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The following is a conversation with Yann LeCun, his second time on the podcast.
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He is the chief AI scientist at Meta, formerly Facebook, professor at NYU, Turing Award winner, one of the seminal figures in the history of machine learning and artificial intelligence, and someone who is brilliant and opinionated in the best kind of way, and so is always fun to talk to.
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And fifth is Magic Spoon, low-carb, keto-friendly cereal.
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Choose wisely, my friends.
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I think I use a bunch of other stuff too, but that s what comes to mind.
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Their products often have this minimalist black and white design that I just absolutely find beautiful.
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It doesn t go over the top.
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It doesn t have all these extra things and features that you don t need, just the essentials.
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I ve used them as part of many hiring efforts I ve done for the teams I ve led in the past.
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It s a treacherous and an exciting process.
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So it s a beautiful process, but I think it s one of the most important processes in life.
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It s selecting the group of people with whom you spend your days with.
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Roka was started by two All-American swimmers from Stanford, and it was born out of an obsession with performance.
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I like the way they feel.
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I like the way they look.
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Whether I m running like a fast pace run we re talking about 8 minute mile or faster or if we re doing a slow pace run 9 10 minute mile along the river in the heat or in the cold or if I m just wearing my suit out On a town however that expression goes.
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I m not sure but they look classy with a suit.
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They look badass in running gear It s just my go-to sunglasses
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I m not sure why I was doing up-speak on that sentence.
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Maybe because I m very excited about NetSuite.
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Anyway, there s a lot of messy things you have to get right when running a company.
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to make sure all the messy things required to run a business are taken care of for you so you can focus on the things that you re best at, where your brilliance shines.
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This episode is also brought to you by Magic Spoon, the OG, not quite OG, but really old school sponsor of this podcast that I love.
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I don t know why I keep saying that, but it seems to be true.
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This is the Lex Friedman podcast and here s my conversation with Yann LeCun.
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You co-wrote the article, Self-Supervised Learning, The Dark Matter of Intelligence.
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Great title, by the way, with Ishan Misra.
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So let me ask, what is self-supervised learning, and why is it the dark matter of intelligence?
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I ll start by the dark matter part.
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There is obviously a kind of learning that humans and animals are doing that we currently are not reproducing properly with machines, with AI.
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So the most popular approaches to machine learning today, or paradigms I should say, are supervised learning and reinforcement learning.
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And they are extremely inefficient.
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Supervised learning requires many samples for learning anything.
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And reinforcement learning requires a ridiculously large number of trials and errors for a system to learn anything.
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And that s why we don t have self-driving cars.
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That was a big leap from one to the other.
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Okay.
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So that to solve difficult problems, you have to have a lot of human annotation for supervised learning to work.
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And to solve those difficult problems with reinforcement learning, you have to have some way to maybe simulate that problem such that you can do that large scale kind of learning that reinforcement learning requires.
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Right.
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So how is it that, you know, most teenagers can learn to drive a car in about 20 hours of practice?
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Whereas even with millions of hours of simulated practice, a self-driving car can t actually learn to drive itself properly.
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And so obviously we re missing something, right?
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And it s quite obvious for a lot of people that, you know, the immediate response you get from many people is, well, you know, humans use their background knowledge to learn faster.
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And they re right.
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Now, how was that background knowledge acquired?
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And that s the big question.
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So now you have to ask, you know, how do babies in the first few months of life learn how the world works?
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Mostly by observation, because they can hardly act in the world.
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And they learn an enormous amount of background knowledge about the world that may be the basis of what we call common sense.
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This type of learning, it s not learning a task, it s not being reinforced for anything, it s just observing the world and figuring out how it works.
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building world models, learning world models.
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How do we do this?
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And how do we reproduce this in machines?
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So self-supervised learning is one instance or one attempt at trying to reproduce this kind of learning.
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Okay, so you re looking at just observation, so not even the interacting part of a child.
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It s just sitting there watching mom and dad walk around, pick up stuff, all of that.
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That s what you mean about background knowledge.
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Perhaps not even watching mom and dad, just watching the world go by.
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Just having eyes open or having eyes closed, or the very act of opening and closing eyes that the world appears and disappears, all of that basic information.
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And you re saying in order to learn to drive,
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Like the reason humans are able to learn to drive quickly, some faster than others, is because of the background knowledge they were able to watch cars operate in the world in the many years leading up to it, the physics of basic objects, all that kind of stuff.
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That s right.
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I mean, the basic physics of objects, you don t even know, you don t even need to know, you know, how a car works, right?
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Because that you can learn fairly quickly.
