vtt/episode_038_small.vtt

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 The following is a conversation with Francois Chalet.

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 He's the creator of Keras, which is an open source deep learning

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 library that is designed to enable fast, user friendly

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 experimentation 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, and use

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 neural networks, 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 and popular

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 library, 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, if not controversial,

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 personality in the AI world, especially

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 in the realm of ideas around the future

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 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 us five stars on iTunes, support on Patreon,

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 or simply connect with me on Twitter

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

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

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 You're known for not sugarcoating your opinions

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 and speaking your mind about ideas in AI, especially

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 on Twitter.

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 That'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 of maintaining

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 Twitter accounts, you might as well speak your mind.

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 Otherwise, what's even the point of doing Twitter accounts,

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 like getting an eye scar and just leaving it in the garage?

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 Yeah, so that's one thing for which

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 I got a lot of pushback.

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 Perhaps 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 and the reasoning behind this

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 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, I'm sure you're familiar

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 with the idea, but it's the idea

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 that if you were to build general AI problems

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 solving algorithms, 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 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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 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, like embodied intelligence,

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 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, you're crushing the dreams of many people.

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 So let's look at Sam Harris.

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 Actually, a lot of physicists, Max Tegmark,

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 people who think the universe is an information processing

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 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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 It doesn't make sense that there should be some,

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 it seems naive to think that our own brain is somehow

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 the limit of the capabilities and this information.

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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, if you're

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 able to build something 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 will improve exponentially.

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 So that's the logic that's used actually

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 by almost everybody that is worried

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 about super human intelligence.

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 Yeah, so you're trying to make, so most people

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 who are skeptical of that are kind of like,

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 this doesn't, their thought process,

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 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, the whole thing is shrouded

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 in mystery 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 post

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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,

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 exists alone, it exists within the environment.

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 So you can't exponentially, you would have to somehow

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 exponentially improve the environment

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 and the brain together, almost yet in order

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 to create something that's much smarter

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 in some kind of, of course we don't have

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 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

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 to the even humans, not even talking about AI's.

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 I don't think their brain and the performance

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 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 the capabilities, like what emerges

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 out of this system to just, you know,

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 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, you know,

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 they're not actually solving any big scientific problems.

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 They're not Einstein.

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 They're like Einstein, but, you know,

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 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, there's 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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 Well, that's brilliant. 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 of 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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 Yeah, exactly. Intelligence is the meaning of

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 great problem solving capabilities 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 in intelligence.

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 All you're left with is potential intelligence,

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 like the performance of your brain or, you know,

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 how high your IQ is, 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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 What, 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 intelligence 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 and that shows at various levels,

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 that shows in some prior knowledge,

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 that's innate, that we have at birth,

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 knowledge about things like agents,

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 goal driven behavior, visual priors about what makes an object,

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 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 easy for us 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 problems

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 and we are quite useless 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, you know?

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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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 we're talking about scale of years, millennia,

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 what do you mean by long term, we're not very good?

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 Well, human intelligence is specialized in the human experience

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 and human experience is very short, like one lifetime is short.

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 Even within one lifetime, we have a very hard time envisioning,

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 you know, things on a scale of years.

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 Like it's very difficult to project yourself at the scale of five,

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 at the scale of 10 years and so on.

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 Right. We can solve only fairly narrowly scoped problems.

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 So when it comes to solving bigger problems, 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 artificial intelligence system, right?

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 And it's not running on one brain, it's running on a network of brains.

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 In fact, it's running on much more than a network of brains.

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 It's running on a lot of infrastructure, like books and computers

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 and the internet and human institutions and so on.

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 And that is capable of handling problems on a much greater scale

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 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 and it is super human, right?

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 It operates on a greater scale, 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 algorithm

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 that is super human.

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 Yeah, it's a computer science is like a theorem prover

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 at a scale of thousands, 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 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 and even the universe as a single organism.

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 Do you think, how do you think about scale and defining intelligent systems?

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

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 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 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, like a brain, a jar

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 that's controlling a body in a very 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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 then the body is manipulating objects in an environment and so on.

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 So everything is subordinate to this one thing, this epicenter,

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 which is the brain.

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 But in real life, intelligent agents don't really work like this.

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 There is no strong delimitation between the brain and the body to start with.

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 You have to look not just at the brain, but at the nervous system.

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 But then the nervous system and the body 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 over any length of time

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 that a lot of the intelligence of an animal 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, there is externalized intelligence.

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 When you write a computer program, you are externalizing cognition.

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 So it's externalized in books.

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 It's externalized in computers, the internet, in other humans.

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 It's externalized in language and so on.

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 So there is no hard delimitation of what makes an intelligent agent.

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 It's all about context.

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 OK, but AlphaGo is better at Go than the best human player.

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 There's levels of skill here.

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 So do you think there is such a concept as an intelligence explosion

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 in a specific task?

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 And then, well, yeah, do you think it's possible to have a category of tasks

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 on which you do have something 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, 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 of free classivity self improving

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 intelligent systems.

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 For instance, science is a problem solving system, 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 and it is clearly recursively self improving

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 because science fits into technology.

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 Technology can be used to build better tools, better computers,

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 better instrumentation and so on, which in turn can make science faster.

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 So science is probably the closest thing we have today

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 to a real civility self improving superhuman AI.

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 And you can just observe, is science, is scientific progress today 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 what

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 will happen with a superhuman AI that has 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 is not possible?

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 Like taking the scientific, 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 any problem solving process.

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 So recursively, recursive self improvement 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 because no system exists in isolation.

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 And so tweaking one part of the system 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, which is clearly a recursively self improving,

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 clearly a problem solving system, scientific progress is not actually exploding.

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 If you look at science, what you see is the picture of a system that is consuming

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 an exponentially increasing amount of resources.

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 But it's having a linear output in terms of scientific progress.

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 And maybe that will seem like a very strong claim.

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 Many people are actually saying that scientific progress is exponential.

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 But when they're claiming this, they're actually looking at indicators of resource

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 consumption by science.

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 For instance, the number of papers being published, the number of patterns being

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 filed, and so on, which are just completely correlated with how many people are working

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 on science today.

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 So it's actually an indicator of resource consumption.

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 But what you should look at is the output is progress in terms of the knowledge that

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 science generates in terms of the scope and significance of the problems that we solve.

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 And some people have actually been trying to measure that.

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 Like Michael Nielsen, for instance, he had a very nice paper, I think that was last

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 year about it.

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 So his approach to measure scientific progress was to look at the timeline of scientific

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 discoveries over the past 100, 150 years.

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 And for each major discovery, ask a panel of experts to rate the significance of the

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 discovery.

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 And if the output of sciences in the institution were exponential, you would expect the temporal

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 density of significance to go up exponentially, maybe because there's a faster rate of discoveries,

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 maybe because the discoveries are increasingly more important.

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 And what actually happens if you plot this temporal density of significance measured

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 in this way, is that you see very much a flat graph.

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 You see a flat graph across all disciplines, across physics, biology, medicine and so on.

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 And it actually makes a lot of sense if you think about it, because think about the progress

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 of physics 110 years ago.

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 It was a time of crazy change.

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 Think about the progress of technology 170 years ago, when we started replacing horses,

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 with cars, when we started having electricity and so on.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

23:01.080 --> 23:02.080
 Yes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

25:08.520 --> 25:09.520
 systems.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

30:37.920 --> 30:38.920
 Right.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

31:46.240 --> 31:47.240
 Absolutely.

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

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

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

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

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

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

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

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

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

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

32:39.600 --> 32:40.600
 networks.

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

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

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

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

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

33:10.560 --> 33:11.560
 not.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

34:06.680 --> 34:07.680
 Exactly.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

34:52.320 --> 34:53.320
 popular.

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

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

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

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

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

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

35:16.200 --> 35:17.200
 Yeah.

35:17.200 --> 35:18.200
 Okay.

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

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

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

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

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

35:34.760 --> 35:35.760
 implementation.

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

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

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

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

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

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

36:00.720 --> 36:01.720
 vision.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

38:10.680 --> 38:11.680
 later.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

42:15.400 --> 42:16.400
 Yeah.

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

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

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

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

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

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

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

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

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

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

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

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

43:07.160 --> 43:08.160
 Yes.

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

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

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

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

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

43:39.920 --> 43:40.920
 base.

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

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

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

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

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

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

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

44:10.560 --> 44:11.560
 right?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

46:02.960 --> 46:03.960
 Yes.

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

46:10.000 --> 46:11.000
 API.

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

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

46:22.360 --> 46:24.560
 Brilliant.

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

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

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

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

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

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

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

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

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

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

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

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

47:22.440 --> 47:23.440
 Yes.

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

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

47:31.120 --> 47:32.120
 Exactly.

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

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

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

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

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

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

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

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

48:05.200 --> 48:06.200
 data?

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

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

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

48:22.840 --> 48:24.040
 gaps.

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

48:27.120 --> 48:28.120
 Right.

48:28.120 --> 48:29.120
 Yes.

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

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

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

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

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

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

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

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

49:08.160 --> 49:09.160
 data.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

51:09.040 --> 51:10.040
 Yeah.

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

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

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

51:20.120 --> 51:21.120
 Yeah.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

53:03.320 --> 53:05.320
 lane.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

54:26.480 --> 54:27.480
 human.

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

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

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

54:41.520 --> 54:42.520
 behavior.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

56:08.800 --> 56:13.040
 to be outside the reach of current perception systems.

56:13.040 --> 56:19.240
 So do you think larger networks will be able to start to understand the physics and the

56:19.240 --> 56:23.960
 physics of the scene, the three dimensional structure and relationships of objects in

56:23.960 --> 56:25.720
 the scene, and so on?

56:25.720 --> 56:28.880
 Or really, that's where symbolic at has to step in?

56:28.880 --> 56:37.680
 Well, it's always possible to solve these problems with deep learning is just extremely

56:37.680 --> 56:38.680
 inefficient.

56:38.680 --> 56:45.240
 A model would be an explicit rule based abstract model would be a far better, more compressed

56:45.240 --> 56:50.280
 representation of physics than learning just this mapping between in this situation, this

56:50.280 --> 56:51.280
 thing happens.

56:51.280 --> 56:54.520
 If you change the situation slightly, then this other thing happens and so on.

56:54.520 --> 57:00.840
 Do you think it's possible to automatically generate the programs that would require that

57:00.840 --> 57:01.840
 kind of reasoning?

57:01.840 --> 57:07.120
 Or does it have to, so where expert systems fail, there's so many facts about the world

57:07.120 --> 57:08.640
 had to be hand coded in.

57:08.640 --> 57:15.360
 Do you think it's possible to learn those logical statements that are true about the

57:15.360 --> 57:17.120
 world and their relationships?

57:17.120 --> 57:22.640
 I mean, that's kind of what they're improving at a basic level is trying to do, right?

57:22.640 --> 57:28.360
 Yeah, except it's much harder to formulate statements about the world compared to fermenting

57:28.360 --> 57:30.680
 mathematical statements.

57:30.680 --> 57:34.320
 Statements about the world tend to be subjective.

57:34.320 --> 57:39.320
 So can you learn rule based models?

57:39.320 --> 57:40.320
 Yes.

57:40.320 --> 57:41.320
 Yes, definitely.

57:41.320 --> 57:43.720
 That's the field of program synthesis.

57:43.720 --> 57:48.080
 However, today we just don't really know how to do it.

