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- The following is a conversation with Francois Chollet.
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- He's the creator of Keras,
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- which is an open source deep learning library
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- that is designed to enable fast, user friendly experimentation
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- with deep neural networks.
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- It serves as an interface to several deep learning libraries,
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- most popular of which is TensorFlow,
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- and it was integrated into the TensorFlow main code base
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- a while ago.
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- Meaning, if you want to create, train,
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- and use neural networks,
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- probably the easiest and most popular option
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- is to use Keras inside TensorFlow.
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- Aside from creating an exceptionally useful
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- and popular library,
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- Francois is also a world class AI researcher
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- and software engineer at Google.
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- And he's definitely an outspoken,
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- if not controversial personality in the AI world,
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- especially in the realm of ideas
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- around the future of artificial intelligence.
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- This is the Artificial Intelligence Podcast.
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- If you enjoy it, subscribe on YouTube,
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- give it five stars on iTunes,
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- support it on Patreon,
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- or simply connect with me on Twitter
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- at Lex Friedman, spelled F R I D M A N.
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- And now, here's my conversation with Francois Chollet.
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- You're known for not sugarcoating your opinions
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- and speaking your mind about ideas in AI,
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- especially on Twitter.
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- It's one of my favorite Twitter accounts.
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- So what's one of the more controversial ideas
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- you've expressed online and gotten some heat for?
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- How do you pick?
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- How do I pick?
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- Yeah, no, I think if you go through the trouble
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- of maintaining a Twitter account,
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- you might as well speak your mind, you know?
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- Otherwise, what's even the point of having a Twitter account?
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- It's like having a nice car
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- and just leaving it in the garage.
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- Yeah, so what's one thing for which I got
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- a lot of pushback?
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- Perhaps, you know, that time I wrote something
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- about the idea of intelligence explosion,
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- and I was questioning the idea
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- and the reasoning behind this idea.
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- And I got a lot of pushback on that.
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- I got a lot of flak for it.
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- So yeah, so intelligence explosion,
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- I'm sure you're familiar with the idea,
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- but it's the idea that if you were to build
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- general AI problem solving algorithms,
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- well, the problem of building such an AI,
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- that itself is a problem that could be solved by your AI,
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- and maybe it could be solved better
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- than what humans can do.
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- So your AI could start tweaking its own algorithm,
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- could start making a better version of itself,
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- and so on iteratively in a recursive fashion.
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- And so you would end up with an AI
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- with exponentially increasing intelligence.
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- That's right.
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- And I was basically questioning this idea,
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- first of all, because the notion of intelligence explosion
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- uses an implicit definition of intelligence
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- that doesn't sound quite right to me.
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- It considers intelligence as a property of a brain
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- that you can consider in isolation,
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- like the height of a building, for instance.
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- But that's not really what intelligence is.
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- Intelligence emerges from the interaction
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- between a brain, a body,
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- like embodied intelligence, and an environment.
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- And if you're missing one of these pieces,
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- then you cannot really define intelligence anymore.
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- So just tweaking a brain to make it smaller and smaller
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- doesn't actually make any sense to me.
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- So first of all,
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- you're crushing the dreams of many people, right?
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- So there's a, let's look at like Sam Harris.
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- Actually, a lot of physicists, Max Tegmark,
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- people who think the universe
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- is an information processing system,
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- our brain is kind of an information processing system.
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- So what's the theoretical limit?
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- Like, it doesn't make sense that there should be some,
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- it seems naive to think that our own brain
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- is somehow the limit of the capabilities
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- of this information system.
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- I'm playing devil's advocate here.
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- This information processing system.
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- And then if you just scale it,
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- if you're able to build something
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- that's on par with the brain,
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- you just, the process that builds it just continues
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- and it'll improve exponentially.
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- So that's the logic that's used actually
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- by almost everybody
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- that is worried about super human intelligence.
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- So you're trying to make,
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- so most people who are skeptical of that
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- are kind of like, this doesn't,
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- their thought process, this doesn't feel right.
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- Like that's for me as well.
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- So I'm more like, it doesn't,
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- the whole thing is shrouded in mystery
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- where you can't really say anything concrete,
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- but you could say this doesn't feel right.
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- This doesn't feel like that's how the brain works.
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- And you're trying to with your blog posts
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- and now making it a little more explicit.
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- So one idea is that the brain isn't exist alone.
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- It exists within the environment.
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- So you can't exponentially,
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- you would have to somehow exponentially improve
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- the environment and the brain together almost.
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- Yeah, in order to create something that's much smarter
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- in some kind of,
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- of course we don't have a definition of intelligence.
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- That's correct, that's correct.
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- I don't think, you should look at very smart people today,
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- even humans, not even talking about AIs.
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- I don't think their brain
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- and the performance of their brain is the bottleneck
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- to their expressed intelligence, to their achievements.
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- You cannot just tweak one part of this system,
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- like of this brain, body, environment system
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- and expect that capabilities like what emerges
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- out of this system to just explode exponentially.
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- Because anytime you improve one part of a system
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- with many interdependencies like this,
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- there's a new bottleneck that arises, right?
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- And I don't think even today for very smart people,
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- their brain is not the bottleneck
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- to the sort of problems they can solve, right?
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- In fact, many very smart people today,
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- you know, they are not actually solving
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- any big scientific problems, they're not Einstein.
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- They're like Einstein, but you know, the patent clerk days.
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- Like Einstein became Einstein
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- because this was a meeting of a genius
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- with a big problem at the right time, right?
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- But maybe this meeting could have never happened
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- and then Einstein would have just been a patent clerk, right?
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- And in fact, many people today are probably like
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- genius level smart, but you wouldn't know
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- because they're not really expressing any of that.
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- Wow, that's brilliant.
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- So we can think of the world, Earth,
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- but also the universe as just as a space of problems.
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- So all these problems and tasks are roaming it
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- of various difficulty.
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- And there's agents, creatures like ourselves
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- and animals and so on that are also roaming it.
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- And then you get coupled with a problem
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- and then you solve it.
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- But without that coupling,
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- you can't demonstrate your quote unquote intelligence.
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- Exactly, intelligence is the meeting
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- of great problem solving capabilities
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- with a great problem.
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- And if you don't have the problem,
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- you don't really express any intelligence.
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- All you're left with is potential intelligence,
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- like the performance of your brain
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- or how high your IQ is,
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- which in itself is just a number, right?
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- So you mentioned problem solving capacity.
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- Yeah.
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- What do you think of as problem solving capacity?
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- Can you try to define intelligence?
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- Like what does it mean to be more or less intelligent?
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- Is it completely coupled to a particular problem
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- or is there something a little bit more universal?
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- Yeah, I do believe all intelligence
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- is specialized intelligence.
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- Even human intelligence has some degree of generality.
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- Well, all intelligent systems have some degree of generality
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- but they're always specialized in one category of problems.
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- So the human intelligence is specialized
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- in the human experience.
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- And that shows at various levels,
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- that shows in some prior knowledge that's innate
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- that we have at birth.
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- Knowledge about things like agents,
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- goal driven behavior, visual priors
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- about what makes an object, priors about time and so on.
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- That shows also in the way we learn.
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- For instance, it's very, very easy for us
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- to pick up language.
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- It's very, very easy for us to learn certain things
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- because we are basically hard coded to learn them.
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- And we are specialized in solving certain kinds of problem
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- and we are quite useless
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- when it comes to other kinds of problems.
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- For instance, we are not really designed
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- to handle very long term problems.
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- We have no capability of seeing the very long term.
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- We don't have very much working memory.
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- So how do you think about long term?
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- Do you think long term planning,
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- are we talking about scale of years, millennia?
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- What do you mean by long term?
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- We're not very good.
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- Well, human intelligence is specialized
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- in the human experience.
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- And human experience is very short.
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- One lifetime is short.
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- Even within one lifetime,
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- we have a very hard time envisioning things
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- on a scale of years.
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- It's very difficult to project yourself
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- at a scale of five years, at a scale of 10 years and so on.
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- We can solve only fairly narrowly scoped problems.
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- So when it comes to solving bigger problems,
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- larger scale problems,
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- we are not actually doing it on an individual level.
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- So it's not actually our brain doing it.
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- We have this thing called civilization, right?
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- Which is itself a sort of problem solving system,
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- a sort of artificially intelligent system, right?
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- And it's not running on one brain,
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- it's running on a network of brains.
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- In fact, it's running on much more
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- than a network of brains.
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- It's running on a lot of infrastructure,
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- like books and computers and the internet
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- and human institutions and so on.
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- And that is capable of handling problems
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- on a much greater scale than any individual human.
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- If you look at computer science, for instance,
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- that's an institution that solves problems
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- and it is superhuman, right?
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- It operates on a greater scale.
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- It can solve much bigger problems
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- than an individual human could.
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- And science itself, science as a system, as an institution,
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- is a kind of artificially intelligent problem solving
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- algorithm that is superhuman.
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- Yeah, it's, at least computer science
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- is like a theorem prover at a scale of thousands,
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- maybe hundreds of thousands of human beings.
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- At that scale, what do you think is an intelligent agent?
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- So there's us humans at the individual level,
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- there is millions, maybe billions of bacteria in our skin.
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- There is, that's at the smaller scale.
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- You can even go to the particle level
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- as systems that behave,
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- you can say intelligently in some ways.
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- And then you can look at the earth as a single organism,
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- you can look at our galaxy
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- and even the universe as a single organism.
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- Do you think, how do you think about scale
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- in defining intelligent systems?
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- And we're here at Google, there is millions of devices
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- doing computation just in a distributed way.
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- How do you think about intelligence versus scale?
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- You can always characterize anything as a system.
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- I think people who talk about things
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- like intelligence explosion,
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- tend to focus on one agent is basically one brain,
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- like one brain considered in isolation,
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- like a brain, a jaw that's controlling a body
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- in a very like top to bottom kind of fashion.
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- And that body is pursuing goals into an environment.
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- So it's a very hierarchical view.
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- You have the brain at the top of the pyramid,
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- then you have the body just plainly receiving orders.
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- And then the body is manipulating objects
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- in the environment and so on.
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- So everything is subordinate to this one thing,
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- this epicenter, which is the brain.
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- But in real life, intelligent agents
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- don't really work like this, right?
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- There is no strong delimitation
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- between the brain and the body to start with.
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- You have to look not just at the brain,
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- but at the nervous system.
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- But then the nervous system and the body
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- are naturally two separate entities.
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- So you have to look at an entire animal as one agent.
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- But then you start realizing as you observe an animal
-
-12:57.000 --> 13:00.200
- over any length of time,
-
-13:00.200 --> 13:03.160
- that a lot of the intelligence of an animal
-
-13:03.160 --> 13:04.600
- is actually externalized.
-
-13:04.600 --> 13:06.240
- That's especially true for humans.
-
-13:06.240 --> 13:08.880
- A lot of our intelligence is externalized.
-
-13:08.880 --> 13:10.360
- When you write down some notes,
-
-13:10.360 --> 13:11.960
- that is externalized intelligence.
-
-13:11.960 --> 13:14.000
- When you write a computer program,
-
-13:14.000 --> 13:16.000
- you are externalizing cognition.
-
-13:16.000 --> 13:19.720
- So it's externalizing books, it's externalized in computers,
-
-13:19.720 --> 13:21.520
- the internet, in other humans.
-
-13:23.080 --> 13:25.400
- It's externalizing language and so on.
-
-13:25.400 --> 13:30.400
- So there is no hard delimitation
-
-13:30.480 --> 13:32.640
- of what makes an intelligent agent.
-
-13:32.640 --> 13:33.880
- It's all about context.
-
-13:34.960 --> 13:38.800
- Okay, but AlphaGo is better at Go
-
-13:38.800 --> 13:40.200
- than the best human player.
-
-13:42.520 --> 13:45.000
- There's levels of skill here.
-
-13:45.000 --> 13:48.600
- So do you think there's such a ability,
-
-13:48.600 --> 13:52.800
- such a concept as intelligence explosion
-
-13:52.800 --> 13:54.760
- in a specific task?
-
-13:54.760 --> 13:57.360
- And then, well, yeah.
-
-13:57.360 --> 14:00.120
- Do you think it's possible to have a category of tasks
-
-14:00.120 --> 14:02.080
- on which you do have something
-
-14:02.080 --> 14:05.040
- like an exponential growth of ability
-
-14:05.040 --> 14:07.440
- to solve that particular problem?
-
-14:07.440 --> 14:10.320
- I think if you consider a specific vertical,
-
-14:10.320 --> 14:13.720
- it's probably possible to some extent.
-
-14:15.320 --> 14:18.320
- I also don't think we have to speculate about it
-
-14:18.320 --> 14:22.280
- because we have real world examples
-
-14:22.280 --> 14:26.920
- of recursively self improving intelligent systems, right?
-
-14:26.920 --> 14:30.920
- So for instance, science is a problem solving system,
-
-14:30.920 --> 14:32.600
- a knowledge generation system,
-
-14:32.600 --> 14:36.240
- like a system that experiences the world in some sense
-
-14:36.240 --> 14:40.160
- and then gradually understands it and can act on it.
-
-14:40.160 --> 14:42.120
- And that system is superhuman
-
-14:42.120 --> 14:45.600
- and it is clearly recursively self improving
-
-14:45.600 --> 14:47.560
- because science feeds into technology.
-
-14:47.560 --> 14:50.200
- Technology can be used to build better tools,
-
-14:50.200 --> 14:52.880
- better computers, better instrumentation and so on,
-
-14:52.880 --> 14:56.720
- which in turn can make science faster, right?
-
-14:56.720 --> 15:00.560
- So science is probably the closest thing we have today
-
-15:00.560 --> 15:04.760
- to a recursively self improving superhuman AI.
-
-15:04.760 --> 15:08.520
- And you can just observe is science,
-
-15:08.520 --> 15:10.320
- is scientific progress to the exploding,
-
-15:10.320 --> 15:12.800
- which itself is an interesting question.
-
-15:12.800 --> 15:15.560
- You can use that as a basis to try to understand
-
-15:15.560 --> 15:17.920
- what will happen with a superhuman AI
-
-15:17.920 --> 15:21.000
- that has a science like behavior.
-
-15:21.000 --> 15:23.320
- Let me linger on it a little bit more.
-
-15:23.320 --> 15:27.600
- What is your intuition why an intelligence explosion
-
-15:27.600 --> 15:28.560
- is not possible?
-
-15:28.560 --> 15:30.920
- Like taking the scientific,
-
-15:30.920 --> 15:33.240
- all the semi scientific revolutions,
-
-15:33.240 --> 15:38.080
- why can't we slightly accelerate that process?
-
-15:38.080 --> 15:41.200
- So you can absolutely accelerate
-
-15:41.200 --> 15:43.120
- any problem solving process.
-
-15:43.120 --> 15:46.720
- So a recursively self improvement
-
-15:46.720 --> 15:48.640
- is absolutely a real thing.
-
-15:48.640 --> 15:51.880
- But what happens with a recursively self improving system
-
-15:51.880 --> 15:53.680
- is typically not explosion
-
-15:53.680 --> 15:56.520
- because no system exists in isolation.
-
-15:56.520 --> 15:58.640
- And so tweaking one part of the system
-
-15:58.640 --> 16:00.880
- means that suddenly another part of the system
-
-16:00.880 --> 16:02.200
- becomes a bottleneck.
-
-16:02.200 --> 16:03.800
- And if you look at science, for instance,
-
-16:03.800 --> 16:06.800
- which is clearly a recursively self improving,
-
-16:06.800 --> 16:09.040
- clearly a problem solving system,
-
-16:09.040 --> 16:12.000
- scientific progress is not actually exploding.
-
-16:12.000 --> 16:13.520
- If you look at science,
-
-16:13.520 --> 16:16.480
- what you see is the picture of a system
-
-16:16.480 --> 16:19.240
- that is consuming an exponentially increasing
-
-16:19.240 --> 16:20.520
- amount of resources,
-
-16:20.520 --> 16:23.960
- but it's having a linear output
-
-16:23.960 --> 16:26.000
- in terms of scientific progress.
-
-16:26.000 --> 16:28.960
- And maybe that will seem like a very strong claim.
-
-16:28.960 --> 16:31.160
- Many people are actually saying that,
-
-16:31.160 --> 16:34.560
- scientific progress is exponential,
-
-16:34.560 --> 16:36.120
- but when they're claiming this,
-
-16:36.120 --> 16:38.400
- they're actually looking at indicators
-
-16:38.400 --> 16:43.080
- of resource consumption by science.
-
-16:43.080 --> 16:46.680
- For instance, the number of papers being published,
-
-16:47.560 --> 16:49.960
- the number of patents being filed and so on,
-
-16:49.960 --> 16:53.600
- which are just completely correlated
-
-16:53.600 --> 16:58.480
- with how many people are working on science today.
-
-16:58.480 --> 17:00.640
- So it's actually an indicator of resource consumption,
-
-17:00.640 --> 17:03.200
- but what you should look at is the output,
-
-17:03.200 --> 17:06.680
- is progress in terms of the knowledge
-
-17:06.680 --> 17:08.040
- that science generates,
-
-17:08.040 --> 17:10.640
- in terms of the scope and significance
-
-17:10.640 --> 17:12.520
- of the problems that we solve.
-
-17:12.520 --> 17:16.720
- And some people have actually been trying to measure that.
-
-17:16.720 --> 17:20.160
- Like Michael Nielsen, for instance,
-
-17:20.160 --> 17:21.920
- he had a very nice paper,
-
-17:21.920 --> 17:23.720
- I think that was last year about it.
-
-17:25.200 --> 17:28.360
- So his approach to measure scientific progress
-
-17:28.360 --> 17:33.360
- was to look at the timeline of scientific discoveries
-
-17:33.720 --> 17:37.160
- over the past, you know, 100, 150 years.
-
-17:37.160 --> 17:41.360
- And for each major discovery,
-
-17:41.360 --> 17:44.360
- ask a panel of experts to rate
-
-17:44.360 --> 17:46.760
- the significance of the discovery.
-
-17:46.760 --> 17:49.600
- And if the output of science as an institution
-
-17:49.600 --> 17:50.440
- were exponential,
-
-17:50.440 --> 17:55.440
- you would expect the temporal density of significance
-
-17:56.600 --> 17:58.160
- to go up exponentially.
-
-17:58.160 --> 18:00.960
- Maybe because there's a faster rate of discoveries,
-
-18:00.960 --> 18:02.960
- maybe because the discoveries are, you know,
-
-18:02.960 --> 18:04.920
- increasingly more important.
-
-18:04.920 --> 18:06.800
- And what actually happens
-
-18:06.800 --> 18:10.040
- if you plot this temporal density of significance
-
-18:10.040 --> 18:11.320
- measured in this way,
-
-18:11.320 --> 18:14.520
- is that you see very much a flat graph.
-
-18:14.520 --> 18:16.600
- You see a flat graph across all disciplines,
-
-18:16.600 --> 18:19.720
- across physics, biology, medicine, and so on.
-
-18:19.720 --> 18:22.480
- And it actually makes a lot of sense
-
-18:22.480 --> 18:23.320
- if you think about it,
-
-18:23.320 --> 18:26.000
- because think about the progress of physics
-
-18:26.000 --> 18:28.000
- 110 years ago, right?
-
-18:28.000 --> 18:30.080
- It was a time of crazy change.
-
-18:30.080 --> 18:31.960
- Think about the progress of technology,
-
-18:31.960 --> 18:34.360
- you know, 170 years ago,
-
-18:34.360 --> 18:35.400
- when we started having, you know,
-
-18:35.400 --> 18:37.560
- replacing horses with cars,
-
-18:37.560 --> 18:40.000
- when we started having electricity and so on.
-
-18:40.000 --> 18:41.520
- It was a time of incredible change.
-
-18:41.520 --> 18:44.600
- And today is also a time of very, very fast change,
-
-18:44.600 --> 18:48.040
- but it would be an unfair characterization
-
-18:48.040 --> 18:50.560
- to say that today technology and science
-
-18:50.560 --> 18:52.920
- are moving way faster than they did 50 years ago
-
-18:52.920 --> 18:54.360
- or 100 years ago.
-
-18:54.360 --> 18:59.360
- And if you do try to rigorously plot
-
-18:59.520 --> 19:04.520
- the temporal density of the significance,
-
-19:04.880 --> 19:07.320
- yeah, of significance, sorry,
-
-19:07.320 --> 19:09.720
- you do see very flat curves.
-
-19:09.720 --> 19:12.040
- And you can check out the paper
-
-19:12.040 --> 19:16.000
- that Michael Nielsen had about this idea.
-
-19:16.000 --> 19:20.000
- And so the way I interpret it is,
-
-19:20.000 --> 19:24.160
- as you make progress in a given field,
-
-19:24.160 --> 19:26.120
- or in a given subfield of science,
-
-19:26.120 --> 19:28.680
- it becomes exponentially more difficult
-
-19:28.680 --> 19:30.440
- to make further progress.
-
-19:30.440 --> 19:35.000
- Like the very first person to work on information theory.
-
-19:35.000 --> 19:36.440
- If you enter a new field,
-
-19:36.440 --> 19:37.920
- and it's still the very early years,
-
-19:37.920 --> 19:41.160
- there's a lot of low hanging fruit you can pick.
-
-19:41.160 --> 19:42.000
- That's right, yeah.
-
-19:42.000 --> 19:43.960
- But the next generation of researchers
-
-19:43.960 --> 19:48.160
- is gonna have to dig much harder, actually,
-
-19:48.160 --> 19:50.640
- to make smaller discoveries,
-
-19:50.640 --> 19:52.640
- probably larger number of smaller discoveries,
-
-19:52.640 --> 19:54.640
- and to achieve the same amount of impact,
-
-19:54.640 --> 19:57.480
- you're gonna need a much greater head count.
-
-19:57.480 --> 20:00.040
- And that's exactly the picture you're seeing with science,
-
-20:00.040 --> 20:03.760
- that the number of scientists and engineers
-
-20:03.760 --> 20:06.520
- is in fact increasing exponentially.
-
-20:06.520 --> 20:08.400
- The amount of computational resources
-
-20:08.400 --> 20:10.040
- that are available to science
-
-20:10.040 --> 20:11.880
- is increasing exponentially and so on.
-
-20:11.880 --> 20:15.560
- So the resource consumption of science is exponential,
-
-20:15.560 --> 20:18.200
- but the output in terms of progress,
-
-20:18.200 --> 20:21.000
- in terms of significance, is linear.
-
-20:21.000 --> 20:23.120
- And the reason why is because,
-
-20:23.120 --> 20:26.000
- and even though science is regressively self improving,
-
-20:26.000 --> 20:28.440
- meaning that scientific progress
-
-20:28.440 --> 20:30.240
- turns into technological progress,
-
-20:30.240 --> 20:32.960
- which in turn helps science.
