Datasets:

shubhamg2208
/

lexicap

	WEBVTT

	00:00.000 --> 00:03.720
	The following is a conversation with Francois Chalet.

	00:03.720 --> 00:07.300
	He's the creator of Keras, which is an open source deep learning

	00:07.300 --> 00:10.560
	library that is designed to enable fast, user friendly

	00:10.560 --> 00:13.600
	experimentation with deep neural networks.

	00:13.600 --> 00:16.680
	It serves as an interface to several deep learning libraries,

	00:16.680 --> 00:19.040
	most popular of which is TensorFlow.

	00:19.040 --> 00:22.600
	And it was integrated into the TensorFlow main code base

	00:22.600 --> 00:24.120
	a while ago.

	00:24.120 --> 00:27.560
	Meaning, if you want to create, train, and use

	00:27.560 --> 00:31.040
	neural networks, probably the easiest and most popular option

	00:31.040 --> 00:34.840
	is to use Keras inside TensorFlow.

	00:34.840 --> 00:37.760
	Aside from creating an exceptionally useful and popular

	00:37.760 --> 00:41.920
	library, Francois is also a world class AI researcher

	00:41.920 --> 00:44.560
	and software engineer at Google.

	00:44.560 --> 00:48.080
	And he's definitely an outspoken, if not controversial,

	00:48.080 --> 00:51.480
	personality in the AI world, especially

	00:51.480 --> 00:53.720
	in the realm of ideas around the future

	00:53.720 --> 00:55.920
	of artificial intelligence.

	00:55.920 --> 00:58.600
	This is the Artificial Intelligence Podcast.

	00:58.600 --> 01:01.000
	If you enjoy it, subscribe on YouTube,

	01:01.000 --> 01:04.160
	give us five stars on iTunes, support on Patreon,

	01:04.160 --> 01:06.080
	or simply connect with me on Twitter

	01:06.080 --> 01:09.960
	at Lex Freedman, spelled F R I D M A N.

	01:09.960 --> 01:14.880
	And now, here's my conversation with Francois Chalet.

	01:14.880 --> 01:17.320
	You're known for not sugarcoating your opinions

	01:17.320 --> 01:19.640
	and speaking your mind about ideas in AI, especially

	01:19.640 --> 01:21.120
	on Twitter.

	01:21.120 --> 01:22.800
	That's one of my favorite Twitter accounts.

	01:22.800 --> 01:26.360
	So what's one of the more controversial ideas

	01:26.360 --> 01:30.440
	you've expressed online and gotten some heat for?

	01:30.440 --> 01:33.080
	How do you pick?

	01:33.080 --> 01:33.920
	How do I pick?

	01:33.920 --> 01:38.280
	Yeah, no, I think if you go through the trouble of maintaining

	01:38.280 --> 01:41.880
	Twitter accounts, you might as well speak your mind.

	01:41.880 --> 01:44.640
	Otherwise, what's even the point of doing Twitter accounts,

	01:44.640 --> 01:48.600
	like getting an eye scar and just leaving it in the garage?

	01:48.600 --> 01:50.360
	Yeah, so that's one thing for which

	01:50.360 --> 01:53.640
	I got a lot of pushback.

	01:53.640 --> 01:56.720
	Perhaps that time, I wrote something

	01:56.720 --> 02:00.960
	about the idea of intelligence explosion.

	02:00.960 --> 02:05.680
	And I was questioning the idea and the reasoning behind this

	02:05.680 --> 02:06.880
	idea.

	02:06.880 --> 02:09.720
	And I got a lot of pushback on that.

	02:09.720 --> 02:11.840
	I got a lot of flak for it.

	02:11.840 --> 02:14.360
	So yeah, so intelligence explosion, I'm sure you're familiar

	02:14.360 --> 02:15.800
	with the idea, but it's the idea

	02:15.800 --> 02:21.360
	that if you were to build general AI problems

	02:21.360 --> 02:27.600
	solving algorithms, well, the problem of building such an AI,

	02:27.600 --> 02:30.640
	that itself is a problem that could be solved by your AI.

	02:30.640 --> 02:33.840
	And maybe it could be solved better than what humans can do.

	02:33.840 --> 02:36.920
	So your AI could start tweaking its own algorithm,

	02:36.920 --> 02:39.640
	could start making a better version of itself.

	02:39.640 --> 02:43.320
	And so on, iteratively, in a recursive fashion,

	02:43.320 --> 02:47.360
	and so you would end up with an AI

	02:47.360 --> 02:50.920
	with exponentially increasing intelligence.

	02:50.920 --> 02:55.880
	And I was basically questioning this idea.

	02:55.880 --> 02:59.080
	First of all, because the notion of intelligence explosion

	02:59.080 --> 03:02.240
	uses an implicit definition of intelligence

	03:02.240 --> 03:05.400
	that doesn't sound quite right to me.

	03:05.400 --> 03:11.200
	It considers intelligence as a property of a brain

	03:11.200 --> 03:13.680
	that you can consider in isolation,

	03:13.680 --> 03:16.640
	like the height of a building, for instance.

	03:16.640 --> 03:19.040
	But that's not really what intelligence is.

	03:19.040 --> 03:22.200
	Intelligence emerges from the interaction

	03:22.200 --> 03:26.720
	between a brain, a body, like embodied intelligence,

	03:26.720 --> 03:28.320
	and an environment.

	03:28.320 --> 03:30.720
	And if you're missing one of these pieces,

	03:30.720 --> 03:33.840
	then you cannot really define intelligence anymore.

	03:33.840 --> 03:36.800
	So just tweaking a brain to make it smaller and smaller

	03:36.800 --> 03:39.120
	doesn't actually make any sense to me.

	03:39.120 --> 03:43.000
	So first of all, you're crushing the dreams of many people.

	03:43.000 --> 03:46.000
	So let's look at Sam Harris.

	03:46.000 --> 03:48.680
	Actually, a lot of physicists, Max Tegmark,

	03:48.680 --> 03:53.600
	people who think the universe is an information processing

	03:53.600 --> 03:54.640
	system.

	03:54.640 --> 03:57.680
	Our brain is kind of an information processing system.

	03:57.680 --> 04:00.040
	So what's the theoretical limit?

	04:00.040 --> 04:04.840
	It doesn't make sense that there should be some,

	04:04.840 --> 04:08.080
	it seems naive to think that our own brain is somehow

	04:08.080 --> 04:11.600
	the limit of the capabilities and this information.

	04:11.600 --> 04:13.600
	I'm playing devil's advocate here.

	04:13.600 --> 04:15.600
	This information processing system.

	04:15.600 --> 04:18.040
	And then if you just scale it, if you're

	04:18.040 --> 04:20.880
	able to build something that's on par with the brain,

	04:20.880 --> 04:24.000
	you just, the process that builds it just continues

	04:24.000 --> 04:26.360
	and it will improve exponentially.

	04:26.360 --> 04:30.120
	So that's the logic that's used actually

	04:30.120 --> 04:33.920
	by almost everybody that is worried

	04:33.920 --> 04:36.880
	about super human intelligence.

	04:36.880 --> 04:39.800
	Yeah, so you're trying to make, so most people

	04:39.800 --> 04:42.320
	who are skeptical of that are kind of like,

	04:42.320 --> 04:44.360
	this doesn't, their thought process,

	04:44.360 --> 04:46.520
	this doesn't feel right.

	04:46.520 --> 04:47.680
	Like that's for me as well.

	04:47.680 --> 04:52.320
	So I'm more like, it doesn't, the whole thing is shrouded

	04:52.320 --> 04:55.840
	in mystery where you can't really say anything concrete,

	04:55.840 --> 04:57.880
	but you could say this doesn't feel right.

	04:57.880 --> 05:00.680
	This doesn't feel like that's how the brain works.

	05:00.680 --> 05:02.400
	And you're trying to, with your blog post

	05:02.400 --> 05:05.680
	and now making it a little more explicit.

	05:05.680 --> 05:10.280
	So one idea is that the brain isn't,

	05:10.280 --> 05:13.840
	exists alone, it exists within the environment.

	05:13.840 --> 05:17.520
	So you can't exponentially, you would have to somehow

	05:17.520 --> 05:19.360
	exponentially improve the environment

	05:19.360 --> 05:22.280
	and the brain together, almost yet in order

	05:22.280 --> 05:26.280
	to create something that's much smarter

	05:26.280 --> 05:29.120
	in some kind of, of course we don't have

	05:29.120 --> 05:30.560
	a definition of intelligence.

	05:30.560 --> 05:31.880
	That's correct, that's correct.

	05:31.880 --> 05:34.560
	I don't think, you should look at very smart people

	05:34.560 --> 05:37.840
	to the even humans, not even talking about AI's.

	05:37.840 --> 05:40.000
	I don't think their brain and the performance

	05:40.000 --> 05:42.520
	of their brain is the bottleneck

	05:42.520 --> 05:45.760
	to their expressed intelligence, to their achievements.

	05:47.160 --> 05:50.480
	You cannot just tweak one part of this system,

	05:50.480 --> 05:53.360
	like of this brain, body, environment system

	05:53.360 --> 05:56.480
	and expect the capabilities, like what emerges

	05:56.480 --> 05:59.000
	out of this system to just, you know,

	05:59.000 --> 06:00.800
	explode exponentially.

	06:00.800 --> 06:04.720
	Because anytime you improve one part of a system

	06:04.720 --> 06:07.280
	with many interdependencies like this,

	06:07.280 --> 06:09.520
	there's a new bottleneck that arises, right?

	06:09.520 --> 06:12.280
	And I don't think even today for very smart people,

	06:12.280 --> 06:14.960
	their brain is not the bottleneck

	06:14.960 --> 06:17.560
	to the sort of problems they can solve, right?

	06:17.560 --> 06:21.480
	In fact, many very smart people today, you know,

	06:21.480 --> 06:23.760
	they're not actually solving any big scientific problems.

	06:23.760 --> 06:24.800
	They're not Einstein.

	06:24.800 --> 06:26.560
	They're like Einstein, but, you know,

	06:26.560 --> 06:28.280
	the patent clerk days.

	06:28.280 --> 06:31.920
	Like Einstein became Einstein

	06:31.920 --> 06:36.080
	because this was a meeting of a genius

	06:36.080 --> 06:39.480
	with a big problem at the right time, right?

	06:39.480 --> 06:42.480
	But maybe this meeting could have never happened

	06:42.480 --> 06:44.960
	and then Einstein, there's just been a patent clerk, right?

	06:44.960 --> 06:48.400
	And in fact, many people today are probably like

	06:49.760 --> 06:52.240
	genius level smart, but you wouldn't know

	06:52.240 --> 06:54.800
	because they're not really expressing any of that.

	06:54.800 --> 06:58.520
	Well, that's brilliant. So we can think of the world, earth,

	06:58.520 --> 07:02.720
	but also the universe as just, as a space of problems.

	07:02.720 --> 07:05.160
	So all of these problems and tasks are roaming it

	07:05.160 --> 07:06.880
	of various difficulty.

	07:06.880 --> 07:10.120
	And there's agents, creatures like ourselves

	07:10.120 --> 07:13.360
	and animals and so on that are also roaming it.

	07:13.360 --> 07:16.480
	And then you get coupled with a problem

	07:16.480 --> 07:17.640
	and then you solve it.

	07:17.640 --> 07:19.880
	But without that coupling,

	07:19.880 --> 07:22.560
	you can't demonstrate your quote unquote intelligence.

	07:22.560 --> 07:25.440
	Yeah, exactly. Intelligence is the meaning of

	07:25.440 --> 07:28.760
	great problem solving capabilities with a great problem.

	07:28.760 --> 07:30.560
	And if you don't have the problem,

	07:30.560 --> 07:32.280
	you don't really express in intelligence.

	07:32.280 --> 07:34.760
	All you're left with is potential intelligence,

	07:34.760 --> 07:36.920
	like the performance of your brain or, you know,

	07:36.920 --> 07:41.920
	how high your IQ is, which in itself is just a number, right?

	07:42.080 --> 07:46.520
	So you mentioned problem solving capacity.

	07:46.520 --> 07:47.360
	Yeah.

	07:47.360 --> 07:51.040
	What do you think of as problem solving capacity?

	07:51.040 --> 07:55.200
	What, can you try to define intelligence?

	07:56.680 --> 08:00.040
	Like, what does it mean to be more or less intelligent?

	08:00.040 --> 08:03.040
	Is it completely coupled to a particular problem?

	08:03.040 --> 08:05.760
	Or is there something a little bit more universal?

	08:05.760 --> 08:07.480
	Yeah, I do believe all intelligence

	08:07.480 --> 08:09.120
	is specialized intelligence.

	08:09.120 --> 08:12.280
	Even human intelligence has some degree of generality.

	08:12.280 --> 08:15.400
	Well, all intelligence systems have some degree of generality,

	08:15.400 --> 08:19.480
	but they're always specialized in one category of problems.

	08:19.480 --> 08:21.920
	So the human intelligence is specialized

	08:21.920 --> 08:25.560
	in the human experience and that shows at various levels,

	08:25.560 --> 08:29.320
	that shows in some prior knowledge,

	08:29.320 --> 08:32.040
	that's innate, that we have at birth,

	08:32.040 --> 08:35.360
	knowledge about things like agents,

	08:35.360 --> 08:40.440
	goal driven behavior, visual priors about what makes an object,

	08:40.440 --> 08:43.520
	priors about time, and so on.

	08:43.520 --> 08:45.360
	That shows also in the way we learn,

	08:45.360 --> 08:48.920
	for instance, it's very easy for us to pick up language,

	08:48.920 --> 08:52.080
	it's very, very easy for us to learn certain things

	08:52.080 --> 08:54.920
	because we are basically hard coded to learn them.

	08:54.920 --> 08:58.280
	And we are specialized in solving certain kinds of problems

	08:58.280 --> 09:01.440
	and we are quite useless when it comes to other kinds of problems.

	09:01.440 --> 09:06.160
	For instance, we are not really designed

	09:06.160 --> 09:08.800
	to handle very long term problems.

	09:08.800 --> 09:12.840
	We have no capability of seeing the very long term.

	09:12.840 --> 09:17.840
	We don't have very much working memory, you know?

	09:17.840 --> 09:19.960
	So how do you think about long term?

	09:19.960 --> 09:21.280
	Do you think long term planning,

	09:21.280 --> 09:24.760
	we're talking about scale of years, millennia,

	09:24.760 --> 09:27.960
	what do you mean by long term, we're not very good?

	09:27.960 --> 09:30.600
	Well, human intelligence is specialized in the human experience

	09:30.600 --> 09:34.120
	and human experience is very short, like one lifetime is short.

	09:34.120 --> 09:38.600
	Even within one lifetime, we have a very hard time envisioning,

	09:38.600 --> 09:41.080
	you know, things on a scale of years.

	09:41.080 --> 09:43.920
	Like it's very difficult to project yourself at the scale of five,

	09:43.920 --> 09:46.080
	at the scale of 10 years and so on.

	09:46.080 --> 09:49.960
	Right. We can solve only fairly narrowly scoped problems.

	09:49.960 --> 09:53.720
	So when it comes to solving bigger problems, larger scale problems,

	09:53.720 --> 09:56.320
	we are not actually doing it on an individual level.

	09:56.320 --> 09:59.240
	So it's not actually our brain doing it.

	09:59.240 --> 10:03.040
	We have this thing called civilization, right?

	10:03.040 --> 10:06.600
	Which is itself a sort of problem solving system,

	10:06.600 --> 10:10.000
	a sort of artificial intelligence system, right?

	10:10.000 --> 10:14.080
	And it's not running on one brain, it's running on a network of brains.

	10:14.080 --> 10:16.760
	In fact, it's running on much more than a network of brains.

	10:16.760 --> 10:21.960
	It's running on a lot of infrastructure, like books and computers

	10:21.960 --> 10:25.760
	and the internet and human institutions and so on.

	10:25.760 --> 10:31.640
	And that is capable of handling problems on a much greater scale

	10:31.640 --> 10:33.720
	than any individual human.

	10:33.720 --> 10:37.560
	If you look at computer science, for instance,

	10:37.560 --> 10:42.480
	that's an institution that solves problems and it is super human, right?

	10:42.480 --> 10:46.840
	It operates on a greater scale, it can solve much bigger problems

	10:46.840 --> 10:49.040
	than an individual human could.

	10:49.040 --> 10:52.120
	And science itself, science as a system, as an institution,

	10:52.120 --> 10:57.640
	is a kind of artificially intelligent problem solving algorithm

	10:57.640 --> 10:59.360
	that is super human.

	10:59.360 --> 11:06.080
	Yeah, it's a computer science is like a theorem prover

	11:06.080 --> 11:10.360
	at a scale of thousands, maybe hundreds of thousands of human beings.

	11:10.360 --> 11:14.640
	At that scale, what do you think is an intelligent agent?

	11:14.640 --> 11:18.280
	So there's us humans at the individual level.

	11:18.280 --> 11:23.880
	There is millions, maybe billions of bacteria in our skin.

	11:23.880 --> 11:26.400
	There is, that's at the smaller scale.

	11:26.400 --> 11:31.880
	You can even go to the particle level as systems that behave.

	11:31.880 --> 11:35.400
	You can say intelligently in some ways.

	11:35.400 --> 11:37.840
	And then you can look at the Earth as a single organism.

	11:37.840 --> 11:42.160
	You can look at our galaxy and even the universe as a single organism.

	11:42.160 --> 11:46.320
	Do you think, how do you think about scale and defining intelligent systems?

	11:46.320 --> 11:51.840
	And we're here at Google, there is millions of devices doing computation

	11:51.840 --> 11:53.400
	in a distributed way.

	11:53.400 --> 11:55.880
	How do you think about intelligence versus scale?

	11:55.880 --> 12:00.640
	You can always characterize anything as a system.

	12:00.640 --> 12:05.320
	I think people who talk about things like intelligence explosion

	12:05.320 --> 12:08.760
	tend to focus on one agent is basically one brain,

	12:08.760 --> 12:11.920
	like one brain considered in isolation, like a brain, a jar

	12:11.920 --> 12:16.280
	that's controlling a body in a very top to bottom kind of fashion.

	12:16.280 --> 12:19.480
	And that body is pursuing goals into an environment.

	12:19.480 --> 12:20.720
	So it's a very hierarchical view.

	12:20.720 --> 12:22.840
	You have the brain at the top of the pyramid,

	12:22.840 --> 12:25.960
	then you have the body just plainly receiving orders,

	12:25.960 --> 12:28.920
	then the body is manipulating objects in an environment and so on.

	12:28.920 --> 12:33.680
	So everything is subordinate to this one thing, this epicenter,

	12:33.680 --> 12:34.760
	which is the brain.

	12:34.760 --> 12:39.240
	But in real life, intelligent agents don't really work like this.

	12:39.240 --> 12:43.400
	There is no strong delimitation between the brain and the body to start with.

	12:43.400 --> 12:46.520
	You have to look not just at the brain, but at the nervous system.

	12:46.520 --> 12:50.760
	But then the nervous system and the body are naturally two separate entities.

	12:50.760 --> 12:53.960
	So you have to look at an entire animal as one agent.

