WEBVTT 00:00.000 --> 00:04.320 What difference between biological neural networks and artificial neural networks 00:04.320 --> 00:07.680 is most mysterious, captivating and profound for you? 00:11.120 --> 00:15.280 First of all, there's so much we don't know about biological neural networks, 00:15.280 --> 00:21.840 and that's very mysterious and captivating because maybe it holds the key to improving 00:21.840 --> 00:29.840 artificial neural networks. One of the things I studied recently is something that 00:29.840 --> 00:36.160 we don't know how biological neural networks do, but would be really useful for artificial ones, 00:37.120 --> 00:43.440 is the ability to do credit assignment through very long time spans. 00:44.080 --> 00:49.680 There are things that we can in principle do with artificial neural nets, but it's not very 00:49.680 --> 00:55.920 convenient and it's not biologically plausible. And this mismatch, I think this kind of mismatch, 00:55.920 --> 01:03.600 maybe an interesting thing to study, to A, understand better how brains might do these 01:03.600 --> 01:08.720 things because we don't have good corresponding theories with artificial neural nets, and B, 01:10.240 --> 01:19.040 maybe provide new ideas that we could explore about things that brain do differently and 01:19.040 --> 01:22.160 that we could incorporate in artificial neural nets. 01:22.160 --> 01:27.680 So let's break credit assignment up a little bit. So what? It's a beautifully technical term, 01:27.680 --> 01:34.560 but it could incorporate so many things. So is it more on the RNN memory side, 01:35.840 --> 01:39.760 thinking like that, or is it something about knowledge, building up common sense knowledge 01:39.760 --> 01:46.560 over time, or is it more in the reinforcement learning sense that you're picking up rewards 01:46.560 --> 01:50.080 over time for a particular to achieve a certain kind of goal? 01:50.080 --> 01:58.080 So I was thinking more about the first two meanings whereby we store all kinds of memories, 01:59.120 --> 02:09.680 episodic memories in our brain, which we can access later in order to help us both infer 02:10.560 --> 02:19.520 causes of things that we are observing now and assign credit to decisions or interpretations 02:19.520 --> 02:26.960 we came up with a while ago when those memories were stored. And then we can change the way we 02:26.960 --> 02:34.800 would have reacted or interpreted things in the past, and now that's credit assignment used for learning. 02:36.320 --> 02:43.760 So in which way do you think artificial neural networks, the current LSTM, 02:43.760 --> 02:52.240 the current architectures are not able to capture the presumably you're thinking of very long term? 02:52.240 --> 03:00.720 Yes. So current, the current nets are doing a fairly good jobs for sequences with dozens or say 03:00.720 --> 03:06.560 hundreds of time steps. And then it gets sort of harder and harder and depending on what you 03:06.560 --> 03:13.120 have to remember and so on as you consider longer durations. Whereas humans seem to be able to 03:13.120 --> 03:18.080 do credit assignment through essentially arbitrary times like I could remember something I did last 03:18.080 --> 03:23.360 year. And then now because I see some new evidence, I'm going to change my mind about 03:23.360 --> 03:29.040 the way I was thinking last year, and hopefully not do the same mistake again. 03:31.040 --> 03:36.800 I think a big part of that is probably forgetting. You're only remembering the really important 03:36.800 --> 03:43.680 things that's very efficient forgetting. Yes. So there's a selection of what we remember. 03:43.680 --> 03:49.120 And I think there are really cool connection to higher level cognitions here regarding 03:49.120 --> 03:55.760 consciousness, deciding and emotions. So deciding what comes to consciousness and what gets stored 03:55.760 --> 04:04.800 in memory, which are not trivial either. So you've been at the forefront there all along 04:04.800 --> 04:10.800 showing some of the amazing things that neural networks, deep neural networks can do in the 04:10.800 --> 04:16.560 field of artificial intelligence is just broadly in all kinds of applications. But we can talk 04:16.560 --> 04:23.200 about that forever. But what in your view, because we're thinking towards the future is the weakest 04:23.200 --> 04:29.120 aspect of the way deep neural networks represent the world. What is that? What is in your view 04:29.120 --> 04:41.200 is missing? So current state of the art neural nets trained on large quantities of images or texts 04:43.840 --> 04:49.760 have some level of understanding of what explains those data sets, but it's very 04:49.760 --> 05:01.440 basic. It's very low level. And it's not nearly as robust and abstract and general as our understanding. 05:02.960 --> 05:09.760 Okay, so that doesn't tell us how to fix things. But I think it encourages us to think about 05:09.760 --> 05:21.200 how we can maybe train our neural nets differently, so that they would focus, for example, on causal 05:21.200 --> 05:30.000 explanations, something that we don't do currently with neural net training. Also, one thing I'll 05:30.000 --> 05:37.920 talk about in my talk this afternoon is instead of learning separately from images and videos on 05:37.920 --> 05:45.600 one hand and from texts on the other hand, we need to do a better job of jointly learning about 05:45.600 --> 05:54.320 language and about the world to which it refers. So that, you know, both sides can help each other. 