duck. Yeah. How far are we from integrating predicates? You know that when you consider complete theory of machine learning. So, what it does, you have a lot of functions. And then you're talking it looks like a duck. You see your training data. From training data you recognize like expected duck should look. Then you remove all functions which does not look like you think it should look from training data. So, you decrease amount of function from which you pick up one. Then you give a second predicate and again decrease the set of function. And after that you pick up the best function you can find. It is standard machine learning. So, why you need not too many examples? Because your predicates aren't very good? That means that predicates are very good because every predicate is invented to decrease admissible set of function. So, you talk about admissible set of functions and you talk about good functions. So, what makes a good function? So, admissible set of function is set of function which has small capacity or small diversity, small VC dimension example. Which contain good function inside. So, by the way for people who don't know VC, you're the V in the VC. So, how would you describe to lay person what VC theory is? How would you describe VC? So, when you have a machine. So, machine capable to pick up one function from the admissible set of function. But set of admissible function can be big. So, it contain all continuous functions and it's useless. You don't have so many examples to pick up function. But it can be small. Small, we call it capacity but maybe better called diversity. So, not very different function in the set. It's infinite set of function but not very diverse. So, it is small VC dimension. When VC dimension is small, you need small amount of training data. So, the goal is to create admissible set of functions which is have small VC dimension and contain good function. Then you will be able to pick up the function using small amount of observations. So, that is the task of learning? Yeah. Is creating a set of admissible functions that has a small VC dimension and then you've figure out a clever way of picking up? No, that is goal of learning which I formulated yesterday. Statistical learning theory does not involve in creating admissible set of function. In classical learning theory, everywhere, 100% in textbook, the set of function, admissible set of function is given. But this is science about nothing because the most difficult problem to create admissible set of functions given, say, a lot of functions, continuum set of function, create admissible set of functions. That means that it has finite VC dimension, small VC dimension and contain good function. So, this was out of consideration. So, what's the process of doing that? I mean, it's fascinating. What is the process of creating this admissible set of functions? That is invariant. That's invariant. Yeah, you're looking of properties of training data and properties means that you have some function and you just count what is value, average value of function on training data. You have model and what is expectation of this function on the model and they should coincide. So, the problem is about how to pick up functions. It can be any function. In fact, it is true for all functions. But because when we're talking, say, duck does not jumping, so you don't ask question jump like a duck because it is trivial. It does not jumping and doesn't help you to recognize jump. But you know something, which question to ask and you're asking it seems like a duck, but looks like a duck at this general situation. Looks like, say, guy who have this illness, this disease. It is legal. So, there is a general type of predicate looks like and special type of predicate, which related to this specific problem. And that is intelligence part of all this business and that where teacher is involved. Incorporating the specialized predicates. What do you think about deep learning as neural networks, these arbitrary architectures as helping accomplish some of the tasks you're thinking about? Their effectiveness or lack thereof? What are the weaknesses and what are the possible strengths? You know, I think that this is fantasy, everything which like deep learning, like features. Let me give you this example. One of the greatest books is Churchill book about history of Second World War. And he started this book describing that in old time when war is over, so the great kings, they gathered together, almost all of them were relatives, and they discussed what should be done, how to create peace. And they came to agreement. And when happened First World War, the general public came in power. And they were so greedy that robbed Germany. And it was clear for everybody that it is not peace, that peace will last only 20 years because they were not professionals. And the same I see in machine learning. There are mathematicians who are looking for the problem from a very deep point of view, mathematical point of view. And there are computer scientists who mostly does not know mathematics. They just have interpretation of that. And they invented a lot of blah, blah, blah interpretations like deep learning. Why you need deep learning? Mathematic does not know deep learning. Mathematic does not know neurons. It is just function. If you like to say piecewise linear function, say that and do in class of piecewise linear function. But they invent something. And then they try to prove advantage of that through interpretations, which mostly wrong. And when it's not enough, they appeal to brain, which they know nothing about that. Nobody knows what's going on in the brain. So, I think that more reliable work on math. This is a mathematical problem. Do your best to solve this problem. Try to understand that there is not only one way of convergence, which is strong way of convergence. There is a weak way of convergence, which requires predicate. And if you will go through all this stuff, you will see that you don't need deep learning. Even more, I would say one of the theory, which