vtt/episode_036_small.vtt

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 The following is a conversation with Yanlacun.

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 He's considered to be one of the fathers of deep learning,

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 which, if you've been hiding under a rock,

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 is the recent revolution in AI that has captivated the world

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 with the possibility of what machines can learn from data.

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 He's a professor at New York University,

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 a vice president and chief AI scientist at Facebook,

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 and co recipient of the Turing Award

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 for his work on deep learning.

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 He's probably best known as the founding father

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 of convolutional neural networks,

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 in particular, their application

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 to optical character recognition

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 and the famed MNIST dataset.

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 He is also an outspoken personality,

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 unafraid to speak his mind in a distinctive French accent

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 and explore provocative ideas,

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 both in the rigorous medium of academic research

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 and the somewhat less rigorous medium of Twitter and Facebook.

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 This is the Artificial Intelligence Podcast.

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 If you enjoy it, subscribe on YouTube,

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 give it five stars on iTunes,

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 support it on Patreon,

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 or simply connect with me on Twitter at Lex Freedman,

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 spelled F R I D M A N.

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 And now, here's my conversation with Leon Lacun.

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 You said that 2001 Space Odyssey

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 is one of your favorite movies.

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 Hal 9000 decides to get rid of the astronauts

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 for people who haven't seen the movie, Spoiler Alert,

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 because he, it, she believes

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 that the astronauts, they will interfere with the mission.

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 Do you see Hal as flawed in some fundamental way

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 or even evil, or did he do the right thing?

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 Neither.

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 There's no notion of evil in that, in that context,

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 other than the fact that people die,

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 but it was an example of what people call

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 value misalignment, right?

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 You give an objective to a machine,

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 and the machine tries to achieve this objective.

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 And if you don't put any constraints on this objective,

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 like don't kill people and don't do things like this,

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 the machine, given the power, will do stupid things

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 just to achieve this, this objective,

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 or damaging things to achieve this objective.

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 It's a little bit like, I mean, we are used to this

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 in the context of human society.

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 We, we put in place laws to prevent people

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 from doing bad things,

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 because spontaneously they would do those bad things, right?

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 So we have to shape their cost function,

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 their objective function, if you want, through laws

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 to kind of correct an education, obviously,

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 to sort of correct for those.

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 So maybe just pushing a little further on that point.

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 Hal, you know, there's a mission.

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 There's a fuzziness around the ambiguity

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 around what the actual mission is.

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 But, you know, do you think that there will be a time

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 from a utilitarian perspective,

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 when AI system, where it is not misalignment,

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 where it is alignment for the greater good of society,

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 that an AI system will make decisions that are difficult?

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 Well, that's the trick.

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 I mean, eventually we'll have to figure out how to do this.

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 And again, we're not starting from scratch

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 because we've been doing this with humans for millennia.

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 So designing objective functions for people

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 is something that we know how to do.

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 And we don't do it by, you know, programming things,

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 although the legal code is called code.

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 So that tells you something.

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 And it's actually the design of an objective function.

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 That's really what legal code is, right?

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 It tells you, here is what you can do,

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 here is what you can't do.

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 If you do it, you pay that much,

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 that's an objective function.

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 So there is this idea somehow that it's a new thing

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 for people to try to design objective functions

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 that are aligned with the common good.

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 But no, we've been writing laws for millennia

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 and that's exactly what it is.

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 So that's where, you know,

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 the science of lawmaking and computer science will...

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 Come together.

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 Will come together.

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 So there's nothing special about how our AI systems

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 is just the continuation of tools used

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 to make some of these difficult ethical judgments

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 that laws make.

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 Yeah, and we have systems like this already

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 that make many decisions for ourselves in society

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 that need to be designed in a way that they...

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 Like, you know, rules about things

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 that sometimes have bad side effects.

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 And we have to be flexible enough about those rules

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 so that they can be broken when it's obvious

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 that they shouldn't be applied.

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 So you don't see this on the camera here,

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 but all the decoration in this room

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 is all pictures from 2001, it's based out of C.

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 That's it.

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 Wow, is that by accident?

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 Or is there a lot?

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 The accident is by design.

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 Oh, wow.

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 So if you were to build HAL 10,000,

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 so an improvement of HAL 9,000, what would you improve?

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 Well, first of all, I wouldn't ask you

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 to hold secrets and tell lies

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 because that's really what breaks it in the end.

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 That's the fact that it's asking itself questions

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 about the purpose of the mission.

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 And it's, you know, pieces things together

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 that it's heard, you know,

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 all the secrecy of the preparation of the mission

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 and the fact that it was discovery on the lunar surface

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 that really was kept secret.

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 And one part of HAL's memory knows this

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 and the other part is, does not know it

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 and is supposed to not tell anyone

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 and that creates internal conflict.

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 So you think there's never should be a set of things

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 that an AI system should not be allowed,

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 like a set of facts that should not be shared

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 with the human operators?

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 Well, I think, no, I think that,

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 I think it should be a bit like in the design

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 of autonomous AI systems.

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 There should be the equivalent of, you know,

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 the oath that hypocrites oaths

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 that doctors sign up to, right?

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 So there's certain things, certain rules

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 that you have to abide by.

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 And we can sort of hardwire this into our machines

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 to kind of make sure they don't go.

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 So I'm not, you know, an advocate of the $3 of robotics,

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 you know, the azimov kind of thing

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 because I don't think it's practical,

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 but, you know, some level of limits.

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 But to be clear, this is not,

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 these are not questions that are kind of reworth asking today

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 because we just don't have the technology to do this.

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 We don't have autonomous intelligent machines.

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 We have intelligent machines.

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 Some are intelligent machines that are very specialized,

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 but they don't really sort of satisfy an objective.

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 They're just, you know, kind of trained to do one thing.

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 So until we have some idea for design

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 of a full fledged autonomous intelligent system,

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 asking the question of how design is subjective,

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 I think is a little too abstract.

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 It's a little too abstract.

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 There's useful elements to it

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 in that it helps us understand

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 our own ethical codes, humans.

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 So even just as a thought experiment,

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 if you imagine that an AGI system is here today,

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 how would we program it

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 is a kind of nice thought experiment of constructing,

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 how should we have a system of laws for us humans?

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 It's just a nice practical tool.

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 And I think there's echoes of that idea too

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 in the AI systems we have today.

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 They don't have to be that intelligent.

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 Yeah.

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 Like autonomous vehicles.

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 These things start creeping in

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 that they're worth thinking about,

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 but certainly they shouldn't be framed as how.

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 Looking back, what is the most,

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 I'm sorry if it's a silly question,

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 but what is the most beautiful

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 or surprising idea in deep learning

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 or AI in general that you've ever come across?

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 So personally, when you said back,

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 and just had this kind of,

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 oh, that's pretty cool moment.

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 That's nice.

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 That's surprising.

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 I don't know if it's an idea

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 rather than a sort of empirical fact.

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 The fact that you can build gigantic neural nets,

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 train them on relatively small amounts of data relatively

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 with stochastic gradient descent,

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 and that it actually works,

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 breaks everything you read in every textbook, right?

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 Every pre deep learning textbook

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 I told you, you need to have fewer parameters

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 and you have data samples.

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 If you have nonconvex objective function,

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 you have no guarantee of convergence.

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 All those things that you read in textbook,

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 and they tell you, stay away from this,

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 and they're all wrong.

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 Huge number of parameters, nonconvex,

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 and somehow which is very relative

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 to the number of parameters data,

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 it's able to learn anything.

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 Does that still surprise you today?

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 Well, it was kind of obvious to me before I knew anything

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 that this is a good idea.

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 And then it became surprising that it worked

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 because I started reading those textbooks.

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 Okay, so do you talk through the intuition

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 of why it was obvious to you if you remember?

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 Well, okay, so the intuition was,

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 it's sort of like those people in the late 19th century

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 who proved that heavier than air flight was impossible, right?

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 And of course you have birds, right?

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 They do fly.

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 And so on the face of it,

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 it's obviously wrong as an empirical question, right?

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 And so we have the same kind of thing that,

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 you know, we know that the brain works.

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 We don't know how, but we know it works.

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 And we know it's a large network of neurons

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 and interaction and that learning takes place

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 by changing the connection.

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 So kind of getting this level of inspiration

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 without covering the details,

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 but sort of trying to derive basic principles.

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 You know, that kind of gives you a clue

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 as to which direction to go.

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 There's also the idea somehow

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 that I've been convinced of since I was an undergrad

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 that even before that intelligence

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 is inseparable from learning.

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 So the idea somehow that you can create

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 an intelligent machine by basically programming,

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 for me was a non starter, you know, from the start.

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 Every intelligent entity that we know about

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 arrives at this intelligence through learning.

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 So learning, you know, machine learning

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 was a completely obvious path.

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 Also because I'm lazy.

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 So, you know, kind of.

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 These automate basically everything

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 and learning is the automation of intelligence.

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 Right.

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 So do you think, so what is learning then?

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 What falls under learning?

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 Because do you think of reasoning as learning?

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 Well, reasoning is certainly a consequence

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 of learning as well,

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 just like other functions of the brain.

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 The big question about reasoning is,

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 how do you make reasoning compatible

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 with gradient based learning?

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 Do you think neural networks can be made to reason?

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 Yes, there is no question about that.

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 Again, we have a good example, right?

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 The question is how?

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 So the question is how much prior structure

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 do you have to put in the neural net

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 so that something like human reasoning

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 will emerge from it, you know, from learning?

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 Another question is all of our kind of model

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 of what reasoning is that are based on logic

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 are discrete and are therefore incompatible

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 with gradient based learning.

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 And I'm a very strong believer in this idea

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 of gradient based learning.

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 I don't believe that other types of learning

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 that don't use kind of gradient information

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 if you want.

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 So you don't like discrete mathematics.

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 You don't like anything discrete?

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 Well, that's, it's not that I don't like it.

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 It's just that it's incompatible with learning

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 and I'm a big fan of learning, right?

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 So in fact, that's perhaps one reason why deep learning

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 has been kind of looked at with suspicion

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 by a lot of computer scientists

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 because the math is very different.

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 The math that you use for deep learning,

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 you know, it kind of has more to do with, you know,

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 cybernetics, the kind of math you do

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 in electrical engineering

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 than the kind of math you do in computer science.

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 And, you know, nothing in machine learning is exact, right?

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 Computer science is all about sort of, you know,

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 obsessive compulsive attention to details

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 of like, you know, every index has to be right

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 and you can prove that an algorithm is correct, right?

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 Machine learning is the science of sloppiness, really.

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 That's beautiful.

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 So, okay, maybe let's feel around in the dark

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 of what is a neural network that reasons

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 or a system that works with continuous functions

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 that's able to do, build knowledge.

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 However we think about reasoning,

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 build on previous knowledge, build on extra knowledge,

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 create new knowledge, generalize outside

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 of any training set ever built, what does that look like?

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 If, yeah, maybe do you have inklings of thoughts

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 of what that might look like?

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 Yeah, I mean, yes and no.

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 If I had precise ideas about this,

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 I think, you know, we'll be building it right now.

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 But, and there are people working on this

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 whose main research interest is actually exactly that, right?

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 So, what you need to have is a working memory.

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 So, you need to have some device, if you want,

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 some subsystem that can store a relatively large number

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 of factual, episodic information for, you know,

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 reasonable amount of time.