05:54.880 --> 06:02.480 We need to have good world models in our neural nets for them to really understand sentences 06:02.480 --> 06:10.000 which talk about what's going on in the world. And I think we need language input to help 06:10.640 --> 06:17.760 provide clues about what high level concepts like semantic concepts should be represented 06:17.760 --> 06:26.400 at the top levels of these neural nets. In fact, there is evidence that the purely unsupervised 06:26.400 --> 06:33.840 learning of representations doesn't give rise to high level representations that are as powerful 06:33.840 --> 06:40.320 as the ones we're getting from supervised learning. And so the clues we're getting just with the labels, 06:40.320 --> 06:46.960 not even sentences, is already very powerful. Do you think that's an architecture challenge 06:46.960 --> 06:55.920 or is it a data set challenge? Neither. I'm tempted to just end it there. 07:02.960 --> 07:06.800 Of course, data sets and architectures are something you want to always play with. But 07:06.800 --> 07:13.040 I think the crucial thing is more the training objectives, the training frameworks. For example, 07:13.040 --> 07:20.240 going from passive observation of data to more active agents, which 07:22.320 --> 07:27.280 learn by intervening in the world, the relationships between causes and effects, 07:28.480 --> 07:36.240 the sort of objective functions which could be important to allow the highest level 07:36.240 --> 07:44.000 of explanations to rise from the learning, which I don't think we have now. The kinds of 07:44.000 --> 07:50.320 objective functions which could be used to reward exploration, the right kind of exploration. So 07:50.320 --> 07:56.160 these kinds of questions are neither in the data set nor in the architecture, but more in 07:56.800 --> 08:03.920 how we learn under what objectives and so on. Yeah, that's a, I've heard you mention in several 08:03.920 --> 08:08.080 contexts, the idea of sort of the way children learn, they interact with objects in the world. 08:08.080 --> 08:15.040 And it seems fascinating because in some sense, except with some cases in reinforcement learning, 08:15.760 --> 08:23.600 that idea is not part of the learning process in artificial neural networks. It's almost like 08:24.320 --> 08:33.120 do you envision something like an objective function saying, you know what, if you poke this 08:33.120 --> 08:38.800 object in this kind of way, it would be really helpful for me to further, further learn. 08:39.920 --> 08:44.880 Sort of almost guiding some aspect of learning. Right, right, right. So I was talking to Rebecca 08:44.880 --> 08:54.240 Sachs just an hour ago and she was talking about lots and lots of evidence from infants seem to 08:54.240 --> 09:04.880 clearly pick what interests them in a directed way. And so they're not passive learners. 09:04.880 --> 09:11.680 They, they focus their attention on aspects of the world, which are most interesting, 09:11.680 --> 09:17.760 surprising in a non trivial way that makes them change their theories of the world. 09:17.760 --> 09:29.120 So that's a fascinating view of the future progress. But on a more maybe boring question, 09:30.000 --> 09:37.440 do you think going deeper and larger? So do you think just increasing the size of the things 09:37.440 --> 09:43.520 that have been increasing a lot in the past few years will, will also make significant progress? 09:43.520 --> 09:49.760 So some of the representational issues that you, you mentioned, they're kind of shallow 09:50.560 --> 09:54.880 in some sense. Oh, you mean in the sense of abstraction, 09:54.880 --> 09:59.040 abstract in the sense of abstraction, they're not getting some, I don't think that having 10:00.400 --> 10:05.520 more depth in the network in the sense of instead of 100 layers, we have 10,000 is going to solve 10:05.520 --> 10:13.120 our problem. You don't think so? Is that obvious to you? Yes. What is clear to me is that 10:13.120 --> 10:21.600 engineers and companies and labs, grad students will continue to tune architectures and explore 10:21.600 --> 10:27.520 all kinds of tweaks to make the current state of the art slightly ever slightly better. But 10:27.520 --> 10:31.840 I don't think that's going to be nearly enough. I think we need some fairly drastic changes in 10:31.840 --> 10:39.680 the way that we're considering learning to achieve the goal that these learners actually 10:39.680 --> 10:45.680 understand in a deep way the environment in which they are, you know, observing and acting. 10:46.480 --> 10:51.920 But I guess I was trying to ask a question that's more interesting than just more layers 10:53.040 --> 11:00.800 is basically once you figure out a way to learn through interacting, how many parameters does 11:00.800 --> 11:07.760 it take to store that information? So I think our brain is quite bigger than most neural networks. 11:07.760 --> 11:13.120 Right, right. Oh, I see what you mean. Oh, I'm with you there. So I agree that in order to 11:14.240 --> 11:19.760 build neural nets with the kind of broad knowledge of the world that typical adult humans have, 11:20.960 --> 11:24.880 probably the kind of computing power we have now is going to be insufficient. 