57:48.080 --> 57:52.640
 So it's very much a grass search or tree search problem.

57:52.640 --> 57:58.080
 And so we are limited to the sort of a tree session grass search algorithms that we have

57:58.080 --> 57:59.080
 today.

57:59.080 --> 58:02.080
 Personally, I think genetic algorithms are very promising.

58:02.080 --> 58:04.640
 So it's almost like genetic programming.

58:04.640 --> 58:05.760
 Genetic programming, exactly.

58:05.760 --> 58:12.200
 Can you discuss the field of program synthesis, like what, how many people are working and

58:12.200 --> 58:13.840
 thinking about it?

58:13.840 --> 58:20.360
 What, where we are in the history of program synthesis and what are your hopes for it?

58:20.360 --> 58:24.760
 Well, if it were deep learning, this is like the 90s.

58:24.760 --> 58:29.320
 So meaning that we already have existing solutions.

58:29.320 --> 58:35.720
 We are starting to have some basic understanding of what this is about.

58:35.720 --> 58:38.120
 But it's still a field that is in its infancy.

58:38.120 --> 58:40.560
 There are very few people working on it.

58:40.560 --> 58:44.520
 There are very few real world applications.

58:44.520 --> 58:51.960
 So the one real world application I'm aware of is Flash Fill in Excel.

58:51.960 --> 58:58.240
 It's a way to automatically learn very simple programs to format cells in an Excel spreadsheet

58:58.240 --> 58:59.840
 from a few examples.

58:59.840 --> 59:02.840
 For instance, learning a way to format a date, things like that.

59:02.840 --> 59:03.840
 Oh, that's fascinating.

59:03.840 --> 59:04.840
 Yeah.

59:04.840 --> 59:06.280
 You know, okay, that's that's fascinating topic.

59:06.280 --> 59:12.880
 I was wondering when I provide a few samples to Excel, what it's able to figure out, like

59:12.880 --> 59:18.280
 just giving it a few dates, what are you able to figure out from the pattern I just gave

59:18.280 --> 59:19.280
 you?

59:19.280 --> 59:20.280
 That's a fascinating question.

59:20.280 --> 59:24.240
 It's fascinating whether that's learnable patterns and you're saying they're working

59:24.240 --> 59:25.240
 on that.

59:25.240 --> 59:26.240
 Yeah.

59:26.240 --> 59:27.240
 How big is the toolbox currently?

59:27.240 --> 59:28.240
 Yeah.

59:28.240 --> 59:29.240
 Are we completely in the dark?

59:29.240 --> 59:30.240
 So if you set the 90s.

59:30.240 --> 59:32.240
 In terms of program synthesis?

59:32.240 --> 59:33.240
 No.

59:33.240 --> 59:40.520
 So I would say, so maybe 90s is even too optimistic because by the 90s, you know, we already understood

59:40.520 --> 59:41.520
 backprop.

59:41.520 --> 59:44.720
 We already understood, you know, the engine of deep learning, even though we couldn't

59:44.720 --> 59:50.440
 really see its potential quite today, I don't think we found the engine of program synthesis.

59:50.440 --> 59:52.960
 So we're in the winter before backprop.

59:52.960 --> 59:53.960
 Yeah.

59:53.960 --> 59:55.760
 In a way, yes.

59:55.760 --> 1:00:02.400
 So I do believe program synthesis, in general, discrete search over rule based models is going

1:00:02.400 --> 1:00:06.960
 to be a cornerstone of AI research in the next century, right?

1:00:06.960 --> 1:00:10.240
 And that doesn't mean we're going to drop deep learning.

1:00:10.240 --> 1:00:11.960
 Deep learning is immensely useful.

1:00:11.960 --> 1:00:19.480
 Like being able to learn this is a very flexible, adaptable, parametric models, that's actually

1:00:19.480 --> 1:00:20.480
 immensely useful.

1:00:20.480 --> 1:00:24.960
 Like all it's doing, it's pattern cognition, but being good at pattern cognition, given

1:00:24.960 --> 1:00:27.880
 lots of data is just extremely powerful.

1:00:27.880 --> 1:00:31.000
 So we are still going to be working on deep learning and we're going to be working on

1:00:31.000 --> 1:00:32.000
 program synthesis.

1:00:32.000 --> 1:00:36.520
 We're going to be combining the two in increasingly automated ways.

1:00:36.520 --> 1:00:38.640
 So let's talk a little bit about data.

1:00:38.640 --> 1:00:46.120
 You've tweeted about 10,000 deep learning papers have been written about hard coding

1:00:46.120 --> 1:00:50.280
 priors, about a specific task in a neural network architecture, it works better than

1:00:50.280 --> 1:00:52.760
 a lack of a prior.

1:00:52.760 --> 1:00:57.480
 By summarizing all these efforts, they put a name to an architecture, but really what

1:00:57.480 --> 1:01:01.680
 they're doing is hard coding some priors that improve the performance of the system.

1:01:01.680 --> 1:01:07.000
 But we get straight to the point, it's probably true.

1:01:07.000 --> 1:01:12.080
 So you say that you can always buy performance, buy in quotes performance by either training

1:01:12.080 --> 1:01:17.520
 on more data, better data, or by injecting task information to the architecture of the

1:01:17.520 --> 1:01:18.520
 preprocessing.

1:01:18.520 --> 1:01:22.720
 However, this is informative about the generalization power the techniques use, the fundamentals

1:01:22.720 --> 1:01:23.720
 of ability to generalize.

1:01:23.720 --> 1:01:30.040
 Do you think we can go far by coming up with better methods for this kind of cheating,

1:01:30.040 --> 1:01:35.320
 for better methods of large scale annotation of data, so building better priors?

1:01:35.320 --> 1:01:37.400
 If you've made it, it's not cheating anymore.

1:01:37.400 --> 1:01:38.400
 Right.

1:01:38.400 --> 1:01:46.480
 I'm joking about the cheating, but large scale, so basically I'm asking about something

1:01:46.480 --> 1:01:54.300
 that hasn't, from my perspective, been researched too much is exponential improvement in annotation

1:01:54.300 --> 1:01:56.800
 of data.

1:01:56.800 --> 1:01:58.120
 You often think about...

1:01:58.120 --> 1:02:00.880
 I think it's actually been researched quite a bit.

1:02:00.880 --> 1:02:06.120
 You just don't see publications about it, because people who publish papers are going

1:02:06.120 --> 1:02:10.000
 to publish about known benchmarks, sometimes they're going to read a new benchmark.

1:02:10.000 --> 1:02:14.360
 People who actually have real world large scale defining problems, they're going to spend

1:02:14.360 --> 1:02:18.800
 a lot of resources into data annotation and good data annotation pipelines, but you don't

1:02:18.800 --> 1:02:19.800
 see any papers about it.

1:02:19.800 --> 1:02:20.800
 That's interesting.

1:02:20.800 --> 1:02:24.600
 Do you think there are certain resources, but do you think there's innovation happening?

1:02:24.600 --> 1:02:25.920
 Oh, yeah.

1:02:25.920 --> 1:02:33.960
 To clarify at the point in the twist, machine learning in general is the science of generalization.

1:02:33.960 --> 1:02:41.080
 You want to generate knowledge that can be reused across different datasets, across different

1:02:41.080 --> 1:02:42.680
 tasks.

1:02:42.680 --> 1:02:49.320
 If instead you're looking at one dataset, and then you are hard coding knowledge about

1:02:49.320 --> 1:02:55.920
 this task into your architecture, this is no more useful than training a network and

1:02:55.920 --> 1:03:03.160
 then saying, oh, I found these weight values perform well.

1:03:03.160 --> 1:03:08.720
 David Ha, I don't know if you know David, he had a paper the other day about weight

1:03:08.720 --> 1:03:13.840
 agnostic neural networks, and this is very interesting paper because it really illustrates

1:03:13.840 --> 1:03:20.800
 the fact that an architecture, even without weight, an architecture is a knowledge about

1:03:20.800 --> 1:03:21.800
 a task.

1:03:21.800 --> 1:03:24.280
 It encodes knowledge.

1:03:24.280 --> 1:03:31.560
 When it comes to architectures that are uncrafted by researchers, in some cases, it is very,

1:03:31.560 --> 1:03:39.400
 very clear that all they are doing is artificially reencoding the template that corresponds

1:03:39.400 --> 1:03:45.240
 to the proper way to solve the task and coding in a given dataset.

1:03:45.240 --> 1:03:52.120
 For instance, if you've looked at the baby dataset, which is about natural language

1:03:52.120 --> 1:03:55.800
 question answering, it is generated by an algorithm.

1:03:55.800 --> 1:03:59.320
 This is a question under pairs that are generated by an algorithm.

1:03:59.320 --> 1:04:01.680
 The algorithm is solving a certain template.

1:04:01.680 --> 1:04:06.760
 Turns out, if you craft a network that literally encodes this template, you can solve this

1:04:06.760 --> 1:04:13.160
 dataset with nearly 100% accuracy, but that doesn't actually tell you anything about how

1:04:13.160 --> 1:04:17.760
 to solve question answering in general, which is the point.

1:04:17.760 --> 1:04:21.560
 The question is just the linger on it, whether it's from the data side or from the size of

1:04:21.560 --> 1:04:22.560
 the network.

1:04:22.560 --> 1:04:27.960
 I don't know if you've read the blog post by Ray Sutton, the bitter lesson, where he

1:04:27.960 --> 1:04:33.480
 says the biggest lesson that we can read from 70 years of AI research is that general methods

1:04:33.480 --> 1:04:38.120
 that leverage computation are ultimately the most effective.

1:04:38.120 --> 1:04:45.520
 As opposed to figuring out methods that can generalize effectively, do you think we can

1:04:45.520 --> 1:04:50.720
 get pretty far by just having something that leverages computation and the improvement of

1:04:50.720 --> 1:04:51.720
 computation?

1:04:51.720 --> 1:04:52.720
 Yes.

1:04:52.720 --> 1:04:56.880
 I think Rich is making a very good point, which is that a lot of these papers, which

1:04:56.880 --> 1:05:03.760
 are actually all about manually hard coding prior knowledge about a task into some system,

1:05:03.760 --> 1:05:08.720
 doesn't have to be deeply architected into some system, right?

1:05:08.720 --> 1:05:11.560
 These papers are not actually making any impact.

1:05:11.560 --> 1:05:18.680
 Instead, what's making really long term impact is very simple, very general systems that

1:05:18.680 --> 1:05:23.560
 are really agnostic to all these tricks, because these tricks do not generalize.

1:05:23.560 --> 1:05:31.680
 And of course, the one general and simple thing that you should focus on is that which

1:05:31.680 --> 1:05:37.360
 leverages computation, because computation, the availability of large scale computation

1:05:37.360 --> 1:05:40.720
 has been increasing exponentially, following Morse law.

1:05:40.720 --> 1:05:46.160
 So if your algorithm is all about exploiting this, then your algorithm is suddenly exponentially

1:05:46.160 --> 1:05:47.640
 improving, right?

1:05:47.640 --> 1:05:51.800
 So I think Rich is definitely right.

1:05:51.800 --> 1:05:59.520
 However, he's right about the past 70 years, he's like assessing the past 70 years.

1:05:59.520 --> 1:06:05.440
 I am not sure that this assessment will still hold true for the next 70 years.