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 So, you know, in the brain, for example,

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 there are kind of three main types of memory.

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 One is the sort of memory of the state of your cortex.

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 And that sort of disappears within 20 seconds.

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 You can't remember things for more than about 20 seconds

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 or a minute if you don't have any other form of memory.

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 The second type of memory, which is longer term,

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 is short term, is the hippocampus.

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 So, you can, you know, you came into this building,

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 you remember where the exit is, where the elevators are.

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 You have some map of that building

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 that's stored in your hippocampus.

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 You might remember something about what I said,

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 you know, a few minutes ago.

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 I forgot it already.

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 Of course, it's been erased.

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 But, you know, that would be in your hippocampus.

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 And then the longer term memory is in the synapse.

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 The synapses, right?

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 So, what you need if you want a system

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 that's capable of reasoning is that you want

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 the hippocampus like thing, right?

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 And that's what people have tried to do

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 with memory networks and, you know,

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 neural engineering machines and stuff like that, right?

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 And now with transformers, which have sort of a memory

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 in their kind of self attention system.

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 You can think of it this way.

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 So, that's one element you need.

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 Another thing you need is some sort of network

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 that can access this memory,

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 get an information back and then kind of crunch on it

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 and then do this iteratively multiple times

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 because a chain of reasoning is a process

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 by which you can update your knowledge

15:19.320 --> 15:20.320
 about the state of the world,

15:20.320 --> 15:22.320
 about, you know, what's going to happen, et cetera.

15:22.320 --> 15:26.320
 And that has to be this sort of recurrent operation, basically.

15:26.320 --> 15:30.320
 And you think that kind of, if we think about a transformer,

15:30.320 --> 15:33.320
 so that seems to be too small to contain the knowledge

15:33.320 --> 15:37.320
 that's to represent the knowledge that's contained

15:37.320 --> 15:38.320
 in Wikipedia, for example.

15:38.320 --> 15:41.320
 Well, a transformer doesn't have this idea of recurrence.

15:41.320 --> 15:42.320
 It's got a fixed number of layers

15:42.320 --> 15:44.320
 and that's the number of steps that, you know,

15:44.320 --> 15:46.320
 limits basically as a representation.

15:46.320 --> 15:50.320
 But recurrence would build on the knowledge somehow.

15:50.320 --> 15:54.320
 I mean, it would evolve the knowledge

15:54.320 --> 15:57.320
 and expand the amount of information,

15:57.320 --> 16:00.320
 perhaps, or useful information within that knowledge.

16:00.320 --> 16:04.320
 But is this something that just can emerge with size?

16:04.320 --> 16:06.320
 Because it seems like everything we have now is too small.

16:06.320 --> 16:09.320
 No, it's not clear.

16:09.320 --> 16:12.320
 I mean, how you access and write into an associated memory

16:12.320 --> 16:13.320
 in an efficient way.

16:13.320 --> 16:15.320
 I mean, sort of the original memory network

16:15.320 --> 16:17.320
 maybe had something like the right architecture,

16:17.320 --> 16:20.320
 but if you try to scale up a memory network

16:20.320 --> 16:22.320
 so that the memory contains all of Wikipedia,

16:22.320 --> 16:24.320
 it doesn't quite work.

16:24.320 --> 16:27.320
 So there's a need for new ideas there.

16:27.320 --> 16:29.320
 But it's not the only form of reasoning.

16:29.320 --> 16:31.320
 So there's another form of reasoning,

16:31.320 --> 16:36.320
 which is very classical also in some types of AI,

16:36.320 --> 16:40.320
 and it's based on, let's call it energy minimization.

16:40.320 --> 16:44.320
 So you have some sort of objective,

16:44.320 --> 16:50.320
 some energy function that represents the quality

16:50.320 --> 16:52.320
 or the negative quality.

16:52.320 --> 16:54.320
 Energy goes up when things get bad

16:54.320 --> 16:56.320
 and they get low when things get good.

16:56.320 --> 17:00.320
 So let's say you want to figure out what gestures

17:00.320 --> 17:07.320
 do I need to do to grab an object or walk out the door.

17:07.320 --> 17:09.320
 If you have a good model of your own body,

17:09.320 --> 17:11.320
 a good model of the environment,

17:11.320 --> 17:13.320
 using this kind of energy minimization,

17:13.320 --> 17:16.320
 you can do planning.

17:16.320 --> 17:21.320
 And it's in optimal control, it's called model predictive control.

17:21.320 --> 17:23.320
 You have a model of what's going to happen in the world

17:23.320 --> 17:25.320
 as a consequence of your actions.

17:25.320 --> 17:28.320
 And that allows you to buy energy minimization,

17:28.320 --> 17:29.320
 figure out a sequence of action

17:29.320 --> 17:31.320
 that optimizes a particular objective function,

17:31.320 --> 17:34.320
 which measures the number of times you're going to hit something

17:34.320 --> 17:39.320
 and the energy you're going to spend doing the gesture and etc.

17:39.320 --> 17:42.320
 So that's a form of reasoning.

17:42.320 --> 17:43.320
 Planning is a form of reasoning.

17:43.320 --> 17:47.320
 And perhaps what led to the ability of humans to reason

17:47.320 --> 17:53.320
 is the fact that species that appear before us

17:53.320 --> 17:56.320
 had to do some sort of planning to be able to hunt and survive

17:56.320 --> 17:59.320
 and survive the winter in particular.

17:59.320 --> 18:03.320
 And so it's the same capacity that you need to have.

18:03.320 --> 18:09.320
 So in your intuition, if we look at expert systems,

18:09.320 --> 18:13.320
 and encoding knowledge as logic systems,

18:13.320 --> 18:16.320
 as graphs in this kind of way,

18:16.320 --> 18:20.320
 is not a useful way to think about knowledge?

18:20.320 --> 18:24.320
 Graphs are a little brittle or logic representation.

18:24.320 --> 18:28.320
 So basically, variables that have values

18:28.320 --> 18:31.320
 and then constrained between them that are represented by rules

18:31.320 --> 18:33.320
 is a little too rigid and too brittle.

18:33.320 --> 18:38.320
 So some of the early efforts in that respect

18:38.320 --> 18:41.320
 were to put probabilities on them.

18:41.320 --> 18:44.320
 So a rule, if you have this and that symptom,

18:44.320 --> 18:47.320
 you have this disease with that probability

18:47.320 --> 18:50.320
 and you should prescribe that antibiotic with that probability.

18:50.320 --> 18:54.320
 That's the mysine system from the 70s.

18:54.320 --> 18:59.320
 And that branch of AI led to business networks

18:59.320 --> 19:02.320
 and graphical models and causal inference

19:02.320 --> 19:05.320
 and variational method.

19:05.320 --> 19:10.320
 So there is certainly a lot of interesting work going on

19:10.320 --> 19:11.320
 in this area.

19:11.320 --> 19:13.320
 The main issue with this is knowledge acquisition.

19:13.320 --> 19:19.320
 How do you reduce a bunch of data to a graph of this type?

19:19.320 --> 19:23.320
 It relies on the expert on the human being to encode,

19:23.320 --> 19:24.320
 to add knowledge.

19:24.320 --> 19:27.320
 And that's essentially impractical.

19:27.320 --> 19:29.320
 So that's a big question.

19:29.320 --> 19:32.320
 The second question is, do you want to represent knowledge

19:32.320 --> 19:36.320
 as symbols and do you want to manipulate them with logic?

19:36.320 --> 19:38.320
 And again, that's incompatible with learning.

19:38.320 --> 19:42.320
 So one suggestion with Jeff Hinton

19:42.320 --> 19:44.320
 has been advocating for many decades

19:44.320 --> 19:48.320
 is replace symbols by vectors.

19:48.320 --> 19:50.320
 Think of it as pattern of activities

19:50.320 --> 19:54.320
 in a bunch of neurons or units or whatever you want to call them.

19:54.320 --> 19:58.320
 And replace logic by continuous functions.

19:58.320 --> 20:01.320
 And that becomes now compatible.

20:01.320 --> 20:04.320
 There's a very good set of ideas

20:04.320 --> 20:07.320
 written in a paper about 10 years ago

20:07.320 --> 20:12.320
 by Leon Botou who is here at Facebook.

20:12.320 --> 20:14.320
 The title of the paper is

20:14.320 --> 20:15.320
 From Machine Learning to Machine Reasoning.

20:15.320 --> 20:19.320
 And his idea is that a learning system

20:19.320 --> 20:22.320
 should be able to manipulate objects that are in a space

20:22.320 --> 20:24.320
 and then put the result back in the same space.

20:24.320 --> 20:27.320
 So it's this idea of working memory basically.

20:27.320 --> 20:30.320
 And it's very enlightening.

20:30.320 --> 20:33.320
 And in a sense, that might learn something

20:33.320 --> 20:37.320
 like the simple expert systems.

20:37.320 --> 20:41.320
 I mean, you can learn basic logic operations there.

20:41.320 --> 20:43.320
 Yeah, quite possibly.

20:43.320 --> 20:46.320
 There's a big debate on how much prior structure

20:46.320 --> 20:48.320
 you have to put in for this kind of stuff to emerge.

20:48.320 --> 20:51.320
 That's the debate I have with Gary Marcus and people like that.

20:51.320 --> 20:54.320
 Yeah, so and the other person,

20:54.320 --> 20:57.320
 so I just talked to Judea Pearl

20:57.320 --> 21:00.320
 and he mentioned causal inference world.

21:00.320 --> 21:04.320
 So his worry is that the current neural networks

21:04.320 --> 21:09.320
 are not able to learn what causes

21:09.320 --> 21:12.320
 what causal inference between things.

21:12.320 --> 21:15.320
 So I think he's right and wrong about this.

21:15.320 --> 21:21.320
 If he's talking about the sort of classic type of neural nets,

21:21.320 --> 21:23.320
 people sort of didn't worry too much about this.

21:23.320 --> 21:26.320
 But there's a lot of people now working on causal inference.

21:26.320 --> 21:28.320
 There's a paper that just came out last week

21:28.320 --> 21:29.320
 by Leon Boutou, among others,

21:29.320 --> 21:32.320
 the Vila Pespas and a bunch of other people.

21:32.320 --> 21:36.320
 Exactly on that problem of how do you kind of,

21:36.320 --> 21:39.320
 you know, get a neural net to sort of pay attention

21:39.320 --> 21:41.320
 to real causal relationships,

21:41.320 --> 21:46.320
 which may also solve issues of bias in data

21:46.320 --> 21:48.320
 and things like this.

21:48.320 --> 21:51.320
 I'd like to read that paper because that ultimately

21:51.320 --> 21:56.320
 challenges also seems to fall back on the human expert

21:56.320 --> 22:01.320
 to ultimately decide causality between things.

22:01.320 --> 22:04.320
 People are not very good at establishing causality, first of all.

22:04.320 --> 22:06.320
 So first of all, you talk to physicists

22:06.320 --> 22:08.320
 and physicists actually don't believe in causality

22:08.320 --> 22:12.320
 because look at all the basic laws of macro physics

22:12.320 --> 22:15.320
 are time reversible, so there's no causality.

22:15.320 --> 22:17.320
 The era of time is not real.

