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	WEBVTT

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	The following is a conversation with Francois Chollet.

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	He's the creator of Keras,

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	which is an open source deep learning library

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	that is designed to enable fast, user friendly experimentation

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	with deep neural networks.

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	It serves as an interface to several deep learning libraries,

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	most popular of which is TensorFlow,

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	and it was integrated into the TensorFlow main code base

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	a while ago.

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	Meaning, if you want to create, train,

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	and use neural networks,

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	probably the easiest and most popular option

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	is to use Keras inside TensorFlow.

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	Aside from creating an exceptionally useful

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	and popular library,

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	Francois is also a world class AI researcher

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	and software engineer at Google.

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	And he's definitely an outspoken,

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	if not controversial personality in the AI world,

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	especially in the realm of ideas

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	around the future of artificial intelligence.

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	This is the Artificial Intelligence Podcast.

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	If you enjoy it, subscribe on YouTube,

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	give it five stars on iTunes,

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	support it on Patreon,

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	or simply connect with me on Twitter

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	at Lex Friedman, spelled F R I D M A N.

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	And now, here's my conversation with Francois Chollet.

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	You're known for not sugarcoating your opinions

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	and speaking your mind about ideas in AI,

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	especially on Twitter.

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	It's one of my favorite Twitter accounts.

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	So what's one of the more controversial ideas

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	you've expressed online and gotten some heat for?

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	How do you pick?

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	How do I pick?

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	Yeah, no, I think if you go through the trouble

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	of maintaining a Twitter account,

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	you might as well speak your mind, you know?

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	Otherwise, what's even the point of having a Twitter account?

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	It's like having a nice car

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	and just leaving it in the garage.

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	Yeah, so what's one thing for which I got

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	a lot of pushback?

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	Perhaps, you know, that time I wrote something

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	about the idea of intelligence explosion,

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	and I was questioning the idea

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	and the reasoning behind this idea.

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	And I got a lot of pushback on that.

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	I got a lot of flak for it.

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	So yeah, so intelligence explosion,

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	I'm sure you're familiar with the idea,

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	but it's the idea that if you were to build

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	general AI problem solving algorithms,

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	well, the problem of building such an AI,

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	that itself is a problem that could be solved by your AI,

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	and maybe it could be solved better

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	than what humans can do.

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	So your AI could start tweaking its own algorithm,

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	could start making a better version of itself,

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	and so on iteratively in a recursive fashion.

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	And so you would end up with an AI

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	with exponentially increasing intelligence.

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	That's right.

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	And I was basically questioning this idea,

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	first of all, because the notion of intelligence explosion

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	uses an implicit definition of intelligence

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	that doesn't sound quite right to me.

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	It considers intelligence as a property of a brain

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	that you can consider in isolation,

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	like the height of a building, for instance.

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	But that's not really what intelligence is.

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	Intelligence emerges from the interaction

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	between a brain, a body,

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	like embodied intelligence, and an environment.

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	And if you're missing one of these pieces,

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	then you cannot really define intelligence anymore.

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	So just tweaking a brain to make it smaller and smaller

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	doesn't actually make any sense to me.

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	So first of all,

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	you're crushing the dreams of many people, right?

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	So there's a, let's look at like Sam Harris.

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	Actually, a lot of physicists, Max Tegmark,

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	people who think the universe

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	is an information processing system,

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	our brain is kind of an information processing system.

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	So what's the theoretical limit?

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	Like, it doesn't make sense that there should be some,

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	it seems naive to think that our own brain

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	is somehow the limit of the capabilities

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	of this information system.

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	I'm playing devil's advocate here.

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	This information processing system.

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	And then if you just scale it,

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	if you're able to build something

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	that's on par with the brain,

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	you just, the process that builds it just continues

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	and it'll improve exponentially.

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	So that's the logic that's used actually

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	by almost everybody

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	that is worried about super human intelligence.

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	So you're trying to make,

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	so most people who are skeptical of that

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	are kind of like, this doesn't,

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	their thought process, this doesn't feel right.

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	Like that's for me as well.

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	So I'm more like, it doesn't,

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	the whole thing is shrouded in mystery

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	where you can't really say anything concrete,

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	but you could say this doesn't feel right.

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	This doesn't feel like that's how the brain works.

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	And you're trying to with your blog posts

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	and now making it a little more explicit.

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	So one idea is that the brain isn't exist alone.

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	It exists within the environment.

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	So you can't exponentially,

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	you would have to somehow exponentially improve

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	the environment and the brain together almost.

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	Yeah, in order to create something that's much smarter

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	in some kind of,

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	of course we don't have a definition of intelligence.

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	That's correct, that's correct.

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	I don't think, you should look at very smart people today,

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	even humans, not even talking about AIs.

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	I don't think their brain

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	and the performance of their brain is the bottleneck

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	to their expressed intelligence, to their achievements.

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	You cannot just tweak one part of this system,

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	like of this brain, body, environment system

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	and expect that capabilities like what emerges

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	out of this system to just explode exponentially.

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	Because anytime you improve one part of a system

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	with many interdependencies like this,

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	there's a new bottleneck that arises, right?

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	And I don't think even today for very smart people,

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	their brain is not the bottleneck

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	to the sort of problems they can solve, right?

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	In fact, many very smart people today,

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	you know, they are not actually solving

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	any big scientific problems, they're not Einstein.

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	They're like Einstein, but you know, the patent clerk days.

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	Like Einstein became Einstein

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	because this was a meeting of a genius

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	with a big problem at the right time, right?

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	But maybe this meeting could have never happened

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	and then Einstein would have just been a patent clerk, right?

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	And in fact, many people today are probably like

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	genius level smart, but you wouldn't know

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	because they're not really expressing any of that.

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	Wow, that's brilliant.

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	So we can think of the world, Earth,

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	but also the universe as just as a space of problems.

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	So all these problems and tasks are roaming it

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	of various difficulty.

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	And there's agents, creatures like ourselves

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	and animals and so on that are also roaming it.

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	And then you get coupled with a problem

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	and then you solve it.

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	But without that coupling,

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	you can't demonstrate your quote unquote intelligence.

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	Exactly, intelligence is the meeting

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	of great problem solving capabilities

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	with a great problem.

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	And if you don't have the problem,

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	you don't really express any intelligence.

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	All you're left with is potential intelligence,

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	like the performance of your brain

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	or how high your IQ is,

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	which in itself is just a number, right?

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	So you mentioned problem solving capacity.

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	Yeah.

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	What do you think of as problem solving capacity?

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	Can you try to define intelligence?

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	Like what does it mean to be more or less intelligent?

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	Is it completely coupled to a particular problem

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	or is there something a little bit more universal?

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	Yeah, I do believe all intelligence

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	is specialized intelligence.

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	Even human intelligence has some degree of generality.

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	Well, all intelligent systems have some degree of generality

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	but they're always specialized in one category of problems.

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	So the human intelligence is specialized

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	in the human experience.

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	And that shows at various levels,

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	that shows in some prior knowledge that's innate

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	that we have at birth.

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	Knowledge about things like agents,

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	goal driven behavior, visual priors

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	about what makes an object, priors about time and so on.

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	That shows also in the way we learn.

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	For instance, it's very, very easy for us

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	to pick up language.

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	It's very, very easy for us to learn certain things

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	because we are basically hard coded to learn them.

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	And we are specialized in solving certain kinds of problem

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	and we are quite useless

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	when it comes to other kinds of problems.

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	For instance, we are not really designed

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	to handle very long term problems.

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	We have no capability of seeing the very long term.

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	We don't have very much working memory.

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	So how do you think about long term?

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	Do you think long term planning,

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	are we talking about scale of years, millennia?

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	What do you mean by long term?

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	We're not very good.

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	Well, human intelligence is specialized

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	in the human experience.

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	And human experience is very short.

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	One lifetime is short.

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	Even within one lifetime,

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	we have a very hard time envisioning things

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	on a scale of years.

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	It's very difficult to project yourself

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	at a scale of five years, at a scale of 10 years and so on.

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	We can solve only fairly narrowly scoped problems.

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	So when it comes to solving bigger problems,

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	larger scale problems,

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	we are not actually doing it on an individual level.

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	So it's not actually our brain doing it.

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	We have this thing called civilization, right?

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	Which is itself a sort of problem solving system,

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	a sort of artificially intelligent system, right?

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	And it's not running on one brain,

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	it's running on a network of brains.

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	In fact, it's running on much more

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	than a network of brains.

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	It's running on a lot of infrastructure,

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	like books and computers and the internet

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	and human institutions and so on.

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	And that is capable of handling problems

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	on a much greater scale than any individual human.

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	If you look at computer science, for instance,

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	that's an institution that solves problems

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	and it is superhuman, right?

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	It operates on a greater scale.

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	It can solve much bigger problems

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	than an individual human could.

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	And science itself, science as a system, as an institution,

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	is a kind of artificially intelligent problem solving

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	algorithm that is superhuman.

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	Yeah, it's, at least computer science

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	is like a theorem prover at a scale of thousands,

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	maybe hundreds of thousands of human beings.

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	At that scale, what do you think is an intelligent agent?

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	So there's us humans at the individual level,

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	there is millions, maybe billions of bacteria in our skin.

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	There is, that's at the smaller scale.

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	You can even go to the particle level

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	as systems that behave,

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	you can say intelligently in some ways.

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	And then you can look at the earth as a single organism,

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	you can look at our galaxy

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	and even the universe as a single organism.

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	Do you think, how do you think about scale

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	in defining intelligent systems?

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	And we're here at Google, there is millions of devices

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	doing computation just in a distributed way.

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	How do you think about intelligence versus scale?

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	You can always characterize anything as a system.

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	I think people who talk about things

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	like intelligence explosion,

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	tend to focus on one agent is basically one brain,

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	like one brain considered in isolation,

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	like a brain, a jaw that's controlling a body

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	in a very like top to bottom kind of fashion.

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	And that body is pursuing goals into an environment.

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	So it's a very hierarchical view.

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	You have the brain at the top of the pyramid,

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	then you have the body just plainly receiving orders.

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	And then the body is manipulating objects

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	in the environment and so on.

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	So everything is subordinate to this one thing,

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	this epicenter, which is the brain.

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	But in real life, intelligent agents

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	don't really work like this, right?

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	There is no strong delimitation

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	between the brain and the body to start with.

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	You have to look not just at the brain,

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	but at the nervous system.

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	But then the nervous system and the body

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	are naturally two separate entities.

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	So you have to look at an entire animal as one agent.

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	But then you start realizing as you observe an animal

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	over any length of time,

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	that a lot of the intelligence of an animal

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	is actually externalized.

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	That's especially true for humans.

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	A lot of our intelligence is externalized.

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	When you write down some notes,

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	that is externalized intelligence.

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	When you write a computer program,

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	you are externalizing cognition.

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	So it's externalizing books, it's externalized in computers,

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	the internet, in other humans.

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	It's externalizing language and so on.

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	So there is no hard delimitation

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	of what makes an intelligent agent.

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	It's all about context.

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	Okay, but AlphaGo is better at Go

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	than the best human player.

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	There's levels of skill here.

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	So do you think there's such a ability,

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	such a concept as intelligence explosion

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	in a specific task?

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	And then, well, yeah.

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	Do you think it's possible to have a category of tasks

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	on which you do have something

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	like an exponential growth of ability

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	to solve that particular problem?

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	I think if you consider a specific vertical,

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	it's probably possible to some extent.

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	I also don't think we have to speculate about it

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	because we have real world examples

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	of recursively self improving intelligent systems, right?

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	So for instance, science is a problem solving system,

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	a knowledge generation system,

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	like a system that experiences the world in some sense

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	and then gradually understands it and can act on it.

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	And that system is superhuman

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	and it is clearly recursively self improving

	14:45.600 --> 14:47.560
	because science feeds into technology.

	14:47.560 --> 14:50.200
	Technology can be used to build better tools,

	14:50.200 --> 14:52.880
	better computers, better instrumentation and so on,

	14:52.880 --> 14:56.720
	which in turn can make science faster, right?

	14:56.720 --> 15:00.560
	So science is probably the closest thing we have today

	15:00.560 --> 15:04.760
	to a recursively self improving superhuman AI.

	15:04.760 --> 15:08.520
	And you can just observe is science,

	15:08.520 --> 15:10.320
	is scientific progress to the exploding,

	15:10.320 --> 15:12.800
	which itself is an interesting question.

	15:12.800 --> 15:15.560
	You can use that as a basis to try to understand

	15:15.560 --> 15:17.920
	what will happen with a superhuman AI

	15:17.920 --> 15:21.000
	that has a science like behavior.

	15:21.000 --> 15:23.320
	Let me linger on it a little bit more.

	15:23.320 --> 15:27.600
	What is your intuition why an intelligence explosion

	15:27.600 --> 15:28.560
	is not possible?

	15:28.560 --> 15:30.920
	Like taking the scientific,

	15:30.920 --> 15:33.240
	all the semi scientific revolutions,

	15:33.240 --> 15:38.080
	why can't we slightly accelerate that process?

	15:38.080 --> 15:41.200
	So you can absolutely accelerate

	15:41.200 --> 15:43.120
	any problem solving process.

	15:43.120 --> 15:46.720
	So a recursively self improvement

	15:46.720 --> 15:48.640
	is absolutely a real thing.

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

	15:51.880 --> 15:53.680
	is typically not explosion

	15:53.680 --> 15:56.520
	because no system exists in isolation.

	15:56.520 --> 15:58.640
	And so tweaking one part of the system

	15:58.640 --> 16:00.880
	means that suddenly another part of the system

	16:00.880 --> 16:02.200
	becomes a bottleneck.

	16:02.200 --> 16:03.800
	And if you look at science, for instance,

	16:03.800 --> 16:06.800
	which is clearly a recursively self improving,

	16:06.800 --> 16:09.040
	clearly a problem solving system,

	16:09.040 --> 16:12.000
	scientific progress is not actually exploding.

	16:12.000 --> 16:13.520
	If you look at science,

	16:13.520 --> 16:16.480
	what you see is the picture of a system

	16:16.480 --> 16:19.240
	that is consuming an exponentially increasing

	16:19.240 --> 16:20.520
	amount of resources,

	16:20.520 --> 16:23.960
	but it's having a linear output

	16:23.960 --> 16:26.000
	in terms of scientific progress.

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

	16:28.960 --> 16:31.160
	Many people are actually saying that,

	16:31.160 --> 16:34.560
	scientific progress is exponential,

	16:34.560 --> 16:36.120
	but when they're claiming this,

	16:36.120 --> 16:38.400
	they're actually looking at indicators

	16:38.400 --> 16:43.080
	of resource consumption by science.

	16:43.080 --> 16:46.680
	For instance, the number of papers being published,

	16:47.560 --> 16:49.960
	the number of patents being filed and so on,

	16:49.960 --> 16:53.600
	which are just completely correlated

	16:53.600 --> 16:58.480
	with how many people are working on science today.

	16:58.480 --> 17:00.640
	So it's actually an indicator of resource consumption,

	17:00.640 --> 17:03.200
	but what you should look at is the output,

	17:03.200 --> 17:06.680
	is progress in terms of the knowledge

	17:06.680 --> 17:08.040
	that science generates,

	17:08.040 --> 17:10.640
	in terms of the scope and significance

	17:10.640 --> 17:12.520
	of the problems that we solve.

	17:12.520 --> 17:16.720
	And some people have actually been trying to measure that.

	17:16.720 --> 17:20.160
	Like Michael Nielsen, for instance,

	17:20.160 --> 17:21.920
	he had a very nice paper,

	17:21.920 --> 17:23.720
	I think that was last year about it.

	17:25.200 --> 17:28.360
	So his approach to measure scientific progress

	17:28.360 --> 17:33.360
	was to look at the timeline of scientific discoveries

	17:33.720 --> 17:37.160
	over the past, you know, 100, 150 years.

	17:37.160 --> 17:41.360
	And for each major discovery,

	17:41.360 --> 17:44.360
	ask a panel of experts to rate

	17:44.360 --> 17:46.760
	the significance of the discovery.

	17:46.760 --> 17:49.600
	And if the output of science as an institution

	17:49.600 --> 17:50.440
	were exponential,

	17:50.440 --> 17:55.440
	you would expect the temporal density of significance

	17:56.600 --> 17:58.160
	to go up exponentially.

	17:58.160 --> 18:00.960
	Maybe because there's a faster rate of discoveries,

	18:00.960 --> 18:02.960
	maybe because the discoveries are, you know,

	18:02.960 --> 18:04.920
	increasingly more important.

	18:04.920 --> 18:06.800
	And what actually happens

	18:06.800 --> 18:10.040
	if you plot this temporal density of significance

	18:10.040 --> 18:11.320
	measured in this way,

	18:11.320 --> 18:14.520
	is that you see very much a flat graph.

	18:14.520 --> 18:16.600
	You see a flat graph across all disciplines,

	18:16.600 --> 18:19.720
	across physics, biology, medicine, and so on.

	18:19.720 --> 18:22.480
	And it actually makes a lot of sense

	18:22.480 --> 18:23.320
	if you think about it,

	18:23.320 --> 18:26.000
	because think about the progress of physics

	18:26.000 --> 18:28.000
	110 years ago, right?

	18:28.000 --> 18:30.080
	It was a time of crazy change.

	18:30.080 --> 18:31.960
	Think about the progress of technology,

	18:31.960 --> 18:34.360
	you know, 170 years ago,

	18:34.360 --> 18:35.400
	when we started having, you know,

	18:35.400 --> 18:37.560
	replacing horses with cars,

	18:37.560 --> 18:40.000
	when we started having electricity and so on.

	18:40.000 --> 18:41.520
	It was a time of incredible change.

	18:41.520 --> 18:44.600
	And today is also a time of very, very fast change,

	18:44.600 --> 18:48.040
	but it would be an unfair characterization

	18:48.040 --> 18:50.560
	to say that today technology and science

	18:50.560 --> 18:52.920
	are moving way faster than they did 50 years ago

	18:52.920 --> 18:54.360
	or 100 years ago.

	18:54.360 --> 18:59.360
	And if you do try to rigorously plot

	18:59.520 --> 19:04.520
	the temporal density of the significance,

	19:04.880 --> 19:07.320
	yeah, of significance, sorry,

	19:07.320 --> 19:09.720
	you do see very flat curves.

	19:09.720 --> 19:12.040
	And you can check out the paper

	19:12.040 --> 19:16.000
	that Michael Nielsen had about this idea.

	19:16.000 --> 19:20.000
	And so the way I interpret it is,

	19:20.000 --> 19:24.160
	as you make progress in a given field,

	19:24.160 --> 19:26.120
	or in a given subfield of science,

	19:26.120 --> 19:28.680
	it becomes exponentially more difficult

	19:28.680 --> 19:30.440
	to make further progress.

	19:30.440 --> 19:35.000
	Like the very first person to work on information theory.

	19:35.000 --> 19:36.440
	If you enter a new field,

	19:36.440 --> 19:37.920
	and it's still the very early years,

	19:37.920 --> 19:41.160
	there's a lot of low hanging fruit you can pick.

	19:41.160 --> 19:42.000
	That's right, yeah.

	19:42.000 --> 19:43.960
	But the next generation of researchers

	19:43.960 --> 19:48.160
	is gonna have to dig much harder, actually,

	19:48.160 --> 19:50.640
	to make smaller discoveries,

	19:50.640 --> 19:52.640
	probably larger number of smaller discoveries,

	19:52.640 --> 19:54.640
	and to achieve the same amount of impact,

	19:54.640 --> 19:57.480
	you're gonna need a much greater head count.

	19:57.480 --> 20:00.040
	And that's exactly the picture you're seeing with science,

	20:00.040 --> 20:03.760
	that the number of scientists and engineers

	20:03.760 --> 20:06.520
	is in fact increasing exponentially.

	20:06.520 --> 20:08.400
	The amount of computational resources

	20:08.400 --> 20:10.040
	that are available to science

	20:10.040 --> 20:11.880
	is increasing exponentially and so on.

	20:11.880 --> 20:15.560
	So the resource consumption of science is exponential,

	20:15.560 --> 20:18.200
	but the output in terms of progress,

	20:18.200 --> 20:21.000
	in terms of significance, is linear.

	20:21.000 --> 20:23.120
	And the reason why is because,

	20:23.120 --> 20:26.000
	and even though science is regressively self improving,

	20:26.000 --> 20:28.440
	meaning that scientific progress

	20:28.440 --> 20:30.240
	turns into technological progress,

	20:30.240 --> 20:32.960
	which in turn helps science.

	20:32.960 --> 20:35.280
	If you look at computers, for instance,

	20:35.280 --> 20:38.480
	our products of science and computers

	20:38.480 --> 20:41.560
	are tremendously useful in speeding up science.

	20:41.560 --> 20:43.840
	The internet, same thing, the internet is a technology

	20:43.840 --> 20:47.480
	that's made possible by very recent scientific advances.

	20:47.480 --> 20:52.400
	And itself, because it enables scientists to network,

	20:52.400 --> 20:55.520
	to communicate, to exchange papers and ideas much faster,

	20:55.520 --> 20:57.440
	it is a way to speed up scientific progress.

	20:57.440 --> 20:58.440
	So even though you're looking

	20:58.440 --> 21:01.440
	at a regressively self improving system,

	21:01.440 --> 21:04.080
	it is consuming exponentially more resources

	21:04.080 --> 21:09.080
	to produce the same amount of problem solving, very much.

