back to indexFrançois Chollet: Measures of Intelligence | Lex Fridman Podcast #120
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The following is a conversation with Francois Chollet, his second time in the podcast.
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He's both a world class engineer and a philosopher in the realm of deep learning and artificial
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This time, we talk a lot about his paper titled On the Measure of Intelligence, that discusses
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how we may define and measure general intelligence in our computing machinery.
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Quick summary of the sponsors Babel, Masterclass, and Cash App.
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Take the sponsor links in the description to get a discount and to support this podcast.
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As a side note, let me say that the serious, rigorous, scientific study of artificial general
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intelligence is a rare thing.
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The mainstream machine learning community works on very narrow AI with very narrow benchmarks.
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This is very good for incremental and sometimes big incremental progress.
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On the other hand, the outside the mainstream, renegade, you could say, AGI community, works
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on approaches that verge on the philosophical and even the literary without big public benchmarks.
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Walking the line between the two worlds is a rare breed, but it doesn't have to be.
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I ran the AGI series at MIT as an attempt to inspire more people to walk this line.
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Keep mind and open AI for time and still, on occasion, walk this line.
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Francois Chollet does as well.
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It's a beautiful dream to work towards and to make real one day.
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If you enjoy this thing, subscribe on YouTube, review it with 5 stars on Apple Podcast, follow
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Since Cash App allows you to send and receive money digitally, let me mention a surprising
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And now, here's my conversation with Francois Chalet.
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What philosophers, thinkers or ideas had a big impact on you growing up and today?
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So one author that had a big impact on me when I read his books as a teenager was Jean
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Piaget, who is a Swiss psychologist, is considered to be the father of developmental psychology
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and he has a large body of work about basically how intelligence develops in children.
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And so it's very old work, like most of it is from the 1930s, 1940s, so it's not quite
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It's actually superseded by many newer developments in developmental psychology.
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But to me, it was very, very interesting, very striking and actually shaped the early
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ways in which I started thinking about the mind and the development of intelligence as
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His actual ideas or the way he thought about it or just the fact that you could think about
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the developing mind at all?
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Jean Piaget is the author that's really introduced me to the notion that intelligence and the
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mind is something that you construct throughout your life and that the children construct
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And I thought that was a very interesting idea, which is, of course, very relevant to AI,
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to building artificial minds.
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Another book that I read around the same time that had a big impact on me, and there was
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actually a little bit of overlap with Jean Piaget as well, and I read it around the same
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time, is Jeff Hawkins on Intelligence, which is a classic.
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And he has this vision of the mind as a multiscale hierarchy of temporal prediction modules.
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And these ideas really resonated with me, the notion of a modular hierarchy of compression
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functions or prediction functions.
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I thought it was really, really interesting, and it shaped the way I started thinking about
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how to build minds.
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The hierarchical nature, which aspect?
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Also he's a neuroscientist.
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He was thinking, he was basically talking about how our mind works.
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Yeah, the notion that cognition is prediction was an idea that was kind of new to me at
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the time, and that I really loved at the time.
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And yeah, the notion that there are multiple scales of processing in the brain.
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This is before deep learning.
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These ideas of hierarchies in AI have been around for a long time, even before on intelligence
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I mean, they've been around since the 1980s.
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And yeah, that was before deep learning.
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But of course, I think these ideas really found their practical implementation in deep
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What about the memory side of things?
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I think he was talking about knowledge representation.
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Do you think about memory a lot?
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One way you can think of neural networks as a kind of memory, you're memorizing things,
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but it doesn't seem to be the kind of memory that's in our brains, or it doesn't have the
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same rich complexity, long term nature that's in our brains.
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The brain is more of a sparse access memory so that you can actually retrieve very precisely
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like bits of your experience.
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The retrieval aspect, you can like introspect, you can ask yourself questions, I guess.
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You can program your own memory, and language is actually the tool you used to do that.
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I think language is a kind of operating system for the mind.
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And you use language, well, one of the uses of language is as a query that you run over
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your own memory, use words as keys to retrieve specific experiences or specific concepts,
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specific thoughts.
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Like language is a way you store thoughts, not just in writing, in the physical world,
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but also in your own mind.
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And it's also how you retrieve them, like imagine if you didn't have language, then
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you would have to, you would not really have a self internally triggered way of retrieving
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You would have to rely on external experiences.
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For instance, you see a specific site, you smell a specific smell, and that brings up
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memories, but you would not really have a way to deliberately access these memories
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Well, the interesting thing you mentioned is you can also program the memory.
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You can change it probably with language.
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Yeah, using language.
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Well, let me ask you a Chomsky question, which is like, first of all, do you think language
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is like fundamental, like there's turtles, what's at the bottom of the turtles?
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They don't go, it can't be turtles all the way down, is language at the bottom of cognition
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of everything is like language, the fundamental aspect of like what it means to be a thinking
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No, I don't think so.
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I think language is...
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You disagree with Norm Chomsky?
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In language is a layer on top of cognition.
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So it is fundamental to cognition in the sense that to use a computing metaphor, I see language
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as the operating system of the brain, of the human mind.
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And the operating system is a layer on top of the computer.
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The computer exists before the operating system, but the operating system is how you
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make it truly useful.
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And the operating system is most likely Windows, not Linux, because its language is messy.
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Yeah, it's messy and it's pretty difficult to inspect it, introspect it.
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How do you think about language?
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We use actually sort of human interpretable language, but is there something deeper that's
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closer to like logical type of statements?
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Yeah, what is the nature of language, do you think?
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Is there something deeper than like the syntactic rules we construct?
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Is there something that doesn't require utterances or writing or so on?
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Now you're asking about the possibility that there could exist languages for thinking that
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are not made of words?
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So the mind is layers, right?
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And language is almost like the uttermost, the uppermost layer.
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But before we think in words, I think we think in terms of emotion in space and we think
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in terms of physical actions.
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And I think babies in particular probably express its thoughts in terms of the actions
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that they've seen or that they can perform and in terms of the emotions of objects in
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the environment before they start thinking in terms of words.
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It's amazing to think about that as the building blocks of language, so like the kind of actions
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and ways the babies see the world as like more fundamental than the beautiful Shakespearean
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language you construct on top of it.
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And we probably don't have any idea what that looks like, right?
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Because it's important for them trying to engineer it into AI systems.
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I think visual analogies and motion is a fundamental building block of the mind and you actually
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see it reflected in language, like language is full of special metaphors.
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And when you think about things, I consider myself very much as a visual thinker.
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You often express its thoughts by using things like visualizing concepts into the space or
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like you solve problems by imagining yourself, navigating a concept space.
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I don't know if you have this sort of experience.
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You said visualizing concept space.
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So I certainly think about, I certainly visualized mathematical concepts, but you mean like in
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concept space visually you're embedding ideas into three dimensional space you can explore
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with your mind essentially.
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You should be more like 2D, but yeah.
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You're a flatlander.
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I always have to, before I jump from concept to concept, I have to put it back down.
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It has to be on paper.
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I can only travel on 2D paper, not inside my mind.
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You're able to move inside your mind.
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And even if you're writing like a paper, for instance, don't you have like a spatial representation
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Like you visualize where ideas lie topologically in relationship to other ideas, kind of like
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a software map of the ideas in your paper.
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I mean, there is, in papers, I don't know about you, but there feels like there's a destination.
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There's a key idea that you want to write that and a lot of it is in the fog and you're
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trying to kind of...
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It's almost like...
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What's that called when you do a path planning search from both directions from the start
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And then you find, you do like shortest path, but like, in game playing, you do this with
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like A star from both sides.
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And you see where they join.
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So you kind of do, at least for me, I think like, first of all, just exploring from the
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start from like, first principles, what do I know?
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What can I start proving from that, right?
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And then from the destination, if I use their backtracking, like, if I want to show some
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kind of sets of ideas, what would it take to show them and kind of backtrack?
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But like, yeah, I don't think I'm doing all that in my mind though, like putting it down
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Do you use mind maps to organize your ideas?
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Let's get into this because it's, I've been so jealous of people, I haven't really tried
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I've been jealous of people that seem to like, they get like this fire of passion in their
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eyes because everything starts making sense.
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It's like Tom Cruise in the movie was like moving stuff around some of the most brilliant
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people I know use mind maps.
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I haven't tried really.
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Can you explain what the hell a mind map is?
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I guess a mind map is the way to make kind of like the mess inside your mind to just
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put it on paper so that you gain more control over it.
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It's the way to organize things on paper and as kind of like a consequence of organizing
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things on paper, it starts being more organized inside your mind.
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So what does that look like?
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You put, like, do you have an example, like, what do you, what's the first thing you write
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What's the second thing you write?
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I mean, typically, you draw a mind map to organize the way you think about the topic.
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So you would start by writing down like the key concept about that topic, like you would
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write intelligence or something.
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And then you would start adding associative connections.
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Like what do you think about when you think about intelligence?
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What do you think are the key elements of intelligence?
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So maybe you would have language, for instance, and you would have motion.
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And so you would start drawing notes with these things.
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And then you would see what do you think about when you think about motion and so on.
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And you would go like that, like a tree.
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Is it a tree or a tree most or is it a graph too, like a tree?
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Oh, it's more of a graph than a tree.
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And it's not limited to just writing down words.
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You can also draw things.
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And it's not supposed to be purely hierarchical, right?
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Like you can, the point is that you can start, once you start writing it down, you can start
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reorganizing it so that it makes more sense, so that it's connected in a more effective
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See, but I'm so OCD that you just mentioned intelligence and language and motion.
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I'll start becoming paranoid that the categorization isn't perfect.
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Like that I would become paralyzed with the mind map that like this may not be so like
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the, even though you're just doing associative kind of connections, there's an implied hierarchy
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And I would start becoming paranoid that it's not the proper hierarchy.
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So you're not just one way to see mind maps is you're putting thoughts on paper.
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It's like a stream of consciousness.
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But then you can also start getting paranoid.
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Well, if it's just the right hierarchy, sure, which is, but it's a mind map, it's your mind
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map, you're free to draw anything you want, you're free to draw any connection you want.
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And you can just make a different mind map if you think the central node is not the right
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Yeah, I suppose there's a fear of being wrong.
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If you want to organize your ideas by writing down what you think, which I think is very
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Like, how do you know what you think about something if you don't write it down, right?
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If you do that, the thing is that it imposes much more syntactic structure over your ideas,
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which is not required with a mind map.
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So a mind map is kind of like a lower level, more freehand way of organizing your thoughts.
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And once you've drawn it, then you can start actually voicing your thoughts in terms of,
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you know, paragraphs.
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It's a two dimensional aspect of layout too, right?
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And it's a kind of flower, I guess, you start, there's usually, you want to start with a
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Typically, it ends up more like a subway map, so it ends up more like a graph, a topological
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Without a root node.
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Yeah, so like in a subway map, there are some nodes that are more connected than others
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and there are some nodes that are more important than others, right?
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So there are destinations, but it's not going to be purely like a tree, for instance.
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Yeah, it's fascinating to think that if there's something to that about our, about the way
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By the way, I just kind of remembered obvious thing that I have probably thousands of documents
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in Google Doc at this point that are bullet point lists, which is you can probably map
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a mind map to a bullet point list.
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It's a, no, it's not.
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So I create trees, but also they don't have the visual element.
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Like I guess I'm comfortable with the structure.
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It feels like the narrowness, the constraints feel more comforting.
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If you have thousands of documents with your own thoughts in Google Docs, why don't you
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write some kind of search engine, like maybe a mind map, a piece of software, a mind mapping
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software where you write down a concept and then it gives you sentences or paragraphs
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from your thousands Google Docs document that match this concept.
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The problem is it's so deeply unlike mind maps, it's so deeply rooted in natural language.
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So it's not, it's not semantically searchable, I would say, because the categories are very,
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you kind of mention intelligence, language and motion, they're very strong, semantic,
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like it feels like the mind map forces you to be semantically clear and specific.
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The bullet points list I have are sparse, disparate thoughts that poetically represent
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a category like motion as opposed to saying motion.
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So unfortunately, that's the same problem with the internet, that's why the idea of
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semantic web is difficult to get.
