Datasets:

shubhamg2208
/

lexicap

	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.