	12:53.960 --> 13:00.200
	But then you start realizing as you observe an animal over any length of time

	13:00.200 --> 13:04.600
	that a lot of the intelligence of an animal is actually externalized.

	13:04.600 --> 13:06.240
	That's especially true for humans.

	13:06.240 --> 13:08.880
	A lot of our intelligence is externalized.

	13:08.880 --> 13:11.960
	When you write down some notes, there is externalized intelligence.

	13:11.960 --> 13:16.000
	When you write a computer program, you are externalizing cognition.

	13:16.000 --> 13:17.320
	So it's externalized in books.

	13:17.320 --> 13:23.040
	It's externalized in computers, the internet, in other humans.

	13:23.040 --> 13:25.400
	It's externalized in language and so on.

	13:25.400 --> 13:32.640
	So there is no hard delimitation of what makes an intelligent agent.

	13:32.640 --> 13:34.920
	It's all about context.

	13:34.920 --> 13:42.440
	OK, but AlphaGo is better at Go than the best human player.

	13:42.440 --> 13:44.960
	There's levels of skill here.

	13:44.960 --> 13:52.680
	So do you think there is such a concept as an intelligence explosion

	13:52.680 --> 13:54.720
	in a specific task?

	13:54.720 --> 14:00.080
	And then, well, yeah, do you think it's possible to have a category of tasks

	14:00.080 --> 14:05.000
	on which you do have something like an exponential growth of ability

	14:05.000 --> 14:07.400
	to solve that particular problem?

	14:07.400 --> 14:15.280
	I think if you consider a specific vertical, it's probably possible to some extent.

	14:15.280 --> 14:18.320
	I also don't think we have to speculate about it

	14:18.320 --> 14:24.760
	because we have real world examples of free classivity self improving

	14:24.760 --> 14:26.880
	intelligent systems.

	14:26.880 --> 14:32.560
	For instance, science is a problem solving system, a knowledge generation system,

	14:32.560 --> 14:36.240
	like a system that experiences the world in some sense

	14:36.240 --> 14:40.120
	and then gradually understands it and can act on it.

	14:40.120 --> 14:45.560
	And that system is superhuman and it is clearly recursively self improving

	14:45.560 --> 14:47.520
	because science fits into technology.

	14:47.520 --> 14:51.120
	Technology can be used to build better tools, better computers,

	14:51.120 --> 14:56.720
	better instrumentation and so on, which in turn can make science faster.

	14:56.720 --> 15:00.520
	So science is probably the closest thing we have today

	15:00.520 --> 15:04.720
	to a real civility self improving superhuman AI.

	15:04.720 --> 15:10.280
	And you can just observe, is science, is scientific progress today exploding,

	15:10.280 --> 15:12.760
	which itself is an interesting question.

	15:12.760 --> 15:15.800
	You can use that as a basis to try to understand what

	15:15.800 --> 15:20.960
	will happen with a superhuman AI that has science like behavior.

	15:20.960 --> 15:23.320
	Let me linger on it a little bit more.

	15:23.320 --> 15:28.520
	What is your intuition why an intelligence explosion is not possible?

	15:28.520 --> 15:34.400
	Like taking the scientific, all the semi scientific revolutions.

	15:34.400 --> 15:38.080
	Why can't we slightly accelerate that process?

	15:38.080 --> 15:43.160
	So you can absolutely accelerate any problem solving process.

	15:43.160 --> 15:48.640
	So recursively, recursive self improvement is absolutely a real thing.

	15:48.640 --> 15:51.880
	But what happens with a recursively self improving system

	15:51.880 --> 15:56.480
	is typically not explosion because no system exists in isolation.

	15:56.480 --> 16:00.840
	And so tweaking one part of the system means that suddenly another part of the system

	16:00.840 --> 16:02.120
	becomes a bottleneck.

	16:02.120 --> 16:06.760
	And if you look at science, for instance, which is clearly a recursively self improving,

	16:06.760 --> 16:11.960
	clearly a problem solving system, scientific progress is not actually exploding.

	16:11.960 --> 16:17.840
	If you look at science, what you see is the picture of a system that is consuming

	16:17.840 --> 16:20.440
	an exponentially increasing amount of resources.

	16:20.440 --> 16:26.000
	But it's having a linear output in terms of scientific progress.

	16:26.000 --> 16:28.960
	And maybe that will seem like a very strong claim.

	16:28.960 --> 16:34.520
	Many people are actually saying that scientific progress is exponential.

	16:34.520 --> 16:40.000
	But when they're claiming this, they're actually looking at indicators of resource

	16:40.000 --> 16:43.080
	consumption by science.

	16:43.080 --> 16:49.200
	For instance, the number of papers being published, the number of patterns being

	16:49.200 --> 16:55.760
	filed, and so on, which are just completely correlated with how many people are working

	16:55.760 --> 16:57.640
	on science today.

	16:57.640 --> 17:00.720
	So it's actually an indicator of resource consumption.

	17:00.720 --> 17:06.760
	But what you should look at is the output is progress in terms of the knowledge that

	17:06.760 --> 17:12.840
	science generates in terms of the scope and significance of the problems that we solve.

	17:12.840 --> 17:16.920
	And some people have actually been trying to measure that.

	17:16.920 --> 17:22.800
	Like Michael Nielsen, for instance, he had a very nice paper, I think that was last

	17:22.800 --> 17:25.280
	year about it.

	17:25.280 --> 17:32.760
	So his approach to measure scientific progress was to look at the timeline of scientific

	17:32.760 --> 17:37.400
	discoveries over the past 100, 150 years.

	17:37.400 --> 17:46.120
	And for each major discovery, ask a panel of experts to rate the significance of the

	17:46.120 --> 17:47.120
	discovery.

	17:47.120 --> 17:54.440
	And if the output of sciences in the institution were exponential, you would expect the temporal

	17:54.440 --> 18:01.080
	density of significance to go up exponentially, maybe because there's a faster rate of discoveries,

	18:01.080 --> 18:05.120
	maybe because the discoveries are increasingly more important.

	18:05.120 --> 18:10.360
	And what actually happens if you plot this temporal density of significance measured

	18:10.360 --> 18:14.600
	in this way, is that you see very much a flat graph.

	18:14.600 --> 18:20.040
	You see a flat graph across all disciplines, across physics, biology, medicine and so on.

	18:20.040 --> 18:24.400
	And it actually makes a lot of sense if you think about it, because think about the progress

	18:24.400 --> 18:28.120
	of physics 110 years ago.

	18:28.120 --> 18:30.240
	It was a time of crazy change.

	18:30.240 --> 18:36.640
	Think about the progress of technology 170 years ago, when we started replacing horses,

	18:36.640 --> 18:40.080
	with cars, when we started having electricity and so on.

	18:40.080 --> 18:41.640
	It was a time of incredible change.

	18:41.640 --> 18:44.800
	And today is also a time of very, very fast change.

	18:44.800 --> 18:50.480
	But it would be an unfair characterization to say that today, technology and science

	18:50.480 --> 18:54.600
	are moving way faster than they did 50 years ago or 100 years ago.

	18:54.600 --> 19:08.800
	And if you do try to rigorously plot the temporal density of the significance, you do see very

	19:08.800 --> 19:16.240
	flat curves and you can check out the paper that Michael Nielsen had about this idea.

	19:16.240 --> 19:25.280
	And so the way I interpret it is as you make progress in a given field or in a given subfield

	19:25.280 --> 19:30.640
	of science, it becomes exponentially more difficult to make further progress, like the

	19:30.640 --> 19:35.120
	very first person to work on information theory.

	19:35.120 --> 19:40.320
	If you enter a new field and it's still the very early years, there's a lot of low hanging

	19:40.320 --> 19:42.200
	fruit you can pick.

	19:42.200 --> 19:48.240
	But the next generation of researchers is going to have to dig much harder, actually,

	19:48.240 --> 19:52.800
	to make smaller discoveries, probably larger numbers, smaller discoveries.

	19:52.800 --> 19:57.640
	And to achieve the same amount of impact, you're going to need a much greater head count.

	19:57.640 --> 20:02.840
	And that's exactly the picture you're seeing with science, is that the number of scientists

	20:02.840 --> 20:06.680
	and engineers is, in fact, increasing exponentially.

	20:06.680 --> 20:11.520
	The amount of computational resources that are available to science is increasing exponentially

	20:11.520 --> 20:12.520
	and so on.

	20:12.520 --> 20:18.240
	So the resource consumption of science is exponential, but the output in terms of progress,

	20:18.240 --> 20:21.160
	in terms of significance, is linear.

	20:21.160 --> 20:26.200
	And the reason why is because, and even though science is rigorously self improving, meaning

	20:26.200 --> 20:33.000
	that scientific progress turns into technological progress, which in turn helps science.

	20:33.000 --> 20:39.240
	If you look at computers, for instance, our products of science and computers are tremendously

	20:39.240 --> 20:41.600
	useful in spinning up science.

	20:41.600 --> 20:42.600
	The internet, same thing.

	20:42.600 --> 20:47.680
	The internet is a technology that's made possible by very recent scientific advances.

	20:47.680 --> 20:53.960
	And itself, because it enables scientists to network, to communicate, to exchange papers

	20:53.960 --> 20:57.480
	and ideas much faster, it is a way to speed up scientific progress.

	20:57.480 --> 21:02.800
	So even though you're looking at a recursively self improving system, it is consuming exponentially

	21:02.800 --> 21:09.240
	more resources to produce the same amount of problem solving, in fact.

	21:09.240 --> 21:11.200
	So that's a fascinating way to paint it.

	21:11.200 --> 21:14.960
	And certainly that holds for the deep learning community, right?

	21:14.960 --> 21:18.040
	If you look at the temporal, what did you call it?

	21:18.040 --> 21:21.260
	The temporal density of significant ideas.

	21:21.260 --> 21:27.440
	If you look at in deep learning, I think, I'd have to think about that, but if you really

	21:27.440 --> 21:32.480
	look at significant ideas in deep learning, they might even be decreasing.

	21:32.480 --> 21:39.720
	So I do believe the per paper significance is decreasing.

	21:39.720 --> 21:43.480
	But the amount of papers is still today, exponentially increasing.

	21:43.480 --> 21:49.480
	So I think if you look at an aggregate, my guess is that you would see a linear progress.

	21:49.480 --> 21:58.720
	If you were to sum the significance of all papers, you would see a roughly linear progress.

	21:58.720 --> 22:05.680
	And in my opinion, it is not a coincidence that you're seeing linear progress in science

	22:05.680 --> 22:07.640
	despite exponential resource consumption.

	22:07.640 --> 22:15.840
	I think the resource consumption is dynamically adjusting itself to maintain linear progress

	22:15.840 --> 22:21.360
	because we as a community expect linear progress, meaning that if we start investing less and

	22:21.360 --> 22:26.160
	seeing less progress, it means that suddenly there are some lower hanging fruits that become

	22:26.160 --> 22:31.320
	available and someone's going to step up and pick them.

	22:31.320 --> 22:37.200
	So it's very much like a market for discoveries and ideas.

	22:37.200 --> 22:41.640
	But there's another fundamental part which you're highlighting, which as a hypothesis

	22:41.640 --> 22:49.440
	as science or the space of ideas, any one path you travel down, it gets exponentially

	22:49.440 --> 22:54.800
	more difficult to develop new ideas.

	22:54.800 --> 23:01.080
	And your sense is that's going to hold across our mysterious universe.

	23:01.080 --> 23:02.080
	Yes.

	23:02.080 --> 23:06.800
	Well, exponential progress triggers exponential friction so that if you tweak one part of

	23:06.800 --> 23:10.200
	the system, suddenly some other part becomes a bottleneck.

	23:10.200 --> 23:17.440
	For instance, let's say we develop some device that measures its own acceleration and then

	23:17.440 --> 23:22.240
	it has some engine and it outputs even more acceleration in proportion of its own acceleration

	23:22.240 --> 23:23.240
	and you drop it somewhere.

	23:23.240 --> 23:29.120
	It's not going to reach infinite speed because it exists in a certain context.

	23:29.120 --> 23:32.960
	So the error on this is going to generate friction and it's going to block it at some

	23:32.960 --> 23:34.440
	top speed.

	23:34.440 --> 23:39.880
	And even if you were to consider a broader context and lift the bottleneck there, like

	23:39.880 --> 23:46.200
	the bottleneck of friction, then some other part of the system would start stepping in

	23:46.200 --> 23:50.040
	and creating exponential friction, maybe the speed of flight or whatever.

	23:50.040 --> 23:55.400
	And this definitely holds true when you look at the problem solving algorithm that is being

	23:55.400 --> 23:59.780
	run by science as an institution, science as a system.

	23:59.780 --> 24:06.880
	As you make more and more progress, despite having this recursive self improvement component,

	24:06.880 --> 24:11.840
	you are encountering exponential friction, like the more researchers you have working

	24:11.840 --> 24:18.200
	on different ideas, the more overhead you have in terms of communication across researchers.

	24:18.200 --> 24:23.160
	If you look at, you were mentioning quantum mechanics, right?

	24:23.160 --> 24:28.480
	Well if you want to start making significant discoveries today, significant progress in

	24:28.480 --> 24:34.200
	quantum mechanics, there is an amount of knowledge you have to ingest, which is huge.

	24:34.200 --> 24:40.000
	But there is a very large overhead to even start to contribute, there is a large amount

	24:40.000 --> 24:44.240
	of overhead to synchronize across researchers and so on.

	24:44.240 --> 24:50.720
	And of course, the significant practical experiments are going to require exponentially

	24:50.720 --> 24:57.920
	expensive equipment because the easier ones have already been run, right?

	24:57.920 --> 25:08.520
	So in your senses, there is no way of escaping this kind of friction with artificial intelligence

	25:08.520 --> 25:09.520
	systems.

	25:09.520 --> 25:15.360
	Yeah, no, I think science is a very good way to model what would happen with a superhuman

	25:15.360 --> 25:17.880
	recursive research improving AI.

	25:17.880 --> 25:20.960
	That's my intuition.

	25:20.960 --> 25:26.680
	It's not like a mathematical proof of anything, that's not my point, like I'm not trying

	25:26.680 --> 25:31.440
	to prove anything, I'm just trying to make an argument to question the narrative of intelligence

	25:31.440 --> 25:35.600
	explosion, which is quite a dominant narrative and you do get a lot of pushback if you go

	25:35.600 --> 25:36.920
	against it.

	25:36.920 --> 25:43.280
	Because so for many people, right, AI is not just a subfield of computer science, it's

	25:43.280 --> 25:49.560
	more like a belief system, like this belief that the world is headed towards an event,

	25:49.560 --> 25:58.000
	the singularity, past which, you know, AI will become, will go exponential very much

	25:58.000 --> 26:02.160
	and the world will be transformed and humans will become obsolete.

	26:02.160 --> 26:07.880
	And if you go against this narrative, because it is not really a scientific argument but

	26:07.880 --> 26:12.240
	more of a belief system, it is part of the identity of many people.

	26:12.240 --> 26:15.680
	If you go against this narrative, it's like you're attacking the identity of people who

	26:15.680 --> 26:16.680
	believe in it.

	26:16.680 --> 26:22.880
	It's almost like saying God doesn't exist or something, so you do get a lot of pushback

	26:22.880 --> 26:25.200
	if you try to question his ideas.

	26:25.200 --> 26:29.880
	First of all, I believe most people, they might not be as eloquent or explicit as you're

	26:29.880 --> 26:34.400
	being, but most people in computer science are most people who actually have built anything

	26:34.400 --> 26:39.160
	that you could call AI, quote unquote, would agree with you.

	26:39.160 --> 26:43.880
	They might not be describing in the same kind of way, it's more, so the pushback you're

	26:43.880 --> 26:51.120
	getting is from people who get attached to the narrative from, not from a place of science,

	26:51.120 --> 26:53.520
	but from a place of imagination.

	26:53.520 --> 26:54.520
	That's correct.

	26:54.520 --> 26:55.520
	That's correct.

	26:55.520 --> 26:57.240
	So why do you think that's so appealing?

	26:57.240 --> 27:03.880
	Because the usual dreams that people have when you create a superintelligence system

	27:03.880 --> 27:09.520
	past the singularity, that what people imagine is somehow always destructive.

	27:09.520 --> 27:13.760
	Do you have, if you were put on your psychology hat, what's, why is it so?

	27:13.760 --> 27:20.200
	Why is it so appealing to imagine the ways that all of human civilization will be destroyed?

	27:20.200 --> 27:22.200
	I think it's a good story.

	27:22.200 --> 27:23.200
	You know, it's a good story.

	27:23.200 --> 27:30.680
	And very interestingly, it mirrors religious stories, right, religious mythology.

	27:30.680 --> 27:36.960
	If you look at the mythology of most civilizations, it's about the world being headed towards

	27:36.960 --> 27:42.240
	some final events in which the world will be destroyed and some new world order will

	27:42.240 --> 27:49.640
	arise that will be mostly spiritual, like the apocalypse followed by a paradise, probably.

	27:49.640 --> 27:52.880
	It's a very appealing story on a fundamental level.

	27:52.880 --> 27:54.640
	And we all need stories.

	27:54.640 --> 27:59.920
	We all need stories to structure in the way we see the world, especially at timescales

	27:59.920 --> 28:04.600
	that are beyond our ability to make predictions.

	28:04.600 --> 28:14.920
	So on a more serious non exponential explosion question, do you think there will be a time

	28:14.920 --> 28:21.880
	when we'll create something like human level intelligence or intelligence systems that

	28:21.880 --> 28:28.720
	will make you sit back and be just surprised at damn how smart this thing is?

	28:28.720 --> 28:32.360
	That doesn't require exponential growth or an exponential improvement.

	28:32.360 --> 28:39.840
	But what's your sense of the timeline and so on, that you'll be really surprised at

	28:39.840 --> 28:40.840
	certain capabilities?

	28:40.840 --> 28:44.360
	And we'll talk about limitations and deep learning, so do you think in your lifetime

	28:44.360 --> 28:46.760
	you'll be really damn surprised?

	28:46.760 --> 28:53.960
	Around 2013, 2014, I was many times surprised by the capabilities of deep learning, actually.

	28:53.960 --> 28:57.880
	That was before we had assessed exactly what deep learning could do and could not do and

	28:57.880 --> 29:00.680
	it felt like a time of immense potential.

	29:00.680 --> 29:03.120
	And then we started narrowing it down.

	29:03.120 --> 29:07.240
	But I was very surprised, so I would say it has already happened.

	29:07.240 --> 29:13.640
	Was there a moment, there must have been a day in there where your surprise was almost

	29:13.640 --> 29:19.640
	bordering on the belief of the narrative that we just discussed?

	29:19.640 --> 29:23.200
	Was there a moment, because you've written quite eloquently about the limits of deep

	29:23.200 --> 29:28.600
	learning, was there a moment that you thought that maybe deep learning is limitless?

	29:28.600 --> 29:32.520
	No, I don't think I've ever believed this.

	29:32.520 --> 29:35.120
	What was really shocking is that it worked.

	29:35.120 --> 29:37.800
	It worked at all, yeah.

	29:37.800 --> 29:43.880
	But there's a big jump between being able to do really good computer vision and human

	29:43.880 --> 29:45.040
	level intelligence.