	21:09.200 --> 21:11.080
	So that's a fascinating way to paint it,

	21:11.080 --> 21:14.960
	and certainly that holds for the deep learning community.

	21:14.960 --> 21:18.120
	If you look at the temporal, what did you call it,

	21:18.120 --> 21:21.240
	the temporal density of significant ideas,

	21:21.240 --> 21:23.920
	if you look at in deep learning,

	21:24.840 --> 21:26.960
	I think, I'd have to think about that,

	21:26.960 --> 21:29.040
	but if you really look at significant ideas

	21:29.040 --> 21:32.400
	in deep learning, they might even be decreasing.

	21:32.400 --> 21:37.400
	So I do believe the per paper significance is decreasing,

	21:39.600 --> 21:41.240
	but the amount of papers

	21:41.240 --> 21:43.440
	is still today exponentially increasing.

	21:43.440 --> 21:45.880
	So I think if you look at an aggregate,

	21:45.880 --> 21:48.840
	my guess is that you would see a linear progress.

	21:48.840 --> 21:53.840
	If you were to sum the significance of all papers,

	21:56.120 --> 21:58.640
	you would see roughly in your progress.

	21:58.640 --> 22:03.640
	And in my opinion, it is not a coincidence

	22:03.880 --> 22:05.800
	that you're seeing linear progress in science

	22:05.800 --> 22:07.720
	despite exponential resource consumption.

	22:07.720 --> 22:10.280
	I think the resource consumption

	22:10.280 --> 22:15.280
	is dynamically adjusting itself to maintain linear progress

	22:15.880 --> 22:18.560
	because we as a community expect linear progress,

	22:18.560 --> 22:21.240
	meaning that if we start investing less

	22:21.240 --> 22:23.600
	and seeing less progress, it means that suddenly

	22:23.600 --> 22:26.800
	there are some lower hanging fruits that become available

	22:26.800 --> 22:31.280
	and someone's gonna step up and pick them, right?

	22:31.280 --> 22:36.280
	So it's very much like a market for discoveries and ideas.

	22:36.920 --> 22:38.720
	But there's another fundamental part

	22:38.720 --> 22:41.800
	which you're highlighting, which as a hypothesis

	22:41.800 --> 22:45.160
	as science or like the space of ideas,

	22:45.160 --> 22:48.160
	any one path you travel down,

	22:48.160 --> 22:51.080
	it gets exponentially more difficult

	22:51.080 --> 22:54.720
	to get a new way to develop new ideas.

	22:54.720 --> 22:57.640
	And your sense is that's gonna hold

	22:57.640 --> 23:01.520
	across our mysterious universe.

	23:01.520 --> 23:03.360
	Yes, well, exponential progress

	23:03.360 --> 23:05.480
	triggers exponential friction.

	23:05.480 --> 23:07.440
	So that if you tweak one part of the system,

	23:07.440 --> 23:10.680
	suddenly some other part becomes a bottleneck, right?

	23:10.680 --> 23:14.880
	For instance, let's say you develop some device

	23:14.880 --> 23:17.160
	that measures its own acceleration

	23:17.160 --> 23:18.720
	and then it has some engine

	23:18.720 --> 23:20.800
	and it outputs even more acceleration

	23:20.800 --> 23:22.360
	in proportion of its own acceleration

	23:22.360 --> 23:23.320
	and you drop it somewhere,

	23:23.320 --> 23:25.240
	it's not gonna reach infinite speed

	23:25.240 --> 23:27.880
	because it exists in a certain context.

	23:29.080 --> 23:31.000
	So the air around it is gonna generate friction

	23:31.000 --> 23:34.320
	and it's gonna block it at some top speed.

	23:34.320 --> 23:37.480
	And even if you were to consider the broader context

	23:37.480 --> 23:39.840
	and lift the bottleneck there,

	23:39.840 --> 23:42.240
	like the bottleneck of friction,

	23:43.120 --> 23:45.120
	then some other part of the system

	23:45.120 --> 23:48.120
	would start stepping in and creating exponential friction,

	23:48.120 --> 23:49.920
	maybe the speed of flight or whatever.

	23:49.920 --> 23:51.920
	And this definitely holds true

	23:51.920 --> 23:54.960
	when you look at the problem solving algorithm

	23:54.960 --> 23:58.160
	that is being run by science as an institution,

	23:58.160 --> 23:59.720
	science as a system.

	23:59.720 --> 24:01.720
	As you make more and more progress,

	24:01.720 --> 24:05.800
	despite having this recursive self improvement component,

	24:06.760 --> 24:09.840
	you are encountering exponential friction.

	24:09.840 --> 24:13.480
	The more researchers you have working on different ideas,

	24:13.480 --> 24:14.880
	the more overhead you have

	24:14.880 --> 24:18.040
	in terms of communication across researchers.

	24:18.040 --> 24:22.920
	If you look at, you were mentioning quantum mechanics, right?

	24:22.920 --> 24:26.880
	Well, if you want to start making significant discoveries

	24:26.880 --> 24:29.680
	today, significant progress in quantum mechanics,

	24:29.680 --> 24:33.000
	there is an amount of knowledge you have to ingest,

	24:33.000 --> 24:34.080
	which is huge.

	24:34.080 --> 24:36.520
	So there's a very large overhead

	24:36.520 --> 24:39.240
	to even start to contribute.

	24:39.240 --> 24:40.680
	There's a large amount of overhead

	24:40.680 --> 24:44.040
	to synchronize across researchers and so on.

	24:44.040 --> 24:47.440
	And of course, the significant practical experiments

	24:48.600 --> 24:52.160
	are going to require exponentially expensive equipment

	24:52.160 --> 24:56.480
	because the easier ones have already been run, right?

	24:56.480 --> 25:00.480
	So in your senses, there's no way escaping,

	25:00.480 --> 25:04.480
	there's no way of escaping this kind of friction

	25:04.480 --> 25:08.600
	with artificial intelligence systems.

	25:08.600 --> 25:11.520
	Yeah, no, I think science is a very good way

	25:11.520 --> 25:14.280
	to model what would happen with a superhuman

	25:14.280 --> 25:16.440
	recursive research improving AI.

	25:16.440 --> 25:18.240
	That's your sense, I mean, the...

	25:18.240 --> 25:19.680
	That's my intuition.

	25:19.680 --> 25:23.400
	It's not like a mathematical proof of anything.

	25:23.400 --> 25:24.400
	That's not my point.

	25:24.400 --> 25:26.600
	Like, I'm not trying to prove anything.

	25:26.600 --> 25:27.920
	I'm just trying to make an argument

	25:27.920 --> 25:31.160
	to question the narrative of intelligence explosion,

	25:31.160 --> 25:32.880
	which is quite a dominant narrative.

	25:32.880 --> 25:35.840
	And you do get a lot of pushback if you go against it.

	25:35.840 --> 25:39.320
	Because, so for many people, right,

	25:39.320 --> 25:42.200
	AI is not just a subfield of computer science.

	25:42.200 --> 25:44.120
	It's more like a belief system.

	25:44.120 --> 25:48.640
	Like this belief that the world is headed towards an event,

	25:48.640 --> 25:55.040
	the singularity, past which, you know, AI will become...

	25:55.040 --> 25:57.080
	will go exponential very much,

	25:57.080 --> 25:58.600
	and the world will be transformed,

	25:58.600 --> 26:00.840
	and humans will become obsolete.

	26:00.840 --> 26:03.880
	And if you go against this narrative,

	26:03.880 --> 26:06.920
	because it is not really a scientific argument,

	26:06.920 --> 26:08.880
	but more of a belief system,

	26:08.880 --> 26:11.240
	it is part of the identity of many people.

	26:11.240 --> 26:12.600
	If you go against this narrative,

	26:12.600 --> 26:14.400
	it's like you're attacking the identity

	26:14.400 --> 26:15.560
	of people who believe in it.

	26:15.560 --> 26:17.640
	It's almost like saying God doesn't exist,

	26:17.640 --> 26:19.000
	or something.

	26:19.000 --> 26:21.880
	So you do get a lot of pushback

	26:21.880 --> 26:24.040
	if you try to question these ideas.

	26:24.040 --> 26:26.520
	First of all, I believe most people,

	26:26.520 --> 26:29.240
	they might not be as eloquent or explicit as you're being,

	26:29.240 --> 26:30.920
	but most people in computer science

	26:30.920 --> 26:33.000
	are most people who actually have built

	26:33.000 --> 26:36.360
	anything that you could call AI, quote, unquote,

	26:36.360 --> 26:38.080
	would agree with you.

	26:38.080 --> 26:40.560
	They might not be describing in the same kind of way.

	26:40.560 --> 26:43.960
	It's more, so the pushback you're getting

	26:43.960 --> 26:48.080
	is from people who get attached to the narrative

	26:48.080 --> 26:51.000
	from, not from a place of science,

	26:51.000 --> 26:53.400
	but from a place of imagination.

	26:53.400 --> 26:54.760
	That's correct, that's correct.

	26:54.760 --> 26:56.920
	So why do you think that's so appealing?

	26:56.920 --> 27:01.920
	Because the usual dreams that people have

	27:02.120 --> 27:03.960
	when you create a superintelligence system

	27:03.960 --> 27:05.120
	past the singularity,

	27:05.120 --> 27:08.600
	that what people imagine is somehow always destructive.

	27:09.440 --> 27:12.240
	Do you have, if you were put on your psychology hat,

	27:12.240 --> 27:17.240
	what's, why is it so appealing to imagine

	27:17.400 --> 27:20.760
	the ways that all of human civilization will be destroyed?

	27:20.760 --> 27:22.080
	I think it's a good story.

	27:22.080 --> 27:23.120
	You know, it's a good story.

	27:23.120 --> 27:28.120
	And very interestingly, it mirrors a religious stories,

	27:28.160 --> 27:30.560
	right, religious mythology.

	27:30.560 --> 27:34.360
	If you look at the mythology of most civilizations,

	27:34.360 --> 27:38.280
	it's about the world being headed towards some final events

	27:38.280 --> 27:40.480
	in which the world will be destroyed

	27:40.480 --> 27:42.800
	and some new world order will arise

	27:42.800 --> 27:44.920
	that will be mostly spiritual,

	27:44.920 --> 27:49.400
	like the apocalypse followed by a paradise probably, right?

	27:49.400 --> 27:52.600
	It's a very appealing story on a fundamental level.

	27:52.600 --> 27:54.560
	And we all need stories.

	27:54.560 --> 27:58.160
	We all need stories to structure the way we see the world,

	27:58.160 --> 27:59.960
	especially at timescales

	27:59.960 --> 28:04.520
	that are beyond our ability to make predictions, right?

	28:04.520 --> 28:08.840
	So on a more serious non exponential explosion,

	28:08.840 --> 28:13.840
	question, do you think there will be a time

	28:15.000 --> 28:19.800
	when we'll create something like human level intelligence

	28:19.800 --> 28:23.800
	or intelligent systems that will make you sit back

	28:23.800 --> 28:28.520
	and be just surprised at damn how smart this thing is?

	28:28.520 --> 28:30.160
	That doesn't require exponential growth

	28:30.160 --> 28:32.120
	or an exponential improvement,

	28:32.120 --> 28:35.600
	but what's your sense of the timeline and so on

	28:35.600 --> 28:40.600
	that you'll be really surprised at certain capabilities?

	28:41.080 --> 28:42.560
	And we'll talk about limitations and deep learning.

	28:42.560 --> 28:44.480
	So do you think in your lifetime,

	28:44.480 --> 28:46.600
	you'll be really damn surprised?

	28:46.600 --> 28:51.440
	Around 2013, 2014, I was many times surprised

	28:51.440 --> 28:53.960
	by the capabilities of deep learning actually.

	28:53.960 --> 28:55.920
	That was before we had assessed exactly

	28:55.920 --> 28:57.880
	what deep learning could do and could not do.

	28:57.880 --> 29:00.600
	And it felt like a time of immense potential.

	29:00.600 --> 29:03.080
	And then we started narrowing it down,

	29:03.080 --> 29:04.360
	but I was very surprised.

	29:04.360 --> 29:07.120
	I would say it has already happened.

	29:07.120 --> 29:10.800
	Was there a moment, there must've been a day in there

	29:10.800 --> 29:14.360
	where your surprise was almost bordering

	29:14.360 --> 29:19.360
	on the belief of the narrative that we just discussed.

	29:19.440 --> 29:20.800
	Was there a moment,

	29:20.800 --> 29:22.400
	because you've written quite eloquently

	29:22.400 --> 29:23.960
	about the limits of deep learning,

	29:23.960 --> 29:25.760
	was there a moment that you thought

	29:25.760 --> 29:27.720
	that maybe deep learning is limitless?

	29:30.000 --> 29:32.400
	No, I don't think I've ever believed this.

	29:32.400 --> 29:35.560
	What was really shocking is that it worked.

	29:35.560 --> 29:37.640
	It worked at all, yeah.

	29:37.640 --> 29:40.520
	But there's a big jump between being able

	29:40.520 --> 29:43.400
	to do really good computer vision

	29:43.400 --> 29:44.920
	and human level intelligence.

	29:44.920 --> 29:49.520
	So I don't think at any point I wasn't under the impression

	29:49.520 --> 29:51.280
	that the results we got in computer vision

	29:51.280 --> 29:54.080
	meant that we were very close to human level intelligence.

	29:54.080 --> 29:56.040
	I don't think we're very close to human level intelligence.

	29:56.040 --> 29:58.520
	I do believe that there's no reason

	29:58.520 --> 30:01.760
	why we won't achieve it at some point.

	30:01.760 --> 30:06.400
	I also believe that it's the problem

	30:06.400 --> 30:08.560
	with talking about human level intelligence

	30:08.560 --> 30:11.240
	that implicitly you're considering

	30:11.240 --> 30:14.360
	like an axis of intelligence with different levels,

	30:14.360 --> 30:16.720
	but that's not really how intelligence works.

	30:16.720 --> 30:19.480
	Intelligence is very multi dimensional.

	30:19.480 --> 30:22.480
	And so there's the question of capabilities,

	30:22.480 --> 30:25.560
	but there's also the question of being human like,

	30:25.560 --> 30:27.040
	and it's two very different things.

	30:27.040 --> 30:28.280
	Like you can build potentially

	30:28.280 --> 30:30.640
	very advanced intelligent agents

	30:30.640 --> 30:32.640
	that are not human like at all.

	30:32.640 --> 30:35.240
	And you can also build very human like agents.

	30:35.240 --> 30:37.840
	And these are two very different things, right?

	30:37.840 --> 30:38.760
	Right.

	30:38.760 --> 30:42.240
	Let's go from the philosophical to the practical.

	30:42.240 --> 30:44.240
	Can you give me a history of Keras

	30:44.240 --> 30:46.440
	and all the major deep learning frameworks

	30:46.440 --> 30:48.480
	that you kind of remember in relation to Keras

	30:48.480 --> 30:52.040
	and in general, TensorFlow, Theano, the old days.

	30:52.040 --> 30:55.400
	Can you give a brief overview Wikipedia style history

	30:55.400 --> 30:59.120
	and your role in it before we return to AGI discussions?

	30:59.120 --> 31:00.720
	Yeah, that's a broad topic.

	31:00.720 --> 31:04.040
	So I started working on Keras.

	31:04.920 --> 31:06.240
	It was the name Keras at the time.

	31:06.240 --> 31:08.320
	I actually picked the name like

	31:08.320 --> 31:10.200
	just the day I was going to release it.

	31:10.200 --> 31:14.800
	So I started working on it in February, 2015.

	31:14.800 --> 31:17.240
	And so at the time there weren't too many people

	31:17.240 --> 31:20.320
	working on deep learning, maybe like fewer than 10,000.

	31:20.320 --> 31:22.840
	The software tooling was not really developed.

	31:25.320 --> 31:28.800
	So the main deep learning library was Cafe,

	31:28.800 --> 31:30.840
	which was mostly C++.

	31:30.840 --> 31:32.760
	Why do you say Cafe was the main one?

	31:32.760 --> 31:36.000
	Cafe was vastly more popular than Theano

	31:36.000 --> 31:38.920
	in late 2014, early 2015.

	31:38.920 --> 31:42.400
	Cafe was the one library that everyone was using

	31:42.400 --> 31:43.400
	for computer vision.

	31:43.400 --> 31:46.120
	And computer vision was the most popular problem

	31:46.120 --> 31:46.960
	in deep learning at the time.

	31:46.960 --> 31:47.800
	Absolutely.

	31:47.800 --> 31:50.440
	Like ConvNets was like the subfield of deep learning

	31:50.440 --> 31:53.160
	that everyone was working on.

	31:53.160 --> 31:57.680
	So myself, so in late 2014,

	31:57.680 --> 32:00.600
	I was actually interested in RNNs,

	32:00.600 --> 32:01.760
	in recurrent neural networks,

	32:01.760 --> 32:05.800
	which was a very niche topic at the time, right?

	32:05.800 --> 32:08.640
	It really took off around 2016.

	32:08.640 --> 32:11.080
	And so I was looking for good tools.

	32:11.080 --> 32:14.800
	I had used Torch 7, I had used Theano,

	32:14.800 --> 32:17.640
	used Theano a lot in Kaggle competitions.

	32:19.320 --> 32:20.840
	I had used Cafe.

	32:20.840 --> 32:25.840
	And there was no like good solution for RNNs at the time.

	32:25.840 --> 32:28.640
	Like there was no reusable open source implementation

	32:28.640 --> 32:30.000
	of an LSTM, for instance.

	32:30.000 --> 32:32.920
	So I decided to build my own.

	32:32.920 --> 32:35.440
	And at first, the pitch for that was,

	32:35.440 --> 32:39.960
	it was gonna be mostly around LSTM recurrent neural networks.

	32:39.960 --> 32:41.360
	It was gonna be in Python.

	32:42.280 --> 32:44.280
	An important decision at the time

	32:44.280 --> 32:45.440
	that was kind of not obvious

	32:45.440 --> 32:50.360
	is that the models would be defined via Python code,

	32:50.360 --> 32:54.400
	which was kind of like going against the mainstream

	32:54.400 --> 32:58.000
	at the time because Cafe, Pylon 2, and so on,

	32:58.000 --> 33:00.600
	like all the big libraries were actually going

	33:00.600 --> 33:03.520
	with the approach of setting configuration files

	33:03.520 --> 33:05.560
	in YAML to define models.

	33:05.560 --> 33:08.840
	So some libraries were using code to define models,

	33:08.840 --> 33:12.280
	like Torch 7, obviously, but that was not Python.

	33:12.280 --> 33:16.680
	Lasagne was like a Theano based very early library

	33:16.680 --> 33:18.640
	that was, I think, developed, I don't remember exactly,

	33:18.640 --> 33:20.240
	probably late 2014.

	33:20.240 --> 33:21.200
	It's Python as well.

	33:21.200 --> 33:22.040
	It's Python as well.

	33:22.040 --> 33:24.320
	It was like on top of Theano.

	33:24.320 --> 33:28.320
	And so I started working on something

	33:29.480 --> 33:32.520
	and the value proposition at the time was that

	33:32.520 --> 33:36.240
	not only what I think was the first

	33:36.240 --> 33:38.800
	reusable open source implementation of LSTM,

	33:40.400 --> 33:44.440
	you could combine RNNs and covenants

	33:44.440 --> 33:45.440
	with the same library,

	33:45.440 --> 33:46.920
	which is not really possible before,

	33:46.920 --> 33:49.080
	like Cafe was only doing covenants.

	33:50.440 --> 33:52.560
	And it was kind of easy to use

	33:52.560 --> 33:54.440
	because, so before I was using Theano,

	33:54.440 --> 33:55.680
	I was actually using scikitlin

	33:55.680 --> 33:58.320
	and I loved scikitlin for its usability.

	33:58.320 --> 34:01.560
	So I drew a lot of inspiration from scikitlin

	34:01.560 --> 34:02.400
	when I made Keras.

	34:02.400 --> 34:05.600
	It's almost like scikitlin for neural networks.

	34:05.600 --> 34:06.680
	The fit function.

	34:06.680 --> 34:07.960
	Exactly, the fit function,

	34:07.960 --> 34:10.800
	like reducing a complex string loop

	34:10.800 --> 34:12.880
	to a single function call, right?

	34:12.880 --> 34:14.880
	And of course, some people will say,

	34:14.880 --> 34:16.320
	this is hiding a lot of details,

	34:16.320 --> 34:18.680
	but that's exactly the point, right?

	34:18.680 --> 34:20.280
	The magic is the point.

	34:20.280 --> 34:22.680
	So it's magical, but in a good way.

	34:22.680 --> 34:24.960
	It's magical in the sense that it's delightful.

	34:24.960 --> 34:26.160
	Yeah, yeah.

	34:26.160 --> 34:27.640
	I'm actually quite surprised.

	34:27.640 --> 34:29.600
	I didn't know that it was born out of desire

	34:29.600 --> 34:32.480
	to implement RNNs and LSTMs.

	34:32.480 --> 34:33.320
	It was.

	34:33.320 --> 34:34.160
	That's fascinating.

	34:34.160 --> 34:36.040
	So you were actually one of the first people

	34:36.040 --> 34:37.960
	to really try to attempt

	34:37.960 --> 34:41.000
	to get the major architectures together.