-
-20:32.960 --> 20:35.280
- If you look at computers, for instance,
-
-20:35.280 --> 20:38.480
- our products of science and computers
-
-20:38.480 --> 20:41.560
- are tremendously useful in speeding up science.
-
-20:41.560 --> 20:43.840
- The internet, same thing, the internet is a technology
-
-20:43.840 --> 20:47.480
- that's made possible by very recent scientific advances.
-
-20:47.480 --> 20:52.400
- And itself, because it enables scientists to network,
-
-20:52.400 --> 20:55.520
- to communicate, to exchange papers and ideas much faster,
-
-20:55.520 --> 20:57.440
- it is a way to speed up scientific progress.
-
-20:57.440 --> 20:58.440
- So even though you're looking
-
-20:58.440 --> 21:01.440
- at a regressively self improving system,
-
-21:01.440 --> 21:04.080
- it is consuming exponentially more resources
-
-21:04.080 --> 21:09.080
- to produce the same amount of problem solving, very much.
-
-21:09.200 --> 21:11.080
- So that's a fascinating way to paint it,
-
-21:11.080 --> 21:14.960
- and certainly that holds for the deep learning community.
-
-21:14.960 --> 21:18.120
- If you look at the temporal, what did you call it,
-
-21:18.120 --> 21:21.240
- the temporal density of significant ideas,
-
-21:21.240 --> 21:23.920
- if you look at in deep learning,
-
-21:24.840 --> 21:26.960
- I think, I'd have to think about that,
-
-21:26.960 --> 21:29.040
- but if you really look at significant ideas
-
-21:29.040 --> 21:32.400
- in deep learning, they might even be decreasing.
-
-21:32.400 --> 21:37.400
- So I do believe the per paper significance is decreasing,
-
-21:39.600 --> 21:41.240
- but the amount of papers
-
-21:41.240 --> 21:43.440
- is still today exponentially increasing.
-
-21:43.440 --> 21:45.880
- So I think if you look at an aggregate,
-
-21:45.880 --> 21:48.840
- my guess is that you would see a linear progress.
-
-21:48.840 --> 21:53.840
- If you were to sum the significance of all papers,
-
-21:56.120 --> 21:58.640
- you would see roughly in your progress.
-
-21:58.640 --> 22:03.640
- And in my opinion, it is not a coincidence
-
-22:03.880 --> 22:05.800
- that you're seeing linear progress in science
-
-22:05.800 --> 22:07.720
- despite exponential resource consumption.
-
-22:07.720 --> 22:10.280
- I think the resource consumption
-
-22:10.280 --> 22:15.280
- is dynamically adjusting itself to maintain linear progress
-
-22:15.880 --> 22:18.560
- because we as a community expect linear progress,
-
-22:18.560 --> 22:21.240
- meaning that if we start investing less
-
-22:21.240 --> 22:23.600
- and seeing less progress, it means that suddenly
-
-22:23.600 --> 22:26.800
- there are some lower hanging fruits that become available
-
-22:26.800 --> 22:31.280
- and someone's gonna step up and pick them, right?
-
-22:31.280 --> 22:36.280
- So it's very much like a market for discoveries and ideas.
-
-22:36.920 --> 22:38.720
- But there's another fundamental part
-
-22:38.720 --> 22:41.800
- which you're highlighting, which as a hypothesis
-
-22:41.800 --> 22:45.160
- as science or like the space of ideas,
-
-22:45.160 --> 22:48.160
- any one path you travel down,
-
-22:48.160 --> 22:51.080
- it gets exponentially more difficult
-
-22:51.080 --> 22:54.720
- to get a new way to develop new ideas.
-
-22:54.720 --> 22:57.640
- And your sense is that's gonna hold
-
-22:57.640 --> 23:01.520
- across our mysterious universe.
-
-23:01.520 --> 23:03.360
- Yes, well, exponential progress
-
-23:03.360 --> 23:05.480
- triggers exponential friction.
-
-23:05.480 --> 23:07.440
- So that if you tweak one part of the system,
-
-23:07.440 --> 23:10.680
- suddenly some other part becomes a bottleneck, right?
-
-23:10.680 --> 23:14.880
- For instance, let's say you develop some device
-
-23:14.880 --> 23:17.160
- that measures its own acceleration
-
-23:17.160 --> 23:18.720
- and then it has some engine
-
-23:18.720 --> 23:20.800
- and it outputs even more acceleration
-
-23:20.800 --> 23:22.360
- in proportion of its own acceleration
-
-23:22.360 --> 23:23.320
- and you drop it somewhere,
-
-23:23.320 --> 23:25.240
- it's not gonna reach infinite speed
-
-23:25.240 --> 23:27.880
- because it exists in a certain context.
-
-23:29.080 --> 23:31.000
- So the air around it is gonna generate friction
-
-23:31.000 --> 23:34.320
- and it's gonna block it at some top speed.
-
-23:34.320 --> 23:37.480
- And even if you were to consider the broader context
-
-23:37.480 --> 23:39.840
- and lift the bottleneck there,
-
-23:39.840 --> 23:42.240
- like the bottleneck of friction,
-
-23:43.120 --> 23:45.120
- then some other part of the system
-
-23:45.120 --> 23:48.120
- would start stepping in and creating exponential friction,
-
-23:48.120 --> 23:49.920
- maybe the speed of flight or whatever.
-
-23:49.920 --> 23:51.920
- And this definitely holds true
-
-23:51.920 --> 23:54.960
- when you look at the problem solving algorithm
-
-23:54.960 --> 23:58.160
- that is being run by science as an institution,
-
-23:58.160 --> 23:59.720
- science as a system.
-
-23:59.720 --> 24:01.720
- As you make more and more progress,
-
-24:01.720 --> 24:05.800
- despite having this recursive self improvement component,
-
-24:06.760 --> 24:09.840
- you are encountering exponential friction.
-
-24:09.840 --> 24:13.480
- The more researchers you have working on different ideas,
-
-24:13.480 --> 24:14.880
- the more overhead you have
-
-24:14.880 --> 24:18.040
- in terms of communication across researchers.
-
-24:18.040 --> 24:22.920
- If you look at, you were mentioning quantum mechanics, right?
-
-24:22.920 --> 24:26.880
- Well, if you want to start making significant discoveries
-
-24:26.880 --> 24:29.680
- today, significant progress in quantum mechanics,
-
-24:29.680 --> 24:33.000
- there is an amount of knowledge you have to ingest,
-
-24:33.000 --> 24:34.080
- which is huge.
-
-24:34.080 --> 24:36.520
- So there's a very large overhead
-
-24:36.520 --> 24:39.240
- to even start to contribute.
-
-24:39.240 --> 24:40.680
- There's a large amount of overhead
-
-24:40.680 --> 24:44.040
- to synchronize across researchers and so on.
-
-24:44.040 --> 24:47.440
- And of course, the significant practical experiments
-
-24:48.600 --> 24:52.160
- are going to require exponentially expensive equipment
-
-24:52.160 --> 24:56.480
- because the easier ones have already been run, right?
-
-24:56.480 --> 25:00.480
- So in your senses, there's no way escaping,
-
-25:00.480 --> 25:04.480
- there's no way of escaping this kind of friction
-
-25:04.480 --> 25:08.600
- with artificial intelligence systems.
-
-25:08.600 --> 25:11.520
- Yeah, no, I think science is a very good way
-
-25:11.520 --> 25:14.280
- to model what would happen with a superhuman
-
-25:14.280 --> 25:16.440
- recursive research improving AI.
-
-25:16.440 --> 25:18.240
- That's your sense, I mean, the...
-
-25:18.240 --> 25:19.680
- That's my intuition.
-
-25:19.680 --> 25:23.400
- It's not like a mathematical proof of anything.
-
-25:23.400 --> 25:24.400
- That's not my point.
-
-25:24.400 --> 25:26.600
- Like, I'm not trying to prove anything.
-
-25:26.600 --> 25:27.920
- I'm just trying to make an argument
-
-25:27.920 --> 25:31.160
- to question the narrative of intelligence explosion,
-
-25:31.160 --> 25:32.880
- which is quite a dominant narrative.
-
-25:32.880 --> 25:35.840
- And you do get a lot of pushback if you go against it.
-
-25:35.840 --> 25:39.320
- Because, so for many people, right,
-
-25:39.320 --> 25:42.200
- AI is not just a subfield of computer science.
-
-25:42.200 --> 25:44.120
- It's more like a belief system.
-
-25:44.120 --> 25:48.640
- Like this belief that the world is headed towards an event,
-
-25:48.640 --> 25:55.040
- the singularity, past which, you know, AI will become...
-
-25:55.040 --> 25:57.080
- will go exponential very much,
-
-25:57.080 --> 25:58.600
- and the world will be transformed,
-
-25:58.600 --> 26:00.840
- and humans will become obsolete.
-
-26:00.840 --> 26:03.880
- And if you go against this narrative,
-
-26:03.880 --> 26:06.920
- because it is not really a scientific argument,
-
-26:06.920 --> 26:08.880
- but more of a belief system,
-
-26:08.880 --> 26:11.240
- it is part of the identity of many people.
-
-26:11.240 --> 26:12.600
- If you go against this narrative,
-
-26:12.600 --> 26:14.400
- it's like you're attacking the identity
-
-26:14.400 --> 26:15.560
- of people who believe in it.
-
-26:15.560 --> 26:17.640
- It's almost like saying God doesn't exist,
-
-26:17.640 --> 26:19.000
- or something.
-
-26:19.000 --> 26:21.880
- So you do get a lot of pushback
-
-26:21.880 --> 26:24.040
- if you try to question these ideas.
-
-26:24.040 --> 26:26.520
- First of all, I believe most people,
-
-26:26.520 --> 26:29.240
- they might not be as eloquent or explicit as you're being,
-
-26:29.240 --> 26:30.920
- but most people in computer science
-
-26:30.920 --> 26:33.000
- are most people who actually have built
-
-26:33.000 --> 26:36.360
- anything that you could call AI, quote, unquote,
-
-26:36.360 --> 26:38.080
- would agree with you.
-
-26:38.080 --> 26:40.560
- They might not be describing in the same kind of way.
-
-26:40.560 --> 26:43.960
- It's more, so the pushback you're getting
-
-26:43.960 --> 26:48.080
- is from people who get attached to the narrative
-
-26:48.080 --> 26:51.000
- from, not from a place of science,
-
-26:51.000 --> 26:53.400
- but from a place of imagination.
-
-26:53.400 --> 26:54.760
- That's correct, that's correct.
-
-26:54.760 --> 26:56.920
- So why do you think that's so appealing?
-
-26:56.920 --> 27:01.920
- Because the usual dreams that people have
-
-27:02.120 --> 27:03.960
- when you create a superintelligence system
-
-27:03.960 --> 27:05.120
- past the singularity,
-
-27:05.120 --> 27:08.600
- that what people imagine is somehow always destructive.
-
-27:09.440 --> 27:12.240
- Do you have, if you were put on your psychology hat,
-
-27:12.240 --> 27:17.240
- what's, why is it so appealing to imagine
-
-27:17.400 --> 27:20.760
- the ways that all of human civilization will be destroyed?
-
-27:20.760 --> 27:22.080
- I think it's a good story.
-
-27:22.080 --> 27:23.120
- You know, it's a good story.
-
-27:23.120 --> 27:28.120
- And very interestingly, it mirrors a religious stories,
-
-27:28.160 --> 27:30.560
- right, religious mythology.
-
-27:30.560 --> 27:34.360
- If you look at the mythology of most civilizations,
-
-27:34.360 --> 27:38.280
- it's about the world being headed towards some final events
-
-27:38.280 --> 27:40.480
- in which the world will be destroyed
-
-27:40.480 --> 27:42.800
- and some new world order will arise
-
-27:42.800 --> 27:44.920
- that will be mostly spiritual,
-
-27:44.920 --> 27:49.400
- like the apocalypse followed by a paradise probably, right?
-
-27:49.400 --> 27:52.600
- It's a very appealing story on a fundamental level.
-
-27:52.600 --> 27:54.560
- And we all need stories.
-
-27:54.560 --> 27:58.160
- We all need stories to structure the way we see the world,
-
-27:58.160 --> 27:59.960
- especially at timescales
-
-27:59.960 --> 28:04.520
- that are beyond our ability to make predictions, right?
-
-28:04.520 --> 28:08.840
- So on a more serious non exponential explosion,
-
-28:08.840 --> 28:13.840
- question, do you think there will be a time
-
-28:15.000 --> 28:19.800
- when we'll create something like human level intelligence
-
-28:19.800 --> 28:23.800
- or intelligent systems that will make you sit back
-
-28:23.800 --> 28:28.520
- and be just surprised at damn how smart this thing is?
-
-28:28.520 --> 28:30.160
- That doesn't require exponential growth
-
-28:30.160 --> 28:32.120
- or an exponential improvement,
-
-28:32.120 --> 28:35.600
- but what's your sense of the timeline and so on
-
-28:35.600 --> 28:40.600
- that you'll be really surprised at certain capabilities?
-
-28:41.080 --> 28:42.560
- And we'll talk about limitations and deep learning.
-
-28:42.560 --> 28:44.480
- So do you think in your lifetime,
-
-28:44.480 --> 28:46.600
- you'll be really damn surprised?
-
-28:46.600 --> 28:51.440
- Around 2013, 2014, I was many times surprised
-
-28:51.440 --> 28:53.960
- by the capabilities of deep learning actually.
-
-28:53.960 --> 28:55.920
- That was before we had assessed exactly
-
-28:55.920 --> 28:57.880
- what deep learning could do and could not do.
-
-28:57.880 --> 29:00.600
- And it felt like a time of immense potential.
-
-29:00.600 --> 29:03.080
- And then we started narrowing it down,
-
-29:03.080 --> 29:04.360
- but I was very surprised.
-
-29:04.360 --> 29:07.120
- I would say it has already happened.
-
-29:07.120 --> 29:10.800
- Was there a moment, there must've been a day in there
-
-29:10.800 --> 29:14.360
- where your surprise was almost bordering
-
-29:14.360 --> 29:19.360
- on the belief of the narrative that we just discussed.
-
-29:19.440 --> 29:20.800
- Was there a moment,
-
-29:20.800 --> 29:22.400
- because you've written quite eloquently
-
-29:22.400 --> 29:23.960
- about the limits of deep learning,
-
-29:23.960 --> 29:25.760
- was there a moment that you thought
-
-29:25.760 --> 29:27.720
- that maybe deep learning is limitless?
-
-29:30.000 --> 29:32.400
- No, I don't think I've ever believed this.
-
-29:32.400 --> 29:35.560
- What was really shocking is that it worked.
-
-29:35.560 --> 29:37.640
- It worked at all, yeah.
-
-29:37.640 --> 29:40.520
- But there's a big jump between being able
-
-29:40.520 --> 29:43.400
- to do really good computer vision
-
-29:43.400 --> 29:44.920
- and human level intelligence.
-
-29:44.920 --> 29:49.520
- So I don't think at any point I wasn't under the impression
-
-29:49.520 --> 29:51.280
- that the results we got in computer vision
-
-29:51.280 --> 29:54.080
- meant that we were very close to human level intelligence.
-
-29:54.080 --> 29:56.040
- I don't think we're very close to human level intelligence.
-
-29:56.040 --> 29:58.520
- I do believe that there's no reason
-
-29:58.520 --> 30:01.760
- why we won't achieve it at some point.
-
-30:01.760 --> 30:06.400
- I also believe that it's the problem
-
-30:06.400 --> 30:08.560
- with talking about human level intelligence
-
-30:08.560 --> 30:11.240
- that implicitly you're considering
-
-30:11.240 --> 30:14.360
- like an axis of intelligence with different levels,
-
-30:14.360 --> 30:16.720
- but that's not really how intelligence works.
-
-30:16.720 --> 30:19.480
- Intelligence is very multi dimensional.
-
-30:19.480 --> 30:22.480
- And so there's the question of capabilities,
-
-30:22.480 --> 30:25.560
- but there's also the question of being human like,
-
-30:25.560 --> 30:27.040
- and it's two very different things.
-
-30:27.040 --> 30:28.280
- Like you can build potentially
-
-30:28.280 --> 30:30.640
- very advanced intelligent agents
-
-30:30.640 --> 30:32.640
- that are not human like at all.
-
-30:32.640 --> 30:35.240
- And you can also build very human like agents.
-
-30:35.240 --> 30:37.840
- And these are two very different things, right?
-
-30:37.840 --> 30:38.760
- Right.
-
-30:38.760 --> 30:42.240
- Let's go from the philosophical to the practical.
-
-30:42.240 --> 30:44.240
- Can you give me a history of Keras
-
-30:44.240 --> 30:46.440
- and all the major deep learning frameworks
-
-30:46.440 --> 30:48.480
- that you kind of remember in relation to Keras
-
-30:48.480 --> 30:52.040
- and in general, TensorFlow, Theano, the old days.
-
-30:52.040 --> 30:55.400
- Can you give a brief overview Wikipedia style history
-
-30:55.400 --> 30:59.120
- and your role in it before we return to AGI discussions?
-
-30:59.120 --> 31:00.720
- Yeah, that's a broad topic.
-
-31:00.720 --> 31:04.040
- So I started working on Keras.
-
-31:04.920 --> 31:06.240
- It was the name Keras at the time.
-
-31:06.240 --> 31:08.320
- I actually picked the name like
-
-31:08.320 --> 31:10.200
- just the day I was going to release it.
-
-31:10.200 --> 31:14.800
- So I started working on it in February, 2015.
-
-31:14.800 --> 31:17.240
- And so at the time there weren't too many people
-
-31:17.240 --> 31:20.320
- working on deep learning, maybe like fewer than 10,000.
-
-31:20.320 --> 31:22.840
- The software tooling was not really developed.
-
-31:25.320 --> 31:28.800
- So the main deep learning library was Cafe,
-
-31:28.800 --> 31:30.840
- which was mostly C++.
-
-31:30.840 --> 31:32.760
- Why do you say Cafe was the main one?
-
-31:32.760 --> 31:36.000
- Cafe was vastly more popular than Theano
-
-31:36.000 --> 31:38.920
- in late 2014, early 2015.
-
-31:38.920 --> 31:42.400
- Cafe was the one library that everyone was using
-
-31:42.400 --> 31:43.400
- for computer vision.
-
-31:43.400 --> 31:46.120
- And computer vision was the most popular problem
-
-31:46.120 --> 31:46.960
- in deep learning at the time.
-
-31:46.960 --> 31:47.800
- Absolutely.
-
-31:47.800 --> 31:50.440
- Like ConvNets was like the subfield of deep learning
-
-31:50.440 --> 31:53.160
- that everyone was working on.
-
-31:53.160 --> 31:57.680
- So myself, so in late 2014,
-
-31:57.680 --> 32:00.600
- I was actually interested in RNNs,
-
-32:00.600 --> 32:01.760
- in recurrent neural networks,
-
-32:01.760 --> 32:05.800
- which was a very niche topic at the time, right?
-
-32:05.800 --> 32:08.640
- It really took off around 2016.
-
-32:08.640 --> 32:11.080
- And so I was looking for good tools.
-
-32:11.080 --> 32:14.800
- I had used Torch 7, I had used Theano,
-
-32:14.800 --> 32:17.640
- used Theano a lot in Kaggle competitions.
-
-32:19.320 --> 32:20.840
- I had used Cafe.
-
-32:20.840 --> 32:25.840
- And there was no like good solution for RNNs at the time.
-
-32:25.840 --> 32:28.640
- Like there was no reusable open source implementation
-
-32:28.640 --> 32:30.000
- of an LSTM, for instance.
-
-32:30.000 --> 32:32.920
- So I decided to build my own.
-
-32:32.920 --> 32:35.440
- And at first, the pitch for that was,
-
-32:35.440 --> 32:39.960
- it was gonna be mostly around LSTM recurrent neural networks.
-
-32:39.960 --> 32:41.360
- It was gonna be in Python.
-
-32:42.280 --> 32:44.280
- An important decision at the time
-
-32:44.280 --> 32:45.440
- that was kind of not obvious
-
-32:45.440 --> 32:50.360
- is that the models would be defined via Python code,
-
-32:50.360 --> 32:54.400
- which was kind of like going against the mainstream
-
-32:54.400 --> 32:58.000
- at the time because Cafe, Pylon 2, and so on,
-
-32:58.000 --> 33:00.600
- like all the big libraries were actually going
-
-33:00.600 --> 33:03.520
- with the approach of setting configuration files
-
-33:03.520 --> 33:05.560
- in YAML to define models.
-
-33:05.560 --> 33:08.840
- So some libraries were using code to define models,
-
-33:08.840 --> 33:12.280
- like Torch 7, obviously, but that was not Python.
-
-33:12.280 --> 33:16.680
- Lasagne was like a Theano based very early library
-
-33:16.680 --> 33:18.640
- that was, I think, developed, I don't remember exactly,
-
-33:18.640 --> 33:20.240
- probably late 2014.
-
-33:20.240 --> 33:21.200
- It's Python as well.
-
-33:21.200 --> 33:22.040
- It's Python as well.
-
-33:22.040 --> 33:24.320
- It was like on top of Theano.
-
-33:24.320 --> 33:28.320
- And so I started working on something
-
-33:29.480 --> 33:32.520
- and the value proposition at the time was that
-
-33:32.520 --> 33:36.240
- not only what I think was the first
-
-33:36.240 --> 33:38.800
- reusable open source implementation of LSTM,
-
-33:40.400 --> 33:44.440
- you could combine RNNs and covenants
-
-33:44.440 --> 33:45.440
- with the same library,
-
-33:45.440 --> 33:46.920
- which is not really possible before,
-
-33:46.920 --> 33:49.080
- like Cafe was only doing covenants.
-
-33:50.440 --> 33:52.560
- And it was kind of easy to use
-
-33:52.560 --> 33:54.440
- because, so before I was using Theano,
-
-33:54.440 --> 33:55.680
- I was actually using scikitlin
-
-33:55.680 --> 33:58.320
- and I loved scikitlin for its usability.
-
-33:58.320 --> 34:01.560
- So I drew a lot of inspiration from scikitlin
-
-34:01.560 --> 34:02.400
- when I made Keras.
-
-34:02.400 --> 34:05.600
- It's almost like scikitlin for neural networks.
-
-34:05.600 --> 34:06.680
- The fit function.
-
-34:06.680 --> 34:07.960
- Exactly, the fit function,
-
-34:07.960 --> 34:10.800
- like reducing a complex string loop
-
-34:10.800 --> 34:12.880
- to a single function call, right?
-
-34:12.880 --> 34:14.880
- And of course, some people will say,
-
-34:14.880 --> 34:16.320
- this is hiding a lot of details,
-
-34:16.320 --> 34:18.680
- but that's exactly the point, right?