	29:45.040 --> 29:50.840
	So I don't think at any point, I wasn't an impression that the results we got in computer

	29:50.840 --> 29:54.040
	vision meant that we were very close to human level intelligence.

	29:54.040 --> 29:56.000
	I don't think we're very close to human level intelligence.

	29:56.000 --> 30:01.720
	I do believe that there's no reason why we won't achieve it at some point.

	30:01.720 --> 30:10.280
	I also believe that the problem with talking about human level intelligence is that implicitly

	30:10.280 --> 30:13.920
	you're considering an axis of intelligence with different levels.

	30:13.920 --> 30:17.200
	But that's not really how intelligence works.

	30:17.200 --> 30:19.600
	Intelligence is very multidimensional.

	30:19.600 --> 30:24.440
	And so there's the question of capabilities, but there's also the question of being human

	30:24.440 --> 30:29.640
	like, and it's two very different things, like you can build potentially very advanced

	30:29.640 --> 30:32.760
	intelligent agents that are not human like at all.

	30:32.760 --> 30:35.320
	And you can also build very human like agents.

	30:35.320 --> 30:37.920
	And these are two very different things, right?

	30:37.920 --> 30:38.920
	Right.

	30:38.920 --> 30:42.360
	Let's go from the philosophical to the practical.

	30:42.360 --> 30:46.560
	Can you give me a history of Keras and all the major deep learning frameworks that you

	30:46.560 --> 30:51.600
	kind of remember in relation to Keras and in general, TensorFlow, Theano, the old days.

	30:51.600 --> 30:57.440
	Can you give a brief overview, Wikipedia style history, and your role in it before we return

	30:57.440 --> 30:58.840
	to AGI discussions?

	30:58.840 --> 31:00.840
	Yeah, that's a broad topic.

	31:00.840 --> 31:06.800
	So I started working on Keras, it was a name Keras at the time, I actually picked the

	31:06.800 --> 31:09.920
	name like just the day I was going to release it.

	31:09.920 --> 31:15.040
	So I started working on it in February 2015.

	31:15.040 --> 31:18.440
	And so at the time, there weren't too many people working on deep learning, maybe like

	31:18.440 --> 31:25.480
	fewer than 10,000, the software tooling was not really developed.

	31:25.480 --> 31:30.960
	So the main deep learning library was Cafe, which was mostly C++.

	31:30.960 --> 31:33.040
	Why do you say Cafe was the main one?

	31:33.040 --> 31:39.120
	Cafe was vastly more popular than Theano in late 2014, early 2015.

	31:39.120 --> 31:43.480
	Cafe was the one library that everyone was using for computer vision.

	31:43.480 --> 31:46.240
	And computer vision was the most popular problem.

	31:46.240 --> 31:47.240
	Absolutely.

	31:47.240 --> 31:53.280
	Like, Covenant was like the subfield of deep learning that everyone was working on.

	31:53.280 --> 32:01.840
	So myself, so in late 2014, I was actually interested in RNNs, in recurrent neural networks,

	32:01.840 --> 32:08.800
	which was a very niche topic at the time, right, it really took off around 2016.

	32:08.800 --> 32:11.520
	And so I was looking for good tools.

	32:11.520 --> 32:19.480
	I had used Torch 7, I had used Theano, used Theano a lot in Kaggle competitions, I had

	32:19.480 --> 32:21.240
	used Cafe.

	32:21.240 --> 32:27.880
	And there was no like good solution for RNNs at the time, like there was no reusable open

	32:27.880 --> 32:30.280
	source implementation of an LSTM, for instance.

	32:30.280 --> 32:33.200
	So I decided to build my own.

	32:33.200 --> 32:39.600
	And at first, the pitch for that was it was going to be mostly around LSTM recurrent neural

	32:39.600 --> 32:40.600
	networks.

	32:40.600 --> 32:46.000
	So in Python, an important decision at the time that was kind of nonobvious is that the

	32:46.000 --> 32:54.520
	models would be defined via Python code, which was kind of like going against the mainstream

	32:54.520 --> 33:00.320
	at the time, because Cafe, Pylon 2 and so on, like all the big libraries were actually

	33:00.320 --> 33:05.840
	going with you, approaching static configuration files in YAML to define models.

	33:05.840 --> 33:10.560
	So some libraries were using code to define models like Torch 7, obviously, but that was

	33:10.560 --> 33:11.560
	not.

	33:11.560 --> 33:17.840
	Python Lasagne was like a Theano based very early library that was, I think, developed.

	33:17.840 --> 33:18.840
	I don't remember exactly.

	33:18.840 --> 33:19.840
	Probably late 2014.

	33:19.840 --> 33:20.840
	It's Python as well.

	33:20.840 --> 33:21.840
	It's Python as well.

	33:21.840 --> 33:25.040
	It was like on top of Theano.

	33:25.040 --> 33:32.760
	And so I started working on something and the value proposition at the time was that not

	33:32.760 --> 33:40.920
	only that what I think was the first reusable open source implementation of LSTM, you could

	33:40.920 --> 33:47.080
	combine RNNs and covenants with the same library, which is not really possible before.

	33:47.080 --> 33:50.760
	Like Cafe was only doing covenants.

	33:50.760 --> 33:52.880
	And it was kind of easy to use.

	33:52.880 --> 33:55.760
	Because so before I was using Theano, I was actually using Psykitlin.

	33:55.760 --> 33:58.480
	And I loved Psykitlin for its usability.

	33:58.480 --> 34:02.440
	So I drew a lot of inspiration from Psykitlin when I met Keras.

	34:02.440 --> 34:05.680
	It's almost like Psykitlin for neural networks.

	34:05.680 --> 34:06.680
	The fit function.

	34:06.680 --> 34:07.680
	Exactly.

	34:07.680 --> 34:08.680
	The fit function.

	34:08.680 --> 34:13.000
	Like reducing a complex string loop to a single function call.

	34:13.000 --> 34:17.480
	And of course, some people will say, this is hiding a lot of details, but that's exactly

	34:17.480 --> 34:18.480
	the point.

	34:18.480 --> 34:20.360
	The magic is the point.

	34:20.360 --> 34:25.280
	So it's magical, but in a good way, it's magical in the sense that it's delightful.

	34:25.280 --> 34:27.600
	I'm actually quite surprised.

	34:27.600 --> 34:31.920
	I didn't know that it was born out of desire to implement RNNs and LSTMs.

	34:31.920 --> 34:32.920
	It was.

	34:32.920 --> 34:33.920
	That's fascinating.

	34:33.920 --> 34:39.160
	So you were actually one of the first people to really try to attempt to get the major

	34:39.160 --> 34:41.160
	architecture together.

	34:41.160 --> 34:45.160
	And it's also interesting, I mean, you realize that that was a design decision at all is

	34:45.160 --> 34:47.480
	defining the model and code.

	34:47.480 --> 34:52.320
	Just I'm putting myself in your shoes, whether the YAML, especially if Cafe was the most

	34:52.320 --> 34:53.320
	popular.

	34:53.320 --> 34:54.760
	It was the most popular by far.

	34:54.760 --> 35:01.880
	If I was if I were, yeah, I don't, I didn't like the YAML thing, but it makes more sense

	35:01.880 --> 35:05.760
	that you will put in a configuration file, the definition of a model.

	35:05.760 --> 35:10.160
	That's an interesting gutsy move to stick with defining it in code.

	35:10.160 --> 35:14.800
	Just if you look back, other libraries, we're doing it as well, but it was definitely the

	35:14.800 --> 35:16.200
	more niche option.

	35:16.200 --> 35:17.200
	Yeah.

	35:17.200 --> 35:18.200
	Okay.

	35:18.200 --> 35:19.200
	Keras and then Keras.

	35:19.200 --> 35:24.220
	So I released Keras in March, 2015, and it got users pretty much from the start.

	35:24.220 --> 35:27.480
	So the deep learning community was very, very small at the time.

	35:27.480 --> 35:30.640
	Lots of people were starting to be interested in LSTMs.

	35:30.640 --> 35:34.760
	So it was going to release at the right time because it was offering an easy to use LSTM

	35:34.760 --> 35:35.760
	implementation.

	35:35.760 --> 35:40.840
	Exactly at the time where lots of you started to be intrigued by the capabilities of RNN,

	35:40.840 --> 35:42.340
	RNNs 1LP.

	35:42.340 --> 35:47.000
	So it grew from there.

	35:47.000 --> 35:53.760
	Then I joined Google about six months later, and that was actually completely unrelated

	35:53.760 --> 35:54.760
	to Keras.

	35:54.760 --> 36:00.720
	Keras actually joined a research team working on image classification mostly like computer

	36:00.720 --> 36:01.720
	vision.

	36:01.720 --> 36:03.840
	So I was doing computer vision research at Google initially.

	36:03.840 --> 36:11.440
	And immediately when I joined Google, I was exposed to the early internal version of TensorFlow.

	36:11.440 --> 36:15.400
	And the way it appeared to me at the time, and it was definitely the way it was at the

	36:15.400 --> 36:20.880
	time, is that this was an improved version of Tiano.

	36:20.880 --> 36:27.040
	So I immediately knew I had to port Keras to this new TensorFlow thing.

	36:27.040 --> 36:31.760
	And I was actually very busy as a new Googler.

	36:31.760 --> 36:34.600
	So I had not time to work on that.

	36:34.600 --> 36:41.360
	But then in November, I think it was November 2015, TensorFlow got released.

	36:41.360 --> 36:47.440
	And it was kind of like my wake up call that, hey, I had to actually go and make it happen.

	36:47.440 --> 36:53.360
	So in December, I ported Keras to run on TensorFlow, but it was not exactly a port.

	36:53.360 --> 36:59.360
	It was more like a refactoring where I was abstracting away all the backend functionality

	36:59.360 --> 37:05.200
	into one module so that the same code base could run on top of multiple backends.

	37:05.200 --> 37:07.560
	So on top of TensorFlow or Tiano.

	37:07.560 --> 37:21.000
	And for the next year, Tiano stayed as the default option, it was easier to use, it was

	37:21.000 --> 37:23.440
	much faster, especially when it came to on it.

	37:23.440 --> 37:27.560
	But eventually, TensorFlow overtook it.

	37:27.560 --> 37:34.000
	And TensorFlow, the early TensorFlow has similar architectural decisions as Tiano.

	37:34.000 --> 37:38.360
	So it was a natural transition.

	37:38.360 --> 37:45.360
	So what, I mean, that still carries as a side, almost one project, right?

	37:45.360 --> 37:50.280
	Yeah, so it was not my job assignment, it was not.

	37:50.280 --> 37:52.360
	I was doing it on the side.

	37:52.360 --> 37:57.840
	And even though it grew to have a lot of uses for deep learning library at the time, like

	37:57.840 --> 38:02.560
	Stroud 2016, but I wasn't doing it as my main job.

	38:02.560 --> 38:10.680
	So things started changing in, I think it must have been maybe October 2016, so one year

	38:10.680 --> 38:11.680
	later.

	38:11.680 --> 38:18.440
	So Rajat, who has the lead in TensorFlow, basically showed up one day in our building

	38:18.440 --> 38:23.040
	where I was doing like, so I was doing research and things like, so I did a lot of computer

	38:23.040 --> 38:29.040
	vision research, also collaborations with Christian Zegedi and Deep Learning for Theraim

	38:29.040 --> 38:34.720
	Proving, that was a really interesting research topic.

	38:34.720 --> 38:42.600
	And so Rajat was saying, hey, we saw Keras, we like it, we saw that you had Google, why

	38:42.600 --> 38:46.960
	don't you come over for like a quarter and work with us?

	38:46.960 --> 38:50.560
	And I was like, yeah, that sounds like a great opportunity, let's do it.

	38:50.560 --> 38:57.520
	And so I started working on integrating the Keras API into TensorFlow more tightly.

	38:57.520 --> 39:06.000
	So what followed up is a sort of temporary TensorFlow only version of Keras that was

	39:06.000 --> 39:12.560
	in TensorFlow.contrib for a while, and finally moved to TensorFlow Core.

	39:12.560 --> 39:17.320
	And I've never actually gotten back to my old team doing research.

	39:17.320 --> 39:27.360
	Well, it's kind of funny that somebody like you who dreams of or at least sees the power

	39:27.360 --> 39:33.800
	of AI systems that reason and Theraim Proving will talk about has also created a system

	39:33.800 --> 39:41.600
	that makes the most basic kind of Lego building that is deep learning, super accessible, super

	39:41.600 --> 39:43.840
	easy, so beautifully so.

	39:43.840 --> 39:50.280
	It's a funny irony that you're both, you're responsible for both things.

	39:50.280 --> 39:55.360
	So TensorFlow 2.0 is kind of, there's a sprint, I don't know how long it'll take, but there's

	39:55.360 --> 39:57.080
	a sprint towards the finish.

	39:57.080 --> 40:01.120
	What do you look, what are you working on these days?

	40:01.120 --> 40:02.120
	What are you excited about?

	40:02.120 --> 40:05.040
	What are you excited about in 2.0?

	40:05.040 --> 40:09.880
	Eager execution, there's so many things that just make it a lot easier to work.

	40:09.880 --> 40:11.640
	What are you excited about?

	40:11.640 --> 40:13.800
	And what's also really hard?

	40:13.800 --> 40:15.880
	What are the problems you have to kind of solve?

	40:15.880 --> 40:22.880
	So I've spent the past year and a half working on TensorFlow 2.0 and it's been a long journey.

	40:22.880 --> 40:25.040
	I'm actually extremely excited about it.

	40:25.040 --> 40:26.560
	I think it's a great product.

	40:26.560 --> 40:29.440
	It's a delightful product compared to TensorFlow 1.0.

	40:29.440 --> 40:32.800
	We've made huge progress.

	40:32.800 --> 40:40.640
	So on the Keras side, what I'm really excited about is that, so previously Keras has been

	40:40.640 --> 40:50.880
	this very easy to use high level interface to do deep learning, but if you wanted to,

	40:50.880 --> 40:57.760
	if you wanted a lot of flexibility, the Keras framework was probably not the optimal way

	40:57.760 --> 41:02.160
	to do things compared to just writing everything from scratch.

	41:02.160 --> 41:05.040
	So in some way, the framework was getting in the way.

	41:05.040 --> 41:08.280
	And in TensorFlow 2.0, you don't have this at all, actually.

	41:08.280 --> 41:13.600
	You have the usability of the high level interface, but you have the flexibility of this lower

	41:13.600 --> 41:20.520
	level interface, and you have this spectrum of workflows where you can get more or less

	41:20.520 --> 41:26.960
	usability and flexibility, the tradeoffs, depending on your needs.

	41:26.960 --> 41:33.800
	You can write everything from scratch and you get a lot of help doing so by subclassing

	41:33.800 --> 41:38.520
	models and writing some train loops using eager execution.

	41:38.520 --> 41:39.520
	It's very flexible.

	41:39.520 --> 41:40.520
	It's very easy to debug.

	41:40.520 --> 41:42.400
	It's very powerful.

	41:42.400 --> 41:48.600
	But all of this integrates seamlessly with higher level features up to the classic Keras

	41:48.600 --> 41:56.440
	workflows, which are very psychedelic and ideal for a data scientist, machine learning

	41:56.440 --> 41:58.320
	engineer type of profile.

	41:58.320 --> 42:04.320
	So now you can have the same framework offering the same set of APIs that enable a spectrum

	42:04.320 --> 42:11.000
	of workflows that are lower level, more or less high level, that are suitable for profiles

	42:11.000 --> 42:15.400
	ranging from researchers to data scientists and everything in between.

	42:15.400 --> 42:16.400
	Yeah.

	42:16.400 --> 42:17.400
	So that's super exciting.

	42:17.400 --> 42:18.600
	I mean, it's not just that.

	42:18.600 --> 42:21.560
	It's connected to all kinds of tooling.

	42:21.560 --> 42:26.760
	You can go on mobile, you can go with TensorFlow Lite, you can go in the cloud or serving

	42:26.760 --> 42:29.240
	and so on, it all is connected together.

	42:29.240 --> 42:37.440
	Some of the best software written ever is often done by one person, sometimes two.

	42:37.440 --> 42:42.920
	So with a Google, you're now seeing sort of Keras having to be integrated in TensorFlow.

	42:42.920 --> 42:46.520
	I'm sure it has a ton of engineers working on.

	42:46.520 --> 42:52.320
	So I'm sure there are a lot of tricky design decisions to be made.

	42:52.320 --> 42:54.600
	How does that process usually happen?

	42:54.600 --> 43:00.800
	At least your perspective, what are the debates like?

	43:00.800 --> 43:07.160
	Is there a lot of thinking considering different options and so on?

	43:07.160 --> 43:08.160
	Yes.

	43:08.160 --> 43:17.920
	So a lot of the time I spend at Google is actually discussing design discussions, writing design

	43:17.920 --> 43:22.200
	docs, participating in design review meetings and so on.

	43:22.200 --> 43:25.520
	This is as important as actually writing a code.

	43:25.520 --> 43:34.080
	So there's a lot of thought and a lot of care that is taken in coming up with these decisions

	43:34.080 --> 43:39.920
	and taking into account all of our users because TensorFlow has this extremely diverse user

	43:39.920 --> 43:40.920
	base.

	43:40.920 --> 43:45.560
	It's not like just one user segment where everyone has the same needs.

	43:45.560 --> 43:49.640
	We have small scale production users, large scale production users.

	43:49.640 --> 43:56.520
	We have startups, we have researchers, it's all over the place, and we have to cater to

	43:56.520 --> 43:57.520
	all of their needs.

	43:57.520 --> 44:04.160
	If I just look at the standard debates of C++ or Python, there's some heated debates.

	44:04.160 --> 44:05.680
	Do you have those at Google?

	44:05.680 --> 44:10.560
	I mean, they're not heated in terms of emotionally, but there's probably multiple ways to do it,

	44:10.560 --> 44:11.560
	right?

	44:11.560 --> 44:16.080
	So how do you arrive through those design meetings at the best way to do it, especially in deep

	44:16.080 --> 44:21.960
	learning where the field is evolving as you're doing it?

	44:21.960 --> 44:23.440
	Is there some magic to it?

	44:23.440 --> 44:25.240
	Is there some magic to the process?

	44:25.240 --> 44:30.800
	I don't know if there's magic to the process, but there definitely is a process.

	44:30.800 --> 44:37.240
	So making design decisions is about satisfying a set of constraints, but also trying to do

	44:37.240 --> 44:42.720
	so in the simplest way possible because this is what can be maintained, this is what can

	44:42.720 --> 44:45.080
	be expanded in the future.

	44:45.080 --> 44:51.200
	So you don't want to naively satisfy the constraints by just, you know, for each capability you

	44:51.200 --> 44:54.760
	need available, you're going to come up with one argument in your API and so on.

	44:54.760 --> 45:03.920
	You want to design APIs that are modular and hierarchical so that they have an API surface

	45:03.920 --> 45:07.520
	that is as small as possible, right?

	45:07.520 --> 45:14.800
	And you want this modular hierarchical architecture to reflect the way that domain experts think

	45:14.800 --> 45:19.960
	about the problem because as a domain expert, when you're reading about a new API, you're

	45:19.960 --> 45:27.120
	reading a tutorial or some docs, pages, you already have a way that you're thinking about

	45:27.120 --> 45:28.120
	the problem.