11:25.600 --> 11:30.320 So the good news is there are hardware companies building neural net chips. And so 11:30.320 --> 11:39.280 it's going to get better. However, the good news in a way, which is also a bad news, is that even 11:39.280 --> 11:47.840 our state of the art deep learning methods fail to learn models that understand even very simple 11:47.840 --> 11:53.680 environments like some grid worlds that we have built. Even these fairly simple environments, 11:53.680 --> 11:57.120 I mean, of course, if you train them with enough examples, eventually they get it, 11:57.120 --> 12:05.200 but it's just like instead of what humans might need just dozens of examples, these things will 12:05.200 --> 12:12.720 need millions, right, for very, very, very simple tasks. And so I think there's an opportunity 12:13.520 --> 12:18.080 for academics who don't have the kind of computing power that say Google has 12:19.280 --> 12:25.360 to do really important and exciting research to advance the state of the art in training 12:25.360 --> 12:32.720 frameworks, learning models, agent learning in even simple environments that are synthetic, 12:33.440 --> 12:37.200 that seem trivial, but yet current machine learning fails on. 12:38.240 --> 12:48.240 We talked about priors and common sense knowledge. It seems like we humans take a lot of knowledge 12:48.240 --> 12:57.040 for granted. So what's your view of these priors of forming this broad view of the world, this 12:57.040 --> 13:02.560 accumulation of information, and how we can teach neural networks or learning systems to pick that 13:02.560 --> 13:10.880 knowledge up? So knowledge, you know, for a while, the artificial intelligence, maybe in the 80, 13:10.880 --> 13:16.880 like there's a time where knowledge representation, knowledge, acquisition, expert systems, I mean, 13:16.880 --> 13:24.080 though, the symbolic AI was a view, was an interesting problem set to solve. And it was kind 13:24.080 --> 13:29.440 of put on hold a little bit, it seems like because it doesn't work. It doesn't work. That's right. 13:29.440 --> 13:37.840 But that's right. But the goals of that remain important. Yes, remain important. And how do you 13:37.840 --> 13:45.920 think those goals can be addressed? Right. So first of all, I believe that one reason why the 13:45.920 --> 13:52.560 classical expert systems approach failed is because a lot of the knowledge we have, so you talked 13:52.560 --> 14:01.760 about common sense and tuition, there's a lot of knowledge like this, which is not consciously 14:01.760 --> 14:06.320 accessible. There are lots of decisions we're taking that we can't really explain, even if 14:06.320 --> 14:16.160 sometimes we make up a story. And that knowledge is also necessary for machines to take good 14:16.160 --> 14:22.320 decisions. And that knowledge is hard to codify in expert systems, rule based systems, and, you 14:22.320 --> 14:27.920 know, classical AI formalism. And there are other issues, of course, with the old AI, like, 14:29.680 --> 14:34.320 not really good ways of handling uncertainty, I would say something more subtle, 14:34.320 --> 14:40.480 which we understand better now, but I think still isn't enough in the minds of people. 14:41.360 --> 14:48.480 There's something really powerful that comes from distributed representations, the thing that really 14:49.120 --> 14:58.480 makes neural nets work so well. And it's hard to replicate that kind of power in a symbolic world. 14:58.480 --> 15:05.200 The knowledge in expert systems and so on is nicely decomposed into like a bunch of rules. 15:05.760 --> 15:11.280 Whereas if you think about a neural net, it's the opposite. You have this big blob of parameters 15:11.280 --> 15:16.480 which work intensely together to represent everything the network knows. And it's not 15:16.480 --> 15:22.880 sufficiently factorized. And so I think this is one of the weaknesses of current neural nets, 15:22.880 --> 15:30.080 that we have to take lessons from classical AI in order to bring in another kind of 15:30.080 --> 15:35.920 compositionality, which is common in language, for example, and in these rules. But that isn't 15:35.920 --> 15:45.040 so native to neural nets. And on that line of thinking, disentangled representations. Yes. So 15:46.320 --> 15:51.680 let me connect with disentangled representations. If you might, if you don't mind. Yes, exactly. 15:51.680 --> 15:58.080 Yeah. So for many years, I thought, and I still believe that it's really important that we come 15:58.080 --> 16:04.080 up with learning algorithms, either unsupervised or supervised, but reinforcement, whatever, 16:04.720 --> 16:11.600 that build representations in which the important factors, hopefully causal factors are nicely 16:11.600 --> 16:16.240 separated and easy to pick up from the representation. So that's the idea of disentangled 16:16.240 --> 16:22.560 representations. It says transfer the data into a space where everything becomes easy, we can maybe 16:22.560 --> 16:29.360 just learn with linear models about the things we care about. And I still think this is important, 16:29.360 --> 16:36.880 but I think this is missing out on a very important ingredient, which classical