-
-34:18.680 --> 34:20.280
- The magic is the point.
-
-34:20.280 --> 34:22.680
- So it's magical, but in a good way.
-
-34:22.680 --> 34:24.960
- It's magical in the sense that it's delightful.
-
-34:24.960 --> 34:26.160
- Yeah, yeah.
-
-34:26.160 --> 34:27.640
- I'm actually quite surprised.
-
-34:27.640 --> 34:29.600
- I didn't know that it was born out of desire
-
-34:29.600 --> 34:32.480
- to implement RNNs and LSTMs.
-
-34:32.480 --> 34:33.320
- It was.
-
-34:33.320 --> 34:34.160
- That's fascinating.
-
-34:34.160 --> 34:36.040
- So you were actually one of the first people
-
-34:36.040 --> 34:37.960
- to really try to attempt
-
-34:37.960 --> 34:41.000
- to get the major architectures together.
-
-34:41.000 --> 34:42.760
- And it's also interesting.
-
-34:42.760 --> 34:45.160
- You made me realize that that was a design decision at all
-
-34:45.160 --> 34:47.360
- is defining the model and code.
-
-34:47.360 --> 34:49.920
- Just, I'm putting myself in your shoes,
-
-34:49.920 --> 34:53.200
- whether the YAML, especially if cafe was the most popular.
-
-34:53.200 --> 34:54.720
- It was the most popular by far.
-
-34:54.720 --> 34:58.480
- If I was, if I were, yeah, I don't,
-
-34:58.480 --> 34:59.560
- I didn't like the YAML thing,
-
-34:59.560 --> 35:02.840
- but it makes more sense that you will put
-
-35:02.840 --> 35:05.720
- in a configuration file, the definition of a model.
-
-35:05.720 --> 35:07.200
- That's an interesting gutsy move
-
-35:07.200 --> 35:10.040
- to stick with defining it in code.
-
-35:10.040 --> 35:11.600
- Just if you look back.
-
-35:11.600 --> 35:13.480
- Other libraries were doing it as well,
-
-35:13.480 --> 35:16.320
- but it was definitely the more niche option.
-
-35:16.320 --> 35:17.160
- Yeah.
-
-35:17.160 --> 35:18.360
- Okay, Keras and then.
-
-35:18.360 --> 35:21.520
- So I released Keras in March, 2015,
-
-35:21.520 --> 35:24.160
- and it got users pretty much from the start.
-
-35:24.160 --> 35:25.800
- So the deep learning community was very, very small
-
-35:25.800 --> 35:27.240
- at the time.
-
-35:27.240 --> 35:30.600
- Lots of people were starting to be interested in LSTM.
-
-35:30.600 --> 35:32.440
- So it was gonna release it at the right time
-
-35:32.440 --> 35:35.560
- because it was offering an easy to use LSTM implementation.
-
-35:35.560 --> 35:37.680
- Exactly at the time where lots of people started
-
-35:37.680 --> 35:42.280
- to be intrigued by the capabilities of RNN, RNNs for NLP.
-
-35:42.280 --> 35:43.920
- So it grew from there.
-
-35:43.920 --> 35:48.920
- Then I joined Google about six months later,
-
-35:51.480 --> 35:54.920
- and that was actually completely unrelated to Keras.
-
-35:54.920 --> 35:57.080
- So I actually joined a research team
-
-35:57.080 --> 35:59.520
- working on image classification,
-
-35:59.520 --> 36:00.680
- mostly like computer vision.
-
-36:00.680 --> 36:02.320
- So I was doing computer vision research
-
-36:02.320 --> 36:03.640
- at Google initially.
-
-36:03.640 --> 36:05.520
- And immediately when I joined Google,
-
-36:05.520 --> 36:10.520
- I was exposed to the early internal version of TensorFlow.
-
-36:10.520 --> 36:13.920
- And the way it appeared to me at the time,
-
-36:13.920 --> 36:15.720
- and it was definitely the way it was at the time
-
-36:15.720 --> 36:20.760
- is that this was an improved version of Theano.
-
-36:20.760 --> 36:24.720
- So I immediately knew I had to port Keras
-
-36:24.720 --> 36:26.800
- to this new TensorFlow thing.
-
-36:26.800 --> 36:29.800
- And I was actually very busy as a noobler,
-
-36:29.800 --> 36:30.720
- as a new Googler.
-
-36:31.600 --> 36:34.520
- So I had not time to work on that.
-
-36:34.520 --> 36:38.680
- But then in November, I think it was November, 2015,
-
-36:38.680 --> 36:41.240
- TensorFlow got released.
-
-36:41.240 --> 36:44.560
- And it was kind of like my wake up call
-
-36:44.560 --> 36:47.320
- that, hey, I had to actually go and make it happen.
-
-36:47.320 --> 36:52.200
- So in December, I ported Keras to run on top of TensorFlow,
-
-36:52.200 --> 36:53.320
- but it was not exactly a port.
-
-36:53.320 --> 36:55.280
- It was more like a refactoring
-
-36:55.280 --> 36:57.920
- where I was abstracting away
-
-36:57.920 --> 37:00.480
- all the backend functionality into one module
-
-37:00.480 --> 37:02.320
- so that the same code base
-
-37:02.320 --> 37:05.080
- could run on top of multiple backends.
-
-37:05.080 --> 37:07.440
- So on top of TensorFlow or Theano.
-
-37:07.440 --> 37:09.760
- And for the next year,
-
-37:09.760 --> 37:14.760
- Theano stayed as the default option.
-
-37:15.400 --> 37:20.400
- It was easier to use, somewhat less buggy.
-
-37:20.640 --> 37:23.360
- It was much faster, especially when it came to audience.
-
-37:23.360 --> 37:26.360
- But eventually, TensorFlow overtook it.
-
-37:27.480 --> 37:30.200
- And TensorFlow, the early TensorFlow,
-
-37:30.200 --> 37:33.960
- has similar architectural decisions as Theano, right?
-
-37:33.960 --> 37:37.440
- So it was a natural transition.
-
-37:37.440 --> 37:38.320
- Yeah, absolutely.
-
-37:38.320 --> 37:42.960
- So what, I mean, that still Keras is a side,
-
-37:42.960 --> 37:45.280
- almost fun project, right?
-
-37:45.280 --> 37:49.040
- Yeah, so it was not my job assignment.
-
-37:49.040 --> 37:50.360
- It was not.
-
-37:50.360 --> 37:52.240
- I was doing it on the side.
-
-37:52.240 --> 37:55.840
- And even though it grew to have a lot of users
-
-37:55.840 --> 37:59.600
- for a deep learning library at the time, like Stroud 2016,
-
-37:59.600 --> 38:02.480
- but I wasn't doing it as my main job.
-
-38:02.480 --> 38:04.760
- So things started changing in,
-
-38:04.760 --> 38:09.760
- I think it must have been maybe October, 2016.
-
-38:10.200 --> 38:11.320
- So one year later.
-
-38:12.360 --> 38:15.240
- So Rajat, who was the lead on TensorFlow,
-
-38:15.240 --> 38:19.240
- basically showed up one day in our building
-
-38:19.240 --> 38:20.080
- where I was doing like,
-
-38:20.080 --> 38:21.640
- so I was doing research and things like,
-
-38:21.640 --> 38:24.640
- so I did a lot of computer vision research,
-
-38:24.640 --> 38:27.560
- also collaborations with Christian Zighetti
-
-38:27.560 --> 38:29.640
- and deep learning for theorem proving.
-
-38:29.640 --> 38:32.920
- It was a really interesting research topic.
-
-38:34.520 --> 38:37.640
- And so Rajat was saying,
-
-38:37.640 --> 38:41.040
- hey, we saw Keras, we like it.
-
-38:41.040 --> 38:42.440
- We saw that you're at Google.
-
-38:42.440 --> 38:45.280
- Why don't you come over for like a quarter
-
-38:45.280 --> 38:47.280
- and work with us?
-
-38:47.280 --> 38:49.240
- And I was like, yeah, that sounds like a great opportunity.
-
-38:49.240 --> 38:50.400
- Let's do it.
-
-38:50.400 --> 38:55.400
- And so I started working on integrating the Keras API
-
-38:55.720 --> 38:57.320
- into TensorFlow more tightly.
-
-38:57.320 --> 39:02.320
- So what followed up is a sort of like temporary
-
-39:02.640 --> 39:05.480
- TensorFlow only version of Keras
-
-39:05.480 --> 39:09.320
- that was in TensorFlow.com Trib for a while.
-
-39:09.320 --> 39:12.200
- And finally moved to TensorFlow Core.
-
-39:12.200 --> 39:15.360
- And I've never actually gotten back
-
-39:15.360 --> 39:17.600
- to my old team doing research.
-
-39:17.600 --> 39:22.320
- Well, it's kind of funny that somebody like you
-
-39:22.320 --> 39:27.320
- who dreams of, or at least sees the power of AI systems
-
-39:28.960 --> 39:31.680
- that reason and theorem proving we'll talk about
-
-39:31.680 --> 39:36.520
- has also created a system that makes the most basic
-
-39:36.520 --> 39:40.400
- kind of Lego building that is deep learning
-
-39:40.400 --> 39:42.640
- super accessible, super easy.
-
-39:42.640 --> 39:43.800
- So beautifully so.
-
-39:43.800 --> 39:47.720
- It's a funny irony that you're both,
-
-39:47.720 --> 39:49.120
- you're responsible for both things,
-
-39:49.120 --> 39:54.000
- but so TensorFlow 2.0 is kind of, there's a sprint.
-
-39:54.000 --> 39:55.080
- I don't know how long it'll take,
-
-39:55.080 --> 39:56.960
- but there's a sprint towards the finish.
-
-39:56.960 --> 40:01.040
- What do you look, what are you working on these days?
-
-40:01.040 --> 40:02.160
- What are you excited about?
-
-40:02.160 --> 40:04.280
- What are you excited about in 2.0?
-
-40:04.280 --> 40:05.760
- I mean, eager execution.
-
-40:05.760 --> 40:08.440
- There's so many things that just make it a lot easier
-
-40:08.440 --> 40:09.760
- to work.
-
-40:09.760 --> 40:13.640
- What are you excited about and what's also really hard?
-
-40:13.640 --> 40:15.800
- What are the problems you have to kind of solve?
-
-40:15.800 --> 40:19.080
- So I've spent the past year and a half working on
-
-40:19.080 --> 40:22.920
- TensorFlow 2.0 and it's been a long journey.
-
-40:22.920 --> 40:25.080
- I'm actually extremely excited about it.
-
-40:25.080 --> 40:26.440
- I think it's a great product.
-
-40:26.440 --> 40:29.360
- It's a delightful product compared to TensorFlow 1.0.
-
-40:29.360 --> 40:31.440
- We've made huge progress.
-
-40:32.640 --> 40:37.400
- So on the Keras side, what I'm really excited about is that,
-
-40:37.400 --> 40:42.400
- so previously Keras has been this very easy to use
-
-40:42.400 --> 40:45.840
- high level interface to do deep learning.
-
-40:45.840 --> 40:47.280
- But if you wanted to,
-
-40:50.520 --> 40:53.040
- if you wanted a lot of flexibility,
-
-40:53.040 --> 40:57.520
- the Keras framework was probably not the optimal way
-
-40:57.520 --> 40:59.760
- to do things compared to just writing everything
-
-40:59.760 --> 41:00.600
- from scratch.
-
-41:01.800 --> 41:04.680
- So in some way, the framework was getting in the way.
-
-41:04.680 --> 41:07.960
- And in TensorFlow 2.0, you don't have this at all, actually.
-
-41:07.960 --> 41:11.040
- You have the usability of the high level interface,
-
-41:11.040 --> 41:14.480
- but you have the flexibility of this lower level interface.
-
-41:14.480 --> 41:16.800
- And you have this spectrum of workflows
-
-41:16.800 --> 41:21.560
- where you can get more or less usability
-
-41:21.560 --> 41:26.560
- and flexibility trade offs depending on your needs, right?
-
-41:26.640 --> 41:29.680
- You can write everything from scratch
-
-41:29.680 --> 41:32.320
- and you get a lot of help doing so
-
-41:32.320 --> 41:36.400
- by subclassing models and writing some train loops
-
-41:36.400 --> 41:38.200
- using ego execution.
-
-41:38.200 --> 41:40.160
- It's very flexible, it's very easy to debug,
-
-41:40.160 --> 41:41.400
- it's very powerful.
-
-41:42.280 --> 41:45.000
- But all of this integrates seamlessly
-
-41:45.000 --> 41:49.440
- with higher level features up to the classic Keras workflows,
-
-41:49.440 --> 41:51.560
- which are very scikit learn like
-
-41:51.560 --> 41:56.040
- and are ideal for a data scientist,
-
-41:56.040 --> 41:58.240
- machine learning engineer type of profile.
-
-41:58.240 --> 42:00.840
- So now you can have the same framework
-
-42:00.840 --> 42:02.880
- offering the same set of APIs
-
-42:02.880 --> 42:05.000
- that enable a spectrum of workflows
-
-42:05.000 --> 42:08.560
- that are more or less low level, more or less high level
-
-42:08.560 --> 42:13.520
- that are suitable for profiles ranging from researchers
-
-42:13.520 --> 42:15.560
- to data scientists and everything in between.
-
-42:15.560 --> 42:16.960
- Yeah, so that's super exciting.
-
-42:16.960 --> 42:18.400
- I mean, it's not just that,
-
-42:18.400 --> 42:21.680
- it's connected to all kinds of tooling.
-
-42:21.680 --> 42:24.520
- You can go on mobile, you can go with TensorFlow Lite,
-
-42:24.520 --> 42:27.240
- you can go in the cloud or serving and so on.
-
-42:27.240 --> 42:28.960
- It all is connected together.
-
-42:28.960 --> 42:31.880
- Now some of the best software written ever
-
-42:31.880 --> 42:36.880
- is often done by one person, sometimes two.
-
-42:36.880 --> 42:40.800
- So with a Google, you're now seeing sort of Keras
-
-42:40.800 --> 42:42.840
- having to be integrated in TensorFlow,
-
-42:42.840 --> 42:46.800
- I'm sure has a ton of engineers working on.
-
-42:46.800 --> 42:51.040
- And there's, I'm sure a lot of tricky design decisions
-
-42:51.040 --> 42:52.200
- to be made.
-
-42:52.200 --> 42:54.440
- How does that process usually happen
-
-42:54.440 --> 42:56.800
- from at least your perspective?
-
-42:56.800 --> 42:59.800
- What are the debates like?
-
-43:00.720 --> 43:04.200
- Is there a lot of thinking,
-
-43:04.200 --> 43:06.880
- considering different options and so on?
-
-43:06.880 --> 43:08.160
- Yes.
-
-43:08.160 --> 43:12.640
- So a lot of the time I spend at Google
-
-43:12.640 --> 43:17.280
- is actually discussing design discussions, right?
-
-43:17.280 --> 43:20.480
- Writing design docs, participating in design review meetings
-
-43:20.480 --> 43:22.080
- and so on.
-
-43:22.080 --> 43:25.240
- This is as important as actually writing a code.
-
-43:25.240 --> 43:26.080
- Right.
-
-43:26.080 --> 43:28.120
- So there's a lot of thought, there's a lot of thought
-
-43:28.120 --> 43:32.280
- and a lot of care that is taken
-
-43:32.280 --> 43:34.160
- in coming up with these decisions
-
-43:34.160 --> 43:37.160
- and taking into account all of our users
-
-43:37.160 --> 43:40.680
- because TensorFlow has this extremely diverse user base,
-
-43:40.680 --> 43:41.520
- right?
-
-43:41.520 --> 43:43.120
- It's not like just one user segment
-
-43:43.120 --> 43:45.480
- where everyone has the same needs.
-
-43:45.480 --> 43:47.640
- We have small scale production users,
-
-43:47.640 --> 43:49.520
- large scale production users.
-
-43:49.520 --> 43:52.800
- We have startups, we have researchers,
-
-43:53.720 --> 43:55.080
- you know, it's all over the place.
-
-43:55.080 --> 43:57.560
- And we have to cater to all of their needs.
-
-43:57.560 --> 44:00.040
- If I just look at the standard debates
-
-44:00.040 --> 44:04.000
- of C++ or Python, there's some heated debates.
-
-44:04.000 --> 44:06.000
- Do you have those at Google?
-
-44:06.000 --> 44:08.080
- I mean, they're not heated in terms of emotionally,
-
-44:08.080 --> 44:10.800
- but there's probably multiple ways to do it, right?
-
-44:10.800 --> 44:14.040
- So how do you arrive through those design meetings
-
-44:14.040 --> 44:15.440
- at the best way to do it?
-
-44:15.440 --> 44:19.280
- Especially in deep learning where the field is evolving
-
-44:19.280 --> 44:20.880
- as you're doing it.
-
-44:21.880 --> 44:23.600
- Is there some magic to it?
-
-44:23.600 --> 44:26.240
- Is there some magic to the process?
-
-44:26.240 --> 44:28.280
- I don't know if there's magic to the process,
-
-44:28.280 --> 44:30.640
- but there definitely is a process.
-
-44:30.640 --> 44:33.760
- So making design decisions
-
-44:33.760 --> 44:36.080
- is about satisfying a set of constraints,
-
-44:36.080 --> 44:39.920
- but also trying to do so in the simplest way possible,
-
-44:39.920 --> 44:42.240
- because this is what can be maintained,
-
-44:42.240 --> 44:44.920
- this is what can be expanded in the future.
-
-44:44.920 --> 44:49.120
- So you don't want to naively satisfy the constraints
-
-44:49.120 --> 44:51.880
- by just, you know, for each capability you need available,
-
-44:51.880 --> 44:53.960
- you're gonna come up with one argument in your API
-
-44:53.960 --> 44:54.800
- and so on.
-
-44:54.800 --> 44:59.800
- You want to design APIs that are modular and hierarchical
-
-45:00.640 --> 45:04.080
- so that they have an API surface
-
-45:04.080 --> 45:07.040
- that is as small as possible, right?
-
-45:07.040 --> 45:11.640
- And you want this modular hierarchical architecture
-
-45:11.640 --> 45:14.560
- to reflect the way that domain experts
-
-45:14.560 --> 45:16.400
- think about the problem.
-
-45:16.400 --> 45:17.880
- Because as a domain expert,
-
-45:17.880 --> 45:19.840
- when you are reading about a new API,
-
-45:19.840 --> 45:24.760
- you're reading a tutorial or some docs pages,
-
-45:24.760 --> 45:28.200
- you already have a way that you're thinking about the problem.
-
-45:28.200 --> 45:32.320
- You already have like certain concepts in mind
-
-45:32.320 --> 45:35.680
- and you're thinking about how they relate together.
-
-45:35.680 --> 45:37.200
- And when you're reading docs,
-
-45:37.200 --> 45:40.280
- you're trying to build as quickly as possible
-
-45:40.280 --> 45:45.280
- a mapping between the concepts featured in your API
-
-45:45.280 --> 45:46.800
- and the concepts in your mind.
-
-45:46.800 --> 45:48.880
- So you're trying to map your mental model
-
-45:48.880 --> 45:53.600
- as a domain expert to the way things work in the API.
-
-45:53.600 --> 45:57.040
- So you need an API and an underlying implementation
-
-45:57.040 --> 46:00.120
- that are reflecting the way people think about these things.
-
-46:00.120 --> 46:02.880
- So in minimizing the time it takes to do the mapping.
-
-46:02.880 --> 46:04.680
- Yes, minimizing the time,
-
-46:04.680 --> 46:06.560
- the cognitive load there is
-
-46:06.560 --> 46:10.920
- in ingesting this new knowledge about your API.
-
-46:10.920 --> 46:13.160
- An API should not be self referential
-
-46:13.160 --> 46:15.520
- or referring to implementation details.
-
-46:15.520 --> 46:19.160
- It should only be referring to domain specific concepts
-
-46:19.160 --> 46:21.360
- that people already understand.
-
-46:23.240 --> 46:24.480
- Brilliant.
-
-46:24.480 --> 46:27.560
- So what's the future of Keras and TensorFlow look like?
-
-46:27.560 --> 46:29.640
- What does TensorFlow 3.0 look like?
-
-46:30.600 --> 46:33.720
- So that's kind of too far in the future for me to answer,
-
-46:33.720 --> 46:37.800
- especially since I'm not even the one making these decisions.
-
-46:37.800 --> 46:39.080
- Okay.
-
-46:39.080 --> 46:41.240
- But so from my perspective,
-
-46:41.240 --> 46:43.200
- which is just one perspective
-
-46:43.200 --> 46:46.040
- among many different perspectives on the TensorFlow team,
-
-46:47.200 --> 46:52.200
- I'm really excited by developing even higher level APIs,
-
-46:52.360 --> 46:53.560
- higher level than Keras.
-
-46:53.560 --> 46:56.480
- I'm really excited by hyperparameter tuning,
-
-46:56.480 --> 46:59.240
- by automated machine learning, AutoML.
-
-47:01.120 --> 47:03.200
- I think the future is not just, you know,
-
-47:03.200 --> 47:07.600
- defining a model like you were assembling Lego blocks
-
-47:07.600 --> 47:09.200
- and then collect fit on it.
-
-47:09.200 --> 47:13.680
- It's more like an automagical model
-
-47:13.680 --> 47:16.080
- that would just look at your data
-
-47:16.080 --> 47:19.040
- and optimize the objective you're after, right?
-
-47:19.040 --> 47:23.040
- So that's what I'm looking into.
-
-47:23.040 --> 47:26.480
- Yeah, so you put the baby into a room with the problem
-
-47:26.480 --> 47:28.760
- and come back a few hours later
-
-47:28.760 --> 47:30.960
- with a fully solved problem.
-
-47:30.960 --> 47:33.560
- Exactly, it's not like a box of Legos.
-
-47:33.560 --> 47:35.920
- It's more like the combination of a kid
-
-47:35.920 --> 47:38.800
- that's really good at Legos and a box of Legos.
-
-47:38.800 --> 47:41.520
- It's just building the thing on its own.
-
-47:41.520 --> 47:42.680
- Very nice.
-
-47:42.680 --> 47:44.160
- So that's an exciting future.
-
-47:44.160 --> 47:46.080
- I think there's a huge amount of applications
-
-47:46.080 --> 47:48.560
- and revolutions to be had
-
-47:49.920 --> 47:52.640
- under the constraints of the discussion we previously had.
-
-47:52.640 --> 47:57.480
- But what do you think of the current limits of deep learning?
-
-47:57.480 --> 48:02.480
- If we look specifically at these function approximators
-
-48:03.840 --> 48:06.160
- that tries to generalize from data.
-
-48:06.160 --> 48:10.160
- You've talked about local versus extreme generalization.