	45:28.120 --> 45:35.600
	You already have certain concepts in mind and you're thinking about how they relate together

	45:35.600 --> 45:41.280
	and when you're reading docs, you're trying to build as quickly as possible a mapping

	45:41.280 --> 45:47.240
	between the concepts featured in your API and the concepts in your mind so you're trying

	45:47.240 --> 45:53.720
	to map your mental model as a domain expert to the way things work in the API.

	45:53.720 --> 45:59.320
	So you need an API and an underlying implementation that are reflecting the way people think about

	45:59.320 --> 46:00.320
	these things.

	46:00.320 --> 46:02.960
	So in minimizing the time it takes to do the mapping?

	46:02.960 --> 46:03.960
	Yes.

	46:03.960 --> 46:10.000
	Minimizing the time, the cognitive load there is in ingesting this new knowledge about your

	46:10.000 --> 46:11.000
	API.

	46:11.000 --> 46:16.080
	An API should not be self referential or referring to implementation details, it should only

	46:16.080 --> 46:22.360
	be referring to domain specific concepts that people already understand.

	46:22.360 --> 46:24.560
	Brilliant.

	46:24.560 --> 46:27.640
	So what's the future of Keras and TensorFlow look like?

	46:27.640 --> 46:30.680
	What does TensorFlow 3.0 look like?

	46:30.680 --> 46:36.440
	So that's kind of too far in the future for me to answer, especially since I'm not even

	46:36.440 --> 46:39.480
	the one making these decisions.

	46:39.480 --> 46:44.840
	But so from my perspective, which is just one perspective among many different perspectives

	46:44.840 --> 46:52.600
	on the TensorFlow team, I'm really excited by developing even higher level APIs, higher

	46:52.600 --> 46:53.600
	level than Keras.

	46:53.600 --> 47:01.040
	I'm really excited by hyperparameter tuning, by automated machine learning, AutoML.

	47:01.040 --> 47:07.480
	I think the future is not just defining a model like you were assembling Lego blocks

	47:07.480 --> 47:14.280
	and then colleague fit on it, it's more like an automagical model that would just look

	47:14.280 --> 47:19.120
	at your data and optimize the objective you're after.

	47:19.120 --> 47:22.440
	So that's what I'm looking into.

	47:22.440 --> 47:23.440
	Yes.

	47:23.440 --> 47:30.120
	So you put the baby into a room with the problem and come back a few hours later with a fully

	47:30.120 --> 47:31.120
	solved problem.

	47:31.120 --> 47:32.120
	Exactly.

	47:32.120 --> 47:36.520
	It's not like a box of Legos, it's more like the combination of a kid that's really good

	47:36.520 --> 47:41.560
	at Legos, and a box of Legos, and just building the thing on the song.

	47:41.560 --> 47:42.760
	Very nice.

	47:42.760 --> 47:44.080
	So that's an exciting feature.

	47:44.080 --> 47:50.680
	I think there's a huge amount of applications and revolutions to be had under the constraints

	47:50.680 --> 47:52.800
	of the discussion we previously had.

	47:52.800 --> 47:57.520
	But what do you think are the current limits of deep learning?

	47:57.520 --> 48:05.200
	If we look specifically at these function approximators that tries to generalize from

	48:05.200 --> 48:06.200
	data?

	48:06.200 --> 48:11.800
	If you've talked about local versus extreme generalization, you mentioned that neural

	48:11.800 --> 48:17.840
	networks don't generalize well and humans do, so there's this gap.

	48:17.840 --> 48:22.840
	And you've also mentioned that extreme generalization requires something like reasoning to fill those

	48:22.840 --> 48:24.040
	gaps.

	48:24.040 --> 48:27.120
	So how can we start trying to build systems like that?

	48:27.120 --> 48:28.120
	Right.

	48:28.120 --> 48:29.120
	Yes.

	48:29.120 --> 48:30.640
	So this is by design, right?

	48:30.640 --> 48:39.600
	And deep learning models are huge, parametric models, differentiable, so continuous, that

	48:39.600 --> 48:42.840
	go from an input space to an output space.

	48:42.840 --> 48:46.560
	And they're trained with gradient descent, so they're trained pretty much point by point.

	48:46.560 --> 48:53.560
	They're learning a continuous geometric morphing from an input vector space to an output vector

	48:53.560 --> 48:55.640
	space, right?

	48:55.640 --> 49:02.920
	And because this is done point by point, a deep neural network can only make sense of

	49:02.920 --> 49:08.160
	points in experience space that are very close to things that it has already seen in string

	49:08.160 --> 49:09.160
	data.

	49:09.160 --> 49:14.040
	At best, it can do interpolation across points.

	49:14.040 --> 49:20.560
	But that means in order to train your network, you need a dense sampling of the input cross

	49:20.560 --> 49:27.040
	output space, almost a point by point sampling, which can be very expensive if you're dealing

	49:27.040 --> 49:33.760
	with complex real world problems like autonomous driving, for instance, or robotics.

	49:33.760 --> 49:37.240
	It's doable if you're looking at the subset of the visual space.

	49:37.240 --> 49:41.200
	But even then, it's still fairly expensive, you still need millions of examples.

	49:41.200 --> 49:45.600
	And it's only going to be able to make sense of things that are very close to ways that's

	49:45.600 --> 49:47.000
	seen before.

	49:47.000 --> 49:50.720
	And in contrast to that, well, of course, you have human intelligence, but even if you're

	49:50.720 --> 49:56.840
	not looking at human intelligence, you can look at very simple rules, algorithms.

	49:56.840 --> 50:03.080
	If you have a symbolic rule, it can actually apply to a very, very large set of inputs

	50:03.080 --> 50:04.920
	because it is abstract.

	50:04.920 --> 50:10.760
	It is not obtained by doing a point by point mapping, right?

	50:10.760 --> 50:15.640
	For instance, if you try to learn a sorting algorithm using a deep neural network, well,

	50:15.640 --> 50:21.800
	you're very much limited to learning point by point what the sorted representation of

	50:21.800 --> 50:24.520
	this specific list is like.

	50:24.520 --> 50:32.120
	But instead, you could have a very, very simple sorting algorithm written in a few lines.

	50:32.120 --> 50:35.720
	Maybe it's just two nested loops.

	50:35.720 --> 50:42.320
	And it can process any list at all because it is abstract, because it is a set of rules.

	50:42.320 --> 50:47.440
	So deep learning is really like point by point geometric morphings, morphings trained with

	50:47.440 --> 50:48.880
	God and Descent.

	50:48.880 --> 50:54.200
	And meanwhile, abstract rules can generalize much better.

	50:54.200 --> 50:56.400
	And I think the future is really to combine the two.

	50:56.400 --> 50:59.720
	So how do we, do you think, combine the two?

	50:59.720 --> 51:08.040
	How do we combine good point by point functions with programs, which is what the symbolic AI

	51:08.040 --> 51:09.040
	type systems?

	51:09.040 --> 51:10.040
	Yeah.

	51:10.040 --> 51:11.600
	At which levels the combination happened.

	51:11.600 --> 51:17.480
	I mean, obviously, we're jumping into the realm of where there's no good answers.

	51:17.480 --> 51:20.120
	It's just kind of ideas and intuitions and so on.

	51:20.120 --> 51:21.120
	Yeah.

	51:21.120 --> 51:25.200
	Well, if you look at the really successful AI systems today, I think there are already

	51:25.200 --> 51:29.600
	hybrid systems that are combining symbolic AI with deep learning.

	51:29.600 --> 51:36.120
	For instance, successful robotics systems are already mostly model based, rule based

	51:36.120 --> 51:39.560
	things like planning algorithms and so on.

	51:39.560 --> 51:44.320
	At the same time, they're using deep learning as perception modules.

	51:44.320 --> 51:49.120
	Sometimes they're using deep learning as a way to inject fuzzy intuition into a rule

	51:49.120 --> 51:51.000
	based process.

	51:51.000 --> 51:56.720
	If you look at a system like a self driving car, it's not just one big end to end neural

	51:56.720 --> 52:00.920
	network that wouldn't work at all, precisely because in order to train that, you would

	52:00.920 --> 52:06.960
	need a dense sampling of experience space when it comes to driving, which is completely

	52:06.960 --> 52:08.480
	unrealistic, obviously.

	52:08.480 --> 52:18.560
	Instead, the self driving car is mostly symbolic, it's software, it's programmed by hand.

	52:18.560 --> 52:25.760
	It's mostly based on explicit models, in this case, mostly 3D models of the environment

	52:25.760 --> 52:31.600
	around the car, but it's interfacing with the real world, using deep learning modules.

	52:31.600 --> 52:36.480
	The deep learning there serves as a way to convert the raw sensory information to something

	52:36.480 --> 52:38.600
	usable by symbolic systems.

	52:38.600 --> 52:42.440
	Okay, well, let's linger on that a little more.

	52:42.440 --> 52:48.400
	So dense sampling from input to output, you said it's obviously very difficult.

	52:48.400 --> 52:49.400
	Is it possible?

	52:49.400 --> 52:51.960
	In the case of self driving, you mean?

	52:51.960 --> 52:53.240
	Let's say self driving, right?

	52:53.240 --> 52:57.760
	Self driving for many people.

	52:57.760 --> 53:03.320
	Let's not even talk about self driving, let's talk about steering, so staying inside the

	53:03.320 --> 53:05.320
	lane.

	53:05.320 --> 53:09.200
	It's definitely a problem you can solve with an end to end deep learning model, but that's

	53:09.200 --> 53:10.200
	like one small subset.

	53:10.200 --> 53:14.600
	Hold on a second, I don't know how you're jumping from the extreme so easily, because

	53:14.600 --> 53:17.800
	I disagree with you on that.

	53:17.800 --> 53:23.240
	I think, well, it's not obvious to me that you can solve lane following.

	53:23.240 --> 53:25.720
	No, it's not obvious, I think it's doable.

	53:25.720 --> 53:33.800
	I think in general, there is no hard limitations to what you can learn with a deep neural network,

	53:33.800 --> 53:42.160
	as long as the search space is rich enough, is flexible enough, and as long as you have

	53:42.160 --> 53:47.640
	this dense sampling of the input cross output space, the problem is that this dense sampling

	53:47.640 --> 53:52.920
	could mean anything from 10,000 examples to trillions and trillions.

	53:52.920 --> 53:54.440
	So that's my question.

	53:54.440 --> 53:56.360
	So what's your intuition?

	53:56.360 --> 54:01.800
	And if you could just give it a chance and think what kind of problems can be solved

	54:01.800 --> 54:08.080
	by getting a huge amounts of data and thereby creating a dense mapping.

	54:08.080 --> 54:14.040
	So let's think about natural language dialogue, the Turing test.

	54:14.040 --> 54:20.080
	Do you think the Turing test can be solved with a neural network alone?

	54:20.080 --> 54:26.480
	Well, the Turing test is all about tricking people into believing they're talking to a

	54:26.480 --> 54:27.480
	human.

	54:27.480 --> 54:35.720
	It's actually very difficult because it's more about exploiting human perception and

	54:35.720 --> 54:37.680
	not so much about intelligence.

	54:37.680 --> 54:41.520
	There's a big difference between mimicking into Asian behavior and actually into Asian

	54:41.520 --> 54:42.520
	behavior.

	54:42.520 --> 54:46.680
	So, okay, let's look at maybe the Alexa prize and so on, the different formulations of the

	54:46.680 --> 54:51.720
	natural language conversation that are less about mimicking and more about maintaining

	54:51.720 --> 54:54.920
	a fun conversation that lasts for 20 minutes.

	54:54.920 --> 54:59.240
	It's a little less about mimicking and that's more about, I mean, it's still mimicking,

	54:59.240 --> 55:03.200
	but it's more about being able to carry forward a conversation with all the tangents that

	55:03.200 --> 55:05.120
	happen in dialogue and so on.

	55:05.120 --> 55:12.480
	Do you think that problem is learnable with this kind of neural network that does the

	55:12.480 --> 55:14.600
	point to point mapping?

	55:14.600 --> 55:17.800
	So I think it would be very, very challenging to do this with deep learning.

	55:17.800 --> 55:21.480
	I don't think it's out of the question either.

	55:21.480 --> 55:23.440
	I wouldn't read it out.

	55:23.440 --> 55:27.080
	The space of problems that can be solved with a large neural network.

	55:27.080 --> 55:31.280
	What's your sense about the space of those problems?

	55:31.280 --> 55:32.680
	Useful problems for us.

	55:32.680 --> 55:33.960
	In theory, it's infinite.

	55:33.960 --> 55:36.320
	You can solve any problem.

	55:36.320 --> 55:45.400
	In practice, while deep learning is a great fit for perception problems, in general, any

	55:45.400 --> 55:52.120
	problem which is naturally amenable to explicit handcrafted rules or rules that you can generate

	55:52.120 --> 55:56.160
	by exhaustive search over some program space.

	55:56.160 --> 56:03.400
	So perception, artificial intuition, as long as you have a sufficient training data set.

	56:03.400 --> 56:04.400
	And that's the question.

	56:04.400 --> 56:08.800
	I mean, perception, there's interpretation and understanding of the scene, which seems

	56:08.800 --> 56:13.040
	to be outside the reach of current perception systems.

	56:13.040 --> 56:19.240
	So do you think larger networks will be able to start to understand the physics and the

	56:19.240 --> 56:23.960
	physics of the scene, the three dimensional structure and relationships of objects in

	56:23.960 --> 56:25.720
	the scene, and so on?

	56:25.720 --> 56:28.880
	Or really, that's where symbolic at has to step in?

	56:28.880 --> 56:37.680
	Well, it's always possible to solve these problems with deep learning is just extremely

	56:37.680 --> 56:38.680
	inefficient.

	56:38.680 --> 56:45.240
	A model would be an explicit rule based abstract model would be a far better, more compressed

	56:45.240 --> 56:50.280
	representation of physics than learning just this mapping between in this situation, this

	56:50.280 --> 56:51.280
	thing happens.

	56:51.280 --> 56:54.520
	If you change the situation slightly, then this other thing happens and so on.

	56:54.520 --> 57:00.840
	Do you think it's possible to automatically generate the programs that would require that

	57:00.840 --> 57:01.840
	kind of reasoning?

	57:01.840 --> 57:07.120
	Or does it have to, so where expert systems fail, there's so many facts about the world

	57:07.120 --> 57:08.640
	had to be hand coded in.

	57:08.640 --> 57:15.360
	Do you think it's possible to learn those logical statements that are true about the

	57:15.360 --> 57:17.120
	world and their relationships?

	57:17.120 --> 57:22.640
	I mean, that's kind of what they're improving at a basic level is trying to do, right?

	57:22.640 --> 57:28.360
	Yeah, except it's much harder to formulate statements about the world compared to fermenting

	57:28.360 --> 57:30.680
	mathematical statements.

	57:30.680 --> 57:34.320
	Statements about the world tend to be subjective.

	57:34.320 --> 57:39.320
	So can you learn rule based models?

	57:39.320 --> 57:40.320
	Yes.

	57:40.320 --> 57:41.320
	Yes, definitely.

	57:41.320 --> 57:43.720
	That's the field of program synthesis.

	57:43.720 --> 57:48.080
	However, today we just don't really know how to do it.

	57:48.080 --> 57:52.640
	So it's very much a grass search or tree search problem.

	57:52.640 --> 57:58.080
	And so we are limited to the sort of a tree session grass search algorithms that we have

	57:58.080 --> 57:59.080
	today.

	57:59.080 --> 58:02.080
	Personally, I think genetic algorithms are very promising.

	58:02.080 --> 58:04.640
	So it's almost like genetic programming.

	58:04.640 --> 58:05.760
	Genetic programming, exactly.

	58:05.760 --> 58:12.200
	Can you discuss the field of program synthesis, like what, how many people are working and

	58:12.200 --> 58:13.840
	thinking about it?

	58:13.840 --> 58:20.360
	What, where we are in the history of program synthesis and what are your hopes for it?

	58:20.360 --> 58:24.760
	Well, if it were deep learning, this is like the 90s.

	58:24.760 --> 58:29.320
	So meaning that we already have existing solutions.

	58:29.320 --> 58:35.720
	We are starting to have some basic understanding of what this is about.

	58:35.720 --> 58:38.120
	But it's still a field that is in its infancy.

	58:38.120 --> 58:40.560
	There are very few people working on it.

	58:40.560 --> 58:44.520
	There are very few real world applications.

	58:44.520 --> 58:51.960
	So the one real world application I'm aware of is Flash Fill in Excel.

	58:51.960 --> 58:58.240
	It's a way to automatically learn very simple programs to format cells in an Excel spreadsheet

	58:58.240 --> 58:59.840
	from a few examples.

	58:59.840 --> 59:02.840
	For instance, learning a way to format a date, things like that.

	59:02.840 --> 59:03.840
	Oh, that's fascinating.

	59:03.840 --> 59:04.840
	Yeah.

	59:04.840 --> 59:06.280
	You know, okay, that's that's fascinating topic.

	59:06.280 --> 59:12.880
	I was wondering when I provide a few samples to Excel, what it's able to figure out, like

	59:12.880 --> 59:18.280
	just giving it a few dates, what are you able to figure out from the pattern I just gave

	59:18.280 --> 59:19.280
	you?

	59:19.280 --> 59:20.280
	That's a fascinating question.

	59:20.280 --> 59:24.240
	It's fascinating whether that's learnable patterns and you're saying they're working

	59:24.240 --> 59:25.240
	on that.

	59:25.240 --> 59:26.240
	Yeah.

	59:26.240 --> 59:27.240
	How big is the toolbox currently?

	59:27.240 --> 59:28.240
	Yeah.

	59:28.240 --> 59:29.240
	Are we completely in the dark?

	59:29.240 --> 59:30.240
	So if you set the 90s.

	59:30.240 --> 59:32.240
	In terms of program synthesis?

	59:32.240 --> 59:33.240
	No.

	59:33.240 --> 59:40.520
	So I would say, so maybe 90s is even too optimistic because by the 90s, you know, we already understood

	59:40.520 --> 59:41.520
	backprop.

	59:41.520 --> 59:44.720
	We already understood, you know, the engine of deep learning, even though we couldn't

	59:44.720 --> 59:50.440
	really see its potential quite today, I don't think we found the engine of program synthesis.

	59:50.440 --> 59:52.960
	So we're in the winter before backprop.

	59:52.960 --> 59:53.960
	Yeah.

	59:53.960 --> 59:55.760
	In a way, yes.

	59:55.760 --> 1:00:02.400
	So I do believe program synthesis, in general, discrete search over rule based models is going

	1:00:02.400 --> 1:00:06.960
	to be a cornerstone of AI research in the next century, right?

	1:00:06.960 --> 1:00:10.240
	And that doesn't mean we're going to drop deep learning.

	1:00:10.240 --> 1:00:11.960
	Deep learning is immensely useful.

	1:00:11.960 --> 1:00:19.480
	Like being able to learn this is a very flexible, adaptable, parametric models, that's actually

	1:00:19.480 --> 1:00:20.480
	immensely useful.