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It's most language on the internet is a giant mess of natural language that's hard to interpret.
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So do you think there's something to mind maps as, you actually originally brought it
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up as when we're talking about kind of cognition and language, do you think there's something
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to mind maps about how our brain actually deals, like, think reasons about things?
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I think it's reasonable to assume that there is some level of topological processing in
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the brain, that the brain is very associative in nature.
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And I also believe that a topological space is a better medium to include thoughts than
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a geometric space.
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What's the difference in a topological and a geometric space?
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Well, if you're talking about topologies, then points are either connected or not.
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So the topology is more like a subway map.
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And geometry is when you're interested in the distance between things.
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In subway maps, you don't really have the concept of distance, you only have the concept
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of whether there is a train going from station A to station B.
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And what we do in deep learning is that we're actually dealing with geometric spaces, we
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are dealing with concept vectors, word vectors, that have a distance between the distance
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We are not really building topological models, usually.
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I think you're absolutely right.
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Distance is a fundamental importance in deep learning.
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I mean, it's the continuous aspect of it.
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Because everything is a vector, and everything has to be a vector because everything has
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to be differentiable.
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If your space is discrete, it's no longer differentiable, you cannot do deep learning
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Well, you could, but you could only do it by embedding it in a bigger, continuous space.
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So if you do topology in the context of deep learning, you have to do it by embedding your
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topology in a geometry.
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Well, let me zoom out for a second.
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Let's get into your paper on the measure of intelligence that, did you put it on 2019?
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That was a different time.
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It feels like a different world.
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You could travel, or you could actually go outside and see friends.
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Let me ask the most absurd question.
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I think there's some nonzero probability there'll be a textbook one day, like 200 years from
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now on artificial intelligence, or it'll be called like just intelligence because humans
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will already be gone, and it'll be your picture with a quote.
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This is, you know, one of the early biological systems would consider the nature of intelligence,
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and there'll be like a definition of how they thought about intelligence, which is one of
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the things you do in your paper on measure intelligence is to ask like, well, what is
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intelligence and how to test for intelligence and so on.
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So is there a spiffy quote about what is intelligence?
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What is the definition of intelligence, according to your friends, Warshale?
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So do you think the superintended AIs of the future will want to remember us?
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The way we remember humans from the past, and do you think they won't be ashamed of
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having a biological origin?
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No, I think it'll be a niche topic.
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It won't be that interesting, but it'll be like the people that study in certain contexts
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like historical civilization that no longer exists, the Aztecs and so on.
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That's how it'll be seen, and it'll be study in also the context on social media.
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There will be hashtags about the atrocity committed to human beings when the robots
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finally got rid of them.
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It'll be seen as a giant mistake, but ultimately in the name of progress, and it created a
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better world because humans were over consuming the resources and they were not very rational
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and were destructive in the end, in terms of productivity, and putting more love in
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And so within that context, there'll be a chapter about these biological systems.
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It seems to have a very detailed vision of that feature.
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You should write a sci fi novel about it.
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I'm working on a sci fi novel currently, yes.
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Self published, yeah.
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The definition of intelligence, so intelligence is the efficiency with which you acquire new
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skills at tasks that you did not previously know about, that you did not prepare for,
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So intelligence is not skill itself.
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It's not what you know, it's not what you can do.
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It's how well and how efficiently you can learn new things.
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The idea of newness there seems to be fundamentally important.
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So you would see intelligence on display, for instance, whenever you see a human being
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or an AI creature adapt to a new environment that it does not see before, that its creators
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did not anticipate.
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When you see adaptation, when you see improvisation, when you see generalization, that's intelligence.
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In reverse, if you have a system that when you put it in a slightly new environment,
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it cannot adapt, it cannot improvise, it cannot deviate from what it's hardcoded to do or
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what it has been trained to do, that is a system that is not intelligence.
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There's actually a quote from Einstein that captures this idea, which is, the measure
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of intelligence is the ability to change.
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I like that quote.
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I think it captures at least part of this idea.
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You know, there might be something interesting about the difference between your definition
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I mean, he's just being Einstein and clever, but acquisition of new ability to deal with
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new things versus ability to just change.
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What's the difference between those two things?
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To just change in itself.
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Do you think there's something to that?
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Just being able to change.
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Yes, being able to adapt.
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So not change, but certainly a change in direction.
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Being able to adapt yourself to your environment.
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Whatever the environment is.
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That's a big part of intelligence, yes.
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Intelligence is most precisely how efficiently you're able to adapt, how efficiently you're
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able to basically master your environment, how efficiently you can acquire new skills.
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And I think there's a big distinction to be drawn between intelligence, which is a process
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and the output of that process, which is skill.
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So for instance, if you have a very smart human programmer that considers the game of
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chess and that writes down a static program that can play chess.
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Then the intelligence is the process of developing that program.
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But the program itself is just encoding the output artifact of that process.
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The program itself is not intelligent.
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And the way you tell it's not intelligent is that if you put it in a different context,
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you ask it to play go or something, it's not going to be able to perform well with that
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human involvement because the source of intelligence, the entity that is capable of that process
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is the human programmer.
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So we should be able to tell a difference between the process and its output.
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We should not confuse the output and the process.
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It's the same as do not confuse a road building company and one specific road.
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Because one specific road takes you from point A to point B. But a road building company
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can take you from, can make a path from anywhere to anywhere else.
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Yeah, that's beautifully put, but it's also to play devil's advocate a little bit.
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It's possible that there is something more fundamental than us humans.
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So you said the programmer creates the difference between the choir of the skill and the skill
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There could be something like you could argue the universe is more intelligent, like the
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deep, the base intelligence that we should be trying to measure is something that created
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humans should be measuring God or the source of the universe as opposed to like there could
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be a deeper intelligence, there's always deeper intelligence.
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You can argue that, but that does not take anything away from the fact that humans are
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intelligent and you can tell that because they are capable of adaptation and generality.
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And you see that in particular in the fact that humans are capable of handling situations
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and tasks that are quite different from anything that any of our evolutionary ancestors has
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So we are capable of generalizing very much out of distribution if you consider our evolutionary
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history as being in a way out of training data.
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Of course evolutionary biologists would argue that we're not going too far out of the distribution.
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We're like mapping the skills we've learned previously, desperately trying to like jam
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them into like these new situations.
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I mean, there's definitely a little bit of that, but it's pretty clear to me that we're
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able to, you know, most of the things we do any given day in our modern civilization are
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things that are very, very different from what our ancestors a million years ago would
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have been doing in a given day.
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And our environment is very different.
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So I agree that everything we do, we do it with cognitive building blocks that we acquire
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over the course of evolution, and that anchors our cognition to certain contexts, which is
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the human condition very much.
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But still, our mind is capable of a pretty remarkable degree of generality far beyond
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anything we can create in artificial systems today, like the degree in which the mind can
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generalize from its evolutionary history, can generalize away from its evolutionary history
link |
is much greater than the degree to which a deep learning system today can generalize
link |
away from its training data.
link |
And the key point you're making, which I think is quite beautiful, is we shouldn't measure,
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if we talk about measurement, we shouldn't measure the skill.
link |
We should measure the creation of the new skill, the ability to create that new skill.
link |
But it's tempting.
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It's weird because the skill is a little bit of a small window into the system.
link |
So whenever you have a lot of skills, it's tempting to measure the skills.
link |
I mean, the skill is the only thing you can objectively measure.
link |
But yeah, so the thing to keep in mind is that when you see skill in the human, it gives
link |
you a strong signal that human is intelligent because you know they weren't born with that
link |
Like, you see a very strong chess player, maybe you're a very strong chess player yourself.
link |
I think you're saying that because I'm Russian and now you're prejudiced, you assume all
link |
of us are some degree of chess.
link |
Well, you're dead.
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So if you see a very strong chess player, you know they weren't born knowing how to
link |
So they had to acquire that skill with their limited resources, with their limited lifetime.
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And you know they did that because they are generally intelligent.
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And so they may as well have acquired any other skill.
link |
You know they have this potential.
link |
And on the other hand, if you see a computer playing chess, you cannot make the same assumptions
link |
because you cannot just assume the computer is generally intelligent.
link |
The computer may be born knowing how to play chess in the sense that it may have been programmed
link |
by a human that has understood chess for the computer and that has just encoded the output
link |
of that understanding in a static program.
link |
And that program is not intelligent.
link |
So let's zoom out just for a second and say like, what is the goal of the on the measure
link |
of intelligence paper?
link |
Like, what do you hope to achieve with it?
link |
So the goal of the paper is to clear up some longstanding misunderstandings about the way
link |
we've been conceptualizing intelligence in the AI community and in the way we've been
link |
evaluating progress in AI.
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There's been a lot of progress recently in machine learning and people are extrapolating
link |
from that progress that we are about to solve general intelligence.
link |
And if you want to be able to evaluate these statements, you need to precisely define what
link |
you're talking about when you're talking about general intelligence.
link |
And you need a formal way, a reliable way to measure how much intelligence, how much
link |
general intelligence a system processes.
link |
And ideally this measure of intelligence should be actionable.
link |
So it should not just describe what intelligence is, it should not just be a binary indicator
link |
that tells you the system is intelligent or it isn't.
link |
It should be actionable, it should have explanatory power, right?
link |
So you could use it as a feedback signal.
link |
It would show you the way towards building more intelligent systems.
link |
So at the first level, you draw a distinction between two divergent views of intelligence.
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As we just talked about, intelligence is a collection of task specific skills and a general
link |
So what's the difference between this memorization of skills and a general learning ability?
link |
We've talked about it a little bit, but can you try to link around this topic for a bit?
link |
Yes, so the first part of the paper is an assessment of the different ways we've been
link |
thinking about intelligence and the different ways we've been evaluating progress in AI.
link |
And this tree of cognitive sciences has been shaped by two views of the human mind.
link |
And one view is the evolutionary psychology view in which the mind is a collection of
link |
fairly static, special purpose ad hoc mechanisms that have been hard coded by evolution over
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our history as a species over a very long time.
link |
And early AI researchers, people like Marvin Minsky, for instance, they clearly subscribed
link |
And they saw the mind as a kind of collection of static programs similar to the programs
link |
they would run on like mainframe computers.
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And in fact, I think they very much understood the mind through the metaphor of the mainframe
link |
computer because that was the tool they were working with.
link |
And so you had these static programs, this collection of very different static programs
link |
operating over a database like memory.
link |
And in this picture, learning was not very important.
link |
Learning was considered to be just memorization.
link |
And in fact, learning is basically not featured in AI textbooks until the 1980s with the rise
link |
of machine learning.
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It's kind of fun to think about that learning was the outcast, like the weird people working
link |
Like the mainstream AI world was, I mean, I don't know what the best term is, but it's
link |
It was seen as like reasoning would not be learning based.
link |
Yes, it was considered that the mind was a collection of programs that were primarily
link |
logical in nature.
link |
And that's all you needed to do to create a mind was to write down these programs.
link |
And they would operate over knowledge, which will be stored in some kind of database.
link |
And as long as your database would encompass everything about the world and your logical
link |
rules were comprehensive, then you would have a mind.
link |
So the other view of the mind is the brain as sort of blank slate.
link |
This is a very old idea.
link |
You find it in John Locke's writings.
link |
This is the Tabulaza.
link |
And this is this idea that the mind is some kind of like information sponge that starts
link |
empty, that starts blank, and that absorbs knowledge and skills from experience, right?
link |
So it's a sponge that reflects the complexity of the world, the complexity of your life
link |
experience, essentially, that everything you know and everything you can do is a reflection
link |
of something you found in the outside world, essentially.
link |
So this is an idea that's very old, that was not very popular, for instance, in the 1970s.
link |
But that gained a lot of vitality recently with the rise of connectionism in particular
link |
And so today, deep learning is the dominant paradigm in AI.
link |
And I feel like lots of AI researchers are conceptualizing the mind via a deep learning
link |
metaphor, like they see the mind as a kind of randomly initialized neural network that
link |
starts blank when you're born, and then that gets trained via exposure to training data
link |
that requires knowledge and skills, exposure to training data.
link |
By the way, it's a small tangent.