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I mean, the example I use very often is you re driving next to a cliff.
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And you know in advance, because of your understanding of intuitive physics, that if you turn the wheel to the right, the car will veer to the right, will run off the cliff, fall off the cliff, and nothing good will come out of this, right?
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But if you are a sort of
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tabula rasa reinforcement learning system that doesn t have a model of the world, you have to repeat falling off this cliff thousands of times before you figure out it s a bad idea.
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And then a few more thousand times before you figure out how to not do it.
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And then a few more million times before you figure out how to not do it in every situation you ever encounter.
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So self-supervised learning still has to have some source of truth being told to it by somebody.
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So you have to figure out a way without human assistance or without significant amount of human assistance to get that truth from the world.
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So the mystery there is how much signal is there, how much truth is there that the world gives you, whether it s the human world, like you watch YouTube or something like that, or it s the more natural world.
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So how much signal is there?
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So here s the trick.
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There is way more signal in sort of a self-supervised setting than there is in either a supervised or reinforcement setting.
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And this is going to my, you know, analogy of the cake.
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You know, le cake as someone has called it, where when you try to figure out how much information you ask the machine to predict and how much feedback you give the machine at every trial,
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In reinforcement learning, you give the machine a single scalar, you tell the machine you did good, you did bad, and you only tell this to the machine once in a while.
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When I say you, it could be the universe telling the machine, right?
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But it s just one scalar.
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So as a consequence, you cannot possibly learn something very complicated without many, many, many trials where you get many, many feedbacks of this type.
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Supervised learning, you give a few bits to the machine at every sample.
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Let s say you re training a system on recognizing images on ImageNet.
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There is 1,000 categories.
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That s a little less than 10 bits of information per sample.
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But self-supervised learning here is a setting.
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Ideally, we don t know how to do this yet, but ideally you would show a machine a segment of video and then stop the video and ask the machine to predict what s going to happen next.
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So you let the machine predict and then you let time go by and show the machine what actually happened and hope the machine will learn to do a better job at predicting next time around.
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There s a huge amount of information you give the machine because it s an entire video clip of the future after the video clip you fed it in the first place.
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So both for language and for vision, there s a subtle, seemingly trivial construction, but maybe that s representative of what is required to create intelligence, which is filling the gap.
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fill in the gaps.
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It sounds dumb, but can you, it is possible you can solve all of intelligence in this way, just for both language, just give a sentence and continue it, or give a sentence and there s a gap in it, some words blanked out, and you fill in what words go there.
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For vision, you give a sequence of images and predict what s gonna happen next, or you fill in what happened in between.
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Do you think it s possible that formulation alone as a signal for self-supervised learning can solve intelligence for vision and language?
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I think that s our best shot at the moment.
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So whether this will take us all the way to human-level intelligence or something, or just cat-level intelligence is not clear, but among all the possible approaches that people have proposed, I think it s our best shot.
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So I think this idea of an intelligent system filling in the blanks, either predicting the future, inferring the past, filling in missing information.
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I m currently filling the blank of what is behind your head and what your head looks like from the back, because I have basic knowledge about how humans are made.
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And I don t know what you re going to say, at which point you re going to speak, whether you re going to move your head this way or that way, which way you re going to look.
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But I know you re not going to just dematerialize and reappear three meters down the hall.
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Because I know what s possible and what s impossible according to intuitive physics.
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You have a model of what s possible and what s impossible, and then you d be very surprised if it happens, and then you ll have to reconstruct your model.
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Right.
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So that s the model of the world.
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It s what tells you what fills in the blanks.
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So given your partial information about the state of the world, given by your perception, your model of the world fills in the missing information.
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And that includes predicting the future, retradicting the past, filling in things you don t immediately perceive.
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And that doesn t have to be purely generic vision or visual information or generic language.
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You can go to specifics like predicting what control decision you make when you re driving in a lane.
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You have a sequence of images from a vehicle.
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and then you have information, if you record it on video, where the car ended up going, so you can go back in time and predict where the car went based on the visual information.
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That s very specific, domain specific.
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Right.
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But the question is whether we can come up with a generic method for training machines to do this kind of prediction or filling in the blanks.
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So right now, this type of approach has been unbelievably successful in the context of natural language processing.
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Every modern natural language processing is pre-trained in a self-supervised manner.
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to fill in the blanks.
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You show it a sequence of words, you remove 10% of them, and then you train some gigantic neural net to predict the words that are missing.
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And once you ve pre-trained that network, you can use the internal representation learned by it as input to something that you trained, supervised, or whatever.
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That s been incredibly successful.