AI systems can remind 16:36.880 --> 16:41.920 us of. So let's say we have these disentangled representations, you still need to learn about 16:41.920 --> 16:47.120 the, the relationships between the variables, those high level semantic variables, they're not 16:47.120 --> 16:52.000 going to be independent. I mean, this is like too much of an assumption. They're going to have some 16:52.000 --> 16:56.400 interesting relationships that allow to predict things in the future to explain what happened in 16:56.400 --> 17:01.840 the past. The kind of knowledge about those relationships in a classical AI system is 17:01.840 --> 17:06.640 encoded in the rules, like a rule is just like a little piece of knowledge that says, oh, I have 17:06.640 --> 17:12.160 these two, three, four variables that are linked in this interesting way. Then I can say something 17:12.160 --> 17:17.280 about one or two of them given a couple of others, right? In addition to disentangling the, 17:18.880 --> 17:23.520 the elements of the representation, which are like the variables in a rule based system, 17:24.080 --> 17:33.200 you also need to disentangle the, the mechanisms that relate those variables to each other. 17:33.200 --> 17:37.760 So like the rules. So if the rules are neatly separated, like each rule is, you know, living 17:37.760 --> 17:44.960 on its own. And when I, I change a rule because I'm learning, it doesn't need to break other rules. 17:44.960 --> 17:49.280 Whereas current neural nets, for example, are very sensitive to what's called catastrophic 17:49.280 --> 17:54.800 forgetting, where after I've learned some things, and then they learn new things, they can destroy 17:54.800 --> 18:00.480 the old things that I had learned, right? If the knowledge was better factorized and, and 18:00.480 --> 18:08.240 and separated disentangled, then you would avoid a lot of that. Now you can't do this in the 18:08.880 --> 18:17.200 sensory domain, but my idea in like a pixel space, but, but my idea is that when you project the 18:17.200 --> 18:22.560 data in the right semantic space, it becomes possible to now represent this extra knowledge 18:23.440 --> 18:27.760 beyond the transformation from input to representations, which is how representations 18:27.760 --> 18:33.120 act on each other and predict the future and so on, in a way that can be neatly 18:34.560 --> 18:38.560 disentangled. So now it's the rules that are disentangled from each other and not just the 18:38.560 --> 18:43.680 variables that are disentangled from each other. And you draw distinction between semantic space 18:43.680 --> 18:48.400 and pixel, like, does there need to be an architectural difference? Well, yeah. So, so 18:48.400 --> 18:51.840 there's the sensory space like pixels, which where everything is entangled, 18:51.840 --> 18:58.000 and the information, like the variables are completely interdependent in very complicated 18:58.000 --> 19:03.760 ways. And also computation, like the, it's not just variables, it's also how they are 19:03.760 --> 19:10.240 related to each other is, is all intertwined. But, but I'm hypothesizing that in the right 19:10.240 --> 19:16.800 high level representation space, both the variables and how they relate to each other 19:16.800 --> 19:22.960 can be disentangled and that will provide a lot of generalization power. Generalization power. 19:22.960 --> 19:29.760 Yes. Distribution of the test set, it's assumed to be the same as a distribution of the training 19:29.760 --> 19:36.640 set. Right. This is where current machine learning is too weak. It doesn't tell us anything, 19:36.640 --> 19:41.120 is not able to tell us anything about how our neural nets, say, are going to generalize to a 19:41.120 --> 19:46.160 new distribution. And, and, you know, people may think, well, but there's nothing we can say if 19:46.160 --> 19:51.840 we don't know what the new distribution will be. The truth is, humans are able to generalize to 19:51.840 --> 19:56.560 new distributions. Yeah, how are we able to do that? So yeah, because there is something, these 19:56.560 --> 20:00.720 new distributions, even though they could look very different from the training distributions, 20:01.520 --> 20:05.360 they have things in common. So let me give you a concrete example. You read a science fiction 20:05.360 --> 20:12.560 novel, the science fiction novel, maybe, you know, brings you in some other planet where 20:12.560 --> 20:17.760 things look very different on the surface, but it's still the same laws of physics. 20:18.560 --> 20:21.440 All right. And so you can read the book and you understand what's going on. 20:22.960 --> 20:29.200 So the distribution is very different. But because you can transport a lot of the knowledge you had 20:29.200 --> 20:35.680 from Earth about the underlying cause and effect relationships and physical mechanisms and all 20:35.680 --> 20:40.880 that, and maybe even social interactions, you can now make sense of what is going on on this 20:40.880 --> 20:43.920 planet where like visually, for example, things are totally different. 20:45.920 --> 20:52.000 Taking that analogy further and distorting it, let's enter a science fiction world of, say, 20:52.000 --> 21:00.720 Space Odyssey 2001 with Hal. Yeah. Or maybe, which is probably one of my favorite AI movies. 