	1:00:20.480 --> 1:00:24.960
	Like all it's doing, it's pattern cognition, but being good at pattern cognition, given

	1:00:24.960 --> 1:00:27.880
	lots of data is just extremely powerful.

	1:00:27.880 --> 1:00:31.000
	So we are still going to be working on deep learning and we're going to be working on

	1:00:31.000 --> 1:00:32.000
	program synthesis.

	1:00:32.000 --> 1:00:36.520
	We're going to be combining the two in increasingly automated ways.

	1:00:36.520 --> 1:00:38.640
	So let's talk a little bit about data.

	1:00:38.640 --> 1:00:46.120
	You've tweeted about 10,000 deep learning papers have been written about hard coding

	1:00:46.120 --> 1:00:50.280
	priors, about a specific task in a neural network architecture, it works better than

	1:00:50.280 --> 1:00:52.760
	a lack of a prior.

	1:00:52.760 --> 1:00:57.480
	By summarizing all these efforts, they put a name to an architecture, but really what

	1:00:57.480 --> 1:01:01.680
	they're doing is hard coding some priors that improve the performance of the system.

	1:01:01.680 --> 1:01:07.000
	But we get straight to the point, it's probably true.

	1:01:07.000 --> 1:01:12.080
	So you say that you can always buy performance, buy in quotes performance by either training

	1:01:12.080 --> 1:01:17.520
	on more data, better data, or by injecting task information to the architecture of the

	1:01:17.520 --> 1:01:18.520
	preprocessing.

	1:01:18.520 --> 1:01:22.720
	However, this is informative about the generalization power the techniques use, the fundamentals

	1:01:22.720 --> 1:01:23.720
	of ability to generalize.

	1:01:23.720 --> 1:01:30.040
	Do you think we can go far by coming up with better methods for this kind of cheating,

	1:01:30.040 --> 1:01:35.320
	for better methods of large scale annotation of data, so building better priors?

	1:01:35.320 --> 1:01:37.400
	If you've made it, it's not cheating anymore.

	1:01:37.400 --> 1:01:38.400
	Right.

	1:01:38.400 --> 1:01:46.480
	I'm joking about the cheating, but large scale, so basically I'm asking about something

	1:01:46.480 --> 1:01:54.300
	that hasn't, from my perspective, been researched too much is exponential improvement in annotation

	1:01:54.300 --> 1:01:56.800
	of data.

	1:01:56.800 --> 1:01:58.120
	You often think about...

	1:01:58.120 --> 1:02:00.880
	I think it's actually been researched quite a bit.

	1:02:00.880 --> 1:02:06.120
	You just don't see publications about it, because people who publish papers are going

	1:02:06.120 --> 1:02:10.000
	to publish about known benchmarks, sometimes they're going to read a new benchmark.

	1:02:10.000 --> 1:02:14.360
	People who actually have real world large scale defining problems, they're going to spend

	1:02:14.360 --> 1:02:18.800
	a lot of resources into data annotation and good data annotation pipelines, but you don't

	1:02:18.800 --> 1:02:19.800
	see any papers about it.

	1:02:19.800 --> 1:02:20.800
	That's interesting.

	1:02:20.800 --> 1:02:24.600
	Do you think there are certain resources, but do you think there's innovation happening?

	1:02:24.600 --> 1:02:25.920
	Oh, yeah.

	1:02:25.920 --> 1:02:33.960
	To clarify at the point in the twist, machine learning in general is the science of generalization.

	1:02:33.960 --> 1:02:41.080
	You want to generate knowledge that can be reused across different datasets, across different

	1:02:41.080 --> 1:02:42.680
	tasks.

	1:02:42.680 --> 1:02:49.320
	If instead you're looking at one dataset, and then you are hard coding knowledge about

	1:02:49.320 --> 1:02:55.920
	this task into your architecture, this is no more useful than training a network and

	1:02:55.920 --> 1:03:03.160
	then saying, oh, I found these weight values perform well.

	1:03:03.160 --> 1:03:08.720
	David Ha, I don't know if you know David, he had a paper the other day about weight

	1:03:08.720 --> 1:03:13.840
	agnostic neural networks, and this is very interesting paper because it really illustrates

	1:03:13.840 --> 1:03:20.800
	the fact that an architecture, even without weight, an architecture is a knowledge about

	1:03:20.800 --> 1:03:21.800
	a task.

	1:03:21.800 --> 1:03:24.280
	It encodes knowledge.

	1:03:24.280 --> 1:03:31.560
	When it comes to architectures that are uncrafted by researchers, in some cases, it is very,

	1:03:31.560 --> 1:03:39.400
	very clear that all they are doing is artificially reencoding the template that corresponds

	1:03:39.400 --> 1:03:45.240
	to the proper way to solve the task and coding in a given dataset.

	1:03:45.240 --> 1:03:52.120
	For instance, if you've looked at the baby dataset, which is about natural language

	1:03:52.120 --> 1:03:55.800
	question answering, it is generated by an algorithm.

	1:03:55.800 --> 1:03:59.320
	This is a question under pairs that are generated by an algorithm.

	1:03:59.320 --> 1:04:01.680
	The algorithm is solving a certain template.

	1:04:01.680 --> 1:04:06.760
	Turns out, if you craft a network that literally encodes this template, you can solve this

	1:04:06.760 --> 1:04:13.160
	dataset with nearly 100% accuracy, but that doesn't actually tell you anything about how

	1:04:13.160 --> 1:04:17.760
	to solve question answering in general, which is the point.

	1:04:17.760 --> 1:04:21.560
	The question is just the linger on it, whether it's from the data side or from the size of

	1:04:21.560 --> 1:04:22.560
	the network.

	1:04:22.560 --> 1:04:27.960
	I don't know if you've read the blog post by Ray Sutton, the bitter lesson, where he

	1:04:27.960 --> 1:04:33.480
	says the biggest lesson that we can read from 70 years of AI research is that general methods

	1:04:33.480 --> 1:04:38.120
	that leverage computation are ultimately the most effective.

	1:04:38.120 --> 1:04:45.520
	As opposed to figuring out methods that can generalize effectively, do you think we can

	1:04:45.520 --> 1:04:50.720
	get pretty far by just having something that leverages computation and the improvement of

	1:04:50.720 --> 1:04:51.720
	computation?

	1:04:51.720 --> 1:04:52.720
	Yes.

	1:04:52.720 --> 1:04:56.880
	I think Rich is making a very good point, which is that a lot of these papers, which

	1:04:56.880 --> 1:05:03.760
	are actually all about manually hard coding prior knowledge about a task into some system,

	1:05:03.760 --> 1:05:08.720
	doesn't have to be deeply architected into some system, right?

	1:05:08.720 --> 1:05:11.560
	These papers are not actually making any impact.

	1:05:11.560 --> 1:05:18.680
	Instead, what's making really long term impact is very simple, very general systems that

	1:05:18.680 --> 1:05:23.560
	are really agnostic to all these tricks, because these tricks do not generalize.

	1:05:23.560 --> 1:05:31.680
	And of course, the one general and simple thing that you should focus on is that which

	1:05:31.680 --> 1:05:37.360
	leverages computation, because computation, the availability of large scale computation

	1:05:37.360 --> 1:05:40.720
	has been increasing exponentially, following Morse law.

	1:05:40.720 --> 1:05:46.160
	So if your algorithm is all about exploiting this, then your algorithm is suddenly exponentially

	1:05:46.160 --> 1:05:47.640
	improving, right?

	1:05:47.640 --> 1:05:51.800
	So I think Rich is definitely right.

	1:05:51.800 --> 1:05:59.520
	However, he's right about the past 70 years, he's like assessing the past 70 years.

	1:05:59.520 --> 1:06:05.440
	I am not sure that this assessment will still hold true for the next 70 years.

	1:06:05.440 --> 1:06:12.040
	It might, to some extent, I suspect it will not, because the truth of his assessment is

	1:06:12.040 --> 1:06:17.040
	a function of the context, right, in which this research took place.

	1:06:17.040 --> 1:06:22.560
	And the context is changing, like Morse law might not be applicable anymore, for instance,

	1:06:22.560 --> 1:06:24.080
	in the future.

	1:06:24.080 --> 1:06:32.320
	And I do believe that when you tweak one aspect of a system, when you exploit one aspect

	1:06:32.320 --> 1:06:36.680
	of a system, some other aspect starts becoming the bottleneck.

	1:06:36.680 --> 1:06:41.640
	Let's say you have unlimited computation, well, then data is the bottleneck.

	1:06:41.640 --> 1:06:46.560
	And I think we are already starting to be in a regime where our systems are so large

	1:06:46.560 --> 1:06:50.960
	in scale and so data ingrained, the data today, and the quality of data, and the scale of

	1:06:50.960 --> 1:06:53.280
	data is the bottleneck.

	1:06:53.280 --> 1:07:00.960
	And in this environment, the beta lesson from Rich is not going to be true anymore, right?

	1:07:00.960 --> 1:07:08.000
	So I think we are going to move from a focus on a scale of a competition scale to focus

	1:07:08.000 --> 1:07:10.080
	on data efficiency.

	1:07:10.080 --> 1:07:11.080
	Data efficiency.

	1:07:11.080 --> 1:07:13.240
	So that's getting to the question of symbolic AI.

	1:07:13.240 --> 1:07:19.120
	But the linger on the deep learning approaches, do you have hope for either unsupervised learning

	1:07:19.120 --> 1:07:28.280
	or reinforcement learning, which are ways of being more data efficient in terms of the

	1:07:28.280 --> 1:07:31.720
	amount of data they need that require human annotation?

	1:07:31.720 --> 1:07:36.320
	So unsupervised learning and reinforcement learning are frameworks for learning, but

	1:07:36.320 --> 1:07:39.080
	they are not like any specific technique.

	1:07:39.080 --> 1:07:42.800
	So usually when people say reinforcement learning, what they really mean is deep reinforcement

	1:07:42.800 --> 1:07:47.440
	learning, which is like one approach which is actually very questionable.

	1:07:47.440 --> 1:07:53.440
	The question I was asking was unsupervised learning with deep neural networks and deeper

	1:07:53.440 --> 1:07:54.440
	reinforcement learning.

	1:07:54.440 --> 1:07:58.840
	Well, these are not really data efficient because you're still leveraging these huge

	1:07:58.840 --> 1:08:03.760
	parametric models, point by point with gradient descent.

	1:08:03.760 --> 1:08:09.000
	It is more efficient in terms of the number of annotations, the density of annotations

	1:08:09.000 --> 1:08:10.000
	you need.

	1:08:10.000 --> 1:08:16.680
	The idea being to learn the latent space around which the data is organized and then map the

	1:08:16.680 --> 1:08:18.960
	sparse annotations into it.

	1:08:18.960 --> 1:08:23.640
	And sure, I mean, that's clearly a very good idea.

	1:08:23.640 --> 1:08:27.960
	It's not really a topic I would be working on, but it's clearly a good idea.

	1:08:27.960 --> 1:08:32.040
	So it would get us to solve some problems that...

	1:08:32.040 --> 1:08:38.280
	It will get us to incremental improvements in labeled data efficiency.

	1:08:38.280 --> 1:08:46.640
	Do you have concerns about short term or long term threats from AI, from artificial intelligence?

	1:08:46.640 --> 1:08:50.720
	Yes, definitely to some extent.

	1:08:50.720 --> 1:08:52.360
	And what's the shape of those concerns?

	1:08:52.360 --> 1:08:57.200
	This is actually something I've briefly written about.

	1:08:57.200 --> 1:09:06.160
	But the capabilities of deep learning technology can be used in many ways that are concerning

	1:09:06.160 --> 1:09:13.920
	from mass surveillance with things like facial recognition, in general, tracking lots of

	1:09:13.920 --> 1:09:20.040
	data about everyone and then being able to making sense of this data, to do identification,

	1:09:20.040 --> 1:09:22.520
	to do prediction.

	1:09:22.520 --> 1:09:23.520
	That's concerning.

	1:09:23.520 --> 1:09:31.680
	That's something that's being very aggressively pursued by totalitarian states like China.

	1:09:31.680 --> 1:09:40.760
	One thing I am very much concerned about is that our lives are increasingly online, are

	1:09:40.760 --> 1:09:45.960
	increasingly digital, made of information, made of information consumption and information

	1:09:45.960 --> 1:09:52.160
	production or digital footprint, I would say.

	1:09:52.160 --> 1:10:01.200
	And if you absorb all of this data and you are in control of where you consume information,

	1:10:01.200 --> 1:10:10.160
	social networks and so on, recommendation engines, then you can build a sort of reinforcement

	1:10:10.160 --> 1:10:13.920
	loop for human behavior.

	1:10:13.920 --> 1:10:18.440
	You can observe the state of your mind at time t.

	1:10:18.440 --> 1:10:25.040
	You can predict how you would react to different pieces of content, how to get you to move

	1:10:25.040 --> 1:10:33.280
	your mind in a certain direction, then you can feed the specific piece of content that

	1:10:33.280 --> 1:10:35.920
	would move you in a specific direction.

	1:10:35.920 --> 1:10:45.000
	And you can do this at scale in terms of doing it continuously in real time.

	1:10:45.000 --> 1:10:50.560
	You can also do it at scale in terms of scaling this to many, many people, to entire populations.

	1:10:50.560 --> 1:10:57.800
	So potentially, artificial intelligence, even in its current state, if you combine it with

	1:10:57.800 --> 1:11:04.120
	the internet, with the fact that we have all of our lives are moving to digital devices

	1:11:04.120 --> 1:11:11.800
	and digital information consumption and creation, what you get is the possibility to achieve

	1:11:11.800 --> 1:11:16.960
	mass manipulation of behavior and mass psychological control.

	1:11:16.960 --> 1:11:18.360
	And this is a very real possibility.

	1:11:18.360 --> 1:11:22.240
	Yeah, so you're talking about any kind of recommender system.

	1:11:22.240 --> 1:11:28.160
	Let's look at the YouTube algorithm, Facebook, anything that recommends content you should

	1:11:28.160 --> 1:11:35.480
	watch next, and it's fascinating to think that there's some aspects of human behavior

	1:11:35.480 --> 1:11:45.520
	that you can say a problem of, is this person hold Republican beliefs or Democratic beliefs?

	1:11:45.520 --> 1:11:52.720
	And it's a trivial, that's an objective function, and you can optimize and you can measure and

	1:11:52.720 --> 1:11:55.720
	you can turn everybody into a Republican or everybody into a Democrat.

	1:11:55.720 --> 1:11:56.720
	Absolutely, yeah.

	1:11:56.720 --> 1:11:57.960
	I do believe it's true.

	1:11:57.960 --> 1:12:02.520
	So the human mind is very...

	1:12:02.520 --> 1:12:06.760
	If you look at the human mind as a kind of computer program, it has a very large exploit

	1:12:06.760 --> 1:12:07.760
	surface, right?

	1:12:07.760 --> 1:12:08.760
	It has many, many vulnerabilities.

	1:12:08.760 --> 1:12:09.760
	Exploit surfaces, yeah.

	1:12:09.760 --> 1:12:16.920
	Where you can control it, for instance, when it comes to your political beliefs, this is

	1:12:16.920 --> 1:12:19.360
	very much tied to your identity.

	1:12:19.360 --> 1:12:26.080
	So for instance, if I'm in control of your news feed on your favorite social media platforms,

	1:12:26.080 --> 1:12:29.680
	this is actually where you're getting your news from.

	1:12:29.680 --> 1:12:35.560
	And of course, I can choose to only show you news that will make you see the world in a

	1:12:35.560 --> 1:12:37.200
	specific way, right?

	1:12:37.200 --> 1:12:44.720
	But I can also create incentives for you to post about some political beliefs.

	1:12:44.720 --> 1:12:52.720
	And then when I get you to express a statement, if it's a statement that me as a controller,

	1:12:52.720 --> 1:12:53.720
	I want to reinforce.

	1:12:53.720 --> 1:12:57.080
	I can just show it to people who will agree and they will like it.

	1:12:57.080 --> 1:12:59.400
	And that will reinforce the statement in your mind.

	1:12:59.400 --> 1:13:06.280
	If this is a statement I want you to, this is a belief I want you to abandon, I can,

	1:13:06.280 --> 1:13:10.800
	on the other hand, show it to opponents, right, will attack you.

	1:13:10.800 --> 1:13:16.440
	And because they attack you at the very least, next time you will think twice about posting

	1:13:16.440 --> 1:13:17.440
	it.

	1:13:17.440 --> 1:13:22.920
	But maybe you will even, you know, stop believing this because you got pushed back, right?

	1:13:22.920 --> 1:13:30.560
	So there are many ways in which social media platforms can potentially control your opinions.

	1:13:30.560 --> 1:13:38.320
	And today, the, so all of these things are already being controlled by algorithms.

	1:13:38.320 --> 1:13:43.080
	These algorithms do not have any explicit political goal today.

	1:13:43.080 --> 1:13:50.960
	Well, potentially they could, like if some totalitarian government takes over, you know,

	1:13:50.960 --> 1:13:55.280
	social media platforms and decides that, you know, now we're going to use this not just

	1:13:55.280 --> 1:13:59.960
	for my surveillance, but also for my opinion control and behavior control, very bad things

	1:13:59.960 --> 1:14:02.000
	could happen.

	1:14:02.000 --> 1:14:08.680
	But what's really fascinating and actually quite concerning is that even without an

	1:14:08.680 --> 1:14:15.480
	explicit intent to manipulate, you're already seeing very dangerous dynamics in terms of

	1:14:15.480 --> 1:14:19.960
	how this content recommendation algorithms behave.

	1:14:19.960 --> 1:14:26.920
	Because right now, the goal, the objective function of these algorithms is to maximize

	1:14:26.920 --> 1:14:32.600
	engagement, right, which seems fairly innocuous at first, right?

	1:14:32.600 --> 1:14:40.400
	However, it is not because content that will maximally engage people, you know, get people

	1:14:40.400 --> 1:14:44.480
	to react in an emotional way, get people to click on something.

	1:14:44.480 --> 1:14:54.480
	It is very often content that, you know, is not healthy to the public discourse.

	1:14:54.480 --> 1:15:01.560
	For instance, fake news are far more likely to get you to click on them than real news,

	1:15:01.560 --> 1:15:07.080
	simply because they are not constrained to reality.

	1:15:07.080 --> 1:15:14.120
	So they can be as outrageous, as surprising as good stories as you want, because they

	1:15:14.120 --> 1:15:15.120
	are artificial, right?

	1:15:15.120 --> 1:15:16.120
	Yeah.

	1:15:16.120 --> 1:15:19.640
	To me, that's an exciting world because so much good can come.

	1:15:19.640 --> 1:15:24.680
	So there's an opportunity to educate people.

	1:15:24.680 --> 1:15:31.200
	You can balance people's worldview with other ideas.

	1:15:31.200 --> 1:15:33.880
	So there's so many objective functions.

	1:15:33.880 --> 1:15:41.080
	The space of objective functions that create better civilizations is large, arguably infinite.

	1:15:41.080 --> 1:15:51.720
	But there's also a large space that creates division and destruction, civil war, a lot

	1:15:51.720 --> 1:15:53.360
	of bad stuff.