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I feel like people who are thinking about intelligence are not conceptualizing it that
link |
I actually haven't met too many people who believe that a neural network will be able
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to reason, who seriously think that, rigorously, because I think it's an actually interesting
link |
And we'll talk about it more, but it's been impressive what neural networks have been
link |
able to accomplish.
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And it's an eye to me, I don't know, you might disagree, but it's an open question whether
link |
scaling size eventually might lead to incredible results to us mere humans will appear as if
link |
I mean, if you ask people who are seriously thinking about intelligence, they will definitely
link |
not say that all you need to do is like the mind is just a neural network.
link |
However, it's actually a view that's very popular, I think, in the deep learning community,
link |
that many people are kind of conceptually intellectually lazy about it.
link |
But I guess what I'm saying exactly right is I haven't met many people, and I think
link |
it would be interesting to meet a person who is not intellectually lazy about this particular
link |
topic and still believes that neural networks will go all the way.
link |
I think Yalla is probably closest to that with self supervisor who argue that currently
link |
planning techniques are already the way to general artificial intelligence and that all
link |
you need to do is to scale it up to all the available train data.
link |
And that's if you look at the waves that open AI is GPT stream model is made, you see echoes
link |
So on that topic, GPT three, similar to GPT two actually, have captivated some part of
link |
the imagination of the public.
link |
This is just a bunch of hype of different kind that's, I would say it's emergent.
link |
It's not artificially manufactured.
link |
It's just like people just get excited for some strange reason.
link |
In the case of GPT three, which is funny, that there's, I believe a couple of months
link |
delay from release to hype, maybe I'm not historically correct on that, but it feels
link |
like there was a little bit of a lack of hype and then there's a phase shift into hype.
link |
But nevertheless, there's a bunch of cool applications that seem to captivate the imagination
link |
of the public about what this language model that's trained in unsupervised way without
link |
any fine tuning is able to achieve.
link |
So what do you make of that?
link |
What are your thoughts about GPT three?
link |
So I think what's interesting about GPT three is the idea that it may be able to learn new
link |
tasks in after just being shown a few examples.
link |
So I think if it's actually capable of doing that, that's novel and that's very interesting
link |
and that's something we should investigate.
link |
That said, I must say, I'm not entirely convinced that we have shown it's capable of doing that
link |
but it's very likely given the amount of data that the model is trained on that what it's
link |
actually doing is pattern matching a new task you give it with a task that it's been exposed
link |
to in its train data.
link |
It's just recognizing the task instead of just developing a model of the task.
link |
But there's, sorry to interrupt, there's a parallel as to what you said before, which
link |
is it's possible to see GPT three as like the prompts it's given as a kind of SQL query
link |
into this thing that it's learned similar to what you said before, which is language
link |
is used to query the memory.
link |
So is it possible that neural network is a giant memorization thing, but then if it's
link |
gets sufficiently giant, it'll memorize sufficiently large amounts of thing in the world or intelligence
link |
becomes a querying machine.
link |
I think it's possible that a significant chunk of intelligence is this giant associative
link |
I definitely don't believe that intelligence is just a giant associative memory, but it
link |
may well be a big component.
link |
So do you think GPT three, four, five, GPT 10 will eventually like what do you think?
link |
Where's the ceiling?
link |
Do you think they'll be able to reason?
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No, that's a bad question.
link |
Like what is the ceiling is the better question?
link |
Well, what is going to scale?
link |
How good is GPT N going to be?
link |
So I believe GPT N is going to improve on the strength of GPT two and three, which is it
link |
will be able to generate, you know, ever more plausible text in context.
link |
Just monetize the performance.
link |
Yes, if you train a bigger model on more data, then your text will be increasingly more context
link |
aware and increasingly more plausible in the same way that GPT three is much better at
link |
generating plausible texts compared to GPT two.
link |
So that said, I don't think just getting up the model to more transformer layers and
link |
more train data is going to address the flaws of GPT three, which is that it can generate
link |
plausible texts, but that text is not constrained by anything else other than plausibility.
link |
So in particular, it's not constrained by factualness or even consistency, which is
link |
why it's very easy to get GPT three to generate statements that are factually untrue or to
link |
generate statements that are even self contradictory, right?
link |
Because it's only goal is plausibility, and it has no other constraints.
link |
It's not constrained to be self consistent, right?
link |
And so for this reason, one thing that I thought was very interesting with GPT three
link |
is that you can put it in mind the answer it will give you by asking the question in
link |
a specific way, because it's very responsive to the way you ask the question since it has
link |
no understanding of the content of the question.
link |
And if you are the same question in two different ways that are basically adversely engineered
link |
to produce a certain answer, you will get two different answers, two contradictory answers.
link |
It's very susceptible to adversarial attacks essentially.
link |
So in general, the problem with these models, these generative models is that they are very
link |
good at generating plausible texts, but that's just not enough, right?
link |
You need, I think one avenue that would be very interesting to make progress is to make
link |
it possible to write programs over the latent space that these models operate on, that you
link |
would rely on these self supervised models to generate a sort of lack pool of knowledge
link |
and concepts and common sense, and then you would be able to write explicit reasoning
link |
Because the current problem with GPT three is that you can be quite difficult to get
link |
it to do what you want to do.
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If you want to turn GPT three into products, you need to put constraints on it.
link |
You need to force it to obey certain rules.
link |
So you need a way to program it explicitly.
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Yeah, so if you look at its ability to do program synthesis, it generates, like you
link |
said, something that's plausible.
link |
Yeah, so if you try to make it generate programs, it will perform well for any program that
link |
it has seen it in its training data, but because a program space is not interpretative, right?
link |
It's not going to be able to generalize two problems it hasn't seen before.
link |
Now that's currently, do you think sort of an absurd, but I think useful, I guess, intuition
link |
builder is, you know, the GPT three has 175 billion parameters.
link |
Human brain has 100 has about 1000 times that or more in terms of number of synapses.
link |
Do you think, obviously, very different kinds of things, but there is some degree of similarity.
link |
Do you think, what do you think GPT will look like when it has 100 trillion parameters?
link |
You think our conversation might be in nature different, like, because you've criticized
link |
GPT three very effectively now.
link |
No, I don't think so.
link |
So to begin with, the bottleneck with scaling up GPT three GPT models, genetic pretrained
link |
transformer models, is not going to be the size of the model or how long it takes to
link |
The bottleneck is going to be the training data because OpenEye is already training GPT
link |
three on a crawl of basically the entire web, right?
link |
And that's a lot of data.
link |
So you could imagine training on more data than that, like Google could train on more
link |
data than that, but it would still be only incrementally more data.
link |
And I don't recall exactly how much more data GPT three was trained on compared to GPT
link |
two, but it's probably at least like 100, maybe even 1000x, don't have the exact number.
link |
You're not going to be able to train a model on 100 more data than what you're already
link |
So that's brilliant.
link |
So it's easier to think of compute as a bottleneck and then arguing that we can remove that
link |
We can remove the compute bottleneck, I don't think it's a big problem.
link |
If you look at the pace at which we've improved the efficiency of deep learning models in
link |
the past a few years, I'm not worried about training time bottlenecks or model size bottlenecks.
link |
The bottleneck in the case of these generative transformer models is absolutely the trained
link |
What about the quality of the data?
link |
So the quality of the data is an interesting point.
link |
The thing is, if you're going to want to use these models in real products, then you want
link |
to feed them data that's as high quality as factual, I would say as unbiased as possible,
link |
but there's not really such a thing as unbiased data in the first place.
link |
But you probably don't want to train it on Reddit, for instance.
link |
Sounds like a bad plan.
link |
So from my personal experience, working with large scale deep learning models.
link |
So at some point, I was working on a model at Google that's trained on like 350 million
link |
It's an image classification model.
link |
That's a lot of images.
link |
That's like probably most publicly available images on the web at the time.
link |
And it was a very noisy data set because the labels were not originally annotated by hand
link |
They were automatically derived from tags on social media or just keywords in the same
link |
page as the image was found and so on.
link |
So it was very noisy.
link |
And it turned out that you could easily get a better model, not just by training.
link |
Like if you train on more of the noisy data, you get an incrementally better model, but
link |
you very quickly eat diminishing returns.
link |
On the other hand, if you train on a smaller data set with higher quality annotations,
link |
quality that are annotations that are actually made by humans, you get a better model.
link |
And it also takes less time to train it.
link |
Yeah, that's fascinating.
link |
It's the self supervised learning.
link |
If there's a way to get better doing the automated labeling.
link |
Yeah, so you can enrich or refine your labels in an automated way.
link |
Do you have a hope for, I don't know if you're familiar with the idea of a semantic web.
link |
Is this a semantic web, just what people are not familiar, and is the idea of being able
link |
to convert the internet or be able to attach semantic meaning to the words on the internet?
link |
The sentences, the paragraphs, to be able to convert information on the internet or
link |
some fraction of the internet into something that's interpretable by machines.
link |
That was kind of a dream for, I think the semantic web papers in the 90s, it's kind
link |
of the dream that the internet is full of rich, exciting information.
link |
Even just looking at Wikipedia, we should be able to use that as data for machines.
link |
Information is not in a format that's available to machines.
link |
So no, I don't think the semantic web will ever work simply because it would be a lot
link |
of work to provide that information in a structured form.
link |
And there is not really any incentive for anyone to provide that work.
link |
So I think the way forward to make the knowledge on the web available to machines is actually
link |
something closer to unsupervised deep learning.
link |
So GBT3 is actually a bigger step in the direction of making the knowledge of the web available
link |
to machines than the semantic web was.
link |
In a human centric sense, it feels like GBT3 hasn't learned anything that could be used
link |
But that might be just the early days.
link |
I think that's correct.
link |
I think the forms of reasoning that you see it perform are basically just reproducing
link |
patterns that it has seen in string data.
link |
So of course, if you're trained on the entire web, then you can produce an illusion of reasoning
link |
in many different situations, but it will break down if it's presented with a novel
link |
That's the open question between the illusion of reasoning and actual reasoning, yeah.
link |
The power to adapt to something that is genuinely new.
link |
Because the thing is, even imagine you had, you could train on every bit of data ever
link |
generated in the history of humanity.
link |
It remains, that model would be capable of anticipating many different possible situations,
link |
but it remains that the future is going to be something different.
link |
Like, for instance, if you train a GBT3 model on data from the year 2002, for instance,
link |
and then you use it today, it's going to be missing many things, it's going to be missing
link |
many common sense facts about the world.
link |
It's even going to be missing vocabulary and so on.
link |
Yeah, it's interesting that GBT3 even doesn't have, I think, any information about the coronavirus.
link |
Which is why a system that's, you tell that the system is intelligent when it's capable
link |
So intelligence is going to require some amount of continuous learning.
link |
It's also going to require some amount of improvisation.
link |
It's not enough to assume that what you're going to be asked to do is something that
link |
you've seen before, or something that is a simple interpolation of things you've seen
link |
In fact, that model breaks down for even very tasks that look relatively simple from
link |
a distance, like L5 self driving, for instance.
link |
Google at the paper a couple of years back showing that something like 30 million different
link |
road situations were actually completely insufficient to train a driving model.
link |
It wasn't even L2, right?
link |
And that's a lot of data.
link |
That's a lot more data than the 20 or 30 hours of driving that a human needs to learn to
link |
drive given the knowledge they've already accumulated.
link |
Well, let me ask you on that topic, Elon Musk, Tesla autopilot, one of the only companies,
link |
I believe, is really pushing for a learning based approach.
link |
You're skeptical that that kind of network can achieve level four?
link |
L4 is probably achievable, L5 is probably not.
link |
What's the distinction there?
link |
Is L5 is completely, you can just fall asleep?
link |
Yeah, L5 is basically human level.
link |
Well, it would drive, you have to be careful saying human level because that's the most
link |
Yeah, that's the clearest example of cars will most likely be much safer than humans
link |
in many situations where humans fail.
link |
It's the vice versa question.
link |
I'll tell you, the thing is the amounts of train data you would need to anticipate for
link |
pretty much every possible situation you learn content in the real world is such that
link |
it's not entirely unrealistic to think that at some point in the future we'll develop
link |
a system that's trying enough data, especially provided that we can simulate a lot of that
link |
We don't necessarily need actual cars on the road for everything, but it's a massive effort.