21:00.720 --> 21:06.080 Me too. And then there's another one that a lot of people love that may be a little bit outside 21:06.080 --> 21:13.120 of the AI community is Ex Machina. I don't know if you've seen it. Yes. By the way, what are your 21:13.120 --> 21:19.600 reviews on that movie? Are you able to enjoy it? So there are things I like and things I hate. 21:21.120 --> 21:25.760 So let me, you could talk about that in the context of a question I want to ask, 21:25.760 --> 21:31.920 which is there's quite a large community of people from different backgrounds off and outside of AI 21:31.920 --> 21:36.480 who are concerned about existential threat of artificial intelligence. Right. You've seen 21:36.480 --> 21:41.920 now this community develop over time. You've seen you have a perspective. So what do you think is 21:41.920 --> 21:47.680 the best way to talk about AI safety, to think about it, to have discourse about it within AI 21:47.680 --> 21:53.920 community and outside and grounded in the fact that Ex Machina is one of the main sources of 21:53.920 --> 21:59.040 information for the general public about AI. So I think you're putting it right. There's a big 21:59.040 --> 22:04.400 difference between the sort of discussion we ought to have within the AI community 22:05.200 --> 22:11.600 and the sort of discussion that really matter in the general public. So I think the picture of 22:11.600 --> 22:19.040 Terminator and, you know, AI loose and killing people and super intelligence that's going to 22:19.040 --> 22:26.320 destroy us, whatever we try, isn't really so useful for the public discussion because 22:26.320 --> 22:32.960 for the public discussion that things I believe really matter are the short term and 22:32.960 --> 22:40.560 mini term, very likely negative impacts of AI on society, whether it's from security, 22:40.560 --> 22:45.680 like, you know, big brother scenarios with face recognition or killer robots, or the impact on 22:45.680 --> 22:52.400 the job market, or concentration of power and discrimination, all kinds of social issues, 22:52.400 --> 22:58.240 which could actually, some of them could really threaten democracy, for example. 22:58.800 --> 23:04.000 Just to clarify, when you said killer robots, you mean autonomous weapons as a weapon system? 23:04.000 --> 23:10.400 Yes, I don't mean, no, that's right. So I think these short and medium term concerns 23:11.280 --> 23:18.560 should be important parts of the public debate. Now, existential risk, for me, is a very unlikely 23:18.560 --> 23:26.880 consideration, but still worth academic investigation. In the same way that you could say, 23:26.880 --> 23:32.640 should we study what could happen if meteorite, you know, came to earth and destroyed it. 23:32.640 --> 23:37.680 So I think it's very unlikely that this is going to happen in or happen in a reasonable future. 23:37.680 --> 23:45.520 It's very, the sort of scenario of an AI getting loose goes against my understanding of at least 23:45.520 --> 23:50.160 current machine learning and current neural nets and so on. It's not plausible to me. 23:50.160 --> 23:54.320 But of course, I don't have a crystal ball and who knows what AI will be in 50 years from now. 23:54.320 --> 23:59.280 So I think it is worth that scientists study those problems. It's just not a pressing question, 23:59.280 --> 24:04.880 as far as I'm concerned. So before I continue down that line, I have a few questions there, but 24:06.640 --> 24:11.440 what do you like and not like about X Machina as a movie? Because I actually watched it for the 24:11.440 --> 24:17.840 second time and enjoyed it. I hated it the first time and I enjoyed it quite a bit more the second 24:17.840 --> 24:26.080 time when I sort of learned to accept certain pieces of it. See it as a concept movie. What 24:26.080 --> 24:36.160 was your experience? What were your thoughts? So the negative is the picture it paints of science 24:36.160 --> 24:41.760 is totally wrong. Science in general and AI in particular. Science is not happening 24:43.120 --> 24:51.840 in some hidden place by some really smart guy. One person. One person. This is totally unrealistic. 24:51.840 --> 24:58.240 This is not how it happens. Even a team of people in some isolated place will not make it. 24:58.240 --> 25:07.920 Science moves by small steps thanks to the collaboration and community of a large number 25:07.920 --> 25:16.000 of people interacting and all the scientists who are expert in their field kind of know what is 25:16.000 --> 25:24.000 going on even in the industrial labs. Information flows and leaks and so on. And the spirit of 25:24.000 --> 25:30.320 it is very different from the way science is painted in this movie. Yeah, let me ask on that 25:30.320 --> 25:36.400 point. It's been the case to this point that kind of even if the research happens inside 25:36.400 --> 25:42.000 Google or Facebook, inside companies, it still kind of comes out. Do you think that will always be 25:42.000 --> 25:48.960 the case with AI? Is it possible to bottle ideas to the point where there's a set of breakthroughs 25:48.960 --> 25:53.120 that go completely undiscovered by the general research community? Do you think that's even 25:53.120 --> 26:02.240 possible? It's possible, but it's unlikely. It's not how it is done now. It's not how I can force 