called represented theory. It says that optimal solution of mathematical problem, which is described learning is on shadow network, not on deep learning. And a shallow network. Yeah. The ultimate problem is there. Absolutely. In the end, what you're saying is exactly right. The question is you have no value for throwing something on the table, playing with it, not math. It's like a neural network where you said throwing something in the bucket or the biological example and looking at kings and queens or the cells or the microscope. You don't see value in imagining the cells or kings and queens and using that as inspiration and imagination for where the math will eventually lead you. You think that interpretation basically deceives you in a way that's not productive. I think that if you're trying to analyze this business of learning and especially discussion about deep learning, it is discussion about interpretation, not about things, about what you can say about things. That's right. But aren't you surprised by the beauty of it? So not mathematical beauty, but the fact that it works at all or are you criticizing that very beauty, our human desire to interpret, to find our silly interpretations in these constructs? Let me ask you this. Are you surprised and does it inspire you? How do you feel about the success of a system like AlphaGo at beating the game of Go? Using neural networks to estimate the quality of a board and the quality of the position. That is your interpretation, quality of the board. Yeah, yes. Yeah. So it's not our interpretation. The fact is a neural network system, it doesn't matter, a learning system that we don't I think mathematically understand that well, beats the best human player, does something that was thought impossible. That means that it's not a very difficult problem. So you empirically, we've empirically have discovered that this is not a very difficult problem. Yeah. It's true. So maybe, can't argue. So even more I would say that if they use deep learning, it is not the most effective way of learning theory. And usually when people use deep learning, they're using zillions of training data. Yeah. But you don't need this. So I describe challenge, can we do some problems which do well deep learning method, this deep net, using hundred times less training data. Even more, some problems deep learning cannot solve because it's not necessary they create admissible set of function. To create deep architecture means to create admissible set of functions. You cannot say that you're creating good admissible set of functions. You just, it's your fantasy. It does not come from us. But it is possible to create admissible set of functions because you have your training data. That actually for mathematicians, when you consider a variant, you need to use law of large numbers. When you're making training in existing algorithm, you need uniform law of large numbers, which is much more difficult, it requires VC dimension and all this stuff. But nevertheless, if you use both weak and strong way of convergence, you can decrease a lot of training data. You could do the three, the swims like a duck and quacks like a duck. So let's step back and think about human intelligence in general. Clearly that has evolved in a non mathematical way. It wasn't, as far as we know, God or whoever didn't come up with a model and place in our brain of admissible functions. It kind of evolved. I don't know, maybe you have a view on this. So Alan Turing in the 50s, in his paper, asked and rejected the question, can machines think? It's not a very useful question, but can you briefly entertain this useful, useless question? Can machines think? So talk about intelligence and your view of it. I don't know that. I know that Turing described imitation. If computer can imitate human being, let's call it intelligent. And he understands that it is not thinking computer. He completely understands what he's doing. But he set up problem of imitation. So now we understand that the problem is not in imitation. I'm not sure that intelligence is just inside of us. It may be also outside of us. I have several observations. So when I prove some theorem, it's very difficult theorem, in couple of years, in several places, people prove the same theorem, say, Sawyer Lemma, after us was done, then another guys proved the same theorem. In the history of science, it's happened all the time. For example, geometry, it's happened simultaneously, first it did Lobachevsky and then Gauss and Boyai and another guys, and it's approximately in 10 times period, 10 years period of time. And I saw a lot of examples like that. And many mathematicians think that when they develop something, they develop something in general which affect everybody. So maybe our model that intelligence is only inside of us is incorrect. It's our interpretation. It might be there exists some connection with world intelligence. I don't know. You're almost like plugging in into... Yeah, exactly. And contributing to this... Into a big network. Into a big, maybe in your own network. On the flip side of that, maybe you can comment on big O complexity and how you see classifying algorithms by worst case running time in relation to their input. So that way of thinking about functions, do you think p equals np, do you think that's an interesting question? Yeah, it is an interesting question. But let me talk about complexity in about worst case scenario. There is a mathematical setting. When I came to United States in 1990, people did not know, they did not know statistical learning theory. So in Russia, it was published to monographs, our monographs, but in America they didn't know. Then they learned and somebody told me that it is worst case theory and they will create real case theory, but till now it did not. Because it is mathematical too. You can do only what you can do using mathematics. And which has a clear understanding and clear description. And for this reason, we introduce complexity. And we need this because using, actually it is diversity, I like this