link |
And it turns out you can create a system that's much more adaptive, that can generalize much
link |
better if you just add explicit models of the surroundings of the car.
link |
And if you use deep learning for what it's good at, which is to provide perceptive information.
link |
So in general, deep learning is a way to encode perception and a way to encode intuition,
link |
but it is not a good medium for any sort of explicit reasoning.
link |
And in AI systems today, strong generalization tends to come from explicit models, tend to
link |
come from abstractions in the human mind that are encoded in program form by a human engineer.
link |
These are the abstractions that can actually generalize, not the sort of weak abstraction
link |
that is learned by a neural network.
link |
And the question is how much reasoning, how much strong abstractions are required to solve
link |
particular tasks like driving?
link |
That's the question.
link |
Or human life, existence, how much strong abstractions does existence require, but more
link |
specifically on driving?
link |
That seems to be a coupled question about intelligence is like, how much intelligence
link |
like, how do you build an intelligent system?
link |
And the coupled problem, how hard is this problem?
link |
How much intelligence does this problem actually require?
link |
So we're, we get to cheat, right?
link |
Because we get to look at the problem.
link |
Like it's not like you get to close our eyes and completely new to driving.
link |
We get to do what we do as human beings, which is for the majority of our life, before we
link |
ever learn quote unquote to drive, you get to watch other cars and other people drive.
link |
We get to be in cars, we get to watch, we get to go and see movies about cars.
link |
We get to, you know, we get to observe all this stuff.
link |
And that's similar to what neural networks are doing.
link |
It's getting a lot of data.
link |
And the question is, yeah, how much is, how many leaps of reasoning genius is required
link |
to be able to actually effectively drive?
link |
Oh, it's for example, driving, I mean, sure, you've seen a lot of cars in your life before
link |
you learn to drive.
link |
But let's say you've learned to drive in Silicon Valley and now you rent a car in Tokyo.
link |
Well now everyone is driving on the other side of the road.
link |
And the signs are different and the roads are more narrow and so on.
link |
So it's a very, very different environment.
link |
And a smart human, even an average human should be able to just zero shot it to just be operational
link |
in this, in this very different environment right away, despite having had no contact
link |
with the novel complexity that is contained in this environment, right?
link |
And that is novel complexity is not just interpolation over the situations that you've encountered
link |
previously, like learning to drive in the US, right?
link |
I would say the reason I ask is one of the most interesting tests of intelligence we
link |
have today, actively, which is driving in terms of having an impact on the world.
link |
Like when do you think we'll pass that test of intelligence?
link |
So I don't think driving is that much of a test of intelligence because again, there
link |
is no task for which skill at that task demonstrates intelligence unless it's a kind of meta tasks
link |
that involves acquiring new skills.
link |
So I don't think I think you can actually solve driving without having any, any real
link |
amount of intelligence.
link |
For instance, if you really did have infinite train there, you could just literally train
link |
an end to end deep learning model that does driving provided infinite train data.
link |
The only problem with the whole idea is collecting a data sets that's sufficiently comprehensive
link |
that covers the very long tail of possible situations you might encounter.
link |
And it's really just a scale problem.
link |
So I think there's nothing fundamentally wrong with this plan, with this idea.
link |
It's just that it strikes me as a fairly inefficient thing to do because you run into this scaling
link |
issue with diminishing returns, whereas if instead you took a more manual engineering
link |
approach where you use deep learning modules in combination with engineering an explicit
link |
model of the surrounding of the cars and you bridge the two in a clever way.
link |
Your model will actually start generalizing much earlier and more effectively than the
link |
end to end deep learning model.
link |
So why would you not go with the more manual engineering oriented approach?
link |
Like even if you created that system, either the end to end deep learning model system
link |
that's infinite data or the slightly more human system.
link |
I don't think achieving alpha would demonstrate general intelligence or intelligence of any
link |
generality at all, again, the only possible test of generality in AI would be a test that
link |
looks at skill acquisition over unknown tasks.
link |
For instance, you could take your L5 driver and ask it to learn to pilot a commercial
link |
airplane, for instance, and then you would look at how much human involvement is required
link |
and how much training data is required for the system to learn to pilot an airplane.
link |
And that gives you a measure of how intelligent that system really is.
link |
Yeah, I mean, that's a big leap.
link |
I get you, but I'm more interested as a problem.
link |
I would see, to me, driving is a black box that can generate novel situations at some
link |
rate, what people call edge cases.
link |
So it does have newness that keeps being like we're confronted, let's say once a month.
link |
It is a very long tail.
link |
That doesn't mean you cannot solve it just by training as a school model and out of data.
link |
Huge amount of data.
link |
It's really amount of scale.
link |
But I guess what I'm saying is if you have a vehicle that achieves level five, it is
link |
going to be able to deal with new situations.
link |
Or I mean, the data is so large that the rate of new situations is very low.
link |
That's not intelligent.
link |
So if we go back to your definition of intelligence, it's the efficiency with which you can adapt
link |
to new situations, to truly new situations, not situations you've seen before, not situations
link |
that could be anticipated by your creators, by the creators of the system, but true new
link |
The efficiency with which you acquire new skills.
link |
If you require, in order to pick up a new skill, you require a very extensive training
link |
dataset of most possible situations that can occur in the practice of that skill, then
link |
the system is not intelligent.
link |
It is mostly just a lookup table.
link |
So if, in order to acquire a skill, you need a human engineer to write down a bunch of
link |
rules that cover most or every possible situation, likewise, the system is not intelligent.
link |
The system is merely the output artifact of a process that happens in the minds of the
link |
engineers that are creating it.
link |
It is encoding an abstraction that's produced by the human mind, and intelligence would
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actually be the process of producing, of autonomously producing this abstraction.
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Not like, if you take an abstraction and you encode it on a piece of paper or in a computer
link |
program, the abstraction itself is not intelligent.
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This intelligent is the agent that's capable of producing these abstractions, right?
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It feels like there's a little bit of a gray area, like, because you're basically saying
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that deep learning forms abstractions too, but those abstractions do not seem to be effective
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for generalizing far outside of the things that you've already seen, but generalize a
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No, deep learning does generalize a little bit.
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But generalization is not a binary, it's more like a spectrum.
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And there's a certain point, it's a gray area, but there's a certain point where there's
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an impressive degree of generalization that happens.
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No, I guess exactly what you were saying is intelligence is how efficiently you're able
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to generalize far outside of the distribution of things you've seen already.
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It's just like the distance of how far you can, like, how new, how radically new something
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is and how efficiently you're able to deal with that.
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You can think of intelligence as a measure of an information conversion ratio.
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Like, imagine a space of possible situations, and you've covered some of them, so you have
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some amount of information about your space of possible situations that's provided by
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the situations you already know, and that's, on the other hand, also provided by the prior
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knowledge that the system brings to the table or the prior knowledge that's embedded in
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So the system starts with some information, right, about the problem, about the task.
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And it's about going from that information to a program, what you would call a skill
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program, a behavioral program that can cover a large area of possible situation space.
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And essentially, the ratio between that area and the amount of information you start with
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So a very smart agent can make efficient users of very little information about a new problem
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and very little prior knowledge as well to cover a very large area of potential situations
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in that problem, without knowing what these future new situations are going to be.
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So one of the other big things you talk about in the paper, we've talked about it a little
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bit already, but let's talk about it some more as the actual tests of intelligence.
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So if we look at like human and machine intelligence, do you think tests of intelligence should
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be different for humans and machines, or how we think about testing of intelligence?
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These fundamentally the same kind of intelligence that we're after, and therefore the tests
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should be similar?
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So if your goal is to create AIs that are more human like, then it will be super valuable,
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obviously, to have a test that's universal, that applies to both AIs and humans, so that
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you could establish a comparison between the two that you could tell exactly how intelligence,
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in terms of human intelligence, a given system is.
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So that said, the constraints that apply to artificial intelligence and to human intelligence
link |
are very different, and your test should account for this difference.
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Because if you look at artificial systems, it's always possible for an experimenter to
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buy arbitrary levels of skill at arbitrary tasks, either by injecting a hard coded prior
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knowledge into the system via rules and so on that come from the human mind, from the
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minds of the programmers, and also buying higher levels of skill just by training on
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more data, for instance, you could generate an infinity of different Go games, and you
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could train a Go playing system that way, but you could not directly compare it to human
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Go playing skills, because a human that plays Go had to develop that skill in a very constrained
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They had the limited amount of time, they had the limited amount of energy, and of course,
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they started from a different set of priors, they started from innate human priors.
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So I think if you want to compare the intelligence of two systems, like the intelligence of an
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AI and the intelligence of a human, you have to control for priors.
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You have to start from the same set of knowledge priors about the task, and you have to control
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for experience, that is to say, for training data.
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So priors is whatever information you have about a given task before you start learning
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And how's the difference from experience?
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Well experience is acquired, right.
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For instance, if you're trying to play Go, your experience with Go is all the Go games
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you've played or you've seen or you've simulated in your mind, let's say.
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And your priors are things like, well, Go is a game on a 2D grid, and we have lots of
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hard coded priors about the organization of 2D space.
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And the rules of how the dynamics of the physics of this game in this 2D space.
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And the idea that you have what winning is.
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And other board games can also share some similarities with Go, and if you've played
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these board games, then with respect to the game of Go, that would be part of your priors
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Well, it's interesting to think about the game of Go is how many priors are actually
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brought to the table.
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When you look at self play, reinforcement learning based mechanisms that do learning,
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it seems like the number of priors is pretty low.
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But you're saying you should be...
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There is a 2D special priors in the cabinet.
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But you should be clear at making those priors explicit.
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So in part, I think if your goal is to measure a human life form of intelligence, then you
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should clearly establish that you want the AI you're testing to start from the same set
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of priors that humans start with.
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So, I mean, to me personally, but I think to a lot of people, the human side of things
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is very interesting.
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So testing intelligence for humans, what do you think is a good test of human intelligence?
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Well, let's do a question that Psychometrics is interested in.
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There's an entire subfield of psychology that deals with this question.
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So what's Psychometrics?
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The Psychometrics is the subfield of psychology that tries to measure, quantify aspects of
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So in particular, cognitive abilities, intelligence, and personality traits as well.
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So what are, might be a weird question, but what are the first principles of Psychometrics
link |
that operates on, you know, what are the priors it brings to the table?
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So it's a field with a fairly long history.
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It's, so you know, psychology sometimes gets a bad reputation for not having very reproducible
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results and some Psychometrics has actually some fairly slightly reproducible results.
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So the ideal goals of the field is, you know, tests should be reliable, which is an ocean
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tide to your productivity.
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It should be valid, meaning that it should actually measure what you say it measures.
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So for instance, if you're saying that you're measuring intelligence, then your test results
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should be correlated with things that you expect to be correlated with intelligence like success
link |
in school or success in the workplace and so on, should be standardized, meaning that
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you can administer your tests to many different people in some conditions, and it should be
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free from bias, meaning that for instance, if your, if your test involves the English
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language, then you have to be aware that this creates a bias against people who have English
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as their second language or people who can't speak English at all.
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So of course, these, these principles for creating Psychometric tests are very much
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I don't think every Psychometric test is, is really either reliable, valid, or offered
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from bias, but at least the field is aware of these weaknesses and is trying to address
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So it's kind of interesting, ultimately, you're only able to measure like you said previously
link |
the skill, but you're trying to do a bunch of measures of different skills that correlate.
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You mentioned strongly with some general concept of cognitive ability.
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So what's the G factor?
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So right, there are many different kinds of tests, tests of intelligence and each of them
link |
is interested in different aspects of intelligence.
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You know, some of them will deal with language, some of them will deal with special vision,
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maybe mental rotations, numbers and so on.
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When you run these very different tests at scale, what you start seeing is that there
link |
are clusters of correlations among test results.
link |
So for instance, if you look at homework at school, you will see that people who do well
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at math are also likely statistically to do well in physics.
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And what's more, there are also people who do well at math and physics are also statistically
link |
likely to do well in things that sound completely unrelated, like writing in English, for instance.
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And so when you see clusters of correlations in statistical terms, you would explain them
link |
with a latent variable.