	1:15:53.360 --> 1:15:59.480
	And the worry is, naturally, probably that space is bigger, first of all.

	1:15:59.480 --> 1:16:06.920
	And if we don't explicitly think about what kind of effects are going to be observed from

	1:16:06.920 --> 1:16:10.280
	different objective functions, then we're going to get into trouble.

	1:16:10.280 --> 1:16:16.400
	Because the question is, how do we get into rooms and have discussions?

	1:16:16.400 --> 1:16:22.200
	So inside Google, inside Facebook, inside Twitter, and think about, okay, how can we

	1:16:22.200 --> 1:16:28.240
	drive up engagement and at the same time create a good society?

	1:16:28.240 --> 1:16:31.760
	Is it even possible to have that kind of philosophical discussion?

	1:16:31.760 --> 1:16:33.200
	I think you can definitely try.

	1:16:33.200 --> 1:16:40.160
	So from my perspective, I would feel rather uncomfortable with companies that are in control

	1:16:40.160 --> 1:16:49.760
	of these new algorithms, with them making explicit decisions to manipulate people's opinions

	1:16:49.760 --> 1:16:55.360
	or behaviors, even if the intent is good, because that's a very totalitarian mindset.

	1:16:55.360 --> 1:16:59.840
	So instead, what I would like to see is probably never going to happen, because it's not super

	1:16:59.840 --> 1:17:02.560
	realistic, but that's actually something I really care about.

	1:17:02.560 --> 1:17:10.680
	I would like all these algorithms to present configuration settings to their users, so

	1:17:10.680 --> 1:17:17.960
	that the users can actually make the decision about how they want to be impacted by these

	1:17:17.960 --> 1:17:22.080
	information recommendation, content recommendation algorithms.

	1:17:22.080 --> 1:17:27.120
	For instance, as a user of something like YouTube or Twitter, maybe I want to maximize

	1:17:27.120 --> 1:17:30.480
	learning about a specific topic.

	1:17:30.480 --> 1:17:38.720
	So I want the algorithm to feed my curiosity, which is in itself a very interesting problem.

	1:17:38.720 --> 1:17:44.840
	So instead of maximizing my engagement, it will maximize how fast and how much I'm learning,

	1:17:44.840 --> 1:17:50.880
	and it will also take into account the accuracy, hopefully, of the information I'm learning.

	1:17:50.880 --> 1:17:57.800
	So yeah, the user should be able to determine exactly how these algorithms are affecting

	1:17:57.800 --> 1:17:58.800
	their lives.

	1:17:58.800 --> 1:18:08.240
	I don't want actually any entity making decisions about in which direction they're going to

	1:18:08.240 --> 1:18:09.480
	try to manipulate me.

	1:18:09.480 --> 1:18:11.840
	I want technology.

	1:18:11.840 --> 1:18:18.520
	So AI, these algorithms are increasingly going to be our interface to a world that is increasingly

	1:18:18.520 --> 1:18:20.280
	made of information.

	1:18:20.280 --> 1:18:27.440
	And I want everyone to be in control of this interface, to interface with the world on

	1:18:27.440 --> 1:18:29.160
	their own terms.

	1:18:29.160 --> 1:18:38.040
	So if someone wants these algorithms to serve their own personal growth goals, they should

	1:18:38.040 --> 1:18:41.920
	be able to configure these algorithms in such a way.

	1:18:41.920 --> 1:18:50.400
	Yeah, but so I know it's painful to have explicit decisions, but there is underlying explicit

	1:18:50.400 --> 1:18:57.240
	decisions, which is some of the most beautiful fundamental philosophy that we have before

	1:18:57.240 --> 1:19:01.200
	us, which is personal growth.

	1:19:01.200 --> 1:19:08.080
	If I want to watch videos from which I can learn, what does that mean?

	1:19:08.080 --> 1:19:13.600
	So if I have a checkbox that wants to emphasize learning, there's still an algorithm with

	1:19:13.600 --> 1:19:18.000
	explicit decisions in it that would promote learning.

	1:19:18.000 --> 1:19:19.000
	What does that mean for me?

	1:19:19.000 --> 1:19:25.440
	Like, for example, I've watched a documentary on Flat Earth theory, I guess.

	1:19:25.440 --> 1:19:28.200
	It was very, like, I learned a lot.

	1:19:28.200 --> 1:19:29.880
	I'm really glad I watched it.

	1:19:29.880 --> 1:19:35.480
	It was a friend recommended it to me, because I don't have such an allergic reaction to

	1:19:35.480 --> 1:19:37.800
	crazy people as my fellow colleagues do.

	1:19:37.800 --> 1:19:42.320
	But it was very eye opening, and for others, it might not be.

	1:19:42.320 --> 1:19:47.640
	From others, they might just get turned off from the same with the Republican and Democrat.

	1:19:47.640 --> 1:19:50.480
	And it's a non trivial problem.

	1:19:50.480 --> 1:19:56.440
	And first of all, if it's done well, I don't think it's something that wouldn't happen

	1:19:56.440 --> 1:20:00.160
	that the YouTube wouldn't be promoting or Twitter wouldn't be.

	1:20:00.160 --> 1:20:02.400
	It's just a really difficult problem.

	1:20:02.400 --> 1:20:05.080
	How do we do, how do give people control?

	1:20:05.080 --> 1:20:09.000
	Well, it's mostly an interface design problem.

	1:20:09.000 --> 1:20:16.280
	The way I see it, you want to create technology that's like a mentor or a coach or an assistant

	1:20:16.280 --> 1:20:22.680
	so that it's not your boss, right, you are in control of it.

	1:20:22.680 --> 1:20:25.920
	You are telling it what to do for you.

	1:20:25.920 --> 1:20:30.760
	And if you feel like it's manipulating you, it's not actually, it's not actually doing

	1:20:30.760 --> 1:20:31.920
	what you want.

	1:20:31.920 --> 1:20:35.040
	You should be able to switch to a different algorithm, you know.

	1:20:35.040 --> 1:20:39.720
	So that fine tune control, you kind of learn, you're trusting the human collaboration.

	1:20:39.720 --> 1:20:44.440
	I mean, that's how I see autonomous vehicles, too, is giving as much information as possible

	1:20:44.440 --> 1:20:46.560
	and you learn that dance yourself.

	1:20:46.560 --> 1:20:51.040
	Yeah, Adobe, I don't know if you use Adobe product for like Photoshop.

	1:20:51.040 --> 1:20:56.600
	Yeah, they're trying to see if they can inject YouTube into their interface, but basically

	1:20:56.600 --> 1:21:01.920
	allow you to show you all these videos that, because everybody's confused about what to

	1:21:01.920 --> 1:21:03.360
	do with features.

	1:21:03.360 --> 1:21:09.720
	So basically teach people by linking to, in that way, it's an assistant that shows, uses

	1:21:09.720 --> 1:21:12.960
	videos as a basic element of information.

	1:21:12.960 --> 1:21:23.080
	Okay, so what practically should people do to try to, to try to fight against abuses of

	1:21:23.080 --> 1:21:26.880
	these algorithms or algorithms that manipulate us?

	1:21:26.880 --> 1:21:31.080
	Honestly, it's a very, very difficult problem because to start with, there is very little

	1:21:31.080 --> 1:21:34.120
	public awareness of these issues.

	1:21:34.120 --> 1:21:39.960
	Very few people would think that, you know, anything wrong with their new algorithm, even

	1:21:39.960 --> 1:21:44.440
	though there is actually something wrong already, which is that it's trying to maximize engagement

	1:21:44.440 --> 1:21:50.000
	most of the time, which has very negative side effects, right?

	1:21:50.000 --> 1:21:59.760
	So ideally, so the very first thing is to stop trying to purely maximize engagement, try

	1:21:59.760 --> 1:22:11.000
	to propagate content based on popularity, right, instead take into account the goals

	1:22:11.000 --> 1:22:13.640
	and the profiles of each user.

	1:22:13.640 --> 1:22:20.200
	So you will, you will be, one example is, for instance, when I look at topic recommendations

	1:22:20.200 --> 1:22:25.640
	on Twitter, it's like, you know, they have this news tab with switch recommendations.

	1:22:25.640 --> 1:22:33.480
	That's always the worst garbage because it's content that appeals to the smallest command

	1:22:33.480 --> 1:22:37.560
	denominator to all Twitter users because they're trying to optimize, they're purely

	1:22:37.560 --> 1:22:41.680
	trying to obtain us popularity, they're purely trying to optimize engagement, but that's

	1:22:41.680 --> 1:22:43.080
	not what I want.

	1:22:43.080 --> 1:22:50.440
	So they should put me in control of some setting so that I define what's the objective function

	1:22:50.440 --> 1:22:54.280
	that Twitter is going to be following to show me this content.

	1:22:54.280 --> 1:22:59.320
	And honestly, so this is all about interface design, and we are not, it's not realistic

	1:22:59.320 --> 1:23:04.760
	to give users control of a bunch of knobs that define an algorithm, instead, we should

	1:23:04.760 --> 1:23:11.200
	purely put them in charge of defining the objective function, like let the user tell

	1:23:11.200 --> 1:23:15.320
	us what they want to achieve, how they want this algorithm to impact their lives.

	1:23:15.320 --> 1:23:20.200
	So do you think it is that or do they provide individual article by article reward structure

	1:23:20.200 --> 1:23:24.760
	where you give a signal, I'm glad I saw this or I'm glad I didn't?

	1:23:24.760 --> 1:23:31.520
	So like a Spotify type feedback mechanism, it works to some extent, I'm kind of skeptical

	1:23:31.520 --> 1:23:38.920
	about it because the only way the algorithm, the algorithm will attempt to relate your choices

	1:23:38.920 --> 1:23:45.040
	with the choices of everyone else, which might, you know, if you have an average profile that

	1:23:45.040 --> 1:23:49.680
	works fine, I'm sure Spotify accommodations work fine if you just like mainstream stuff.

	1:23:49.680 --> 1:23:54.040
	But if you don't, it can be, it's not optimal at all, actually.

	1:23:54.040 --> 1:24:00.880
	It'll be in an efficient search for the part of the Spotify world that represents you.

	1:24:00.880 --> 1:24:09.000
	So it's a tough problem, but do note that even a feedback system like what Spotify has

	1:24:09.000 --> 1:24:15.680
	does not give me control over why the algorithm is trying to optimize for.

	1:24:15.680 --> 1:24:21.440
	Well, public awareness, which is what we're doing now, is a good place to start.

	1:24:21.440 --> 1:24:27.760
	Do you have concerns about long term existential threats of artificial intelligence?

	1:24:27.760 --> 1:24:34.800
	Well, as I was saying, our world is increasingly made of information, AI algorithms are increasingly

	1:24:34.800 --> 1:24:40.280
	going to be our interface to this world of information, and somebody will be in control

	1:24:40.280 --> 1:24:46.000
	of these algorithms, and that puts us in any kind of bad situation, right?

	1:24:46.000 --> 1:24:48.120
	It has risks.

	1:24:48.120 --> 1:24:55.000
	It has risks coming from potentially large companies wanting to optimize their own goals,

	1:24:55.000 --> 1:25:01.760
	maybe profit, maybe something else, also from governments who might want to use these algorithms

	1:25:01.760 --> 1:25:04.720
	as a means of control of the entire population.

	1:25:04.720 --> 1:25:07.560
	Do you think there's existential threat that could arise from that?

	1:25:07.560 --> 1:25:15.840
	So existential threat, so maybe you're referring to the singularity narrative where robots

	1:25:15.840 --> 1:25:16.840
	just take over?

	1:25:16.840 --> 1:25:22.040
	Well, I don't not terminate a robot, and I don't believe it has to be a singularity.

	1:25:22.040 --> 1:25:30.000
	We're just talking to, just like you said, the algorithm controlling masses of populations,

	1:25:30.000 --> 1:25:37.840
	the existential threat being hurt ourselves much like a nuclear war would hurt ourselves,

	1:25:37.840 --> 1:25:38.840
	that kind of thing.

	1:25:38.840 --> 1:25:44.600
	I don't think that requires a singularity, that requires a loss of control over AI algorithms.

	1:25:44.600 --> 1:25:47.920
	So I do agree there are concerning trends.

	1:25:47.920 --> 1:25:53.600
	Honestly, I wouldn't want to make any long term predictions.

	1:25:53.600 --> 1:25:59.560
	I don't think today we really have the capability to see what the dangers of AI are going to

	1:25:59.560 --> 1:26:02.240
	be in 50 years, in 100 years.

	1:26:02.240 --> 1:26:11.480
	I do see that we are already faced with concrete and present dangers surrounding the negative

	1:26:11.480 --> 1:26:17.280
	side effects of content recombination systems of new seed algorithms concerning algorithmic

	1:26:17.280 --> 1:26:19.520
	bias as well.

	1:26:19.520 --> 1:26:26.000
	So we are delegating more and more decision processes to algorithms.

	1:26:26.000 --> 1:26:30.160
	Some of these algorithms are uncrafted, some are learned from data.

	1:26:30.160 --> 1:26:34.040
	But we are delegating control.

	1:26:34.040 --> 1:26:37.240
	Sometimes it's a good thing, sometimes not so much.

	1:26:37.240 --> 1:26:41.720
	And there is in general very little supervision of this process.

	1:26:41.720 --> 1:26:50.160
	So we are still in this period of very fast change, even chaos, where society is restructuring

	1:26:50.160 --> 1:26:56.160
	itself, turning into an information society, which itself is turning into an increasingly

	1:26:56.160 --> 1:26:59.240
	automated information processing society.

	1:26:59.240 --> 1:27:05.760
	And well, yeah, I think the best we can do today is try to raise awareness around some

	1:27:05.760 --> 1:27:06.760
	of these issues.

	1:27:06.760 --> 1:27:13.000
	And I think we are actually making good progress if you look at algorithmic bias, for instance.

	1:27:13.000 --> 1:27:17.240
	Three years ago, even two years ago, very, very few people were talking about it.

	1:27:17.240 --> 1:27:22.400
	And now all the big companies are talking about it, often not in a very serious way,

	1:27:22.400 --> 1:27:24.600
	but at least it is part of the public discourse.

	1:27:24.600 --> 1:27:27.360
	You see people in Congress talking about it.

	1:27:27.360 --> 1:27:32.840
	And it all started from raising awareness.

	1:27:32.840 --> 1:27:40.200
	So in terms of alignment problem, trying to teach as we allow algorithms, just even recommend

	1:27:40.200 --> 1:27:50.280
	their systems on Twitter, encoding human values and morals, decisions that touch on ethics.

	1:27:50.280 --> 1:27:52.640
	How hard do you think that problem is?

	1:27:52.640 --> 1:27:59.800
	How do we have lost functions in neural networks that have some component, some fuzzy components

	1:27:59.800 --> 1:28:01.280
	of human morals?

	1:28:01.280 --> 1:28:07.400
	Well, I think this is really all about objective function engineering, which is probably going

	1:28:07.400 --> 1:28:10.680
	to be increasingly a topic of concern in the future.

	1:28:10.680 --> 1:28:16.160
	Like for now, we are just using very naive loss functions because the hard part is not

	1:28:16.160 --> 1:28:19.240
	actually what you're trying to minimize, it's everything else.

	1:28:19.240 --> 1:28:25.280
	But as the everything else is going to be increasingly automated, we're going to be

	1:28:25.280 --> 1:28:30.920
	focusing our human attention on increasingly high level components, like what's actually

	1:28:30.920 --> 1:28:34.040
	driving the whole learning system, like the objective function.

	1:28:34.040 --> 1:28:38.360
	So loss function engineering is going to be, loss function engineer is probably going to

	1:28:38.360 --> 1:28:40.760
	be a job title in the future.

	1:28:40.760 --> 1:28:46.200
	And then the tooling you're creating with Keras essentially takes care of all the details

	1:28:46.200 --> 1:28:52.960
	underneath and basically the human expert is needed for exactly that.

	1:28:52.960 --> 1:28:59.240
	Keras is the interface between the data you're collecting and the business goals.

	1:28:59.240 --> 1:29:04.280
	And your job as an engineer is going to be to express your business goals and your understanding

	1:29:04.280 --> 1:29:10.440
	of your business or your product, your system as a kind of loss function or a kind of set

	1:29:10.440 --> 1:29:11.440
	of constraints.

	1:29:11.440 --> 1:29:19.560
	Does the possibility of creating an AGI system excite you or scare you or bore you?

	1:29:19.560 --> 1:29:23.600
	So intelligence can never really be general, you know, at best it can have some degree

	1:29:23.600 --> 1:29:26.600
	of generality, like human intelligence.

	1:29:26.600 --> 1:29:30.720
	And it's also always as some specialization in the same way that human intelligence is

	1:29:30.720 --> 1:29:35.680
	specialized in a certain category of problems, is specialized in the human experience.

	1:29:35.680 --> 1:29:41.440
	And when people talk about AGI, I'm never quite sure if they're talking about very,

	1:29:41.440 --> 1:29:46.200
	very smart AI, so smart that it's even smarter than humans, or they're talking about human

	1:29:46.200 --> 1:29:49.880
	like intelligence, because these are different things.

	1:29:49.880 --> 1:29:54.840
	Let's say, presumably I'm oppressing you today with my humanness.

	1:29:54.840 --> 1:29:59.400
	So imagine that I was in fact a robot.

	1:29:59.400 --> 1:30:02.400
	So what does that mean?

	1:30:02.400 --> 1:30:05.160
	I'm oppressing you with natural language processing.

	1:30:05.160 --> 1:30:08.320
	Maybe if you weren't able to see me, maybe this is a phone call.

	1:30:08.320 --> 1:30:09.320
	That kind of system.

	1:30:09.320 --> 1:30:10.320
	Okay.

	1:30:10.320 --> 1:30:11.320
	So companion.

	1:30:11.320 --> 1:30:15.200
	So that's very much about building human like AI.

	1:30:15.200 --> 1:30:18.200
	And you're asking me, you know, is this an exciting perspective?

	1:30:18.200 --> 1:30:19.200
	Yes.

	1:30:19.200 --> 1:30:21.960
	I think so, yes.

	1:30:21.960 --> 1:30:29.640
	Not so much because of what artificial human like intelligence could do, but, you know,

	1:30:29.640 --> 1:30:34.240
	from an intellectual perspective, I think if you could build truly human like intelligence,

	1:30:34.240 --> 1:30:40.160
	that means you could actually understand human intelligence, which is fascinating, right?

	1:30:40.160 --> 1:30:44.480
	Human like intelligence is going to require emotions, it's going to require consciousness,

	1:30:44.480 --> 1:30:48.640
	which is not things that would normally be required by an intelligent system.

	1:30:48.640 --> 1:30:55.560
	If you look at, you know, we were mentioning earlier like science as a superhuman problem

	1:30:55.560 --> 1:31:02.240
	solving agent or system, it does not have consciousness, it doesn't have emotions.

	1:31:02.240 --> 1:31:07.760
	In general, so emotions, I see consciousness as being on the same spectrum as emotions.

	1:31:07.760 --> 1:31:17.560
	It is a component of the subjective experience that is meant very much to guide behavior

	1:31:17.560 --> 1:31:20.880
	generation, right, it's meant to guide your behavior.