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Not so successful in images, although it s making progress.
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And it s based on sort of manual data augmentation.
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We can go into this later.
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But what has not been successful yet is training from video.
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So getting a machine to learn to represent the visual world, for example, by just watching video.
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Nobody has really succeeded in doing this.
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Okay, well, let s kind of give a high-level overview.
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What s the difference in
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kind and in difficulty between vision and language.
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So you said people haven t been able to really kind of crack the problem of vision open in terms of self-supervised learning, but that may not be necessarily because it s fundamentally more difficult.
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Maybe like when we re talking about achieving like passing
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the Turing test in the full spirit of the Turing test in language might be harder than vision.
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That s not obvious.
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So in your view, which is harder, or perhaps are they just the same problem?
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The farther we get to solving each, the more we realize it s all the same thing.
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It s all the same cake.
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I think what I m looking for are methods that make them look essentially like the same cake, but currently they re not.
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And the main issue,
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with learning world models or learning predictive models is that the prediction is never a single thing because the world is not entirely predictable.
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It may be deterministic or stochastic, we can get into the philosophical discussion about it, but even if it s deterministic, it s not entirely predictable.
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And so if I play a short video clip and then I ask you to predict what s going to happen next, there s many, many plausible continuations for that video clip.
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And the number of continuation grows with the interval of time that you re asking the system to make a prediction for.
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And so one big question with self-supervision is how you represent this uncertainty, how you represent
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multiple discrete outcomes, how you represent a continuum of possible outcomes, etc.
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If you are a classical machine learning person, you say, oh, you just represent a distribution, right?
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And that we know how to do when we re predicting words, missing words in the text, because you can have a neural net give a score for every word in a dictionary.
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It s a big, you know, it s a big list of numbers, you know, maybe a hundred thousand or so.
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And you can turn them into a probability distribution that gives, that tells you when I say a sentence, you know, the, you know, the cat is chasing the blank in the kitchen.
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There are only a few words that make sense there.
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It could be a mouse or it could be a lizard spot or something like that, right?
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And if I say the blank is chasing the blank in the savannah, you also have a bunch of plausible options for those two words, right?
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Because you have kind of an underlying reality that you can refer to to sort of fill in those blanks.
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You cannot say for sure in the savannah if it s a lion or a cheetah or whatever.
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You cannot know if it s a zebra or a goo or whatever.
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Well, the beast, the same thing.
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But you can represent the uncertainty by just a long list of numbers.
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Now,
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If I do the same thing with video and I ask you to predict a video clip, it s not a discrete set of potential frames.
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You have to have somewhere representing an infinite number of plausible continuations of multiple frames in a high-dimensional continuous space.
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And we just have no idea how to do this properly.
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a finite high dimensional.
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So like you.
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It s finite high dimensional, yes.
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Just like the words, they try to get it down to a small finite set of like under a million, something like that.
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Something like that.
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I mean it s kind of ridiculous that
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we re doing a distribution over every single possible word for language, and it works.
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It feels like that s a really dumb way to do it.
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It seems to be like there should be some more compressed representation of the distribution of the words.
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And so, do you have any interesting ideas about how to represent all of reality in a compressed way such that you can form a distribution over it?
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That s one of the big questions, you know, how do you do that?
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Right.
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I mean, what s what s kind of, you know, another thing that really is.
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stupid about, I shouldn t say stupid, but like simplistic about current approaches to self-supervision in NLP, in text, is that not only do you represent a giant distribution over words, but for multiple words that are missing, those distributions are essentially independent of each other.
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And, you know, you don t pay too much of a price for this.
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So you can t, so, you know, the system, you know, in the sentence that I gave earlier,
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If it gives a certain probability for a lion and a cheetah, and then a certain probability for a gazelle, wildebeest, and zebra, those two probabilities are independent of each other.
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And it s not the case that those things are independent.
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Lions actually attack bigger animals than cheetahs.
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So there s a huge independent hypothesis in this process, which is not actually true.
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The reason for this is that we don t know how to represent properly distributions over combinatorial sequences of symbols, essentially, because the number grows exponentially with the length of the symbols.
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And so we have to use tricks for this, but those techniques can get around, don t even deal with it.
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So the big question is, would there be some sort of abstract latent representation of text that would say that when I switch lion for cheetah, I also have to switch zebra for gazelle?
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Yeah, so this independence assumption, let me throw some criticism at you that I often hear and see how you respond.
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So this kind of filling in the blanks is just statistics.
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You re not learning anything like the deep underlying concepts.
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You re just mimicking stuff from the past.
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You re not learning anything new such that you can use it to generalize about the world.