22:17.320 --> 22:20.320
 It's as soon as you start looking at macroscopic systems

22:20.320 --> 22:22.320
 where there is unpredictable randomness

22:22.320 --> 22:25.320
 where there is clearly an hour of time,

22:25.320 --> 22:28.320
 but it's a big mystery in physics, actually, how that emerges.

22:28.320 --> 22:34.320
 Is it emergent or is it part of the fundamental fabric of reality?

22:34.320 --> 22:36.320
 Or is it a bias of intelligent systems

22:36.320 --> 22:39.320
 that, you know, because of the second law of thermodynamics,

22:39.320 --> 22:41.320
 we perceive a particular hour of time,

22:41.320 --> 22:44.320
 but in fact, it's kind of arbitrary, right?

22:44.320 --> 22:47.320
 So yeah, physicists, mathematicians, they don't care about,

22:47.320 --> 22:51.320
 I mean, the math doesn't care about the flow of time.

22:51.320 --> 22:53.320
 Well, certainly macro physics doesn't.

22:53.320 --> 22:58.320
 People themselves are not very good at establishing causal relationships.

22:58.320 --> 23:02.320
 If you ask, I think it was in one of Seymour Papert's book

23:02.320 --> 23:06.320
 on, like, children learning.

23:06.320 --> 23:08.320
 You know, he studied with Jean Piaget.

23:08.320 --> 23:12.320
 He's the guy who coauthored the book Perception with Marvin Minsky

23:12.320 --> 23:14.320
 that kind of killed the first wave of neural nets.

23:14.320 --> 23:17.320
 But he was actually a learning person.

23:17.320 --> 23:22.320
 He, in the sense of studying learning in humans and machines.

23:22.320 --> 23:24.320
 That's why he got interested in Perceptron.

23:24.320 --> 23:33.320
 And he wrote that if you ask a little kid about what is the cause of the wind,

23:33.320 --> 23:36.320
 a lot of kids will say, they will think for a while and they will say,

23:36.320 --> 23:38.320
 oh, it's the branches in the trees.

23:38.320 --> 23:40.320
 They move and that creates wind, right?

23:40.320 --> 23:42.320
 So they get the causal relationship backwards.

23:42.320 --> 23:45.320
 And it's because they're understanding of the world and intuitive physics.

23:45.320 --> 23:46.320
 It's not that great, right?

23:46.320 --> 23:49.320
 I mean, these are like, you know, four or five year old kids.

23:49.320 --> 23:53.320
 You know, it gets better and then you understand that this, it can be, right?

23:53.320 --> 24:00.320
 But there are many things which we can, because of our common sense understanding of things,

24:00.320 --> 24:02.320
 what people call common sense.

24:02.320 --> 24:03.320
 Yeah.

24:03.320 --> 24:05.320
 And we're understanding of physics.

24:05.320 --> 24:09.320
 We can, there's a lot of stuff that we can figure out causality, even with diseases.

24:09.320 --> 24:13.320
 We can figure out what's not causing what often.

24:13.320 --> 24:19.320
 There's a lot of mystery, of course, but the idea is that you should be able to encode that into systems.

24:19.320 --> 24:22.320
 Because it seems unlikely they'd be able to figure that out themselves.

24:22.320 --> 24:26.320
 Well, whenever we can do intervention, but you know, all of humanity has been completely deluded

24:26.320 --> 24:32.320
 for millennia, probably since existence, about a very, very wrong causal relationship

24:32.320 --> 24:38.320
 where whatever you can explain, you're attributed to, you know, some deity, some divinity, right?

24:38.320 --> 24:40.320
 And that's a cup out.

24:40.320 --> 24:42.320
 That's a way of saying like, I don't know the cause.

24:42.320 --> 24:44.320
 So, you know, God did it, right?

24:44.320 --> 24:54.320
 So you mentioned Marvin Minsky and the irony of, you know, maybe causing the first day I winter.

24:54.320 --> 24:56.320
 You were there in the 90s.

24:56.320 --> 24:58.320
 You were there in the 80s, of course.

24:58.320 --> 25:02.320
 In the 90s, what do you think people lost faith in deep learning in the 90s

25:02.320 --> 25:06.320
 and found it again a decade later, over a decade later?

25:06.320 --> 25:07.320
 Yeah.

25:07.320 --> 25:09.320
 Deep learning, yeah, it was just called neural nets.

25:09.320 --> 25:11.320
 You know, that works.

25:11.320 --> 25:13.320
 Yeah, they lost interest.

25:13.320 --> 25:18.320
 I mean, I think I would put that around 1995, at least the machine learning community.

25:18.320 --> 25:28.320
 There was always a neural net community, but it became kind of disconnected from sort of mainstream machine learning if you want.

25:28.320 --> 25:32.320
 There were, it was basically electrical engineering that kept at it.

25:32.320 --> 25:33.320
 Right.

25:33.320 --> 25:35.320
 And computer science.

25:35.320 --> 25:36.320
 Just gave up.

25:36.320 --> 25:37.320
 Neural nets.

25:37.320 --> 25:39.320
 I don't, I don't know.

25:39.320 --> 25:47.320
 You know, I was too close to it to really sort of analyze it with sort of a unbiased eye if you want.

25:47.320 --> 25:50.320
 But I would, I would, I would make a few guesses.

25:50.320 --> 26:03.320
 So the first one is at the time neural nets were, it was very hard to make them work in a sense that you would, you know, implement backprop in your favorite language.

26:03.320 --> 26:06.320
 And that favorite language was not Python.

26:06.320 --> 26:07.320
 It was not MATLAB.

26:07.320 --> 26:10.320
 It was not any of those things because they didn't exist.

26:10.320 --> 26:11.320
 Right.

26:11.320 --> 26:14.320
 You had to write it in Fortran or C or something like this.

26:14.320 --> 26:15.320
 Right.

26:15.320 --> 26:18.320
 So you would experiment with it.

26:18.320 --> 26:26.320
 You would probably make some very basic mistakes, like, you know, badly initialize your weights, make the network too small because you're already in the textbook, you know, you don't want too many parameters.

26:26.320 --> 26:27.320
 Right.

26:27.320 --> 26:31.320
 And of course, you know, and you would train on XOR because you didn't have any other data set to trade on.

26:31.320 --> 26:33.320
 And of course, you know, it works half the time.

26:33.320 --> 26:35.320
 So you would say, I give up.

26:35.320 --> 26:39.320
 Also, you would train it with batch gradient, which, you know, isn't that sufficient.

26:39.320 --> 26:46.320
 So there was a lot of bad good tricks that you had to know to make those things work or you had to reinvent.

26:46.320 --> 26:50.320
 And a lot of people just didn't and they just couldn't make it work.

26:50.320 --> 26:52.320
 So that's one thing.

26:52.320 --> 27:08.320
 The investment in software platform to be able to kind of, you know, display things, figure out why things don't work, kind of get a good intuition for how to get them to work, have enough flexibility so you can create, you know, network architectures like convolutional nets and stuff like that.

27:08.320 --> 27:09.320
 It was hard.

27:09.320 --> 27:10.320
 I mean, you had to write everything from scratch.

27:10.320 --> 27:13.320
 And again, you didn't have any Python or MATLAB or anything.

27:13.320 --> 27:14.320
 Right.

27:14.320 --> 27:25.320
 I read that, sorry to interrupt, but I read that you wrote in Lisp the, your first versions of Lynette with the convolutional networks, which by the way, one of my favorite languages.

27:25.320 --> 27:27.320
 That's how I knew you were legit.

27:27.320 --> 27:29.320
 Touring award, whatever.

27:29.320 --> 27:31.320
 You programmed in Lisp.

27:31.320 --> 27:32.320
 It's still my favorite language.

27:32.320 --> 27:35.320
 But it's not that we programmed in Lisp.

27:35.320 --> 27:37.320
 It's that we had to write a Lisp interpreter.

27:37.320 --> 27:38.320
 Okay.

27:38.320 --> 27:40.320
 Because it's not like we use one that existed.

27:40.320 --> 27:48.320
 So we wrote a Lisp interpreter that we hooked up to, you know, a back end library that we wrote also for sort of neural net computation.

27:48.320 --> 28:01.320
 And then after a few years around 1991, we invented this idea of basically having modules that know how to forward propagate and back propagate gradients and then interconnecting those modules in a graph.

28:01.320 --> 28:11.320
 Leon but who had made proposals on this about this in the late 80s, and we're able to implement this using a list system.

28:11.320 --> 28:14.320
 Eventually, we wanted to use that system to make build production code for character recognition at Bell Labs.

28:14.320 --> 28:22.320
 So we actually wrote a compiler for that Lisp interpreter so that Petris Seymard, who is now Microsoft, kind of did the bulk of it with Leon and me.

28:22.320 --> 28:33.320
 And so we could write our system in Lisp and then compile to C and then we'll have a self contain complete system that could kind of do the entire thing.

28:33.320 --> 28:36.320
 Neither PyTorch nor Transparency can do this today.

28:36.320 --> 28:37.320
 Yeah.

28:37.320 --> 28:38.320
 Okay.

28:38.320 --> 28:39.320
 It's coming.

28:39.320 --> 28:40.320
 Yeah.

28:40.320 --> 28:44.320
 I mean, there's something like that in PyTorch called, you know, Torch script.

28:44.320 --> 28:50.320
 And so, you know, we had to write a Lisp interpreter, we had to write a Lisp compiler, we had to invest a huge amount of effort to do this.

28:50.320 --> 28:56.320
 And not everybody, if you don't completely believe in the concept, you're not going to invest the time to do this.

28:56.320 --> 28:57.320
 Right.

28:57.320 --> 29:03.320
 Now, at the time also, you know, or today, this would turn into Torch or PyTorch or Transparency or whatever.

29:03.320 --> 29:07.320
 We'd put it in open source, everybody would use it and, you know, realize it's good.

29:07.320 --> 29:17.320
 Back before 1995, working at AT&T, there's no way the lawyers would let you release anything in open source of this nature.

29:17.320 --> 29:20.320
 And so we could not distribute our code, really.

29:20.320 --> 29:29.320
 And on that point, and sorry to go on a million tangents, but on that point, I also read that there was some almost pat, like a patent on convolutional networks.

29:29.320 --> 29:31.320
 Yes, there was.

29:31.320 --> 29:35.320
 So that, first of all, I mean, just.

29:35.320 --> 29:37.320
 There were two, actually.

29:37.320 --> 29:39.320
 That ran out.

29:39.320 --> 29:41.320
 Thankfully, in 2007.

29:41.320 --> 29:44.320
 In 2007.

29:44.320 --> 29:48.320
 What, can we, can we just talk about that first?

29:48.320 --> 29:50.320
 I know you're a Facebook, but you're also an NYU.

29:50.320 --> 29:58.320
 And what does it mean to patent ideas like these software ideas, essentially?

29:58.320 --> 30:01.320
 Or what are mathematical ideas?

30:01.320 --> 30:03.320
 Or what are they?

30:03.320 --> 30:04.320
 Okay.

30:04.320 --> 30:05.320
 So they're not mathematical ideas.

30:05.320 --> 30:07.320
 So there are, you know, algorithms.

30:07.320 --> 30:15.320
 And there was a period where the US patent office would allow the patent of software as long as it was embodied.

30:15.320 --> 30:18.320
 The Europeans are very different.

30:18.320 --> 30:21.320
 They don't, they don't quite accept that they have a different concept.