1:06:05.440 --> 1:06:12.040
 It might, to some extent, I suspect it will not, because the truth of his assessment is

1:06:12.040 --> 1:06:17.040
 a function of the context, right, in which this research took place.

1:06:17.040 --> 1:06:22.560
 And the context is changing, like Morse law might not be applicable anymore, for instance,

1:06:22.560 --> 1:06:24.080
 in the future.

1:06:24.080 --> 1:06:32.320
 And I do believe that when you tweak one aspect of a system, when you exploit one aspect

1:06:32.320 --> 1:06:36.680
 of a system, some other aspect starts becoming the bottleneck.

1:06:36.680 --> 1:06:41.640
 Let's say you have unlimited computation, well, then data is the bottleneck.

1:06:41.640 --> 1:06:46.560
 And I think we are already starting to be in a regime where our systems are so large

1:06:46.560 --> 1:06:50.960
 in scale and so data ingrained, the data today, and the quality of data, and the scale of

1:06:50.960 --> 1:06:53.280
 data is the bottleneck.

1:06:53.280 --> 1:07:00.960
 And in this environment, the beta lesson from Rich is not going to be true anymore, right?

1:07:00.960 --> 1:07:08.000
 So I think we are going to move from a focus on a scale of a competition scale to focus

1:07:08.000 --> 1:07:10.080
 on data efficiency.

1:07:10.080 --> 1:07:11.080
 Data efficiency.

1:07:11.080 --> 1:07:13.240
 So that's getting to the question of symbolic AI.

1:07:13.240 --> 1:07:19.120
 But the linger on the deep learning approaches, do you have hope for either unsupervised learning

1:07:19.120 --> 1:07:28.280
 or reinforcement learning, which are ways of being more data efficient in terms of the

1:07:28.280 --> 1:07:31.720
 amount of data they need that require human annotation?

1:07:31.720 --> 1:07:36.320
 So unsupervised learning and reinforcement learning are frameworks for learning, but

1:07:36.320 --> 1:07:39.080
 they are not like any specific technique.

1:07:39.080 --> 1:07:42.800
 So usually when people say reinforcement learning, what they really mean is deep reinforcement

1:07:42.800 --> 1:07:47.440
 learning, which is like one approach which is actually very questionable.

1:07:47.440 --> 1:07:53.440
 The question I was asking was unsupervised learning with deep neural networks and deeper

1:07:53.440 --> 1:07:54.440
 reinforcement learning.

1:07:54.440 --> 1:07:58.840
 Well, these are not really data efficient because you're still leveraging these huge

1:07:58.840 --> 1:08:03.760
 parametric models, point by point with gradient descent.

1:08:03.760 --> 1:08:09.000
 It is more efficient in terms of the number of annotations, the density of annotations

1:08:09.000 --> 1:08:10.000
 you need.

1:08:10.000 --> 1:08:16.680
 The idea being to learn the latent space around which the data is organized and then map the

1:08:16.680 --> 1:08:18.960
 sparse annotations into it.

1:08:18.960 --> 1:08:23.640
 And sure, I mean, that's clearly a very good idea.

1:08:23.640 --> 1:08:27.960
 It's not really a topic I would be working on, but it's clearly a good idea.

1:08:27.960 --> 1:08:32.040
 So it would get us to solve some problems that...

1:08:32.040 --> 1:08:38.280
 It will get us to incremental improvements in labeled data efficiency.

1:08:38.280 --> 1:08:46.640
 Do you have concerns about short term or long term threats from AI, from artificial intelligence?

1:08:46.640 --> 1:08:50.720
 Yes, definitely to some extent.

1:08:50.720 --> 1:08:52.360
 And what's the shape of those concerns?

1:08:52.360 --> 1:08:57.200
 This is actually something I've briefly written about.

1:08:57.200 --> 1:09:06.160
 But the capabilities of deep learning technology can be used in many ways that are concerning

1:09:06.160 --> 1:09:13.920
 from mass surveillance with things like facial recognition, in general, tracking lots of

1:09:13.920 --> 1:09:20.040
 data about everyone and then being able to making sense of this data, to do identification,

1:09:20.040 --> 1:09:22.520
 to do prediction.

1:09:22.520 --> 1:09:23.520
 That's concerning.

1:09:23.520 --> 1:09:31.680
 That's something that's being very aggressively pursued by totalitarian states like China.

1:09:31.680 --> 1:09:40.760
 One thing I am very much concerned about is that our lives are increasingly online, are

1:09:40.760 --> 1:09:45.960
 increasingly digital, made of information, made of information consumption and information

1:09:45.960 --> 1:09:52.160
 production or digital footprint, I would say.

1:09:52.160 --> 1:10:01.200
 And if you absorb all of this data and you are in control of where you consume information,

1:10:01.200 --> 1:10:10.160
 social networks and so on, recommendation engines, then you can build a sort of reinforcement

1:10:10.160 --> 1:10:13.920
 loop for human behavior.

1:10:13.920 --> 1:10:18.440
 You can observe the state of your mind at time t.

1:10:18.440 --> 1:10:25.040
 You can predict how you would react to different pieces of content, how to get you to move

1:10:25.040 --> 1:10:33.280
 your mind in a certain direction, then you can feed the specific piece of content that

1:10:33.280 --> 1:10:35.920
 would move you in a specific direction.

1:10:35.920 --> 1:10:45.000
 And you can do this at scale in terms of doing it continuously in real time.

1:10:45.000 --> 1:10:50.560
 You can also do it at scale in terms of scaling this to many, many people, to entire populations.

1:10:50.560 --> 1:10:57.800
 So potentially, artificial intelligence, even in its current state, if you combine it with

1:10:57.800 --> 1:11:04.120
 the internet, with the fact that we have all of our lives are moving to digital devices

1:11:04.120 --> 1:11:11.800
 and digital information consumption and creation, what you get is the possibility to achieve

1:11:11.800 --> 1:11:16.960
 mass manipulation of behavior and mass psychological control.

1:11:16.960 --> 1:11:18.360
 And this is a very real possibility.

1:11:18.360 --> 1:11:22.240
 Yeah, so you're talking about any kind of recommender system.

1:11:22.240 --> 1:11:28.160
 Let's look at the YouTube algorithm, Facebook, anything that recommends content you should

1:11:28.160 --> 1:11:35.480
 watch next, and it's fascinating to think that there's some aspects of human behavior

1:11:35.480 --> 1:11:45.520
 that you can say a problem of, is this person hold Republican beliefs or Democratic beliefs?

1:11:45.520 --> 1:11:52.720
 And it's a trivial, that's an objective function, and you can optimize and you can measure and

1:11:52.720 --> 1:11:55.720
 you can turn everybody into a Republican or everybody into a Democrat.

1:11:55.720 --> 1:11:56.720
 Absolutely, yeah.

1:11:56.720 --> 1:11:57.960
 I do believe it's true.

1:11:57.960 --> 1:12:02.520
 So the human mind is very...

1:12:02.520 --> 1:12:06.760
 If you look at the human mind as a kind of computer program, it has a very large exploit

1:12:06.760 --> 1:12:07.760
 surface, right?

1:12:07.760 --> 1:12:08.760
 It has many, many vulnerabilities.

1:12:08.760 --> 1:12:09.760
 Exploit surfaces, yeah.

1:12:09.760 --> 1:12:16.920
 Where you can control it, for instance, when it comes to your political beliefs, this is

1:12:16.920 --> 1:12:19.360
 very much tied to your identity.

1:12:19.360 --> 1:12:26.080
 So for instance, if I'm in control of your news feed on your favorite social media platforms,

1:12:26.080 --> 1:12:29.680
 this is actually where you're getting your news from.

1:12:29.680 --> 1:12:35.560
 And of course, I can choose to only show you news that will make you see the world in a

1:12:35.560 --> 1:12:37.200
 specific way, right?

1:12:37.200 --> 1:12:44.720
 But I can also create incentives for you to post about some political beliefs.

1:12:44.720 --> 1:12:52.720
 And then when I get you to express a statement, if it's a statement that me as a controller,

1:12:52.720 --> 1:12:53.720
 I want to reinforce.

1:12:53.720 --> 1:12:57.080
 I can just show it to people who will agree and they will like it.

1:12:57.080 --> 1:12:59.400
 And that will reinforce the statement in your mind.

1:12:59.400 --> 1:13:06.280
 If this is a statement I want you to, this is a belief I want you to abandon, I can,

1:13:06.280 --> 1:13:10.800
 on the other hand, show it to opponents, right, will attack you.

1:13:10.800 --> 1:13:16.440
 And because they attack you at the very least, next time you will think twice about posting

1:13:16.440 --> 1:13:17.440
 it.

1:13:17.440 --> 1:13:22.920
 But maybe you will even, you know, stop believing this because you got pushed back, right?

1:13:22.920 --> 1:13:30.560
 So there are many ways in which social media platforms can potentially control your opinions.

1:13:30.560 --> 1:13:38.320
 And today, the, so all of these things are already being controlled by algorithms.

1:13:38.320 --> 1:13:43.080
 These algorithms do not have any explicit political goal today.

1:13:43.080 --> 1:13:50.960
 Well, potentially they could, like if some totalitarian government takes over, you know,

1:13:50.960 --> 1:13:55.280
 social media platforms and decides that, you know, now we're going to use this not just

1:13:55.280 --> 1:13:59.960
 for my surveillance, but also for my opinion control and behavior control, very bad things

1:13:59.960 --> 1:14:02.000
 could happen.

1:14:02.000 --> 1:14:08.680
 But what's really fascinating and actually quite concerning is that even without an

1:14:08.680 --> 1:14:15.480
 explicit intent to manipulate, you're already seeing very dangerous dynamics in terms of

1:14:15.480 --> 1:14:19.960
 how this content recommendation algorithms behave.

1:14:19.960 --> 1:14:26.920
 Because right now, the goal, the objective function of these algorithms is to maximize

1:14:26.920 --> 1:14:32.600
 engagement, right, which seems fairly innocuous at first, right?

1:14:32.600 --> 1:14:40.400
 However, it is not because content that will maximally engage people, you know, get people

1:14:40.400 --> 1:14:44.480
 to react in an emotional way, get people to click on something.

1:14:44.480 --> 1:14:54.480
 It is very often content that, you know, is not healthy to the public discourse.

1:14:54.480 --> 1:15:01.560
 For instance, fake news are far more likely to get you to click on them than real news,

1:15:01.560 --> 1:15:07.080
 simply because they are not constrained to reality.

1:15:07.080 --> 1:15:14.120
 So they can be as outrageous, as surprising as good stories as you want, because they

1:15:14.120 --> 1:15:15.120
 are artificial, right?

1:15:15.120 --> 1:15:16.120
 Yeah.

1:15:16.120 --> 1:15:19.640
 To me, that's an exciting world because so much good can come.

1:15:19.640 --> 1:15:24.680
 So there's an opportunity to educate people.

1:15:24.680 --> 1:15:31.200
 You can balance people's worldview with other ideas.

1:15:31.200 --> 1:15:33.880
 So there's so many objective functions.

1:15:33.880 --> 1:15:41.080
 The space of objective functions that create better civilizations is large, arguably infinite.

1:15:41.080 --> 1:15:51.720
 But there's also a large space that creates division and destruction, civil war, a lot

1:15:51.720 --> 1:15:53.360
 of bad stuff.