	34:41.000 --> 34:42.760
	And it's also interesting.

	34:42.760 --> 34:45.160
	You made me realize that that was a design decision at all

	34:45.160 --> 34:47.360
	is defining the model and code.

	34:47.360 --> 34:49.920
	Just, I'm putting myself in your shoes,

	34:49.920 --> 34:53.200
	whether the YAML, especially if cafe was the most popular.

	34:53.200 --> 34:54.720
	It was the most popular by far.

	34:54.720 --> 34:58.480
	If I was, if I were, yeah, I don't,

	34:58.480 --> 34:59.560
	I didn't like the YAML thing,

	34:59.560 --> 35:02.840
	but it makes more sense that you will put

	35:02.840 --> 35:05.720
	in a configuration file, the definition of a model.

	35:05.720 --> 35:07.200
	That's an interesting gutsy move

	35:07.200 --> 35:10.040
	to stick with defining it in code.

	35:10.040 --> 35:11.600
	Just if you look back.

	35:11.600 --> 35:13.480
	Other libraries were doing it as well,

	35:13.480 --> 35:16.320
	but it was definitely the more niche option.

	35:16.320 --> 35:17.160
	Yeah.

	35:17.160 --> 35:18.360
	Okay, Keras and then.

	35:18.360 --> 35:21.520
	So I released Keras in March, 2015,

	35:21.520 --> 35:24.160
	and it got users pretty much from the start.

	35:24.160 --> 35:25.800
	So the deep learning community was very, very small

	35:25.800 --> 35:27.240
	at the time.

	35:27.240 --> 35:30.600
	Lots of people were starting to be interested in LSTM.

	35:30.600 --> 35:32.440
	So it was gonna release it at the right time

	35:32.440 --> 35:35.560
	because it was offering an easy to use LSTM implementation.

	35:35.560 --> 35:37.680
	Exactly at the time where lots of people started

	35:37.680 --> 35:42.280
	to be intrigued by the capabilities of RNN, RNNs for NLP.

	35:42.280 --> 35:43.920
	So it grew from there.

	35:43.920 --> 35:48.920
	Then I joined Google about six months later,

	35:51.480 --> 35:54.920
	and that was actually completely unrelated to Keras.

	35:54.920 --> 35:57.080
	So I actually joined a research team

	35:57.080 --> 35:59.520
	working on image classification,

	35:59.520 --> 36:00.680
	mostly like computer vision.

	36:00.680 --> 36:02.320
	So I was doing computer vision research

	36:02.320 --> 36:03.640
	at Google initially.

	36:03.640 --> 36:05.520
	And immediately when I joined Google,

	36:05.520 --> 36:10.520
	I was exposed to the early internal version of TensorFlow.

	36:10.520 --> 36:13.920
	And the way it appeared to me at the time,

	36:13.920 --> 36:15.720
	and it was definitely the way it was at the time

	36:15.720 --> 36:20.760
	is that this was an improved version of Theano.

	36:20.760 --> 36:24.720
	So I immediately knew I had to port Keras

	36:24.720 --> 36:26.800
	to this new TensorFlow thing.

	36:26.800 --> 36:29.800
	And I was actually very busy as a noobler,

	36:29.800 --> 36:30.720
	as a new Googler.

	36:31.600 --> 36:34.520
	So I had not time to work on that.

	36:34.520 --> 36:38.680
	But then in November, I think it was November, 2015,

	36:38.680 --> 36:41.240
	TensorFlow got released.

	36:41.240 --> 36:44.560
	And it was kind of like my wake up call

	36:44.560 --> 36:47.320
	that, hey, I had to actually go and make it happen.

	36:47.320 --> 36:52.200
	So in December, I ported Keras to run on top of TensorFlow,

	36:52.200 --> 36:53.320
	but it was not exactly a port.

	36:53.320 --> 36:55.280
	It was more like a refactoring

	36:55.280 --> 36:57.920
	where I was abstracting away

	36:57.920 --> 37:00.480
	all the backend functionality into one module

	37:00.480 --> 37:02.320
	so that the same code base

	37:02.320 --> 37:05.080
	could run on top of multiple backends.

	37:05.080 --> 37:07.440
	So on top of TensorFlow or Theano.

	37:07.440 --> 37:09.760
	And for the next year,

	37:09.760 --> 37:14.760
	Theano stayed as the default option.

	37:15.400 --> 37:20.400
	It was easier to use, somewhat less buggy.

	37:20.640 --> 37:23.360
	It was much faster, especially when it came to audience.

	37:23.360 --> 37:26.360
	But eventually, TensorFlow overtook it.

	37:27.480 --> 37:30.200
	And TensorFlow, the early TensorFlow,

	37:30.200 --> 37:33.960
	has similar architectural decisions as Theano, right?

	37:33.960 --> 37:37.440
	So it was a natural transition.

	37:37.440 --> 37:38.320
	Yeah, absolutely.

	37:38.320 --> 37:42.960
	So what, I mean, that still Keras is a side,

	37:42.960 --> 37:45.280
	almost fun project, right?

	37:45.280 --> 37:49.040
	Yeah, so it was not my job assignment.

	37:49.040 --> 37:50.360
	It was not.

	37:50.360 --> 37:52.240
	I was doing it on the side.

	37:52.240 --> 37:55.840
	And even though it grew to have a lot of users

	37:55.840 --> 37:59.600
	for a deep learning library at the time, like Stroud 2016,

	37:59.600 --> 38:02.480
	but I wasn't doing it as my main job.

	38:02.480 --> 38:04.760
	So things started changing in,

	38:04.760 --> 38:09.760
	I think it must have been maybe October, 2016.

	38:10.200 --> 38:11.320
	So one year later.

	38:12.360 --> 38:15.240
	So Rajat, who was the lead on TensorFlow,

	38:15.240 --> 38:19.240
	basically showed up one day in our building

	38:19.240 --> 38:20.080
	where I was doing like,

	38:20.080 --> 38:21.640
	so I was doing research and things like,

	38:21.640 --> 38:24.640
	so I did a lot of computer vision research,

	38:24.640 --> 38:27.560
	also collaborations with Christian Zighetti

	38:27.560 --> 38:29.640
	and deep learning for theorem proving.

	38:29.640 --> 38:32.920
	It was a really interesting research topic.

	38:34.520 --> 38:37.640
	And so Rajat was saying,

	38:37.640 --> 38:41.040
	hey, we saw Keras, we like it.

	38:41.040 --> 38:42.440
	We saw that you're at Google.

	38:42.440 --> 38:45.280
	Why don't you come over for like a quarter

	38:45.280 --> 38:47.280
	and work with us?

	38:47.280 --> 38:49.240
	And I was like, yeah, that sounds like a great opportunity.

	38:49.240 --> 38:50.400
	Let's do it.

	38:50.400 --> 38:55.400
	And so I started working on integrating the Keras API

	38:55.720 --> 38:57.320
	into TensorFlow more tightly.

	38:57.320 --> 39:02.320
	So what followed up is a sort of like temporary

	39:02.640 --> 39:05.480
	TensorFlow only version of Keras

	39:05.480 --> 39:09.320
	that was in TensorFlow.com Trib for a while.

	39:09.320 --> 39:12.200
	And finally moved to TensorFlow Core.

	39:12.200 --> 39:15.360
	And I've never actually gotten back

	39:15.360 --> 39:17.600
	to my old team doing research.

	39:17.600 --> 39:22.320
	Well, it's kind of funny that somebody like you

	39:22.320 --> 39:27.320
	who dreams of, or at least sees the power of AI systems

	39:28.960 --> 39:31.680
	that reason and theorem proving we'll talk about

	39:31.680 --> 39:36.520
	has also created a system that makes the most basic

	39:36.520 --> 39:40.400
	kind of Lego building that is deep learning

	39:40.400 --> 39:42.640
	super accessible, super easy.

	39:42.640 --> 39:43.800
	So beautifully so.

	39:43.800 --> 39:47.720
	It's a funny irony that you're both,

	39:47.720 --> 39:49.120
	you're responsible for both things,

	39:49.120 --> 39:54.000
	but so TensorFlow 2.0 is kind of, there's a sprint.

	39:54.000 --> 39:55.080
	I don't know how long it'll take,

	39:55.080 --> 39:56.960
	but there's a sprint towards the finish.

	39:56.960 --> 40:01.040
	What do you look, what are you working on these days?

	40:01.040 --> 40:02.160
	What are you excited about?

	40:02.160 --> 40:04.280
	What are you excited about in 2.0?

	40:04.280 --> 40:05.760
	I mean, eager execution.

	40:05.760 --> 40:08.440
	There's so many things that just make it a lot easier

	40:08.440 --> 40:09.760
	to work.

	40:09.760 --> 40:13.640
	What are you excited about and what's also really hard?

	40:13.640 --> 40:15.800
	What are the problems you have to kind of solve?

	40:15.800 --> 40:19.080
	So I've spent the past year and a half working on

	40:19.080 --> 40:22.920
	TensorFlow 2.0 and it's been a long journey.

	40:22.920 --> 40:25.080
	I'm actually extremely excited about it.

	40:25.080 --> 40:26.440
	I think it's a great product.

	40:26.440 --> 40:29.360
	It's a delightful product compared to TensorFlow 1.0.

	40:29.360 --> 40:31.440
	We've made huge progress.

	40:32.640 --> 40:37.400
	So on the Keras side, what I'm really excited about is that,

	40:37.400 --> 40:42.400
	so previously Keras has been this very easy to use

	40:42.400 --> 40:45.840
	high level interface to do deep learning.

	40:45.840 --> 40:47.280
	But if you wanted to,

	40:50.520 --> 40:53.040
	if you wanted a lot of flexibility,

	40:53.040 --> 40:57.520
	the Keras framework was probably not the optimal way

	40:57.520 --> 40:59.760
	to do things compared to just writing everything

	40:59.760 --> 41:00.600
	from scratch.

	41:01.800 --> 41:04.680
	So in some way, the framework was getting in the way.

	41:04.680 --> 41:07.960
	And in TensorFlow 2.0, you don't have this at all, actually.

	41:07.960 --> 41:11.040
	You have the usability of the high level interface,

	41:11.040 --> 41:14.480
	but you have the flexibility of this lower level interface.

	41:14.480 --> 41:16.800
	And you have this spectrum of workflows

	41:16.800 --> 41:21.560
	where you can get more or less usability

	41:21.560 --> 41:26.560
	and flexibility trade offs depending on your needs, right?

	41:26.640 --> 41:29.680
	You can write everything from scratch

	41:29.680 --> 41:32.320
	and you get a lot of help doing so

	41:32.320 --> 41:36.400
	by subclassing models and writing some train loops

	41:36.400 --> 41:38.200
	using ego execution.

	41:38.200 --> 41:40.160
	It's very flexible, it's very easy to debug,

	41:40.160 --> 41:41.400
	it's very powerful.

	41:42.280 --> 41:45.000
	But all of this integrates seamlessly

	41:45.000 --> 41:49.440
	with higher level features up to the classic Keras workflows,

	41:49.440 --> 41:51.560
	which are very scikit learn like

	41:51.560 --> 41:56.040
	and are ideal for a data scientist,

	41:56.040 --> 41:58.240
	machine learning engineer type of profile.

	41:58.240 --> 42:00.840
	So now you can have the same framework

	42:00.840 --> 42:02.880
	offering the same set of APIs

	42:02.880 --> 42:05.000
	that enable a spectrum of workflows

	42:05.000 --> 42:08.560
	that are more or less low level, more or less high level

	42:08.560 --> 42:13.520
	that are suitable for profiles ranging from researchers

	42:13.520 --> 42:15.560
	to data scientists and everything in between.

	42:15.560 --> 42:16.960
	Yeah, so that's super exciting.

	42:16.960 --> 42:18.400
	I mean, it's not just that,

	42:18.400 --> 42:21.680
	it's connected to all kinds of tooling.

	42:21.680 --> 42:24.520
	You can go on mobile, you can go with TensorFlow Lite,

	42:24.520 --> 42:27.240
	you can go in the cloud or serving and so on.

	42:27.240 --> 42:28.960
	It all is connected together.

	42:28.960 --> 42:31.880
	Now some of the best software written ever

	42:31.880 --> 42:36.880
	is often done by one person, sometimes two.

	42:36.880 --> 42:40.800
	So with a Google, you're now seeing sort of Keras

	42:40.800 --> 42:42.840
	having to be integrated in TensorFlow,

	42:42.840 --> 42:46.800
	I'm sure has a ton of engineers working on.

	42:46.800 --> 42:51.040
	And there's, I'm sure a lot of tricky design decisions

	42:51.040 --> 42:52.200
	to be made.

	42:52.200 --> 42:54.440
	How does that process usually happen

	42:54.440 --> 42:56.800
	from at least your perspective?

	42:56.800 --> 42:59.800
	What are the debates like?

	43:00.720 --> 43:04.200
	Is there a lot of thinking,

	43:04.200 --> 43:06.880
	considering different options and so on?

	43:06.880 --> 43:08.160
	Yes.

	43:08.160 --> 43:12.640
	So a lot of the time I spend at Google

	43:12.640 --> 43:17.280
	is actually discussing design discussions, right?

	43:17.280 --> 43:20.480
	Writing design docs, participating in design review meetings

	43:20.480 --> 43:22.080
	and so on.

	43:22.080 --> 43:25.240
	This is as important as actually writing a code.

	43:25.240 --> 43:26.080
	Right.

	43:26.080 --> 43:28.120
	So there's a lot of thought, there's a lot of thought

	43:28.120 --> 43:32.280
	and a lot of care that is taken

	43:32.280 --> 43:34.160
	in coming up with these decisions

	43:34.160 --> 43:37.160
	and taking into account all of our users

	43:37.160 --> 43:40.680
	because TensorFlow has this extremely diverse user base,

	43:40.680 --> 43:41.520
	right?

	43:41.520 --> 43:43.120
	It's not like just one user segment

	43:43.120 --> 43:45.480
	where everyone has the same needs.

	43:45.480 --> 43:47.640
	We have small scale production users,

	43:47.640 --> 43:49.520
	large scale production users.

	43:49.520 --> 43:52.800
	We have startups, we have researchers,

	43:53.720 --> 43:55.080
	you know, it's all over the place.

	43:55.080 --> 43:57.560
	And we have to cater to all of their needs.

	43:57.560 --> 44:00.040
	If I just look at the standard debates

	44:00.040 --> 44:04.000
	of C++ or Python, there's some heated debates.

	44:04.000 --> 44:06.000
	Do you have those at Google?

	44:06.000 --> 44:08.080
	I mean, they're not heated in terms of emotionally,

	44:08.080 --> 44:10.800
	but there's probably multiple ways to do it, right?

	44:10.800 --> 44:14.040
	So how do you arrive through those design meetings

	44:14.040 --> 44:15.440
	at the best way to do it?

	44:15.440 --> 44:19.280
	Especially in deep learning where the field is evolving

	44:19.280 --> 44:20.880
	as you're doing it.

	44:21.880 --> 44:23.600
	Is there some magic to it?

	44:23.600 --> 44:26.240
	Is there some magic to the process?

	44:26.240 --> 44:28.280
	I don't know if there's magic to the process,

	44:28.280 --> 44:30.640
	but there definitely is a process.

	44:30.640 --> 44:33.760
	So making design decisions

	44:33.760 --> 44:36.080
	is about satisfying a set of constraints,

	44:36.080 --> 44:39.920
	but also trying to do so in the simplest way possible,

	44:39.920 --> 44:42.240
	because this is what can be maintained,

	44:42.240 --> 44:44.920
	this is what can be expanded in the future.

	44:44.920 --> 44:49.120
	So you don't want to naively satisfy the constraints

	44:49.120 --> 44:51.880
	by just, you know, for each capability you need available,

	44:51.880 --> 44:53.960
	you're gonna come up with one argument in your API

	44:53.960 --> 44:54.800
	and so on.

	44:54.800 --> 44:59.800
	You want to design APIs that are modular and hierarchical

	45:00.640 --> 45:04.080
	so that they have an API surface

	45:04.080 --> 45:07.040
	that is as small as possible, right?

	45:07.040 --> 45:11.640
	And you want this modular hierarchical architecture

	45:11.640 --> 45:14.560
	to reflect the way that domain experts

	45:14.560 --> 45:16.400
	think about the problem.

	45:16.400 --> 45:17.880
	Because as a domain expert,

	45:17.880 --> 45:19.840
	when you are reading about a new API,

	45:19.840 --> 45:24.760
	you're reading a tutorial or some docs pages,

	45:24.760 --> 45:28.200
	you already have a way that you're thinking about the problem.

	45:28.200 --> 45:32.320
	You already have like certain concepts in mind

	45:32.320 --> 45:35.680
	and you're thinking about how they relate together.

	45:35.680 --> 45:37.200
	And when you're reading docs,

	45:37.200 --> 45:40.280
	you're trying to build as quickly as possible

	45:40.280 --> 45:45.280
	a mapping between the concepts featured in your API

	45:45.280 --> 45:46.800
	and the concepts in your mind.

	45:46.800 --> 45:48.880
	So you're trying to map your mental model

	45:48.880 --> 45:53.600
	as a domain expert to the way things work in the API.

	45:53.600 --> 45:57.040
	So you need an API and an underlying implementation

	45:57.040 --> 46:00.120
	that are reflecting the way people think about these things.

	46:00.120 --> 46:02.880
	So in minimizing the time it takes to do the mapping.

	46:02.880 --> 46:04.680
	Yes, minimizing the time,

	46:04.680 --> 46:06.560
	the cognitive load there is

	46:06.560 --> 46:10.920
	in ingesting this new knowledge about your API.

	46:10.920 --> 46:13.160
	An API should not be self referential

	46:13.160 --> 46:15.520
	or referring to implementation details.

	46:15.520 --> 46:19.160
	It should only be referring to domain specific concepts

	46:19.160 --> 46:21.360
	that people already understand.

	46:23.240 --> 46:24.480
	Brilliant.

	46:24.480 --> 46:27.560
	So what's the future of Keras and TensorFlow look like?

	46:27.560 --> 46:29.640
	What does TensorFlow 3.0 look like?

	46:30.600 --> 46:33.720
	So that's kind of too far in the future for me to answer,

	46:33.720 --> 46:37.800
	especially since I'm not even the one making these decisions.

	46:37.800 --> 46:39.080
	Okay.

	46:39.080 --> 46:41.240
	But so from my perspective,

	46:41.240 --> 46:43.200
	which is just one perspective

	46:43.200 --> 46:46.040
	among many different perspectives on the TensorFlow team,

	46:47.200 --> 46:52.200
	I'm really excited by developing even higher level APIs,

	46:52.360 --> 46:53.560
	higher level than Keras.

	46:53.560 --> 46:56.480
	I'm really excited by hyperparameter tuning,

	46:56.480 --> 46:59.240
	by automated machine learning, AutoML.

	47:01.120 --> 47:03.200
	I think the future is not just, you know,

	47:03.200 --> 47:07.600
	defining a model like you were assembling Lego blocks

	47:07.600 --> 47:09.200
	and then collect fit on it.

	47:09.200 --> 47:13.680
	It's more like an automagical model

	47:13.680 --> 47:16.080
	that would just look at your data

	47:16.080 --> 47:19.040
	and optimize the objective you're after, right?

	47:19.040 --> 47:23.040
	So that's what I'm looking into.

	47:23.040 --> 47:26.480
	Yeah, so you put the baby into a room with the problem

	47:26.480 --> 47:28.760
	and come back a few hours later

	47:28.760 --> 47:30.960
	with a fully solved problem.

	47:30.960 --> 47:33.560
	Exactly, it's not like a box of Legos.

	47:33.560 --> 47:35.920
	It's more like the combination of a kid

	47:35.920 --> 47:38.800
	that's really good at Legos and a box of Legos.

	47:38.800 --> 47:41.520
	It's just building the thing on its own.

	47:41.520 --> 47:42.680
	Very nice.

	47:42.680 --> 47:44.160
	So that's an exciting future.

	47:44.160 --> 47:46.080
	I think there's a huge amount of applications

	47:46.080 --> 47:48.560
	and revolutions to be had

	47:49.920 --> 47:52.640
	under the constraints of the discussion we previously had.

	47:52.640 --> 47:57.480
	But what do you think of the current limits of deep learning?

	47:57.480 --> 48:02.480
	If we look specifically at these function approximators

	48:03.840 --> 48:06.160
	that tries to generalize from data.

	48:06.160 --> 48:10.160
	You've talked about local versus extreme generalization.

	48:11.120 --> 48:13.280
	You mentioned that neural networks don't generalize well

	48:13.280 --> 48:14.560
	and humans do.

	48:14.560 --> 48:15.760
	So there's this gap.

	48:17.640 --> 48:20.840
	And you've also mentioned that extreme generalization

	48:20.840 --> 48:23.960
	requires something like reasoning to fill those gaps.

	48:23.960 --> 48:27.560
	So how can we start trying to build systems like that?

	48:27.560 --> 48:30.600
	Right, yeah, so this is by design, right?

	48:30.600 --> 48:37.080
	Deep learning models are like huge parametric models,

	48:37.080 --> 48:39.280
	differentiable, so continuous,

	48:39.280 --> 48:42.680
	that go from an input space to an output space.