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Or, okay, let me just say the crude version, which is it s just statistics.
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It s not intelligence.
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What do you have to say to that?
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What do you usually say to that if you kind of hear this kind of thing?
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I don t get into those discussions because they are kind of pointless.
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So first of all, it s quite possible that intelligence is just statistics.
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It s just statistics of a particular kind.
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Where- This is the philosophical question.
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Is it possible that intelligence is just statistics?
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Yeah.
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But what kind of statistics?
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So if you are asking the question,
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Are the models of the world that we learn, do they have some notion of causality?
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Yes.
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So if the criticism comes from people who say, you know, a current machine learning system don t care about causality, which by the way is wrong, you know, I agree with them.
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Your model of the world should have your actions as one of the inputs, and that will drive you to learn causal models of the world where you know what
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you know, what intervention in the world will cause what result.
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Or you can do this by observation of other agents acting in the world and observing the effect.
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Other humans, for example.
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So I think, you know, at some level of description, intelligence is just statistics.
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But that doesn t mean you won t have models that have deep mechanistic explanation for what goes on.
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The question is, how do you learn them?
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SPEAKER_01
That s the question I m interested in.
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SPEAKER_01
Because a lot of people who actually voice their criticism say that those mechanistic models have to come from someplace else.
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SPEAKER_01
They have to come from human designers.
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SPEAKER_01
They have to come from I don t know what.
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SPEAKER_01
And obviously, we learn them.
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SPEAKER_01
Or if we don t learn them as an individual, nature learned them for us using evolution.
1
SPEAKER_01
So regardless of what you think, those processes have been learned somehow.
1
SPEAKER_00
So if you look at the human brain, just like when we humans introspect about how the brain works, it seems like when we think about what is intelligence, we think about the high-level stuff, like the models we ve constructed, concepts like cognitive science, like concepts of memory and reasoning module, almost like these high-level modules.
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SPEAKER_00
Does this serve as a good analogy?
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SPEAKER_00
Are we ignoring the,
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The dark matter, the basic low-level mechanisms, just like we ignore the way the operating system works, we re just using the high-level software, we re ignoring that at the low level, the neural network might be doing something like statistics.
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Meaning, sorry to use this word probably incorrectly and crudely,
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SPEAKER_00
doing this kind of fill-in-the-gap kind of learning and just kind of updating the model constantly in order to be able to support the raw sensory information, to predict it and then adjust to the prediction when it s wrong.
1
SPEAKER_00
But when we look at our brain at the high level, it feels like we re playing chess, like we re playing with high-level concepts and we re stitching them together and we re putting them into long-term memory.
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SPEAKER_00
But really what s going underneath
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SPEAKER_00
is something we re not able to introspect, which is this kind of simple, large neural network that s just filling in the gaps.
1
SPEAKER_01
Right, well, okay, so there s a lot of questions and a lot of answers there.
1
SPEAKER_01
Okay, so first of all, there s a whole school of thought in neuroscience, computational neuroscience in particular, that likes the idea of predictive coding, which is really related to the idea I was talking about in self-supervised learning.
1
SPEAKER_01
So everything is about prediction, the essence of intelligence is the ability to predict, and everything the brain does is trying to predict everything from everything else.
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SPEAKER_01
And that s really the underlying principle, if you want, that self-supervised learning is trying to reproduce this idea of prediction as an essential mechanism of task-independent learning, if you want.
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SPEAKER_01
The next step is, what kind of intelligence are you interested in reproducing?
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SPEAKER_01
And of course, we all think about trying to reproduce high-level cognitive processes in humans,
1
SPEAKER_01
But with machines, we re not even at the level of even reproducing the learning processes in a cat brain.
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SPEAKER_01
The most intelligent of our intelligent systems don t have as much common sense as a house cat.
1
SPEAKER_01
So how is it that cats learn?
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SPEAKER_01
And, you know, cats don t do a whole lot of reasoning.
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SPEAKER_01
They certainly have causal models.
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SPEAKER_01
They certainly have, because, you know, many cats can figure out like how they can act on the world to get what they want.
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SPEAKER_01
They certainly have a fantastic model of intuitive physics, certainly of the dynamics of their own bodies, but also of praise and things like that, right?
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So they re pretty smart.
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SPEAKER_01
They only do this with about 800 million neurons.
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SPEAKER_01
We are not anywhere close to reproducing this kind of thing.
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SPEAKER_01
So to some extent, I could say, let s not even worry about the high-level cognition and long-term planning and reasoning that humans can do until we figure out, can we even reproduce what cats are doing?