1:15:53.360 --> 1:15:59.480
 And the worry is, naturally, probably that space is bigger, first of all.

1:15:59.480 --> 1:16:06.920
 And if we don't explicitly think about what kind of effects are going to be observed from

1:16:06.920 --> 1:16:10.280
 different objective functions, then we're going to get into trouble.

1:16:10.280 --> 1:16:16.400
 Because the question is, how do we get into rooms and have discussions?

1:16:16.400 --> 1:16:22.200
 So inside Google, inside Facebook, inside Twitter, and think about, okay, how can we

1:16:22.200 --> 1:16:28.240
 drive up engagement and at the same time create a good society?

1:16:28.240 --> 1:16:31.760
 Is it even possible to have that kind of philosophical discussion?

1:16:31.760 --> 1:16:33.200
 I think you can definitely try.

1:16:33.200 --> 1:16:40.160
 So from my perspective, I would feel rather uncomfortable with companies that are in control

1:16:40.160 --> 1:16:49.760
 of these new algorithms, with them making explicit decisions to manipulate people's opinions

1:16:49.760 --> 1:16:55.360
 or behaviors, even if the intent is good, because that's a very totalitarian mindset.

1:16:55.360 --> 1:16:59.840
 So instead, what I would like to see is probably never going to happen, because it's not super

1:16:59.840 --> 1:17:02.560
 realistic, but that's actually something I really care about.

1:17:02.560 --> 1:17:10.680
 I would like all these algorithms to present configuration settings to their users, so

1:17:10.680 --> 1:17:17.960
 that the users can actually make the decision about how they want to be impacted by these

1:17:17.960 --> 1:17:22.080
 information recommendation, content recommendation algorithms.

1:17:22.080 --> 1:17:27.120
 For instance, as a user of something like YouTube or Twitter, maybe I want to maximize

1:17:27.120 --> 1:17:30.480
 learning about a specific topic.

1:17:30.480 --> 1:17:38.720
 So I want the algorithm to feed my curiosity, which is in itself a very interesting problem.

1:17:38.720 --> 1:17:44.840
 So instead of maximizing my engagement, it will maximize how fast and how much I'm learning,

1:17:44.840 --> 1:17:50.880
 and it will also take into account the accuracy, hopefully, of the information I'm learning.

1:17:50.880 --> 1:17:57.800
 So yeah, the user should be able to determine exactly how these algorithms are affecting

1:17:57.800 --> 1:17:58.800
 their lives.

1:17:58.800 --> 1:18:08.240
 I don't want actually any entity making decisions about in which direction they're going to

1:18:08.240 --> 1:18:09.480
 try to manipulate me.

1:18:09.480 --> 1:18:11.840
 I want technology.

1:18:11.840 --> 1:18:18.520
 So AI, these algorithms are increasingly going to be our interface to a world that is increasingly

1:18:18.520 --> 1:18:20.280
 made of information.

1:18:20.280 --> 1:18:27.440
 And I want everyone to be in control of this interface, to interface with the world on

1:18:27.440 --> 1:18:29.160
 their own terms.

1:18:29.160 --> 1:18:38.040
 So if someone wants these algorithms to serve their own personal growth goals, they should

1:18:38.040 --> 1:18:41.920
 be able to configure these algorithms in such a way.

1:18:41.920 --> 1:18:50.400
 Yeah, but so I know it's painful to have explicit decisions, but there is underlying explicit

1:18:50.400 --> 1:18:57.240
 decisions, which is some of the most beautiful fundamental philosophy that we have before

1:18:57.240 --> 1:19:01.200
 us, which is personal growth.

1:19:01.200 --> 1:19:08.080
 If I want to watch videos from which I can learn, what does that mean?

1:19:08.080 --> 1:19:13.600
 So if I have a checkbox that wants to emphasize learning, there's still an algorithm with

1:19:13.600 --> 1:19:18.000
 explicit decisions in it that would promote learning.

1:19:18.000 --> 1:19:19.000
 What does that mean for me?

1:19:19.000 --> 1:19:25.440
 Like, for example, I've watched a documentary on Flat Earth theory, I guess.

1:19:25.440 --> 1:19:28.200
 It was very, like, I learned a lot.

1:19:28.200 --> 1:19:29.880
 I'm really glad I watched it.

1:19:29.880 --> 1:19:35.480
 It was a friend recommended it to me, because I don't have such an allergic reaction to

1:19:35.480 --> 1:19:37.800
 crazy people as my fellow colleagues do.

1:19:37.800 --> 1:19:42.320
 But it was very eye opening, and for others, it might not be.

1:19:42.320 --> 1:19:47.640
 From others, they might just get turned off from the same with the Republican and Democrat.

1:19:47.640 --> 1:19:50.480
 And it's a non trivial problem.

1:19:50.480 --> 1:19:56.440
 And first of all, if it's done well, I don't think it's something that wouldn't happen

1:19:56.440 --> 1:20:00.160
 that the YouTube wouldn't be promoting or Twitter wouldn't be.

1:20:00.160 --> 1:20:02.400
 It's just a really difficult problem.

1:20:02.400 --> 1:20:05.080
 How do we do, how do give people control?

1:20:05.080 --> 1:20:09.000
 Well, it's mostly an interface design problem.

1:20:09.000 --> 1:20:16.280
 The way I see it, you want to create technology that's like a mentor or a coach or an assistant

1:20:16.280 --> 1:20:22.680
 so that it's not your boss, right, you are in control of it.

1:20:22.680 --> 1:20:25.920
 You are telling it what to do for you.

1:20:25.920 --> 1:20:30.760
 And if you feel like it's manipulating you, it's not actually, it's not actually doing

1:20:30.760 --> 1:20:31.920
 what you want.

1:20:31.920 --> 1:20:35.040
 You should be able to switch to a different algorithm, you know.

1:20:35.040 --> 1:20:39.720
 So that fine tune control, you kind of learn, you're trusting the human collaboration.

1:20:39.720 --> 1:20:44.440
 I mean, that's how I see autonomous vehicles, too, is giving as much information as possible

1:20:44.440 --> 1:20:46.560
 and you learn that dance yourself.

1:20:46.560 --> 1:20:51.040
 Yeah, Adobe, I don't know if you use Adobe product for like Photoshop.

1:20:51.040 --> 1:20:56.600
 Yeah, they're trying to see if they can inject YouTube into their interface, but basically

1:20:56.600 --> 1:21:01.920
 allow you to show you all these videos that, because everybody's confused about what to

1:21:01.920 --> 1:21:03.360
 do with features.

1:21:03.360 --> 1:21:09.720
 So basically teach people by linking to, in that way, it's an assistant that shows, uses

1:21:09.720 --> 1:21:12.960
 videos as a basic element of information.

1:21:12.960 --> 1:21:23.080
 Okay, so what practically should people do to try to, to try to fight against abuses of

1:21:23.080 --> 1:21:26.880
 these algorithms or algorithms that manipulate us?

1:21:26.880 --> 1:21:31.080
 Honestly, it's a very, very difficult problem because to start with, there is very little

1:21:31.080 --> 1:21:34.120
 public awareness of these issues.

1:21:34.120 --> 1:21:39.960
 Very few people would think that, you know, anything wrong with their new algorithm, even

1:21:39.960 --> 1:21:44.440
 though there is actually something wrong already, which is that it's trying to maximize engagement

1:21:44.440 --> 1:21:50.000
 most of the time, which has very negative side effects, right?

1:21:50.000 --> 1:21:59.760
 So ideally, so the very first thing is to stop trying to purely maximize engagement, try

1:21:59.760 --> 1:22:11.000
 to propagate content based on popularity, right, instead take into account the goals

1:22:11.000 --> 1:22:13.640
 and the profiles of each user.

1:22:13.640 --> 1:22:20.200
 So you will, you will be, one example is, for instance, when I look at topic recommendations

1:22:20.200 --> 1:22:25.640
 on Twitter, it's like, you know, they have this news tab with switch recommendations.

1:22:25.640 --> 1:22:33.480
 That's always the worst garbage because it's content that appeals to the smallest command

1:22:33.480 --> 1:22:37.560
 denominator to all Twitter users because they're trying to optimize, they're purely

1:22:37.560 --> 1:22:41.680
 trying to obtain us popularity, they're purely trying to optimize engagement, but that's

1:22:41.680 --> 1:22:43.080
 not what I want.

1:22:43.080 --> 1:22:50.440
 So they should put me in control of some setting so that I define what's the objective function

1:22:50.440 --> 1:22:54.280
 that Twitter is going to be following to show me this content.

1:22:54.280 --> 1:22:59.320
 And honestly, so this is all about interface design, and we are not, it's not realistic

1:22:59.320 --> 1:23:04.760
 to give users control of a bunch of knobs that define an algorithm, instead, we should

1:23:04.760 --> 1:23:11.200
 purely put them in charge of defining the objective function, like let the user tell

1:23:11.200 --> 1:23:15.320
 us what they want to achieve, how they want this algorithm to impact their lives.

1:23:15.320 --> 1:23:20.200
 So do you think it is that or do they provide individual article by article reward structure

1:23:20.200 --> 1:23:24.760
 where you give a signal, I'm glad I saw this or I'm glad I didn't?

1:23:24.760 --> 1:23:31.520
 So like a Spotify type feedback mechanism, it works to some extent, I'm kind of skeptical

1:23:31.520 --> 1:23:38.920
 about it because the only way the algorithm, the algorithm will attempt to relate your choices

1:23:38.920 --> 1:23:45.040
 with the choices of everyone else, which might, you know, if you have an average profile that

1:23:45.040 --> 1:23:49.680
 works fine, I'm sure Spotify accommodations work fine if you just like mainstream stuff.

1:23:49.680 --> 1:23:54.040
 But if you don't, it can be, it's not optimal at all, actually.

1:23:54.040 --> 1:24:00.880
 It'll be in an efficient search for the part of the Spotify world that represents you.

1:24:00.880 --> 1:24:09.000
 So it's a tough problem, but do note that even a feedback system like what Spotify has

1:24:09.000 --> 1:24:15.680
 does not give me control over why the algorithm is trying to optimize for.

1:24:15.680 --> 1:24:21.440
 Well, public awareness, which is what we're doing now, is a good place to start.

1:24:21.440 --> 1:24:27.760
 Do you have concerns about long term existential threats of artificial intelligence?

1:24:27.760 --> 1:24:34.800
 Well, as I was saying, our world is increasingly made of information, AI algorithms are increasingly

1:24:34.800 --> 1:24:40.280
 going to be our interface to this world of information, and somebody will be in control

1:24:40.280 --> 1:24:46.000
 of these algorithms, and that puts us in any kind of bad situation, right?

1:24:46.000 --> 1:24:48.120
 It has risks.

1:24:48.120 --> 1:24:55.000
 It has risks coming from potentially large companies wanting to optimize their own goals,

1:24:55.000 --> 1:25:01.760
 maybe profit, maybe something else, also from governments who might want to use these algorithms

1:25:01.760 --> 1:25:04.720
 as a means of control of the entire population.

1:25:04.720 --> 1:25:07.560
 Do you think there's existential threat that could arise from that?

1:25:07.560 --> 1:25:15.840
 So existential threat, so maybe you're referring to the singularity narrative where robots

1:25:15.840 --> 1:25:16.840
 just take over?