-
-48:11.120 --> 48:13.280
- You mentioned that neural networks don't generalize well
-
-48:13.280 --> 48:14.560
- and humans do.
-
-48:14.560 --> 48:15.760
- So there's this gap.
-
-48:17.640 --> 48:20.840
- And you've also mentioned that extreme generalization
-
-48:20.840 --> 48:23.960
- requires something like reasoning to fill those gaps.
-
-48:23.960 --> 48:27.560
- So how can we start trying to build systems like that?
-
-48:27.560 --> 48:30.600
- Right, yeah, so this is by design, right?
-
-48:30.600 --> 48:37.080
- Deep learning models are like huge parametric models,
-
-48:37.080 --> 48:39.280
- differentiable, so continuous,
-
-48:39.280 --> 48:42.680
- that go from an input space to an output space.
-
-48:42.680 --> 48:44.120
- And they're trained with gradient descent.
-
-48:44.120 --> 48:47.160
- So they're trained pretty much point by point.
-
-48:47.160 --> 48:50.520
- They are learning a continuous geometric morphing
-
-48:50.520 --> 48:55.320
- from an input vector space to an output vector space.
-
-48:55.320 --> 48:58.960
- And because this is done point by point,
-
-48:58.960 --> 49:02.200
- a deep neural network can only make sense
-
-49:02.200 --> 49:05.880
- of points in experience space that are very close
-
-49:05.880 --> 49:08.520
- to things that it has already seen in string data.
-
-49:08.520 --> 49:12.520
- At best, it can do interpolation across points.
-
-49:13.840 --> 49:17.360
- But that means in order to train your network,
-
-49:17.360 --> 49:21.680
- you need a dense sampling of the input cross output space,
-
-49:22.880 --> 49:25.240
- almost a point by point sampling,
-
-49:25.240 --> 49:27.160
- which can be very expensive if you're dealing
-
-49:27.160 --> 49:29.320
- with complex real world problems,
-
-49:29.320 --> 49:33.240
- like autonomous driving, for instance, or robotics.
-
-49:33.240 --> 49:36.000
- It's doable if you're looking at the subset
-
-49:36.000 --> 49:37.120
- of the visual space.
-
-49:37.120 --> 49:38.800
- But even then, it's still fairly expensive.
-
-49:38.800 --> 49:40.920
- You still need millions of examples.
-
-49:40.920 --> 49:44.240
- And it's only going to be able to make sense of things
-
-49:44.240 --> 49:46.880
- that are very close to what it has seen before.
-
-49:46.880 --> 49:49.160
- And in contrast to that, well, of course,
-
-49:49.160 --> 49:50.160
- you have human intelligence.
-
-49:50.160 --> 49:53.240
- But even if you're not looking at human intelligence,
-
-49:53.240 --> 49:56.800
- you can look at very simple rules, algorithms.
-
-49:56.800 --> 49:58.080
- If you have a symbolic rule,
-
-49:58.080 --> 50:03.080
- it can actually apply to a very, very large set of inputs
-
-50:03.120 --> 50:04.880
- because it is abstract.
-
-50:04.880 --> 50:09.560
- It is not obtained by doing a point by point mapping.
-
-50:10.720 --> 50:14.000
- For instance, if you try to learn a sorting algorithm
-
-50:14.000 --> 50:15.520
- using a deep neural network,
-
-50:15.520 --> 50:18.520
- well, you're very much limited to learning point by point
-
-50:20.080 --> 50:24.360
- what the sorted representation of this specific list is like.
-
-50:24.360 --> 50:29.360
- But instead, you could have a very, very simple
-
-50:29.400 --> 50:31.920
- sorting algorithm written in a few lines.
-
-50:31.920 --> 50:34.520
- Maybe it's just two nested loops.
-
-50:35.560 --> 50:40.560
- And it can process any list at all because it is abstract,
-
-50:41.040 --> 50:42.240
- because it is a set of rules.
-
-50:42.240 --> 50:45.160
- So deep learning is really like point by point
-
-50:45.160 --> 50:48.640
- geometric morphings, train with good and decent.
-
-50:48.640 --> 50:53.640
- And meanwhile, abstract rules can generalize much better.
-
-50:53.640 --> 50:56.160
- And I think the future is we need to combine the two.
-
-50:56.160 --> 50:59.160
- So how do we, do you think, combine the two?
-
-50:59.160 --> 51:03.040
- How do we combine good point by point functions
-
-51:03.040 --> 51:08.040
- with programs, which is what the symbolic AI type systems?
-
-51:08.920 --> 51:11.600
- At which levels the combination happen?
-
-51:11.600 --> 51:14.680
- I mean, obviously we're jumping into the realm
-
-51:14.680 --> 51:16.880
- of where there's no good answers.
-
-51:16.880 --> 51:20.280
- It's just kind of ideas and intuitions and so on.
-
-51:20.280 --> 51:23.080
- Well, if you look at the really successful AI systems
-
-51:23.080 --> 51:26.320
- today, I think they are already hybrid systems
-
-51:26.320 --> 51:29.520
- that are combining symbolic AI with deep learning.
-
-51:29.520 --> 51:32.520
- For instance, successful robotics systems
-
-51:32.520 --> 51:36.400
- are already mostly model based, rule based,
-
-51:37.400 --> 51:39.400
- things like planning algorithms and so on.
-
-51:39.400 --> 51:42.200
- At the same time, they're using deep learning
-
-51:42.200 --> 51:43.840
- as perception modules.
-
-51:43.840 --> 51:46.000
- Sometimes they're using deep learning as a way
-
-51:46.000 --> 51:50.920
- to inject fuzzy intuition into a rule based process.
-
-51:50.920 --> 51:54.560
- If you look at the system like in a self driving car,
-
-51:54.560 --> 51:57.240
- it's not just one big end to end neural network.
-
-51:57.240 --> 51:59.000
- You know, that wouldn't work at all.
-
-51:59.000 --> 52:00.760
- Precisely because in order to train that,
-
-52:00.760 --> 52:05.160
- you would need a dense sampling of experience base
-
-52:05.160 --> 52:06.200
- when it comes to driving,
-
-52:06.200 --> 52:08.880
- which is completely unrealistic, obviously.
-
-52:08.880 --> 52:12.440
- Instead, the self driving car is mostly
-
-52:13.920 --> 52:18.360
- symbolic, you know, it's software, it's programmed by hand.
-
-52:18.360 --> 52:21.640
- So it's mostly based on explicit models.
-
-52:21.640 --> 52:25.840
- In this case, mostly 3D models of the environment
-
-52:25.840 --> 52:29.520
- around the car, but it's interfacing with the real world
-
-52:29.520 --> 52:31.440
- using deep learning modules, right?
-
-52:31.440 --> 52:33.440
- So the deep learning there serves as a way
-
-52:33.440 --> 52:36.080
- to convert the raw sensory information
-
-52:36.080 --> 52:38.320
- to something usable by symbolic systems.
-
-52:39.760 --> 52:42.400
- Okay, well, let's linger on that a little more.
-
-52:42.400 --> 52:45.440
- So dense sampling from input to output.
-
-52:45.440 --> 52:48.240
- You said it's obviously very difficult.
-
-52:48.240 --> 52:50.120
- Is it possible?
-
-52:50.120 --> 52:51.800
- In the case of self driving, you mean?
-
-52:51.800 --> 52:53.040
- Let's say self driving, right?
-
-52:53.040 --> 52:55.760
- Self driving for many people,
-
-52:57.560 --> 52:59.520
- let's not even talk about self driving,
-
-52:59.520 --> 53:03.880
- let's talk about steering, so staying inside the lane.
-
-53:05.040 --> 53:07.080
- Lane following, yeah, it's definitely a problem
-
-53:07.080 --> 53:08.880
- you can solve with an end to end deep learning model,
-
-53:08.880 --> 53:10.600
- but that's like one small subset.
-
-53:10.600 --> 53:11.440
- Hold on a second.
-
-53:11.440 --> 53:12.760
- Yeah, I don't know why you're jumping
-
-53:12.760 --> 53:14.480
- from the extreme so easily,
-
-53:14.480 --> 53:16.280
- because I disagree with you on that.
-
-53:16.280 --> 53:21.000
- I think, well, it's not obvious to me
-
-53:21.000 --> 53:23.400
- that you can solve lane following.
-
-53:23.400 --> 53:25.840
- No, it's not obvious, I think it's doable.
-
-53:25.840 --> 53:30.840
- I think in general, there is no hard limitations
-
-53:31.200 --> 53:33.680
- to what you can learn with a deep neural network,
-
-53:33.680 --> 53:38.680
- as long as the search space is rich enough,
-
-53:40.320 --> 53:42.240
- is flexible enough, and as long as you have
-
-53:42.240 --> 53:45.360
- this dense sampling of the input cross output space.
-
-53:45.360 --> 53:47.720
- The problem is that this dense sampling
-
-53:47.720 --> 53:51.120
- could mean anything from 10,000 examples
-
-53:51.120 --> 53:52.840
- to like trillions and trillions.
-
-53:52.840 --> 53:54.360
- So that's my question.
-
-53:54.360 --> 53:56.200
- So what's your intuition?
-
-53:56.200 --> 53:58.720
- And if you could just give it a chance
-
-53:58.720 --> 54:01.880
- and think what kind of problems can be solved
-
-54:01.880 --> 54:04.240
- by getting a huge amounts of data
-
-54:04.240 --> 54:08.000
- and thereby creating a dense mapping.
-
-54:08.000 --> 54:12.480
- So let's think about natural language dialogue,
-
-54:12.480 --> 54:14.000
- the Turing test.
-
-54:14.000 --> 54:17.000
- Do you think the Turing test can be solved
-
-54:17.000 --> 54:21.120
- with a neural network alone?
-
-54:21.120 --> 54:24.440
- Well, the Turing test is all about tricking people
-
-54:24.440 --> 54:26.880
- into believing they're talking to a human.
-
-54:26.880 --> 54:29.040
- And I don't think that's actually very difficult
-
-54:29.040 --> 54:34.040
- because it's more about exploiting human perception
-
-54:35.600 --> 54:37.520
- and not so much about intelligence.
-
-54:37.520 --> 54:39.680
- There's a big difference between mimicking
-
-54:39.680 --> 54:42.080
- intelligent behavior and actual intelligent behavior.
-
-54:42.080 --> 54:45.360
- So, okay, let's look at maybe the Alexa prize and so on.
-
-54:45.360 --> 54:47.480
- The different formulations of the natural language
-
-54:47.480 --> 54:50.520
- conversation that are less about mimicking
-
-54:50.520 --> 54:52.800
- and more about maintaining a fun conversation
-
-54:52.800 --> 54:54.720
- that lasts for 20 minutes.
-
-54:54.720 --> 54:56.200
- That's a little less about mimicking
-
-54:56.200 --> 54:59.080
- and that's more about, I mean, it's still mimicking,
-
-54:59.080 --> 55:01.440
- but it's more about being able to carry forward
-
-55:01.440 --> 55:03.640
- a conversation with all the tangents that happen
-
-55:03.640 --> 55:05.080
- in dialogue and so on.
-
-55:05.080 --> 55:08.320
- Do you think that problem is learnable
-
-55:08.320 --> 55:13.320
- with a neural network that does the point to point mapping?
-
-55:14.520 --> 55:16.280
- So I think it would be very, very challenging
-
-55:16.280 --> 55:17.800
- to do this with deep learning.
-
-55:17.800 --> 55:21.480
- I don't think it's out of the question either.
-
-55:21.480 --> 55:23.240
- I wouldn't rule it out.
-
-55:23.240 --> 55:25.400
- The space of problems that can be solved
-
-55:25.400 --> 55:26.920
- with a large neural network.
-
-55:26.920 --> 55:30.080
- What's your sense about the space of those problems?
-
-55:30.080 --> 55:32.560
- So useful problems for us.
-
-55:32.560 --> 55:34.800
- In theory, it's infinite, right?
-
-55:34.800 --> 55:36.200
- You can solve any problem.
-
-55:36.200 --> 55:39.800
- In practice, well, deep learning is a great fit
-
-55:39.800 --> 55:41.800
- for perception problems.
-
-55:41.800 --> 55:46.800
- In general, any problem which is naturally amenable
-
-55:47.640 --> 55:52.200
- to explicit handcrafted rules or rules that you can generate
-
-55:52.200 --> 55:54.960
- by exhaustive search over some program space.
-
-55:56.080 --> 55:59.320
- So perception, artificial intuition,
-
-55:59.320 --> 56:03.240
- as long as you have a sufficient training dataset.
-
-56:03.240 --> 56:05.360
- And that's the question, I mean, perception,
-
-56:05.360 --> 56:08.400
- there's interpretation and understanding of the scene,
-
-56:08.400 --> 56:10.280
- which seems to be outside the reach
-
-56:10.280 --> 56:12.960
- of current perception systems.
-
-56:12.960 --> 56:15.920
- So do you think larger networks will be able
-
-56:15.920 --> 56:18.280
- to start to understand the physics
-
-56:18.280 --> 56:21.080
- and the physics of the scene,
-
-56:21.080 --> 56:23.400
- the three dimensional structure and relationships
-
-56:23.400 --> 56:25.560
- of objects in the scene and so on?
-
-56:25.560 --> 56:28.320
- Or really that's where symbolic AI has to step in?
-
-56:28.320 --> 56:34.480
- Well, it's always possible to solve these problems
-
-56:34.480 --> 56:36.800
- with deep learning.
-
-56:36.800 --> 56:38.560
- It's just extremely inefficient.
-
-56:38.560 --> 56:42.000
- A model would be an explicit rule based abstract model
-
-56:42.000 --> 56:45.240
- would be a far better, more compressed
-
-56:45.240 --> 56:46.840
- representation of physics.
-
-56:46.840 --> 56:49.080
- Then learning just this mapping between
-
-56:49.080 --> 56:50.960
- in this situation, this thing happens.
-
-56:50.960 --> 56:52.720
- If you change the situation slightly,
-
-56:52.720 --> 56:54.760
- then this other thing happens and so on.
-
-56:54.760 --> 56:57.440
- Do you think it's possible to automatically generate
-
-56:57.440 --> 57:02.200
- the programs that would require that kind of reasoning?
-
-57:02.200 --> 57:05.360
- Or does it have to, so the way the expert systems fail,
-
-57:05.360 --> 57:07.120
- there's so many facts about the world
-
-57:07.120 --> 57:08.960
- had to be hand coded in.
-
-57:08.960 --> 57:14.600
- Do you think it's possible to learn those logical statements
-
-57:14.600 --> 57:18.200
- that are true about the world and their relationships?
-
-57:18.200 --> 57:20.360
- Do you think, I mean, that's kind of what theorem proving
-
-57:20.360 --> 57:22.680
- at a basic level is trying to do, right?
-
-57:22.680 --> 57:26.160
- Yeah, except it's much harder to formulate statements
-
-57:26.160 --> 57:28.480
- about the world compared to formulating
-
-57:28.480 --> 57:30.320
- mathematical statements.
-
-57:30.320 --> 57:34.200
- Statements about the world tend to be subjective.
-
-57:34.200 --> 57:39.600
- So can you learn rule based models?
-
-57:39.600 --> 57:40.920
- Yes, definitely.
-
-57:40.920 --> 57:43.640
- That's the field of program synthesis.
-
-57:43.640 --> 57:48.040
- However, today we just don't really know how to do it.
-
-57:48.040 --> 57:52.400
- So it's very much a grass search or tree search problem.
-
-57:52.400 --> 57:56.800
- And so we are limited to the sort of tree session grass
-
-57:56.800 --> 57:58.560
- search algorithms that we have today.
-
-57:58.560 --> 58:02.760
- Personally, I think genetic algorithms are very promising.
-
-58:02.760 --> 58:04.360
- So almost like genetic programming.
-
-58:04.360 --> 58:05.560
- Genetic programming, exactly.
-
-58:05.560 --> 58:08.840
- Can you discuss the field of program synthesis?
-
-58:08.840 --> 58:14.560
- Like how many people are working and thinking about it?
-
-58:14.560 --> 58:17.960
- Where we are in the history of program synthesis
-
-58:17.960 --> 58:20.720
- and what are your hopes for it?
-
-58:20.720 --> 58:24.600
- Well, if it were deep learning, this is like the 90s.
-
-58:24.600 --> 58:29.120
- So meaning that we already have existing solutions.
-
-58:29.120 --> 58:34.280
- We are starting to have some basic understanding
-
-58:34.280 --> 58:35.480
- of what this is about.
-
-58:35.480 --> 58:38.000
- But it's still a field that is in its infancy.
-
-58:38.000 --> 58:40.440
- There are very few people working on it.
-
-58:40.440 --> 58:44.480
- There are very few real world applications.
-
-58:44.480 --> 58:47.640
- So the one real world application I'm aware of
-
-58:47.640 --> 58:51.680
- is Flash Fill in Excel.
-
-58:51.680 --> 58:55.080
- It's a way to automatically learn very simple programs
-
-58:55.080 --> 58:58.200
- to format cells in an Excel spreadsheet
-
-58:58.200 --> 59:00.240
- from a few examples.
-
-59:00.240 --> 59:02.800
- For instance, learning a way to format a date, things like that.
-
-59:02.800 --> 59:03.680
- Oh, that's fascinating.
-
-59:03.680 --> 59:04.560
- Yeah.
-
-59:04.560 --> 59:06.280
- You know, OK, that's a fascinating topic.
-
-59:06.280 --> 59:10.480
- I always wonder when I provide a few samples to Excel,
-
-59:10.480 --> 59:12.600
- what it's able to figure out.
-
-59:12.600 --> 59:15.960
- Like just giving it a few dates, what
-
-59:15.960 --> 59:18.480
- are you able to figure out from the pattern I just gave you?
-
-59:18.480 --> 59:19.760
- That's a fascinating question.
-
-59:19.760 --> 59:23.320
- And it's fascinating whether that's learnable patterns.
-
-59:23.320 --> 59:25.520
- And you're saying they're working on that.
-
-59:25.520 --> 59:28.200
- How big is the toolbox currently?
-
-59:28.200 --> 59:29.520
- Are we completely in the dark?
-
-59:29.520 --> 59:30.440
- So if you said the 90s.
-
-59:30.440 --> 59:31.720
- In terms of program synthesis?
-
-59:31.720 --> 59:32.360
- No.
-
-59:32.360 --> 59:37.720
- So I would say, so maybe 90s is even too optimistic.
-
-59:37.720 --> 59:41.080
- Because by the 90s, we already understood back prop.
-
-59:41.080 --> 59:43.960
- We already understood the engine of deep learning,
-
-59:43.960 --> 59:47.280
- even though we couldn't really see its potential quite.
-
-59:47.280 --> 59:48.520
- Today, I don't think we have found
-
-59:48.520 --> 59:50.400
- the engine of program synthesis.
-
-59:50.400 --> 59:52.880
- So we're in the winter before back prop.
-
-59:52.880 --> 59:54.160
- Yeah.
-
-59:54.160 --> 59:55.720
- In a way, yes.
-
-59:55.720 --> 1:00:00.120
- So I do believe program synthesis and general discrete search
-
-1:00:00.120 --> 1:00:02.760
- over rule based models is going to be
-
-1:00:02.760 --> 1:00:06.640
- a cornerstone of AI research in the next century.
-
-1:00:06.640 --> 1:00:10.200
- And that doesn't mean we are going to drop deep learning.
-
-1:00:10.200 --> 1:00:11.880
- Deep learning is immensely useful.
-
-1:00:11.880 --> 1:00:17.200
- Like, being able to learn is a very flexible, adaptable,
-
-1:00:17.200 --> 1:00:18.120
- parametric model.
-
-1:00:18.120 --> 1:00:20.720
- So it's got to understand that's actually immensely useful.
-
-1:00:20.720 --> 1:00:23.040
- All it's doing is pattern cognition.
-
-1:00:23.040 --> 1:00:25.640
- But being good at pattern cognition, given lots of data,
-
-1:00:25.640 --> 1:00:27.920
- is just extremely powerful.
-
-1:00:27.920 --> 1:00:30.320
- So we are still going to be working on deep learning.
-
-1:00:30.320 --> 1:00:31.840
- We are going to be working on program synthesis.
-
-1:00:31.840 --> 1:00:34.680
- We are going to be combining the two in increasingly automated
-
-1:00:34.680 --> 1:00:36.400
- ways.
-
-1:00:36.400 --> 1:00:38.520
- So let's talk a little bit about data.
-
-1:00:38.520 --> 1:00:44.600
- You've tweeted, about 10,000 deep learning papers
-
-1:00:44.600 --> 1:00:47.080
- have been written about hard coding priors
-
-1:00:47.080 --> 1:00:49.600
- about a specific task in a neural network architecture
-
-1:00:49.600 --> 1:00:52.440
- works better than a lack of a prior.
-
-1:00:52.440 --> 1:00:55.120
- Basically, summarizing all these efforts,
-
-1:00:55.120 --> 1:00:56.920
- they put a name to an architecture.
-
-1:00:56.920 --> 1:00:59.280
- But really, what they're doing is hard coding some priors
-
-1:00:59.280 --> 1:01:01.560
- that improve the performance of the system.
-
-1:01:01.560 --> 1:01:06.880
- But which gets straight to the point is probably true.
-
-1:01:06.880 --> 1:01:09.800
- So you say that you can always buy performance by,
-
-1:01:09.800 --> 1:01:12.920
- in quotes, performance by either training on more data,
-
-1:01:12.920 --> 1:01:15.480
- better data, or by injecting task information
-
-1:01:15.480 --> 1:01:18.400
- to the architecture of the preprocessing.
-
-1:01:18.400 --> 1:01:21.280
- However, this isn't informative about the generalization power
-
-1:01:21.280 --> 1:01:23.080
- the techniques use, the fundamental ability
-
-1:01:23.080 --> 1:01:24.200
- to generalize.
-
-1:01:24.200 --> 1:01:26.800
- Do you think we can go far by coming up
-
-1:01:26.800 --> 1:01:29.920
- with better methods for this kind of cheating,
-
-1:01:29.920 --> 1:01:33.520
- for better methods of large scale annotation of data?
-
-1:01:33.520 --> 1:01:34.960
- So building better priors.
-
-1:01:34.960 --> 1:01:37.280
- If you automate it, it's not cheating anymore.
-
-1:01:37.280 --> 1:01:38.360
- Right.
-
-1:01:38.360 --> 1:01:41.600
- I'm joking about the cheating, but large scale.
-
-1:01:41.600 --> 1:01:46.560
- So basically, I'm asking about something
-
-1:01:46.560 --> 1:01:48.280
- that hasn't, from my perspective,
-
-1:01:48.280 --> 1:01:53.360
- been researched too much is exponential improvement
-
-1:01:53.360 --> 1:01:55.960
- in annotation of data.