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And the latent variable that would, for instance, explain the relationship between being good
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at math and being good at physics would be cognitive ability, right?
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And the G factor is the latent variable that explains the fact that every test of intelligence
link |
that you can come up with results on this test end up being correlated.
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So there is some single, unique variable that explains these correlations, that's the G factor.
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So it's a statistical construct.
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It's not really something you can directly measure, for instance, in a person.
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It's there at scale.
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And that's also one thing I want to mention about psychometrics.
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Like, you know, when you talk about measuring intelligence in humans, for instance, some
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people get a little bit worried, they will say, you know, that sounds dangerous, maybe
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that sounds potentially discriminatory and so on.
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And they are not wrong.
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And the thing is, personally, I'm not interested in psychometrics as a way to characterize one
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individual person, like if I get your psychometric personality assessment or your IQ, I don't
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think that actually tells me much about you as a person.
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I think psychometrics is most useful as a statistical tool.
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So it's most useful at scale.
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It's most useful when you start getting test results for a large number of people and you
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start cross correlating these test results, because that gives you information about the
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structure of the human mind, in particular about the structure of human cognitive abilities.
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So at scale, psychometrics paints a certain picture of the human mind.
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And that's interesting.
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And that's what's relevant to AI, the structure of human cognitive abilities.
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Yeah, it gives you an insight into, I mean, to me, I remember when I learned about GFactor,
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it seemed like it would be impossible for it to be real, even as a statistical variable.
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It felt kind of like astrology, like it's like wishful thinking about psychologists.
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But the more I learned, I realized that there's some, I mean, I'm not sure what to make about
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human beings, the fact that the GFactor is a thing, that there's a commonality across all
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of human species, that there does seem to be a strong correlation between cognitive abilities.
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That's kind of fascinating.
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So human connectivities have a structure, like the most mainstream theory of the structure
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of connectivities is called a CHC theory.
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It's a cattle horn, Carol, it's named after the three psychologists who contributed key pieces of it.
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And it describes cognitive abilities as a hierarchy with three levels.
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And at the top, you have the GFactor, then you have broad cognitive abilities, for instance,
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fluid intelligence, that encompass a broad set of possible kinds of tasks that are all related.
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And then you have narrow cognitive abilities at the last level, which is closer to task specific
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skill. And there are actually different theories of the structure of cognitive abilities.
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They just emerge from different statistical analysis of IQ test results.
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But they all describe a hierarchy with a kind of GFactor at the top.
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And you're right that the GFactor is, it's not quite real in the sense that it's not
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something you can observe and measure, like your height, for instance.
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But it's really in the sense that you see it in a statistical analysis of the data.
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One thing I want to mention is that the fact that there is a GFactor does not really mean that
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human intelligence is general in a strong sense, does not mean human intelligence can be applied
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to any problem at all and that someone who has a high IQ is going to be able to solve any problem
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at all. That's not quite what it means, I think.
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One popular analogy to understand it is the sports analogy. If you consider the concept
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of physical fitness, it's a concept that's very similar to intelligence because it's a useful
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concept. It's something you can intuitively understand. Some people are fit, maybe like you,
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some people are not as fit, maybe like me. But none of us can fly.
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Absolutely. Even if you're very fit, that doesn't mean you can do anything at all in
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any environment. You obviously cannot fly, you cannot serve at the bottom of the ocean and so
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on. And if you were a scientist and you wanted to precisely define and measure physical fitness
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in humans, then you would come up with a battery of tests, like you would have running 100 meter,
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playing soccer, playing table tennis, swimming, and so on. And if you ran these tests over many
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different people, you would start seeing correlations and test results. For instance,
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people who are good at soccer are also good at sprinting. And you would explain these correlations
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with physical abilities that are strictly analogous to cognitive abilities. And then you would
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start also observing correlations between biological characteristics, like maybe lung
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volume is correlated with being a fast runner, for instance. In the same way that there are
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neurophysical correlates of cognitive abilities. And at the top of the hierarchy of physical
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abilities that you would be able to observe, you would have a g factor, a physical g factor,
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which would map to physical fitness. And as you just said, that doesn't mean that people with
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high physical fitness can't fly. It doesn't mean human morphology and human physiology is universal.
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It's actually super specialized. We can only do the things that we evolve to do. We are not
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appropriate to... You could not exist on Venus or Mars or in the void of space or the bottom of the
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ocean. So that said, one thing that's really striking and remarkable is that our morphology
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generalizes far beyond the environments that we evolved for. Like in a way, you could say we evolved
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to run after prey in the savanna, right? That's very much where our human morphology comes from.
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And that said, we can do a lot of things that are completely unrelated to that. We can climb
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mountains. We can swim across lakes. We can play table tennis. I mean, table tennis is very different
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from what we were evolved to do, right? So our morphology, our bodies, our sense and motor
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affordances have a degree of generality that is absolutely remarkable, right? And I think cognition
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is very similar to that. Our cognitive abilities have a degree of generality that goes far beyond
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what the mind was initially supposed to do, which is why we can play music and write novels and go
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to Mars and do all kinds of crazy things. But it's not universal in the same way that human
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morphology and our body is not appropriate for actually most of the universe by volume,
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in the same way you could say that the human mind is not really appropriate for most of
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problem space, potential problem space by volume. So we have very strong cognitive biases,
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actually. That means that there are certain types of problems that we handle very well and certain
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types of problems that we are completely inadapted for. So that's really how we interpret
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the g factor. It's not a sign of strong generality. It's really just the broadest cognitive ability.
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But our abilities, whether we are talking about sensory motor abilities or cognitive abilities,
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they still, they remain very specialized in the human condition, right?
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Within the constraints of the human cognition, they're general.
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But the constraints, as you're saying, are very limited.
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I think what's limiting.
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So we evolved our cognition and our body evolved in very specific environments
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because our environment was so valuable, fast changing and so unpredictable.
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Part of the constraints that drove our evolution is generality itself. So we were in a way evolved
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to be able to improvise in all kinds of physical or cognitive environments, right?
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And for this reason, it turns out that the minds and bodies that we ended up with
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can be applied to much, much broader scope than what they were evolved for, right?
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And that's truly remarkable. And that goes, that's a degree of generalization that is far beyond
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anything you can see in artificial systems today, right?
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That's it. It does not mean that human intelligence is anywhere universal.
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Yeah, it's not general. You know, it's a kind of exciting topic for people even outside of
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artificial intelligence IQ tests. I think it's Mensa, whatever, there's different degrees of
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difficulty for questions. We talked about this offline a little bit too about sort of difficult
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questions. What makes a question on an IQ test more difficult or less difficult, do you think?
link |
So the thing to keep in mind is that there's no such thing as a question that's intrinsically
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difficult. It has to be difficult to suspect to the things you already know, and the things you
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cannot really do, right? So in terms of an IQ test question, typically it would be structured,
link |
for instance, as a set of demonstration input and output pairs, right? And then you would be given
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a test input, a prompt, and you would need to recognize or produce the corresponding output.
link |
And in that narrow context, you could say a difficult question is a question where
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the input prompt is very surprising and unexpected given the training example.
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Just even the nature of the patterns that you're observing in the input prompt.
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For instance, let's say you have a rotation problem. You must rotate the shape by 90 degrees.
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If I give you two examples, and then I give you one prompt, which is actually one of the two
link |
training examples, then there is zero generalization difficulty for the task. It's actually a trivial
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task. You just recognize that it's one of the training examples and you produce the same answer.
link |
Now, if it's a more complex shape, there is a little bit more generalization, but it remains
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that you are still doing the same thing at this time as you were being demonstrated at
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training time. A difficult task does require some amount of test time adaptation, some amount of
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improvisation, right? So consider, I don't know, you're teaching a class on quantum physics or
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something. If you wanted to kind of test the understanding that students have of the material,
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you would come up with an exam that's very different from anything they've seen,
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like on the Internet when they were cramming. On the other hand, if you wanted to make it easy,
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you would just give them something that's very similar to the mock exams that they've taken,
link |
something that's just a simple interpolation of questions that they've already seen.
link |
And so that would be an easy exam. It's very similar to what you've been trained on. And a
link |
difficult exam is one that really probes your understanding because it forces you to improvise.
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It forces you to do things that are different from what you were exposed to before. So that said,
link |
it doesn't mean that the exam that requires improvisation is intrinsically hard, right?
link |
Because maybe you're a quantum physics expert. So when you take the exam, this is actually stuff
link |
that despite being new to the students, it's not new to you, right? So it can only be difficult
link |
with respect to what the test taker already knows, and with respect to the information that
link |
the test taker has about the task. So that's what I mean by controlling for priors what you,
link |
the information you bring to the table. And the experience, which is the training data. So in the
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case of the quantum physics exam, that would be all the course material itself and all the mock
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exams that students might have taken online. Yeah, it's interesting because I've also,
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I sent you an email and asked you, like, I've been, this just this curious question of,
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you know, what's a really hard IQ test question? And I've been talking to also people who have
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designed IQ tests as a few folks on the internet. It's like a thing. People are really curious
link |
about it. First of all, most of the IQ tests they designed, they like religiously protect against
link |
the correct answers. Like you can't find the correct answers anywhere. In fact, the question
link |
is ruined once you know, even like the approach you're supposed to take. So they're very
link |
that said, the approach is implicit in the training examples. So if you release the train
link |
examples, it's over. Well, which is why in arc, for instance, there's a test set that is private
link |
and no one has seen it. No, for really tough IQ questions, it's not obvious. It's not because
link |
the ambiguity. Like it's, I mean, we'll have to look through them, but like some number sequences
link |
and so on, it's not completely clear. So like, you can get a sense, but there's like some,
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you know, when you look at a number sequence, I don't know,
link |
like your Fibonacci number sequence, if you look at the first few numbers, that sequence
link |
could be completed in a lot of different ways. And, you know, some are, if you think deeply,
link |
are more correct than others. Like there's a kind of intuitive simplicity and elegance
link |
to the correct solution. Yes, I am personally not a fan of ambiguity in test questions,
link |
actually. But I think you can have difficulty without requiring ambiguity simply by making the
link |
test require a lot of extrapolation over the training examples. But the beautiful question
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is difficult, but gives away everything when you give the training example.
link |
Basically, yes. Meaning that, so the tests I'm interested in creating are not necessarily
link |
difficult for humans, because human intelligence is the benchmark. They're supposed to be difficult
link |
for machines in ways that are easy for humans. Like I think an ideal test of human and machine
link |
intelligence is a test that is actionable, that highlights the need for progress, and that
link |
highlights the direction in which you should be making progress. I think we'll talk about
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the RR challenge and the test you've constructed, and you have these elegant examples. I think
link |
that highlight, like this is really easy for us humans, but it's really hard for machines.
link |
But on the designing an IQ test for IQs of like a higher than 160 and so on,
link |
you have to say, you have to take that and put it on steroids, right? You have to think like,
link |
what is hard for humans? And that's a fascinating exercise in itself, I think.
link |
And it was an interesting question of what it takes to create a really hard question for humans,
link |
because you again have to do the same process as you mentioned, which is something basically
link |
where the experience that you have likely to have encountered throughout your whole life,
link |
even if you've prepared for IQ tests, which is a big challenge, that this will still be novel for
link |
you. Yeah, I mean, novelty is a requirement. You should not be able to practice for the questions
link |
that you're going to be tested on. That's important. Because otherwise, what you're doing
link |
is not exhibiting intelligence, what you're doing is just retrieving what you've been exposed before.
link |
It's the same thing as a deep learning model. If you train a deep learning model on
link |
all the possible answers, then it will ace your test. In the same way that a stupid student can
link |
still ace the test, if they cram for it, they memorize 100 different possible mock exams.
link |
And then they hope that the actual exam will be a very simple interpolation of the mock exams.
link |
And that student could just be a deep learning model at that point.
link |
And that student could just be a deep learning model at that point. But you can actually do that
link |
without any understanding of the material. And in fact, many students pass the exams in exactly
link |
this way. And if you want to avoid that, you need an exam that's unlike anything they've seen,
link |
that really probes their understanding. So how do we design an IQ test for machines?