1:25:16.840 --> 1:25:22.040
 Well, I don't not terminate a robot, and I don't believe it has to be a singularity.

1:25:22.040 --> 1:25:30.000
 We're just talking to, just like you said, the algorithm controlling masses of populations,

1:25:30.000 --> 1:25:37.840
 the existential threat being hurt ourselves much like a nuclear war would hurt ourselves,

1:25:37.840 --> 1:25:38.840
 that kind of thing.

1:25:38.840 --> 1:25:44.600
 I don't think that requires a singularity, that requires a loss of control over AI algorithms.

1:25:44.600 --> 1:25:47.920
 So I do agree there are concerning trends.

1:25:47.920 --> 1:25:53.600
 Honestly, I wouldn't want to make any long term predictions.

1:25:53.600 --> 1:25:59.560
 I don't think today we really have the capability to see what the dangers of AI are going to

1:25:59.560 --> 1:26:02.240
 be in 50 years, in 100 years.

1:26:02.240 --> 1:26:11.480
 I do see that we are already faced with concrete and present dangers surrounding the negative

1:26:11.480 --> 1:26:17.280
 side effects of content recombination systems of new seed algorithms concerning algorithmic

1:26:17.280 --> 1:26:19.520
 bias as well.

1:26:19.520 --> 1:26:26.000
 So we are delegating more and more decision processes to algorithms.

1:26:26.000 --> 1:26:30.160
 Some of these algorithms are uncrafted, some are learned from data.

1:26:30.160 --> 1:26:34.040
 But we are delegating control.

1:26:34.040 --> 1:26:37.240
 Sometimes it's a good thing, sometimes not so much.

1:26:37.240 --> 1:26:41.720
 And there is in general very little supervision of this process.

1:26:41.720 --> 1:26:50.160
 So we are still in this period of very fast change, even chaos, where society is restructuring

1:26:50.160 --> 1:26:56.160
 itself, turning into an information society, which itself is turning into an increasingly

1:26:56.160 --> 1:26:59.240
 automated information processing society.

1:26:59.240 --> 1:27:05.760
 And well, yeah, I think the best we can do today is try to raise awareness around some

1:27:05.760 --> 1:27:06.760
 of these issues.

1:27:06.760 --> 1:27:13.000
 And I think we are actually making good progress if you look at algorithmic bias, for instance.

1:27:13.000 --> 1:27:17.240
 Three years ago, even two years ago, very, very few people were talking about it.

1:27:17.240 --> 1:27:22.400
 And now all the big companies are talking about it, often not in a very serious way,

1:27:22.400 --> 1:27:24.600
 but at least it is part of the public discourse.

1:27:24.600 --> 1:27:27.360
 You see people in Congress talking about it.

1:27:27.360 --> 1:27:32.840
 And it all started from raising awareness.

1:27:32.840 --> 1:27:40.200
 So in terms of alignment problem, trying to teach as we allow algorithms, just even recommend

1:27:40.200 --> 1:27:50.280
 their systems on Twitter, encoding human values and morals, decisions that touch on ethics.

1:27:50.280 --> 1:27:52.640
 How hard do you think that problem is?

1:27:52.640 --> 1:27:59.800
 How do we have lost functions in neural networks that have some component, some fuzzy components

1:27:59.800 --> 1:28:01.280
 of human morals?

1:28:01.280 --> 1:28:07.400
 Well, I think this is really all about objective function engineering, which is probably going

1:28:07.400 --> 1:28:10.680
 to be increasingly a topic of concern in the future.

1:28:10.680 --> 1:28:16.160
 Like for now, we are just using very naive loss functions because the hard part is not

1:28:16.160 --> 1:28:19.240
 actually what you're trying to minimize, it's everything else.

1:28:19.240 --> 1:28:25.280
 But as the everything else is going to be increasingly automated, we're going to be

1:28:25.280 --> 1:28:30.920
 focusing our human attention on increasingly high level components, like what's actually

1:28:30.920 --> 1:28:34.040
 driving the whole learning system, like the objective function.

1:28:34.040 --> 1:28:38.360
 So loss function engineering is going to be, loss function engineer is probably going to

1:28:38.360 --> 1:28:40.760
 be a job title in the future.

1:28:40.760 --> 1:28:46.200
 And then the tooling you're creating with Keras essentially takes care of all the details

1:28:46.200 --> 1:28:52.960
 underneath and basically the human expert is needed for exactly that.

1:28:52.960 --> 1:28:59.240
 Keras is the interface between the data you're collecting and the business goals.

1:28:59.240 --> 1:29:04.280
 And your job as an engineer is going to be to express your business goals and your understanding

1:29:04.280 --> 1:29:10.440
 of your business or your product, your system as a kind of loss function or a kind of set

1:29:10.440 --> 1:29:11.440
 of constraints.

1:29:11.440 --> 1:29:19.560
 Does the possibility of creating an AGI system excite you or scare you or bore you?

1:29:19.560 --> 1:29:23.600
 So intelligence can never really be general, you know, at best it can have some degree

1:29:23.600 --> 1:29:26.600
 of generality, like human intelligence.

1:29:26.600 --> 1:29:30.720
 And it's also always as some specialization in the same way that human intelligence is

1:29:30.720 --> 1:29:35.680
 specialized in a certain category of problems, is specialized in the human experience.

1:29:35.680 --> 1:29:41.440
 And when people talk about AGI, I'm never quite sure if they're talking about very,

1:29:41.440 --> 1:29:46.200
 very smart AI, so smart that it's even smarter than humans, or they're talking about human

1:29:46.200 --> 1:29:49.880
 like intelligence, because these are different things.

1:29:49.880 --> 1:29:54.840
 Let's say, presumably I'm oppressing you today with my humanness.

1:29:54.840 --> 1:29:59.400
 So imagine that I was in fact a robot.

1:29:59.400 --> 1:30:02.400
 So what does that mean?

1:30:02.400 --> 1:30:05.160
 I'm oppressing you with natural language processing.

1:30:05.160 --> 1:30:08.320
 Maybe if you weren't able to see me, maybe this is a phone call.

1:30:08.320 --> 1:30:09.320
 That kind of system.

1:30:09.320 --> 1:30:10.320
 Okay.

1:30:10.320 --> 1:30:11.320
 So companion.

1:30:11.320 --> 1:30:15.200
 So that's very much about building human like AI.

1:30:15.200 --> 1:30:18.200
 And you're asking me, you know, is this an exciting perspective?

1:30:18.200 --> 1:30:19.200
 Yes.

1:30:19.200 --> 1:30:21.960
 I think so, yes.

1:30:21.960 --> 1:30:29.640
 Not so much because of what artificial human like intelligence could do, but, you know,

1:30:29.640 --> 1:30:34.240
 from an intellectual perspective, I think if you could build truly human like intelligence,

1:30:34.240 --> 1:30:40.160
 that means you could actually understand human intelligence, which is fascinating, right?

1:30:40.160 --> 1:30:44.480
 Human like intelligence is going to require emotions, it's going to require consciousness,

1:30:44.480 --> 1:30:48.640
 which is not things that would normally be required by an intelligent system.

1:30:48.640 --> 1:30:55.560
 If you look at, you know, we were mentioning earlier like science as a superhuman problem

1:30:55.560 --> 1:31:02.240
 solving agent or system, it does not have consciousness, it doesn't have emotions.

1:31:02.240 --> 1:31:07.760
 In general, so emotions, I see consciousness as being on the same spectrum as emotions.

1:31:07.760 --> 1:31:17.560
 It is a component of the subjective experience that is meant very much to guide behavior

1:31:17.560 --> 1:31:20.880
 generation, right, it's meant to guide your behavior.

1:31:20.880 --> 1:31:27.080
 In general, human intelligence and animal intelligence has evolved for the purpose of

1:31:27.080 --> 1:31:30.760
 behavior generation, right, including in a social context.

1:31:30.760 --> 1:31:32.600
 So that's why we actually need emotions.

1:31:32.600 --> 1:31:35.080
 That's why we need consciousness.

1:31:35.080 --> 1:31:39.280
 An artificial intelligence system developed in a different context may well never need

1:31:39.280 --> 1:31:43.280
 them, may well never be conscious like science.

1:31:43.280 --> 1:31:50.160
 But on that point, I would argue it's possible to imagine that there's echoes of consciousness

1:31:50.160 --> 1:31:55.640
 in science when viewed as an organism, that science is consciousness.

1:31:55.640 --> 1:31:59.320
 So I mean, how would you go about testing this hypothesis?

1:31:59.320 --> 1:32:07.240
 How do you probe the subjective experience of an abstract system like science?

1:32:07.240 --> 1:32:12.280
 Well the point of probing any subjective experience is impossible, because I'm not science, I'm

1:32:12.280 --> 1:32:13.280
 a science.

1:32:13.280 --> 1:32:20.720
 So I can't probe another entity's, another, it's no more than bacteria on my skin.

1:32:20.720 --> 1:32:25.360
 Your legs, I can ask you questions about your subjective experience and you can answer me.

1:32:25.360 --> 1:32:27.720
 And that's how I know you're conscious.

1:32:27.720 --> 1:32:32.080
 Yes, but that's because we speak the same language.

1:32:32.080 --> 1:32:35.800
 You perhaps, we have to speak the language of science and we have to ask it.

1:32:35.800 --> 1:32:41.120
 Honestly, I don't think consciousness, just like emotions of pain and pleasure, is not

1:32:41.120 --> 1:32:47.120
 something that inevitably arises from any sort of sufficiently intelligent information

1:32:47.120 --> 1:32:48.120
 processing.

1:32:48.120 --> 1:32:54.080
 It is a feature of the mind and if you've not implemented it explicitly, it is not there.

1:32:54.080 --> 1:32:59.120
 So you think it's an emergent feature of a particular architecture.

1:32:59.120 --> 1:33:00.120
 So do you think?

1:33:00.120 --> 1:33:02.080
 It's a feature in the same sense.

1:33:02.080 --> 1:33:09.800
 So again, the subjective experience is all about guiding behavior.

1:33:09.800 --> 1:33:15.560
 If the problems you're trying to solve don't really involve embedded agents, maybe in a

1:33:15.560 --> 1:33:19.800
 social context, generating behavior and pursuing goals like this.

1:33:19.800 --> 1:33:23.280
 And if you look at science, that's not really what's happening, even though it is, it is

1:33:23.280 --> 1:33:29.600
 a form of artificial air in this artificial intelligence in the sense that it is solving

1:33:29.600 --> 1:33:35.240
 problems, it is committing knowledge, committing solutions and so on.

1:33:35.240 --> 1:33:41.120
 So if you're not explicitly implementing a subjective experience, implementing certain

1:33:41.120 --> 1:33:47.120
 emotions and implementing consciousness, it's not going to just spontaneously emerge.

1:33:47.120 --> 1:33:48.360
 Yeah.

1:33:48.360 --> 1:33:53.640
 But so for a system like human like intelligent system that has consciousness, do you think

1:33:53.640 --> 1:33:55.240
 it needs to have a body?

1:33:55.240 --> 1:33:56.240
 Yes, definitely.

1:33:56.240 --> 1:33:59.920
 I mean, it doesn't have to be a physical body, right?

1:33:59.920 --> 1:34:03.680
 And there's not that much difference between a realistic simulation in the real world.

1:34:03.680 --> 1:34:06.560
 So there has to be something you have to preserve kind of thing.