-
-1:01:55.960 --> 1:01:58.120
- Do you often think about?
-
-1:01:58.120 --> 1:02:00.840
- I think it's actually been researched quite a bit.
-
-1:02:00.840 --> 1:02:02.720
- You just don't see publications about it.
-
-1:02:02.720 --> 1:02:05.840
- Because people who publish papers
-
-1:02:05.840 --> 1:02:07.920
- are going to publish about known benchmarks.
-
-1:02:07.920 --> 1:02:09.800
- Sometimes they're going to read a new benchmark.
-
-1:02:09.800 --> 1:02:12.200
- People who actually have real world large scale
-
-1:02:12.200 --> 1:02:13.880
- depending on problems, they're going
-
-1:02:13.880 --> 1:02:16.960
- to spend a lot of resources into data annotation
-
-1:02:16.960 --> 1:02:18.400
- and good data annotation pipelines,
-
-1:02:18.400 --> 1:02:19.640
- but you don't see any papers about it.
-
-1:02:19.640 --> 1:02:20.400
- That's interesting.
-
-1:02:20.400 --> 1:02:22.720
- So do you think, certainly resources,
-
-1:02:22.720 --> 1:02:24.840
- but do you think there's innovation happening?
-
-1:02:24.840 --> 1:02:25.880
- Oh, yeah.
-
-1:02:25.880 --> 1:02:28.880
- To clarify the point in the tweet.
-
-1:02:28.880 --> 1:02:31.160
- So machine learning in general is
-
-1:02:31.160 --> 1:02:33.840
- the science of generalization.
-
-1:02:33.840 --> 1:02:37.800
- You want to generate knowledge that
-
-1:02:37.800 --> 1:02:40.440
- can be reused across different data sets,
-
-1:02:40.440 --> 1:02:42.000
- across different tasks.
-
-1:02:42.000 --> 1:02:45.280
- And if instead you're looking at one data set
-
-1:02:45.280 --> 1:02:50.000
- and then you are hard coding knowledge about this task
-
-1:02:50.000 --> 1:02:54.040
- into your architecture, this is no more useful
-
-1:02:54.040 --> 1:02:56.760
- than training a network and then saying, oh, I
-
-1:02:56.760 --> 1:03:01.920
- found these weight values perform well.
-
-1:03:01.920 --> 1:03:05.680
- So David Ha, I don't know if you know David,
-
-1:03:05.680 --> 1:03:08.760
- he had a paper the other day about weight
-
-1:03:08.760 --> 1:03:10.400
- agnostic neural networks.
-
-1:03:10.400 --> 1:03:12.120
- And this is a very interesting paper
-
-1:03:12.120 --> 1:03:14.400
- because it really illustrates the fact
-
-1:03:14.400 --> 1:03:17.400
- that an architecture, even without weights,
-
-1:03:17.400 --> 1:03:21.360
- an architecture is knowledge about a task.
-
-1:03:21.360 --> 1:03:23.640
- It encodes knowledge.
-
-1:03:23.640 --> 1:03:25.840
- And when it comes to architectures
-
-1:03:25.840 --> 1:03:30.440
- that are uncrafted by researchers, in some cases,
-
-1:03:30.440 --> 1:03:34.160
- it is very, very clear that all they are doing
-
-1:03:34.160 --> 1:03:38.880
- is artificially reencoding the template that
-
-1:03:38.880 --> 1:03:44.400
- corresponds to the proper way to solve the task encoding
-
-1:03:44.400 --> 1:03:45.200
- a given data set.
-
-1:03:45.200 --> 1:03:48.120
- For instance, I know if you looked
-
-1:03:48.120 --> 1:03:52.280
- at the baby data set, which is about natural language
-
-1:03:52.280 --> 1:03:55.520
- question answering, it is generated by an algorithm.
-
-1:03:55.520 --> 1:03:57.680
- So this is a question answer pairs
-
-1:03:57.680 --> 1:03:59.280
- that are generated by an algorithm.
-
-1:03:59.280 --> 1:04:01.520
- The algorithm is solving a certain template.
-
-1:04:01.520 --> 1:04:04.400
- Turns out, if you craft a network that
-
-1:04:04.400 --> 1:04:06.360
- literally encodes this template, you
-
-1:04:06.360 --> 1:04:09.640
- can solve this data set with nearly 100% accuracy.
-
-1:04:09.640 --> 1:04:11.160
- But that doesn't actually tell you
-
-1:04:11.160 --> 1:04:14.640
- anything about how to solve question answering
-
-1:04:14.640 --> 1:04:17.680
- in general, which is the point.
-
-1:04:17.680 --> 1:04:19.400
- The question is just to linger on it,
-
-1:04:19.400 --> 1:04:21.560
- whether it's from the data side or from the size
-
-1:04:21.560 --> 1:04:23.280
- of the network.
-
-1:04:23.280 --> 1:04:25.920
- I don't know if you've read the blog post by Rich Sutton,
-
-1:04:25.920 --> 1:04:28.400
- The Bitter Lesson, where he says,
-
-1:04:28.400 --> 1:04:31.480
- the biggest lesson that we can read from 70 years of AI
-
-1:04:31.480 --> 1:04:34.720
- research is that general methods that leverage computation
-
-1:04:34.720 --> 1:04:37.160
- are ultimately the most effective.
-
-1:04:37.160 --> 1:04:39.720
- So as opposed to figuring out methods
-
-1:04:39.720 --> 1:04:41.840
- that can generalize effectively, do you
-
-1:04:41.840 --> 1:04:47.720
- think we can get pretty far by just having something
-
-1:04:47.720 --> 1:04:51.520
- that leverages computation and the improvement of computation?
-
-1:04:51.520 --> 1:04:54.960
- Yeah, so I think Rich is making a very good point, which
-
-1:04:54.960 --> 1:04:57.560
- is that a lot of these papers, which are actually
-
-1:04:57.560 --> 1:05:02.800
- all about manually hardcoding prior knowledge about a task
-
-1:05:02.800 --> 1:05:04.720
- into some system, it doesn't have
-
-1:05:04.720 --> 1:05:08.600
- to be deep learning architecture, but into some system.
-
-1:05:08.600 --> 1:05:11.920
- These papers are not actually making any impact.
-
-1:05:11.920 --> 1:05:14.800
- Instead, what's making really long term impact
-
-1:05:14.800 --> 1:05:18.520
- is very simple, very general systems
-
-1:05:18.520 --> 1:05:21.280
- that are really agnostic to all these tricks.
-
-1:05:21.280 --> 1:05:23.320
- Because these tricks do not generalize.
-
-1:05:23.320 --> 1:05:27.480
- And of course, the one general and simple thing
-
-1:05:27.480 --> 1:05:33.160
- that you should focus on is that which leverages computation.
-
-1:05:33.160 --> 1:05:36.200
- Because computation, the availability
-
-1:05:36.200 --> 1:05:39.400
- of large scale computation has been increasing exponentially
-
-1:05:39.400 --> 1:05:40.560
- following Moore's law.
-
-1:05:40.560 --> 1:05:44.080
- So if your algorithm is all about exploiting this,
-
-1:05:44.080 --> 1:05:47.440
- then your algorithm is suddenly exponentially improving.
-
-1:05:47.440 --> 1:05:52.400
- So I think Rich is definitely right.
-
-1:05:52.400 --> 1:05:57.120
- However, he's right about the past 70 years.
-
-1:05:57.120 --> 1:05:59.440
- He's like assessing the past 70 years.
-
-1:05:59.440 --> 1:06:02.360
- I am not sure that this assessment will still
-
-1:06:02.360 --> 1:06:04.880
- hold true for the next 70 years.
-
-1:06:04.880 --> 1:06:07.160
- It might to some extent.
-
-1:06:07.160 --> 1:06:08.560
- I suspect it will not.
-
-1:06:08.560 --> 1:06:11.560
- Because the truth of his assessment
-
-1:06:11.560 --> 1:06:16.800
- is a function of the context in which this research took place.
-
-1:06:16.800 --> 1:06:18.600
- And the context is changing.
-
-1:06:18.600 --> 1:06:21.440
- Moore's law might not be applicable anymore,
-
-1:06:21.440 --> 1:06:23.760
- for instance, in the future.
-
-1:06:23.760 --> 1:06:31.200
- And I do believe that when you tweak one aspect of a system,
-
-1:06:31.200 --> 1:06:32.920
- when you exploit one aspect of a system,
-
-1:06:32.920 --> 1:06:36.480
- some other aspect starts becoming the bottleneck.
-
-1:06:36.480 --> 1:06:38.800
- Let's say you have unlimited computation.
-
-1:06:38.800 --> 1:06:41.440
- Well, then data is the bottleneck.
-
-1:06:41.440 --> 1:06:43.560
- And I think we are already starting
-
-1:06:43.560 --> 1:06:45.720
- to be in a regime where our systems are
-
-1:06:45.720 --> 1:06:48.120
- so large in scale and so data ingrained
-
-1:06:48.120 --> 1:06:50.360
- that data today and the quality of data
-
-1:06:50.360 --> 1:06:53.040
- and the scale of data is the bottleneck.
-
-1:06:53.040 --> 1:06:58.160
- And in this environment, the bitter lesson from Rich
-
-1:06:58.160 --> 1:07:00.800
- is not going to be true anymore.
-
-1:07:00.800 --> 1:07:03.960
- So I think we are going to move from a focus
-
-1:07:03.960 --> 1:07:09.840
- on a computation scale to focus on data efficiency.
-
-1:07:09.840 --> 1:07:10.720
- Data efficiency.
-
-1:07:10.720 --> 1:07:13.120
- So that's getting to the question of symbolic AI.
-
-1:07:13.120 --> 1:07:16.280
- But to linger on the deep learning approaches,
-
-1:07:16.280 --> 1:07:19.240
- do you have hope for either unsupervised learning
-
-1:07:19.240 --> 1:07:23.280
- or reinforcement learning, which are
-
-1:07:23.280 --> 1:07:28.120
- ways of being more data efficient in terms
-
-1:07:28.120 --> 1:07:31.560
- of the amount of data they need that required human annotation?
-
-1:07:31.560 --> 1:07:34.280
- So unsupervised learning and reinforcement learning
-
-1:07:34.280 --> 1:07:36.640
- are frameworks for learning, but they are not
-
-1:07:36.640 --> 1:07:39.000
- like any specific technique.
-
-1:07:39.000 --> 1:07:41.200
- So usually when people say reinforcement learning,
-
-1:07:41.200 --> 1:07:43.320
- what they really mean is deep reinforcement learning,
-
-1:07:43.320 --> 1:07:47.440
- which is like one approach which is actually very questionable.
-
-1:07:47.440 --> 1:07:50.920
- The question I was asking was unsupervised learning
-
-1:07:50.920 --> 1:07:54.680
- with deep neural networks and deep reinforcement learning.
-
-1:07:54.680 --> 1:07:56.840
- Well, these are not really data efficient
-
-1:07:56.840 --> 1:08:00.520
- because you're still leveraging these huge parametric models
-
-1:08:00.520 --> 1:08:03.720
- point by point with gradient descent.
-
-1:08:03.720 --> 1:08:08.000
- It is more efficient in terms of the number of annotations,
-
-1:08:08.000 --> 1:08:09.520
- the density of annotations you need.
-
-1:08:09.520 --> 1:08:13.840
- So the idea being to learn the latent space around which
-
-1:08:13.840 --> 1:08:17.960
- the data is organized and then map the sparse annotations
-
-1:08:17.960 --> 1:08:18.760
- into it.
-
-1:08:18.760 --> 1:08:23.560
- And sure, I mean, that's clearly a very good idea.
-
-1:08:23.560 --> 1:08:26.080
- It's not really a topic I would be working on,
-
-1:08:26.080 --> 1:08:28.040
- but it's clearly a good idea.
-
-1:08:28.040 --> 1:08:31.760
- So it would get us to solve some problems that?
-
-1:08:31.760 --> 1:08:34.880
- It will get us to incremental improvements
-
-1:08:34.880 --> 1:08:38.240
- in labeled data efficiency.
-
-1:08:38.240 --> 1:08:43.520
- Do you have concerns about short term or long term threats
-
-1:08:43.520 --> 1:08:47.800
- from AI, from artificial intelligence?
-
-1:08:47.800 --> 1:08:50.640
- Yes, definitely to some extent.
-
-1:08:50.640 --> 1:08:52.800
- And what's the shape of those concerns?
-
-1:08:52.800 --> 1:08:56.880
- This is actually something I've briefly written about.
-
-1:08:56.880 --> 1:09:02.680
- But the capabilities of deep learning technology
-
-1:09:02.680 --> 1:09:05.200
- can be used in many ways that are
-
-1:09:05.200 --> 1:09:09.760
- concerning from mass surveillance with things
-
-1:09:09.760 --> 1:09:11.880
- like facial recognition.
-
-1:09:11.880 --> 1:09:15.440
- In general, tracking lots of data about everyone
-
-1:09:15.440 --> 1:09:18.920
- and then being able to making sense of this data
-
-1:09:18.920 --> 1:09:22.240
- to do identification, to do prediction.
-
-1:09:22.240 --> 1:09:23.160
- That's concerning.
-
-1:09:23.160 --> 1:09:26.560
- That's something that's being very aggressively pursued
-
-1:09:26.560 --> 1:09:31.440
- by totalitarian states like China.
-
-1:09:31.440 --> 1:09:34.000
- One thing I am very much concerned about
-
-1:09:34.000 --> 1:09:40.640
- is that our lives are increasingly online,
-
-1:09:40.640 --> 1:09:43.280
- are increasingly digital, made of information,
-
-1:09:43.280 --> 1:09:48.080
- made of information consumption and information production,
-
-1:09:48.080 --> 1:09:51.800
- our digital footprint, I would say.
-
-1:09:51.800 --> 1:09:56.280
- And if you absorb all of this data
-
-1:09:56.280 --> 1:10:01.440
- and you are in control of where you consume information,
-
-1:10:01.440 --> 1:10:06.960
- social networks and so on, recommendation engines,
-
-1:10:06.960 --> 1:10:10.200
- then you can build a sort of reinforcement
-
-1:10:10.200 --> 1:10:13.760
- loop for human behavior.
-
-1:10:13.760 --> 1:10:18.360
- You can observe the state of your mind at time t.
-
-1:10:18.360 --> 1:10:21.080
- You can predict how you would react
-
-1:10:21.080 --> 1:10:23.800
- to different pieces of content, how
-
-1:10:23.800 --> 1:10:27.000
- to get you to move your mind in a certain direction.
-
-1:10:27.000 --> 1:10:33.160
- And then you can feed you the specific piece of content
-
-1:10:33.160 --> 1:10:35.680
- that would move you in a specific direction.
-
-1:10:35.680 --> 1:10:41.800
- And you can do this at scale in terms
-
-1:10:41.800 --> 1:10:44.960
- of doing it continuously in real time.
-
-1:10:44.960 --> 1:10:46.440
- You can also do it at scale in terms
-
-1:10:46.440 --> 1:10:50.480
- of scaling this to many, many people, to entire populations.
-
-1:10:50.480 --> 1:10:53.840
- So potentially, artificial intelligence,
-
-1:10:53.840 --> 1:10:57.440
- even in its current state, if you combine it
-
-1:10:57.440 --> 1:11:01.760
- with the internet, with the fact that all of our lives
-
-1:11:01.760 --> 1:11:05.120
- are moving to digital devices and digital information
-
-1:11:05.120 --> 1:11:08.720
- consumption and creation, what you get
-
-1:11:08.720 --> 1:11:14.480
- is the possibility to achieve mass manipulation of behavior
-
-1:11:14.480 --> 1:11:16.840
- and mass psychological control.
-
-1:11:16.840 --> 1:11:18.520
- And this is a very real possibility.
-
-1:11:18.520 --> 1:11:22.080
- Yeah, so you're talking about any kind of recommender system.
-
-1:11:22.080 --> 1:11:26.160
- Let's look at the YouTube algorithm, Facebook,
-
-1:11:26.160 --> 1:11:29.720
- anything that recommends content you should watch next.
-
-1:11:29.720 --> 1:11:32.960
- And it's fascinating to think that there's
-
-1:11:32.960 --> 1:11:41.120
- some aspects of human behavior that you can say a problem of,
-
-1:11:41.120 --> 1:11:45.400
- is this person hold Republican beliefs or Democratic beliefs?
-
-1:11:45.400 --> 1:11:50.240
- And this is a trivial, that's an objective function.
-
-1:11:50.240 --> 1:11:52.600
- And you can optimize, and you can measure,
-
-1:11:52.600 --> 1:11:54.360
- and you can turn everybody into a Republican
-
-1:11:54.360 --> 1:11:56.080
- or everybody into a Democrat.
-
-1:11:56.080 --> 1:11:57.840
- I do believe it's true.
-
-1:11:57.840 --> 1:12:03.680
- So the human mind is very, if you look at the human mind
-
-1:12:03.680 --> 1:12:05.320
- as a kind of computer program, it
-
-1:12:05.320 --> 1:12:07.560
- has a very large exploit surface.
-
-1:12:07.560 --> 1:12:09.360
- It has many, many vulnerabilities.
-
-1:12:09.360 --> 1:12:10.840
- Exploit surfaces, yeah.
-
-1:12:10.840 --> 1:12:13.520
- Ways you can control it.
-
-1:12:13.520 --> 1:12:16.680
- For instance, when it comes to your political beliefs,
-
-1:12:16.680 --> 1:12:19.400
- this is very much tied to your identity.
-
-1:12:19.400 --> 1:12:23.040
- So for instance, if I'm in control of your news feed
-
-1:12:23.040 --> 1:12:26.000
- on your favorite social media platforms,
-
-1:12:26.000 --> 1:12:29.360
- this is actually where you're getting your news from.
-
-1:12:29.360 --> 1:12:32.960
- And of course, I can choose to only show you
-
-1:12:32.960 --> 1:12:37.120
- news that will make you see the world in a specific way.
-
-1:12:37.120 --> 1:12:41.920
- But I can also create incentives for you
-
-1:12:41.920 --> 1:12:44.720
- to post about some political beliefs.
-
-1:12:44.720 --> 1:12:47.960
- And then when I get you to express a statement,
-
-1:12:47.960 --> 1:12:51.840
- if it's a statement that me as the controller,
-
-1:12:51.840 --> 1:12:53.800
- I want to reinforce.
-
-1:12:53.800 --> 1:12:55.560
- I can just show it to people who will agree,
-
-1:12:55.560 --> 1:12:56.880
- and they will like it.
-
-1:12:56.880 --> 1:12:59.280
- And that will reinforce the statement in your mind.
-
-1:12:59.280 --> 1:13:02.760
- If this is a statement I want you to,
-
-1:13:02.760 --> 1:13:05.320
- this is a belief I want you to abandon,
-
-1:13:05.320 --> 1:13:09.600
- I can, on the other hand, show it to opponents.
-
-1:13:09.600 --> 1:13:10.640
- We'll attack you.
-
-1:13:10.640 --> 1:13:12.840
- And because they attack you, at the very least,
-
-1:13:12.840 --> 1:13:16.840
- next time you will think twice about posting it.
-
-1:13:16.840 --> 1:13:20.280
- But maybe you will even start believing this
-
-1:13:20.280 --> 1:13:22.840
- because you got pushback.
-
-1:13:22.840 --> 1:13:28.440
- So there are many ways in which social media platforms
-
-1:13:28.440 --> 1:13:30.520
- can potentially control your opinions.
-
-1:13:30.520 --> 1:13:35.040
- And today, so all of these things
-
-1:13:35.040 --> 1:13:38.240
- are already being controlled by AI algorithms.
-
-1:13:38.240 --> 1:13:41.880
- These algorithms do not have any explicit political goal
-
-1:13:41.880 --> 1:13:42.880
- today.
-
-1:13:42.880 --> 1:13:48.680
- Well, potentially they could, like if some totalitarian
-
-1:13:48.680 --> 1:13:52.720
- government takes over social media platforms
-
-1:13:52.720 --> 1:13:55.360
- and decides that now we are going to use this not just
-
-1:13:55.360 --> 1:13:58.040
- for mass surveillance, but also for mass opinion control
-
-1:13:58.040 --> 1:13:59.360
- and behavior control.
-
-1:13:59.360 --> 1:14:01.840
- Very bad things could happen.
-
-1:14:01.840 --> 1:14:06.480
- But what's really fascinating and actually quite concerning
-
-1:14:06.480 --> 1:14:11.280
- is that even without an explicit intent to manipulate,
-
-1:14:11.280 --> 1:14:14.760
- you're already seeing very dangerous dynamics
-
-1:14:14.760 --> 1:14:18.160
- in terms of how these content recommendation
-
-1:14:18.160 --> 1:14:19.800
- algorithms behave.
-
-1:14:19.800 --> 1:14:24.920
- Because right now, the goal, the objective function
-
-1:14:24.920 --> 1:14:28.640
- of these algorithms is to maximize engagement,
-
-1:14:28.640 --> 1:14:32.520
- which seems fairly innocuous at first.
-
-1:14:32.520 --> 1:14:36.480
- However, it is not because content
-
-1:14:36.480 --> 1:14:42.000
- that will maximally engage people, get people to react
-
-1:14:42.000 --> 1:14:44.720
- in an emotional way, get people to click on something.
-
-1:14:44.720 --> 1:14:52.200
- It is very often content that is not
-
-1:14:52.200 --> 1:14:54.400
- healthy to the public discourse.
-
-1:14:54.400 --> 1:14:58.200
- For instance, fake news are far more
-
-1:14:58.200 --> 1:15:01.320
- likely to get you to click on them than real news
-
-1:15:01.320 --> 1:15:06.960
- simply because they are not constrained to reality.
-
-1:15:06.960 --> 1:15:11.360
- So they can be as outrageous, as surprising,
-
-1:15:11.360 --> 1:15:15.880
- as good stories as you want because they're artificial.
-
-1:15:15.880 --> 1:15:18.880
- To me, that's an exciting world because so much good
-
-1:15:18.880 --> 1:15:19.560
- can come.
-
-1:15:19.560 --> 1:15:24.520
- So there's an opportunity to educate people.
-
-1:15:24.520 --> 1:15:31.200
- You can balance people's worldview with other ideas.
-
-1:15:31.200 --> 1:15:33.800
- So there's so many objective functions.
-
-1:15:33.800 --> 1:15:35.840
- The space of objective functions that
-
-1:15:35.840 --> 1:15:40.720
- create better civilizations is large, arguably infinite.
-
-1:15:40.720 --> 1:15:43.720
- But there's also a large space that
-
-1:15:43.720 --> 1:15:51.480
- creates division and destruction, civil war,
-
-1:15:51.480 --> 1:15:53.160
- a lot of bad stuff.