1
SPEAKER_01
Now, that said, this ability to learn world models, I think, is the key to the possibility of learning machines that can also reason.
1
SPEAKER_01
So whenever I give a talk, I say there are three main challenges in machine learning.
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The first one is getting machines to learn to represent the world, and I m proposing self-supervised learning.
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The second is getting machines to reason in ways that are compatible with
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essentially gradient-based learning because this is what deep learning is all about really.
1
SPEAKER_01
And the third one is something we have no idea how to solve, at least I have no idea how to solve, is can we get machines to learn hierarchical representations of action plans?
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SPEAKER_01
You know, like, you know, we know how to train them to learn hierarchical representations of perception, you know, with convolutional nets and things like that and transformers.
1
SPEAKER_01
But what about action plans?
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SPEAKER_01
Can we get them to spontaneously learn good hierarchical representations of actions?
1
SPEAKER_00
Also gradient based.
1
SPEAKER_01
Yeah, all of that needs to be somewhat differentiable so that you can apply sort of gradient-based learning, which is really what deep learning is about.
1
SPEAKER_00
So it s background knowledge, ability to reason in a way that s differentiable, that is somehow connected, deeply integrated with that background knowledge, or builds on top of that background knowledge.
1
SPEAKER_00
And then given that background knowledge, be able to make hierarchical plans in the world.
1
SPEAKER_01
So if you take classical optimal control, there s something in classical optimal control called model predictive control.
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SPEAKER_01
It s been around since the early 60s.
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SPEAKER_01
NASA uses that to compute trajectories of rockets.
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SPEAKER_01
The basic idea is that you have a predictive model of the rocket, let s say, or whatever system you intend to control.
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SPEAKER_01
which, given the state of the system at time t and given an action that you re taking on the system, so for a rocket to be thrust and all the controls you can have, it gives you the state of the system at time t plus delta t. So basically a differential equation, something like that.
1
SPEAKER_01
And if you have this model, and you have this model in the form of some sort of neural net, or some sort of set of formula that you can back propagate gradient through, you can do what s called model predictive control, or gradient-based model predictive control.
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SPEAKER_01
So you can unroll that model in time.
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SPEAKER_01
You feed it a hypothesized sequence of actions.
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SPEAKER_01
And then you have some objective function that measures how well, at the end of the trajectory, the system has succeeded or matched what you wanted to do.
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SPEAKER_01
Is it a robot harm?
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Have you grasped the object you want to grasp?
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If it s a rocket, are you at the right place near the space station?
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Things like that.
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And by backpropagation through time, and again this was invented in the 1960s by optimal control theorists, you can figure out what is the optimal sequence of actions that will get my system to the best final state.
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SPEAKER_01
That s a form of reasoning.
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SPEAKER_01
It s basically planning.
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SPEAKER_01
And a lot of planning systems in robotics are actually based on this.
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SPEAKER_01
And you can think of this as a form of reasoning.
1
SPEAKER_01
So to take the example of the teenager driving a car again, you have a pretty good dynamical model of the car.
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SPEAKER_01
It doesn t need to be very accurate.
1
SPEAKER_01
But you know, again, that if you turn the wheel to the right and there is a cliff, you re going to run off the cliff.
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SPEAKER_01
You don t need to have a very accurate model to predict that.
1
SPEAKER_01
And you can run this in your mind and decide not to do it for that reason.
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SPEAKER_01
because you can predict in advance that the result is going to be bad.
1
SPEAKER_01
So you can imagine different scenarios and then employ or take the first step in the scenario that is most favourable and then repeat the process of planning.
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SPEAKER_01
That s called receding horizon model predictive control.
1
SPEAKER_01
So all those things have names going back decades.
1
SPEAKER_01
In classical optimal control, the model of the world is not generally learned.
1
SPEAKER_01
There s sometimes a few parameters you have to identify, that s called systems identification.
1
SPEAKER_01
But generally, the model is mostly deterministic and mostly built by hand.
1
SPEAKER_01
So the big question of AI, I think the big challenge of AI for the next decade, is how do we get machines to run predictive models of the world that deal with uncertainty,
1
SPEAKER_01
and deal with the real world in all this complexity.
1
SPEAKER_01
So it s not just the trajectory of a rocket, which you can reduce to first principles.
1
SPEAKER_01
It s not even just the trajectory of a robot arm, which again, you can model by careful mathematics, but it s everything else, everything we observe in the world, people, behavior.
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SPEAKER_01
you know, physical systems that involve collective phenomena like water or, you know, trees and, you know, branches in a tree or something, or like complex things that, you know, humans have no trouble developing abstract representations and predictive models for, but we still don t know how to do with machines.