one more. You see the mention, you can prove some theorems. But we also create theory for case when you know probability measure. And that is the best case which can happen, it is entropy theory. So from mathematical point of view, you know the best possible case and the worst possible case. You can derive different model in medium, but it's not so interesting. You think the edges are interesting? The edges are interesting because it is not so easy to get good bound, exact bound. It's not many cases where you have the bound is not exact. But interesting principles which discover the mass. Do you think it's interesting because it's challenging and reveals interesting principles that allow you to get those bounds? Or do you think it's interesting because it's actually very useful for understanding the essence of a function of an algorithm? So it's like me judging your life as a human being by the worst thing you did and the best thing you did versus all the stuff in the middle. It seems not productive. I don't think so because you cannot describe situation in the middle. So it will be not general. So you can describe edges cases and it is clear it has some model, but you cannot describe model for every new case. So you will be never accurate when you're using model. But from a statistical point of view, the way you've studied functions and the nature of learning in the world, don't you think that the real world has a very long tail? That the edge cases are very far away from the mean, the stuff in the middle or no? I don't know that. I think that from my point of view, if you will use formal statistic, you need uniform law of large numbers. If you will use this invariance business, you will need just law of large numbers. And there's this huge difference between uniform law of large numbers and large numbers. Is it useful to describe that a little more or should we just take it to... For example, when I'm talking about duck, I give three predicates and that was enough. But if you will try to do formal distinguish, you will need a lot of observations. So that means that information about looks like a duck contain a lot of bit of information, formal bits of information. So we don't know that how much bit of information contain things from artificial and from intelligence. And that is the subject of analysis. Till now, all business, I don't like how people consider artificial intelligence. They consider us some codes which imitate activity of human being. It is not science, it is applications. You would like to imitate go ahead, it is very useful and a good problem. But you need to learn something more. How people try to do, how people can to develop, say, predicates seems like a duck or play like butterfly or something like that. Not the teacher says you, how it came in his mind, how he choose this image. So that process... That is problem of intelligence. That is the problem of intelligence and you see that connected to the problem of learning? Absolutely. Because you immediately give this predicate like specific predicate seems like a duck or quack like a duck. It was chosen somehow. So what is the line of work, would you say? If you were to formulate as a set of open problems, that will take us there, to play like a butterfly. We'll get a system to be able to... Let's separate two stories. One mathematical story that if you have predicate, you can do something. And another story how to get predicate. It is intelligence problem and people even did not start to understand intelligence. Because to understand intelligence, first of all, try to understand what do teachers. How teacher teach, why one teacher better than another one. Yeah. And so you think we really even haven't started on the journey of generating the predicates? No. We don't understand. We even don't understand that this problem exists. Because did you hear... You do. No, I just know name. I want to understand why one teacher better than another and how affect teacher, student. It is not because he repeating the problem which is in textbook. He makes some remarks. He makes some philosophy of reasoning. Yeah, that's a beautiful... So it is a formulation of a question that is the open problem. Why is one teacher better than another? Right. What he does better. Yeah. What... What... Why in... At every level? How do they get better? What does it mean to be better? The whole... Yeah. Yeah. From whatever model I have, one teacher can give a very good predicate. One teacher can say swims like a dog and another can say jump like a dog. And jump like a dog carries zero information. So what is the most exciting problem in statistical learning you've ever worked on or are working on now? I just finished this invariant story and I'm happy that... I believe that it is ultimate learning story. At least I can show that there are no another mechanism, only two mechanisms. But they separate statistical part from intelligent part and I know nothing about intelligent part. And if you will know this intelligent part, so it will help us a lot in teaching, in learning. In learning. Yeah. You will know it when we see it? So for example, in my talk, the last slide was a challenge. So you have say NIST digit recognition problem and deep learning claims that they did it very well, say 99.5% of correct answers. But they use 60,000 observations. Can you do the same using hundred times less? But incorporating invariants, what it means, you know, digit one, two, three. But looking on that, explain to me which invariant I should keep to use hundred examples or say hundred times less examples to do the same job. Yeah, that last slide, unfortunately your talk ended quickly, but that last slide was a powerful open challenge and a formulation of the essence here. What is the exact problem of intelligence? Because everybody, when machine learning started and it was developed by mathematicians, they immediately recognized that we use much more training data than humans needed. But now again, we came to the same story, have to decrease. That is the problem of learning. It is not