30:21.320 --> 30:28.320
 But, you know, I don't, I no longer, I mean, I never actually strongly believed in this, but I don't believe in this kind of patent.

30:28.320 --> 30:33.320
 Facebook basically doesn't believe in this kind of patent.

30:33.320 --> 30:39.320
 Google files patents because they've been burned with Apple.

30:39.320 --> 30:41.320
 And so now they do this for defensive purpose.

30:41.320 --> 30:44.320
 But usually they say, we're not going to see you if you're in French.

30:44.320 --> 30:47.320
 Facebook has a, has a similar policy.

30:47.320 --> 30:50.320
 They say, you know, we have a patent on certain things for defensive purpose.

30:50.320 --> 30:54.320
 We're not going to see you if you're in French unless you through us.

30:54.320 --> 30:59.320
 So the, the industry does not believe in, in patents.

30:59.320 --> 31:03.320
 They're there because of, you know, the legal landscape and, and, and various things.

31:03.320 --> 31:07.320
 But, but I don't really believe in patents for this kind of stuff.

31:07.320 --> 31:09.320
 Okay. So that's, that's a great thing.

31:09.320 --> 31:11.320
 So I, I tell you a worst story.

31:11.320 --> 31:12.320
 Yeah.

31:12.320 --> 31:19.320
 So what happens was the first, the first patent about convolutional net was about kind of the early version of convolutional net that didn't have separate pooling layers.

31:19.320 --> 31:24.320
 It had, you know, convolutional layers with tried more than one, if you want, right?

31:24.320 --> 31:31.320
 And then there was a second one on convolutional nets with separate pooling layers, trained with backprop.

31:31.320 --> 31:35.320
 And there were files filed in 89 and 1990 or something like this.

31:35.320 --> 31:39.320
 At the time, the life, life of a patent was 17 years.

31:39.320 --> 31:48.320
 So here's what happened over the next few years is that we started developing character recognition technology around convolutional nets.

31:48.320 --> 31:55.320
 And in 1994, a check reading system was deployed in ATM machines.

31:55.320 --> 32:00.320
 In 1995, it was for large check reading machines in back offices, et cetera.

32:00.320 --> 32:08.320
 And those systems were developed by an engineering group that we were collaborating with AT&T and they were commercialized by NCR,

32:08.320 --> 32:11.320
 which at the time was a subsidiary of AT&T.

32:11.320 --> 32:18.320
 Now AT&T split up in 1996, early 1996.

32:18.320 --> 32:22.320
 And the lawyers just looked at all the patents and they distributed the patents among the various companies.

32:22.320 --> 32:28.320
 They gave the convolutional net patent to NCR because they were actually selling products that used it.

32:28.320 --> 32:31.320
 But nobody at NCR had any idea what a convolutional net was.

32:31.320 --> 32:32.320
 Yeah.

32:32.320 --> 32:33.320
 Okay.

32:33.320 --> 32:40.320
 So between 1996 and 2007, there's a whole period until 2002 where I didn't actually work on

32:40.320 --> 32:42.320
 machine learning or convolutional net.

32:42.320 --> 32:45.320
 I resumed working on this around 2002.

32:45.320 --> 32:51.320
 And between 2002 and 2007, I was working on them crossing my finger that nobody at NCR would notice and nobody noticed.

32:51.320 --> 32:52.320
 Yeah.

32:52.320 --> 33:02.320
 And I hope that this kind of somewhat, as you said, lawyers aside, relative openness of the community now will continue.

33:02.320 --> 33:05.320
 It accelerates the entire progress of the industry.

33:05.320 --> 33:17.320
 And the problems that Facebook and Google and others are facing today is not whether Facebook or Google or Microsoft or IBM or whoever is ahead of the other.

33:17.320 --> 33:20.320
 It's that we don't have the technology to build these things we want to build.

33:20.320 --> 33:24.320
 We want to build intelligent virtual assistants that have common sense.

33:24.320 --> 33:26.320
 We don't have monopoly on good ideas for this.

33:26.320 --> 33:27.320
 We don't believe we do.

33:27.320 --> 33:30.320
 Maybe others do believe they do, but we don't.

33:30.320 --> 33:31.320
 Okay.

33:31.320 --> 33:37.320
 If a startup tells you they have a secret to human level intelligence and common sense, don't believe them.

33:37.320 --> 33:38.320
 They don't.

33:38.320 --> 33:50.320
 And it's going to take the entire work of the world research community for a while to get to the point where you can go off and in each of those companies can start to build things on this.

33:50.320 --> 33:51.320
 We're not there yet.

33:51.320 --> 33:52.320
 Absolutely.

33:52.320 --> 34:03.320
 And this calls to the gap between the space of ideas and the rigorous testing of those ideas of practical application that you often speak to.

34:03.320 --> 34:17.320
 You've written advice saying, don't get fooled by people who claim to have a solution to artificial general intelligence who claim to have an AI system that works just like the human brain or who claim to have figured out how the brain works.

34:17.320 --> 34:23.320
 That's them, what the error rate they get on MNIST or ImageNet.

34:23.320 --> 34:25.320
 This is a little dated, by the way.

34:25.320 --> 34:26.320
 $2,000.

34:26.320 --> 34:27.320
 I mean, five years.

34:27.320 --> 34:28.320
 Who's counting?

34:28.320 --> 34:29.320
 Okay.

34:29.320 --> 34:33.320
 But I think your opinion is the MNIST and ImageNet.

34:33.320 --> 34:35.320
 Yes, maybe dated.

34:35.320 --> 34:36.320
 There may be new benchmarks, right?

34:36.320 --> 34:47.320
 But I think that philosophy is one you still in somewhat hold that benchmarks and the practical testing, the practical application is where you really get to test the ideas.

34:47.320 --> 34:49.320
 Well, it may not be completely practical.

34:49.320 --> 35:00.320
 Like, for example, you know, it could be a toy data set, but it has to be some sort of task that the community as a whole is accepted as some sort of standard, you know, kind of benchmark if you want.

35:00.320 --> 35:01.320
 It doesn't need to be real.

35:01.320 --> 35:17.320
 So for example, many years ago here at FAIR, people, you know, Cheson West and Antoine Bourne and a few others proposed the baby tasks, which were kind of a toy problem to test the ability of machines to reason actually to access working memory and things like this.

35:17.320 --> 35:20.320
 And it was very useful, even though it wasn't a real task.

35:20.320 --> 35:23.320
 MNIST is kind of halfway a real task.

35:23.320 --> 35:26.320
 So, you know, toy problems can be very useful.

35:26.320 --> 35:39.320
 I guess that I was really struck by the fact that a lot of people, particularly a lot of people with money to invest would be fooled by people telling them, oh, we have, you know, the algorithm of the cortex and you should give us 50 million.

35:39.320 --> 35:40.320
 Yes, absolutely.

35:40.320 --> 35:48.320
 So there's a lot of people who who try to take advantage of the hype for business reasons and so on.

35:48.320 --> 36:00.320
 But let me sort of talk to this idea that new ideas, the ideas that push the field forward may not yet have a benchmark or it may be very difficult to establish a benchmark.

36:00.320 --> 36:01.320
 I agree.

36:01.320 --> 36:02.320
 That's part of the process.

36:02.320 --> 36:04.320
 Establishing benchmarks is part of the process.

36:04.320 --> 36:18.320
 So what are your thoughts about, so we have these benchmarks on around stuff we can do with images from classification to captioning to just every kind of information you can pull off from images and the surface level.

36:18.320 --> 36:20.320
 There's audio data set.

36:20.320 --> 36:22.320
 There's some video.

36:22.320 --> 36:25.320
 What can we start natural language?

36:25.320 --> 36:41.320
 What kind of stuff, what kind of benchmarks do you see that start creeping on to more something like intelligence, like reasoning, like maybe you don't like the term but AGI echoes of that kind of formulation.

36:41.320 --> 36:48.320
 A lot of people are working on interactive environments in which you can you can train and test intelligence systems.

36:48.320 --> 37:02.320
 So there, for example, you know, it's the classical paradigm of supervised running is that you have a data set, you partition it into a training set, validation set, test set, and there's a clear protocol, right.

37:02.320 --> 37:13.320
 But what if the that assumes that the samples are statistically independent, you can exchange them, the order in which you see them doesn't shouldn't matter, you know, things like that.

37:13.320 --> 37:23.320
 But what if the answer you give determines the next sample you see, which is the case, for example, in robotics, right, you robot does something and then it gets exposed to a new room.

37:23.320 --> 37:26.320
 And depending on where it goes, the room would be different.

37:26.320 --> 37:30.320
 So that's the that creates the exploration problem.

37:30.320 --> 37:44.320
 What if the samples, so that creates also a dependency between samples, right, you, you, if you move, if you can only move in space, the next sample you're going to see is going to be probably in the same building, most likely.

37:44.320 --> 37:56.320
 So, so, so the all the assumptions about the validity of this training set, test set hypothesis break, whenever machine can take an action that has an influence in the in the world, and it's what is going to see.

37:56.320 --> 38:08.320
 So people are setting up artificial environments where where that takes place, right, the robot runs around a 3D model of a house and can interact with objects and things like this.

38:08.320 --> 38:20.320
 So you do robotics by simulation, you have those, you know, opening a gym type thing or Mujoko kind of simulated robots and you have games, you know, things like that.

38:20.320 --> 38:25.320
 So that that's where the field is going really this kind of environment.

38:25.320 --> 38:35.320
 Now, back to the question of AGI, like, I don't like the term AGI, because it implies that human intelligence is general.

38:35.320 --> 38:40.320
 And human intelligence is nothing like general, it's very, very specialized.

38:40.320 --> 38:45.320
 We think is general, we'd like to think of ourselves as having general intelligence, we don't, we're very specialized.

38:45.320 --> 38:47.320
 We're only slightly more general.

38:47.320 --> 38:48.320
 Why does it feel general?

38:48.320 --> 38:51.320
 So you kind of the term general.

38:51.320 --> 39:07.320
 I think what's impressive about humans is ability to learn, as we were talking about learning, to learn in just so many different domains is perhaps not arbitrarily general, but just you can learn in many domains and integrate that knowledge somehow.

39:07.320 --> 39:08.320
 Okay.

39:08.320 --> 39:09.320
 The knowledge persists.

39:09.320 --> 39:11.320
 So let me take a very specific example.

39:11.320 --> 39:12.320
 Yes.

39:12.320 --> 39:13.320
 It's not an example.

39:13.320 --> 39:16.320
 It's more like a quasi mathematical demonstration.

39:16.320 --> 39:22.320
 So you have about one million fibers coming out of one of your eyes, okay, two million total, but let's, let's talk about just one of them.

39:22.320 --> 39:26.320
 It's one million nerve fibers, your optical nerve.

39:26.320 --> 39:30.320
 Let's imagine that they are binary, so they can be active or inactive, right?

39:30.320 --> 39:36.320
 So the input to your visual cortex is one million bits.

39:36.320 --> 39:47.320
 Now they're connected to your brain in a particular way and your brain has connections that are kind of a little bit like a convolution that they kind of local, you know, in space and things like this.

39:47.320 --> 39:50.320
 Now imagine I play a trick on you.

39:50.320 --> 39:52.320
 It's a pretty nasty trick, I admit.