-
-1:15:53.160 --> 1:15:56.920
- And the worry is, naturally, probably that space
-
-1:15:56.920 --> 1:15:59.160
- is bigger, first of all.
-
-1:15:59.160 --> 1:16:04.920
- And if we don't explicitly think about what kind of effects
-
-1:16:04.920 --> 1:16:08.320
- are going to be observed from different objective functions,
-
-1:16:08.320 --> 1:16:10.160
- then we're going to get into trouble.
-
-1:16:10.160 --> 1:16:14.480
- But the question is, how do we get into rooms
-
-1:16:14.480 --> 1:16:18.560
- and have discussions, so inside Google, inside Facebook,
-
-1:16:18.560 --> 1:16:21.840
- inside Twitter, and think about, OK,
-
-1:16:21.840 --> 1:16:24.840
- how can we drive up engagement and, at the same time,
-
-1:16:24.840 --> 1:16:28.200
- create a good society?
-
-1:16:28.200 --> 1:16:29.560
- Is it even possible to have that kind
-
-1:16:29.560 --> 1:16:31.720
- of philosophical discussion?
-
-1:16:31.720 --> 1:16:33.080
- I think you can definitely try.
-
-1:16:33.080 --> 1:16:37.280
- So from my perspective, I would feel rather uncomfortable
-
-1:16:37.280 --> 1:16:41.560
- with companies that are uncomfortable with these new
-
-1:16:41.560 --> 1:16:47.120
- student algorithms, with them making explicit decisions
-
-1:16:47.120 --> 1:16:50.440
- to manipulate people's opinions or behaviors,
-
-1:16:50.440 --> 1:16:53.480
- even if the intent is good, because that's
-
-1:16:53.480 --> 1:16:55.200
- a very totalitarian mindset.
-
-1:16:55.200 --> 1:16:57.440
- So instead, what I would like to see
-
-1:16:57.440 --> 1:16:58.880
- is probably never going to happen,
-
-1:16:58.880 --> 1:17:00.360
- because it's not super realistic,
-
-1:17:00.360 --> 1:17:02.520
- but that's actually something I really care about.
-
-1:17:02.520 --> 1:17:06.280
- I would like all these algorithms
-
-1:17:06.280 --> 1:17:10.560
- to present configuration settings to their users,
-
-1:17:10.560 --> 1:17:14.600
- so that the users can actually make the decision about how
-
-1:17:14.600 --> 1:17:19.000
- they want to be impacted by these information
-
-1:17:19.000 --> 1:17:21.960
- recommendation, content recommendation algorithms.
-
-1:17:21.960 --> 1:17:24.240
- For instance, as a user of something
-
-1:17:24.240 --> 1:17:26.520
- like YouTube or Twitter, maybe I want
-
-1:17:26.520 --> 1:17:30.280
- to maximize learning about a specific topic.
-
-1:17:30.280 --> 1:17:36.800
- So I want the algorithm to feed my curiosity,
-
-1:17:36.800 --> 1:17:38.760
- which is in itself a very interesting problem.
-
-1:17:38.760 --> 1:17:41.200
- So instead of maximizing my engagement,
-
-1:17:41.200 --> 1:17:44.600
- it will maximize how fast and how much I'm learning.
-
-1:17:44.600 --> 1:17:47.360
- And it will also take into account the accuracy,
-
-1:17:47.360 --> 1:17:50.680
- hopefully, of the information I'm learning.
-
-1:17:50.680 --> 1:17:55.680
- So yeah, the user should be able to determine exactly
-
-1:17:55.680 --> 1:17:58.560
- how these algorithms are affecting their lives.
-
-1:17:58.560 --> 1:18:03.520
- I don't want actually any entity making decisions
-
-1:18:03.520 --> 1:18:09.480
- about in which direction they're going to try to manipulate me.
-
-1:18:09.480 --> 1:18:11.680
- I want technology.
-
-1:18:11.680 --> 1:18:14.280
- So AI, these algorithms are increasingly
-
-1:18:14.280 --> 1:18:18.560
- going to be our interface to a world that is increasingly
-
-1:18:18.560 --> 1:18:19.960
- made of information.
-
-1:18:19.960 --> 1:18:25.840
- And I want everyone to be in control of this interface,
-
-1:18:25.840 --> 1:18:29.160
- to interface with the world on their own terms.
-
-1:18:29.160 --> 1:18:32.840
- So if someone wants these algorithms
-
-1:18:32.840 --> 1:18:37.640
- to serve their own personal growth goals,
-
-1:18:37.640 --> 1:18:40.640
- they should be able to configure these algorithms
-
-1:18:40.640 --> 1:18:41.800
- in such a way.
-
-1:18:41.800 --> 1:18:46.680
- Yeah, but so I know it's painful to have explicit decisions.
-
-1:18:46.680 --> 1:18:51.080
- But there is underlying explicit decisions,
-
-1:18:51.080 --> 1:18:53.360
- which is some of the most beautiful fundamental
-
-1:18:53.360 --> 1:18:57.400
- philosophy that we have before us,
-
-1:18:57.400 --> 1:19:01.120
- which is personal growth.
-
-1:19:01.120 --> 1:19:05.680
- If I want to watch videos from which I can learn,
-
-1:19:05.680 --> 1:19:08.080
- what does that mean?
-
-1:19:08.080 --> 1:19:11.800
- So if I have a checkbox that wants to emphasize learning,
-
-1:19:11.800 --> 1:19:15.480
- there's still an algorithm with explicit decisions in it
-
-1:19:15.480 --> 1:19:17.800
- that would promote learning.
-
-1:19:17.800 --> 1:19:19.200
- What does that mean for me?
-
-1:19:19.200 --> 1:19:22.800
- For example, I've watched a documentary on flat Earth
-
-1:19:22.800 --> 1:19:23.640
- theory, I guess.
-
-1:19:27.280 --> 1:19:28.240
- I learned a lot.
-
-1:19:28.240 --> 1:19:29.800
- I'm really glad I watched it.
-
-1:19:29.800 --> 1:19:32.560
- It was a friend recommended it to me.
-
-1:19:32.560 --> 1:19:35.800
- Because I don't have such an allergic reaction to crazy
-
-1:19:35.800 --> 1:19:37.640
- people, as my fellow colleagues do.
-
-1:19:37.640 --> 1:19:40.360
- But it was very eye opening.
-
-1:19:40.360 --> 1:19:42.120
- And for others, it might not be.
-
-1:19:42.120 --> 1:19:45.560
- From others, they might just get turned off from that, same
-
-1:19:45.560 --> 1:19:47.160
- with Republican and Democrat.
-
-1:19:47.160 --> 1:19:50.200
- And it's a non trivial problem.
-
-1:19:50.200 --> 1:19:52.880
- And first of all, if it's done well,
-
-1:19:52.880 --> 1:19:56.560
- I don't think it's something that wouldn't happen,
-
-1:19:56.560 --> 1:19:59.280
- that YouTube wouldn't be promoting,
-
-1:19:59.280 --> 1:20:00.200
- or Twitter wouldn't be.
-
-1:20:00.200 --> 1:20:02.280
- It's just a really difficult problem,
-
-1:20:02.280 --> 1:20:05.520
- how to give people control.
-
-1:20:05.520 --> 1:20:08.960
- Well, it's mostly an interface design problem.
-
-1:20:08.960 --> 1:20:11.080
- The way I see it, you want to create technology
-
-1:20:11.080 --> 1:20:16.400
- that's like a mentor, or a coach, or an assistant,
-
-1:20:16.400 --> 1:20:20.520
- so that it's not your boss.
-
-1:20:20.520 --> 1:20:22.560
- You are in control of it.
-
-1:20:22.560 --> 1:20:25.760
- You are telling it what to do for you.
-
-1:20:25.760 --> 1:20:27.840
- And if you feel like it's manipulating you,
-
-1:20:27.840 --> 1:20:31.760
- it's not actually doing what you want.
-
-1:20:31.760 --> 1:20:34.920
- You should be able to switch to a different algorithm.
-
-1:20:34.920 --> 1:20:36.440
- So that's fine tune control.
-
-1:20:36.440 --> 1:20:38.840
- You kind of learn that you're trusting
-
-1:20:38.840 --> 1:20:40.080
- the human collaboration.
-
-1:20:40.080 --> 1:20:41.920
- I mean, that's how I see autonomous vehicles too,
-
-1:20:41.920 --> 1:20:44.480
- is giving as much information as possible,
-
-1:20:44.480 --> 1:20:47.240
- and you learn that dance yourself.
-
-1:20:47.240 --> 1:20:50.280
- Yeah, Adobe, I don't know if you use Adobe product
-
-1:20:50.280 --> 1:20:52.280
- for like Photoshop.
-
-1:20:52.280 --> 1:20:55.040
- They're trying to see if they can inject YouTube
-
-1:20:55.040 --> 1:20:57.120
- into their interface, but basically allow you
-
-1:20:57.120 --> 1:20:59.840
- to show you all these videos,
-
-1:20:59.840 --> 1:21:03.320
- that everybody's confused about what to do with features.
-
-1:21:03.320 --> 1:21:07.120
- So basically teach people by linking to,
-
-1:21:07.120 --> 1:21:10.280
- in that way, it's an assistant that uses videos
-
-1:21:10.280 --> 1:21:13.440
- as a basic element of information.
-
-1:21:13.440 --> 1:21:18.240
- Okay, so what practically should people do
-
-1:21:18.240 --> 1:21:24.000
- to try to fight against abuses of these algorithms,
-
-1:21:24.000 --> 1:21:27.400
- or algorithms that manipulate us?
-
-1:21:27.400 --> 1:21:29.280
- Honestly, it's a very, very difficult problem,
-
-1:21:29.280 --> 1:21:32.800
- because to start with, there is very little public awareness
-
-1:21:32.800 --> 1:21:35.040
- of these issues.
-
-1:21:35.040 --> 1:21:38.520
- Very few people would think there's anything wrong
-
-1:21:38.520 --> 1:21:39.720
- with the unused algorithm,
-
-1:21:39.720 --> 1:21:42.040
- even though there is actually something wrong already,
-
-1:21:42.040 --> 1:21:44.480
- which is that it's trying to maximize engagement
-
-1:21:44.480 --> 1:21:49.880
- most of the time, which has very negative side effects.
-
-1:21:49.880 --> 1:21:56.160
- So ideally, so the very first thing is to stop
-
-1:21:56.160 --> 1:21:59.560
- trying to purely maximize engagement,
-
-1:21:59.560 --> 1:22:06.560
- try to propagate content based on popularity, right?
-
-1:22:06.560 --> 1:22:11.040
- Instead, take into account the goals
-
-1:22:11.040 --> 1:22:13.560
- and the profiles of each user.
-
-1:22:13.560 --> 1:22:16.920
- So you will be, one example is, for instance,
-
-1:22:16.920 --> 1:22:20.800
- when I look at topic recommendations on Twitter,
-
-1:22:20.800 --> 1:22:24.480
- it's like, you know, they have this news tab
-
-1:22:24.480 --> 1:22:25.480
- with switch recommendations.
-
-1:22:25.480 --> 1:22:28.480
- It's always the worst coverage,
-
-1:22:28.480 --> 1:22:30.360
- because it's content that appeals
-
-1:22:30.360 --> 1:22:34.080
- to the smallest common denominator
-
-1:22:34.080 --> 1:22:37.080
- to all Twitter users, because they're trying to optimize.
-
-1:22:37.080 --> 1:22:39.040
- They're purely trying to optimize popularity.
-
-1:22:39.040 --> 1:22:41.320
- They're purely trying to optimize engagement.
-
-1:22:41.320 --> 1:22:42.960
- But that's not what I want.
-
-1:22:42.960 --> 1:22:46.080
- So they should put me in control of some setting
-
-1:22:46.080 --> 1:22:50.360
- so that I define what's the objective function
-
-1:22:50.360 --> 1:22:52.200
- that Twitter is going to be following
-
-1:22:52.200 --> 1:22:54.120
- to show me this content.
-
-1:22:54.120 --> 1:22:57.360
- And honestly, so this is all about interface design.
-
-1:22:57.360 --> 1:22:59.440
- And we are not, it's not realistic
-
-1:22:59.440 --> 1:23:01.760
- to give users control of a bunch of knobs
-
-1:23:01.760 --> 1:23:03.400
- that define algorithm.
-
-1:23:03.400 --> 1:23:06.760
- Instead, we should purely put them in charge
-
-1:23:06.760 --> 1:23:09.400
- of defining the objective function.
-
-1:23:09.400 --> 1:23:13.240
- Like, let the user tell us what they want to achieve,
-
-1:23:13.240 --> 1:23:15.280
- how they want this algorithm to impact their lives.
-
-1:23:15.280 --> 1:23:16.680
- So do you think it is that,
-
-1:23:16.680 --> 1:23:19.360
- or do they provide individual article by article
-
-1:23:19.360 --> 1:23:21.600
- reward structure where you give a signal,
-
-1:23:21.600 --> 1:23:24.720
- I'm glad I saw this, or I'm glad I didn't?
-
-1:23:24.720 --> 1:23:28.480
- So like a Spotify type feedback mechanism,
-
-1:23:28.480 --> 1:23:30.680
- it works to some extent.
-
-1:23:30.680 --> 1:23:32.000
- I'm kind of skeptical about it
-
-1:23:32.000 --> 1:23:34.880
- because the only way the algorithm,
-
-1:23:34.880 --> 1:23:39.120
- the algorithm will attempt to relate your choices
-
-1:23:39.120 --> 1:23:41.040
- with the choices of everyone else,
-
-1:23:41.040 --> 1:23:45.000
- which might, you know, if you have an average profile
-
-1:23:45.000 --> 1:23:47.880
- that works fine, I'm sure Spotify accommodations work fine
-
-1:23:47.880 --> 1:23:49.560
- if you just like mainstream stuff.
-
-1:23:49.560 --> 1:23:53.960
- If you don't, it can be, it's not optimal at all actually.
-
-1:23:53.960 --> 1:23:56.040
- It'll be in an efficient search
-
-1:23:56.040 --> 1:24:00.800
- for the part of the Spotify world that represents you.
-
-1:24:00.800 --> 1:24:02.960
- So it's a tough problem,
-
-1:24:02.960 --> 1:24:07.960
- but do note that even a feedback system
-
-1:24:07.960 --> 1:24:10.880
- like what Spotify has does not give me control
-
-1:24:10.880 --> 1:24:15.000
- over what the algorithm is trying to optimize for.
-
-1:24:16.320 --> 1:24:19.360
- Well, public awareness, which is what we're doing now,
-
-1:24:19.360 --> 1:24:21.360
- is a good place to start.
-
-1:24:21.360 --> 1:24:25.960
- Do you have concerns about longterm existential threats
-
-1:24:25.960 --> 1:24:27.360
- of artificial intelligence?
-
-1:24:28.280 --> 1:24:31.040
- Well, as I was saying,
-
-1:24:31.040 --> 1:24:33.360
- our world is increasingly made of information.
-
-1:24:33.360 --> 1:24:36.240
- AI algorithms are increasingly going to be our interface
-
-1:24:36.240 --> 1:24:37.880
- to this world of information,
-
-1:24:37.880 --> 1:24:41.480
- and somebody will be in control of these algorithms.
-
-1:24:41.480 --> 1:24:45.920
- And that puts us in any kind of a bad situation, right?
-
-1:24:45.920 --> 1:24:46.880
- It has risks.
-
-1:24:46.880 --> 1:24:50.840
- It has risks coming from potentially large companies
-
-1:24:50.840 --> 1:24:53.760
- wanting to optimize their own goals,
-
-1:24:53.760 --> 1:24:55.960
- maybe profit, maybe something else.
-
-1:24:55.960 --> 1:25:00.720
- Also from governments who might want to use these algorithms
-
-1:25:00.720 --> 1:25:03.520
- as a means of control of the population.
-
-1:25:03.520 --> 1:25:05.000
- Do you think there's existential threat
-
-1:25:05.000 --> 1:25:06.320
- that could arise from that?
-
-1:25:06.320 --> 1:25:09.120
- So existential threat.
-
-1:25:09.120 --> 1:25:13.240
- So maybe you're referring to the singularity narrative
-
-1:25:13.240 --> 1:25:15.560
- where robots just take over.
-
-1:25:15.560 --> 1:25:18.320
- Well, I don't, I'm not terminating robots,
-
-1:25:18.320 --> 1:25:21.000
- and I don't believe it has to be a singularity.
-
-1:25:21.000 --> 1:25:24.800
- We're just talking to, just like you said,
-
-1:25:24.800 --> 1:25:27.920
- the algorithm controlling masses of populations.
-
-1:25:28.920 --> 1:25:31.120
- The existential threat being,
-
-1:25:32.640 --> 1:25:36.760
- hurt ourselves much like a nuclear war would hurt ourselves.
-
-1:25:36.760 --> 1:25:37.600
- That kind of thing.
-
-1:25:37.600 --> 1:25:39.480
- I don't think that requires a singularity.
-
-1:25:39.480 --> 1:25:42.560
- That requires a loss of control over AI algorithm.
-
-1:25:42.560 --> 1:25:43.560
- Yes.
-
-1:25:43.560 --> 1:25:47.000
- So I do agree there are concerning trends.
-
-1:25:47.000 --> 1:25:52.000
- Honestly, I wouldn't want to make any longterm predictions.
-
-1:25:52.960 --> 1:25:56.000
- I don't think today we really have the capability
-
-1:25:56.000 --> 1:25:58.560
- to see what the dangers of AI
-
-1:25:58.560 --> 1:26:01.360
- are going to be in 50 years, in 100 years.
-
-1:26:01.360 --> 1:26:04.800
- I do see that we are already faced
-
-1:26:04.800 --> 1:26:08.840
- with concrete and present dangers
-
-1:26:08.840 --> 1:26:11.560
- surrounding the negative side effects
-
-1:26:11.560 --> 1:26:14.960
- of content recombination systems, of newsfeed algorithms
-
-1:26:14.960 --> 1:26:17.640
- concerning algorithmic bias as well.
-
-1:26:18.640 --> 1:26:21.200
- So we are delegating more and more
-
-1:26:22.240 --> 1:26:25.080
- decision processes to algorithms.
-
-1:26:25.080 --> 1:26:26.760
- Some of these algorithms are uncrafted,
-
-1:26:26.760 --> 1:26:29.360
- some are learned from data,
-
-1:26:29.360 --> 1:26:31.920
- but we are delegating control.
-
-1:26:32.920 --> 1:26:36.280
- Sometimes it's a good thing, sometimes not so much.
-
-1:26:36.280 --> 1:26:39.480
- And there is in general very little supervision
-
-1:26:39.480 --> 1:26:41.000
- of this process, right?
-
-1:26:41.000 --> 1:26:45.400
- So we are still in this period of very fast change,
-
-1:26:45.400 --> 1:26:50.400
- even chaos, where society is restructuring itself,
-
-1:26:50.920 --> 1:26:53.160
- turning into an information society,
-
-1:26:53.160 --> 1:26:54.520
- which itself is turning into
-
-1:26:54.520 --> 1:26:58.360
- an increasingly automated information passing society.
-
-1:26:58.360 --> 1:27:02.520
- And well, yeah, I think the best we can do today
-
-1:27:02.520 --> 1:27:06.040
- is try to raise awareness around some of these issues.
-
-1:27:06.040 --> 1:27:07.680
- And I think we're actually making good progress.
-
-1:27:07.680 --> 1:27:11.720
- If you look at algorithmic bias, for instance,
-
-1:27:12.760 --> 1:27:14.760
- three years ago, even two years ago,
-
-1:27:14.760 --> 1:27:17.040
- very, very few people were talking about it.
-
-1:27:17.040 --> 1:27:20.320
- And now all the big companies are talking about it.
-
-1:27:20.320 --> 1:27:22.360
- They are often not in a very serious way,
-
-1:27:22.360 --> 1:27:24.560
- but at least it is part of the public discourse.
-
-1:27:24.560 --> 1:27:27.080
- You see people in Congress talking about it.
-
-1:27:27.080 --> 1:27:31.960
- And it all started from raising awareness.
-
-1:27:31.960 --> 1:27:32.800
- Right.
-
-1:27:32.800 --> 1:27:36.080
- So in terms of alignment problem,
-
-1:27:36.080 --> 1:27:39.400
- trying to teach as we allow algorithms,
-
-1:27:39.400 --> 1:27:41.520
- just even recommender systems on Twitter,
-
-1:27:43.640 --> 1:27:47.080
- encoding human values and morals,
-
-1:27:48.280 --> 1:27:50.200
- decisions that touch on ethics,
-
-1:27:50.200 --> 1:27:52.600
- how hard do you think that problem is?
-
-1:27:52.600 --> 1:27:57.240
- How do we have lost functions in neural networks
-
-1:27:57.240 --> 1:27:58.640
- that have some component,
-
-1:27:58.640 --> 1:28:01.080
- some fuzzy components of human morals?
-
-1:28:01.080 --> 1:28:06.080
- Well, I think this is really all about objective function engineering,
-
-1:28:06.080 --> 1:28:10.520
- which is probably going to be increasingly a topic of concern in the future.
-
-1:28:10.520 --> 1:28:14.640
- Like for now, we're just using very naive loss functions
-
-1:28:14.640 --> 1:28:17.760
- because the hard part is not actually what you're trying to minimize.
-
-1:28:17.760 --> 1:28:19.040
- It's everything else.
-
-1:28:19.040 --> 1:28:22.840
- But as the everything else is going to be increasingly automated,
-
-1:28:22.840 --> 1:28:27.040
- we're going to be focusing our human attention
-
-1:28:27.040 --> 1:28:30.240
- on increasingly high level components,
-
-1:28:30.240 --> 1:28:32.680
- like what's actually driving the whole learning system,
-
-1:28:32.680 --> 1:28:33.960
- like the objective function.
-
-1:28:33.960 --> 1:28:36.920
- So loss function engineering is going to be,
-
-1:28:36.920 --> 1:28:40.640
- loss function engineer is probably going to be a job title in the future.
-
-1:28:40.640 --> 1:28:44.520
- And then the tooling you're creating with Keras essentially
-
-1:28:44.520 --> 1:28:47.040
- takes care of all the details underneath.
-
-1:28:47.040 --> 1:28:52.720
- And basically the human expert is needed for exactly that.
-
-1:28:52.720 --> 1:28:53.920
- That's the idea.
-
-1:28:53.920 --> 1:28:57.640
- Keras is the interface between the data you're collecting
-
-1:28:57.640 --> 1:28:59.080
- and the business goals.
-
-1:28:59.080 --> 1:29:03.480
- And your job as an engineer is going to be to express your business goals
-
-1:29:03.480 --> 1:29:06.720
- and your understanding of your business or your product,
-
-1:29:06.720 --> 1:29:11.840
- your system as a kind of loss function or a kind of set of constraints.