like in deep learning, they use zillions of training data because maybe zillions are not enough if you have a good invariants. Maybe you will never collect some number of observations. But now it is a question to intelligence, how to do that? Because statistical part is ready, as soon as you supply us with predicate, we can do good job with small amount of observations. And the very first challenge is well known digit recognition. And you know digits, and please tell me invariants. I think about that, I can say for digit three, I would introduce concept of horizontal symmetry. So the digit three has horizontal symmetry, say more than, say, digit two or something like that. But as soon as I get the idea of horizontal symmetry, I can mathematically invent a lot of measure of horizontal symmetry, or then vertical symmetry, or diagonal symmetry, whatever, if I have idea of symmetry. But what else? I think on digit I see that it is meta predicate, which is not shape, it is something like symmetry, like how dark is whole picture, something like that, which can self rise a predicate. You think such a predicate could rise out of something that is not general, meaning it feels like for me to be able to understand the difference between two and three, I would need to have had a childhood of 10 to 15 years playing with kids, going to school, being yelled by parents, all of that, walking, jumping, looking at ducks, and then I would be able to generate the right predicate for telling the difference between two and a three. Or do you think there's a more efficient way? I don't know. I know for sure that you must know something more than digits. Yes. And that's a powerful statement. Yeah. But maybe there are several languages of description, these elements of digits. So I'm talking about symmetry, about some properties of geometry, I'm talking about something abstract. I don't know that. But this is a problem of intelligence. So in one of our articles, it is trivial to show that every example can carry not more than one bit of information in real. Because when you show example and you say this is one, you can remove, say, a function which does not tell you one, say, is the best strategy. If you can do it perfectly, it's remove half of the functions. But when you use one predicate, which looks like a duck, you can remove much more functions than half. And that means that it contains a lot of bit of information from formal point of view. But when you have a general picture of what you want to recognize and general picture of the world, can you invent this predicate? And that predicate carries a lot of information. Beautifully put. Maybe just me, but in all the math you show, in your work, which is some of the most profound mathematical work in the field of learning AI and just math in general, I hear a lot of poetry and philosophy. You really kind of talk about philosophy of science. There's a poetry and music to a lot of the work you're doing and the way you're thinking about it. So do you, where does that come from? Do you escape to poetry? Do you escape to music or not? I think that there exists ground truth. There exists ground truth? Yeah. And that can be seen everywhere. The smart guy, philosopher, sometimes I'm surprised how they deep see. Sometimes I see that some of them are completely out of subject. But the ground truth I see in music. Music is the ground truth? Yeah. And in poetry, many poets, they believe, they take dictation. So what piece of music as a piece of empirical evidence gave you a sense that they are touching something in the ground truth? It is structure. The structure of the math of music. Yeah, because when you're listening to Bach, you see the structure. Very clear, very classic, very simple, and the same in math when you have axioms in geometry, you have the same feeling. And in poetry, sometimes you see the same. And if you look back at your childhood, you grew up in Russia, you maybe were born as a researcher in Russia, you've developed as a researcher in Russia, you've came to United States and a few places. If you look back, what was some of your happiest moments as a researcher, some of the most profound moments, not in terms of their impact on society, but in terms of their impact on how damn good you feel that day and you remember that moment? You know, every time when you found something, it is great in the life, every simple things. But my general feeling is that most of my time was wrong. You should go again and again and again and try to be honest in front of yourself, not to make interpretation, but try to understand that it's related to ground truth, it is not my blah, blah, blah interpretation and something like that. But you're allowed to get excited at the possibility of discovery. Oh yeah. You have to double check it. No, but how it's related to another ground truth, is it just temporary or it is for forever? You know, you always have a feeling when you found something, how big is that? So 20 years ago when we discovered statistical learning theory, nobody believed, except for one guy, Dudley from MIT, and then in 20 years it became fashion, and the same with support vector machines, that is kernel machines. So with support vector machines and learning theory, when you were working on it, you had a sense, you had a sense of the profundity of it, how this seems to be right, this seems to be powerful. Right. Absolutely. Immediately. I recognized that it will last forever, and now when I found this invariant story, I have a feeling that it is complete learning, because I have proof that there are no different mechanisms. You can have some cosmetic improvement you can do, but in terms of invariants, you need both invariants and statistical learning, and they should work together. But also I'm happy that we can formulate what is intelligence from that, and to separate from technical part, and that is completely different. Absolutely. Well, Vladimir, thank you so much for talking today. Thank you. It's an honor.

Vladimir Vapnik: Statistical Learning | Lex Fridman Podcast #5