	1:31:20.880 --> 1:31:27.080
	In general, human intelligence and animal intelligence has evolved for the purpose of

	1:31:27.080 --> 1:31:30.760
	behavior generation, right, including in a social context.

	1:31:30.760 --> 1:31:32.600
	So that's why we actually need emotions.

	1:31:32.600 --> 1:31:35.080
	That's why we need consciousness.

	1:31:35.080 --> 1:31:39.280
	An artificial intelligence system developed in a different context may well never need

	1:31:39.280 --> 1:31:43.280
	them, may well never be conscious like science.

	1:31:43.280 --> 1:31:50.160
	But on that point, I would argue it's possible to imagine that there's echoes of consciousness

	1:31:50.160 --> 1:31:55.640
	in science when viewed as an organism, that science is consciousness.

	1:31:55.640 --> 1:31:59.320
	So I mean, how would you go about testing this hypothesis?

	1:31:59.320 --> 1:32:07.240
	How do you probe the subjective experience of an abstract system like science?

	1:32:07.240 --> 1:32:12.280
	Well the point of probing any subjective experience is impossible, because I'm not science, I'm

	1:32:12.280 --> 1:32:13.280
	a science.

	1:32:13.280 --> 1:32:20.720
	So I can't probe another entity's, another, it's no more than bacteria on my skin.

	1:32:20.720 --> 1:32:25.360
	Your legs, I can ask you questions about your subjective experience and you can answer me.

	1:32:25.360 --> 1:32:27.720
	And that's how I know you're conscious.

	1:32:27.720 --> 1:32:32.080
	Yes, but that's because we speak the same language.

	1:32:32.080 --> 1:32:35.800
	You perhaps, we have to speak the language of science and we have to ask it.

	1:32:35.800 --> 1:32:41.120
	Honestly, I don't think consciousness, just like emotions of pain and pleasure, is not

	1:32:41.120 --> 1:32:47.120
	something that inevitably arises from any sort of sufficiently intelligent information

	1:32:47.120 --> 1:32:48.120
	processing.

	1:32:48.120 --> 1:32:54.080
	It is a feature of the mind and if you've not implemented it explicitly, it is not there.

	1:32:54.080 --> 1:32:59.120
	So you think it's an emergent feature of a particular architecture.

	1:32:59.120 --> 1:33:00.120
	So do you think?

	1:33:00.120 --> 1:33:02.080
	It's a feature in the same sense.

	1:33:02.080 --> 1:33:09.800
	So again, the subjective experience is all about guiding behavior.

	1:33:09.800 --> 1:33:15.560
	If the problems you're trying to solve don't really involve embedded agents, maybe in a

	1:33:15.560 --> 1:33:19.800
	social context, generating behavior and pursuing goals like this.

	1:33:19.800 --> 1:33:23.280
	And if you look at science, that's not really what's happening, even though it is, it is

	1:33:23.280 --> 1:33:29.600
	a form of artificial air in this artificial intelligence in the sense that it is solving

	1:33:29.600 --> 1:33:35.240
	problems, it is committing knowledge, committing solutions and so on.

	1:33:35.240 --> 1:33:41.120
	So if you're not explicitly implementing a subjective experience, implementing certain

	1:33:41.120 --> 1:33:47.120
	emotions and implementing consciousness, it's not going to just spontaneously emerge.

	1:33:47.120 --> 1:33:48.360
	Yeah.

	1:33:48.360 --> 1:33:53.640
	But so for a system like human like intelligent system that has consciousness, do you think

	1:33:53.640 --> 1:33:55.240
	it needs to have a body?

	1:33:55.240 --> 1:33:56.240
	Yes, definitely.

	1:33:56.240 --> 1:33:59.920
	I mean, it doesn't have to be a physical body, right?

	1:33:59.920 --> 1:34:03.680
	And there's not that much difference between a realistic simulation in the real world.

	1:34:03.680 --> 1:34:06.560
	So there has to be something you have to preserve kind of thing.

	1:34:06.560 --> 1:34:07.560
	Yes.

	1:34:07.560 --> 1:34:12.400
	But human like intelligence can only arise in a human like context.

	1:34:12.400 --> 1:34:13.400
	Intelligence needs to be tired.

	1:34:13.400 --> 1:34:20.480
	You need other humans in order for you to demonstrate that you have human like intelligence, essentially.

	1:34:20.480 --> 1:34:29.240
	So what kind of tests and demonstration would be sufficient for you to demonstrate human

	1:34:29.240 --> 1:34:30.480
	like intelligence?

	1:34:30.480 --> 1:34:31.480
	Yeah.

	1:34:31.480 --> 1:34:37.080
	And just out of curiosity, you talked about in terms of theorem proving and program synthesis,

	1:34:37.080 --> 1:34:40.480
	I think you've written about that there's no good benchmarks for this.

	1:34:40.480 --> 1:34:41.480
	Yeah.

	1:34:41.480 --> 1:34:42.480
	That's one of the problems.

	1:34:42.480 --> 1:34:46.560
	So let's talk programs, program synthesis.

	1:34:46.560 --> 1:34:51.440
	So what do you imagine is a good, I think it's related questions for human like intelligence

	1:34:51.440 --> 1:34:53.720
	and for program synthesis.

	1:34:53.720 --> 1:34:56.160
	What's a good benchmark for either or both?

	1:34:56.160 --> 1:34:57.160
	Right.

	1:34:57.160 --> 1:34:59.400
	So I mean, you're actually asking two questions.

	1:34:59.400 --> 1:35:06.520
	Which is one is about quantifying intelligence and comparing the intelligence of an artificial

	1:35:06.520 --> 1:35:08.800
	system to the intelligence for human.

	1:35:08.800 --> 1:35:13.520
	And the other is about a degree to which this intelligence is human like.

	1:35:13.520 --> 1:35:16.800
	It's actually two different questions.

	1:35:16.800 --> 1:35:19.320
	So if you look, you mentioned earlier the Turing test.

	1:35:19.320 --> 1:35:20.320
	Right.

	1:35:20.320 --> 1:35:24.080
	Well, I actually don't like the Turing test because it's very lazy.

	1:35:24.080 --> 1:35:28.960
	It's all about completely bypassing the problem of defining and measuring intelligence.

	1:35:28.960 --> 1:35:34.400
	And instead delegating to a human judge or a panel of human judges.

	1:35:34.400 --> 1:35:38.400
	So it's a total cobalt, right?

	1:35:38.400 --> 1:35:45.640
	If you want to measure how human like an agent is, I think you have to make it interact

	1:35:45.640 --> 1:35:47.920
	with other humans.

	1:35:47.920 --> 1:35:54.120
	Maybe it's not necessarily a good idea to have these other humans be the judges.

	1:35:54.120 --> 1:36:00.800
	Maybe you should just observe BFU and compare it to what the human would actually have done.

	1:36:00.800 --> 1:36:09.160
	When it comes to measuring how smart, how clever an agent is and comparing that to the

	1:36:09.160 --> 1:36:11.240
	degree of human intelligence.

	1:36:11.240 --> 1:36:13.680
	So we're already talking about two things, right?

	1:36:13.680 --> 1:36:20.600
	The degree, kind of like the magnitude of an intelligence and its direction, right?

	1:36:20.600 --> 1:36:23.560
	Like the norm of a vector and its direction.

	1:36:23.560 --> 1:36:27.200
	And the direction is like human likeness.

	1:36:27.200 --> 1:36:32.880
	And the magnitude, the norm is intelligence.

	1:36:32.880 --> 1:36:34.280
	You could call it intelligence, right?

	1:36:34.280 --> 1:36:42.440
	So the direction, your sense, the space of directions that are human like is very narrow.

	1:36:42.440 --> 1:36:49.880
	So the way you would measure the magnitude of intelligence in a system in a way that

	1:36:49.880 --> 1:36:54.960
	also enables you to compare it to that of a human.

	1:36:54.960 --> 1:37:02.000
	Well, if you look at different benchmarks for intelligence today, they're all too focused

	1:37:02.000 --> 1:37:04.480
	on skill at a given task.

	1:37:04.480 --> 1:37:11.080
	That's skill at playing chess, skill at playing Go, skill at playing Dota.

	1:37:11.080 --> 1:37:17.560
	And I think that's not the right way to go about it because you can always be the human

	1:37:17.560 --> 1:37:20.240
	at one specific task.

	1:37:20.240 --> 1:37:25.320
	The reason why our skill at playing Go or at juggling or anything is impressive is because

	1:37:25.320 --> 1:37:29.480
	we are expressing this skill within a certain set of constraints.

	1:37:29.480 --> 1:37:33.840
	If you remove the constraints, the constraints that we have one lifetime, that we have this

	1:37:33.840 --> 1:37:40.120
	body and so on, if you remove the context, if you have unlimited train data, if you

	1:37:40.120 --> 1:37:44.840
	can have access to, you know, for instance, if you look at juggling, if you have no restriction

	1:37:44.840 --> 1:37:50.040
	on the hardware, then achieving arbitrary levels of skill is not very interesting and

	1:37:50.040 --> 1:37:53.960
	says nothing about the amount of intelligence you've achieved.

	1:37:53.960 --> 1:37:59.320
	So if you want to measure intelligence, you need to rigorously define what intelligence

	1:37:59.320 --> 1:38:04.360
	is, which in itself, you know, it's a very challenging problem.

	1:38:04.360 --> 1:38:05.960
	And do you think that's possible?

	1:38:05.960 --> 1:38:06.960
	To define intelligence?

	1:38:06.960 --> 1:38:07.960
	Yes, absolutely.

	1:38:07.960 --> 1:38:11.680
	I mean, you can provide, many people have provided, you know, some definition.

	1:38:11.680 --> 1:38:13.640
	I have my own definition.

	1:38:13.640 --> 1:38:16.520
	Where does your definition begin if it doesn't end?

	1:38:16.520 --> 1:38:25.560
	Well, I think intelligence is essentially the efficiency with which you turn experience

	1:38:25.560 --> 1:38:29.960
	into generalizable programs.

	1:38:29.960 --> 1:38:35.280
	So what that means is it's the efficiency with which you turn a sampling of experience

	1:38:35.280 --> 1:38:46.200
	space into the ability to process a larger chunk of experience space.

	1:38:46.200 --> 1:38:53.480
	So measuring skill can be one proxy because many, many different tasks can be one proxy

	1:38:53.480 --> 1:38:54.680
	for measure intelligence.

	1:38:54.680 --> 1:38:58.880
	But if you want to only measure skill, you should control for two things.

	1:38:58.880 --> 1:39:07.920
	You should control for the amount of experience that your system has and the priors that your

	1:39:07.920 --> 1:39:08.920
	system has.

	1:39:08.920 --> 1:39:14.120
	But if you control, if you look at two agents and you give them the same priors and you

	1:39:14.120 --> 1:39:21.480
	give them the same amount of experience, there is one of the agents that is going to learn

	1:39:21.480 --> 1:39:27.720
	programs, representation, something, a model that will perform well on the larger chunk

	1:39:27.720 --> 1:39:29.760
	of experience space than the other.

	1:39:29.760 --> 1:39:31.920
	And that is the smaller agent.

	1:39:31.920 --> 1:39:32.920
	Yeah.

	1:39:32.920 --> 1:39:39.920
	So if you fix the experience, which generate better programs, better meaning, more generalizable,

	1:39:39.920 --> 1:39:40.920
	that's really interesting.

	1:39:40.920 --> 1:39:42.760
	That's a very nice, clean definition of...

	1:39:42.760 --> 1:39:49.560
	By the way, in this definition, it is already very obvious that intelligence has to be specialized

	1:39:49.560 --> 1:39:53.600
	because you're talking about experience space and you're talking about segments of experience

	1:39:53.600 --> 1:39:54.600
	space.

	1:39:54.600 --> 1:39:59.680
	You're talking about priors and you're talking about experience, all of these things define

	1:39:59.680 --> 1:40:04.840
	the context in which intelligence emerges.

	1:40:04.840 --> 1:40:10.040
	And you can never look at the totality of experience space.

	1:40:10.040 --> 1:40:12.520
	So intelligence has to be specialized.

	1:40:12.520 --> 1:40:16.760
	But it can be sufficiently large, the experience space, even though specialized is a certain

	1:40:16.760 --> 1:40:22.200
	point when the experience space is large enough to where it might as well be general.

	1:40:22.200 --> 1:40:23.200
	It feels general.

	1:40:23.200 --> 1:40:24.200
	It looks general.

	1:40:24.200 --> 1:40:25.200
	I mean, it's very relative.

	1:40:25.200 --> 1:40:29.560
	For instance, many people would say human intelligence is general.

	1:40:29.560 --> 1:40:32.960
	In fact, it is quite specialized.

	1:40:32.960 --> 1:40:37.960
	We can definitely build systems that start from the same innate priors as what humans

	1:40:37.960 --> 1:40:43.720
	have at birth because we already understand fairly well what sort of priors we have as

	1:40:43.720 --> 1:40:44.720
	humans.

	1:40:44.720 --> 1:40:50.680
	Like many people have worked on this problem, most notably, Elzebeth Spelke from Harvard,

	1:40:50.680 --> 1:40:56.240
	and if you know her, she's worked a lot on what she calls a core knowledge.

	1:40:56.240 --> 1:41:02.560
	And it is very much about trying to determine and describe what priors we are born with.

	1:41:02.560 --> 1:41:06.080
	Like language skills and so on and all that kind of stuff.

	1:41:06.080 --> 1:41:07.080
	Exactly.

	1:41:07.080 --> 1:41:11.520
	So we have some pretty good understanding of what priors we are born with.

	1:41:11.520 --> 1:41:13.960
	So we could...

	1:41:13.960 --> 1:41:18.720
	So I've actually been working on a benchmark for the past couple of years, on and off.

	1:41:18.720 --> 1:41:21.440
	I hope to be able to release it at some point.

	1:41:21.440 --> 1:41:29.120
	The idea is to measure the intelligence of systems by considering for priors, considering

	1:41:29.120 --> 1:41:34.840
	for amount of experience, and by assuming the same priors as what humans are born with

	1:41:34.840 --> 1:41:40.160
	so that you can actually compare these scores to human intelligence and you can actually

	1:41:40.160 --> 1:41:44.440
	have humans pass the same test in a way that's fair.

	1:41:44.440 --> 1:41:54.720
	And so importantly, such a benchmark should be such that any amount of practicing does

	1:41:54.720 --> 1:41:56.800
	not increase your score.

	1:41:56.800 --> 1:42:04.120
	So try to picture a game where no matter how much you play this game, it does not change

	1:42:04.120 --> 1:42:05.400
	your skill at the game.

	1:42:05.400 --> 1:42:08.600
	Can you picture that?

	1:42:08.600 --> 1:42:14.840
	As a person who deeply appreciates practice, I cannot actually...

	1:42:14.840 --> 1:42:19.040
	There's actually a very simple trick.

	1:42:19.040 --> 1:42:24.760
	So in order to come up with a task, so the only thing you can measure is skill at a task.

	1:42:24.760 --> 1:42:28.280
	All tasks are going to involve priors.

	1:42:28.280 --> 1:42:32.480
	The trick is to know what they are and to describe that.

	1:42:32.480 --> 1:42:36.040
	And then you make sure that this is the same set of priors as what humans start with.

	1:42:36.040 --> 1:42:41.080
	So you create a task that assumes these priors, that exactly documents these priors, so that

	1:42:41.080 --> 1:42:44.720
	the priors are made explicit and there are no other priors involved.

	1:42:44.720 --> 1:42:52.240
	And then you generate a certain number of samples in experience space for this task.

	1:42:52.240 --> 1:42:59.480
	And this, for one task, assuming that the task is new for the agent passing it, that's

	1:42:59.480 --> 1:43:07.560
	one test of this definition of intelligence that we set up.

	1:43:07.560 --> 1:43:12.360
	And now you can scale that to many different tasks, that each task should be new to the

	1:43:12.360 --> 1:43:13.360
	agent passing it.

	1:43:13.360 --> 1:43:18.680
	And also should be human interpretable and understandable, so that you can actually have

	1:43:18.680 --> 1:43:21.960
	a human pass the same test and then you can compare the score of your machine and the score

	1:43:21.960 --> 1:43:22.960
	of your human.

	1:43:22.960 --> 1:43:23.960
	Which could be a lot.

	1:43:23.960 --> 1:43:28.580
	It could even start a task like MNIST, just as long as you start with the same set of

	1:43:28.580 --> 1:43:29.580
	priors.

	1:43:29.580 --> 1:43:35.880
	Yeah, so the problem with MNIST, humans are already trained to recognize digits.

	1:43:35.880 --> 1:43:44.240
	But let's say we're considering objects that are not digits, some complete arbitrary patterns.

	1:43:44.240 --> 1:43:50.120
	Well, humans already come with visual priors about how to process that.

	1:43:50.120 --> 1:43:55.760
	So in order to make the game fair, you would have to isolate these priors and describe

	1:43:55.760 --> 1:43:58.720
	them and then express them as computational rules.

	1:43:58.720 --> 1:44:03.760
	Having worked a lot with vision science people has exceptionally difficult, a lot of progress

	1:44:03.760 --> 1:44:07.720
	has been made, there's been a lot of good tests, and basically reducing all of human

	1:44:07.720 --> 1:44:09.360
	vision into some good priors.

	1:44:09.360 --> 1:44:14.640
	We're still probably far away from that perfectly, but as a start for a benchmark, that's an

	1:44:14.640 --> 1:44:15.640
	exciting possibility.

	1:44:15.640 --> 1:44:25.320
	Yeah, so Elisabeth Belke actually lists objectness as one of the core knowledge priors.

	1:44:25.320 --> 1:44:26.320
	Objectness.

	1:44:26.320 --> 1:44:27.320
	Cool.

	1:44:27.320 --> 1:44:28.320
	Objectness.

	1:44:28.320 --> 1:44:29.320
	Yeah.

	1:44:29.320 --> 1:44:33.000
	So we have priors about objectness, like about the visual space, about time, about agents,

	1:44:33.000 --> 1:44:34.600
	about goal oriented behavior.

	1:44:34.600 --> 1:44:42.680
	We have many different priors, but what's interesting is that, sure, we have this pretty

	1:44:42.680 --> 1:44:48.520
	diverse and rich set of priors, but it's also not that diverse, right?

	1:44:48.520 --> 1:44:52.560
	We are not born into this world with a ton of knowledge about the world.

	1:44:52.560 --> 1:44:59.240
	There is only a small set of core knowledge, right?

	1:44:59.240 --> 1:45:00.240
	Yeah.

	1:45:00.240 --> 1:45:07.120
	So do you have a sense of how it feels to us humans that that set is not that large,

	1:45:07.120 --> 1:45:11.920
	but just even the nature of time that we kind of integrate pretty effectively through all

	1:45:11.920 --> 1:45:17.680
	of our perception, all of our reasoning, maybe how, you know, do you have a sense of

	1:45:17.680 --> 1:45:19.880
	how easy it is to encode those priors?

	1:45:19.880 --> 1:45:26.000
	Maybe it requires building a universe, and then the human brain in order to encode those

	1:45:26.000 --> 1:45:27.000
	priors.

	1:45:27.000 --> 1:45:30.680
	Or do you have a hope that it can be listed like an XAMAT?