link |
All right, so in the paper, I outline a number of requirements that you expect of such a test.
link |
And in particular, we should start by acknowledging the priors that we expect to be required
link |
in order to perform the test. So we should be explicit about the priors.
link |
And if the goal is to compare machine intelligence and human intelligence,
link |
then we should assume human cognitive priors. And secondly, we should make sure that we are testing
link |
for skill acquisition ability, skill acquisition efficiency in particular, and not for skill
link |
itself, meaning that every task featured in your test should be novel and should not be
link |
something that you can anticipate. So for instance, it should not be possible to
link |
brute force the space of possible questions to pregenerate every possible question and answer.
link |
So it should be tasks that cannot be anticipated, not just by the system itself,
link |
but by the creators of the system. Yeah, you know what's fascinating? I mean,
link |
one of my favorite aspects of the paper and the work you do, the ARC challenge, is the process
link |
of making priors explicit. Just even that act alone is a really powerful one of like, what are,
link |
it's a really powerful question, ask of us humans, what are the priors that we bring to the table?
link |
So the next step is like, once you have those priors, how do you use them to solve a novel
link |
task? But like just even making the priors explicit is a really difficult and really powerful step.
link |
And that's like visually beautiful and conceptually philosophically beautiful part of the work you
link |
did with, and I guess continue to do probably with the paper and the ARC challenge. Can you
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talk about some of the priors that we're talking about here? Yes. So a researcher has done a lot
link |
of work on what exactly are the knowledge priors that are innate to humans is Elizabeth Spelke
link |
from Harvard. So she developed the core knowledge theory, which outlines four different core
link |
knowledge systems. So systems of knowledge that we are basically either born with or that we are
link |
hardwired to acquire very early on in our development. And there's no strong distinction
link |
between the two. Like if you are primed to acquire a certain type of knowledge, in just a few weeks,
link |
you might as well just be born with it. It's just part of who you are. And so there are four
link |
different core knowledge systems. Like the first one is the notion of objectness and basic physics.
link |
Like you recognize that something that moves currently, for instance, is an object. So we
link |
intuitively naturally, innately divide the world into objects based on this notion of
link |
coherence, physical coherence. And in terms of elementary physics, there's the fact that objects
link |
can bump against each other and the fact that they can occlude each other. So these are things that
link |
we are essentially born with or at least that we are going to be acquiring extremely early because
link |
really hardwired to acquire them. So a bunch of points, pixels that move together on objects
link |
are partly the same object. Yes. I mean, I don't smoke weed, but if I did,
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that's something I could sit all night and just think about. I remember writing in your paper
link |
just objectness. I wasn't self aware of that particular prior. That's such a fascinating
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prior. That's the most basic one. Objectness, just identity, objectness. It's very basic,
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I suppose, but it's so fundamental. It is fundamental to human cognition.
link |
Yeah. And the second prior that's also fundamental is agentness, which is not a real world,
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a real world, but so agentness. The fact that some of these objects that you segment your
link |
environment into, some of these objects are agents. So what's an agent? Basically, it's
link |
an object that has goals. That has what? That has goals. There's capable of
link |
pursuing goals. So for instance, if you see two dots moving in a roughly synchronized fashion,
link |
you will intuitively infer that one of the dots is pursuing the other. So one of the dots is,
link |
and one of the dots is an agent, and its goal is to avoid the other dot. And one of the dots,
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the other dot, is also an agent, and its goal is to catch the first dot. Pelke has shown that
link |
babies as young as three months identify agentness and goal directedness in their environment.
link |
Another prior is basic geometry and topology, like the notion of distance,
link |
the ability to navigate in your environment, and so on. This is something that is fundamentally
link |
hardwired into our brain. It's in fact backed by very specific neural mechanisms, like for instance,
link |
grid cells and plate cells. So it's something that's literally hardcoded at the neural level
link |
in our hippocampus. And the last prior would be the notion of numbers, like numbers are not
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actually a cultural construct. We are intuitively, innately able to do some basic counting and to
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compare quantities. So it doesn't mean we can do arbitrary arithmetic. Counting, the actual counting.
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Like counting one, two, three, then maybe more than three. You can also compare quantities if I give
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you three dots and five dots, you can tell the side with five dots as more dots. So this is
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actually an innate prior. So that said, the list may not be exhaustive. So Spelki is still
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pursuing the potential existence of new knowledge systems, for instance,
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knowledge systems that we deal with social relationships.
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Yeah. Which is much less relevant to something like ARC or IQ test.
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Right. There could be stuff that's, like you said, rotation or symmetry. Is it really interesting?
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It's very likely that there is, speaking about rotation, that there is in the brain
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a hardcoded system that is capable of performing rotations.
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One famous experiment that people did in the, I don't remember who it was exactly, but in the
link |
70s was that people found that if you asked people, if you give them two different shapes,
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and one of the shapes is a rotated version of the first shape, and you ask them,
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is that shape a rotated version of the first shape or not? What you see is that the time it
link |
takes people to answer is linearly proportional, right, to the angle of rotation. So it's almost
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like you have it somewhere in your brain, like a turntable with a fixed speed. And if you want to
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know if two objects are rotated versions of each other, you put the object on the turntable,
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you let it move around a little bit, and then you stop when you have a match.
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And that's really interesting. So what's the arc challenge?
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So in the paper, I outlined all these principles that a good test of machine
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intelligence and human intelligence should follow. And the arc challenge is one attempt
link |
to embody as many of these principles as possible. So I don't think it's anywhere near
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a perfect attempt, right? It does not actually follow every principle, but it is
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what I was able to do given the constraints. So the format of arc is very similar to classic
link |
IQ tests, in particular Raven's Progessive Metruses. Yeah, Raven's Progessive Metruses.
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I mean, if you've done IQ tests in the past, you know where that is probably,
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at least you've seen it, even if you don't know what it's called. And so you have a set of tasks,
link |
that's what they're called. And for each task, you have training data, which is a set of input
link |
and output pairs. So an input or output pair is a grid of colors, basically. The size of the
link |
grid is variable, is the size of the grid is variable. And you're given an input and you
link |
must transform it into the proper output, right? And so you're shown a few demonstrations
link |
of a task in the form of existing input output pairs, and then you're given a new input,
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and you must provide, you must produce the correct output. And the assumptions
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in arc is that every task should only require core knowledge priors, should not require any
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outside knowledge. So for instance, no language, no English, nothing like this, no concepts taken
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from our human experience, like trees, dogs, cats, and so on. So only tasks that are, reasoning tasks
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that are built on top of core knowledge priors. And some of the tasks are actually explicitly
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trying to probe specific forms of abstraction, right? Part of the reason why I wanted to create arc
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is I'm a big believer in, you know, when you're faced with a problem as murky as
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understanding how to autonomously generate abstraction in a machine,
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you have to co evolve the solution and the problem. And so part of the reason why I designed arc
link |
was to clarify my ideas about the nature of abstraction, right? And some of the tasks are
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actually designed to probe bits of that theory. And there are things that are turned out to be
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very easy for humans to perform, including young kids, right? But turned out to be
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not to be near impossible for machines. So what have you learned from the nature of abstraction
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from designing that? Can you clarify what you mean? One of the things you wanted to try to
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understand was this idea of abstraction? Yes. So clarifying my own ideas about abstraction by
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forcing myself to produce tasks that would require the ability to produce that form of
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abstraction in order to solve them. Got it. Okay. So, and by the way, just to, I mean,
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people should check out, I'll probably overlay if you're watching the video part, but the grid input
link |
output with the different colors on the grid. That's it. That's that means a very simple world.
link |
But it's kind of beautiful. It's very similar to classic acutest. Like, it's not very original
link |
in that sense. The main difference with acutest is that we make the priors explicit, which is not
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usually the case in acutest. So you might get explicit that everything should only be built
link |
out of core knowledge priors. I also think it's generally more diverse than acutest in general.
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And it's, it perhaps requires a bit more manual work to produce solutions because you have to
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click around on a grid for a while. Sometimes the grades can be as large as cell by cell cells.
link |
So how did you come up? If you can reveal with the questions, like what's the process
link |
of the questions? Was it mostly you? Yeah, they came up with the questions. What,
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how difficult is it to come up with a question? Like, is this scalable to a much larger number?
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If we think, you know, with acutest, you might not necessarily want it to or need it to be scalable
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with machines. It's possible you could argue that it needs to be scalable.
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So there are a thousand questions, a thousand tasks, including the test set, the private test set.
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I think it's fairly difficult in the sense that a big requirement is that every task should be
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novel and unique and unpredictable, right? Like you don't want to create your own little world
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that is simple enough that it would be possible for a human to reverse and generate
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and write down an algorithm that could generate every possible arc task and their solution.
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So in a sense, that would completely invalidate the test. So you're constantly coming up with new
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stuff. Yeah, you need a source of novelty, of unfakeable novelty. And one thing I found is that
link |
as a human, you are not a very good source of unfakeable novelty. And so you have to
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base the creation of these tasks quite a bit. There are only so many unique tasks that you
link |
can do in a given day. So that means coming up with truly original new ideas. Did psychedelics
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help you at all? No, it's okay. But I mean, that's fascinating to think about. So you would be like
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walking or something like that. Are you constantly thinking of something totally new?
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Yes. I mean, this is hard. This is hard. Yeah, I mean, I'm not saying I've done
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anywhere near perfect job at it. There is some amount of redundancy, and there are many imperfections
link |
in arc. So that said, you should consider arc as a work in progress. It is not the definitive
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state where the arc tasks today are not the definitive state of the test. I want to keep
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refining it. In the future, I also think it should be possible to open up the creation of tasks
link |
to broad audience to do crowdsourcing. That would involve several levels of filtering, obviously.
link |
But I think it's possible to apply crowdsourcing to develop a much bigger and much more diverse
link |
arc data set that would also be free of potentially some of my own personal biases.
link |
Is there always need to be a part of arc that the test is hidden?
link |
Yes, absolutely. It is impressive that the test that you're using to actually benchmark algorithms
link |
is not accessible to the people developing these algorithms. Because otherwise, what's
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going to happen is that the human engineers are just going to solve the tasks themselves
link |
and encode their solution in program form. But that again, what you're seeing here is
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the process of intelligence happening in the mind of the human. And then you're just capturing
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its crystallized output. But that crystallized output is not the same thing as the process
link |
generated. It's not intelligent. So by the way, the idea of crowdsourcing it is fascinating.
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I think the creation of questions is really exciting for people. I think there's a lot
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of really brilliant people out there that love to create these kinds of stuff.
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Yeah. One thing that surprised me that I wasn't expecting is that lots of people seem to actually
link |
enjoy arc as a kind of game. And I was really seeing it as a test, as a benchmark of fluid
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general intelligence. And lots of people, including kids, are just enjoying it as a game. So I think
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that's encouraging. Yeah, I'm fascinated by it. There's a world of people who create IQ questions.
link |
I think that's a cool activity for machines and for humans. And humans are themselves fascinated
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by taking the questions, measuring their own intelligence. That's just really compelling.
link |
It's really interesting to me too. It helps. One of the cool things about arc, you said,
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it's kind of inspired by IQ tests or whatever. It follows a similar process. But because of its
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nature, because of the context in which it lives, it immediately forces you to think about the nature
link |
of intelligence as opposed to just the test of your own. It forces you to really think. I don't
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know if it's within the question, inherent in the question, or just the fact that it lives
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in the test that's supposed to be a test of machine intelligence. Absolutely. As you solve
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arc tasks as a human, you will be forced to basically introspect how you come up with solutions,
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and that forces you to reflect on the human problem solving process and the way your own mind
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generates abstract representations of the problems it's exposed to. I think it's due to the fact that
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the set of core knowledge priors that arc is built upon is so small. It's all a recombination of a
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very, very small set of assumptions. Okay. So what's the future of arc? So you held arc as a
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challenge as part of a Kegel competition. Yes. Kegel competition. And what do you think? Do
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you think that's something that continues for five years, 10 years, just continues growing?
link |
Yes, absolutely. So arc itself will keep evolving. So I've talked about crowd sourcing,
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I think that's a good avenue. Another thing I'm starting is I'll be collaborating with
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folks from the psychology department at NYU to do human testing on arc. And I think there are
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lots of interesting questions you can start asking, especially as you start coordinating machine
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solutions to arc tasks and the human characteristics of solutions. Like for instance, you can try to
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see if there's a relationship between the human perceived difficulty of a task and the machine
link |
perceived. Yes, and exactly some measure of machine perceived difficulty. Yeah, it's a nice
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playground in which to explore this very difference. It's the same thing as we talked
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about the autonomous vehicles. The things that could be difficult for humans might be very
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different than the things that are absolutely and formalizing or making explicit that difference
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and difficulty will teach us something may teach us something fundamental about intelligence.