-
-1:29:11.840 --> 1:29:19.480
- Does the possibility of creating an AGI system excite you or scare you or bore you?
-
-1:29:19.480 --> 1:29:22.080
- So intelligence can never really be general.
-
-1:29:22.080 --> 1:29:26.400
- You know, at best it can have some degree of generality like human intelligence.
-
-1:29:26.400 --> 1:29:30.640
- It also always has some specialization in the same way that human intelligence
-
-1:29:30.640 --> 1:29:33.440
- is specialized in a certain category of problems,
-
-1:29:33.440 --> 1:29:35.440
- is specialized in the human experience.
-
-1:29:35.440 --> 1:29:37.280
- And when people talk about AGI,
-
-1:29:37.280 --> 1:29:42.520
- I'm never quite sure if they're talking about very, very smart AI,
-
-1:29:42.520 --> 1:29:45.080
- so smart that it's even smarter than humans,
-
-1:29:45.080 --> 1:29:48.000
- or they're talking about human like intelligence,
-
-1:29:48.000 --> 1:29:49.680
- because these are different things.
-
-1:29:49.680 --> 1:29:54.760
- Let's say, presumably I'm oppressing you today with my humanness.
-
-1:29:54.760 --> 1:29:59.240
- So imagine that I was in fact a robot.
-
-1:29:59.240 --> 1:30:01.920
- So what does that mean?
-
-1:30:01.920 --> 1:30:04.920
- That I'm impressing you with natural language processing.
-
-1:30:04.920 --> 1:30:07.840
- Maybe if you weren't able to see me, maybe this is a phone call.
-
-1:30:07.840 --> 1:30:10.000
- So that kind of system.
-
-1:30:10.000 --> 1:30:11.120
- Companion.
-
-1:30:11.120 --> 1:30:15.040
- So that's very much about building human like AI.
-
-1:30:15.040 --> 1:30:18.200
- And you're asking me, you know, is this an exciting perspective?
-
-1:30:18.200 --> 1:30:19.440
- Yes.
-
-1:30:19.440 --> 1:30:21.760
- I think so, yes.
-
-1:30:21.760 --> 1:30:28.000
- Not so much because of what artificial human like intelligence could do,
-
-1:30:28.000 --> 1:30:30.880
- but, you know, from an intellectual perspective,
-
-1:30:30.880 --> 1:30:34.120
- I think if you could build truly human like intelligence,
-
-1:30:34.120 --> 1:30:37.240
- that means you could actually understand human intelligence,
-
-1:30:37.240 --> 1:30:39.880
- which is fascinating, right?
-
-1:30:39.880 --> 1:30:42.680
- Human like intelligence is going to require emotions.
-
-1:30:42.680 --> 1:30:44.400
- It's going to require consciousness,
-
-1:30:44.400 --> 1:30:49.720
- which is not things that would normally be required by an intelligent system.
-
-1:30:49.720 --> 1:30:53.160
- If you look at, you know, we were mentioning earlier like science
-
-1:30:53.160 --> 1:30:59.600
- as a superhuman problem solving agent or system,
-
-1:30:59.600 --> 1:31:02.120
- it does not have consciousness, it doesn't have emotions.
-
-1:31:02.120 --> 1:31:04.320
- In general, so emotions,
-
-1:31:04.320 --> 1:31:07.640
- I see consciousness as being on the same spectrum as emotions.
-
-1:31:07.640 --> 1:31:12.280
- It is a component of the subjective experience
-
-1:31:12.280 --> 1:31:18.800
- that is meant very much to guide behavior generation, right?
-
-1:31:18.800 --> 1:31:20.800
- It's meant to guide your behavior.
-
-1:31:20.800 --> 1:31:24.520
- In general, human intelligence and animal intelligence
-
-1:31:24.520 --> 1:31:29.280
- has evolved for the purpose of behavior generation, right?
-
-1:31:29.280 --> 1:31:30.680
- Including in a social context.
-
-1:31:30.680 --> 1:31:32.480
- So that's why we actually need emotions.
-
-1:31:32.480 --> 1:31:34.920
- That's why we need consciousness.
-
-1:31:34.920 --> 1:31:38.360
- An artificial intelligence system developed in a different context
-
-1:31:38.360 --> 1:31:42.800
- may well never need them, may well never be conscious like science.
-
-1:31:42.800 --> 1:31:47.960
- Well, on that point, I would argue it's possible to imagine
-
-1:31:47.960 --> 1:31:51.480
- that there's echoes of consciousness in science
-
-1:31:51.480 --> 1:31:55.480
- when viewed as an organism, that science is consciousness.
-
-1:31:55.480 --> 1:31:59.160
- So, I mean, how would you go about testing this hypothesis?
-
-1:31:59.160 --> 1:32:07.000
- How do you probe the subjective experience of an abstract system like science?
-
-1:32:07.000 --> 1:32:10.400
- Well, the point of probing any subjective experience is impossible
-
-1:32:10.400 --> 1:32:13.200
- because I'm not science, I'm Lex.
-
-1:32:13.200 --> 1:32:20.520
- So I can't probe another entity, it's no more than bacteria on my skin.
-
-1:32:20.520 --> 1:32:24.160
- You're Lex, I can ask you questions about your subjective experience
-
-1:32:24.160 --> 1:32:28.440
- and you can answer me, and that's how I know you're conscious.
-
-1:32:28.440 --> 1:32:31.840
- Yes, but that's because we speak the same language.
-
-1:32:31.840 --> 1:32:35.520
- You perhaps, we have to speak the language of science in order to ask it.
-
-1:32:35.520 --> 1:32:40.320
- Honestly, I don't think consciousness, just like emotions of pain and pleasure,
-
-1:32:40.320 --> 1:32:44.160
- is not something that inevitably arises
-
-1:32:44.160 --> 1:32:47.920
- from any sort of sufficiently intelligent information processing.
-
-1:32:47.920 --> 1:32:53.920
- It is a feature of the mind, and if you've not implemented it explicitly, it is not there.
-
-1:32:53.920 --> 1:32:58.960
- So you think it's an emergent feature of a particular architecture.
-
-1:32:58.960 --> 1:33:00.320
- So do you think...
-
-1:33:00.320 --> 1:33:02.000
- It's a feature in the same sense.
-
-1:33:02.000 --> 1:33:08.240
- So, again, the subjective experience is all about guiding behavior.
-
-1:33:08.240 --> 1:33:15.120
- If the problems you're trying to solve don't really involve an embodied agent,
-
-1:33:15.120 --> 1:33:19.520
- maybe in a social context, generating behavior and pursuing goals like this.
-
-1:33:19.520 --> 1:33:22.160
- And if you look at science, that's not really what's happening.
-
-1:33:22.160 --> 1:33:27.920
- Even though it is, it is a form of artificial AI, artificial intelligence,
-
-1:33:27.920 --> 1:33:31.920
- in the sense that it is solving problems, it is accumulating knowledge,
-
-1:33:31.920 --> 1:33:35.040
- accumulating solutions and so on.
-
-1:33:35.040 --> 1:33:39.440
- So if you're not explicitly implementing a subjective experience,
-
-1:33:39.440 --> 1:33:44.000
- implementing certain emotions and implementing consciousness,
-
-1:33:44.000 --> 1:33:47.360
- it's not going to just spontaneously emerge.
-
-1:33:47.360 --> 1:33:48.080
- Yeah.
-
-1:33:48.080 --> 1:33:53.200
- But so for a system like, human like intelligence system that has consciousness,
-
-1:33:53.200 --> 1:33:55.840
- do you think it needs to have a body?
-
-1:33:55.840 --> 1:33:56.720
- Yes, definitely.
-
-1:33:56.720 --> 1:33:59.600
- I mean, it doesn't have to be a physical body, right?
-
-1:33:59.600 --> 1:34:03.440
- And there's not that much difference between a realistic simulation in the real world.
-
-1:34:03.440 --> 1:34:06.400
- So there has to be something you have to preserve kind of thing.
-
-1:34:06.400 --> 1:34:11.840
- Yes, but human like intelligence can only arise in a human like context.
-
-1:34:11.840 --> 1:34:16.800
- Intelligence needs other humans in order for you to demonstrate
-
-1:34:16.800 --> 1:34:19.040
- that you have human like intelligence, essentially.
-
-1:34:19.040 --> 1:34:19.540
- Yes.
-
-1:34:20.320 --> 1:34:28.080
- So what kind of tests and demonstration would be sufficient for you
-
-1:34:28.080 --> 1:34:30.960
- to demonstrate human like intelligence?
-
-1:34:30.960 --> 1:34:31.360
- Yeah.
-
-1:34:31.360 --> 1:34:35.600
- Just out of curiosity, you've talked about in terms of theorem proving
-
-1:34:35.600 --> 1:34:38.000
- and program synthesis, I think you've written about
-
-1:34:38.000 --> 1:34:40.480
- that there's no good benchmarks for this.
-
-1:34:40.480 --> 1:34:40.720
- Yeah.
-
-1:34:40.720 --> 1:34:42.000
- That's one of the problems.
-
-1:34:42.000 --> 1:34:46.320
- So let's talk program synthesis.
-
-1:34:46.320 --> 1:34:47.760
- So what do you imagine is a good...
-
-1:34:48.800 --> 1:34:51.360
- I think it's related questions for human like intelligence
-
-1:34:51.360 --> 1:34:52.560
- and for program synthesis.
-
-1:34:53.360 --> 1:34:56.080
- What's a good benchmark for either or both?
-
-1:34:56.080 --> 1:34:56.480
- Right.
-
-1:34:56.480 --> 1:34:59.200
- So I mean, you're actually asking two questions,
-
-1:34:59.200 --> 1:35:02.480
- which is one is about quantifying intelligence
-
-1:35:02.480 --> 1:35:06.880
- and comparing the intelligence of an artificial system
-
-1:35:06.880 --> 1:35:08.480
- to the intelligence for human.
-
-1:35:08.480 --> 1:35:13.440
- And the other is about the degree to which this intelligence is human like.
-
-1:35:13.440 --> 1:35:15.120
- It's actually two different questions.
-
-1:35:16.560 --> 1:35:18.960
- So you mentioned earlier the Turing test.
-
-1:35:19.680 --> 1:35:23.200
- Well, I actually don't like the Turing test because it's very lazy.
-
-1:35:23.200 --> 1:35:28.720
- It's all about completely bypassing the problem of defining and measuring intelligence
-
-1:35:28.720 --> 1:35:34.160
- and instead delegating to a human judge or a panel of human judges.
-
-1:35:34.160 --> 1:35:37.120
- So it's a total copout, right?
-
-1:35:38.160 --> 1:35:43.200
- If you want to measure how human like an agent is,
-
-1:35:43.760 --> 1:35:46.640
- I think you have to make it interact with other humans.
-
-1:35:47.600 --> 1:35:53.760
- Maybe it's not necessarily a good idea to have these other humans be the judges.
-
-1:35:53.760 --> 1:35:59.280
- Maybe you should just observe behavior and compare it to what a human would actually have done.
-
-1:36:00.560 --> 1:36:05.120
- When it comes to measuring how smart, how clever an agent is
-
-1:36:05.120 --> 1:36:11.120
- and comparing that to the degree of human intelligence.
-
-1:36:11.120 --> 1:36:12.960
- So we're already talking about two things, right?
-
-1:36:13.520 --> 1:36:20.320
- The degree, kind of like the magnitude of an intelligence and its direction, right?
-
-1:36:20.320 --> 1:36:23.280
- Like the norm of a vector and its direction.
-
-1:36:23.280 --> 1:36:32.000
- And the direction is like human likeness and the magnitude, the norm is intelligence.
-
-1:36:32.720 --> 1:36:34.080
- You could call it intelligence, right?
-
-1:36:34.080 --> 1:36:41.040
- So the direction, your sense, the space of directions that are human like is very narrow.
-
-1:36:41.040 --> 1:36:41.200
- Yeah.
-
-1:36:42.240 --> 1:36:48.880
- So the way you would measure the magnitude of intelligence in a system
-
-1:36:48.880 --> 1:36:54.640
- in a way that also enables you to compare it to that of a human.
-
-1:36:54.640 --> 1:36:59.200
- Well, if you look at different benchmarks for intelligence today,
-
-1:36:59.200 --> 1:37:04.160
- they're all too focused on skill at a given task.
-
-1:37:04.160 --> 1:37:08.720
- Like skill at playing chess, skill at playing Go, skill at playing Dota.
-
-1:37:10.720 --> 1:37:15.600
- And I think that's not the right way to go about it because you can always
-
-1:37:15.600 --> 1:37:18.240
- beat a human at one specific task.
-
-1:37:19.200 --> 1:37:23.920
- The reason why our skill at playing Go or juggling or anything is impressive
-
-1:37:23.920 --> 1:37:28.400
- is because we are expressing this skill within a certain set of constraints.
-
-1:37:28.400 --> 1:37:32.320
- If you remove the constraints, the constraints that we have one lifetime,
-
-1:37:32.320 --> 1:37:36.080
- that we have this body and so on, if you remove the context,
-
-1:37:36.080 --> 1:37:40.480
- if you have unlimited string data, if you can have access to, you know,
-
-1:37:40.480 --> 1:37:44.640
- for instance, if you look at juggling, if you have no restriction on the hardware,
-
-1:37:44.640 --> 1:37:48.400
- then achieving arbitrary levels of skill is not very interesting
-
-1:37:48.400 --> 1:37:52.400
- and says nothing about the amount of intelligence you've achieved.
-
-1:37:52.400 --> 1:37:57.440
- So if you want to measure intelligence, you need to rigorously define what
-
-1:37:57.440 --> 1:38:02.960
- intelligence is, which in itself, you know, it's a very challenging problem.
-
-1:38:02.960 --> 1:38:04.320
- And do you think that's possible?
-
-1:38:04.320 --> 1:38:06.000
- To define intelligence? Yes, absolutely.
-
-1:38:06.000 --> 1:38:09.760
- I mean, you can provide, many people have provided, you know, some definition.
-
-1:38:10.560 --> 1:38:12.000
- I have my own definition.
-
-1:38:12.000 --> 1:38:13.440
- Where does your definition begin?
-
-1:38:13.440 --> 1:38:16.240
- Where does your definition begin if it doesn't end?
-
-1:38:16.240 --> 1:38:21.680
- Well, I think intelligence is essentially the efficiency
-
-1:38:22.320 --> 1:38:29.760
- with which you turn experience into generalizable programs.
-
-1:38:29.760 --> 1:38:32.800
- So what that means is it's the efficiency with which
-
-1:38:32.800 --> 1:38:36.720
- you turn a sampling of experience space into
-
-1:38:36.720 --> 1:38:46.000
- the ability to process a larger chunk of experience space.
-
-1:38:46.000 --> 1:38:52.560
- So measuring skill can be one proxy across many different tasks,
-
-1:38:52.560 --> 1:38:54.480
- can be one proxy for measuring intelligence.
-
-1:38:54.480 --> 1:38:58.720
- But if you want to only measure skill, you should control for two things.
-
-1:38:58.720 --> 1:39:04.960
- You should control for the amount of experience that your system has
-
-1:39:04.960 --> 1:39:08.080
- and the priors that your system has.
-
-1:39:08.080 --> 1:39:13.120
- But if you look at two agents and you give them the same priors
-
-1:39:13.120 --> 1:39:16.160
- and you give them the same amount of experience,
-
-1:39:16.160 --> 1:39:21.360
- there is one of the agents that is going to learn programs,
-
-1:39:21.360 --> 1:39:25.440
- representations, something, a model that will perform well
-
-1:39:25.440 --> 1:39:28.720
- on the larger chunk of experience space than the other.
-
-1:39:28.720 --> 1:39:30.960
- And that is the smaller agent.
-
-1:39:30.960 --> 1:39:36.960
- Yeah. So if you fix the experience, which generate better programs,
-
-1:39:37.680 --> 1:39:39.520
- better meaning more generalizable.
-
-1:39:39.520 --> 1:39:40.560
- That's really interesting.
-
-1:39:40.560 --> 1:39:42.400
- That's a very nice, clean definition of...
-
-1:39:42.400 --> 1:39:47.280
- Oh, by the way, in this definition, it is already very obvious
-
-1:39:47.280 --> 1:39:49.440
- that intelligence has to be specialized
-
-1:39:49.440 --> 1:39:51.680
- because you're talking about experience space
-
-1:39:51.680 --> 1:39:54.080
- and you're talking about segments of experience space.
-
-1:39:54.080 --> 1:39:57.200
- You're talking about priors and you're talking about experience.
-
-1:39:57.200 --> 1:40:02.480
- All of these things define the context in which intelligence emerges.
-
-1:40:04.480 --> 1:40:08.640
- And you can never look at the totality of experience space, right?
-
-1:40:09.760 --> 1:40:12.160
- So intelligence has to be specialized.
-
-1:40:12.160 --> 1:40:14.960
- But it can be sufficiently large, the experience space,
-
-1:40:14.960 --> 1:40:16.080
- even though it's specialized.
-
-1:40:16.080 --> 1:40:19.120
- There's a certain point when the experience space is large enough
-
-1:40:19.120 --> 1:40:21.440
- to where it might as well be general.
-
-1:40:22.000 --> 1:40:23.920
- It feels general. It looks general.
-
-1:40:23.920 --> 1:40:25.680
- Sure. I mean, it's very relative.
-
-1:40:25.680 --> 1:40:29.360
- Like, for instance, many people would say human intelligence is general.
-
-1:40:29.360 --> 1:40:31.200
- In fact, it is quite specialized.
-
-1:40:32.800 --> 1:40:37.120
- We can definitely build systems that start from the same innate priors
-
-1:40:37.120 --> 1:40:39.120
- as what humans have at birth.
-
-1:40:39.120 --> 1:40:42.320
- Because we already understand fairly well
-
-1:40:42.320 --> 1:40:44.480
- what sort of priors we have as humans.
-
-1:40:44.480 --> 1:40:46.080
- Like many people have worked on this problem.
-
-1:40:46.800 --> 1:40:51.040
- Most notably, Elisabeth Spelke from Harvard.
-
-1:40:51.040 --> 1:40:52.240
- I don't know if you know her.
-
-1:40:52.240 --> 1:40:56.000
- She's worked a lot on what she calls core knowledge.
-
-1:40:56.000 --> 1:41:00.640
- And it is very much about trying to determine and describe
-
-1:41:00.640 --> 1:41:02.320
- what priors we are born with.
-
-1:41:02.320 --> 1:41:04.720
- Like language skills and so on, all that kind of stuff.
-
-1:41:04.720 --> 1:41:05.220
- Exactly.
-
-1:41:06.880 --> 1:41:11.440
- So we have some pretty good understanding of what priors we are born with.
-
-1:41:11.440 --> 1:41:12.560
- So we could...
-
-1:41:13.760 --> 1:41:17.760
- So I've actually been working on a benchmark for the past couple years,
-
-1:41:17.760 --> 1:41:18.640
- you know, on and off.
-
-1:41:18.640 --> 1:41:20.480
- I hope to be able to release it at some point.
-
-1:41:20.480 --> 1:41:21.760
- That's exciting.
-
-1:41:21.760 --> 1:41:25.680
- The idea is to measure the intelligence of systems
-
-1:41:26.800 --> 1:41:28.640
- by countering for priors,
-
-1:41:28.640 --> 1:41:30.480
- countering for amount of experience,
-
-1:41:30.480 --> 1:41:34.800
- and by assuming the same priors as what humans are born with.
-
-1:41:34.800 --> 1:41:39.520
- So that you can actually compare these scores to human intelligence.
-
-1:41:39.520 --> 1:41:43.280
- You can actually have humans pass the same test in a way that's fair.
-
-1:41:43.280 --> 1:41:52.320
- Yeah. And so importantly, such a benchmark should be such that any amount
-
-1:41:52.960 --> 1:41:55.920
- of practicing does not increase your score.
-
-1:41:56.480 --> 1:42:00.560
- So try to picture a game where no matter how much you play this game,
-
-1:42:01.600 --> 1:42:05.040
- that does not change your skill at the game.
-
-1:42:05.040 --> 1:42:05.920
- Can you picture that?
-
-1:42:05.920 --> 1:42:11.040
- As a person who deeply appreciates practice, I cannot actually.
-
-1:42:11.040 --> 1:42:16.560
- There's actually a very simple trick.
-
-1:42:16.560 --> 1:42:19.440
- So in order to come up with a task,
-
-1:42:19.440 --> 1:42:21.760
- so the only thing you can measure is skill at the task.
-
-1:42:21.760 --> 1:42:22.320
- Yes.
-
-1:42:22.320 --> 1:42:24.800
- All tasks are going to involve priors.
-
-1:42:24.800 --> 1:42:25.600
- Yes.
-
-1:42:25.600 --> 1:42:29.920
- The trick is to know what they are and to describe that.
-
-1:42:29.920 --> 1:42:33.760
- And then you make sure that this is the same set of priors as what humans start with.
-
-1:42:33.760 --> 1:42:38.560
- So you create a task that assumes these priors, that exactly documents these priors,
-
-1:42:38.560 --> 1:42:42.240
- so that the priors are made explicit and there are no other priors involved.
-
-1:42:42.240 --> 1:42:48.960
- And then you generate a certain number of samples in experience space for this task, right?
-
-1:42:49.840 --> 1:42:56.320
- And this, for one task, assuming that the task is new for the agent passing it,
-
-1:42:56.320 --> 1:43:04.320
- that's one test of this definition of intelligence that we set up.
-
-1:43:04.320 --> 1:43:06.880
- And now you can scale that to many different tasks,
-
-1:43:06.880 --> 1:43:10.480
- that each task should be new to the agent passing it, right?
-
-1:43:11.360 --> 1:43:14.480
- And also it should be human interpretable and understandable
-
-1:43:14.480 --> 1:43:16.880
- so that you can actually have a human pass the same test.
-
-1:43:16.880 --> 1:43:19.760
- And then you can compare the score of your machine and the score of your human.
-
-1:43:19.760 --> 1:43:20.720
- Which could be a lot of stuff.
-
-1:43:20.720 --> 1:43:23.040
- You could even start a task like MNIST.
-
-1:43:23.040 --> 1:43:28.800
- Just as long as you start with the same set of priors.
-
-1:43:28.800 --> 1:43:34.080
- So the problem with MNIST, humans are already trying to recognize digits, right?
-
-1:43:35.600 --> 1:43:40.960
- But let's say we're considering objects that are not digits,
-
-1:43:42.400 --> 1:43:43.920
- some completely arbitrary patterns.
-
-1:43:44.480 --> 1:43:48.880
- Well, humans already come with visual priors about how to process that.