	48:42.680 --> 48:44.120
	And they're trained with gradient descent.

	48:44.120 --> 48:47.160
	So they're trained pretty much point by point.

	48:47.160 --> 48:50.520
	They are learning a continuous geometric morphing

	48:50.520 --> 48:55.320
	from an input vector space to an output vector space.

	48:55.320 --> 48:58.960
	And because this is done point by point,

	48:58.960 --> 49:02.200
	a deep neural network can only make sense

	49:02.200 --> 49:05.880
	of points in experience space that are very close

	49:05.880 --> 49:08.520
	to things that it has already seen in string data.

	49:08.520 --> 49:12.520
	At best, it can do interpolation across points.

	49:13.840 --> 49:17.360
	But that means in order to train your network,

	49:17.360 --> 49:21.680
	you need a dense sampling of the input cross output space,

	49:22.880 --> 49:25.240
	almost a point by point sampling,

	49:25.240 --> 49:27.160
	which can be very expensive if you're dealing

	49:27.160 --> 49:29.320
	with complex real world problems,

	49:29.320 --> 49:33.240
	like autonomous driving, for instance, or robotics.

	49:33.240 --> 49:36.000
	It's doable if you're looking at the subset

	49:36.000 --> 49:37.120
	of the visual space.

	49:37.120 --> 49:38.800
	But even then, it's still fairly expensive.

	49:38.800 --> 49:40.920
	You still need millions of examples.

	49:40.920 --> 49:44.240
	And it's only going to be able to make sense of things

	49:44.240 --> 49:46.880
	that are very close to what it has seen before.

	49:46.880 --> 49:49.160
	And in contrast to that, well, of course,

	49:49.160 --> 49:50.160
	you have human intelligence.

	49:50.160 --> 49:53.240
	But even if you're not looking at human intelligence,

	49:53.240 --> 49:56.800
	you can look at very simple rules, algorithms.

	49:56.800 --> 49:58.080
	If you have a symbolic rule,

	49:58.080 --> 50:03.080
	it can actually apply to a very, very large set of inputs

	50:03.120 --> 50:04.880
	because it is abstract.

	50:04.880 --> 50:09.560
	It is not obtained by doing a point by point mapping.

	50:10.720 --> 50:14.000
	For instance, if you try to learn a sorting algorithm

	50:14.000 --> 50:15.520
	using a deep neural network,

	50:15.520 --> 50:18.520
	well, you're very much limited to learning point by point

	50:20.080 --> 50:24.360
	what the sorted representation of this specific list is like.

	50:24.360 --> 50:29.360
	But instead, you could have a very, very simple

	50:29.400 --> 50:31.920
	sorting algorithm written in a few lines.

	50:31.920 --> 50:34.520
	Maybe it's just two nested loops.

	50:35.560 --> 50:40.560
	And it can process any list at all because it is abstract,

	50:41.040 --> 50:42.240
	because it is a set of rules.

	50:42.240 --> 50:45.160
	So deep learning is really like point by point

	50:45.160 --> 50:48.640
	geometric morphings, train with good and decent.

	50:48.640 --> 50:53.640
	And meanwhile, abstract rules can generalize much better.

	50:53.640 --> 50:56.160
	And I think the future is we need to combine the two.

	50:56.160 --> 50:59.160
	So how do we, do you think, combine the two?

	50:59.160 --> 51:03.040
	How do we combine good point by point functions

	51:03.040 --> 51:08.040
	with programs, which is what the symbolic AI type systems?

	51:08.920 --> 51:11.600
	At which levels the combination happen?

	51:11.600 --> 51:14.680
	I mean, obviously we're jumping into the realm

	51:14.680 --> 51:16.880
	of where there's no good answers.

	51:16.880 --> 51:20.280
	It's just kind of ideas and intuitions and so on.

	51:20.280 --> 51:23.080
	Well, if you look at the really successful AI systems

	51:23.080 --> 51:26.320
	today, I think they are already hybrid systems

	51:26.320 --> 51:29.520
	that are combining symbolic AI with deep learning.

	51:29.520 --> 51:32.520
	For instance, successful robotics systems

	51:32.520 --> 51:36.400
	are already mostly model based, rule based,

	51:37.400 --> 51:39.400
	things like planning algorithms and so on.

	51:39.400 --> 51:42.200
	At the same time, they're using deep learning

	51:42.200 --> 51:43.840
	as perception modules.

	51:43.840 --> 51:46.000
	Sometimes they're using deep learning as a way

	51:46.000 --> 51:50.920
	to inject fuzzy intuition into a rule based process.

	51:50.920 --> 51:54.560
	If you look at the system like in a self driving car,

	51:54.560 --> 51:57.240
	it's not just one big end to end neural network.

	51:57.240 --> 51:59.000
	You know, that wouldn't work at all.

	51:59.000 --> 52:00.760
	Precisely because in order to train that,

	52:00.760 --> 52:05.160
	you would need a dense sampling of experience base

	52:05.160 --> 52:06.200
	when it comes to driving,

	52:06.200 --> 52:08.880
	which is completely unrealistic, obviously.

	52:08.880 --> 52:12.440
	Instead, the self driving car is mostly

	52:13.920 --> 52:18.360
	symbolic, you know, it's software, it's programmed by hand.

	52:18.360 --> 52:21.640
	So it's mostly based on explicit models.

	52:21.640 --> 52:25.840
	In this case, mostly 3D models of the environment

	52:25.840 --> 52:29.520
	around the car, but it's interfacing with the real world

	52:29.520 --> 52:31.440
	using deep learning modules, right?

	52:31.440 --> 52:33.440
	So the deep learning there serves as a way

	52:33.440 --> 52:36.080
	to convert the raw sensory information

	52:36.080 --> 52:38.320
	to something usable by symbolic systems.

	52:39.760 --> 52:42.400
	Okay, well, let's linger on that a little more.

	52:42.400 --> 52:45.440
	So dense sampling from input to output.

	52:45.440 --> 52:48.240
	You said it's obviously very difficult.

	52:48.240 --> 52:50.120
	Is it possible?

	52:50.120 --> 52:51.800
	In the case of self driving, you mean?

	52:51.800 --> 52:53.040
	Let's say self driving, right?

	52:53.040 --> 52:55.760
	Self driving for many people,

	52:57.560 --> 52:59.520
	let's not even talk about self driving,

	52:59.520 --> 53:03.880
	let's talk about steering, so staying inside the lane.

	53:05.040 --> 53:07.080
	Lane following, yeah, it's definitely a problem

	53:07.080 --> 53:08.880
	you can solve with an end to end deep learning model,

	53:08.880 --> 53:10.600
	but that's like one small subset.

	53:10.600 --> 53:11.440
	Hold on a second.

	53:11.440 --> 53:12.760
	Yeah, I don't know why you're jumping

	53:12.760 --> 53:14.480
	from the extreme so easily,

	53:14.480 --> 53:16.280
	because I disagree with you on that.

	53:16.280 --> 53:21.000
	I think, well, it's not obvious to me

	53:21.000 --> 53:23.400
	that you can solve lane following.

	53:23.400 --> 53:25.840
	No, it's not obvious, I think it's doable.

	53:25.840 --> 53:30.840
	I think in general, there is no hard limitations

	53:31.200 --> 53:33.680
	to what you can learn with a deep neural network,

	53:33.680 --> 53:38.680
	as long as the search space is rich enough,

	53:40.320 --> 53:42.240
	is flexible enough, and as long as you have

	53:42.240 --> 53:45.360
	this dense sampling of the input cross output space.

	53:45.360 --> 53:47.720
	The problem is that this dense sampling

	53:47.720 --> 53:51.120
	could mean anything from 10,000 examples

	53:51.120 --> 53:52.840
	to like trillions and trillions.

	53:52.840 --> 53:54.360
	So that's my question.

	53:54.360 --> 53:56.200
	So what's your intuition?

	53:56.200 --> 53:58.720
	And if you could just give it a chance

	53:58.720 --> 54:01.880
	and think what kind of problems can be solved

	54:01.880 --> 54:04.240
	by getting a huge amounts of data

	54:04.240 --> 54:08.000
	and thereby creating a dense mapping.

	54:08.000 --> 54:12.480
	So let's think about natural language dialogue,

	54:12.480 --> 54:14.000
	the Turing test.

	54:14.000 --> 54:17.000
	Do you think the Turing test can be solved

	54:17.000 --> 54:21.120
	with a neural network alone?

	54:21.120 --> 54:24.440
	Well, the Turing test is all about tricking people

	54:24.440 --> 54:26.880
	into believing they're talking to a human.

	54:26.880 --> 54:29.040
	And I don't think that's actually very difficult

	54:29.040 --> 54:34.040
	because it's more about exploiting human perception

	54:35.600 --> 54:37.520
	and not so much about intelligence.

	54:37.520 --> 54:39.680
	There's a big difference between mimicking

	54:39.680 --> 54:42.080
	intelligent behavior and actual intelligent behavior.

	54:42.080 --> 54:45.360
	So, okay, let's look at maybe the Alexa prize and so on.

	54:45.360 --> 54:47.480
	The different formulations of the natural language

	54:47.480 --> 54:50.520
	conversation that are less about mimicking

	54:50.520 --> 54:52.800
	and more about maintaining a fun conversation

	54:52.800 --> 54:54.720
	that lasts for 20 minutes.

	54:54.720 --> 54:56.200
	That's a little less about mimicking

	54:56.200 --> 54:59.080
	and that's more about, I mean, it's still mimicking,

	54:59.080 --> 55:01.440
	but it's more about being able to carry forward

	55:01.440 --> 55:03.640
	a conversation with all the tangents that happen

	55:03.640 --> 55:05.080
	in dialogue and so on.

	55:05.080 --> 55:08.320
	Do you think that problem is learnable

	55:08.320 --> 55:13.320
	with a neural network that does the point to point mapping?

	55:14.520 --> 55:16.280
	So I think it would be very, very challenging

	55:16.280 --> 55:17.800
	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 --> 55:23.240
	I wouldn't rule it out.

	55:23.240 --> 55:25.400
	The space of problems that can be solved

	55:25.400 --> 55:26.920
	with a large neural network.

	55:26.920 --> 55:30.080
	What's your sense about the space of those problems?

	55:30.080 --> 55:32.560
	So useful problems for us.

	55:32.560 --> 55:34.800
	In theory, it's infinite, right?

	55:34.800 --> 55:36.200
	You can solve any problem.

	55:36.200 --> 55:39.800
	In practice, well, deep learning is a great fit

	55:39.800 --> 55:41.800
	for perception problems.

	55:41.800 --> 55:46.800
	In general, any problem which is naturally amenable

	55:47.640 --> 55:52.200
	to explicit handcrafted rules or rules that you can generate

	55:52.200 --> 55:54.960
	by exhaustive search over some program space.

	55:56.080 --> 55:59.320
	So perception, artificial intuition,

	55:59.320 --> 56:03.240
	as long as you have a sufficient training dataset.

	56:03.240 --> 56:05.360
	And that's the question, I mean, perception,

	56:05.360 --> 56:08.400
	there's interpretation and understanding of the scene,

	56:08.400 --> 56:10.280
	which seems to be outside the reach

	56:10.280 --> 56:12.960
	of current perception systems.

	56:12.960 --> 56:15.920
	So do you think larger networks will be able

	56:15.920 --> 56:18.280
	to start to understand the physics

	56:18.280 --> 56:21.080
	and the physics of the scene,

	56:21.080 --> 56:23.400
	the three dimensional structure and relationships

	56:23.400 --> 56:25.560
	of objects in the scene and so on?

	56:25.560 --> 56:28.320
	Or really that's where symbolic AI has to step in?

	56:28.320 --> 56:34.480
	Well, it's always possible to solve these problems

	56:34.480 --> 56:36.800
	with deep learning.

	56:36.800 --> 56:38.560
	It's just extremely inefficient.

	56:38.560 --> 56:42.000
	A model would be an explicit rule based abstract model

	56:42.000 --> 56:45.240
	would be a far better, more compressed

	56:45.240 --> 56:46.840
	representation of physics.

	56:46.840 --> 56:49.080
	Then learning just this mapping between

	56:49.080 --> 56:50.960
	in this situation, this thing happens.

	56:50.960 --> 56:52.720
	If you change the situation slightly,

	56:52.720 --> 56:54.760
	then this other thing happens and so on.

	56:54.760 --> 56:57.440
	Do you think it's possible to automatically generate

	56:57.440 --> 57:02.200
	the programs that would require that kind of reasoning?

	57:02.200 --> 57:05.360
	Or does it have to, so the way the expert systems fail,

	57:05.360 --> 57:07.120
	there's so many facts about the world

	57:07.120 --> 57:08.960
	had to be hand coded in.

	57:08.960 --> 57:14.600
	Do you think it's possible to learn those logical statements

	57:14.600 --> 57:18.200
	that are true about the world and their relationships?

	57:18.200 --> 57:20.360
	Do you think, I mean, that's kind of what theorem proving

	57:20.360 --> 57:22.680
	at a basic level is trying to do, right?

	57:22.680 --> 57:26.160
	Yeah, except it's much harder to formulate statements

	57:26.160 --> 57:28.480
	about the world compared to formulating

	57:28.480 --> 57:30.320
	mathematical statements.

	57:30.320 --> 57:34.200
	Statements about the world tend to be subjective.

	57:34.200 --> 57:39.600
	So can you learn rule based models?

	57:39.600 --> 57:40.920
	Yes, definitely.

	57:40.920 --> 57:43.640
	That's the field of program synthesis.

	57:43.640 --> 57:48.040
	However, today we just don't really know how to do it.

	57:48.040 --> 57:52.400
	So it's very much a grass search or tree search problem.

	57:52.400 --> 57:56.800
	And so we are limited to the sort of tree session grass

	57:56.800 --> 57:58.560
	search algorithms that we have today.

	57:58.560 --> 58:02.760
	Personally, I think genetic algorithms are very promising.

	58:02.760 --> 58:04.360
	So almost like genetic programming.

	58:04.360 --> 58:05.560
	Genetic programming, exactly.

	58:05.560 --> 58:08.840
	Can you discuss the field of program synthesis?

	58:08.840 --> 58:14.560
	Like how many people are working and thinking about it?

	58:14.560 --> 58:17.960
	Where we are in the history of program synthesis

	58:17.960 --> 58:20.720
	and what are your hopes for it?

	58:20.720 --> 58:24.600
	Well, if it were deep learning, this is like the 90s.

	58:24.600 --> 58:29.120
	So meaning that we already have existing solutions.

	58:29.120 --> 58:34.280
	We are starting to have some basic understanding

	58:34.280 --> 58:35.480
	of what this is about.

	58:35.480 --> 58:38.000
	But it's still a field that is in its infancy.

	58:38.000 --> 58:40.440
	There are very few people working on it.

	58:40.440 --> 58:44.480
	There are very few real world applications.

	58:44.480 --> 58:47.640
	So the one real world application I'm aware of

	58:47.640 --> 58:51.680
	is Flash Fill in Excel.

	58:51.680 --> 58:55.080
	It's a way to automatically learn very simple programs

	58:55.080 --> 58:58.200
	to format cells in an Excel spreadsheet

	58:58.200 --> 59:00.240
	from a few examples.

	59:00.240 --> 59:02.800
	For instance, learning a way to format a date, things like that.

	59:02.800 --> 59:03.680
	Oh, that's fascinating.

	59:03.680 --> 59:04.560
	Yeah.

	59:04.560 --> 59:06.280
	You know, OK, that's a fascinating topic.

	59:06.280 --> 59:10.480
	I always wonder when I provide a few samples to Excel,

	59:10.480 --> 59:12.600
	what it's able to figure out.

	59:12.600 --> 59:15.960
	Like just giving it a few dates, what

	59:15.960 --> 59:18.480
	are you able to figure out from the pattern I just gave you?

	59:18.480 --> 59:19.760
	That's a fascinating question.

	59:19.760 --> 59:23.320
	And it's fascinating whether that's learnable patterns.

	59:23.320 --> 59:25.520
	And you're saying they're working on that.

	59:25.520 --> 59:28.200
	How big is the toolbox currently?

	59:28.200 --> 59:29.520
	Are we completely in the dark?

	59:29.520 --> 59:30.440
	So if you said the 90s.

	59:30.440 --> 59:31.720
	In terms of program synthesis?

	59:31.720 --> 59:32.360
	No.

	59:32.360 --> 59:37.720
	So I would say, so maybe 90s is even too optimistic.

	59:37.720 --> 59:41.080
	Because by the 90s, we already understood back prop.

	59:41.080 --> 59:43.960
	We already understood the engine of deep learning,

	59:43.960 --> 59:47.280
	even though we couldn't really see its potential quite.

	59:47.280 --> 59:48.520
	Today, I don't think we have found

	59:48.520 --> 59:50.400
	the engine of program synthesis.

	59:50.400 --> 59:52.880
	So we're in the winter before back prop.

	59:52.880 --> 59:54.160
	Yeah.

	59:54.160 --> 59:55.720
	In a way, yes.

	59:55.720 --> 1:00:00.120
	So I do believe program synthesis and general discrete search

	1:00:00.120 --> 1:00:02.760
	over rule based models is going to be

	1:00:02.760 --> 1:00:06.640
	a cornerstone of AI research in the next century.

	1:00:06.640 --> 1:00:10.200
	And that doesn't mean we are going to drop deep learning.

	1:00:10.200 --> 1:00:11.880
	Deep learning is immensely useful.

	1:00:11.880 --> 1:00:17.200
	Like, being able to learn is a very flexible, adaptable,

	1:00:17.200 --> 1:00:18.120
	parametric model.

	1:00:18.120 --> 1:00:20.720
	So it's got to understand that's actually immensely useful.

	1:00:20.720 --> 1:00:23.040
	All it's doing is pattern cognition.

	1:00:23.040 --> 1:00:25.640
	But being good at pattern cognition, given lots of data,

	1:00:25.640 --> 1:00:27.920
	is just extremely powerful.

	1:00:27.920 --> 1:00:30.320
	So we are still going to be working on deep learning.

	1:00:30.320 --> 1:00:31.840
	We are going to be working on program synthesis.

	1:00:31.840 --> 1:00:34.680
	We are going to be combining the two in increasingly automated

	1:00:34.680 --> 1:00:36.400
	ways.

	1:00:36.400 --> 1:00:38.520
	So let's talk a little bit about data.

	1:00:38.520 --> 1:00:44.600
	You've tweeted, about 10,000 deep learning papers

	1:00:44.600 --> 1:00:47.080
	have been written about hard coding priors

	1:00:47.080 --> 1:00:49.600
	about a specific task in a neural network architecture

	1:00:49.600 --> 1:00:52.440
	works better than a lack of a prior.

	1:00:52.440 --> 1:00:55.120
	Basically, summarizing all these efforts,

	1:00:55.120 --> 1:00:56.920
	they put a name to an architecture.

	1:00:56.920 --> 1:00:59.280
	But really, what they're doing is hard coding some priors

	1:00:59.280 --> 1:01:01.560
	that improve the performance of the system.

	1:01:01.560 --> 1:01:06.880
	But which gets straight to the point is probably true.

	1:01:06.880 --> 1:01:09.800
	So you say that you can always buy performance by,

	1:01:09.800 --> 1:01:12.920
	in quotes, performance by either training on more data,

	1:01:12.920 --> 1:01:15.480
	better data, or by injecting task information

	1:01:15.480 --> 1:01:18.400
	to the architecture of the preprocessing.

	1:01:18.400 --> 1:01:21.280
	However, this isn't informative about the generalization power

	1:01:21.280 --> 1:01:23.080
	the techniques use, the fundamental ability

	1:01:23.080 --> 1:01:24.200
	to generalize.

	1:01:24.200 --> 1:01:26.800
	Do you think we can go far by coming up

	1:01:26.800 --> 1:01:29.920
	with better methods for this kind of cheating,

	1:01:29.920 --> 1:01:33.520
	for better methods of large scale annotation of data?

	1:01:33.520 --> 1:01:34.960
	So building better priors.

	1:01:34.960 --> 1:01:37.280
	If you automate it, it's not cheating anymore.

	1:01:37.280 --> 1:01:38.360
	Right.

	1:01:38.360 --> 1:01:41.600
	I'm joking about the cheating, but large scale.

	1:01:41.600 --> 1:01:46.560
	So basically, I'm asking about something

	1:01:46.560 --> 1:01:48.280
	that hasn't, from my perspective,

	1:01:48.280 --> 1:01:53.360
	been researched too much is exponential improvement

	1:01:53.360 --> 1:01:55.960
	in annotation of data.

	1:01:55.960 --> 1:01:58.120
	Do you often think about?

	1:01:58.120 --> 1:02:00.840
	I think it's actually been researched quite a bit.

	1:02:00.840 --> 1:02:02.720
	You just don't see publications about it.

	1:02:02.720 --> 1:02:05.840
	Because people who publish papers

	1:02:05.840 --> 1:02:07.920
	are going to publish about known benchmarks.

	1:02:07.920 --> 1:02:09.800
	Sometimes they're going to read a new benchmark.

	1:02:09.800 --> 1:02:12.200
	People who actually have real world large scale

	1:02:12.200 --> 1:02:13.880
	depending on problems, they're going

	1:02:13.880 --> 1:02:16.960
	to spend a lot of resources into data annotation

	1:02:16.960 --> 1:02:18.400
	and good data annotation pipelines,

	1:02:18.400 --> 1:02:19.640
	but you don't see any papers about it.