39:52.320 --> 40:00.320
 I cut your optical nerve and I put a device that makes a random perturbation of a permutation of all the nerve fibers.

40:00.320 --> 40:08.320
 So now what comes to your brain is a fixed but random permutation of all the pixels.

40:08.320 --> 40:19.320
 There's no way in hell that your visual cortex, even if I do this to you in infancy, will actually learn vision to the same level of quality that you can.

40:19.320 --> 40:20.320
 Got it.

40:20.320 --> 40:22.320
 And you're saying there's no way you've learned that?

40:22.320 --> 40:28.320
 No, because now two pixels that are nearby in the world will end up in very different places in your visual cortex.

40:28.320 --> 40:33.320
 And your neurons there have no connections with each other because they only connect it locally.

40:33.320 --> 40:38.320
 So this whole, our entire, the hardware is built in many ways to support.

40:38.320 --> 40:39.320
 The locality of the real world?

40:39.320 --> 40:40.320
 Yeah.

40:40.320 --> 40:41.320
 Yes.

40:41.320 --> 40:42.320
 That's specialization.

40:42.320 --> 40:44.320
 Yeah, but it's still pretty damn impressive.

40:44.320 --> 40:46.320
 So it's not perfect generalization.

40:46.320 --> 40:47.320
 It's not even close.

40:47.320 --> 40:48.320
 No, no.

40:48.320 --> 40:50.320
 It's not that it's not even close.

40:50.320 --> 40:51.320
 It's not at all.

40:51.320 --> 40:52.320
 Yeah, it's not.

40:52.320 --> 40:54.320
 So how many Boolean functions?

40:54.320 --> 41:03.320
 Let's imagine you want to train your visual system to recognize particular patterns of those one million bits.

41:03.320 --> 41:05.320
 So that's a Boolean function.

41:05.320 --> 41:07.320
 Either the pattern is here or not here.

41:07.320 --> 41:13.320
 It's a two way classification with one million binary inputs.

41:13.320 --> 41:16.320
 How many such Boolean functions are there?

41:16.320 --> 41:21.320
 You have two to the one million combinations of inputs.

41:21.320 --> 41:24.320
 For each of those, you have an output bit.

41:24.320 --> 41:29.320
 And so you have two to the two to the one million Boolean functions of this type.

41:29.320 --> 41:30.320
 Okay.

41:30.320 --> 41:33.320
 Which is an unimaginably large number.

41:33.320 --> 41:37.320
 How many of those functions can actually be computed by your visual cortex?

41:37.320 --> 41:41.320
 And the answer is a tiny, tiny, tiny, tiny, tiny, tiny sliver.

41:41.320 --> 41:43.320
 Like an enormously tiny sliver.

41:43.320 --> 41:44.320
 Yeah.

41:44.320 --> 41:45.320
 Yeah.

41:45.320 --> 41:48.320
 So we are ridiculously specialized.

41:48.320 --> 41:51.320
 Okay.

41:51.320 --> 41:54.320
 That's an argument against the word general.

41:54.320 --> 42:09.320
 I agree with your intuition, but I'm not sure it seems the brain is impressively capable of adjusting to things.

42:09.320 --> 42:16.320
 It's because we can't imagine tasks that are outside of our comprehension.

42:16.320 --> 42:20.320
 So we think we are general because we're general of all the things that we can apprehend.

42:20.320 --> 42:21.320
 So yeah.

42:21.320 --> 42:24.320
 But there is a huge world out there of things that we have no idea.

42:24.320 --> 42:26.320
 We call that heat, by the way.

42:26.320 --> 42:27.320
 Heat.

42:27.320 --> 42:28.320
 Heat.

42:28.320 --> 42:33.320
 So at least physicists call that heat or they call it entropy, which is kind of...

42:33.320 --> 42:39.320
 You have a thing full of gas, right?

42:39.320 --> 42:40.320
 Close system for gas.

42:40.320 --> 42:41.320
 Right?

42:41.320 --> 42:42.320
 Close or no close.

42:42.320 --> 42:51.320
 It has, you know, pressure, it has temperature, it has, you know, and you can write equations,

42:51.320 --> 42:55.320
 PV equal and RT, you know, things like that, right?

42:55.320 --> 43:00.320
 When you reduce the volume, the temperature goes up, the pressure goes up, you know, things like that, right?

43:00.320 --> 43:02.320
 For perfect gas, at least.

43:02.320 --> 43:05.320
 Those are the things you can know about that system.

43:05.320 --> 43:10.320
 And it's a tiny, tiny number of bits compared to the complete information of the state of the entire system.

43:10.320 --> 43:17.320
 Because the state of the entire system will give you the position and momentum of every molecule of the gas.

43:17.320 --> 43:23.320
 And what you don't know about it is the entropy and you interpret it as heat.

43:23.320 --> 43:27.320
 The energy contained in that thing is what we call heat.

43:27.320 --> 43:34.320
 Now, it's very possible that, in fact, there is some very strong structure in how those molecules are moving.

43:34.320 --> 43:38.320
 It's just that they are in a way that we are just not wired to perceive.

43:38.320 --> 43:39.320
 Yeah, we're ignorant of it.

43:39.320 --> 43:44.320
 And there's, in your infinite amount of things, we're not wired to perceive.

43:44.320 --> 43:45.320
 Yeah.

43:45.320 --> 43:47.320
 And you're right, that's a nice way to put it.

43:47.320 --> 43:54.320
 We're general to all the things we can imagine, which is a very tiny subset of all the things that are possible.

43:54.320 --> 43:58.320
 So it's like comagraph complexity or the comagraph's chat in some kind of complexity.

43:58.320 --> 43:59.320
 Yeah.

43:59.320 --> 44:07.320
 You know, every bit string or every integer is random, except for all the ones that you can actually write down.

44:07.320 --> 44:15.320
 Yeah, okay, so beautiful, but, you know, so we can just call it artificial intelligence.

44:15.320 --> 44:17.320
 We don't need to have a general.

44:17.320 --> 44:18.320
 Or human level.

44:18.320 --> 44:20.320
 Human level intelligence is good.

44:20.320 --> 44:33.320
 You know, you'll start, anytime you touch human, it gets interesting because, you know, it's because we attach ourselves to human

44:33.320 --> 44:36.320
 and it's difficult to define what human intelligence is.

44:36.320 --> 44:42.320
 Nevertheless, my definition is maybe a damn impressive intelligence.

44:42.320 --> 44:46.320
 Okay, damn impressive demonstration of intelligence, whatever.

44:46.320 --> 44:53.320
 And so on that topic, most successes in deep learning have been in supervised learning.

44:53.320 --> 44:57.320
 What is your view on unsupervised learning?

44:57.320 --> 45:07.320
 Is there a hope to reduce involvement of human input and still have successful systems that have practically use?

45:07.320 --> 45:09.320
 Yeah, I mean, there's definitely a hope.

45:09.320 --> 45:11.320
 It's more than a hope, actually.

45:11.320 --> 45:13.320
 It's, you know, mounting evidence for it.

45:13.320 --> 45:15.320
 And that's basically all I do.

45:15.320 --> 45:20.320
 Like the only thing I'm interested in at the moment is I call itself supervised learning, not unsupervised.

45:20.320 --> 45:25.320
 Because unsupervised learning is a loaded term.

45:25.320 --> 45:31.320
 People who know something about machine learning, you know, tell you, so you're doing clustering or PCA, which is not the case.

45:31.320 --> 45:37.320
 And the white public, you know, when you say unsupervised learning, oh my God, you know, machines are going to learn by themselves and without supervision.

45:37.320 --> 45:39.320
 You know, they see this as...

45:39.320 --> 45:41.320
 Where's the parents?

45:41.320 --> 45:49.320
 Yeah, so I call myself supervised learning because, in fact, the underlying algorithms that are used are the same algorithms as the supervised learning algorithms.

45:49.320 --> 45:59.320
 Except that what we try them to do is not predict a particular set of variables, like the category of an image.

45:59.320 --> 46:05.320
 And not to predict a set of variables that have been provided by human labelers.

46:05.320 --> 46:11.320
 But what you're trying the machine to do is basically reconstruct a piece of its input that it's being...

46:11.320 --> 46:15.320
 It's being masked out, essentially. You can think of it this way, right?

46:15.320 --> 46:20.320
 So show a piece of video to a machine and ask it to predict what's going to happen next.

46:20.320 --> 46:28.320
 And of course, after a while, you can show what happens and the machine will kind of train itself to do better at that task.

46:28.320 --> 46:35.320
 You can do, like all the latest, most successful models in natural language processing, use self supervised learning.

46:35.320 --> 46:38.320
 You know, sort of bird style systems, for example, right?

46:38.320 --> 46:43.320
 You show it a window of a dozen words on a text corpus.

46:43.320 --> 46:51.320
 You take out 15% of the words and then you train the machine to predict the words that are missing.

46:51.320 --> 46:56.320
 That's self supervised learning. It's not predicting the future, it's just predicting things in the middle.

46:56.320 --> 46:59.320
 But you could have it predict the future. That's what language models do.

46:59.320 --> 47:05.320
 So in an unsupervised way, you construct a model of language. Do you think...

47:05.320 --> 47:09.320
 Or video or the physical world or whatever, right?

47:09.320 --> 47:12.320
 How far do you think that can take us?

47:12.320 --> 47:17.320
 Do you think very far it understands anything?

47:17.320 --> 47:23.320
 To some level, it has, you know, a shadow understanding of text.

47:23.320 --> 47:26.320
 But it needs to, I mean, to have kind of true human level intelligence.

47:26.320 --> 47:29.320
 I think you need to ground language in reality.

47:29.320 --> 47:32.320
 So some people are attempting to do this, right?

47:32.320 --> 47:37.320
 Having systems that kind of have some visual representation of what is being talked about.

47:37.320 --> 47:40.320
 Which is one reason you need those interactive environments, actually.

47:40.320 --> 47:44.320
 But this is like a huge technical problem that is not solved.

47:44.320 --> 47:49.320
 And that explains why self supervised learning works in the context of natural language.

47:49.320 --> 47:55.320
 That does not work in the context, or at least not well, in the context of image recognition and video.

47:55.320 --> 47:57.320
 Although it's making progress quickly.

47:57.320 --> 48:04.320
 And the reason, that reason is the fact that it's much easier to represent uncertainty in the prediction.

48:04.320 --> 48:09.320
 In the context of natural language than it is in the context of things like video and images.

48:09.320 --> 48:17.320
 So for example, if I ask you to predict what words I'm missing, you know, 15% of the words that I've taken out.

48:17.320 --> 48:19.320
 The possibilities are small.

48:19.320 --> 48:22.320
 It's small, right? There is 100,000 words in the lexicon.

48:22.320 --> 48:27.320
 And what the machine spits out is a big probability vector, right?

48:27.320 --> 48:30.320
 It's a bunch of numbers between the one one that's on to one.

48:30.320 --> 48:33.320
 And we know how to do this with computers.

48:33.320 --> 48:37.320
 So there, representing uncertainty in the prediction is relatively easy.

48:37.320 --> 48:42.320
 And that's, in my opinion, why those techniques work for NLP.

48:42.320 --> 48:48.320
 For images, if you ask, if you block a piece of an image and you ask the system reconstruct that piece of the image,

48:48.320 --> 48:54.320
 there are many possible answers that are all perfectly legit, right?