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So one thing I think we did well with arc is that it's proving to be a very
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actionable test in the sense that machine performance on arc started at very much zero
link |
initially, while humans found actually the task very easy. And that alone was like a big red
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flashing light saying that something is going on and that we are missing something. And at the
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same time, machine performance did not stay at zero for very long actually within two weeks
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of the Kaggle competition, we started having a non zero number. And now the state of the art is
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around 20% of the test set solved. And so arc is actually a challenge where our capabilities
link |
start at zero, which indicates the need for progress. But it's also not an impossible
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challenge. It's not accessible. You can start making progress basically right away. At the
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same time, we are still very far from having solved it. And that's actually a very positive outcome
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of the competition is that the competition has proven that there was no obvious shortcut to
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solve these tasks. Right. Yeah, so the test held up. Yeah, exactly. That was the primary reason
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to do the Kaggle competition is to check if some some, you know, clever person was was going to
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hack the benchmark. And that did not happen, right? Like people who are solving the task are
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essentially doing it. Well, in a way, they're actually exploring some flaws of arc that we
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will need to address in the future, especially they're essentially anticipating what sort of tasks
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may be contained in the test set, right? Right. Which is kind of, yeah, that's the kind of hacking.
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It's human hacking of the test. Yes. That said, you know, with the state of the art, that's like
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a 20% versus very, very far from human level, which is closer to 100 person. And so, and I do
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believe that, you know, it will it will take a while until we reach a human parity on arc. And
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that by the time we have human parity, we will have AI systems that are probably pretty close to
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human level in terms of general fluid intelligence, which is, I mean, it's they're not going to be
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necessarily human like, they're not necessarily, you would not necessarily recognize them as,
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you know, being an AI. But they would be capable of a degree of generalization that matches the
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generalization performed by human fluid intelligence. Sure. I mean, this is a good point
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in terms of general fluid intelligence to mention in your paper, you describe different kinds of
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generalizations, local, broad, extreme, and there's a kind of hierarchy that you form. So when we say
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generalizations, what, what are we talking about? What kinds are there? Right. So generalization is
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very old idea. I mean, it's even older than machine learning. In the context of machine learning,
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you say a system generalizes if it can make sense of an input it has, it has not yet seen.
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And that's what I would call a system centric generalization, you generalization
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with respect to novelty for the specific system you're considering. So I think a good test of
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intelligence should actually deal with developer aware generalization, which is slightly stronger
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than system centric generalization. So developer generalization developer aware generalization
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would be the ability to generalize to novelty or uncertainty that not only the system itself
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has not access to, but the developer of the system could not have access to either.
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Yeah. That's a fascinating, that's a fascinating meta definition. So like the system is, it's
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basically the edge case thing we're talking about with autonomous vehicles, neither the developer
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nor the system know about the edge cases. So it's up to the system should be able to generalize the
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thing that, that nobody expected, neither the designer of the training data, nor obviously
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the contents of the training data. That's a fascinating definition.
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So you can see generalization degrees of generalization as a spectrum.
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And the lowest level is what machine learning is trying to do is the assumption that
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any new situation is going to be sampled from a static distribution of possible situations.
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And that you already have a representative sample of the distribution that's your training data.
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And so in machine learning, you generalize to a new sample from a known distribution.
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And the ways in which your new sample will be new or different are ways that are already understood
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by the developers of the system. So you are generalizing to known unknowns for one specific task.
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That's what you would call robustness. You are robust to things like noise,
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small variations and so on. For one fixed known distribution that you know through your training
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data. And a higher degree would be flexibility in machine intelligence. So flexibility would be
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something like an L5 cell driving car, or maybe a robot that can pass the
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the coffee cup test, which is the notion that you would be given a random kitchen
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somewhere in the country and you would have to go make a cup of coffee in that kitchen.
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So flexibility would be the ability to deal with unknown unknowns. So things that could not,
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dimensions of variability that could not have been possibly foreseen by the creators of the system
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within one specific task. So generalizing to the long tail of situations in cell driving,
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for instance, would be flexibility. So you have robustness, flexibility. And finally,
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we'd have extreme generalization, which is basically flexibility, but instead of just
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considering one specific domain like driving or domestic robotics, you're considering an
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open ended range of possible domains. So a robot would be capable of extreme generalization if
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let's say it's designed and trained for cooking, for instance. And if I buy the robots,
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and if I'm able, if it's able to teach itself gardening in a couple weeks, it would be capable
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of extreme generalization, for instance. So the ultimate goal is extreme generalization. Yes.
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So creating a system that is so general that it could essentially achieve human skill parity over
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arbitrary tasks and arbitrary domains with the same level of improvisation and adaptation power
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as humans when it encounters new situations. And it would do so over basically the same range
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of possible domains and tasks as humans, and using essentially the same amount of training
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experience of practice as humans would require. That would be human level
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of extreme generalization. So I don't actually think humans are anywhere near the
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optimal intelligence bound if there is such a thing. So I think for humans or in general?
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In general. I think it's quite likely that there is a hard limit to how intelligent
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any system can be. But at the same time, I don't think humans are anywhere near that limit.
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Yeah, last time I think we talked, I think you had this idea that we're only as intelligent as
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the problems we face. We are bounded by the problem. In a way, yes. We are bounded by our
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environments and we are bounded by the problems we try to solve. Yeah. What do you make of Neuralink
link |
and outsourcing some of the brain power, like brain computer interfaces? Do you think we can expand
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our, augment our intelligence? I am fairly skeptical of Neuralink interfaces because
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they're trying to fix one specific bottleneck in human mission cognition, which is the bandwidth
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bottleneck input and output of information in the brain. And my perception of the problem is that
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bandwidth is not, at this time, a bottleneck at all, meaning that we already have senses that
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enable us to take in far more information than what we can actually process. Well, to push back
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on that a little bit, to sort of play devil's advocate a little bit, is if you look at the
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internet, the Wikipedia, let's say Wikipedia, I would say that humans, after the advent of Wikipedia,
link |
are much more intelligent. Yes. I think that's a good one. But that's also not about, that's about
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externalizing our intelligence via information processing systems, external information
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processing systems, which is very different from brain computer interfaces. Right. But the question
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is whether if we have direct access, if our brain has direct access to Wikipedia, would our brain
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already has direct access to Wikipedia, it's on your phone, and you have your hands and your eyes
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and your ears and so on to access that information and the speed at which you can access it.
link |
Is bottlenecked by the cognition? I think it's already close, fairly close to optimal, which is
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why speed reading, for instance, does not work. The faster you read, the less you understand.
link |
But maybe it's because it uses the eyes. So maybe, so I don't believe so. I think, you know,
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the brain is very slow. It's speaking operates, you know, the fastest things that happen in the
link |
brain at the level of 50 milliseconds, forming a conscious start can potentially take entire
link |
seconds. Right. And you can already read pretty fast. So I think the speed at which you can
link |
take information in and even the speed at which you can output information can only be very
link |
incrementally improved. Maybe if you're very fast typer, if you're a very trained typer,
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the speed at which you can express your thoughts is already a speed at which you can form your
link |
thoughts. Right. So that's kind of an idea that there are fundamental bottlenecks to the human
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mind. But it's possible that everything we have in the human mind is just to be able to survive
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in the environment. And there's a lot more to expand. Maybe, you know, you said the speed of the
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thought. So yeah, I think augmenting human intelligence is a very valid and very powerful
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avenue. Right. And that's what computers are about. In fact, that's what, you know, all of
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culture and civilization is about. They are culture is externalized cognition. And we rely
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on culture to think constantly. Yeah. Yeah. I mean, that's another yeah, that's not just not
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just computers, not just forms on the internet. I mean, all of culture, like language, for instance,
link |
is a form of externalized cognition. Books are obviously externalized cognition.
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Yeah, that's great. And you can scale that externalized cognition, you know, far beyond
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the capability of the human brain. And you could see, you know, civilization itself is
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it has capabilities that are far beyond any individual brain and we keep scaling it because
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it's not rebound by individual brains. It's a different kind of system. Yeah. And and that
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system includes non human, non humans. First of all, includes all the other biological systems,
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which are probably contributing to the overall intelligence of the organism.
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And then computers are part of it on non human systems, probably not contributing much, but
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AI is definitely contributing to that. Like Google search, for instance, part of it. Yeah. Yeah.
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A huge part, a part that we can't probably introspect. Like how the world has changed in
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the past 20 years. It's probably very difficult for us to be able to understand until, of course,
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whoever created the simulation wherein is probably do metrics measuring the progress.
link |
Yes. There was probably a big spike in performance. They're enjoying, they're enjoying this.
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So what are your thoughts on the Turing test and the Lobner Prize, which is the,
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you know, one of the most famous attempts at the test of human intelligence, sorry,
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of artificial intelligence by doing a natural language open dialogue test that's test that's
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judged by humans as far as how well the machine did.
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So I'm not a fan of the Turing test itself or any of its variants for two reasons.
link |
So first of all, it's really coping out of trying to define and measure intelligence because it's
link |
entirely outsourcing that to a panel of human judges. And these human judges, they may not
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themselves have any proper methodology. They may not themselves have any proper definition of
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intelligence. They may not be reliable. So the Turing test already failing one of the core
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psychometrics principles, which is reliability because you have biased human judges. It's also
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violating the standardization requirement and the freedom from bias requirement. And so it's
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really a coop out because you are outsourcing everything that matters, which is precisely
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describing intelligence and finding a standard on test to measure it. You are sourcing everything
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to people. So it's really a coop out. And by the way, we should keep in mind that when Turing
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proposed the imitation game, it was not meaning for the imitation game to be an actual goal for
link |
the field of AI and actual tests of intelligence. It was using the imitation game as a thought
link |
experiment in a philosophical discussion in his 1950 paper. He was trying to argue that theoretically,
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it should be possible for something very much like the human mind indistinguishable from the
link |
human mind to be encoded in a Turing machine. And at the time, that was a very daring idea.
link |
It was stretching credulity. But nowadays, I think it's fairly well accepted that the mind is an
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information processing system and that you could probably encode it into a computer. So another
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reason why I'm not a fan of this type of test is that the incentives that it creates are incentives
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that are not conducive to proper scientific research. If your goal is to convince a panel of
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human judges that they're talking to a human, then you have an incentive to rely on tricks and
link |
prestidigitation in the same way that, let's say, you're doing physics and you want to solve
link |
teleportation. And what if the test that you set out to pass is you need to convince a panel of
link |
judges that teleportation took place and they're just sitting there and watching what you're doing.
link |
And that is something that you can achieve with David Copperfield could achieve it in his show
link |
at Vegas. And what he's doing is very elaborate. But it's not actually, it's not physics. It's
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not making any progress in our understanding of the universe. To push back on that as possible,
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that's the hope with these kinds of subjective evaluations is that it's easier to solve it
link |
generally than it is to come up with tricks that convince a large number of judges. That's the hope.
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In practice, it turns out that it's very easy to deceive people in the same way that you can do
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magic in Vegas. You can actually very easily convince people that they're talking to a human
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when they're actually talking to an algorithm. I disagree with that. I think it's easy.
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It's not easy. It's doable. It's very easy because I wouldn't say it's very easy though.
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We are biased. We have theory of mind. We are constantly projecting emotions, intentions,
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agentness. Agentness is one of our core innate priors. We are projecting these things on everything
link |
around us. If you paint a smiley on a rock, the rock becomes happy in our eyes. Because
link |
we have this extreme bias that permits everything we see around us, it's actually pretty easy to
link |
trick people. I disagree with that. I totally disagree with that. You brilliantly put the
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anthropomorphization that we naturally do, the agentness of that word. Is that a real word?