	1:45:30.680 --> 1:45:31.680
	I don't think so.

	1:45:31.680 --> 1:45:36.480
	So you have to keep in mind that any knowledge about the world that we are born with is something

	1:45:36.480 --> 1:45:43.280
	that has to have been encoded into our DNA by evolution at some point.

	1:45:43.280 --> 1:45:50.720
	And DNA is a very, very low bandwidth medium, like it's extremely long and expensive to

	1:45:50.720 --> 1:45:57.120
	encode anything into DNA, because first of all, you need some sort of evolutionary pressure

	1:45:57.120 --> 1:45:59.400
	to guide this writing process.

	1:45:59.400 --> 1:46:05.720
	And then, you know, the higher level of information you're trying to write, the longer it's going

	1:46:05.720 --> 1:46:13.960
	to be, and the thing in the environment that you're trying to encode knowledge about has

	1:46:13.960 --> 1:46:17.240
	to be stable over this duration.

	1:46:17.240 --> 1:46:22.840
	So you can only encode into DNA things that constitute an evolutionary advantage.

	1:46:22.840 --> 1:46:27.120
	So this is actually a very small subset of all possible knowledge about the world.

	1:46:27.120 --> 1:46:33.360
	You can only encode things that are stable, that are true over very, very long periods

	1:46:33.360 --> 1:46:35.480
	of time, typically millions of years.

	1:46:35.480 --> 1:46:40.520
	For instance, we might have some visual prior about the shape of snakes, right?

	1:46:40.520 --> 1:46:43.800
	But what makes a face?

	1:46:43.800 --> 1:46:46.440
	What's the difference between a face and a nonface?

	1:46:46.440 --> 1:46:49.840
	But consider this interesting question.

	1:46:49.840 --> 1:46:57.800
	Do we have any innate sense of the visual difference between a male face and a female

	1:46:57.800 --> 1:46:58.800
	face?

	1:46:58.800 --> 1:46:59.800
	What do you think?

	1:46:59.800 --> 1:47:01.320
	For a human, I mean.

	1:47:01.320 --> 1:47:05.920
	I would have to look back into evolutionary history when the genders emerged.

	1:47:05.920 --> 1:47:11.280
	But yeah, most, I mean, the faces of humans are quite different from the faces of great

	1:47:11.280 --> 1:47:14.000
	apes, great apes, right?

	1:47:14.000 --> 1:47:15.000
	Yeah.

	1:47:15.000 --> 1:47:16.000
	That's interesting.

	1:47:16.000 --> 1:47:17.000
	But yeah.

	1:47:17.000 --> 1:47:23.200
	You couldn't tell the face of a female chimpanzee from the face of a male chimpanzee, probably.

	1:47:23.200 --> 1:47:24.200
	Yeah.

	1:47:24.200 --> 1:47:26.720
	And I don't think most humans evolve all that ability.

	1:47:26.720 --> 1:47:33.160
	We do have innate knowledge of what makes a face, but it's actually impossible for us

	1:47:33.160 --> 1:47:39.200
	to have any DNA encoding knowledge of the difference between a female human face and

	1:47:39.200 --> 1:47:40.680
	a male human face.

	1:47:40.680 --> 1:47:50.800
	Because that knowledge, that information came up into the world actually very recently.

	1:47:50.800 --> 1:47:56.920
	If you look at the slowness of the process of encoding knowledge into DNA.

	1:47:56.920 --> 1:47:57.920
	Yeah.

	1:47:57.920 --> 1:47:58.920
	So that's interesting.

	1:47:58.920 --> 1:48:01.640
	That's a really powerful argument that DNA is a low bandwidth and it takes a long time

	1:48:01.640 --> 1:48:05.480
	to encode that naturally creates a very efficient encoding.

	1:48:05.480 --> 1:48:12.400
	But one important consequence of this is that, so yes, we are born into this world with a

	1:48:12.400 --> 1:48:17.440
	bunch of knowledge, sometimes very high level knowledge about the world like the rough shape

	1:48:17.440 --> 1:48:20.800
	of a snake, of the rough shape of a face.

	1:48:20.800 --> 1:48:27.040
	But importantly, because this knowledge takes so long to write, almost all of this innate

	1:48:27.040 --> 1:48:33.360
	knowledge is shared with our cousins, with great apes, right?

	1:48:33.360 --> 1:48:37.600
	So it is not actually this innate knowledge that makes us special.

	1:48:37.600 --> 1:48:44.120
	But to throw it right back at you from the earlier on in our discussion, that encoding

	1:48:44.120 --> 1:48:50.600
	might also include the entirety of the environment of Earth.

	1:48:50.600 --> 1:48:56.520
	To sum up, so it can include things that are important to survival and production.

	1:48:56.520 --> 1:49:01.840
	So for which there is some evolutionary pressure and things that are stable, constant over

	1:49:01.840 --> 1:49:05.240
	very, very, very long time periods.

	1:49:05.240 --> 1:49:07.440
	And honestly, it's not that much information.

	1:49:07.440 --> 1:49:15.600
	There's also, besides the bandwidths, constraints and constraints of the writing process, there's

	1:49:15.600 --> 1:49:22.600
	also memory constraints like DNA, the part of DNA that deals with the human brain, it's

	1:49:22.600 --> 1:49:23.600
	actually fairly small.

	1:49:23.600 --> 1:49:26.360
	It's like, you know, on the order of megabytes, right?

	1:49:26.360 --> 1:49:31.880
	There's not that much high level knowledge about the world you can encode.

	1:49:31.880 --> 1:49:39.400
	That's quite brilliant and hopeful for a benchmark that you're referring to of encoding priors.

	1:49:39.400 --> 1:49:43.680
	I actually look forward to, I'm skeptical that you can do it in the next couple of years,

	1:49:43.680 --> 1:49:44.680
	but hopefully...

	1:49:44.680 --> 1:49:45.960
	I've been working on it.

	1:49:45.960 --> 1:49:50.120
	So honestly, it's a very simple benchmark and it's not like a big breakthrough or anything.

	1:49:50.120 --> 1:49:53.920
	It's more like a fun side project, right?

	1:49:53.920 --> 1:49:56.720
	So is ImageNet.

	1:49:56.720 --> 1:50:04.120
	These fun side projects could launch entire groups of efforts towards creating reasoning

	1:50:04.120 --> 1:50:05.120
	systems and so on.

	1:50:05.120 --> 1:50:06.120
	And I think...

	1:50:06.120 --> 1:50:07.120
	Yeah, that's the goal.

	1:50:07.120 --> 1:50:12.160
	It's trying to measure strong generalization, to measure the strength of abstraction in

	1:50:12.160 --> 1:50:17.160
	our minds, well, in our minds and in an artificially intelligent agency.

	1:50:17.160 --> 1:50:24.960
	And if there's anything true about this science organism, it's individual cells love competition.

	1:50:24.960 --> 1:50:27.000
	So benchmarks encourage competition.

	1:50:27.000 --> 1:50:29.680
	So that's an exciting possibility.

	1:50:29.680 --> 1:50:30.680
	If you...

	1:50:30.680 --> 1:50:35.720
	Do you think an AI winter is coming and how do we prevent it?

	1:50:35.720 --> 1:50:36.720
	Not really.

	1:50:36.720 --> 1:50:42.160
	So an AI winter is something that would occur when there's a big mismatch between how we

	1:50:42.160 --> 1:50:47.560
	are selling the capabilities of AI and the actual capabilities of AI.

	1:50:47.560 --> 1:50:52.000
	And today, deep learning is creating a lot of value and it will keep creating a lot of

	1:50:52.000 --> 1:50:59.360
	value in the sense that these models are applicable to a very wide range of problems that are

	1:50:59.360 --> 1:51:00.360
	even today.

	1:51:00.360 --> 1:51:05.320
	And we are only just getting started with applying algorithms to every problem they

	1:51:05.320 --> 1:51:06.520
	could be solving.

	1:51:06.520 --> 1:51:10.440
	So deep learning will keep creating a lot of value for the time being.

	1:51:10.440 --> 1:51:16.000
	What's concerning, however, is that there's a lot of hype around deep learning and around

	1:51:16.000 --> 1:51:17.000
	AI.

	1:51:17.000 --> 1:51:22.840
	A lot of people are overselling the capabilities of these systems, not just the capabilities

	1:51:22.840 --> 1:51:31.520
	but also overselling the fact that they might be more or less brain like, like given a kind

	1:51:31.520 --> 1:51:40.480
	of a mystical aspect, these technologies, and also overselling the pace of progress,

	1:51:40.480 --> 1:51:46.000
	which it might look fast in the sense that we have this exponentially increasing number

	1:51:46.000 --> 1:51:48.080
	of papers.

	1:51:48.080 --> 1:51:53.000
	But again, that's just a simple consequence of the fact that we have ever more people

	1:51:53.000 --> 1:51:54.000
	coming into the field.

	1:51:54.000 --> 1:51:58.000
	It doesn't mean the progress is actually exponentially fast.

	1:51:58.000 --> 1:52:02.960
	Like, let's say you're trying to raise money for your startup or your research lab.

	1:52:02.960 --> 1:52:09.120
	You might want to tell, you know, a grand yos story to investors about how deep learning

	1:52:09.120 --> 1:52:14.240
	is just like the brain and how it can solve all these incredible problems like self driving

	1:52:14.240 --> 1:52:19.040
	and robotics and so on, and maybe you can tell them that the field is progressing so fast

	1:52:19.040 --> 1:52:27.000
	and we're going to have AI within 15 years or even 10 years, and none of this is true.

	1:52:27.000 --> 1:52:33.320
	And every time you're like saying these things and an investor or, you know, a decision maker

	1:52:33.320 --> 1:52:43.400
	believes them, well, this is like the equivalent of taking on credit card debt, but for trust.

	1:52:43.400 --> 1:52:50.920
	And maybe this will, you know, this will be what enables you to raise a lot of money,

	1:52:50.920 --> 1:52:55.160
	but ultimately you are creating damage, you are damaging the field.

	1:52:55.160 --> 1:53:01.240
	That's the concern is that debt, that's what happens with the other AI winters is the concern

	1:53:01.240 --> 1:53:04.440
	is you actually tweeted about this with autonomous vehicles, right?

	1:53:04.440 --> 1:53:08.960
	There's almost every single company now have promised that they will have full autonomous

	1:53:08.960 --> 1:53:12.000
	vehicles by 2021, 2022.

	1:53:12.000 --> 1:53:18.280
	That's a good example of the consequences of overhyping the capabilities of AI and the

	1:53:18.280 --> 1:53:19.280
	pace of progress.

	1:53:19.280 --> 1:53:25.160
	So because I work especially a lot recently in this area, I have a deep concern of what

	1:53:25.160 --> 1:53:30.480
	happens when all of these companies after every invested billions have a meeting and

	1:53:30.480 --> 1:53:33.720
	say, how much do we actually, first of all, do we have an autonomous vehicle?

	1:53:33.720 --> 1:53:36.360
	The answer will definitely be no.

	1:53:36.360 --> 1:53:40.680
	And second will be, wait a minute, we've invested one, two, three, four billion dollars

	1:53:40.680 --> 1:53:43.400
	into this and we made no profit.

	1:53:43.400 --> 1:53:49.280
	And the reaction to that may be going very hard in other directions that might impact

	1:53:49.280 --> 1:53:50.840
	you that even other industries.

	1:53:50.840 --> 1:53:55.320
	And that's what we call in the air winter is when there is backlash where no one believes

	1:53:55.320 --> 1:54:00.600
	any of these promises anymore because they've turned out to be big lies the first time around.

	1:54:00.600 --> 1:54:06.120
	And this will definitely happen to some extent for autonomous vehicles because the public

	1:54:06.120 --> 1:54:13.440
	and decision makers have been convinced that around 2015, they've been convinced by these

	1:54:13.440 --> 1:54:19.600
	people who are trying to raise money for their startups and so on, that L5 driving was coming

	1:54:19.600 --> 1:54:23.120
	in maybe 2016, maybe 2017, May 2018.

	1:54:23.120 --> 1:54:28.040
	Now in 2019, we're still waiting for it.

	1:54:28.040 --> 1:54:32.880
	And so I don't believe we are going to have a full on AI winter because we have these

	1:54:32.880 --> 1:54:39.480
	technologies that are producing a tremendous amount of real value, but there is also too

	1:54:39.480 --> 1:54:40.480
	much hype.

	1:54:40.480 --> 1:54:45.240
	So there will be some backlash, especially there will be backlash.

	1:54:45.240 --> 1:54:53.080
	So some startups are trying to sell the dream of AGI and the fact that AGI is going to create

	1:54:53.080 --> 1:54:54.080
	infinite value.

	1:54:54.080 --> 1:55:01.240
	AGI is like a freelance, like if you can develop an AI system that passes a certain threshold

	1:55:01.240 --> 1:55:06.440
	of IQ or something, then suddenly you have infinite value.

	1:55:06.440 --> 1:55:11.640
	And well, there are actually lots of investors buying into this idea.

	1:55:11.640 --> 1:55:18.920
	And they will wait maybe 10, 15 years and nothing will happen.

	1:55:18.920 --> 1:55:22.800
	And the next time around, well, maybe there will be a new generation of investors, no

	1:55:22.800 --> 1:55:24.040
	one will care.

	1:55:24.040 --> 1:55:27.160
	Human memory is very short after all.

	1:55:27.160 --> 1:55:34.440
	I don't know about you, but because I've spoken about AGI sometimes poetically, I get a lot

	1:55:34.440 --> 1:55:42.360
	of emails from people giving me, they're usually like a large manifestos of, they say to me

	1:55:42.360 --> 1:55:48.320
	that they have created an AGI system or they know how to do it and there's a long write

	1:55:48.320 --> 1:55:49.320
	up of how to do it.

	1:55:49.320 --> 1:55:51.400
	I get a lot of these emails.

	1:55:51.400 --> 1:55:57.840
	They're a little bit feel like it's generated by an AI system actually, but there's usually

	1:55:57.840 --> 1:55:58.840
	no backup.

	1:55:58.840 --> 1:56:04.920
	Maybe that's recursively self improving AI, it's you have a transformer generating crank

	1:56:04.920 --> 1:56:06.880
	papers about a GI.

	1:56:06.880 --> 1:56:12.160
	So the question is about, because you've been such a good, you have a good radar for crank

	1:56:12.160 --> 1:56:16.960
	papers, how do we know they're not onto something?

	1:56:16.960 --> 1:56:24.320
	How do I, so when you start to talk about AGI or anything like the reasoning benchmarks

	1:56:24.320 --> 1:56:28.720
	and so on, so something that doesn't have a benchmark, it's really difficult to know.

	1:56:28.720 --> 1:56:35.480
	I mean, I talked to Jeff Hawkins who's really looking at neuroscience approaches to how,

	1:56:35.480 --> 1:56:41.800
	and there's some, there's echoes of really interesting ideas in at least Jeff's case,

	1:56:41.800 --> 1:56:43.520
	which he's showing.

	1:56:43.520 --> 1:56:45.840
	How do you usually think about this?

	1:56:45.840 --> 1:56:52.920
	Like preventing yourself from being too narrow minded and elitist about deep learning.

	1:56:52.920 --> 1:56:57.040
	It has to work on these particular benchmarks, otherwise it's trash.

	1:56:57.040 --> 1:57:05.880
	Well, the thing is intelligence does not exist in the abstract.

	1:57:05.880 --> 1:57:07.440
	Intelligence has to be applied.

	1:57:07.440 --> 1:57:11.040
	So if you don't have a benchmark, if you don't have an improvement on some benchmark, maybe

	1:57:11.040 --> 1:57:12.680
	it's a new benchmark.

	1:57:12.680 --> 1:57:16.760
	Maybe it's not something we've been looking at before, but you do need a problem that

	1:57:16.760 --> 1:57:17.760
	you're trying to solve.

	1:57:17.760 --> 1:57:21.040
	You're not going to come up with a solution without a problem.

	1:57:21.040 --> 1:57:26.760
	So you, general intelligence, I mean, you've clearly highlighted generalization.

	1:57:26.760 --> 1:57:31.320
	If you want to claim that you have an intelligence system, it should come with a benchmark.

	1:57:31.320 --> 1:57:35.960
	It should, yes, it should display capabilities of some kind.

	1:57:35.960 --> 1:57:41.920
	It should show that it can create some form of value, even if it's a very artificial form

	1:57:41.920 --> 1:57:43.160
	of value.

	1:57:43.160 --> 1:57:48.840
	And that's also the reason why you don't actually need to care about telling which papers have

	1:57:48.840 --> 1:57:53.520
	actually some hidden potential and which do not.

	1:57:53.520 --> 1:57:58.880
	Because if there is a new technique, it's actually creating value.

	1:57:58.880 --> 1:58:02.640
	This is going to be brought to light very quickly because it's actually making a difference.

	1:58:02.640 --> 1:58:08.240
	So it's a difference between something that is ineffectual and something that is actually

	1:58:08.240 --> 1:58:09.240
	useful.

	1:58:09.240 --> 1:58:14.120
	And ultimately, usefulness is our guide, not just in this field, but if you look at science

	1:58:14.120 --> 1:58:19.560
	in general, maybe there are many, many people over the years that have had some really interesting

	1:58:19.560 --> 1:58:23.120
	theories of everything, but they were just completely useless.

	1:58:23.120 --> 1:58:28.240
	And you don't actually need to tell the interesting theories from the useless theories.

	1:58:28.240 --> 1:58:34.120
	All you need is to see, you know, is this actually having an effect on something else?

	1:58:34.120 --> 1:58:35.600
	You know, is this actually useful?

	1:58:35.600 --> 1:58:37.960
	Is this making an impact or not?

	1:58:37.960 --> 1:58:42.480
	As beautifully put, I mean, the same applies to quantum mechanics, to string theory, to

	1:58:42.480 --> 1:58:43.480
	the holographic principle.

	1:58:43.480 --> 1:58:46.080
	We are doing deep learning because it works.

	1:58:46.080 --> 1:58:52.720
	Before it started working, people considered people working on neural networks as cranks

	1:58:52.720 --> 1:58:53.720
	very much.

	1:58:53.720 --> 1:58:56.560
	Like, you know, no one was working on this anymore.

	1:58:56.560 --> 1:58:59.400
	And now it's working, which is what makes it valuable.

	1:58:59.400 --> 1:59:02.840
	It's not about being right, it's about being effective.

	1:59:02.840 --> 1:59:08.120
	And nevertheless, the individual entities of this scientific mechanism, just like Yoshio

	1:59:08.120 --> 1:59:13.160
	Banjo or Yanlacun, they, while being called cranks, stuck with it, right?

	1:59:13.160 --> 1:59:19.080
	And so, us individual agents, even if everyone's laughing at us, should stick with it.

	1:59:19.080 --> 1:59:23.840
	If you believe you have something, you should stick with it and see it through.

	1:59:23.840 --> 1:59:25.920
	That's a beautiful, inspirational message to end on.

	1:59:25.920 --> 1:59:27.800
	Francois, thank you so much for talking today.

	1:59:27.800 --> 1:59:28.800
	That was amazing.

	1:59:28.800 --> 1:59:35.800
	Thank you.