	1:02:19.640 --> 1:02:20.400
	That's interesting.

	1:02:20.400 --> 1:02:22.720
	So do you think, certainly resources,

	1:02:22.720 --> 1:02:24.840
	but do you think there's innovation happening?

	1:02:24.840 --> 1:02:25.880
	Oh, yeah.

	1:02:25.880 --> 1:02:28.880
	To clarify the point in the tweet.

	1:02:28.880 --> 1:02:31.160
	So machine learning in general is

	1:02:31.160 --> 1:02:33.840
	the science of generalization.

	1:02:33.840 --> 1:02:37.800
	You want to generate knowledge that

	1:02:37.800 --> 1:02:40.440
	can be reused across different data sets,

	1:02:40.440 --> 1:02:42.000
	across different tasks.

	1:02:42.000 --> 1:02:45.280
	And if instead you're looking at one data set

	1:02:45.280 --> 1:02:50.000
	and then you are hard coding knowledge about this task

	1:02:50.000 --> 1:02:54.040
	into your architecture, this is no more useful

	1:02:54.040 --> 1:02:56.760
	than training a network and then saying, oh, I

	1:02:56.760 --> 1:03:01.920
	found these weight values perform well.

	1:03:01.920 --> 1:03:05.680
	So David Ha, I don't know if you know David,

	1:03:05.680 --> 1:03:08.760
	he had a paper the other day about weight

	1:03:08.760 --> 1:03:10.400
	agnostic neural networks.

	1:03:10.400 --> 1:03:12.120
	And this is a very interesting paper

	1:03:12.120 --> 1:03:14.400
	because it really illustrates the fact

	1:03:14.400 --> 1:03:17.400
	that an architecture, even without weights,

	1:03:17.400 --> 1:03:21.360
	an architecture is knowledge about a task.

	1:03:21.360 --> 1:03:23.640
	It encodes knowledge.

	1:03:23.640 --> 1:03:25.840
	And when it comes to architectures

	1:03:25.840 --> 1:03:30.440
	that are uncrafted by researchers, in some cases,

	1:03:30.440 --> 1:03:34.160
	it is very, very clear that all they are doing

	1:03:34.160 --> 1:03:38.880
	is artificially reencoding the template that

	1:03:38.880 --> 1:03:44.400
	corresponds to the proper way to solve the task encoding

	1:03:44.400 --> 1:03:45.200
	a given data set.

	1:03:45.200 --> 1:03:48.120
	For instance, I know if you looked

	1:03:48.120 --> 1:03:52.280
	at the baby data set, which is about natural language

	1:03:52.280 --> 1:03:55.520
	question answering, it is generated by an algorithm.

	1:03:55.520 --> 1:03:57.680
	So this is a question answer pairs

	1:03:57.680 --> 1:03:59.280
	that are generated by an algorithm.

	1:03:59.280 --> 1:04:01.520
	The algorithm is solving a certain template.

	1:04:01.520 --> 1:04:04.400
	Turns out, if you craft a network that

	1:04:04.400 --> 1:04:06.360
	literally encodes this template, you

	1:04:06.360 --> 1:04:09.640
	can solve this data set with nearly 100% accuracy.

	1:04:09.640 --> 1:04:11.160
	But that doesn't actually tell you

	1:04:11.160 --> 1:04:14.640
	anything about how to solve question answering

	1:04:14.640 --> 1:04:17.680
	in general, which is the point.

	1:04:17.680 --> 1:04:19.400
	The question is just to linger on it,

	1:04:19.400 --> 1:04:21.560
	whether it's from the data side or from the size

	1:04:21.560 --> 1:04:23.280
	of the network.

	1:04:23.280 --> 1:04:25.920
	I don't know if you've read the blog post by Rich Sutton,

	1:04:25.920 --> 1:04:28.400
	The Bitter Lesson, where he says,

	1:04:28.400 --> 1:04:31.480
	the biggest lesson that we can read from 70 years of AI

	1:04:31.480 --> 1:04:34.720
	research is that general methods that leverage computation

	1:04:34.720 --> 1:04:37.160
	are ultimately the most effective.

	1:04:37.160 --> 1:04:39.720
	So as opposed to figuring out methods

	1:04:39.720 --> 1:04:41.840
	that can generalize effectively, do you

	1:04:41.840 --> 1:04:47.720
	think we can get pretty far by just having something

	1:04:47.720 --> 1:04:51.520
	that leverages computation and the improvement of computation?

	1:04:51.520 --> 1:04:54.960
	Yeah, so I think Rich is making a very good point, which

	1:04:54.960 --> 1:04:57.560
	is that a lot of these papers, which are actually

	1:04:57.560 --> 1:05:02.800
	all about manually hardcoding prior knowledge about a task

	1:05:02.800 --> 1:05:04.720
	into some system, it doesn't have

	1:05:04.720 --> 1:05:08.600
	to be deep learning architecture, but into some system.

	1:05:08.600 --> 1:05:11.920
	These papers are not actually making any impact.

	1:05:11.920 --> 1:05:14.800
	Instead, what's making really long term impact

	1:05:14.800 --> 1:05:18.520
	is very simple, very general systems

	1:05:18.520 --> 1:05:21.280
	that are really agnostic to all these tricks.

	1:05:21.280 --> 1:05:23.320
	Because these tricks do not generalize.

	1:05:23.320 --> 1:05:27.480
	And of course, the one general and simple thing

	1:05:27.480 --> 1:05:33.160
	that you should focus on is that which leverages computation.

	1:05:33.160 --> 1:05:36.200
	Because computation, the availability

	1:05:36.200 --> 1:05:39.400
	of large scale computation has been increasing exponentially

	1:05:39.400 --> 1:05:40.560
	following Moore's law.

	1:05:40.560 --> 1:05:44.080
	So if your algorithm is all about exploiting this,

	1:05:44.080 --> 1:05:47.440
	then your algorithm is suddenly exponentially improving.

	1:05:47.440 --> 1:05:52.400
	So I think Rich is definitely right.

	1:05:52.400 --> 1:05:57.120
	However, he's right about the past 70 years.

	1:05:57.120 --> 1:05:59.440
	He's like assessing the past 70 years.

	1:05:59.440 --> 1:06:02.360
	I am not sure that this assessment will still

	1:06:02.360 --> 1:06:04.880
	hold true for the next 70 years.

	1:06:04.880 --> 1:06:07.160
	It might to some extent.

	1:06:07.160 --> 1:06:08.560
	I suspect it will not.

	1:06:08.560 --> 1:06:11.560
	Because the truth of his assessment

	1:06:11.560 --> 1:06:16.800
	is a function of the context in which this research took place.

	1:06:16.800 --> 1:06:18.600
	And the context is changing.

	1:06:18.600 --> 1:06:21.440
	Moore's law might not be applicable anymore,

	1:06:21.440 --> 1:06:23.760
	for instance, in the future.

	1:06:23.760 --> 1:06:31.200
	And I do believe that when you tweak one aspect of a system,

	1:06:31.200 --> 1:06:32.920
	when you exploit one aspect of a system,

	1:06:32.920 --> 1:06:36.480
	some other aspect starts becoming the bottleneck.

	1:06:36.480 --> 1:06:38.800
	Let's say you have unlimited computation.

	1:06:38.800 --> 1:06:41.440
	Well, then data is the bottleneck.

	1:06:41.440 --> 1:06:43.560
	And I think we are already starting

	1:06:43.560 --> 1:06:45.720
	to be in a regime where our systems are

	1:06:45.720 --> 1:06:48.120
	so large in scale and so data ingrained

	1:06:48.120 --> 1:06:50.360
	that data today and the quality of data

	1:06:50.360 --> 1:06:53.040
	and the scale of data is the bottleneck.

	1:06:53.040 --> 1:06:58.160
	And in this environment, the bitter lesson from Rich

	1:06:58.160 --> 1:07:00.800
	is not going to be true anymore.

	1:07:00.800 --> 1:07:03.960
	So I think we are going to move from a focus

	1:07:03.960 --> 1:07:09.840
	on a computation scale to focus on data efficiency.

	1:07:09.840 --> 1:07:10.720
	Data efficiency.

	1:07:10.720 --> 1:07:13.120
	So that's getting to the question of symbolic AI.

	1:07:13.120 --> 1:07:16.280
	But to linger on the deep learning approaches,

	1:07:16.280 --> 1:07:19.240
	do you have hope for either unsupervised learning

	1:07:19.240 --> 1:07:23.280
	or reinforcement learning, which are

	1:07:23.280 --> 1:07:28.120
	ways of being more data efficient in terms

	1:07:28.120 --> 1:07:31.560
	of the amount of data they need that required human annotation?

	1:07:31.560 --> 1:07:34.280
	So unsupervised learning and reinforcement learning

	1:07:34.280 --> 1:07:36.640
	are frameworks for learning, but they are not

	1:07:36.640 --> 1:07:39.000
	like any specific technique.

	1:07:39.000 --> 1:07:41.200
	So usually when people say reinforcement learning,

	1:07:41.200 --> 1:07:43.320
	what they really mean is deep reinforcement learning,

	1:07:43.320 --> 1:07:47.440
	which is like one approach which is actually very questionable.

	1:07:47.440 --> 1:07:50.920
	The question I was asking was unsupervised learning

	1:07:50.920 --> 1:07:54.680
	with deep neural networks and deep reinforcement learning.

	1:07:54.680 --> 1:07:56.840
	Well, these are not really data efficient

	1:07:56.840 --> 1:08:00.520
	because you're still leveraging these huge parametric models

	1:08:00.520 --> 1:08:03.720
	point by point with gradient descent.

	1:08:03.720 --> 1:08:08.000
	It is more efficient in terms of the number of annotations,

	1:08:08.000 --> 1:08:09.520
	the density of annotations you need.

	1:08:09.520 --> 1:08:13.840
	So the idea being to learn the latent space around which

	1:08:13.840 --> 1:08:17.960
	the data is organized and then map the sparse annotations

	1:08:17.960 --> 1:08:18.760
	into it.

	1:08:18.760 --> 1:08:23.560
	And sure, I mean, that's clearly a very good idea.

	1:08:23.560 --> 1:08:26.080
	It's not really a topic I would be working on,

	1:08:26.080 --> 1:08:28.040
	but it's clearly a good idea.

	1:08:28.040 --> 1:08:31.760
	So it would get us to solve some problems that?

	1:08:31.760 --> 1:08:34.880
	It will get us to incremental improvements

	1:08:34.880 --> 1:08:38.240
	in labeled data efficiency.

	1:08:38.240 --> 1:08:43.520
	Do you have concerns about short term or long term threats

	1:08:43.520 --> 1:08:47.800
	from AI, from artificial intelligence?

	1:08:47.800 --> 1:08:50.640
	Yes, definitely to some extent.

	1:08:50.640 --> 1:08:52.800
	And what's the shape of those concerns?

	1:08:52.800 --> 1:08:56.880
	This is actually something I've briefly written about.

	1:08:56.880 --> 1:09:02.680
	But the capabilities of deep learning technology

	1:09:02.680 --> 1:09:05.200
	can be used in many ways that are

	1:09:05.200 --> 1:09:09.760
	concerning from mass surveillance with things

	1:09:09.760 --> 1:09:11.880
	like facial recognition.

	1:09:11.880 --> 1:09:15.440
	In general, tracking lots of data about everyone

	1:09:15.440 --> 1:09:18.920
	and then being able to making sense of this data

	1:09:18.920 --> 1:09:22.240
	to do identification, to do prediction.

	1:09:22.240 --> 1:09:23.160
	That's concerning.

	1:09:23.160 --> 1:09:26.560
	That's something that's being very aggressively pursued

	1:09:26.560 --> 1:09:31.440
	by totalitarian states like China.

	1:09:31.440 --> 1:09:34.000
	One thing I am very much concerned about

	1:09:34.000 --> 1:09:40.640
	is that our lives are increasingly online,

	1:09:40.640 --> 1:09:43.280
	are increasingly digital, made of information,

	1:09:43.280 --> 1:09:48.080
	made of information consumption and information production,

	1:09:48.080 --> 1:09:51.800
	our digital footprint, I would say.

	1:09:51.800 --> 1:09:56.280
	And if you absorb all of this data

	1:09:56.280 --> 1:10:01.440
	and you are in control of where you consume information,

	1:10:01.440 --> 1:10:06.960
	social networks and so on, recommendation engines,

	1:10:06.960 --> 1:10:10.200
	then you can build a sort of reinforcement

	1:10:10.200 --> 1:10:13.760
	loop for human behavior.

	1:10:13.760 --> 1:10:18.360
	You can observe the state of your mind at time t.

	1:10:18.360 --> 1:10:21.080
	You can predict how you would react

	1:10:21.080 --> 1:10:23.800
	to different pieces of content, how

	1:10:23.800 --> 1:10:27.000
	to get you to move your mind in a certain direction.

	1:10:27.000 --> 1:10:33.160
	And then you can feed you the specific piece of content

	1:10:33.160 --> 1:10:35.680
	that would move you in a specific direction.

	1:10:35.680 --> 1:10:41.800
	And you can do this at scale in terms

	1:10:41.800 --> 1:10:44.960
	of doing it continuously in real time.

	1:10:44.960 --> 1:10:46.440
	You can also do it at scale in terms

	1:10:46.440 --> 1:10:50.480
	of scaling this to many, many people, to entire populations.

	1:10:50.480 --> 1:10:53.840
	So potentially, artificial intelligence,

	1:10:53.840 --> 1:10:57.440
	even in its current state, if you combine it

	1:10:57.440 --> 1:11:01.760
	with the internet, with the fact that all of our lives

	1:11:01.760 --> 1:11:05.120
	are moving to digital devices and digital information

	1:11:05.120 --> 1:11:08.720
	consumption and creation, what you get

	1:11:08.720 --> 1:11:14.480
	is the possibility to achieve mass manipulation of behavior

	1:11:14.480 --> 1:11:16.840
	and mass psychological control.

	1:11:16.840 --> 1:11:18.520
	And this is a very real possibility.

	1:11:18.520 --> 1:11:22.080
	Yeah, so you're talking about any kind of recommender system.

	1:11:22.080 --> 1:11:26.160
	Let's look at the YouTube algorithm, Facebook,

	1:11:26.160 --> 1:11:29.720
	anything that recommends content you should watch next.

	1:11:29.720 --> 1:11:32.960
	And it's fascinating to think that there's

	1:11:32.960 --> 1:11:41.120
	some aspects of human behavior that you can say a problem of,

	1:11:41.120 --> 1:11:45.400
	is this person hold Republican beliefs or Democratic beliefs?

	1:11:45.400 --> 1:11:50.240
	And this is a trivial, that's an objective function.

	1:11:50.240 --> 1:11:52.600
	And you can optimize, and you can measure,

	1:11:52.600 --> 1:11:54.360
	and you can turn everybody into a Republican

	1:11:54.360 --> 1:11:56.080
	or everybody into a Democrat.

	1:11:56.080 --> 1:11:57.840
	I do believe it's true.

	1:11:57.840 --> 1:12:03.680
	So the human mind is very, if you look at the human mind

	1:12:03.680 --> 1:12:05.320
	as a kind of computer program, it

	1:12:05.320 --> 1:12:07.560
	has a very large exploit surface.

	1:12:07.560 --> 1:12:09.360
	It has many, many vulnerabilities.

	1:12:09.360 --> 1:12:10.840
	Exploit surfaces, yeah.

	1:12:10.840 --> 1:12:13.520
	Ways you can control it.

	1:12:13.520 --> 1:12:16.680
	For instance, when it comes to your political beliefs,

	1:12:16.680 --> 1:12:19.400
	this is very much tied to your identity.

	1:12:19.400 --> 1:12:23.040
	So for instance, if I'm in control of your news feed

	1:12:23.040 --> 1:12:26.000
	on your favorite social media platforms,

	1:12:26.000 --> 1:12:29.360
	this is actually where you're getting your news from.

	1:12:29.360 --> 1:12:32.960
	And of course, I can choose to only show you

	1:12:32.960 --> 1:12:37.120
	news that will make you see the world in a specific way.

	1:12:37.120 --> 1:12:41.920
	But I can also create incentives for you

	1:12:41.920 --> 1:12:44.720
	to post about some political beliefs.

	1:12:44.720 --> 1:12:47.960
	And then when I get you to express a statement,

	1:12:47.960 --> 1:12:51.840
	if it's a statement that me as the controller,

	1:12:51.840 --> 1:12:53.800
	I want to reinforce.

	1:12:53.800 --> 1:12:55.560
	I can just show it to people who will agree,

	1:12:55.560 --> 1:12:56.880
	and they will like it.

	1:12:56.880 --> 1:12:59.280
	And that will reinforce the statement in your mind.

	1:12:59.280 --> 1:13:02.760
	If this is a statement I want you to,

	1:13:02.760 --> 1:13:05.320
	this is a belief I want you to abandon,

	1:13:05.320 --> 1:13:09.600
	I can, on the other hand, show it to opponents.

	1:13:09.600 --> 1:13:10.640
	We'll attack you.

	1:13:10.640 --> 1:13:12.840
	And because they attack you, at the very least,

	1:13:12.840 --> 1:13:16.840
	next time you will think twice about posting it.

	1:13:16.840 --> 1:13:20.280
	But maybe you will even start believing this

	1:13:20.280 --> 1:13:22.840
	because you got pushback.

	1:13:22.840 --> 1:13:28.440
	So there are many ways in which social media platforms

	1:13:28.440 --> 1:13:30.520
	can potentially control your opinions.

	1:13:30.520 --> 1:13:35.040
	And today, so all of these things

	1:13:35.040 --> 1:13:38.240
	are already being controlled by AI algorithms.

	1:13:38.240 --> 1:13:41.880
	These algorithms do not have any explicit political goal

	1:13:41.880 --> 1:13:42.880
	today.

	1:13:42.880 --> 1:13:48.680
	Well, potentially they could, like if some totalitarian

	1:13:48.680 --> 1:13:52.720
	government takes over social media platforms

	1:13:52.720 --> 1:13:55.360
	and decides that now we are going to use this not just

	1:13:55.360 --> 1:13:58.040
	for mass surveillance, but also for mass opinion control

	1:13:58.040 --> 1:13:59.360
	and behavior control.

	1:13:59.360 --> 1:14:01.840
	Very bad things could happen.

	1:14:01.840 --> 1:14:06.480
	But what's really fascinating and actually quite concerning

	1:14:06.480 --> 1:14:11.280
	is that even without an explicit intent to manipulate,

	1:14:11.280 --> 1:14:14.760
	you're already seeing very dangerous dynamics

	1:14:14.760 --> 1:14:18.160
	in terms of how these content recommendation

	1:14:18.160 --> 1:14:19.800
	algorithms behave.

	1:14:19.800 --> 1:14:24.920
	Because right now, the goal, the objective function

	1:14:24.920 --> 1:14:28.640
	of these algorithms is to maximize engagement,

	1:14:28.640 --> 1:14:32.520
	which seems fairly innocuous at first.

	1:14:32.520 --> 1:14:36.480
	However, it is not because content

	1:14:36.480 --> 1:14:42.000
	that will maximally engage people, get people to react

	1:14:42.000 --> 1:14:44.720
	in an emotional way, get people to click on something.

	1:14:44.720 --> 1:14:52.200
	It is very often content that is not

	1:14:52.200 --> 1:14:54.400
	healthy to the public discourse.

	1:14:54.400 --> 1:14:58.200
	For instance, fake news are far more

	1:14:58.200 --> 1:15:01.320
	likely to get you to click on them than real news

	1:15:01.320 --> 1:15:06.960
	simply because they are not constrained to reality.

	1:15:06.960 --> 1:15:11.360
	So they can be as outrageous, as surprising,

	1:15:11.360 --> 1:15:15.880
	as good stories as you want because they're artificial.

	1:15:15.880 --> 1:15:18.880
	To me, that's an exciting world because so much good

	1:15:18.880 --> 1:15:19.560
	can come.

	1:15:19.560 --> 1:15:24.520
	So there's an opportunity to educate people.

	1:15:24.520 --> 1:15:31.200
	You can balance people's worldview with other ideas.

	1:15:31.200 --> 1:15:33.800
	So there's so many objective functions.

	1:15:33.800 --> 1:15:35.840
	The space of objective functions that

	1:15:35.840 --> 1:15:40.720
	create better civilizations is large, arguably infinite.

	1:15:40.720 --> 1:15:43.720
	But there's also a large space that

	1:15:43.720 --> 1:15:51.480
	creates division and destruction, civil war,

	1:15:51.480 --> 1:15:53.160
	a lot of bad stuff.

	1:15:53.160 --> 1:15:56.920
	And the worry is, naturally, probably that space

	1:15:56.920 --> 1:15:59.160
	is bigger, first of all.

	1:15:59.160 --> 1:16:04.920
	And if we don't explicitly think about what kind of effects

	1:16:04.920 --> 1:16:08.320
	are going to be observed from different objective functions,

	1:16:08.320 --> 1:16:10.160
	then we're going to get into trouble.