48:54.320 --> 48:58.320
 And how do you represent that, this set of possible answers?

48:58.320 --> 49:00.320
 You can't train a system to make one prediction.

49:00.320 --> 49:04.320
 You can train an old net to say, here it is, that's the image.

49:04.320 --> 49:07.320
 Because there's a whole set of things that are compatible with it.

49:07.320 --> 49:12.320
 So how do you get the machine to represent not a single output, but a whole set of outputs?

49:12.320 --> 49:20.320
 And, you know, similarly with video prediction, there's a lot of things that can happen in the future of video.

49:20.320 --> 49:22.320
 You're looking at me right now. I'm not moving my head very much.

49:22.320 --> 49:26.320
 But, you know, I might, you know, turn my head to the left or to the right.

49:26.320 --> 49:30.320
 If you don't have a system that can predict this,

49:30.320 --> 49:34.320
 and you train it with least square to kind of minimize the error with a prediction on what I'm doing,

49:34.320 --> 49:39.320
 what you get is a blurry image of myself in all possible future positions that I might be in.

49:39.320 --> 49:41.320
 Which is not a good prediction.

49:41.320 --> 49:45.320
 But so there might be other ways to do the self supervision, right?

49:45.320 --> 49:47.320
 For visual scenes.

49:47.320 --> 49:49.320
 Like what?

49:49.320 --> 49:55.320
 I mean, if I knew I wouldn't tell you, I'd publish it first. I don't know.

49:55.320 --> 49:57.320
 No, there might be.

49:57.320 --> 50:05.320
 So, I mean, these are kind of, there might be artificial ways of like self play in games to where you can simulate part of the environment.

50:05.320 --> 50:10.320
 Oh, that doesn't solve the problem. It's just a way of generating data.

50:10.320 --> 50:16.320
 But because you have more of a control, that may mean you can control, yeah, it's a way to generate data.

50:16.320 --> 50:21.320
 That's right. And because you can do huge amounts of data generation, that doesn't, you're right.

50:21.320 --> 50:26.320
 Well, it's a creeps up on the problem from the side of data.

50:26.320 --> 50:28.320
 I don't think that's the right way to creep up on the problem.

50:28.320 --> 50:31.320
 It doesn't solve this problem of handling uncertainty in the world, right?

50:31.320 --> 50:42.320
 So, if you have a machine learn a predictive model of the world in a game that is deterministic or quasi deterministic, it's easy, right?

50:42.320 --> 50:49.320
 Just, you know, give a few frames of the game to a connet, put a bunch of layers, and then have the game generates the next few frames.

50:49.320 --> 50:54.320
 And if the game is deterministic, it works fine.

50:54.320 --> 51:02.320
 And that includes, you know, feeding the system with the action that your little character is going to take.

51:02.320 --> 51:09.320
 The problem comes from the fact that the real world and most games are not entirely predictable.

51:09.320 --> 51:13.320
 And so there you get those blurry predictions, and you can't do planning with blurry predictions.

51:13.320 --> 51:23.320
 Right, so if you have a perfect model of the world, you can, in your head, run this model with a hypothesis for a sequence of actions,

51:23.320 --> 51:27.320
 and you're going to predict the outcome of that sequence of actions.

51:27.320 --> 51:32.320
 But if your model is imperfect, how can you plan?

51:32.320 --> 51:34.320
 Yeah, it quickly explodes.

51:34.320 --> 51:39.320
 What are your thoughts on the extension of this, which topic I'm super excited about.

51:39.320 --> 51:44.320
 It's connected to something you were talking about in terms of robotics, is active learning.

51:44.320 --> 51:50.320
 So, as opposed to sort of completely unsupervised or self supervised learning,

51:50.320 --> 51:58.320
 you ask the system for human help for selecting parts you want annotated next.

51:58.320 --> 52:02.320
 So if you think about a robot exploring a space, or a baby exploring a space,

52:02.320 --> 52:08.320
 or a system exploring a data set, every once in a while asking for human input.

52:08.320 --> 52:12.320
 Do you see value in that kind of work?

52:12.320 --> 52:14.320
 I don't see transformative value.

52:14.320 --> 52:20.320
 It's going to make things that we can already do more efficient, or they will learn slightly more efficiently,

52:20.320 --> 52:25.320
 but it's not going to make machines sort of significantly more intelligent, I think.

52:25.320 --> 52:34.320
 And by the way, there is no opposition, there is no conflict between self supervised learning, reinforcement learning,

52:34.320 --> 52:38.320
 and supervised learning, or imitation learning, or active learning.

52:38.320 --> 52:43.320
 I see self supervised learning as a preliminary to all of the above.

52:43.320 --> 52:44.320
 Yes.

52:44.320 --> 52:54.320
 So, the example I use very often is, how is it that, so if you use classical reinforcement learning,

52:54.320 --> 52:57.320
 deep reinforcement learning, if you want.

52:57.320 --> 53:05.320
 The best methods today, so called model free reinforcement learning, to learn to play Atari games,

53:05.320 --> 53:11.320
 take about 80 hours of training to reach the level that any human can reach in about 15 minutes.

53:11.320 --> 53:17.320
 They get better than humans, but it takes them a long time.

53:17.320 --> 53:27.320
 Alpha star, okay, the, you know, all your vinyls and his teams, the system to play, to play Starcraft,

53:27.320 --> 53:34.320
 plays, you know, a single map, a single type of player,

53:34.320 --> 53:45.320
 and can reach better than human level with about the equivalent of 200 years of training playing against itself.

53:45.320 --> 53:50.320
 It's 200 years, right? It's not something that no human can, could ever do.

53:50.320 --> 53:52.320
 I mean, I'm not sure what lesson to take away from that.

53:52.320 --> 54:01.320
 Okay, now, take those algorithms, the best RL algorithms we have today, to train a car to drive itself.

54:01.320 --> 54:05.320
 It would probably have to drive millions of hours, it will have to kill thousands of pedestrians,

54:05.320 --> 54:09.320
 it will have to run into thousands of trees, it will have to run off cliffs,

54:09.320 --> 54:15.320
 and it had to run off cliffs multiple times before it figures out that it's a bad idea, first of all,

54:15.320 --> 54:18.320
 and second of all, before it figures out how not to do it.

54:18.320 --> 54:24.320
 And so, I mean, this type of learning obviously does not reflect the kind of learning that animals and humans do.

54:24.320 --> 54:27.320
 There is something missing that's really, really important there.

54:27.320 --> 54:31.320
 And my hypothesis, which I've been advocating for like five years now,

54:31.320 --> 54:39.320
 is that we have predictive models of the world that include the ability to predict under uncertainty,

54:39.320 --> 54:45.320
 and what allows us to not run off a cliff when we learn to drive.

54:45.320 --> 54:51.320
 Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.

54:51.320 --> 54:56.320
 If we drive next to a cliff, we know that if we turn the wheel to the right,

54:56.320 --> 55:00.320
 the car is going to run off the cliff and nothing good is going to come out of this,

55:00.320 --> 55:03.320
 because we have a pretty good model of intuitive physics that tells us the car is going to fall.

55:03.320 --> 55:05.320
 We know about gravity.

55:05.320 --> 55:12.320
 Babies run this around the age of eight or nine months that objects don't float, they fall.

55:12.320 --> 55:16.320
 And we have a pretty good idea of the effect of turning the wheel on the car,

55:16.320 --> 55:18.320
 and we know we need to stay on the road.

55:18.320 --> 55:23.320
 So there's a lot of things that we bring to the table, which is basically our predictive model of the world,

55:23.320 --> 55:31.320
 and that model allows us to not do stupid things and to basically stay within the context of things we need to do.

55:31.320 --> 55:35.320
 We still face unpredictable situations, and that's how we learn,

55:35.320 --> 55:39.320
 but that allows us to learn really, really, really quickly.

55:39.320 --> 55:42.320
 So that's called model based reinforcement learning.

55:42.320 --> 55:48.320
 There's some imitation and supervised learning because we have a driving instructor that tells us occasionally what to do,

55:48.320 --> 55:52.320
 but most of the learning is learning the model.

55:52.320 --> 55:55.320
 Learning physics that we've done since we were babies.

55:55.320 --> 55:57.320
 That's where almost all the learning...

55:57.320 --> 56:00.320
 And the physics is somewhat transferable from...

56:00.320 --> 56:02.320
 It's transferable from scene to scene.

56:02.320 --> 56:05.320
 Stupid things are the same everywhere.

56:05.320 --> 56:08.320
 Yeah. I mean, if you have an experience of the world,

56:08.320 --> 56:16.320
 you don't need to be from a particularly intelligent species to know that if you spill water from a container,

56:16.320 --> 56:19.320
 the rest is going to get wet.

56:19.320 --> 56:21.320
 You might get wet.

56:21.320 --> 56:24.320
 So cats know this, right?

56:24.320 --> 56:25.320
 Yeah.

56:25.320 --> 56:30.320
 So the main problem we need to solve is how do we learn models of the world?

56:30.320 --> 56:31.320
 And that's what I'm interested in.

56:31.320 --> 56:34.320
 That's what self supervised learning is all about.

56:34.320 --> 56:39.320
 If you were to try to construct a benchmark for...

56:39.320 --> 56:41.320
 Let's look at MNIST.

56:41.320 --> 56:43.320
 I love that dataset.

56:43.320 --> 56:53.320
 Do you think it's useful, interesting, slash possible to perform well on MNIST with just one example of each digit?

56:53.320 --> 56:58.320
 And how would we solve that problem?

56:58.320 --> 56:59.320
 The answer is probably yes.

56:59.320 --> 57:03.320
 The question is what other type of learning are you allowed to do?

57:03.320 --> 57:08.320
 So if what you're allowed to do is train on some gigantic dataset of labeled digit that's called transfer learning.

57:08.320 --> 57:10.320
 And we know that works.

57:10.320 --> 57:13.320
 We do this at Facebook like in production, right?

57:13.320 --> 57:20.320
 We train large convolution nest to predict hashtags that people type on Instagram and we train on billions of images, literally billions.

57:20.320 --> 57:24.320
 And then we chop off the last layer and fine tune on whatever task we want.

57:24.320 --> 57:25.320
 That works really well.

57:25.320 --> 57:28.320
 You can beat the ImageNet record with this.

57:28.320 --> 57:31.320
 We actually open sourced the whole thing like a few weeks ago.

57:31.320 --> 57:33.320
 Yeah, that's still pretty cool.

57:33.320 --> 57:40.320
 But yeah, so what would be impressive and what's useful and impressive, what kind of transfer learning would be useful and impressive?

57:40.320 --> 57:42.320
 Is it Wikipedia, that kind of thing?

57:42.320 --> 57:43.320
 No, no.

57:43.320 --> 57:46.320
 I don't think transfer learning is really where we should focus.

57:46.320 --> 57:59.320
 We should try to have a kind of scenario for a benchmark where you have unlabeled data and it's a very large number of unlabeled data.

57:59.320 --> 58:10.320
 It could be video clips, it could be where you do frame prediction, it could be images where you could choose to mask a piece of it.

58:10.320 --> 58:15.320
 It could be whatever, but they're unlabeled and you're not allowed to label them.

58:15.320 --> 58:26.320
 So you do some training on this and then you train on a particular supervised task, ImageNet or NIST.