1:34:06.560 --> 1:34:07.560
 Yes.

1:34:07.560 --> 1:34:12.400
 But human like intelligence can only arise in a human like context.

1:34:12.400 --> 1:34:13.400
 Intelligence needs to be tired.

1:34:13.400 --> 1:34:20.480
 You need other humans in order for you to demonstrate that you have human like intelligence, essentially.

1:34:20.480 --> 1:34:29.240
 So what kind of tests and demonstration would be sufficient for you to demonstrate human

1:34:29.240 --> 1:34:30.480
 like intelligence?

1:34:30.480 --> 1:34:31.480
 Yeah.

1:34:31.480 --> 1:34:37.080
 And just out of curiosity, you talked about in terms of theorem proving and program synthesis,

1:34:37.080 --> 1:34:40.480
 I think you've written about that there's no good benchmarks for this.

1:34:40.480 --> 1:34:41.480
 Yeah.

1:34:41.480 --> 1:34:42.480
 That's one of the problems.

1:34:42.480 --> 1:34:46.560
 So let's talk programs, program synthesis.

1:34:46.560 --> 1:34:51.440
 So what do you imagine is a good, I think it's related questions for human like intelligence

1:34:51.440 --> 1:34:53.720
 and for program synthesis.

1:34:53.720 --> 1:34:56.160
 What's a good benchmark for either or both?

1:34:56.160 --> 1:34:57.160
 Right.

1:34:57.160 --> 1:34:59.400
 So I mean, you're actually asking two questions.

1:34:59.400 --> 1:35:06.520
 Which is one is about quantifying intelligence and comparing the intelligence of an artificial

1:35:06.520 --> 1:35:08.800
 system to the intelligence for human.

1:35:08.800 --> 1:35:13.520
 And the other is about a degree to which this intelligence is human like.

1:35:13.520 --> 1:35:16.800
 It's actually two different questions.

1:35:16.800 --> 1:35:19.320
 So if you look, you mentioned earlier the Turing test.

1:35:19.320 --> 1:35:20.320
 Right.

1:35:20.320 --> 1:35:24.080
 Well, I actually don't like the Turing test because it's very lazy.

1:35:24.080 --> 1:35:28.960
 It's all about completely bypassing the problem of defining and measuring intelligence.

1:35:28.960 --> 1:35:34.400
 And instead delegating to a human judge or a panel of human judges.

1:35:34.400 --> 1:35:38.400
 So it's a total cobalt, right?

1:35:38.400 --> 1:35:45.640
 If you want to measure how human like an agent is, I think you have to make it interact

1:35:45.640 --> 1:35:47.920
 with other humans.

1:35:47.920 --> 1:35:54.120
 Maybe it's not necessarily a good idea to have these other humans be the judges.

1:35:54.120 --> 1:36:00.800
 Maybe you should just observe BFU and compare it to what the human would actually have done.

1:36:00.800 --> 1:36:09.160
 When it comes to measuring how smart, how clever an agent is and comparing that to the

1:36:09.160 --> 1:36:11.240
 degree of human intelligence.

1:36:11.240 --> 1:36:13.680
 So we're already talking about two things, right?

1:36:13.680 --> 1:36:20.600
 The degree, kind of like the magnitude of an intelligence and its direction, right?

1:36:20.600 --> 1:36:23.560
 Like the norm of a vector and its direction.

1:36:23.560 --> 1:36:27.200
 And the direction is like human likeness.

1:36:27.200 --> 1:36:32.880
 And the magnitude, the norm is intelligence.

1:36:32.880 --> 1:36:34.280
 You could call it intelligence, right?

1:36:34.280 --> 1:36:42.440
 So the direction, your sense, the space of directions that are human like is very narrow.

1:36:42.440 --> 1:36:49.880
 So the way you would measure the magnitude of intelligence in a system in a way that

1:36:49.880 --> 1:36:54.960
 also enables you to compare it to that of a human.

1:36:54.960 --> 1:37:02.000
 Well, if you look at different benchmarks for intelligence today, they're all too focused

1:37:02.000 --> 1:37:04.480
 on skill at a given task.

1:37:04.480 --> 1:37:11.080
 That's skill at playing chess, skill at playing Go, skill at playing Dota.

1:37:11.080 --> 1:37:17.560
 And I think that's not the right way to go about it because you can always be the human

1:37:17.560 --> 1:37:20.240
 at one specific task.

1:37:20.240 --> 1:37:25.320
 The reason why our skill at playing Go or at juggling or anything is impressive is because

1:37:25.320 --> 1:37:29.480
 we are expressing this skill within a certain set of constraints.

1:37:29.480 --> 1:37:33.840
 If you remove the constraints, the constraints that we have one lifetime, that we have this

1:37:33.840 --> 1:37:40.120
 body and so on, if you remove the context, if you have unlimited train data, if you

1:37:40.120 --> 1:37:44.840
 can have access to, you know, for instance, if you look at juggling, if you have no restriction

1:37:44.840 --> 1:37:50.040
 on the hardware, then achieving arbitrary levels of skill is not very interesting and

1:37:50.040 --> 1:37:53.960
 says nothing about the amount of intelligence you've achieved.

1:37:53.960 --> 1:37:59.320
 So if you want to measure intelligence, you need to rigorously define what intelligence

1:37:59.320 --> 1:38:04.360
 is, which in itself, you know, it's a very challenging problem.

1:38:04.360 --> 1:38:05.960
 And do you think that's possible?

1:38:05.960 --> 1:38:06.960
 To define intelligence?

1:38:06.960 --> 1:38:07.960
 Yes, absolutely.

1:38:07.960 --> 1:38:11.680
 I mean, you can provide, many people have provided, you know, some definition.

1:38:11.680 --> 1:38:13.640
 I have my own definition.

1:38:13.640 --> 1:38:16.520
 Where does your definition begin if it doesn't end?

1:38:16.520 --> 1:38:25.560
 Well, I think intelligence is essentially the efficiency with which you turn experience

1:38:25.560 --> 1:38:29.960
 into generalizable programs.

1:38:29.960 --> 1:38:35.280
 So what that means is it's the efficiency with which you turn a sampling of experience

1:38:35.280 --> 1:38:46.200
 space into the ability to process a larger chunk of experience space.

1:38:46.200 --> 1:38:53.480
 So measuring skill can be one proxy because many, many different tasks can be one proxy

1:38:53.480 --> 1:38:54.680
 for measure intelligence.

1:38:54.680 --> 1:38:58.880
 But if you want to only measure skill, you should control for two things.

1:38:58.880 --> 1:39:07.920
 You should control for the amount of experience that your system has and the priors that your

1:39:07.920 --> 1:39:08.920
 system has.

1:39:08.920 --> 1:39:14.120
 But if you control, if you look at two agents and you give them the same priors and you

1:39:14.120 --> 1:39:21.480
 give them the same amount of experience, there is one of the agents that is going to learn

1:39:21.480 --> 1:39:27.720
 programs, representation, something, a model that will perform well on the larger chunk

1:39:27.720 --> 1:39:29.760
 of experience space than the other.

1:39:29.760 --> 1:39:31.920
 And that is the smaller agent.

1:39:31.920 --> 1:39:32.920
 Yeah.

1:39:32.920 --> 1:39:39.920
 So if you fix the experience, which generate better programs, better meaning, more generalizable,

1:39:39.920 --> 1:39:40.920
 that's really interesting.

1:39:40.920 --> 1:39:42.760
 That's a very nice, clean definition of...

1:39:42.760 --> 1:39:49.560
 By the way, in this definition, it is already very obvious that intelligence has to be specialized

1:39:49.560 --> 1:39:53.600
 because you're talking about experience space and you're talking about segments of experience

1:39:53.600 --> 1:39:54.600
 space.

1:39:54.600 --> 1:39:59.680
 You're talking about priors and you're talking about experience, all of these things define

1:39:59.680 --> 1:40:04.840
 the context in which intelligence emerges.

1:40:04.840 --> 1:40:10.040
 And you can never look at the totality of experience space.

1:40:10.040 --> 1:40:12.520
 So intelligence has to be specialized.

1:40:12.520 --> 1:40:16.760
 But it can be sufficiently large, the experience space, even though specialized is a certain

1:40:16.760 --> 1:40:22.200
 point when the experience space is large enough to where it might as well be general.

1:40:22.200 --> 1:40:23.200
 It feels general.

1:40:23.200 --> 1:40:24.200
 It looks general.

1:40:24.200 --> 1:40:25.200
 I mean, it's very relative.

1:40:25.200 --> 1:40:29.560
 For instance, many people would say human intelligence is general.

1:40:29.560 --> 1:40:32.960
 In fact, it is quite specialized.

1:40:32.960 --> 1:40:37.960
 We can definitely build systems that start from the same innate priors as what humans

1:40:37.960 --> 1:40:43.720
 have at birth because we already understand fairly well what sort of priors we have as

1:40:43.720 --> 1:40:44.720
 humans.

1:40:44.720 --> 1:40:50.680
 Like many people have worked on this problem, most notably, Elzebeth Spelke from Harvard,

1:40:50.680 --> 1:40:56.240
 and if you know her, she's worked a lot on what she calls a core knowledge.

1:40:56.240 --> 1:41:02.560
 And it is very much about trying to determine and describe what priors we are born with.

1:41:02.560 --> 1:41:06.080
 Like language skills and so on and all that kind of stuff.

1:41:06.080 --> 1:41:07.080
 Exactly.

1:41:07.080 --> 1:41:11.520
 So we have some pretty good understanding of what priors we are born with.

1:41:11.520 --> 1:41:13.960
 So we could...

1:41:13.960 --> 1:41:18.720
 So I've actually been working on a benchmark for the past couple of years, on and off.

1:41:18.720 --> 1:41:21.440
 I hope to be able to release it at some point.

1:41:21.440 --> 1:41:29.120
 The idea is to measure the intelligence of systems by considering for priors, considering

1:41:29.120 --> 1:41:34.840
 for amount of experience, and by assuming the same priors as what humans are born with

1:41:34.840 --> 1:41:40.160
 so that you can actually compare these scores to human intelligence and you can actually

1:41:40.160 --> 1:41:44.440
 have humans pass the same test in a way that's fair.

1:41:44.440 --> 1:41:54.720
 And so importantly, such a benchmark should be such that any amount of practicing does

1:41:54.720 --> 1:41:56.800
 not increase your score.

1:41:56.800 --> 1:42:04.120
 So try to picture a game where no matter how much you play this game, it does not change

1:42:04.120 --> 1:42:05.400
 your skill at the game.

1:42:05.400 --> 1:42:08.600
 Can you picture that?

1:42:08.600 --> 1:42:14.840
 As a person who deeply appreciates practice, I cannot actually...

1:42:14.840 --> 1:42:19.040
 There's actually a very simple trick.

1:42:19.040 --> 1:42:24.760
 So in order to come up with a task, so the only thing you can measure is skill at a task.

1:42:24.760 --> 1:42:28.280
 All tasks are going to involve priors.

1:42:28.280 --> 1:42:32.480
 The trick is to know what they are and to describe that.

1:42:32.480 --> 1:42:36.040
 And then you make sure that this is the same set of priors as what humans start with.

1:42:36.040 --> 1:42:41.080
 So you create a task that assumes these priors, that exactly documents these priors, so that

1:42:41.080 --> 1:42:44.720
 the priors are made explicit and there are no other priors involved.