1
SPEAKER_00
Where do you put in these three, maybe in the planning stages, the game theoretic nature of this world, where your actions not only respond to the dynamic nature of the world, the environment, but also affect it?
1
SPEAKER_00
So if there s other humans involved, is this point number four, or is it somehow integrated into the hierarchical representation of action in your view?
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SPEAKER_01
I think it s integrated.
1
SPEAKER_01
It s just that now your model of the world has to deal with, you know, it just makes it more complicated.
1
SPEAKER_01
The fact that humans are complicated and not easily predictable, that makes your model of the world much more complicated.
1
SPEAKER_01
That much more complicated.
2
SPEAKER_01
Yeah, I mean, I suppose chess is an analogy.
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SPEAKER_01
So Monte Carlo, tree search.
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SPEAKER_01
I mean, there s a, I go, you go, I go, you go.
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SPEAKER_01
Like Andrej Karpathy recently gave a talk at MIT about car doors.
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SPEAKER_01
I think there s some machine learning too, but mostly car doors.
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SPEAKER_01
And there s a dynamic nature to the car, like the person opening the door checking, I mean, he wasn t talking about that, he was talking about the perception problem of what the ontology of what defines a car door, this big philosophical question.
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SPEAKER_01
But to me it was interesting, because it s obvious that the person opening the car doors, they re trying to get out, like here in New York, trying to get out of the car, you slowing down is going to signal something, you speeding up is gonna signal something, and that s a dance.
2
SPEAKER_01
It s a asynchronous chess game, I don t know.
2
SPEAKER_01
So it feels like it s not just, I mean, I guess you can integrate all of that into one giant model, like the entirety of these little interactions.
2
SPEAKER_01
Because it s not as complicated as chess, it s just like a little dance.
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SPEAKER_01
We do like a little dance together, and then we figure it out.
2
SPEAKER_00
Well, in some ways it s way more complicated than chess because it s continuous, it s uncertain in a continuous manner.
2
SPEAKER_00
It doesn t feel more complicated.
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SPEAKER_00
But it doesn t feel more complicated because that s what we ve evolved to solve.
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SPEAKER_00
This is the kind of problem we ve evolved to solve.
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SPEAKER_00
And so we re good at it because, you know, nature has made us good at it.
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SPEAKER_00
nature has not made us good at chess.
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SPEAKER_00
We completely suck at chess.
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SPEAKER_00
In fact, that s why we designed it as a game, is to be challenging.
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SPEAKER_00
And if there is something that, you know, recent progress in chess and Go has made us realize is that humans are really terrible at those things, like really bad.
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SPEAKER_00
You know, there was a story right before AlphaGo that, you know, the best Go players thought there were maybe two or three stones behind, you know, an ideal player that they would call God.
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SPEAKER_00
In fact, no, there are like nine or 10 stones behind.
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SPEAKER_00
I mean, we re just bad.
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SPEAKER_00
So we re not good at, and it s because we have limited working memory.
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SPEAKER_00
We re not very good at doing this tree exploration that computers are much better at doing than we are, but we are much better at learning differentiable models of the world.
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SPEAKER_00
I mean, I say differentiable in a kind of
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SPEAKER_00
I should say, not differentiable in the sense that we went back far through it, but in the sense that our brain has some mechanism for estimating gradients of some kind.
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SPEAKER_00
And that s what makes us efficient.
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SPEAKER_00
So if you have an agent that consists of a model of the world, which in the human brain is basically the entire front half of your brain,
2
SPEAKER_00
an objective function, which in humans is a combination of two things.
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SPEAKER_00
There is your sort of intrinsic motivation module, which is in the basal ganglia, you know, at the base of your brain.
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SPEAKER_00
That s the thing that measures pain and hunger and things like that, like immediate feelings and emotions.
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SPEAKER_00
And then there is
2
SPEAKER_00
know, the equivalent of what people in reinforcement learning call a critic, which is a sort of module that predicts ahead what the outcome of a situation will be.
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SPEAKER_00
And so it s not a cost function, but it s sort of not an objective function, but it s sort of a
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SPEAKER_00
trained predictor of the ultimate objective function.
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SPEAKER_00
And that also is differentiable.
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SPEAKER_00
And so if all of this is differentiable, your cost function, your critic, your world model, then you can use gradient-based type methods to do planning, to do reasoning, to do learning, to do all the things that would like an intelligent agent
2
SPEAKER_00
to do.
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SPEAKER_01
And gradient based learning, like what s your intuition?
