vtt/episode_038_large.vtt

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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

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 because science feeds into technology.

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 Technology can be used to build better tools,

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 better computers, better instrumentation and so on,

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 which in turn can make science faster, right?

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 So science is probably the closest thing we have today

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 to a recursively self improving superhuman AI.

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 And you can just observe is science,

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 is scientific progress to the exploding,

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 which itself is an interesting question.

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 You can use that as a basis to try to understand

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 what will happen with a superhuman AI

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 that has a science like behavior.

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 Let me linger on it a little bit more.

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 What is your intuition why an intelligence explosion

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 is not possible?

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 Like taking the scientific,

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 all the semi scientific revolutions,

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 why can't we slightly accelerate that process?

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 So you can absolutely accelerate

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 any problem solving process.

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 So a recursively self improvement

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 is absolutely a real thing.

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 But what happens with a recursively self improving system

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 is typically not explosion

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 because no system exists in isolation.

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 And so tweaking one part of the system

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 means that suddenly another part of the system

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 becomes a bottleneck.

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 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.