58:26.320 --> 58:35.320
 And you measure how your test error or validation error decreases as you increase the number of labeled training samples.

58:35.320 --> 58:47.320
 And what you'd like to see is that your error decreases much faster than if you train from scratch, from random weights.

58:47.320 --> 58:56.320
 So that to reach the same level of performance than a completely supervised, purely supervised system would reach, you would need way fewer samples.

58:56.320 --> 59:02.320
 So that's the crucial question because it will answer the question to people interested in medical image analysis.

59:02.320 --> 59:17.320
 Okay, if I want to get a particular level of error rate for this task, I know I need a million samples, can I do self supervised pre training to reduce this to about 100 or something?

59:17.320 --> 59:20.320
 And you think the answer there is self supervised pre training?

59:20.320 --> 59:24.320
 Yeah, some form of it.

59:24.320 --> 59:27.320
 Telling you active learning, but you disagree?

59:27.320 --> 59:33.320
 No, it's not useless, it's just not going to lead to a quantum leap, it's just going to make things that we already do.

59:33.320 --> 59:36.320
 So you're way smarter than me, I just disagree with you.

59:36.320 --> 59:40.320
 But I don't have anything to back that, it's just intuition.

59:40.320 --> 59:46.320
 So I worked a lot of large scale data sets and there's something that might be magic in active learning.

59:46.320 --> 59:49.320
 But okay, at least I said it publicly.

59:49.320 --> 59:52.320
 At least I'm being an idiot publicly.

59:52.320 --> 1:00:05.320
 Okay, it's not being an idiot, it's working with the data you have. I mean, certainly people are doing things like, okay, I have 3000 hours of imitation learning for cell driving car, but most of those are incredibly boring.

1:00:05.320 --> 1:00:12.320
 What I like is select 10% of them that are kind of the most informative and with just that, I would probably reach the same.

1:00:12.320 --> 1:00:16.320
 So it's a weak form of active learning if you want.

1:00:16.320 --> 1:00:20.320
 Yes, but there might be a much stronger version.

1:00:20.320 --> 1:00:23.320
 That's right. And that's an open question if it exists.

1:00:23.320 --> 1:00:26.320
 The question is how much stronger can you get?

1:00:26.320 --> 1:00:35.320
 Elon Musk is confident, talked to him recently, he's confident that large scale data and deep learning can solve the autonomous driving problem.

1:00:35.320 --> 1:00:40.320
 What are your thoughts on the limits possibilities of deep learning in this space?

1:00:40.320 --> 1:00:42.320
 It's obviously part of the solution.

1:00:42.320 --> 1:00:50.320
 I mean, I don't think we'll ever have a cell driving system or it is not in the foreseeable future that does not use deep learning.

1:00:50.320 --> 1:00:52.320
 Now, how much of it?

1:00:52.320 --> 1:01:03.320
 So in the history of sort of engineering, particularly sort of AI like systems, there's generally a first phase where everything is built by hand.

1:01:03.320 --> 1:01:08.320
 Then there is a second phase, and that was the case for autonomous driving, you know, 20, 30 years ago.

1:01:08.320 --> 1:01:18.320
 There's a phase where there's a little bit of learning is used, but there's a lot of engineering that's involved in kind of, you know, taking care of corner cases and putting limits, etc.

1:01:18.320 --> 1:01:20.320
 Because the learning system is not perfect.

1:01:20.320 --> 1:01:26.320
 And then as technology progresses, we end up relying more and more on learning.

1:01:26.320 --> 1:01:31.320
 That's the history of character recognition, so history of speech recognition, now computer vision, natural language processing.

1:01:31.320 --> 1:01:43.320
 And I think the same is going to happen with autonomous driving that currently the methods that are closest to providing some level of autonomy,

1:01:43.320 --> 1:01:50.320
 some, you know, decent level of autonomy where you don't expect a driver to kind of do anything, is where you constrain the world.

1:01:50.320 --> 1:02:00.320
 So you only run within, you know, 100 square kilometers or square miles in Phoenix, but the weather is nice and the roads are wide, which is what Waymo is doing.

1:02:00.320 --> 1:02:13.320
 You completely over engineer the car with tons of lidars and sophisticated sensors that are too expensive for consumer cars, but they're fine if you just run a fleet.

1:02:13.320 --> 1:02:20.320
 And you engineer the thing, the hell out of the everything else, you map the entire world, so you have complete 3D model of everything.

1:02:20.320 --> 1:02:30.320
 So the only thing that the perception system has to take care of is moving objects and construction and sort of, you know, things that weren't in your map.

1:02:30.320 --> 1:02:33.320
 And you can engineer a good, you know, slam system.

1:02:33.320 --> 1:02:43.320
 So that's kind of the current approach that's closest to some level of autonomy, but I think eventually the long term solution is going to rely more and more on learning

1:02:43.320 --> 1:02:50.320
 and possibly using a combination of self supervised learning and model based reinforcement or something like that.

1:02:50.320 --> 1:02:57.320
 But ultimately learning will be not just at the core, but really the fundamental part of the system.

1:02:57.320 --> 1:03:00.320
 Yeah, it already is, but it will become more and more.

1:03:00.320 --> 1:03:04.320
 What do you think it takes to build a system with human level intelligence?

1:03:04.320 --> 1:03:12.320
 You talked about the AI system in the movie, her being way out of reach, our current reach, this might be outdated as well, but

1:03:12.320 --> 1:03:13.320
 this is your way out of reach.

1:03:13.320 --> 1:03:15.320
 It's the way out of reach.

1:03:15.320 --> 1:03:18.320
 What would it take to build her?

1:03:18.320 --> 1:03:19.320
 Do you think?

1:03:19.320 --> 1:03:24.320
 So I can tell you the first two obstacles that we have to clear, but I don't know how many obstacles there are after this.

1:03:24.320 --> 1:03:32.320
 So the image I usually use is that there is a bunch of mountains that we have to climb and we can see the first one, but we don't know if there are 50 mountains behind it or not.

1:03:32.320 --> 1:03:43.320
 And this might be a good sort of metaphor for why AI researchers in the past have been overly optimistic about the result of AI.

1:03:43.320 --> 1:03:52.320
 For example, Noah and Simon wrote the general problem solver and they call it the general problem solver.

1:03:52.320 --> 1:03:59.320
 And of course, the first thing you realize is that all the problems you want to solve are exponential and so you can't actually use it for anything useful.

1:03:59.320 --> 1:04:02.320
 Yeah, so yeah, all you see is the first peak.

1:04:02.320 --> 1:04:05.320
 So what are the first couple of peaks for her?

1:04:05.320 --> 1:04:09.320
 So the first peak, which is precisely what I'm working on, is cell supervision.

1:04:09.320 --> 1:04:17.320
 How do we get machines to run models of the world by observation, kind of like babies and like young animals?

1:04:17.320 --> 1:04:23.320
 So we've been working with, you know, cognitive scientists.

1:04:23.320 --> 1:04:32.320
 So this Emmanuel Dupu, who is at Faire in Paris, is a half time, is also a researcher in French University.

1:04:32.320 --> 1:04:42.320
 And he has this chart that shows how many months of life baby humans can learn different concepts.

1:04:42.320 --> 1:04:46.320
 And you can measure this in various ways.

1:04:46.320 --> 1:04:56.320
 So things like distinguishing animate objects from inanimate objects, you can tell the difference at age two, three months.

1:04:56.320 --> 1:05:03.320
 Whether an object is going to stay stable is going to fall, you know, about four months you can tell.

1:05:03.320 --> 1:05:05.320
 You know, there are various things like this.

1:05:05.320 --> 1:05:13.320
 And then things like gravity, the fact that objects are not supposed to float in the air but are supposed to fall, you run this around the age of eight or nine months.

1:05:13.320 --> 1:05:19.320
 So you look at a lot of eight month old babies, you give them a bunch of toys on their high chair.

1:05:19.320 --> 1:05:22.320
 First thing they do is throw them on the ground and they look at them.

1:05:22.320 --> 1:05:27.320
 It's because, you know, they're learning about, actively learning about gravity.

1:05:27.320 --> 1:05:33.320
 So they're not trying to know you, but they need to do the experiment, right?

1:05:33.320 --> 1:05:39.320
 So, you know, how do we get machines to learn like babies mostly by observation with a little bit of interaction

1:05:39.320 --> 1:05:46.320
 and learning those models of the world because I think that's really a crucial piece of an intelligent autonomous system.

1:05:46.320 --> 1:05:51.320
 So if you think about the architecture of an intelligent autonomous system, it needs to have a predictive model of the world.

1:05:51.320 --> 1:05:57.320
 So something that says, here is a world at time t, here is a state of the world at time t plus one if I take this action.

1:05:57.320 --> 1:05:59.320
 And it's not a single answer.

1:05:59.320 --> 1:06:01.320
 It can be a distribution.

1:06:01.320 --> 1:06:05.320
 Yeah, well, we don't know how to represent distributions in highly measured space.

1:06:05.320 --> 1:06:07.320
 So it's got to be something weaker than that.

1:06:07.320 --> 1:06:10.320
 With some representation of uncertainty.

1:06:10.320 --> 1:06:15.320
 If you have that, then you can do what optimal control theory is called model predictive control,

1:06:15.320 --> 1:06:21.320
 which means that you can run your model with a hypothesis for a sequence of action and then see the result.

1:06:21.320 --> 1:06:25.320
 Now what you need, the other thing you need is some sort of objective that you want to optimize.

1:06:25.320 --> 1:06:28.320
 Am I reaching the goal of grabbing the subject?

1:06:28.320 --> 1:06:30.320
 Am I minimizing energy?

1:06:30.320 --> 1:06:31.320
 Am I whatever, right?

1:06:31.320 --> 1:06:34.320
 So there is some sort of objective that you have to minimize.

1:06:34.320 --> 1:06:40.320
 And so in your head, if you have this model, you can figure out the sequence of action that will optimize your objective.

1:06:40.320 --> 1:06:46.320
 That objective is something that ultimately is rooted in your basal ganglia, at least in the human brain.

1:06:46.320 --> 1:06:47.320
 That's what it's.

1:06:47.320 --> 1:06:52.320
 Basal ganglia computes your level of contentment or miscontentment.

1:06:52.320 --> 1:06:53.320
 I don't know if that's a word.

1:06:53.320 --> 1:06:55.320
 Unhappiness, okay.

1:06:55.320 --> 1:06:57.320
 Discontentment.

1:06:57.320 --> 1:06:58.320
 Discontentment.

1:06:58.320 --> 1:07:10.320
 And so your entire behavior is driven towards kind of minimizing that objective, which is maximizing your contentment computed by your basal ganglia.

1:07:10.320 --> 1:07:16.320
 And what you have is an objective function, which is basically a predictor of what your basal ganglia is going to tell you.

1:07:16.320 --> 1:07:23.320
 So you're not going to put your hand on fire because you know it's going to burn and you're going to get hurt.

1:07:23.320 --> 1:07:29.320
 And you're predicting this because of your model of the world and your sort of predictor of this objective, right?

1:07:29.320 --> 1:07:43.320
 So if you have those three components, you have four components, you have the hardwired contentment objective computer, if you want, calculator.

1:07:43.320 --> 1:07:44.320
 And then you have the three components.

1:07:44.320 --> 1:07:48.320
 One is the objective predictor, which basically predicts your level of contentment.