2
SPEAKER_01
That s probably at the core of what can solve intelligence.
2
SPEAKER_01
So you don t need like logic based reasoning in your view.
2
SPEAKER_00
I don t know how to make logic based reasoning compatible with efficient learning.
2
SPEAKER_00
Yeah.
2
SPEAKER_00
Okay.
2
SPEAKER_00
I mean, there is a big question, perhaps a philosophical question.
2
SPEAKER_00
I mean, it s not that philosophical, but that we can ask is, is that, you know, all the learning algorithms we know from engineering and computer science proceed by optimizing some objective function.
2
SPEAKER_01
Yeah.
2
SPEAKER_00
Right.
2
SPEAKER_00
So one question we may ask is, is, does learning in the brain minimize an objective function?
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SPEAKER_00
I mean, it could be a, you know, a composite of multiple objective functions, but it s still an objective function.
2
SPEAKER_00
Second, if it does optimise an objective function, does it do it by some sort of gradient estimation?
2
SPEAKER_00
It doesn t need to be backprop, but some way of estimating the gradient in an efficient manner.
2
SPEAKER_00
whose complexity is on the same order of magnitude as, you know, actually running the inference.
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SPEAKER_00
Because you can t afford to do things like, you know, perturbing a weight in your brain to figure out what the effect is.
2
SPEAKER_00
And then sort of, you know, you can do sort of estimating gradient by perturbation.
2
SPEAKER_00
To me, it seems very implausible that the brain uses some sort of, you know, zeroth order black box, gradient-free optimization,
2
SPEAKER_00
because it s so much less efficient than gradient optimization.
2
SPEAKER_00
So it has to have a way of estimating gradients.
2
SPEAKER_01
Is it possible that some kind of logic-based reasoning emerges in pockets as a useful, like you said, if the brain is an objective function, maybe it s a mechanism for creating objective functions.
2
SPEAKER_01
It s a mechanism for creating knowledge bases, for example, that can then be queried.
2
SPEAKER_01
Like maybe it s an efficient representation of knowledge that s learned in a gradient-based way or something like that.
2
SPEAKER_00
So I think there is a lot of different types of intelligence.
2
SPEAKER_00
So first of all, I think the type of logical reasoning that we think about, that we are maybe stemming from sort of classical AI of the 1970s and 80s, I think humans use that relatively rarely.
2
SPEAKER_00
and are not particularly good at it.
2
SPEAKER_01
But we judge each other based on our ability to solve those rare problems.
2
SPEAKER_01
It s called an IQ test.
2
SPEAKER_00
I don t think so.
2
SPEAKER_00
I m not very good at chess.
2
SPEAKER_01
Yes, I m judging you this whole time.
2
SPEAKER_01
Because, well, we actually.
2
SPEAKER_00
With your heritage, I m sure you re good at chess.
2
SPEAKER_01
No, stereotypes.
2
SPEAKER_01
Not all stereotypes are true.
2
SPEAKER_00
Well, I m terrible at chess.
2
SPEAKER_00
So, you know, but I think perhaps another type of intelligence that I have is this, you know, ability of sort of building models to the world from, you know, reasoning obviously, but also data.
2
SPEAKER_00
And those models generally are more kind of analogical, right?
2
SPEAKER_00
So it s reasoning by simulation and by analogy.
2
SPEAKER_00
where you use one model to apply to a new situation, even though you ve never seen that situation, you can sort of connect it to a situation you ve encountered before.
2
SPEAKER_00
And your reasoning is more, you know, akin to some sort of internal simulation.
2
SPEAKER_00
So you re kind of simulating what s happening when you re building, I don t know, a box out of wood or something, right?
2
SPEAKER_00
You kind of imagine in advance, like, what would be the result of, you know, cutting the wood in this particular way?
2
SPEAKER_00
Are you going to use, you know, screws or nails or whatever?
2
SPEAKER_00
When you are interacting with someone, you also have a model of that person and sort of interact with that person, you know, having this model in mind to kind of tell the person what you think is useful to them.
2
SPEAKER_00
So I think this ability to construct models of the world is basically the essence of intelligence.
2
SPEAKER_00
And the ability to use it then to plan actions that will fulfill a particular criterion, of course, is necessary as well.
2
SPEAKER_01
So I m gonna ask you a series of impossible questions as we keep asking, as I ve been doing.
2
SPEAKER_01
So if that s the fundamental sort of dark matter of intelligence, this ability to form a background model, what s your intuition about how much knowledge is required?