1:42:44.720 --> 1:42:52.240
 And then you generate a certain number of samples in experience space for this task.

1:42:52.240 --> 1:42:59.480
 And this, for one task, assuming that the task is new for the agent passing it, that's

1:42:59.480 --> 1:43:07.560
 one test of this definition of intelligence that we set up.

1:43:07.560 --> 1:43:12.360
 And now you can scale that to many different tasks, that each task should be new to the

1:43:12.360 --> 1:43:13.360
 agent passing it.

1:43:13.360 --> 1:43:18.680
 And also should be human interpretable and understandable, so that you can actually have

1:43:18.680 --> 1:43:21.960
 a human pass the same test and then you can compare the score of your machine and the score

1:43:21.960 --> 1:43:22.960
 of your human.

1:43:22.960 --> 1:43:23.960
 Which could be a lot.

1:43:23.960 --> 1:43:28.580
 It could even start a task like MNIST, just as long as you start with the same set of

1:43:28.580 --> 1:43:29.580
 priors.

1:43:29.580 --> 1:43:35.880
 Yeah, so the problem with MNIST, humans are already trained to recognize digits.

1:43:35.880 --> 1:43:44.240
 But let's say we're considering objects that are not digits, some complete arbitrary patterns.

1:43:44.240 --> 1:43:50.120
 Well, humans already come with visual priors about how to process that.

1:43:50.120 --> 1:43:55.760
 So in order to make the game fair, you would have to isolate these priors and describe

1:43:55.760 --> 1:43:58.720
 them and then express them as computational rules.

1:43:58.720 --> 1:44:03.760
 Having worked a lot with vision science people has exceptionally difficult, a lot of progress

1:44:03.760 --> 1:44:07.720
 has been made, there's been a lot of good tests, and basically reducing all of human

1:44:07.720 --> 1:44:09.360
 vision into some good priors.

1:44:09.360 --> 1:44:14.640
 We're still probably far away from that perfectly, but as a start for a benchmark, that's an

1:44:14.640 --> 1:44:15.640
 exciting possibility.

1:44:15.640 --> 1:44:25.320
 Yeah, so Elisabeth Belke actually lists objectness as one of the core knowledge priors.

1:44:25.320 --> 1:44:26.320
 Objectness.

1:44:26.320 --> 1:44:27.320
 Cool.

1:44:27.320 --> 1:44:28.320
 Objectness.

1:44:28.320 --> 1:44:29.320
 Yeah.

1:44:29.320 --> 1:44:33.000
 So we have priors about objectness, like about the visual space, about time, about agents,

1:44:33.000 --> 1:44:34.600
 about goal oriented behavior.

1:44:34.600 --> 1:44:42.680
 We have many different priors, but what's interesting is that, sure, we have this pretty

1:44:42.680 --> 1:44:48.520
 diverse and rich set of priors, but it's also not that diverse, right?

1:44:48.520 --> 1:44:52.560
 We are not born into this world with a ton of knowledge about the world.

1:44:52.560 --> 1:44:59.240
 There is only a small set of core knowledge, right?

1:44:59.240 --> 1:45:00.240
 Yeah.

1:45:00.240 --> 1:45:07.120
 So do you have a sense of how it feels to us humans that that set is not that large,

1:45:07.120 --> 1:45:11.920
 but just even the nature of time that we kind of integrate pretty effectively through all

1:45:11.920 --> 1:45:17.680
 of our perception, all of our reasoning, maybe how, you know, do you have a sense of

1:45:17.680 --> 1:45:19.880
 how easy it is to encode those priors?

1:45:19.880 --> 1:45:26.000
 Maybe it requires building a universe, and then the human brain in order to encode those

1:45:26.000 --> 1:45:27.000
 priors.

1:45:27.000 --> 1:45:30.680
 Or do you have a hope that it can be listed like an XAMAT?

1:45:30.680 --> 1:45:31.680
 I don't think so.

1:45:31.680 --> 1:45:36.480
 So you have to keep in mind that any knowledge about the world that we are born with is something

1:45:36.480 --> 1:45:43.280
 that has to have been encoded into our DNA by evolution at some point.

1:45:43.280 --> 1:45:50.720
 And DNA is a very, very low bandwidth medium, like it's extremely long and expensive to

1:45:50.720 --> 1:45:57.120
 encode anything into DNA, because first of all, you need some sort of evolutionary pressure

1:45:57.120 --> 1:45:59.400
 to guide this writing process.

1:45:59.400 --> 1:46:05.720
 And then, you know, the higher level of information you're trying to write, the longer it's going

1:46:05.720 --> 1:46:13.960
 to be, and the thing in the environment that you're trying to encode knowledge about has

1:46:13.960 --> 1:46:17.240
 to be stable over this duration.

1:46:17.240 --> 1:46:22.840
 So you can only encode into DNA things that constitute an evolutionary advantage.

1:46:22.840 --> 1:46:27.120
 So this is actually a very small subset of all possible knowledge about the world.

1:46:27.120 --> 1:46:33.360
 You can only encode things that are stable, that are true over very, very long periods

1:46:33.360 --> 1:46:35.480
 of time, typically millions of years.

1:46:35.480 --> 1:46:40.520
 For instance, we might have some visual prior about the shape of snakes, right?

1:46:40.520 --> 1:46:43.800
 But what makes a face?

1:46:43.800 --> 1:46:46.440
 What's the difference between a face and a nonface?

1:46:46.440 --> 1:46:49.840
 But consider this interesting question.

1:46:49.840 --> 1:46:57.800
 Do we have any innate sense of the visual difference between a male face and a female

1:46:57.800 --> 1:46:58.800
 face?

1:46:58.800 --> 1:46:59.800
 What do you think?

1:46:59.800 --> 1:47:01.320
 For a human, I mean.

1:47:01.320 --> 1:47:05.920
 I would have to look back into evolutionary history when the genders emerged.

1:47:05.920 --> 1:47:11.280
 But yeah, most, I mean, the faces of humans are quite different from the faces of great

1:47:11.280 --> 1:47:14.000
 apes, great apes, right?

1:47:14.000 --> 1:47:15.000
 Yeah.

1:47:15.000 --> 1:47:16.000
 That's interesting.

1:47:16.000 --> 1:47:17.000
 But yeah.

1:47:17.000 --> 1:47:23.200
 You couldn't tell the face of a female chimpanzee from the face of a male chimpanzee, probably.

1:47:23.200 --> 1:47:24.200
 Yeah.

1:47:24.200 --> 1:47:26.720
 And I don't think most humans evolve all that ability.

1:47:26.720 --> 1:47:33.160
 We do have innate knowledge of what makes a face, but it's actually impossible for us

1:47:33.160 --> 1:47:39.200
 to have any DNA encoding knowledge of the difference between a female human face and

1:47:39.200 --> 1:47:40.680
 a male human face.

1:47:40.680 --> 1:47:50.800
 Because that knowledge, that information came up into the world actually very recently.

1:47:50.800 --> 1:47:56.920
 If you look at the slowness of the process of encoding knowledge into DNA.

1:47:56.920 --> 1:47:57.920
 Yeah.

1:47:57.920 --> 1:47:58.920
 So that's interesting.

1:47:58.920 --> 1:48:01.640
 That's a really powerful argument that DNA is a low bandwidth and it takes a long time

1:48:01.640 --> 1:48:05.480
 to encode that naturally creates a very efficient encoding.

1:48:05.480 --> 1:48:12.400
 But one important consequence of this is that, so yes, we are born into this world with a

1:48:12.400 --> 1:48:17.440
 bunch of knowledge, sometimes very high level knowledge about the world like the rough shape

1:48:17.440 --> 1:48:20.800
 of a snake, of the rough shape of a face.

1:48:20.800 --> 1:48:27.040
 But importantly, because this knowledge takes so long to write, almost all of this innate

1:48:27.040 --> 1:48:33.360
 knowledge is shared with our cousins, with great apes, right?

1:48:33.360 --> 1:48:37.600
 So it is not actually this innate knowledge that makes us special.

1:48:37.600 --> 1:48:44.120
 But to throw it right back at you from the earlier on in our discussion, that encoding

1:48:44.120 --> 1:48:50.600
 might also include the entirety of the environment of Earth.

1:48:50.600 --> 1:48:56.520
 To sum up, so it can include things that are important to survival and production.

1:48:56.520 --> 1:49:01.840
 So for which there is some evolutionary pressure and things that are stable, constant over

1:49:01.840 --> 1:49:05.240
 very, very, very long time periods.

1:49:05.240 --> 1:49:07.440
 And honestly, it's not that much information.

1:49:07.440 --> 1:49:15.600
 There's also, besides the bandwidths, constraints and constraints of the writing process, there's

1:49:15.600 --> 1:49:22.600
 also memory constraints like DNA, the part of DNA that deals with the human brain, it's

1:49:22.600 --> 1:49:23.600
 actually fairly small.

1:49:23.600 --> 1:49:26.360
 It's like, you know, on the order of megabytes, right?

1:49:26.360 --> 1:49:31.880
 There's not that much high level knowledge about the world you can encode.

1:49:31.880 --> 1:49:39.400
 That's quite brilliant and hopeful for a benchmark that you're referring to of encoding priors.

1:49:39.400 --> 1:49:43.680
 I actually look forward to, I'm skeptical that you can do it in the next couple of years,

1:49:43.680 --> 1:49:44.680
 but hopefully...

1:49:44.680 --> 1:49:45.960
 I've been working on it.

1:49:45.960 --> 1:49:50.120
 So honestly, it's a very simple benchmark and it's not like a big breakthrough or anything.

1:49:50.120 --> 1:49:53.920
 It's more like a fun side project, right?

1:49:53.920 --> 1:49:56.720
 So is ImageNet.

1:49:56.720 --> 1:50:04.120
 These fun side projects could launch entire groups of efforts towards creating reasoning

1:50:04.120 --> 1:50:05.120
 systems and so on.

1:50:05.120 --> 1:50:06.120
 And I think...

1:50:06.120 --> 1:50:07.120
 Yeah, that's the goal.

1:50:07.120 --> 1:50:12.160
 It's trying to measure strong generalization, to measure the strength of abstraction in

1:50:12.160 --> 1:50:17.160
 our minds, well, in our minds and in an artificially intelligent agency.

1:50:17.160 --> 1:50:24.960
 And if there's anything true about this science organism, it's individual cells love competition.

1:50:24.960 --> 1:50:27.000
 So benchmarks encourage competition.

1:50:27.000 --> 1:50:29.680
 So that's an exciting possibility.

1:50:29.680 --> 1:50:30.680
 If you...

1:50:30.680 --> 1:50:35.720
 Do you think an AI winter is coming and how do we prevent it?

1:50:35.720 --> 1:50:36.720
 Not really.

1:50:36.720 --> 1:50:42.160
 So an AI winter is something that would occur when there's a big mismatch between how we

1:50:42.160 --> 1:50:47.560
 are selling the capabilities of AI and the actual capabilities of AI.

1:50:47.560 --> 1:50:52.000
 And today, deep learning is creating a lot of value and it will keep creating a lot of

1:50:52.000 --> 1:50:59.360
 value in the sense that these models are applicable to a very wide range of problems that are

1:50:59.360 --> 1:51:00.360
 even today.