	1:16:10.160 --> 1:16:14.480
	But the question is, how do we get into rooms

	1:16:14.480 --> 1:16:18.560
	and have discussions, so inside Google, inside Facebook,

	1:16:18.560 --> 1:16:21.840
	inside Twitter, and think about, OK,

	1:16:21.840 --> 1:16:24.840
	how can we drive up engagement and, at the same time,

	1:16:24.840 --> 1:16:28.200
	create a good society?

	1:16:28.200 --> 1:16:29.560
	Is it even possible to have that kind

	1:16:29.560 --> 1:16:31.720
	of philosophical discussion?

	1:16:31.720 --> 1:16:33.080
	I think you can definitely try.

	1:16:33.080 --> 1:16:37.280
	So from my perspective, I would feel rather uncomfortable

	1:16:37.280 --> 1:16:41.560
	with companies that are uncomfortable with these new

	1:16:41.560 --> 1:16:47.120
	student algorithms, with them making explicit decisions

	1:16:47.120 --> 1:16:50.440
	to manipulate people's opinions or behaviors,

	1:16:50.440 --> 1:16:53.480
	even if the intent is good, because that's

	1:16:53.480 --> 1:16:55.200
	a very totalitarian mindset.

	1:16:55.200 --> 1:16:57.440
	So instead, what I would like to see

	1:16:57.440 --> 1:16:58.880
	is probably never going to happen,

	1:16:58.880 --> 1:17:00.360
	because it's not super realistic,

	1:17:00.360 --> 1:17:02.520
	but that's actually something I really care about.

	1:17:02.520 --> 1:17:06.280
	I would like all these algorithms

	1:17:06.280 --> 1:17:10.560
	to present configuration settings to their users,

	1:17:10.560 --> 1:17:14.600
	so that the users can actually make the decision about how

	1:17:14.600 --> 1:17:19.000
	they want to be impacted by these information

	1:17:19.000 --> 1:17:21.960
	recommendation, content recommendation algorithms.

	1:17:21.960 --> 1:17:24.240
	For instance, as a user of something

	1:17:24.240 --> 1:17:26.520
	like YouTube or Twitter, maybe I want

	1:17:26.520 --> 1:17:30.280
	to maximize learning about a specific topic.

	1:17:30.280 --> 1:17:36.800
	So I want the algorithm to feed my curiosity,

	1:17:36.800 --> 1:17:38.760
	which is in itself a very interesting problem.

	1:17:38.760 --> 1:17:41.200
	So instead of maximizing my engagement,

	1:17:41.200 --> 1:17:44.600
	it will maximize how fast and how much I'm learning.

	1:17:44.600 --> 1:17:47.360
	And it will also take into account the accuracy,

	1:17:47.360 --> 1:17:50.680
	hopefully, of the information I'm learning.

	1:17:50.680 --> 1:17:55.680
	So yeah, the user should be able to determine exactly

	1:17:55.680 --> 1:17:58.560
	how these algorithms are affecting their lives.

	1:17:58.560 --> 1:18:03.520
	I don't want actually any entity making decisions

	1:18:03.520 --> 1:18:09.480
	about in which direction they're going to try to manipulate me.

	1:18:09.480 --> 1:18:11.680
	I want technology.

	1:18:11.680 --> 1:18:14.280
	So AI, these algorithms are increasingly

	1:18:14.280 --> 1:18:18.560
	going to be our interface to a world that is increasingly

	1:18:18.560 --> 1:18:19.960
	made of information.

	1:18:19.960 --> 1:18:25.840
	And I want everyone to be in control of this interface,

	1:18:25.840 --> 1:18:29.160
	to interface with the world on their own terms.

	1:18:29.160 --> 1:18:32.840
	So if someone wants these algorithms

	1:18:32.840 --> 1:18:37.640
	to serve their own personal growth goals,

	1:18:37.640 --> 1:18:40.640
	they should be able to configure these algorithms

	1:18:40.640 --> 1:18:41.800
	in such a way.

	1:18:41.800 --> 1:18:46.680
	Yeah, but so I know it's painful to have explicit decisions.

	1:18:46.680 --> 1:18:51.080
	But there is underlying explicit decisions,

	1:18:51.080 --> 1:18:53.360
	which is some of the most beautiful fundamental

	1:18:53.360 --> 1:18:57.400
	philosophy that we have before us,

	1:18:57.400 --> 1:19:01.120
	which is personal growth.

	1:19:01.120 --> 1:19:05.680
	If I want to watch videos from which I can learn,

	1:19:05.680 --> 1:19:08.080
	what does that mean?

	1:19:08.080 --> 1:19:11.800
	So if I have a checkbox that wants to emphasize learning,

	1:19:11.800 --> 1:19:15.480
	there's still an algorithm with explicit decisions in it

	1:19:15.480 --> 1:19:17.800
	that would promote learning.

	1:19:17.800 --> 1:19:19.200
	What does that mean for me?

	1:19:19.200 --> 1:19:22.800
	For example, I've watched a documentary on flat Earth

	1:19:22.800 --> 1:19:23.640
	theory, I guess.

	1:19:27.280 --> 1:19:28.240
	I learned a lot.

	1:19:28.240 --> 1:19:29.800
	I'm really glad I watched it.

	1:19:29.800 --> 1:19:32.560
	It was a friend recommended it to me.

	1:19:32.560 --> 1:19:35.800
	Because I don't have such an allergic reaction to crazy

	1:19:35.800 --> 1:19:37.640
	people, as my fellow colleagues do.

	1:19:37.640 --> 1:19:40.360
	But it was very eye opening.

	1:19:40.360 --> 1:19:42.120
	And for others, it might not be.

	1:19:42.120 --> 1:19:45.560
	From others, they might just get turned off from that, same

	1:19:45.560 --> 1:19:47.160
	with Republican and Democrat.

	1:19:47.160 --> 1:19:50.200
	And it's a non trivial problem.

	1:19:50.200 --> 1:19:52.880
	And first of all, if it's done well,

	1:19:52.880 --> 1:19:56.560
	I don't think it's something that wouldn't happen,

	1:19:56.560 --> 1:19:59.280
	that YouTube wouldn't be promoting,

	1:19:59.280 --> 1:20:00.200
	or Twitter wouldn't be.

	1:20:00.200 --> 1:20:02.280
	It's just a really difficult problem,

	1:20:02.280 --> 1:20:05.520
	how to give people control.

	1:20:05.520 --> 1:20:08.960
	Well, it's mostly an interface design problem.

	1:20:08.960 --> 1:20:11.080
	The way I see it, you want to create technology

	1:20:11.080 --> 1:20:16.400
	that's like a mentor, or a coach, or an assistant,

	1:20:16.400 --> 1:20:20.520
	so that it's not your boss.

	1:20:20.520 --> 1:20:22.560
	You are in control of it.

	1:20:22.560 --> 1:20:25.760
	You are telling it what to do for you.

	1:20:25.760 --> 1:20:27.840
	And if you feel like it's manipulating you,

	1:20:27.840 --> 1:20:31.760
	it's not actually doing what you want.

	1:20:31.760 --> 1:20:34.920
	You should be able to switch to a different algorithm.

	1:20:34.920 --> 1:20:36.440
	So that's fine tune control.

	1:20:36.440 --> 1:20:38.840
	You kind of learn that you're trusting

	1:20:38.840 --> 1:20:40.080
	the human collaboration.

	1:20:40.080 --> 1:20:41.920
	I mean, that's how I see autonomous vehicles too,

	1:20:41.920 --> 1:20:44.480
	is giving as much information as possible,

	1:20:44.480 --> 1:20:47.240
	and you learn that dance yourself.

	1:20:47.240 --> 1:20:50.280
	Yeah, Adobe, I don't know if you use Adobe product

	1:20:50.280 --> 1:20:52.280
	for like Photoshop.

	1:20:52.280 --> 1:20:55.040
	They're trying to see if they can inject YouTube

	1:20:55.040 --> 1:20:57.120
	into their interface, but basically allow you

	1:20:57.120 --> 1:20:59.840
	to show you all these videos,

	1:20:59.840 --> 1:21:03.320
	that everybody's confused about what to do with features.

	1:21:03.320 --> 1:21:07.120
	So basically teach people by linking to,

	1:21:07.120 --> 1:21:10.280
	in that way, it's an assistant that uses videos

	1:21:10.280 --> 1:21:13.440
	as a basic element of information.

	1:21:13.440 --> 1:21:18.240
	Okay, so what practically should people do

	1:21:18.240 --> 1:21:24.000
	to try to fight against abuses of these algorithms,

	1:21:24.000 --> 1:21:27.400
	or algorithms that manipulate us?

	1:21:27.400 --> 1:21:29.280
	Honestly, it's a very, very difficult problem,

	1:21:29.280 --> 1:21:32.800
	because to start with, there is very little public awareness

	1:21:32.800 --> 1:21:35.040
	of these issues.

	1:21:35.040 --> 1:21:38.520
	Very few people would think there's anything wrong

	1:21:38.520 --> 1:21:39.720
	with the unused algorithm,

	1:21:39.720 --> 1:21:42.040
	even though there is actually something wrong already,

	1:21:42.040 --> 1:21:44.480
	which is that it's trying to maximize engagement

	1:21:44.480 --> 1:21:49.880
	most of the time, which has very negative side effects.

	1:21:49.880 --> 1:21:56.160
	So ideally, so the very first thing is to stop

	1:21:56.160 --> 1:21:59.560
	trying to purely maximize engagement,

	1:21:59.560 --> 1:22:06.560
	try to propagate content based on popularity, right?

	1:22:06.560 --> 1:22:11.040
	Instead, take into account the goals

	1:22:11.040 --> 1:22:13.560
	and the profiles of each user.

	1:22:13.560 --> 1:22:16.920
	So you will be, one example is, for instance,

	1:22:16.920 --> 1:22:20.800
	when I look at topic recommendations on Twitter,

	1:22:20.800 --> 1:22:24.480
	it's like, you know, they have this news tab

	1:22:24.480 --> 1:22:25.480
	with switch recommendations.

	1:22:25.480 --> 1:22:28.480
	It's always the worst coverage,

	1:22:28.480 --> 1:22:30.360
	because it's content that appeals

	1:22:30.360 --> 1:22:34.080
	to the smallest common denominator

	1:22:34.080 --> 1:22:37.080
	to all Twitter users, because they're trying to optimize.

	1:22:37.080 --> 1:22:39.040
	They're purely trying to optimize popularity.

	1:22:39.040 --> 1:22:41.320
	They're purely trying to optimize engagement.

	1:22:41.320 --> 1:22:42.960
	But that's not what I want.

	1:22:42.960 --> 1:22:46.080
	So they should put me in control of some setting

	1:22:46.080 --> 1:22:50.360
	so that I define what's the objective function

	1:22:50.360 --> 1:22:52.200
	that Twitter is going to be following

	1:22:52.200 --> 1:22:54.120
	to show me this content.

	1:22:54.120 --> 1:22:57.360
	And honestly, so this is all about interface design.

	1:22:57.360 --> 1:22:59.440
	And we are not, it's not realistic

	1:22:59.440 --> 1:23:01.760
	to give users control of a bunch of knobs

	1:23:01.760 --> 1:23:03.400
	that define algorithm.

	1:23:03.400 --> 1:23:06.760
	Instead, we should purely put them in charge

	1:23:06.760 --> 1:23:09.400
	of defining the objective function.

	1:23:09.400 --> 1:23:13.240
	Like, let the user tell us what they want to achieve,

	1:23:13.240 --> 1:23:15.280
	how they want this algorithm to impact their lives.

	1:23:15.280 --> 1:23:16.680
	So do you think it is that,

	1:23:16.680 --> 1:23:19.360
	or do they provide individual article by article

	1:23:19.360 --> 1:23:21.600
	reward structure where you give a signal,

	1:23:21.600 --> 1:23:24.720
	I'm glad I saw this, or I'm glad I didn't?

	1:23:24.720 --> 1:23:28.480
	So like a Spotify type feedback mechanism,

	1:23:28.480 --> 1:23:30.680
	it works to some extent.

	1:23:30.680 --> 1:23:32.000
	I'm kind of skeptical about it

	1:23:32.000 --> 1:23:34.880
	because the only way the algorithm,

	1:23:34.880 --> 1:23:39.120
	the algorithm will attempt to relate your choices

	1:23:39.120 --> 1:23:41.040
	with the choices of everyone else,

	1:23:41.040 --> 1:23:45.000
	which might, you know, if you have an average profile

	1:23:45.000 --> 1:23:47.880
	that works fine, I'm sure Spotify accommodations work fine

	1:23:47.880 --> 1:23:49.560
	if you just like mainstream stuff.

	1:23:49.560 --> 1:23:53.960
	If you don't, it can be, it's not optimal at all actually.

	1:23:53.960 --> 1:23:56.040
	It'll be in an efficient search

	1:23:56.040 --> 1:24:00.800
	for the part of the Spotify world that represents you.

	1:24:00.800 --> 1:24:02.960
	So it's a tough problem,

	1:24:02.960 --> 1:24:07.960
	but do note that even a feedback system

	1:24:07.960 --> 1:24:10.880
	like what Spotify has does not give me control

	1:24:10.880 --> 1:24:15.000
	over what the algorithm is trying to optimize for.

	1:24:16.320 --> 1:24:19.360
	Well, public awareness, which is what we're doing now,

	1:24:19.360 --> 1:24:21.360
	is a good place to start.

	1:24:21.360 --> 1:24:25.960
	Do you have concerns about longterm existential threats

	1:24:25.960 --> 1:24:27.360
	of artificial intelligence?

	1:24:28.280 --> 1:24:31.040
	Well, as I was saying,

	1:24:31.040 --> 1:24:33.360
	our world is increasingly made of information.

	1:24:33.360 --> 1:24:36.240
	AI algorithms are increasingly going to be our interface

	1:24:36.240 --> 1:24:37.880
	to this world of information,

	1:24:37.880 --> 1:24:41.480
	and somebody will be in control of these algorithms.

	1:24:41.480 --> 1:24:45.920
	And that puts us in any kind of a bad situation, right?

	1:24:45.920 --> 1:24:46.880
	It has risks.

	1:24:46.880 --> 1:24:50.840
	It has risks coming from potentially large companies

	1:24:50.840 --> 1:24:53.760
	wanting to optimize their own goals,

	1:24:53.760 --> 1:24:55.960
	maybe profit, maybe something else.

	1:24:55.960 --> 1:25:00.720
	Also from governments who might want to use these algorithms

	1:25:00.720 --> 1:25:03.520
	as a means of control of the population.

	1:25:03.520 --> 1:25:05.000
	Do you think there's existential threat

	1:25:05.000 --> 1:25:06.320
	that could arise from that?

	1:25:06.320 --> 1:25:09.120
	So existential threat.

	1:25:09.120 --> 1:25:13.240
	So maybe you're referring to the singularity narrative

	1:25:13.240 --> 1:25:15.560
	where robots just take over.

	1:25:15.560 --> 1:25:18.320
	Well, I don't, I'm not terminating robots,

	1:25:18.320 --> 1:25:21.000
	and I don't believe it has to be a singularity.

	1:25:21.000 --> 1:25:24.800
	We're just talking to, just like you said,

	1:25:24.800 --> 1:25:27.920
	the algorithm controlling masses of populations.

	1:25:28.920 --> 1:25:31.120
	The existential threat being,

	1:25:32.640 --> 1:25:36.760
	hurt ourselves much like a nuclear war would hurt ourselves.

	1:25:36.760 --> 1:25:37.600
	That kind of thing.

	1:25:37.600 --> 1:25:39.480
	I don't think that requires a singularity.

	1:25:39.480 --> 1:25:42.560
	That requires a loss of control over AI algorithm.

	1:25:42.560 --> 1:25:43.560
	Yes.

	1:25:43.560 --> 1:25:47.000
	So I do agree there are concerning trends.

	1:25:47.000 --> 1:25:52.000
	Honestly, I wouldn't want to make any longterm predictions.

	1:25:52.960 --> 1:25:56.000
	I don't think today we really have the capability

	1:25:56.000 --> 1:25:58.560
	to see what the dangers of AI

	1:25:58.560 --> 1:26:01.360
	are going to be in 50 years, in 100 years.

	1:26:01.360 --> 1:26:04.800
	I do see that we are already faced

	1:26:04.800 --> 1:26:08.840
	with concrete and present dangers

	1:26:08.840 --> 1:26:11.560
	surrounding the negative side effects

	1:26:11.560 --> 1:26:14.960
	of content recombination systems, of newsfeed algorithms

	1:26:14.960 --> 1:26:17.640
	concerning algorithmic bias as well.

	1:26:18.640 --> 1:26:21.200
	So we are delegating more and more

	1:26:22.240 --> 1:26:25.080
	decision processes to algorithms.

	1:26:25.080 --> 1:26:26.760
	Some of these algorithms are uncrafted,

	1:26:26.760 --> 1:26:29.360
	some are learned from data,

	1:26:29.360 --> 1:26:31.920
	but we are delegating control.

	1:26:32.920 --> 1:26:36.280
	Sometimes it's a good thing, sometimes not so much.

	1:26:36.280 --> 1:26:39.480
	And there is in general very little supervision

	1:26:39.480 --> 1:26:41.000
	of this process, right?

	1:26:41.000 --> 1:26:45.400
	So we are still in this period of very fast change,

	1:26:45.400 --> 1:26:50.400
	even chaos, where society is restructuring itself,

	1:26:50.920 --> 1:26:53.160
	turning into an information society,

	1:26:53.160 --> 1:26:54.520
	which itself is turning into

	1:26:54.520 --> 1:26:58.360
	an increasingly automated information passing society.

	1:26:58.360 --> 1:27:02.520
	And well, yeah, I think the best we can do today

	1:27:02.520 --> 1:27:06.040
	is try to raise awareness around some of these issues.

	1:27:06.040 --> 1:27:07.680
	And I think we're actually making good progress.

	1:27:07.680 --> 1:27:11.720
	If you look at algorithmic bias, for instance,

	1:27:12.760 --> 1:27:14.760
	three years ago, even two years ago,

	1:27:14.760 --> 1:27:17.040
	very, very few people were talking about it.

	1:27:17.040 --> 1:27:20.320
	And now all the big companies are talking about it.

	1:27:20.320 --> 1:27:22.360
	They are often not in a very serious way,

	1:27:22.360 --> 1:27:24.560
	but at least it is part of the public discourse.

	1:27:24.560 --> 1:27:27.080
	You see people in Congress talking about it.

	1:27:27.080 --> 1:27:31.960
	And it all started from raising awareness.

	1:27:31.960 --> 1:27:32.800
	Right.

	1:27:32.800 --> 1:27:36.080
	So in terms of alignment problem,

	1:27:36.080 --> 1:27:39.400
	trying to teach as we allow algorithms,

	1:27:39.400 --> 1:27:41.520
	just even recommender systems on Twitter,

	1:27:43.640 --> 1:27:47.080
	encoding human values and morals,

	1:27:48.280 --> 1:27:50.200
	decisions that touch on ethics,

	1:27:50.200 --> 1:27:52.600
	how hard do you think that problem is?

	1:27:52.600 --> 1:27:57.240
	How do we have lost functions in neural networks

	1:27:57.240 --> 1:27:58.640
	that have some component,

	1:27:58.640 --> 1:28:01.080
	some fuzzy components of human morals?

	1:28:01.080 --> 1:28:06.080
	Well, I think this is really all about objective function engineering,

	1:28:06.080 --> 1:28:10.520
	which is probably going to be increasingly a topic of concern in the future.

	1:28:10.520 --> 1:28:14.640
	Like for now, we're just using very naive loss functions

	1:28:14.640 --> 1:28:17.760
	because the hard part is not actually what you're trying to minimize.

	1:28:17.760 --> 1:28:19.040
	It's everything else.

	1:28:19.040 --> 1:28:22.840
	But as the everything else is going to be increasingly automated,

	1:28:22.840 --> 1:28:27.040
	we're going to be focusing our human attention

	1:28:27.040 --> 1:28:30.240
	on increasingly high level components,

	1:28:30.240 --> 1:28:32.680
	like what's actually driving the whole learning system,

	1:28:32.680 --> 1:28:33.960
	like the objective function.

	1:28:33.960 --> 1:28:36.920
	So loss function engineering is going to be,

	1:28:36.920 --> 1:28:40.640
	loss function engineer is probably going to be a job title in the future.

	1:28:40.640 --> 1:28:44.520
	And then the tooling you're creating with Keras essentially

	1:28:44.520 --> 1:28:47.040
	takes care of all the details underneath.

	1:28:47.040 --> 1:28:52.720
	And basically the human expert is needed for exactly that.

	1:28:52.720 --> 1:28:53.920
	That's the idea.

	1:28:53.920 --> 1:28:57.640
	Keras is the interface between the data you're collecting

	1:28:57.640 --> 1:28:59.080
	and the business goals.

	1:28:59.080 --> 1:29:03.480
	And your job as an engineer is going to be to express your business goals

	1:29:03.480 --> 1:29:06.720
	and your understanding of your business or your product,

	1:29:06.720 --> 1:29:11.840
	your system as a kind of loss function or a kind of set of constraints.

	1:29:11.840 --> 1:29:19.480
	Does the possibility of creating an AGI system excite you or scare you or bore you?

	1:29:19.480 --> 1:29:22.080
	So intelligence can never really be general.