26:02.240 --> 26:13.120 it in in the foreseeable future. But of course, I don't have a crystal ball. And so who knows, 26:13.120 --> 26:18.240 this is science fiction after all. But but usually ominous that the lights went off during 26:18.240 --> 26:24.320 during that discussion. So the problem again, there's a you know, one thing is the movie and 26:24.320 --> 26:28.720 you could imagine all kinds of science fiction. The problem with for me, maybe similar to the 26:28.720 --> 26:37.120 question about existential risk is that this kind of movie paints such a wrong picture of what is 26:37.120 --> 26:43.520 actual, you know, the actual science and how it's going on that that it can have unfortunate effects 26:43.520 --> 26:49.040 on people's understanding of current science. And so that's kind of sad. 26:50.560 --> 26:56.800 There's an important principle in research, which is diversity. So in other words, 26:58.000 --> 27:02.720 research is exploration, research is exploration in the space of ideas. And different people 27:03.440 --> 27:09.920 will focus on different directions. And this is not just good, it's essential. So I'm totally fine 27:09.920 --> 27:16.640 with people exploring directions that are contrary to mine or look orthogonal to mine. 27:18.560 --> 27:24.880 I am more than fine, I think it's important. I and my friends don't claim we have universal 27:24.880 --> 27:29.680 truth about what will especially about what will happen in the future. Now that being said, 27:30.320 --> 27:37.600 we have our intuitions and then we act accordingly, according to where we think we can be most useful 27:37.600 --> 27:43.360 and where society has the most to gain or to lose. We should have those debates and 27:45.920 --> 27:50.080 and not end up in a society where there's only one voice and one way of thinking and 27:51.360 --> 27:59.120 research money is spread out. So this agreement is a sign of good research, good science. So 27:59.120 --> 28:08.560 yes. The idea of bias in the human sense of bias. How do you think about instilling in machine 28:08.560 --> 28:15.440 learning something that's aligned with human values in terms of bias? We intuitively assume 28:15.440 --> 28:21.680 beings have a concept of what bias means, of what fundamental respect for other human beings means, 28:21.680 --> 28:25.280 but how do we instill that into machine learning systems, do you think? 28:25.280 --> 28:32.720 So I think there are short term things that are already happening and then there are long term 28:32.720 --> 28:39.040 things that we need to do. In the short term, there are techniques that have been proposed and 28:39.040 --> 28:44.800 I think will continue to be improved and maybe alternatives will come up to take data sets 28:45.600 --> 28:51.200 in which we know there is bias, we can measure it. Pretty much any data set where humans are 28:51.200 --> 28:56.080 being observed taking decisions will have some sort of bias discrimination against particular 28:56.080 --> 29:04.000 groups and so on. And we can use machine learning techniques to try to build predictors, classifiers 29:04.000 --> 29:11.920 that are going to be less biased. We can do it for example using adversarial methods to make our 29:11.920 --> 29:19.520 systems less sensitive to these variables we should not be sensitive to. So these are clear, 29:19.520 --> 29:24.240 well defined ways of trying to address the problem, maybe they have weaknesses and more 29:24.240 --> 29:30.400 research is needed and so on, but I think in fact they're sufficiently mature that governments should 29:30.400 --> 29:36.160 start regulating companies where it matters say like insurance companies so that they use those 29:36.160 --> 29:43.840 techniques because those techniques will probably reduce the bias, but at a cost for example maybe 29:43.840 --> 29:47.920 their predictions will be less accurate and so companies will not do it until you force them. 29:47.920 --> 29:56.000 All right, so this is short term. Long term, I'm really interested in thinking how we can 29:56.000 --> 30:02.160 instill moral values into computers. Obviously this is not something we'll achieve in the next five 30:02.160 --> 30:11.680 or 10 years. There's already work in detecting emotions for example in images and sounds and 30:11.680 --> 30:21.520 texts and also studying how different agents interacting in different ways may correspond to 30:22.960 --> 30:30.000 patterns of say injustice which could trigger anger. So these are things we can do in the 30:30.000 --> 30:42.160 medium term and eventually train computers to model for example how humans react emotionally. I would 30:42.160 --> 30:49.920 say the simplest thing is unfair situations which trigger anger. This is one of the most basic 30:49.920 --> 30:55.360 emotions that we share with other animals. I think it's quite feasible within the next few years so 30:55.360 --> 31:00.800 we can build systems that can detect these kind of things to the extent unfortunately that they 31:00.800 --> 31:07.840 understand enough about the world around us which is a long time away but maybe we can initially do 31:07.840 --> 31:14.800 this in virtual environments so you can imagine like a video game where agents interact in some 31:14.800 --> 31:21.760 ways and then some situations trigger an emotion. I think we could train machines to detect those 31:21.760 --> 31:27.920 situations and predict that the particular emotion will likely be felt if a human was playing one 31:27.920 --> 31:34.080 of the characters. You have shown excitement and done a lot of excellent work with unsupervised 31:34.080 --> 31:42.800 learning but there's been a lot of success on the supervised learning. One of the things I'm 31:42.800 --> 31:48.800 really passionate about is how humans and robots work together and in the context of supervised 31:48.800 --> 31:54.800 learning that means the process of annotation. Do you think about the problem of annotation of 31:55.520 --> 32:04.080 put in a more interesting way is humans teaching machines? Yes, I think it's an important subject. 