1:51:00.360 --> 1:51:05.320
 And we are only just getting started with applying algorithms to every problem they

1:51:05.320 --> 1:51:06.520
 could be solving.

1:51:06.520 --> 1:51:10.440
 So deep learning will keep creating a lot of value for the time being.

1:51:10.440 --> 1:51:16.000
 What's concerning, however, is that there's a lot of hype around deep learning and around

1:51:16.000 --> 1:51:17.000
 AI.

1:51:17.000 --> 1:51:22.840
 A lot of people are overselling the capabilities of these systems, not just the capabilities

1:51:22.840 --> 1:51:31.520
 but also overselling the fact that they might be more or less brain like, like given a kind

1:51:31.520 --> 1:51:40.480
 of a mystical aspect, these technologies, and also overselling the pace of progress,

1:51:40.480 --> 1:51:46.000
 which it might look fast in the sense that we have this exponentially increasing number

1:51:46.000 --> 1:51:48.080
 of papers.

1:51:48.080 --> 1:51:53.000
 But again, that's just a simple consequence of the fact that we have ever more people

1:51:53.000 --> 1:51:54.000
 coming into the field.

1:51:54.000 --> 1:51:58.000
 It doesn't mean the progress is actually exponentially fast.

1:51:58.000 --> 1:52:02.960
 Like, let's say you're trying to raise money for your startup or your research lab.

1:52:02.960 --> 1:52:09.120
 You might want to tell, you know, a grand yos story to investors about how deep learning

1:52:09.120 --> 1:52:14.240
 is just like the brain and how it can solve all these incredible problems like self driving

1:52:14.240 --> 1:52:19.040
 and robotics and so on, and maybe you can tell them that the field is progressing so fast

1:52:19.040 --> 1:52:27.000
 and we're going to have AI within 15 years or even 10 years, and none of this is true.

1:52:27.000 --> 1:52:33.320
 And every time you're like saying these things and an investor or, you know, a decision maker

1:52:33.320 --> 1:52:43.400
 believes them, well, this is like the equivalent of taking on credit card debt, but for trust.

1:52:43.400 --> 1:52:50.920
 And maybe this will, you know, this will be what enables you to raise a lot of money,

1:52:50.920 --> 1:52:55.160
 but ultimately you are creating damage, you are damaging the field.

1:52:55.160 --> 1:53:01.240
 That's the concern is that debt, that's what happens with the other AI winters is the concern

1:53:01.240 --> 1:53:04.440
 is you actually tweeted about this with autonomous vehicles, right?

1:53:04.440 --> 1:53:08.960
 There's almost every single company now have promised that they will have full autonomous

1:53:08.960 --> 1:53:12.000
 vehicles by 2021, 2022.

1:53:12.000 --> 1:53:18.280
 That's a good example of the consequences of overhyping the capabilities of AI and the

1:53:18.280 --> 1:53:19.280
 pace of progress.

1:53:19.280 --> 1:53:25.160
 So because I work especially a lot recently in this area, I have a deep concern of what

1:53:25.160 --> 1:53:30.480
 happens when all of these companies after every invested billions have a meeting and

1:53:30.480 --> 1:53:33.720
 say, how much do we actually, first of all, do we have an autonomous vehicle?

1:53:33.720 --> 1:53:36.360
 The answer will definitely be no.

1:53:36.360 --> 1:53:40.680
 And second will be, wait a minute, we've invested one, two, three, four billion dollars

1:53:40.680 --> 1:53:43.400
 into this and we made no profit.

1:53:43.400 --> 1:53:49.280
 And the reaction to that may be going very hard in other directions that might impact

1:53:49.280 --> 1:53:50.840
 you that even other industries.

1:53:50.840 --> 1:53:55.320
 And that's what we call in the air winter is when there is backlash where no one believes

1:53:55.320 --> 1:54:00.600
 any of these promises anymore because they've turned out to be big lies the first time around.

1:54:00.600 --> 1:54:06.120
 And this will definitely happen to some extent for autonomous vehicles because the public

1:54:06.120 --> 1:54:13.440
 and decision makers have been convinced that around 2015, they've been convinced by these

1:54:13.440 --> 1:54:19.600
 people who are trying to raise money for their startups and so on, that L5 driving was coming

1:54:19.600 --> 1:54:23.120
 in maybe 2016, maybe 2017, May 2018.

1:54:23.120 --> 1:54:28.040
 Now in 2019, we're still waiting for it.

1:54:28.040 --> 1:54:32.880
 And so I don't believe we are going to have a full on AI winter because we have these

1:54:32.880 --> 1:54:39.480
 technologies that are producing a tremendous amount of real value, but there is also too

1:54:39.480 --> 1:54:40.480
 much hype.

1:54:40.480 --> 1:54:45.240
 So there will be some backlash, especially there will be backlash.

1:54:45.240 --> 1:54:53.080
 So some startups are trying to sell the dream of AGI and the fact that AGI is going to create

1:54:53.080 --> 1:54:54.080
 infinite value.

1:54:54.080 --> 1:55:01.240
 AGI is like a freelance, like if you can develop an AI system that passes a certain threshold

1:55:01.240 --> 1:55:06.440
 of IQ or something, then suddenly you have infinite value.

1:55:06.440 --> 1:55:11.640
 And well, there are actually lots of investors buying into this idea.

1:55:11.640 --> 1:55:18.920
 And they will wait maybe 10, 15 years and nothing will happen.

1:55:18.920 --> 1:55:22.800
 And the next time around, well, maybe there will be a new generation of investors, no

1:55:22.800 --> 1:55:24.040
 one will care.

1:55:24.040 --> 1:55:27.160
 Human memory is very short after all.

1:55:27.160 --> 1:55:34.440
 I don't know about you, but because I've spoken about AGI sometimes poetically, I get a lot

1:55:34.440 --> 1:55:42.360
 of emails from people giving me, they're usually like a large manifestos of, they say to me

1:55:42.360 --> 1:55:48.320
 that they have created an AGI system or they know how to do it and there's a long write

1:55:48.320 --> 1:55:49.320
 up of how to do it.

1:55:49.320 --> 1:55:51.400
 I get a lot of these emails.

1:55:51.400 --> 1:55:57.840
 They're a little bit feel like it's generated by an AI system actually, but there's usually

1:55:57.840 --> 1:55:58.840
 no backup.

1:55:58.840 --> 1:56:04.920
 Maybe that's recursively self improving AI, it's you have a transformer generating crank

1:56:04.920 --> 1:56:06.880
 papers about a GI.

1:56:06.880 --> 1:56:12.160
 So the question is about, because you've been such a good, you have a good radar for crank

1:56:12.160 --> 1:56:16.960
 papers, how do we know they're not onto something?

1:56:16.960 --> 1:56:24.320
 How do I, so when you start to talk about AGI or anything like the reasoning benchmarks

1:56:24.320 --> 1:56:28.720
 and so on, so something that doesn't have a benchmark, it's really difficult to know.

1:56:28.720 --> 1:56:35.480
 I mean, I talked to Jeff Hawkins who's really looking at neuroscience approaches to how,

1:56:35.480 --> 1:56:41.800
 and there's some, there's echoes of really interesting ideas in at least Jeff's case,

1:56:41.800 --> 1:56:43.520
 which he's showing.

1:56:43.520 --> 1:56:45.840
 How do you usually think about this?

1:56:45.840 --> 1:56:52.920
 Like preventing yourself from being too narrow minded and elitist about deep learning.

1:56:52.920 --> 1:56:57.040
 It has to work on these particular benchmarks, otherwise it's trash.

1:56:57.040 --> 1:57:05.880
 Well, the thing is intelligence does not exist in the abstract.

1:57:05.880 --> 1:57:07.440
 Intelligence has to be applied.

1:57:07.440 --> 1:57:11.040
 So if you don't have a benchmark, if you don't have an improvement on some benchmark, maybe

1:57:11.040 --> 1:57:12.680
 it's a new benchmark.

1:57:12.680 --> 1:57:16.760
 Maybe it's not something we've been looking at before, but you do need a problem that

1:57:16.760 --> 1:57:17.760
 you're trying to solve.

1:57:17.760 --> 1:57:21.040
 You're not going to come up with a solution without a problem.

1:57:21.040 --> 1:57:26.760
 So you, general intelligence, I mean, you've clearly highlighted generalization.

1:57:26.760 --> 1:57:31.320
 If you want to claim that you have an intelligence system, it should come with a benchmark.

1:57:31.320 --> 1:57:35.960
 It should, yes, it should display capabilities of some kind.

1:57:35.960 --> 1:57:41.920
 It should show that it can create some form of value, even if it's a very artificial form

1:57:41.920 --> 1:57:43.160
 of value.

1:57:43.160 --> 1:57:48.840
 And that's also the reason why you don't actually need to care about telling which papers have

1:57:48.840 --> 1:57:53.520
 actually some hidden potential and which do not.

1:57:53.520 --> 1:57:58.880
 Because if there is a new technique, it's actually creating value.

1:57:58.880 --> 1:58:02.640
 This is going to be brought to light very quickly because it's actually making a difference.

1:58:02.640 --> 1:58:08.240
 So it's a difference between something that is ineffectual and something that is actually

1:58:08.240 --> 1:58:09.240
 useful.

1:58:09.240 --> 1:58:14.120
 And ultimately, usefulness is our guide, not just in this field, but if you look at science

1:58:14.120 --> 1:58:19.560
 in general, maybe there are many, many people over the years that have had some really interesting

1:58:19.560 --> 1:58:23.120
 theories of everything, but they were just completely useless.

1:58:23.120 --> 1:58:28.240
 And you don't actually need to tell the interesting theories from the useless theories.

1:58:28.240 --> 1:58:34.120
 All you need is to see, you know, is this actually having an effect on something else?

1:58:34.120 --> 1:58:35.600
 You know, is this actually useful?

1:58:35.600 --> 1:58:37.960
 Is this making an impact or not?

1:58:37.960 --> 1:58:42.480
 As beautifully put, I mean, the same applies to quantum mechanics, to string theory, to

1:58:42.480 --> 1:58:43.480
 the holographic principle.

1:58:43.480 --> 1:58:46.080
 We are doing deep learning because it works.

1:58:46.080 --> 1:58:52.720
 Before it started working, people considered people working on neural networks as cranks

1:58:52.720 --> 1:58:53.720
 very much.

1:58:53.720 --> 1:58:56.560
 Like, you know, no one was working on this anymore.

1:58:56.560 --> 1:58:59.400
 And now it's working, which is what makes it valuable.

1:58:59.400 --> 1:59:02.840
 It's not about being right, it's about being effective.

1:59:02.840 --> 1:59:08.120
 And nevertheless, the individual entities of this scientific mechanism, just like Yoshio

1:59:08.120 --> 1:59:13.160
 Banjo or Yanlacun, they, while being called cranks, stuck with it, right?

1:59:13.160 --> 1:59:19.080
 And so, us individual agents, even if everyone's laughing at us, should stick with it.

1:59:19.080 --> 1:59:23.840
 If you believe you have something, you should stick with it and see it through.

1:59:23.840 --> 1:59:25.920
 That's a beautiful, inspirational message to end on.

1:59:25.920 --> 1:59:27.800
 Francois, thank you so much for talking today.

1:59:27.800 --> 1:59:28.800
 That was amazing.

1:59:28.800 --> 1:59:35.800
 Thank you.