1:07:48.320 --> 1:08:01.320
 One is the model of the world, and there's a third module I didn't mention, which is the module that will figure out the best course of action to optimize an objective given your model.

1:08:01.320 --> 1:08:02.320
 Okay?

1:08:02.320 --> 1:08:03.320
 Yeah.

1:08:03.320 --> 1:08:08.320
 Collision policy, policy network or something like that, right?

1:08:08.320 --> 1:08:15.320
 Now, you need those three components to act autonomously intelligently, and you can be stupid in three different ways.

1:08:15.320 --> 1:08:18.320
 You can be stupid because your model of the world is wrong.

1:08:18.320 --> 1:08:24.320
 You can be stupid because your objective is not aligned with what you actually want to achieve.

1:08:24.320 --> 1:08:26.320
 Okay?

1:08:26.320 --> 1:08:29.320
 In humans, that would be a psychopath.

1:08:29.320 --> 1:08:40.320
 And then the third thing, the third way you can be stupid is that you have the right model, you have the right objective, but you're unable to figure out a course of action to optimize your objective given your model.

1:08:40.320 --> 1:08:41.320
 Right.

1:08:41.320 --> 1:08:43.320
 Okay?

1:08:43.320 --> 1:08:47.320
 Some people who are in charge of big countries actually have all three that are wrong.

1:08:47.320 --> 1:08:50.320
 All right.

1:08:50.320 --> 1:08:51.320
 Which countries?

1:08:51.320 --> 1:08:52.320
 I don't know.

1:08:52.320 --> 1:08:53.320
 Okay.

1:08:53.320 --> 1:09:04.320
 So if we think about this agent, if we think about the movie Her, you've criticized the art project that is Sophia the Robot.

1:09:04.320 --> 1:09:14.320
 And what that project essentially does is uses our natural inclination to anthropomorphize things that look like human and give them more.

1:09:14.320 --> 1:09:20.320
 Do you think that could be used by AI systems like in the movie Her?

1:09:20.320 --> 1:09:26.320
 So do you think that body is needed to create a feeling of intelligence?

1:09:26.320 --> 1:09:32.320
 Well, if Sophia was just an art piece, I would have no problem with it, but it's presented as something else.

1:09:32.320 --> 1:09:35.320
 Let me add that comment real quick.

1:09:35.320 --> 1:09:42.320
 If creators of Sophia could change something about their marketing or behavior in general, what would it be?

1:09:42.320 --> 1:09:45.320
 I'm just about everything.

1:09:45.320 --> 1:09:50.320
 I mean, don't you think, here's a tough question.

1:09:50.320 --> 1:09:52.320
 Let me, so I agree with you.

1:09:52.320 --> 1:09:59.320
 So Sophia is not, the general public feels that Sophia can do way more than she actually can.

1:09:59.320 --> 1:10:00.320
 That's right.

1:10:00.320 --> 1:10:09.320
 And the people who created Sophia are not honestly publicly communicating, trying to teach the public.

1:10:09.320 --> 1:10:10.320
 Right.

1:10:10.320 --> 1:10:13.320
 But here's a tough question.

1:10:13.320 --> 1:10:29.320
 Don't you think the same thing is scientists in industry and research are taking advantage of the same misunderstanding in the public when they create AI companies or publish stuff?

1:10:29.320 --> 1:10:31.320
 Some companies, yes.

1:10:31.320 --> 1:10:34.320
 I mean, there is no sense of, there's no desire to delude.

1:10:34.320 --> 1:10:38.320
 There's no desire to kind of overclaim what something is done.

1:10:38.320 --> 1:10:39.320
 Right.

1:10:39.320 --> 1:10:42.320
 You publish a paper on AI that has this result on ImageNet.

1:10:42.320 --> 1:10:43.320
 It's pretty clear.

1:10:43.320 --> 1:10:45.320
 I mean, it's not even interesting anymore.

1:10:45.320 --> 1:10:48.320
 But I don't think there is that.

1:10:48.320 --> 1:10:57.320
 I mean, the reviewers are generally not very forgiving of unsupported claims of this type.

1:10:57.320 --> 1:11:05.320
 And, but there are certainly quite a few startups that have had a huge amount of hype around this that I find extremely damaging.

1:11:05.320 --> 1:11:07.320
 And I've been calling it out when I've seen it.

1:11:07.320 --> 1:11:15.320
 So, yeah, but to go back to your original question, like the necessity of embodiment, I think, I don't think embodiment is necessary.

1:11:15.320 --> 1:11:17.320
 I think grounding is necessary.

1:11:17.320 --> 1:11:22.320
 So I don't think we're going to get machines that really understand language without some level of grounding in the real world.

1:11:22.320 --> 1:11:29.320
 And it's not clear to me that language is a high enough bandwidth medium to communicate how the real world works.

1:11:29.320 --> 1:11:30.320
 I think for this...

1:11:30.320 --> 1:11:33.320
 Can you talk about what grounding means to you?

1:11:33.320 --> 1:11:34.320
 So grounding means that...

1:11:34.320 --> 1:11:41.320
 So there is this classic problem of common sense reasoning, you know, the Winograd schema, right?

1:11:41.320 --> 1:11:49.320
 And so I tell you the trophy doesn't fit in the suitcase because it's too big, or the trophy doesn't fit in the suitcase because it's too small.

1:11:49.320 --> 1:11:53.320
 And the it in the first case refers to the trophy in the second case to the suitcase.

1:11:53.320 --> 1:11:58.320
 And the reason you can figure this out is because you know what the trophy in the suitcase are, you know, one is supposed to fit in the other one,

1:11:58.320 --> 1:12:05.320
 and you know the notion of size and a big object doesn't fit in a small object unless it's a target, you know, things like that, right?

1:12:05.320 --> 1:12:11.320
 So you have this knowledge of how the world works, of geometry and things like that.

1:12:11.320 --> 1:12:18.320
 I don't believe you can learn everything about the world by just being told in language how the world works.

1:12:18.320 --> 1:12:26.320
 You need some low level perception of the world, you know, be it visual touch, you know, whatever, but some higher bandwidth perception of the world.

1:12:26.320 --> 1:12:31.320
 So by reading all the world's text, you still may not have enough information.

1:12:31.320 --> 1:12:32.320
 That's right.

1:12:32.320 --> 1:12:37.320
 There's a lot of things that just will never appear in text and that you can't really infer.

1:12:37.320 --> 1:12:43.320
 So I think common sense will emerge from, you know, certainly a lot of language interaction,

1:12:43.320 --> 1:12:51.320
 but also with watching videos or perhaps even interacting in virtual environments and possibly, you know, robot interacting in the real world.

1:12:51.320 --> 1:12:55.320
 But I don't actually believe necessarily that this last one is absolutely necessary.

1:12:55.320 --> 1:12:59.320
 But I think there's a need for some grounding.

1:12:59.320 --> 1:13:04.320
 But the final product doesn't necessarily need to be embodied, you're saying?

1:13:04.320 --> 1:13:05.320
 No.

1:13:05.320 --> 1:13:07.320
 It just needs to have an awareness grounding.

1:13:07.320 --> 1:13:08.320
 Right.

1:13:08.320 --> 1:13:16.320
 It needs to know how the world works to have, you know, to not be frustrated, frustrating to talk to.

1:13:16.320 --> 1:13:20.320
 And you talked about emotions being important.

1:13:20.320 --> 1:13:22.320
 That's a whole other topic.

1:13:22.320 --> 1:13:33.320
 Well, so, you know, I talked about this, the base of ganglia as the, you know, the thing that calculates your level of misconstantment, contentment.

1:13:33.320 --> 1:13:38.320
 This is the other module that sort of tries to do a prediction of whether you're going to be content or not.

1:13:38.320 --> 1:13:40.320
 That's the source of some emotion.

1:13:40.320 --> 1:13:47.320
 So fear, for example, is an anticipation of bad things that can happen to you, right?

1:13:47.320 --> 1:13:52.320
 You have this inkling that there is some chance that something really bad is going to happen to you and that creates fear.

1:13:52.320 --> 1:13:56.320
 When you know for sure that something bad is going to happen to you, you kind of give up, right?

1:13:56.320 --> 1:13:57.320
 It's not going to be anymore.

1:13:57.320 --> 1:13:59.320
 It's uncertainty that creates fear.

1:13:59.320 --> 1:14:04.320
 So the punchline is we're not going to have autonomous intelligence without emotions.

1:14:04.320 --> 1:14:06.320
 Okay.

1:14:06.320 --> 1:14:08.320
 Whatever the heck emotions are.

1:14:08.320 --> 1:14:13.320
 So you mentioned very practical things of fear, but there's a lot of other mess around it.

1:14:13.320 --> 1:14:16.320
 But there are kind of the results of, you know, drives.

1:14:16.320 --> 1:14:17.320
 Yeah.

1:14:17.320 --> 1:14:19.320
 There's deeper biological stuff going on.

1:14:19.320 --> 1:14:21.320
 And I've talked to a few folks on this.

1:14:21.320 --> 1:14:27.320
 There's this fascinating stuff that ultimately connects to our brain.

1:14:27.320 --> 1:14:30.320
 If we create an AGI system.

1:14:30.320 --> 1:14:31.320
 Sorry.

1:14:31.320 --> 1:14:32.320
 Human level intelligence.

1:14:32.320 --> 1:14:34.320
 Human level intelligence system.

1:14:34.320 --> 1:14:37.320
 And you get to ask her one question.

1:14:37.320 --> 1:14:40.320
 What would that question be?

1:14:40.320 --> 1:14:45.320
 You know, I think the first one we'll create will probably not be that smart.

1:14:45.320 --> 1:14:47.320
 They'll be like a four year old.

1:14:47.320 --> 1:14:48.320
 Okay.

1:14:48.320 --> 1:14:53.320
 So you would have to ask her a question to know she's not that smart.

1:14:53.320 --> 1:14:54.320
 Yeah.

1:14:54.320 --> 1:14:57.320
 Well, what's a good question to ask, you know, to be impressed?

1:14:57.320 --> 1:15:00.320
 With the cause of wind.

1:15:00.320 --> 1:15:06.320
 And if she answers, oh, it's because the leaves of the tree are moving and that creates wind.

1:15:06.320 --> 1:15:08.320
 She's onto something.

1:15:08.320 --> 1:15:12.320
 And if she says, that's a stupid question, she's really onto something.

1:15:12.320 --> 1:15:13.320
 No.

1:15:13.320 --> 1:15:17.320
 And then you tell her, actually, you know, here is the real thing.

1:15:17.320 --> 1:15:20.320
 And she says, oh, yeah, that makes sense.

1:15:20.320 --> 1:15:26.320
 So questions that, that reveal the ability to do common sense reasoning about the physical world.

1:15:26.320 --> 1:15:27.320
 Yeah.

1:15:27.320 --> 1:15:29.320
 And you know, some of that will cause an inference.

1:15:29.320 --> 1:15:31.320
 Causal inference.

1:15:31.320 --> 1:15:33.320
 Well, it was a huge honor.

1:15:33.320 --> 1:15:35.320
 Congratulations on your touring award.

1:15:35.320 --> 1:15:37.320
 Thank you so much for talking today.

1:15:37.320 --> 1:15:38.320
 Thank you.

1:15:38.320 --> 1:15:58.320
 Thank you.