	1:29:22.080 --> 1:29:26.400
	You know, at best it can have some degree of generality like human intelligence.

	1:29:26.400 --> 1:29:30.640
	It also always has some specialization in the same way that human intelligence

	1:29:30.640 --> 1:29:33.440
	is specialized in a certain category of problems,

	1:29:33.440 --> 1:29:35.440
	is specialized in the human experience.

	1:29:35.440 --> 1:29:37.280
	And when people talk about AGI,

	1:29:37.280 --> 1:29:42.520
	I'm never quite sure if they're talking about very, very smart AI,

	1:29:42.520 --> 1:29:45.080
	so smart that it's even smarter than humans,

	1:29:45.080 --> 1:29:48.000
	or they're talking about human like intelligence,

	1:29:48.000 --> 1:29:49.680
	because these are different things.

	1:29:49.680 --> 1:29:54.760
	Let's say, presumably I'm oppressing you today with my humanness.

	1:29:54.760 --> 1:29:59.240
	So imagine that I was in fact a robot.

	1:29:59.240 --> 1:30:01.920
	So what does that mean?

	1:30:01.920 --> 1:30:04.920
	That I'm impressing you with natural language processing.

	1:30:04.920 --> 1:30:07.840
	Maybe if you weren't able to see me, maybe this is a phone call.

	1:30:07.840 --> 1:30:10.000
	So that kind of system.

	1:30:10.000 --> 1:30:11.120
	Companion.

	1:30:11.120 --> 1:30:15.040
	So that's very much about building human like AI.

	1:30:15.040 --> 1:30:18.200
	And you're asking me, you know, is this an exciting perspective?

	1:30:18.200 --> 1:30:19.440
	Yes.

	1:30:19.440 --> 1:30:21.760
	I think so, yes.

	1:30:21.760 --> 1:30:28.000
	Not so much because of what artificial human like intelligence could do,

	1:30:28.000 --> 1:30:30.880
	but, you know, from an intellectual perspective,

	1:30:30.880 --> 1:30:34.120
	I think if you could build truly human like intelligence,

	1:30:34.120 --> 1:30:37.240
	that means you could actually understand human intelligence,

	1:30:37.240 --> 1:30:39.880
	which is fascinating, right?

	1:30:39.880 --> 1:30:42.680
	Human like intelligence is going to require emotions.

	1:30:42.680 --> 1:30:44.400
	It's going to require consciousness,

	1:30:44.400 --> 1:30:49.720
	which is not things that would normally be required by an intelligent system.

	1:30:49.720 --> 1:30:53.160
	If you look at, you know, we were mentioning earlier like science

	1:30:53.160 --> 1:30:59.600
	as a superhuman problem solving agent or system,

	1:30:59.600 --> 1:31:02.120
	it does not have consciousness, it doesn't have emotions.

	1:31:02.120 --> 1:31:04.320
	In general, so emotions,

	1:31:04.320 --> 1:31:07.640
	I see consciousness as being on the same spectrum as emotions.

	1:31:07.640 --> 1:31:12.280
	It is a component of the subjective experience

	1:31:12.280 --> 1:31:18.800
	that is meant very much to guide behavior generation, right?

	1:31:18.800 --> 1:31:20.800
	It's meant to guide your behavior.

	1:31:20.800 --> 1:31:24.520
	In general, human intelligence and animal intelligence

	1:31:24.520 --> 1:31:29.280
	has evolved for the purpose of behavior generation, right?

	1:31:29.280 --> 1:31:30.680
	Including in a social context.

	1:31:30.680 --> 1:31:32.480
	So that's why we actually need emotions.

	1:31:32.480 --> 1:31:34.920
	That's why we need consciousness.

	1:31:34.920 --> 1:31:38.360
	An artificial intelligence system developed in a different context

	1:31:38.360 --> 1:31:42.800
	may well never need them, may well never be conscious like science.

	1:31:42.800 --> 1:31:47.960
	Well, on that point, I would argue it's possible to imagine

	1:31:47.960 --> 1:31:51.480
	that there's echoes of consciousness in science

	1:31:51.480 --> 1:31:55.480
	when viewed as an organism, that science is consciousness.

	1:31:55.480 --> 1:31:59.160
	So, I mean, how would you go about testing this hypothesis?

	1:31:59.160 --> 1:32:07.000
	How do you probe the subjective experience of an abstract system like science?

	1:32:07.000 --> 1:32:10.400
	Well, the point of probing any subjective experience is impossible

	1:32:10.400 --> 1:32:13.200
	because I'm not science, I'm Lex.

	1:32:13.200 --> 1:32:20.520
	So I can't probe another entity, it's no more than bacteria on my skin.

	1:32:20.520 --> 1:32:24.160
	You're Lex, I can ask you questions about your subjective experience

	1:32:24.160 --> 1:32:28.440
	and you can answer me, and that's how I know you're conscious.

	1:32:28.440 --> 1:32:31.840
	Yes, but that's because we speak the same language.

	1:32:31.840 --> 1:32:35.520
	You perhaps, we have to speak the language of science in order to ask it.

	1:32:35.520 --> 1:32:40.320
	Honestly, I don't think consciousness, just like emotions of pain and pleasure,

	1:32:40.320 --> 1:32:44.160
	is not something that inevitably arises

	1:32:44.160 --> 1:32:47.920
	from any sort of sufficiently intelligent information processing.

	1:32:47.920 --> 1:32:53.920
	It is a feature of the mind, and if you've not implemented it explicitly, it is not there.

	1:32:53.920 --> 1:32:58.960
	So you think it's an emergent feature of a particular architecture.

	1:32:58.960 --> 1:33:00.320
	So do you think...

	1:33:00.320 --> 1:33:02.000
	It's a feature in the same sense.

	1:33:02.000 --> 1:33:08.240
	So, again, the subjective experience is all about guiding behavior.

	1:33:08.240 --> 1:33:15.120
	If the problems you're trying to solve don't really involve an embodied agent,

	1:33:15.120 --> 1:33:19.520
	maybe in a social context, generating behavior and pursuing goals like this.

	1:33:19.520 --> 1:33:22.160
	And if you look at science, that's not really what's happening.

	1:33:22.160 --> 1:33:27.920
	Even though it is, it is a form of artificial AI, artificial intelligence,

	1:33:27.920 --> 1:33:31.920
	in the sense that it is solving problems, it is accumulating knowledge,

	1:33:31.920 --> 1:33:35.040
	accumulating solutions and so on.

	1:33:35.040 --> 1:33:39.440
	So if you're not explicitly implementing a subjective experience,

	1:33:39.440 --> 1:33:44.000
	implementing certain emotions and implementing consciousness,

	1:33:44.000 --> 1:33:47.360
	it's not going to just spontaneously emerge.

	1:33:47.360 --> 1:33:48.080
	Yeah.

	1:33:48.080 --> 1:33:53.200
	But so for a system like, human like intelligence system that has consciousness,

	1:33:53.200 --> 1:33:55.840
	do you think it needs to have a body?

	1:33:55.840 --> 1:33:56.720
	Yes, definitely.

	1:33:56.720 --> 1:33:59.600
	I mean, it doesn't have to be a physical body, right?

	1:33:59.600 --> 1:34:03.440
	And there's not that much difference between a realistic simulation in the real world.

	1:34:03.440 --> 1:34:06.400
	So there has to be something you have to preserve kind of thing.

	1:34:06.400 --> 1:34:11.840
	Yes, but human like intelligence can only arise in a human like context.

	1:34:11.840 --> 1:34:16.800
	Intelligence needs other humans in order for you to demonstrate

	1:34:16.800 --> 1:34:19.040
	that you have human like intelligence, essentially.

	1:34:19.040 --> 1:34:19.540
	Yes.

	1:34:20.320 --> 1:34:28.080
	So what kind of tests and demonstration would be sufficient for you

	1:34:28.080 --> 1:34:30.960
	to demonstrate human like intelligence?

	1:34:30.960 --> 1:34:31.360
	Yeah.

	1:34:31.360 --> 1:34:35.600
	Just out of curiosity, you've talked about in terms of theorem proving

	1:34:35.600 --> 1:34:38.000
	and program synthesis, I think you've written about

	1:34:38.000 --> 1:34:40.480
	that there's no good benchmarks for this.

	1:34:40.480 --> 1:34:40.720
	Yeah.

	1:34:40.720 --> 1:34:42.000
	That's one of the problems.

	1:34:42.000 --> 1:34:46.320
	So let's talk program synthesis.

	1:34:46.320 --> 1:34:47.760
	So what do you imagine is a good...

	1:34:48.800 --> 1:34:51.360
	I think it's related questions for human like intelligence

	1:34:51.360 --> 1:34:52.560
	and for program synthesis.

	1:34:53.360 --> 1:34:56.080
	What's a good benchmark for either or both?

	1:34:56.080 --> 1:34:56.480
	Right.

	1:34:56.480 --> 1:34:59.200
	So I mean, you're actually asking two questions,

	1:34:59.200 --> 1:35:02.480
	which is one is about quantifying intelligence

	1:35:02.480 --> 1:35:06.880
	and comparing the intelligence of an artificial system

	1:35:06.880 --> 1:35:08.480
	to the intelligence for human.

	1:35:08.480 --> 1:35:13.440
	And the other is about the degree to which this intelligence is human like.

	1:35:13.440 --> 1:35:15.120
	It's actually two different questions.

	1:35:16.560 --> 1:35:18.960
	So you mentioned earlier the Turing test.

	1:35:19.680 --> 1:35:23.200
	Well, I actually don't like the Turing test because it's very lazy.

	1:35:23.200 --> 1:35:28.720
	It's all about completely bypassing the problem of defining and measuring intelligence

	1:35:28.720 --> 1:35:34.160
	and instead delegating to a human judge or a panel of human judges.

	1:35:34.160 --> 1:35:37.120
	So it's a total copout, right?

	1:35:38.160 --> 1:35:43.200
	If you want to measure how human like an agent is,

	1:35:43.760 --> 1:35:46.640
	I think you have to make it interact with other humans.

	1:35:47.600 --> 1:35:53.760
	Maybe it's not necessarily a good idea to have these other humans be the judges.

	1:35:53.760 --> 1:35:59.280
	Maybe you should just observe behavior and compare it to what a human would actually have done.

	1:36:00.560 --> 1:36:05.120
	When it comes to measuring how smart, how clever an agent is

	1:36:05.120 --> 1:36:11.120
	and comparing that to the degree of human intelligence.

	1:36:11.120 --> 1:36:12.960
	So we're already talking about two things, right?

	1:36:13.520 --> 1:36:20.320
	The degree, kind of like the magnitude of an intelligence and its direction, right?

	1:36:20.320 --> 1:36:23.280
	Like the norm of a vector and its direction.

	1:36:23.280 --> 1:36:32.000
	And the direction is like human likeness and the magnitude, the norm is intelligence.

	1:36:32.720 --> 1:36:34.080
	You could call it intelligence, right?

	1:36:34.080 --> 1:36:41.040
	So the direction, your sense, the space of directions that are human like is very narrow.

	1:36:41.040 --> 1:36:41.200
	Yeah.

	1:36:42.240 --> 1:36:48.880
	So the way you would measure the magnitude of intelligence in a system

	1:36:48.880 --> 1:36:54.640
	in a way that also enables you to compare it to that of a human.

	1:36:54.640 --> 1:36:59.200
	Well, if you look at different benchmarks for intelligence today,

	1:36:59.200 --> 1:37:04.160
	they're all too focused on skill at a given task.

	1:37:04.160 --> 1:37:08.720
	Like skill at playing chess, skill at playing Go, skill at playing Dota.

	1:37:10.720 --> 1:37:15.600
	And I think that's not the right way to go about it because you can always

	1:37:15.600 --> 1:37:18.240
	beat a human at one specific task.

	1:37:19.200 --> 1:37:23.920
	The reason why our skill at playing Go or juggling or anything is impressive

	1:37:23.920 --> 1:37:28.400
	is because we are expressing this skill within a certain set of constraints.

	1:37:28.400 --> 1:37:32.320
	If you remove the constraints, the constraints that we have one lifetime,

	1:37:32.320 --> 1:37:36.080
	that we have this body and so on, if you remove the context,

	1:37:36.080 --> 1:37:40.480
	if you have unlimited string data, if you can have access to, you know,

	1:37:40.480 --> 1:37:44.640
	for instance, if you look at juggling, if you have no restriction on the hardware,

	1:37:44.640 --> 1:37:48.400
	then achieving arbitrary levels of skill is not very interesting

	1:37:48.400 --> 1:37:52.400
	and says nothing about the amount of intelligence you've achieved.

	1:37:52.400 --> 1:37:57.440
	So if you want to measure intelligence, you need to rigorously define what

	1:37:57.440 --> 1:38:02.960
	intelligence is, which in itself, you know, it's a very challenging problem.

	1:38:02.960 --> 1:38:04.320
	And do you think that's possible?

	1:38:04.320 --> 1:38:06.000
	To define intelligence? Yes, absolutely.

	1:38:06.000 --> 1:38:09.760
	I mean, you can provide, many people have provided, you know, some definition.

	1:38:10.560 --> 1:38:12.000
	I have my own definition.

	1:38:12.000 --> 1:38:13.440
	Where does your definition begin?

	1:38:13.440 --> 1:38:16.240
	Where does your definition begin if it doesn't end?

	1:38:16.240 --> 1:38:21.680
	Well, I think intelligence is essentially the efficiency

	1:38:22.320 --> 1:38:29.760
	with which you turn experience into generalizable programs.

	1:38:29.760 --> 1:38:32.800
	So what that means is it's the efficiency with which

	1:38:32.800 --> 1:38:36.720
	you turn a sampling of experience space into

	1:38:36.720 --> 1:38:46.000
	the ability to process a larger chunk of experience space.

	1:38:46.000 --> 1:38:52.560
	So measuring skill can be one proxy across many different tasks,

	1:38:52.560 --> 1:38:54.480
	can be one proxy for measuring intelligence.

	1:38:54.480 --> 1:38:58.720
	But if you want to only measure skill, you should control for two things.

	1:38:58.720 --> 1:39:04.960
	You should control for the amount of experience that your system has

	1:39:04.960 --> 1:39:08.080
	and the priors that your system has.

	1:39:08.080 --> 1:39:13.120
	But if you look at two agents and you give them the same priors

	1:39:13.120 --> 1:39:16.160
	and you give them the same amount of experience,

	1:39:16.160 --> 1:39:21.360
	there is one of the agents that is going to learn programs,

	1:39:21.360 --> 1:39:25.440
	representations, something, a model that will perform well

	1:39:25.440 --> 1:39:28.720
	on the larger chunk of experience space than the other.

	1:39:28.720 --> 1:39:30.960
	And that is the smaller agent.

	1:39:30.960 --> 1:39:36.960
	Yeah. So if you fix the experience, which generate better programs,

	1:39:37.680 --> 1:39:39.520
	better meaning more generalizable.

	1:39:39.520 --> 1:39:40.560
	That's really interesting.

	1:39:40.560 --> 1:39:42.400
	That's a very nice, clean definition of...

	1:39:42.400 --> 1:39:47.280
	Oh, by the way, in this definition, it is already very obvious

	1:39:47.280 --> 1:39:49.440
	that intelligence has to be specialized

	1:39:49.440 --> 1:39:51.680
	because you're talking about experience space

	1:39:51.680 --> 1:39:54.080
	and you're talking about segments of experience space.

	1:39:54.080 --> 1:39:57.200
	You're talking about priors and you're talking about experience.

	1:39:57.200 --> 1:40:02.480
	All of these things define the context in which intelligence emerges.

	1:40:04.480 --> 1:40:08.640
	And you can never look at the totality of experience space, right?

	1:40:09.760 --> 1:40:12.160
	So intelligence has to be specialized.

	1:40:12.160 --> 1:40:14.960
	But it can be sufficiently large, the experience space,

	1:40:14.960 --> 1:40:16.080
	even though it's specialized.

	1:40:16.080 --> 1:40:19.120
	There's a certain point when the experience space is large enough

	1:40:19.120 --> 1:40:21.440
	to where it might as well be general.

	1:40:22.000 --> 1:40:23.920
	It feels general. It looks general.

	1:40:23.920 --> 1:40:25.680
	Sure. I mean, it's very relative.

	1:40:25.680 --> 1:40:29.360
	Like, for instance, many people would say human intelligence is general.

	1:40:29.360 --> 1:40:31.200
	In fact, it is quite specialized.

	1:40:32.800 --> 1:40:37.120
	We can definitely build systems that start from the same innate priors

	1:40:37.120 --> 1:40:39.120
	as what humans have at birth.

	1:40:39.120 --> 1:40:42.320
	Because we already understand fairly well

	1:40:42.320 --> 1:40:44.480
	what sort of priors we have as humans.

	1:40:44.480 --> 1:40:46.080
	Like many people have worked on this problem.

	1:40:46.800 --> 1:40:51.040
	Most notably, Elisabeth Spelke from Harvard.

	1:40:51.040 --> 1:40:52.240
	I don't know if you know her.

	1:40:52.240 --> 1:40:56.000
	She's worked a lot on what she calls core knowledge.

	1:40:56.000 --> 1:41:00.640
	And it is very much about trying to determine and describe

	1:41:00.640 --> 1:41:02.320
	what priors we are born with.

	1:41:02.320 --> 1:41:04.720
	Like language skills and so on, all that kind of stuff.

	1:41:04.720 --> 1:41:05.220
	Exactly.

	1:41:06.880 --> 1:41:11.440
	So we have some pretty good understanding of what priors we are born with.

	1:41:11.440 --> 1:41:12.560
	So we could...

	1:41:13.760 --> 1:41:17.760
	So I've actually been working on a benchmark for the past couple years,

	1:41:17.760 --> 1:41:18.640
	you know, on and off.

	1:41:18.640 --> 1:41:20.480
	I hope to be able to release it at some point.

	1:41:20.480 --> 1:41:21.760
	That's exciting.

	1:41:21.760 --> 1:41:25.680
	The idea is to measure the intelligence of systems

	1:41:26.800 --> 1:41:28.640
	by countering for priors,

	1:41:28.640 --> 1:41:30.480
	countering for amount of experience,

	1:41:30.480 --> 1:41:34.800
	and by assuming the same priors as what humans are born with.

	1:41:34.800 --> 1:41:39.520
	So that you can actually compare these scores to human intelligence.

	1:41:39.520 --> 1:41:43.280
	You can actually have humans pass the same test in a way that's fair.

	1:41:43.280 --> 1:41:52.320
	Yeah. And so importantly, such a benchmark should be such that any amount

	1:41:52.960 --> 1:41:55.920
	of practicing does not increase your score.

	1:41:56.480 --> 1:42:00.560
	So try to picture a game where no matter how much you play this game,

	1:42:01.600 --> 1:42:05.040
	that does not change your skill at the game.

	1:42:05.040 --> 1:42:05.920
	Can you picture that?

	1:42:05.920 --> 1:42:11.040
	As a person who deeply appreciates practice, I cannot actually.

	1:42:11.040 --> 1:42:16.560
	There's actually a very simple trick.

	1:42:16.560 --> 1:42:19.440
	So in order to come up with a task,

	1:42:19.440 --> 1:42:21.760
	so the only thing you can measure is skill at the task.

	1:42:21.760 --> 1:42:22.320
	Yes.

	1:42:22.320 --> 1:42:24.800
	All tasks are going to involve priors.

	1:42:24.800 --> 1:42:25.600
	Yes.

	1:42:25.600 --> 1:42:29.920
	The trick is to know what they are and to describe that.

	1:42:29.920 --> 1:42:33.760
	And then you make sure that this is the same set of priors as what humans start with.

	1:42:33.760 --> 1:42:38.560
	So you create a task that assumes these priors, that exactly documents these priors,

	1:42:38.560 --> 1:42:42.240
	so that the priors are made explicit and there are no other priors involved.

	1:42:42.240 --> 1:42:48.960
	And then you generate a certain number of samples in experience space for this task, right?

	1:42:49.840 --> 1:42:56.320
	And this, for one task, assuming that the task is new for the agent passing it,

	1:42:56.320 --> 1:43:04.320
	that's one test of this definition of intelligence that we set up.

	1:43:04.320 --> 1:43:06.880
	And now you can scale that to many different tasks,

	1:43:06.880 --> 1:43:10.480
	that each task should be new to the agent passing it, right?

	1:43:11.360 --> 1:43:14.480
	And also it should be human interpretable and understandable

	1:43:14.480 --> 1:43:16.880
	so that you can actually have a human pass the same test.

	1:43:16.880 --> 1:43:19.760
	And then you can compare the score of your machine and the score of your human.

	1:43:19.760 --> 1:43:20.720
	Which could be a lot of stuff.

	1:43:20.720 --> 1:43:23.040
	You could even start a task like MNIST.

	1:43:23.040 --> 1:43:28.800
	Just as long as you start with the same set of priors.

	1:43:28.800 --> 1:43:34.080
	So the problem with MNIST, humans are already trying to recognize digits, right?

	1:43:35.600 --> 1:43:40.960
	But let's say we're considering objects that are not digits,

	1:43:42.400 --> 1:43:43.920
	some completely arbitrary patterns.

	1:43:44.480 --> 1:43:48.880
	Well, humans already come with visual priors about how to process that.