32:04.880 --> 32:11.280 Reducing it to annotation may be useful for somebody building a system tomorrow but 32:12.560 --> 32:17.600 longer term the process of teaching I think is something that deserves a lot more attention 32:17.600 --> 32:21.840 from the machine learning community so there are people of coin the term machine teaching. 32:22.560 --> 32:30.480 So what are good strategies for teaching a learning agent and can we design, train a system 32:30.480 --> 32:38.000 that is going to be a good teacher? So in my group we have a project called a BBI or BBI game 32:38.640 --> 32:46.000 where there is a game or a scenario where there's a learning agent and a teaching agent 32:46.000 --> 32:54.400 presumably the teaching agent would eventually be a human but we're not there yet and the 32:56.000 --> 33:00.880 role of the teacher is to use its knowledge of the environment which it can acquire using 33:00.880 --> 33:09.680 whatever way brute force to help the learner learn as quickly as possible. So the learner 33:09.680 --> 33:13.920 is going to try to learn by itself maybe using some exploration and whatever 33:13.920 --> 33:21.520 but the teacher can choose, can have an influence on the interaction with the learner 33:21.520 --> 33:28.960 so as to guide the learner maybe teach it the things that the learner has most trouble with 33:28.960 --> 33:34.320 or just add the boundary between what it knows and doesn't know and so on. So there's a tradition 33:34.320 --> 33:41.280 of these kind of ideas from other fields and like tutorial systems for example and AI 33:41.280 --> 33:46.880 and of course people in the humanities have been thinking about these questions but I think 33:46.880 --> 33:52.560 it's time that machine learning people look at this because in the future we'll have more and more 33:53.760 --> 33:59.680 human machine interaction with the human in the loop and I think understanding how to make this 33:59.680 --> 34:04.080 work better. Oh the problems around that are very interesting and not sufficiently addressed. 34:04.080 --> 34:11.440 You've done a lot of work with language too, what aspect of the traditionally formulated 34:11.440 --> 34:17.040 touring test, a test of natural language understanding in generation in your eyes is the 34:17.040 --> 34:22.960 most difficult of conversation, what in your eyes is the hardest part of conversation to solve for 34:22.960 --> 34:30.640 machines. So I would say it's everything having to do with the non linguistic knowledge which 34:30.640 --> 34:36.400 implicitly you need in order to make sense of sentences. Things like the winner grad schemas 34:36.400 --> 34:42.400 so these sentences that are semantically ambiguous. In other words you need to understand enough about 34:42.400 --> 34:48.720 the world in order to really interpret properly those sentences. I think these are interesting 34:48.720 --> 34:55.840 challenges for machine learning because they point in the direction of building systems that 34:55.840 --> 35:02.880 both understand how the world works and there's causal relationships in the world and associate 35:03.520 --> 35:09.760 that knowledge with how to express it in language either for reading or writing. 35:11.840 --> 35:17.600 You speak French? Yes, it's my mother tongue. It's one of the romance languages. Do you think 35:17.600 --> 35:23.040 passing the touring test and all the underlying challenges we just mentioned depend on language? 35:23.040 --> 35:28.000 Do you think it might be easier in French than it is in English or is independent of language? 35:28.800 --> 35:37.680 I think it's independent of language. I would like to build systems that can use the same 35:37.680 --> 35:45.840 principles, the same learning mechanisms to learn from human agents, whatever their language. 35:45.840 --> 35:53.600 Well, certainly us humans can talk more beautifully and smoothly in poetry. So I'm Russian originally. 35:53.600 --> 36:01.360 I know poetry in Russian is maybe easier to convey complex ideas than it is in English 36:02.320 --> 36:09.520 but maybe I'm showing my bias and some people could say that about French. But of course the 36:09.520 --> 36:16.400 goal ultimately is our human brain is able to utilize any kind of those languages to use them 36:16.400 --> 36:21.040 as tools to convey meaning. Yeah, of course there are differences between languages and maybe some 36:21.040 --> 36:25.920 are slightly better at some things but in the grand scheme of things where we're trying to understand 36:25.920 --> 36:31.040 how the brain works and language and so on, I think these differences are minute. 