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No, it's not a real word. I like it. But it's a good word. It's a useful word.
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It's a useful word. Let's make it real. It's a huge help. But I still think it's really difficult
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to convince. If you do like the Alexa Prize formulation where you talk for an hour,
link |
like there's formulations of the test you can create where it's very difficult.
link |
So I like the Alexa Prize better because it's more pragmatic. It's more practical.
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It's actually incentivizing developers to create something that's useful as a human
link |
mission interface. So that's slightly better than just the imitation.
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So your idea is like a test which hopefully will help us in creating intelligent systems
link |
as a result. If you create a system that passes it, it'll be useful for creating
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further intelligent systems. Yes, at least. I'm a little bit surprised
link |
how little inspiration people draw from the Turing test today. The media and the popular
link |
press might write about it every once in a while. The philosophers might talk about it.
link |
But most engineers are not really inspired by it. I know you don't like the Turing test,
link |
but we'll have this argument another time. There's something inspiring it about it,
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I think. As a philosophical device in a philosophical discussion,
link |
I think there is something very interesting about it. I don't think it is in practical terms.
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I don't think it's conducive to progress. And one of the reasons why is that I think
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being very human like being undistinguishable from a human is actually the very last step
link |
in the creation of machine intelligence. That the first AI is that will show strong
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generalization that will actually implement human like broad cognitive abilities.
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They will not actually be able to look anything like humans.
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Human likeness is the very last step in that process. And so a good test is a test that
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points you towards the first step on the ladder, not towards the top of the ladder, right?
link |
So to push back on that, I usually agree with you on most things. I remember you,
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I think, at some point tweeting something about the Turing test not being counterproductive or
link |
something like that. And I think a lot of very smart people agree with that. A computation
link |
speaking not very smart person disagree with that because I think there's some magic to the
link |
interactivity interactivity with other humans. So to push to play devil's advocate on your
link |
statement, it's possible that in order to demonstrate the generalization abilities of a system,
link |
you have to show your in conversation show your ability to adjust, adapt to the conversation
link |
through not just like as a standalone system, but through the process of like the interaction
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that game theoretic, where you're you really are changing the environment by your actions. So
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in the art challenge, for example, you're an observer, you can't you can't scare the test
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into into changing, you can't talk to the test, you can't play with it. So there's some aspect
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of that interactivity that becomes highly subjective, but it feels like it could be conducive
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to yeah, I think you make a great point. The interactivity is very good setting to force
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a system to show adaptation to show generalization. That said, you're at the same time. It's not
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something very scalable because you rely on human judges. It's not something reliable because the
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human judges may not you don't like human judges. Basically, yes. And I think so. I love the idea
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of interactivity. I initially wanted an artist that had some amount of interactivity where your
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score on a task would not be one or zero if you can solve it or not, but would be the number
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of attempts that you can make before you hit the right solution, which means that now you can start
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applying the scientific method as you sort of arc tasks that you can start formulating hypothesis
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and probing the system to see whether the idea of this is the observation will match the hypothesis
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or not. It would be amazing if you could also even higher level than that, measure the quality of
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your attempts, which of course is impossible. But again, that gets subjective. How good was
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your thinking? How efficient was? So one thing that's interesting about this notion of scoring
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you as how many attempts you need is that you can start producing tasks that are way more ambiguous,
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right? Because with the different attempts, you can actually probe that
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ambiguity, right? Right. So that's in a sense, which is how good can you adapt to the uncertainty
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and reduce the uncertainty? Yes. It's half fast. Is the efficiency with which to reduce uncertainty
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in program space? Exactly. Very difficult to come up with that kind of test though.
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Yeah. So I would love to be able to create something like this. In practice, it would be very,
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very difficult. But yes. What you're doing, what you've done with the ARC challenge is brilliant.
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I'm also not, I'm surprised that it's not more popular, but I think it's picking up like that.
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It does its niche. It does its niche. Yeah. What are your thoughts about another test that I talked
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with Marcus Hutter? He has the harder prize for compression of human knowledge and the idea is
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really sort of quantify and reduce the test of intelligence purely to just the ability to
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compress. What's your thoughts about this intelligence as compression? I mean, it's a very
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fun test because it's such a simple idea. Like you're given Wikipedia, basically English Wikipedia,
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and you must compress it. And so it stems from the idea that cognition is compression,
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that the brain is basically a compression algorithm. This is a very old idea. It's a very, I think,
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striking and beautiful idea. I used to believe it. I eventually had to realize that it was,
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it was very much a flawed idea. So I no longer believe that compression is cognition is compression.
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So, but I can tell you what's the difference. So it's very easy to believe that cognition
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and compression are the same thing because, so Jeff Hawkins, for instance, says that
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cognition is prediction. And of course, prediction is basically the same thing as compression, right?
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It's just including the temporal axis. And it's very easy to believe this because compression
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is something that we do all the time very naturally. We are constantly compressing information. We are
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constantly trying, we have this bias towards simplicity. We're constantly trying to organize
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things in our mind and around us to be more regular, right? So it's a beautiful idea. It's
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very easy to believe. There is a big difference between what we do with our brains and compression.
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So compression is actually kind of a tool in the human cognitive tool kit that is used in many
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ways. But it's just a tool. It is not, it is a tool for cognition. It is not cognition itself.
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And the big fundamental difference is that cognition is about being able to operate in
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future situations that include fundamental uncertainty and novelty. So for instance,
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consider a child at age 10. And so they have 10 years of life experience. They've gotten,
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you know, pain, pleasure, rewards, and punishment at a period of time. If you were to generate
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the shortest behavioral program that would have basically run that child over these 10 years
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in an optimal way, right? The shortest optimal behavioral program given the experience of that
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child so far. Well, that program, that compressed program, this is what you would get if the mind
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of the child was a compression algorithm essentially, would be utterly unable, inappropriate to process
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the next 70 years in the life of that child. So in the models we build of the world, we are not
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trying to make them actually optimally compressed. We are using compression as a tool to promote
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simplicity and efficiency in our models. But they are not perfectly compressed because they need to
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include things that are seemingly useless today, that have seemingly been useless so far. But that
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may turn out to be useful in the future because you just don't know the future. And that's the
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fundamental principle that cognition, that intelligence arises from is that you need to be
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able to run appropriate behavioral programs, except you have absolutely no idea what sort of
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context, environment, and situation they're going to be running in. And you have to deal with that,
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with that uncertainty, with that future novelty. So an analogy that you can make is with investing,
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for instance. If I look at the past 20 years of stock market data, and I use a compression
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algorithm to figure out the best trading strategy, it's going to be you buy Apple stock, then maybe
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the past few years you buy Tesla stock or something. But is that strategy still going to be true for
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the next 20 years? Well, actually, probably not. Which is why if you're a smart investor, you're
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not just going to be following the strategy that corresponds to compression of the past.
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You're going to be following, you're going to have a balanced spot for you, right? Because you
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just don't know what's going to happen. I mean, I guess in that same sense, the compression is
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analogous to what you talked about, which is like local or robust generalization versus extreme
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generalization. It's much closer to that side of being able to generalize in the local sense.
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That's why as humans, when we are children, in our education, so a lot of it is driven by place,
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driven by curiosity, we are not efficiently compressing things. We're actually exploring.
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We are retaining all kinds of things from our environment that seem to be completely useless,
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because they might turn out to be eventually useful. That's what cognition is really about,
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and what makes it antagonistic to compression is that it is about hedging for future uncertainty.
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Cognition leverages compression as a tool to promote efficiency.
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So in that sense, in our models. It's like Einstein said, make it simpler,
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but not however that quote goes, but not too simple. So you want to compression,
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simplifies things, but you don't want to make it too simple.
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Yes. So a good model of the world is going to include all kinds of things that are completely
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useless, actually, just in case. Because you need diversity in the same way that in your portfolio,
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you need all kinds of stocks that may not have performed well so far, but you need
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diversity. And the reason you need diversity is because, fundamentally, you don't know what
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you're doing. And the same is true of the human mind, is that it needs to behave appropriately
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in the future. And it has no idea what the future is going to be like. But it's not going to be
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like the past. So compressing the past is not appropriate, because the past is not
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predictive of the future. Yeah. History repeats itself, but not perfectly.
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I don't think I asked you last time the most inappropriately absurd question.
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We've talked a lot about intelligence, but the bigger question from intelligence is of meaning.
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You know, intelligence systems are kind of goal oriented. They're always optimizing for goal.
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You look at the hotter prize, actually. I mean, there's always a clean formulation of a goal.
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But the natural questions for us humans, since we don't know our objective function, is what is
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the meaning of it all? So the absurd question is, what Francois Chalet do you think is the meaning
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of life? What's the meaning of life? Yeah, that's a big question.
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And I think I can give you my answer, at least one of my answers.
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And so you know, the one thing that's very important in understanding who we are is that
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everything that makes up ourselves, that makes up who we are,
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even your most personal thoughts is not actually your own. Even your most personal thoughts
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are expressed in words that you did not invent and are built on concepts and images that you did
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not invent. We are very much cultural beings. We are made of culture. What makes us different
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from animals, for instance. So everything about ourselves is an echo of the past, an echo of
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people who lived before us. That's who we are. And in the same way, if we manage to contribute
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something to the collective edifice of culture, a new idea, maybe a beautiful piece of music,
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a work of art, a grand theory, and new words, maybe, that something is going to become a part
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of the minds of future humans, essentially forever. So everything we do creates repulse
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that propagates into the future. And that's in a way, this is our path to immortality,
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is that as we contribute things to culture, culture in turn becomes future humans. And
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we keep influencing people thousands of years from now. So our actions today create repulse.
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And these repulse, I think, basically sum up the meaning of life. Like in the same way that we are,
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the sum of the interactions between many different repulse that came from our past,
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we are ourselves creating repulse that will propagate into the future. And that's why
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we should be, this seems like perhaps an eighth thing to say, but we should be kind to others
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during our time on Earth, because every act of kindness creates repulse. And in reverse,
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every act of violence also creates repulse. And you want to carefully choose which kind of repulse
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you want to create, and you want to propagate into the future. And in your case, first of all,
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beautifully put, but in your case, creating repulse into the future human and future AGI systems.
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Yes. It's fascinating. Our successors.
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I don't think there's a better way to end it. Francois has always, for a second time, and I'm
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sure many times in the future, it's been a huge honor. You're one of the most brilliant people
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in the machine learning computer science, science world. Again, it's a huge honor. Thanks for talking
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to me. It's been a pleasure. Thanks a lot for having me. We appreciate it. Thanks for listening
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to this conversation with Francois Chollet. And thank you to our sponsors, Babel, Masterclass,
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and Cash App. Click the sponsor links in the description to get a discount and to support
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this podcast. If you enjoy this thing, subscribe on YouTube, review it with five stars on our
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podcast, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Freedman.
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And now let me leave you with some words from Renee Descartes in 1668, an excerpt of which
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Francois includes in his On the Measure of Intelligence paper. If there were machines
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which bore a resemblance to our bodies and imitated our actions as closely as possible
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for all practical purposes, we should still have two very certain means of recognizing
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that they were not real men. The first is that they could never use words or put together
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signs as we do in order to declare our thoughts to others. For we can certainly conceive of a
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machine so constructed that it utters words and even utters words that correspond to bodily
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actions causing a change in its organs. But it is not conceivable that such a machine should
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produce different arrangements of words so as to give it an appropriately meaningful answer
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to whatever is said in its presence as the dullest of men can do. Here Descartes is anticipating
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the touring test, and the argument still continues to this day. Secondly, he continues,
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even though some machines might do some things as well as we do them, or perhaps even better,
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they would inevitably fail in others, which would reveal that they are acting not from
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understanding but only from the disposition of their organs. This is an incredible quote.
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For whereas reason is a universal instrument which can be used in all kinds of situations,
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these organs need some particular action. Hence it is for all practical purposes impossible
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for a machine to have enough different organs to make it act in all the contingencies of life
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in the way in which our reason makes us act. That's the debate between mimicry memorization
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versus understanding. So, thank you for listening and hope to see you next time.