	1:43:48.880 --> 1:43:54.080
	So in order to make the game fair, you would have to isolate these priors

	1:43:54.080 --> 1:43:57.280
	and describe them and then express them as computational rules.

	1:43:57.280 --> 1:44:01.680
	Having worked a lot with vision science people, that's exceptionally difficult.

	1:44:01.680 --> 1:44:03.120
	A lot of progress has been made.

	1:44:03.120 --> 1:44:08.080
	There's been a lot of good tests and basically reducing all of human vision into some good priors.

	1:44:08.640 --> 1:44:10.960
	We're still probably far away from that perfectly,

	1:44:10.960 --> 1:44:14.640
	but as a start for a benchmark, that's an exciting possibility.

	1:44:14.640 --> 1:44:24.240
	Yeah, so Elisabeth Spelke actually lists objectness as one of the core knowledge priors.

	1:44:24.800 --> 1:44:25.920
	Objectness, cool.

	1:44:25.920 --> 1:44:26.880
	Objectness, yeah.

	1:44:27.440 --> 1:44:31.520
	So we have priors about objectness, like about the visual space, about time,

	1:44:31.520 --> 1:44:34.240
	about agents, about goal oriented behavior.

	1:44:35.280 --> 1:44:39.280
	We have many different priors, but what's interesting is that,

	1:44:39.280 --> 1:44:43.920
	sure, we have this pretty diverse and rich set of priors,

	1:44:43.920 --> 1:44:46.880
	but it's also not that diverse, right?

	1:44:46.880 --> 1:44:50.800
	We are not born into this world with a ton of knowledge about the world,

	1:44:50.800 --> 1:44:57.840
	with only a small set of core knowledge.

	1:44:58.640 --> 1:45:05.040
	Yeah, sorry, do you have a sense of how it feels to us humans that that set is not that large?

	1:45:05.040 --> 1:45:09.600
	But just even the nature of time that we kind of integrate pretty effectively

	1:45:09.600 --> 1:45:11.600
	through all of our perception, all of our reasoning,

	1:45:12.640 --> 1:45:17.680
	maybe how, you know, do you have a sense of how easy it is to encode those priors?

	1:45:17.680 --> 1:45:24.560
	Maybe it requires building a universe and then the human brain in order to encode those priors.

	1:45:25.440 --> 1:45:28.640
	Or do you have a hope that it can be listed like an axiomatic?

	1:45:28.640 --> 1:45:29.280
	I don't think so.

	1:45:29.280 --> 1:45:33.040
	So you have to keep in mind that any knowledge about the world that we are

	1:45:33.040 --> 1:45:41.120
	born with is something that has to have been encoded into our DNA by evolution at some point.

	1:45:41.120 --> 1:45:41.440
	Right.

	1:45:41.440 --> 1:45:45.440
	And DNA is a very, very low bandwidth medium.

	1:45:46.000 --> 1:45:51.200
	Like it's extremely long and expensive to encode anything into DNA because first of all,

	1:45:52.560 --> 1:45:57.440
	you need some sort of evolutionary pressure to guide this writing process.

	1:45:57.440 --> 1:46:03.440
	And then, you know, the higher level of information you're trying to write, the longer it's going to take.

	1:46:04.480 --> 1:46:13.520
	And the thing in the environment that you're trying to encode knowledge about has to be stable

	1:46:13.520 --> 1:46:15.280
	over this duration.

	1:46:15.280 --> 1:46:20.960
	So you can only encode into DNA things that constitute an evolutionary advantage.

	1:46:20.960 --> 1:46:25.280
	So this is actually a very small subset of all possible knowledge about the world.

	1:46:25.280 --> 1:46:32.080
	You can only encode things that are stable, that are true, over very, very long periods of time,

	1:46:32.080 --> 1:46:33.680
	typically millions of years.

	1:46:33.680 --> 1:46:38.720
	For instance, we might have some visual prior about the shape of snakes, right?

	1:46:38.720 --> 1:46:43.920
	But what makes a face, what's the difference between a face and an art face?

	1:46:44.560 --> 1:46:48.080
	But consider this interesting question.

	1:46:48.080 --> 1:46:56.640
	Do we have any innate sense of the visual difference between a male face and a female face?

	1:46:56.640 --> 1:46:57.600
	What do you think?

	1:46:58.640 --> 1:46:59.840
	For a human, I mean.

	1:46:59.840 --> 1:47:04.000
	I would have to look back into evolutionary history when the genders emerged.

	1:47:04.000 --> 1:47:06.240
	But yeah, most...

	1:47:06.240 --> 1:47:09.840
	I mean, the faces of humans are quite different from the faces of great apes.

	1:47:10.640 --> 1:47:11.600
	Great apes, right?

	1:47:12.880 --> 1:47:13.600
	Yeah.

	1:47:13.600 --> 1:47:14.800
	That's interesting.

	1:47:14.800 --> 1:47:22.800
	Yeah, you couldn't tell the face of a female chimpanzee from the face of a male chimpanzee,

	1:47:22.800 --> 1:47:23.440
	probably.

	1:47:23.440 --> 1:47:26.160
	Yeah, and I don't think most humans have all that ability.

	1:47:26.160 --> 1:47:33.280
	So we do have innate knowledge of what makes a face, but it's actually impossible for us to

	1:47:33.280 --> 1:47:40.320
	have any DNA encoded knowledge of the difference between a female human face and a male human face

	1:47:40.320 --> 1:47:50.560
	because that knowledge, that information came up into the world actually very recently.

	1:47:50.560 --> 1:47:56.400
	If you look at the slowness of the process of encoding knowledge into DNA.

	1:47:56.400 --> 1:47:57.360
	Yeah, so that's interesting.

	1:47:57.360 --> 1:48:02.080
	That's a really powerful argument that DNA is a low bandwidth and it takes a long time to encode.

	1:48:02.800 --> 1:48:05.200
	That naturally creates a very efficient encoding.

	1:48:05.200 --> 1:48:12.800
	But one important consequence of this is that, so yes, we are born into this world with a bunch of

	1:48:12.800 --> 1:48:17.600
	knowledge, sometimes high level knowledge about the world, like the shape, the rough shape of a

	1:48:17.600 --> 1:48:19.520
	snake, of the rough shape of a face.

	1:48:20.480 --> 1:48:26.960
	But importantly, because this knowledge takes so long to write, almost all of this innate

	1:48:26.960 --> 1:48:32.080
	knowledge is shared with our cousins, with great apes, right?

	1:48:32.080 --> 1:48:35.600
	So it is not actually this innate knowledge that makes us special.

	1:48:36.320 --> 1:48:42.000
	But to throw it right back at you from the earlier on in our discussion, it's that encoding

	1:48:42.960 --> 1:48:48.320
	might also include the entirety of the environment of Earth.

	1:48:49.360 --> 1:48:49.920
	To some extent.

	1:48:49.920 --> 1:48:56.480
	So it can include things that are important to survival and production, so for which there is

	1:48:56.480 --> 1:49:02.880
	some evolutionary pressure, and things that are stable, constant over very, very, very long time

	1:49:02.880 --> 1:49:03.380
	periods.

	1:49:04.160 --> 1:49:06.320
	And honestly, it's not that much information.

	1:49:06.320 --> 1:49:14.400
	There's also, besides the bandwidths constraint and the constraints of the writing process,

	1:49:14.400 --> 1:49:21.440
	there's also memory constraints, like DNA, the part of DNA that deals with the human brain,

	1:49:21.440 --> 1:49:22.640
	it's actually fairly small.

	1:49:22.640 --> 1:49:25.520
	It's like, you know, on the order of megabytes, right?

	1:49:25.520 --> 1:49:29.600
	There's not that much high level knowledge about the world you can encode.

	1:49:31.600 --> 1:49:38.880
	That's quite brilliant and hopeful for a benchmark that you're referring to of encoding

	1:49:38.880 --> 1:49:39.360
	priors.

	1:49:39.360 --> 1:49:43.120
	I actually look forward to, I'm skeptical whether you can do it in the next couple of

	1:49:43.120 --> 1:49:44.320
	years, but hopefully.

	1:49:45.040 --> 1:49:45.760
	I've been working.

	1:49:45.760 --> 1:49:49.920
	So honestly, it's a very simple benchmark, and it's not like a big breakthrough or anything.

	1:49:49.920 --> 1:49:53.200
	It's more like a fun side project, right?

	1:49:53.200 --> 1:49:55.680
	But these fun, so is ImageNet.

	1:49:56.480 --> 1:50:04.080
	These fun side projects could launch entire groups of efforts towards creating reasoning

	1:50:04.080 --> 1:50:04.960
	systems and so on.

	1:50:04.960 --> 1:50:05.440
	And I think...

	1:50:05.440 --> 1:50:06.160
	Yeah, that's the goal.

	1:50:06.160 --> 1:50:12.080
	It's trying to measure strong generalization, to measure the strength of abstraction in

	1:50:12.080 --> 1:50:16.960
	our minds, well, in our minds and in artificial intelligence agencies.

	1:50:16.960 --> 1:50:24.800
	And if there's anything true about this science organism is its individual cells love competition.

	1:50:24.800 --> 1:50:26.800
	So and benchmarks encourage competition.

	1:50:26.800 --> 1:50:29.520
	So that's an exciting possibility.

	1:50:29.520 --> 1:50:32.640
	If you, do you think an AI winter is coming?

	1:50:33.520 --> 1:50:34.640
	And how do we prevent it?

	1:50:35.440 --> 1:50:36.080
	Not really.

	1:50:36.080 --> 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 --> 1:50:47.280
	are selling the capabilities of AI and the actual capabilities of AI.

	1:50:47.280 --> 1:50:50.560
	And today, some deep learning is creating a lot of value.

	1:50:50.560 --> 1:50:56.240
	And it will keep creating a lot of value in the sense that these models are applicable

	1:50:56.240 --> 1:51:00.000
	to a very wide range of problems that are relevant today.

	1:51:00.000 --> 1:51:05.120
	And we are only just getting started with applying these algorithms to every problem

	1:51:05.120 --> 1:51:06.320
	they could be solving.

	1:51:06.320 --> 1:51:10.160
	So deep learning will keep creating a lot of value for the time being.

	1:51:10.160 --> 1:51:15.920
	What's concerning, however, is that there's a lot of hype around deep learning and around

	1:51:15.920 --> 1:51:16.240
	AI.

	1:51:16.240 --> 1:51:22.000
	There are lots of people are overselling the capabilities of these systems, not just

	1:51:22.000 --> 1:51:27.760
	the capabilities, but also overselling the fact that they might be more or less, you

	1:51:27.760 --> 1:51:36.640
	know, brain like, like given the kind of a mystical aspect, these technologies and also

	1:51:36.640 --> 1:51:43.840
	overselling the pace of progress, which, you know, it might look fast in the sense that

	1:51:43.840 --> 1:51:46.480
	we have this exponentially increasing number of papers.

	1:51:47.760 --> 1:51:52.960
	But again, that's just a simple consequence of the fact that we have ever more people

	1:51:52.960 --> 1:51:53.840
	coming into the field.

	1:51:54.400 --> 1:51:57.440
	It doesn't mean the progress is actually exponentially fast.

	1:51:58.640 --> 1:52:02.720
	Let's say you're trying to raise money for your startup or your research lab.

	1:52:02.720 --> 1:52:09.120
	You might want to tell, you know, a grandiose 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 --> 1:52:15.760
	and robotics and so on.

	1:52:15.760 --> 1:52:19.440
	And maybe you can tell them that the field is progressing so fast and we are going to

	1:52:19.440 --> 1:52:23.040
	have AGI within 15 years or even 10 years.

	1:52:23.040 --> 1:52:25.920
	And none of this is true.

	1:52:25.920 --> 1:52:32.800
	And every time you're like saying these things and an investor or, you know, a decision maker

	1:52:32.800 --> 1:52:41.680
	believes them, well, this is like the equivalent of taking on credit card debt, but for trust,

	1:52:41.680 --> 1:52:42.480
	right?

	1:52:42.480 --> 1:52:50.160
	And maybe this will, you know, this will be what enables you to raise a lot of money,

	1:52:50.160 --> 1:52:54.320
	but ultimately you are creating damage, you are damaging the field.

	1:52:54.320 --> 1:53:00.160
	So that's the concern is that that debt, that's what happens with the other AI winters is

	1:53:00.160 --> 1:53:04.160
	the concern is you actually tweeted about this with autonomous vehicles, right?

	1:53:04.160 --> 1:53:08.960
	There's almost every single company now have promised that they will have full autonomous

	1:53:08.960 --> 1:53:11.760
	vehicles by 2021, 2022.

	1:53:11.760 --> 1:53:18.080
	That's a good example of the consequences of over hyping the capabilities of AI and

	1:53:18.080 --> 1:53:19.280
	the pace of progress.

	1:53:19.280 --> 1:53:25.200
	So because I work especially a lot recently in this area, I have a deep concern of what

	1:53:25.200 --> 1:53:30.400
	happens when all of these companies after I've invested billions have a meeting and

	1:53:30.400 --> 1:53:33.600
	say, how much do we actually, first of all, do we have an autonomous vehicle?

	1:53:33.600 --> 1:53:35.280
	The answer will definitely be no.

	1:53:35.840 --> 1:53:40.560
	And second will be, wait a minute, we've invested one, two, three, four billion dollars

	1:53:40.560 --> 1:53:43.120
	into this and we made no profit.

	1:53:43.120 --> 1:53:49.200
	And the reaction to that may be going very hard in other directions that might impact

	1:53:49.200 --> 1:53:50.400
	even other industries.

	1:53:50.400 --> 1:53:55.520
	And that's what we call an AI winter is when there is backlash where no one believes any

	1:53:55.520 --> 1:53:59.360
	of these promises anymore because they've turned that to be big lies the first time

	1:53:59.360 --> 1:54:00.240
	around.

	1:54:00.240 --> 1:54:06.000
	And this will definitely happen to some extent for autonomous vehicles because the public

	1:54:06.000 --> 1:54:13.360
	and decision makers have been convinced that around 2015, they've been convinced by these

	1:54:13.360 --> 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 --> 1:54:22.880
	in maybe 2016, maybe 2017, maybe 2018.

	1:54:22.880 --> 1:54:26.080
	Now we're in 2019, we're still waiting for it.

	1:54:27.600 --> 1:54:32.800
	And so I don't believe we are going to have a full on AI winter because we have these

	1:54:32.800 --> 1:54:36.640
	technologies that are producing a tremendous amount of real value.

	1:54:37.680 --> 1:54:39.920
	But there is also too much hype.

	1:54:39.920 --> 1:54:43.520
	So there will be some backlash, especially there will be backlash.

	1:54:44.960 --> 1:54:53.040
	So some startups are trying to sell the dream of AGI and the fact that AGI is going to create

	1:54:53.040 --> 1:54:53.760
	infinite value.

	1:54:53.760 --> 1:54:55.680
	Like AGI is like a free lunch.

	1:54:55.680 --> 1:55:02.800
	Like if you can develop an AI system that passes a certain threshold of IQ or something,

	1:55:02.800 --> 1:55:04.400
	then suddenly you have infinite value.

	1:55:04.400 --> 1:55:14.160
	And well, there are actually lots of investors buying into this idea and they will wait maybe

	1:55:14.160 --> 1:55:17.760
	10, 15 years and nothing will happen.

	1:55:17.760 --> 1:55:22.560
	And the next time around, well, maybe there will be a new generation of investors.

	1:55:22.560 --> 1:55:23.360
	No one will care.

	1:55:24.800 --> 1:55:27.280
	Human memory is fairly short after all.

	1:55:27.280 --> 1:55:34.320
	I don't know about you, but because I've spoken about AGI sometimes poetically, I get a lot

	1:55:34.320 --> 1:55:42.000
	of emails from people giving me, they're usually like a large manifestos of they've, they say

	1:55:42.000 --> 1:55:47.200
	to me that they have created an AGI system or they know how to do it.

	1:55:47.200 --> 1:55:48.880
	And there's a long write up of how to do it.

	1:55:48.880 --> 1:55:50.560
	I get a lot of these emails, yeah.

	1:55:50.560 --> 1:55:57.760
	They're a little bit feel like it's generated by an AI system actually, but there's usually

	1:55:57.760 --> 1:56:06.640
	no diagram, you have a transformer generating crank papers about AGI.

	1:56:06.640 --> 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 --> 1:56:16.720
	papers, how do we know they're not onto something?

	1:56:16.720 --> 1:56:24.240
	How do I, so when you start to talk about AGI or anything like the reasoning benchmarks

	1:56:24.240 --> 1:56:28.160
	and so on, so something that doesn't have a benchmark, it's really difficult to know.

	1:56:29.120 --> 1:56:34.560
	I mean, I talked to Jeff Hawkins, who's really looking at neuroscience approaches to how,

	1:56:35.200 --> 1:56:41.520
	and there's some, there's echoes of really interesting ideas in at least Jeff's case,

	1:56:41.520 --> 1:56:42.320
	which he's showing.

	1:56:43.280 --> 1:56:45.040
	How do you usually think about this?

	1:56:46.640 --> 1:56:52.880
	Like preventing yourself from being too narrow minded and elitist about deep learning, it

	1:56:52.880 --> 1:56:56.720
	has to work on these particular benchmarks, otherwise it's trash.

	1:56:56.720 --> 1:57:05.280
	Well, you know, the thing is, intelligence does not exist in the abstract.

	1:57:05.280 --> 1:57:07.200
	Intelligence has to be applied.

	1:57:07.200 --> 1:57:11.040
	So if you don't have a benchmark, if you have an improvement in some benchmark, maybe it's

	1:57:11.040 --> 1:57:12.400
	a new benchmark, right?

	1:57:12.400 --> 1:57:16.640
	Maybe it's not something we've been looking at before, but you do need a problem that

	1:57:16.640 --> 1:57:17.360
	you're trying to solve.

	1:57:17.360 --> 1:57:20.000
	You're not going to come up with a solution without a problem.

	1:57:20.000 --> 1:57:25.520
	So you, general intelligence, I mean, you've clearly highlighted generalization.

	1:57:26.320 --> 1:57:31.200
	If you want to claim that you have an intelligence system, it should come with a benchmark.

	1:57:31.200 --> 1:57:35.760
	It should, yes, it should display capabilities of some kind.

	1:57:35.760 --> 1:57:41.840
	It should show that it can create some form of value, even if it's a very artificial form

	1:57:41.840 --> 1:57:42.800
	of value.

	1:57:42.800 --> 1:57:48.800
	And that's also the reason why you don't actually need to care about telling which papers have

	1:57:48.800 --> 1:57:52.000
	actually some hidden potential and which do not.

	1:57:53.120 --> 1:57:59.200
	Because if there is a new technique that's actually creating value, this is going to

	1:57:59.200 --> 1:58:02.480
	be brought to light very quickly because it's actually making a difference.

	1:58:02.480 --> 1:58:08.160
	So it's the difference between something that is ineffectual and something that is actually

	1:58:08.160 --> 1:58:08.800
	useful.

	1:58:08.800 --> 1:58:14.080
	And ultimately usefulness is our guide, not just in this field, but if you look at science

	1:58:14.080 --> 1:58:18.720
	in general, maybe there are many, many people over the years that have had some really interesting

	1:58:19.440 --> 1:58:22.800
	theories of everything, but they were just completely useless.

	1:58:22.800 --> 1:58:27.280
	And you don't actually need to tell the interesting theories from the useless theories.

	1:58:28.000 --> 1:58:34.080
	All you need is to see, is this actually having an effect on something else?

	1:58:34.080 --> 1:58:35.360
	Is this actually useful?

	1:58:35.360 --> 1:58:36.800
	Is this making an impact or not?

	1:58:37.600 --> 1:58:38.640
	That's beautifully put.

	1:58:38.640 --> 1:58:43.680
	I mean, the same applies to quantum mechanics, to string theory, to the holographic principle.

	1:58:43.680 --> 1:58:45.280
	We are doing deep learning because it works.

	1:58:46.960 --> 1:58:52.720
	Before it started working, people considered people working on neural networks as cranks

	1:58:52.720 --> 1:58:53.120
	very much.

	1:58:54.560 --> 1:58:56.320
	No one was working on this anymore.

	1:58:56.320 --> 1:58:59.120
	And now it's working, which is what makes it valuable.

	1:58:59.120 --> 1:59:00.320
	It's not about being right.

	1:59:01.120 --> 1:59:02.560
	It's about being effective.

	1:59:02.560 --> 1:59:08.080
	And nevertheless, the individual entities of this scientific mechanism, just like Yoshua

	1:59:08.080 --> 1:59:12.480
	Banjo or Jan Lekun, they, while being called cranks, stuck with it.

	1:59:12.480 --> 1:59:12.880
	Right?

	1:59:12.880 --> 1:59:13.280
	Yeah.

	1:59:13.280 --> 1:59:17.840
	And so us individual agents, even if everyone's laughing at us, just stick with it.

	1:59:18.880 --> 1:59:21.840
	If you believe you have something, you should stick with it and see it through.

	1:59:23.520 --> 1:59:25.920
	That's a beautiful inspirational message to end on.

	1:59:25.920 --> 1:59:27.600
	Francois, thank you so much for talking today.

	1:59:27.600 --> 1:59:28.640
	That was amazing.

	1:59:28.640 --> 1:59:44.000
	Thank you.