36:31.040 --> 36:42.880 So you've lived perhaps through an AI winter of sorts. Yes. How did you stay warm and continue 36:42.880 --> 36:48.480 with your research? Stay warm with friends. With friends. Okay, so it's important to have friends 36:48.480 --> 36:57.200 and what have you learned from the experience? Listen to your inner voice. Don't, you know, be 36:57.200 --> 37:07.680 trying to just please the crowds and the fashion and if you have a strong intuition about something 37:08.480 --> 37:15.520 that is not contradicted by actual evidence, go for it. I mean, it could be contradicted by people. 37:16.960 --> 37:21.920 Not your own instinct of based on everything you've learned. So of course you have to adapt 37:21.920 --> 37:29.440 your beliefs when your experiments contradict those beliefs but you have to stick to your 37:29.440 --> 37:36.160 beliefs otherwise. It's what allowed me to go through those years. It's what allowed me to 37:37.120 --> 37:44.480 persist in directions that, you know, took time, whatever other people think, took time to mature 37:44.480 --> 37:53.680 and bring fruits. So history of AI is marked with these, of course it's marked with technical 37:53.680 --> 37:58.880 breakthroughs but it's also marked with these seminal events that capture the imagination 37:58.880 --> 38:06.000 of the community. Most recent, I would say AlphaGo beating the world champion human go player 38:06.000 --> 38:14.000 was one of those moments. What do you think the next such moment might be? Okay, sir, first of all, 38:14.000 --> 38:24.880 I think that these so called seminal events are overrated. As I said, science really moves by 38:24.880 --> 38:33.760 small steps. Now what happens is you make one more small step and it's like the drop that, 38:33.760 --> 38:40.560 you know, allows to, that fills the bucket and then you have drastic consequences because now 38:40.560 --> 38:46.240 you're able to do something you were not able to do before or now say the cost of building some 38:46.240 --> 38:51.920 device or solving a problem becomes cheaper than what existed and you have a new market that opens 38:51.920 --> 39:00.080 up. So especially in the world of commerce and applications, the impact of a small scientific 39:00.080 --> 39:07.520 progress could be huge but in the science itself, I think it's very, very gradual and 39:07.520 --> 39:15.280 where are these steps being taken now? So there's unsupervised, right? So if I look at one trend 39:15.280 --> 39:24.080 that I like in my community, for example, and at me line, my institute, what are the two hardest 39:24.080 --> 39:32.800 topics? GANs and reinforcement learning, even though in Montreal in particular, like reinforcement 39:32.800 --> 39:39.600 learning was something pretty much absent just two or three years ago. So it is really a big 39:39.600 --> 39:48.400 interest from students and there's a big interest from people like me. So I would say this is 39:48.400 --> 39:54.960 something where we're going to see more progress even though it hasn't yet provided much in terms of 39:54.960 --> 40:01.280 actual industrial fallout. Like even though there's Alpha Gold, there's no, like Google is not making 40:01.280 --> 40:06.320 money on this right now. But I think over the long term, this is really, really important for many 40:06.320 --> 40:13.760 reasons. So in other words, I would say reinforcement learning maybe more generally agent learning 40:13.760 --> 40:17.520 because it doesn't have to be with rewards. It could be in all kinds of ways that an agent 40:17.520 --> 40:23.040 is learning about its environment. Now, reinforcement learning, you're excited about. Do you think 40:23.040 --> 40:32.320 GANs could provide something? Yes. Some moment in it. Well, GANs or other 40:33.760 --> 40:41.360 generative models, I believe, will be crucial ingredients in building agents that can understand 40:41.360 --> 40:48.880 the world. A lot of the successes in reinforcement learning in the past has been with policy 40:48.880 --> 40:53.360 gradient where you'll just learn a policy. You don't actually learn a model of the world. But 40:53.360 --> 40:58.640 there are lots of issues with that. And we don't know how to do model based RL right now. But I 40:58.640 --> 41:06.080 think this is where we have to go in order to build models that can generalize faster and better, 41:06.080 --> 41:13.200 like to new distributions that capture, to some extent, at least the underlying causal 41:13.200 --> 41:20.320 mechanisms in the world. Last question. What made you fall in love with artificial intelligence? 41:20.960 --> 41:28.400 If you look back, what was the first moment in your life when you were fascinated by either 41:28.400 --> 41:33.600 the human mind or the artificial mind? You know, when I was an adolescent, I was reading a lot. 41:33.600 --> 41:41.920 And then I started reading science fiction. There you go. That's it. That's where I got hooked. 41:41.920 --> 41:50.160 And then, you know, I had one of the first personal computers and I got hooked in programming. 41:50.960 --> 41:55.040 And so it just, you know, start with fiction and then make it a reality. That's right. 41:55.040 --> 42:12.080 Yosha, thank you so much for talking to me. My pleasure.