-
-1:43:48.880 --> 1:43:54.080
- So in order to make the game fair, you would have to isolate these priors
-
-1:43:54.080 --> 1:43:57.280
- and describe them and then express them as computational rules.
-
-1:43:57.280 --> 1:44:01.680
- Having worked a lot with vision science people, that's exceptionally difficult.
-
-1:44:01.680 --> 1:44:03.120
- A lot of progress has been made.
-
-1:44:03.120 --> 1:44:08.080
- There's been a lot of good tests and basically reducing all of human vision into some good priors.
-
-1:44:08.640 --> 1:44:10.960
- We're still probably far away from that perfectly,
-
-1:44:10.960 --> 1:44:14.640
- but as a start for a benchmark, that's an exciting possibility.
-
-1:44:14.640 --> 1:44:24.240
- Yeah, so Elisabeth Spelke actually lists objectness as one of the core knowledge priors.
-
-1:44:24.800 --> 1:44:25.920
- Objectness, cool.
-
-1:44:25.920 --> 1:44:26.880
- Objectness, yeah.
-
-1:44:27.440 --> 1:44:31.520
- So we have priors about objectness, like about the visual space, about time,
-
-1:44:31.520 --> 1:44:34.240
- about agents, about goal oriented behavior.
-
-1:44:35.280 --> 1:44:39.280
- We have many different priors, but what's interesting is that,
-
-1:44:39.280 --> 1:44:43.920
- sure, we have this pretty diverse and rich set of priors,
-
-1:44:43.920 --> 1:44:46.880
- but it's also not that diverse, right?
-
-1:44:46.880 --> 1:44:50.800
- We are not born into this world with a ton of knowledge about the world,
-
-1:44:50.800 --> 1:44:57.840
- with only a small set of core knowledge.
-
-1:44:58.640 --> 1:45:05.040
- Yeah, sorry, do you have a sense of how it feels to us humans that that set is not that large?
-
-1:45:05.040 --> 1:45:09.600
- But just even the nature of time that we kind of integrate pretty effectively
-
-1:45:09.600 --> 1:45:11.600
- through all of our perception, all of our reasoning,
-
-1:45:12.640 --> 1:45:17.680
- maybe how, you know, do you have a sense of how easy it is to encode those priors?
-
-1:45:17.680 --> 1:45:24.560
- Maybe it requires building a universe and then the human brain in order to encode those priors.
-
-1:45:25.440 --> 1:45:28.640
- Or do you have a hope that it can be listed like an axiomatic?
-
-1:45:28.640 --> 1:45:29.280
- I don't think so.
-
-1:45:29.280 --> 1:45:33.040
- So you have to keep in mind that any knowledge about the world that we are
-
-1:45:33.040 --> 1:45:41.120
- born with is something that has to have been encoded into our DNA by evolution at some point.
-
-1:45:41.120 --> 1:45:41.440
- Right.
-
-1:45:41.440 --> 1:45:45.440
- And DNA is a very, very low bandwidth medium.
-
-1:45:46.000 --> 1:45:51.200
- Like it's extremely long and expensive to encode anything into DNA because first of all,
-
-1:45:52.560 --> 1:45:57.440
- you need some sort of evolutionary pressure to guide this writing process.
-
-1:45:57.440 --> 1:46:03.440
- And then, you know, the higher level of information you're trying to write, the longer it's going to take.
-
-1:46:04.480 --> 1:46:13.520
- And the thing in the environment that you're trying to encode knowledge about has to be stable
-
-1:46:13.520 --> 1:46:15.280
- over this duration.
-
-1:46:15.280 --> 1:46:20.960
- So you can only encode into DNA things that constitute an evolutionary advantage.
-
-1:46:20.960 --> 1:46:25.280
- So this is actually a very small subset of all possible knowledge about the world.
-
-1:46:25.280 --> 1:46:32.080
- You can only encode things that are stable, that are true, over very, very long periods of time,
-
-1:46:32.080 --> 1:46:33.680
- typically millions of years.
-
-1:46:33.680 --> 1:46:38.720
- For instance, we might have some visual prior about the shape of snakes, right?
-
-1:46:38.720 --> 1:46:43.920
- But what makes a face, what's the difference between a face and an art face?
-
-1:46:44.560 --> 1:46:48.080
- But consider this interesting question.
-
-1:46:48.080 --> 1:46:56.640
- Do we have any innate sense of the visual difference between a male face and a female face?
-
-1:46:56.640 --> 1:46:57.600
- What do you think?
-
-1:46:58.640 --> 1:46:59.840
- For a human, I mean.
-
-1:46:59.840 --> 1:47:04.000
- I would have to look back into evolutionary history when the genders emerged.
-
-1:47:04.000 --> 1:47:06.240
- But yeah, most...
-
-1:47:06.240 --> 1:47:09.840
- I mean, the faces of humans are quite different from the faces of great apes.
-
-1:47:10.640 --> 1:47:11.600
- Great apes, right?
-
-1:47:12.880 --> 1:47:13.600
- Yeah.
-
-1:47:13.600 --> 1:47:14.800
- That's interesting.
-
-1:47:14.800 --> 1:47:22.800
- Yeah, you couldn't tell the face of a female chimpanzee from the face of a male chimpanzee,
-
-1:47:22.800 --> 1:47:23.440
- probably.
-
-1:47:23.440 --> 1:47:26.160
- Yeah, and I don't think most humans have all that ability.
-
-1:47:26.160 --> 1:47:33.280
- So we do have innate knowledge of what makes a face, but it's actually impossible for us to
-
-1:47:33.280 --> 1:47:40.320
- have any DNA encoded knowledge of the difference between a female human face and a male human face
-
-1:47:40.320 --> 1:47:50.560
- because that knowledge, that information came up into the world actually very recently.
-
-1:47:50.560 --> 1:47:56.400
- If you look at the slowness of the process of encoding knowledge into DNA.
-
-1:47:56.400 --> 1:47:57.360
- Yeah, so that's interesting.
-
-1:47:57.360 --> 1:48:02.080
- That's a really powerful argument that DNA is a low bandwidth and it takes a long time to encode.
-
-1:48:02.800 --> 1:48:05.200
- That naturally creates a very efficient encoding.
-
-1:48:05.200 --> 1:48:12.800
- But one important consequence of this is that, so yes, we are born into this world with a bunch of
-
-1:48:12.800 --> 1:48:17.600
- knowledge, sometimes high level knowledge about the world, like the shape, the rough shape of a
-
-1:48:17.600 --> 1:48:19.520
- snake, of the rough shape of a face.
-
-1:48:20.480 --> 1:48:26.960
- But importantly, because this knowledge takes so long to write, almost all of this innate
-
-1:48:26.960 --> 1:48:32.080
- knowledge is shared with our cousins, with great apes, right?
-
-1:48:32.080 --> 1:48:35.600
- So it is not actually this innate knowledge that makes us special.
-
-1:48:36.320 --> 1:48:42.000
- But to throw it right back at you from the earlier on in our discussion, it's that encoding
-
-1:48:42.960 --> 1:48:48.320
- might also include the entirety of the environment of Earth.
-
-1:48:49.360 --> 1:48:49.920
- To some extent.
-
-1:48:49.920 --> 1:48:56.480
- So it can include things that are important to survival and production, so for which there is
-
-1:48:56.480 --> 1:49:02.880
- some evolutionary pressure, and things that are stable, constant over very, very, very long time
-
-1:49:02.880 --> 1:49:03.380
- periods.
-
-1:49:04.160 --> 1:49:06.320
- And honestly, it's not that much information.
-
-1:49:06.320 --> 1:49:14.400
- There's also, besides the bandwidths constraint and the constraints of the writing process,
-
-1:49:14.400 --> 1:49:21.440
- there's also memory constraints, like DNA, the part of DNA that deals with the human brain,
-
-1:49:21.440 --> 1:49:22.640
- it's actually fairly small.
-
-1:49:22.640 --> 1:49:25.520
- It's like, you know, on the order of megabytes, right?
-
-1:49:25.520 --> 1:49:29.600
- There's not that much high level knowledge about the world you can encode.
-
-1:49:31.600 --> 1:49:38.880
- That's quite brilliant and hopeful for a benchmark that you're referring to of encoding
-
-1:49:38.880 --> 1:49:39.360
- priors.
-
-1:49:39.360 --> 1:49:43.120
- I actually look forward to, I'm skeptical whether you can do it in the next couple of
-
-1:49:43.120 --> 1:49:44.320
- years, but hopefully.
-
-1:49:45.040 --> 1:49:45.760
- I've been working.
-
-1:49:45.760 --> 1:49:49.920
- So honestly, it's a very simple benchmark, and it's not like a big breakthrough or anything.
-
-1:49:49.920 --> 1:49:53.200
- It's more like a fun side project, right?
-
-1:49:53.200 --> 1:49:55.680
- But these fun, so is ImageNet.
-
-1:49:56.480 --> 1:50:04.080
- These fun side projects could launch entire groups of efforts towards creating reasoning
-
-1:50:04.080 --> 1:50:04.960
- systems and so on.
-
-1:50:04.960 --> 1:50:05.440
- And I think...
-
-1:50:05.440 --> 1:50:06.160
- Yeah, that's the goal.
-
-1:50:06.160 --> 1:50:12.080
- It's trying to measure strong generalization, to measure the strength of abstraction in
-
-1:50:12.080 --> 1:50:16.960
- our minds, well, in our minds and in artificial intelligence agencies.
-
-1:50:16.960 --> 1:50:24.800
- And if there's anything true about this science organism is its individual cells love competition.
-
-1:50:24.800 --> 1:50:26.800
- So and benchmarks encourage competition.
-
-1:50:26.800 --> 1:50:29.520
- So that's an exciting possibility.
-
-1:50:29.520 --> 1:50:32.640
- If you, do you think an AI winter is coming?
-
-1:50:33.520 --> 1:50:34.640
- And how do we prevent it?
-
-1:50:35.440 --> 1:50:36.080
- Not really.
-
-1:50:36.080 --> 1:50:42.160
- So an AI winter is something that would occur when there's a big mismatch between how we
-
-1:50:42.160 --> 1:50:47.280
- are selling the capabilities of AI and the actual capabilities of AI.
-
-1:50:47.280 --> 1:50:50.560
- And today, some deep learning is creating a lot of value.
-
-1:50:50.560 --> 1:50:56.240
- And it will keep creating a lot of value in the sense that these models are applicable
-
-1:50:56.240 --> 1:51:00.000
- to a very wide range of problems that are relevant today.
-
-1:51:00.000 --> 1:51:05.120
- And we are only just getting started with applying these algorithms to every problem
-
-1:51:05.120 --> 1:51:06.320
- they could be solving.
-
-1:51:06.320 --> 1:51:10.160
- So deep learning will keep creating a lot of value for the time being.
-
-1:51:10.160 --> 1:51:15.920
- What's concerning, however, is that there's a lot of hype around deep learning and around
-
-1:51:15.920 --> 1:51:16.240
- AI.
-
-1:51:16.240 --> 1:51:22.000
- There are lots of people are overselling the capabilities of these systems, not just
-
-1:51:22.000 --> 1:51:27.760
- the capabilities, but also overselling the fact that they might be more or less, you
-
-1:51:27.760 --> 1:51:36.640
- know, brain like, like given the kind of a mystical aspect, these technologies and also
-
-1:51:36.640 --> 1:51:43.840
- overselling the pace of progress, which, you know, it might look fast in the sense that
-
-1:51:43.840 --> 1:51:46.480
- we have this exponentially increasing number of papers.
-
-1:51:47.760 --> 1:51:52.960
- But again, that's just a simple consequence of the fact that we have ever more people
-
-1:51:52.960 --> 1:51:53.840
- coming into the field.
-
-1:51:54.400 --> 1:51:57.440
- It doesn't mean the progress is actually exponentially fast.
-
-1:51:58.640 --> 1:52:02.720
- Let's say you're trying to raise money for your startup or your research lab.
-
-1:52:02.720 --> 1:52:09.120
- You might want to tell, you know, a grandiose story to investors about how deep learning
-
-1:52:09.120 --> 1:52:14.240
- is just like the brain and how it can solve all these incredible problems like self driving
-
-1:52:14.240 --> 1:52:15.760
- and robotics and so on.
-
-1:52:15.760 --> 1:52:19.440
- And maybe you can tell them that the field is progressing so fast and we are going to
-
-1:52:19.440 --> 1:52:23.040
- have AGI within 15 years or even 10 years.
-
-1:52:23.040 --> 1:52:25.920
- And none of this is true.
-
-1:52:25.920 --> 1:52:32.800
- And every time you're like saying these things and an investor or, you know, a decision maker
-
-1:52:32.800 --> 1:52:41.680
- believes them, well, this is like the equivalent of taking on credit card debt, but for trust,
-
-1:52:41.680 --> 1:52:42.480
- right?
-
-1:52:42.480 --> 1:52:50.160
- And maybe this will, you know, this will be what enables you to raise a lot of money,
-
-1:52:50.160 --> 1:52:54.320
- but ultimately you are creating damage, you are damaging the field.
-
-1:52:54.320 --> 1:53:00.160
- So that's the concern is that that debt, that's what happens with the other AI winters is
-
-1:53:00.160 --> 1:53:04.160
- the concern is you actually tweeted about this with autonomous vehicles, right?
-
-1:53:04.160 --> 1:53:08.960
- There's almost every single company now have promised that they will have full autonomous
-
-1:53:08.960 --> 1:53:11.760
- vehicles by 2021, 2022.
-
-1:53:11.760 --> 1:53:18.080
- That's a good example of the consequences of over hyping the capabilities of AI and
-
-1:53:18.080 --> 1:53:19.280
- the pace of progress.
-
-1:53:19.280 --> 1:53:25.200
- So because I work especially a lot recently in this area, I have a deep concern of what
-
-1:53:25.200 --> 1:53:30.400
- happens when all of these companies after I've invested billions have a meeting and
-
-1:53:30.400 --> 1:53:33.600
- say, how much do we actually, first of all, do we have an autonomous vehicle?
-
-1:53:33.600 --> 1:53:35.280
- The answer will definitely be no.
-
-1:53:35.840 --> 1:53:40.560
- And second will be, wait a minute, we've invested one, two, three, four billion dollars
-
-1:53:40.560 --> 1:53:43.120
- into this and we made no profit.
-
-1:53:43.120 --> 1:53:49.200
- And the reaction to that may be going very hard in other directions that might impact
-
-1:53:49.200 --> 1:53:50.400
- even other industries.
-
-1:53:50.400 --> 1:53:55.520
- And that's what we call an AI winter is when there is backlash where no one believes any
-
-1:53:55.520 --> 1:53:59.360
- of these promises anymore because they've turned that to be big lies the first time
-
-1:53:59.360 --> 1:54:00.240
- around.
-
-1:54:00.240 --> 1:54:06.000
- And this will definitely happen to some extent for autonomous vehicles because the public
-
-1:54:06.000 --> 1:54:13.360
- and decision makers have been convinced that around 2015, they've been convinced by these
-
-1:54:13.360 --> 1:54:19.600
- people who are trying to raise money for their startups and so on, that L5 driving was coming
-
-1:54:19.600 --> 1:54:22.880
- in maybe 2016, maybe 2017, maybe 2018.
-
-1:54:22.880 --> 1:54:26.080
- Now we're in 2019, we're still waiting for it.
-
-1:54:27.600 --> 1:54:32.800
- And so I don't believe we are going to have a full on AI winter because we have these
-
-1:54:32.800 --> 1:54:36.640
- technologies that are producing a tremendous amount of real value.
-
-1:54:37.680 --> 1:54:39.920
- But there is also too much hype.
-
-1:54:39.920 --> 1:54:43.520
- So there will be some backlash, especially there will be backlash.
-
-1:54:44.960 --> 1:54:53.040
- So some startups are trying to sell the dream of AGI and the fact that AGI is going to create
-
-1:54:53.040 --> 1:54:53.760
- infinite value.
-
-1:54:53.760 --> 1:54:55.680
- Like AGI is like a free lunch.
-
-1:54:55.680 --> 1:55:02.800
- Like if you can develop an AI system that passes a certain threshold of IQ or something,
-
-1:55:02.800 --> 1:55:04.400
- then suddenly you have infinite value.
-
-1:55:04.400 --> 1:55:14.160
- And well, there are actually lots of investors buying into this idea and they will wait maybe
-
-1:55:14.160 --> 1:55:17.760
- 10, 15 years and nothing will happen.
-
-1:55:17.760 --> 1:55:22.560
- And the next time around, well, maybe there will be a new generation of investors.
-
-1:55:22.560 --> 1:55:23.360
- No one will care.
-
-1:55:24.800 --> 1:55:27.280
- Human memory is fairly short after all.
-
-1:55:27.280 --> 1:55:34.320
- I don't know about you, but because I've spoken about AGI sometimes poetically, I get a lot
-
-1:55:34.320 --> 1:55:42.000
- of emails from people giving me, they're usually like a large manifestos of they've, they say
-
-1:55:42.000 --> 1:55:47.200
- to me that they have created an AGI system or they know how to do it.
-
-1:55:47.200 --> 1:55:48.880
- And there's a long write up of how to do it.
-
-1:55:48.880 --> 1:55:50.560
- I get a lot of these emails, yeah.
-
-1:55:50.560 --> 1:55:57.760
- They're a little bit feel like it's generated by an AI system actually, but there's usually
-
-1:55:57.760 --> 1:56:06.640
- no diagram, you have a transformer generating crank papers about AGI.
-
-1:56:06.640 --> 1:56:12.160
- So the question is about, because you've been such a good, you have a good radar for crank
-
-1:56:12.160 --> 1:56:16.720
- papers, how do we know they're not onto something?
-
-1:56:16.720 --> 1:56:24.240
- How do I, so when you start to talk about AGI or anything like the reasoning benchmarks
-
-1:56:24.240 --> 1:56:28.160
- and so on, so something that doesn't have a benchmark, it's really difficult to know.
-
-1:56:29.120 --> 1:56:34.560
- I mean, I talked to Jeff Hawkins, who's really looking at neuroscience approaches to how,
-
-1:56:35.200 --> 1:56:41.520
- and there's some, there's echoes of really interesting ideas in at least Jeff's case,
-
-1:56:41.520 --> 1:56:42.320
- which he's showing.
-
-1:56:43.280 --> 1:56:45.040
- How do you usually think about this?
-
-1:56:46.640 --> 1:56:52.880
- Like preventing yourself from being too narrow minded and elitist about deep learning, it
-
-1:56:52.880 --> 1:56:56.720
- has to work on these particular benchmarks, otherwise it's trash.
-
-1:56:56.720 --> 1:57:05.280
- Well, you know, the thing is, intelligence does not exist in the abstract.
-
-1:57:05.280 --> 1:57:07.200
- Intelligence has to be applied.
-
-1:57:07.200 --> 1:57:11.040
- So if you don't have a benchmark, if you have an improvement in some benchmark, maybe it's
-
-1:57:11.040 --> 1:57:12.400
- a new benchmark, right?
-
-1:57:12.400 --> 1:57:16.640
- Maybe it's not something we've been looking at before, but you do need a problem that
-
-1:57:16.640 --> 1:57:17.360
- you're trying to solve.
-
-1:57:17.360 --> 1:57:20.000
- You're not going to come up with a solution without a problem.
-
-1:57:20.000 --> 1:57:25.520
- So you, general intelligence, I mean, you've clearly highlighted generalization.
-
-1:57:26.320 --> 1:57:31.200
- If you want to claim that you have an intelligence system, it should come with a benchmark.
-
-1:57:31.200 --> 1:57:35.760
- It should, yes, it should display capabilities of some kind.
-
-1:57:35.760 --> 1:57:41.840
- It should show that it can create some form of value, even if it's a very artificial form
-
-1:57:41.840 --> 1:57:42.800
- of value.
-
-1:57:42.800 --> 1:57:48.800
- And that's also the reason why you don't actually need to care about telling which papers have
-
-1:57:48.800 --> 1:57:52.000
- actually some hidden potential and which do not.
-
-1:57:53.120 --> 1:57:59.200
- Because if there is a new technique that's actually creating value, this is going to
-
-1:57:59.200 --> 1:58:02.480
- be brought to light very quickly because it's actually making a difference.
-
-1:58:02.480 --> 1:58:08.160
- So it's the difference between something that is ineffectual and something that is actually
-
-1:58:08.160 --> 1:58:08.800
- useful.
-
-1:58:08.800 --> 1:58:14.080
- And ultimately usefulness is our guide, not just in this field, but if you look at science
-
-1:58:14.080 --> 1:58:18.720
- in general, maybe there are many, many people over the years that have had some really interesting
-
-1:58:19.440 --> 1:58:22.800
- theories of everything, but they were just completely useless.
-
-1:58:22.800 --> 1:58:27.280
- And you don't actually need to tell the interesting theories from the useless theories.
-
-1:58:28.000 --> 1:58:34.080
- All you need is to see, is this actually having an effect on something else?
-
-1:58:34.080 --> 1:58:35.360
- Is this actually useful?
-
-1:58:35.360 --> 1:58:36.800
- Is this making an impact or not?
-
-1:58:37.600 --> 1:58:38.640
- That's beautifully put.
-
-1:58:38.640 --> 1:58:43.680
- I mean, the same applies to quantum mechanics, to string theory, to the holographic principle.
-
-1:58:43.680 --> 1:58:45.280
- We are doing deep learning because it works.
-
-1:58:46.960 --> 1:58:52.720
- Before it started working, people considered people working on neural networks as cranks
-
-1:58:52.720 --> 1:58:53.120
- very much.
-
-1:58:54.560 --> 1:58:56.320
- No one was working on this anymore.
-
-1:58:56.320 --> 1:58:59.120
- And now it's working, which is what makes it valuable.
-
-1:58:59.120 --> 1:59:00.320
- It's not about being right.
-
-1:59:01.120 --> 1:59:02.560
- It's about being effective.
-
-1:59:02.560 --> 1:59:08.080
- And nevertheless, the individual entities of this scientific mechanism, just like Yoshua
-
-1:59:08.080 --> 1:59:12.480
- Banjo or Jan Lekun, they, while being called cranks, stuck with it.
-
-1:59:12.480 --> 1:59:12.880
- Right?
-
-1:59:12.880 --> 1:59:13.280
- Yeah.
-
-1:59:13.280 --> 1:59:17.840
- And so us individual agents, even if everyone's laughing at us, just stick with it.
-
-1:59:18.880 --> 1:59:21.840
- If you believe you have something, you should stick with it and see it through.
-
-1:59:23.520 --> 1:59:25.920
- That's a beautiful inspirational message to end on.
-
-1:59:25.920 --> 1:59:27.600
- Francois, thank you so much for talking today.
-
-1:59:27.600 --> 1:59:28.640
- That was amazing.
-
-1:59:28.640 --> 1:59:44.000
- Thank you.
-