2
SPEAKER_01
You know, I think dark matter, you could put a percentage on it of the composition of the universe and how much of it is dark matter, how much of it is dark energy, how much,
2
SPEAKER_01
information do you think is required to be a house cat?
2
SPEAKER_01
So you have to be able to, when you see a box, go in it.
2
SPEAKER_01
When you see a human, compute the most evil action.
2
SPEAKER_01
If there s a thing that s near an edge, you knock it off.
2
SPEAKER_01
All of that, plus the extra stuff you mentioned, which is a great self-awareness of the physics of your own body and the world.
2
SPEAKER_01
How much knowledge is required, do you think, to solve it?
2
SPEAKER_01
I don t even know how to measure an answer to that question.
2
SPEAKER_00
I m not sure how to measure it, but whatever it is, it fits in about 800,000 neurons, 800 million neurons, sorry.
2
SPEAKER_00
The representation does.
2
SPEAKER_00
everything, all knowledge, everything, right?
2
SPEAKER_00
It s less than a billion.
2
SPEAKER_00
A dog is two billion, but a cat is less than one billion.
2
SPEAKER_00
And so multiply that by a thousand and you get the number of synapses.
2
SPEAKER_00
And I think almost all of it is learned through this, you know, a sort of self-supervised learning.
2
SPEAKER_00
Although, you know, I think a tiny sliver is learned through reinforcement learning and certainly very little through
2
SPEAKER_00
you know, classical supervised running, although it s not even clear how supervised running actually works in the biological world.
2
SPEAKER_00
So I think almost all of it is self-supervised running, but it s driven by the sort of ingrained objective functions that a cat or human have at the base of their brain, which kind of drives their behavior.
2
SPEAKER_00
So, you know, nature tells us you re hungry,
2
SPEAKER_00
it doesn t tell us how to feed ourselves.
2
SPEAKER_00
That s something that the rest of our brain has to figure out, right?
2
SPEAKER_01
What s interesting is there might be more like deeper objective functions underlying the whole thing.
2
SPEAKER_01
So hunger may be some kind of, now you go to like neurobiology, it might be just the brain.
2
SPEAKER_01
trying to maintain homeostasis.
2
SPEAKER_01
So hunger is just one of the human perceivable symptoms of the brain being unhappy with the way things are currently.
2
SPEAKER_01
It could be just like one really dumb objective function at the core.
2
SPEAKER_00
But that s how behavior is driven.
2
SPEAKER_00
The fact that the orbital ganglia,
2
SPEAKER_00
drive us to do things that are different from, say, an orangutan or certainly a cat, is what makes human nature versus orangutan nature versus cat nature.
2
SPEAKER_00
So, for example, our basal ganglia drives us to seek the company of other humans.
2
SPEAKER_00
And that s because nature has figured out that we need to be social animals for our species to survive, and it s true of many primates.
2
SPEAKER_00
It s not true of orangutans.
2
SPEAKER_00
Orangutans are solitary animals.
2
SPEAKER_00
They don t seek the company of others.
2
SPEAKER_00
In fact, they avoid them.
2
SPEAKER_00
In fact, they scream at them when they come too close because they re territorial.
2
SPEAKER_00
Because for their survival, evolution has figured out that s the best thing.
2
SPEAKER_00
I mean, they are occasionally social, of course, for reproduction and stuff like that.
2
SPEAKER_00
But they re mostly solitary.
2
SPEAKER_00
So all of those behaviors are not part of intelligence.
2
SPEAKER_00
People say, oh, you re never going to have intelligent machines because human intelligence is social.
2
SPEAKER_00
But then you look at orangutans, you look at octopuses.
2
SPEAKER_00
Octopuses never know their parents.
2
SPEAKER_00
they barely interact with any other, and they get to be really smart in less than a year, in like half a year.
2
SPEAKER_00
In a year, they re adults.
2
SPEAKER_00
In two years, they re dead.
2
SPEAKER_00
So there are things that we think, as humans, are intimately linked with intelligence, like social interaction, like language,
2
SPEAKER_00
We think, I think we give way too much importance to language as a substrate of intelligence, as humans, because we think our reasoning is so linked with language.
2
SPEAKER_01
So for, to solve the house cat intelligence problem, you think you could do it on a desert island?
2
SPEAKER_01
You could have- Pretty much.
2
SPEAKER_01
You could just have a cat sitting there, looking at the waves, at the ocean waves, and figure a lot of it out.
2
SPEAKER_00
It needs to have the right set of drives to get it to do the thing and learn the appropriate things.
2
SPEAKER_00
For example, baby humans are driven to learn to stand up and walk.
2
SPEAKER_00
This desire is hardwired.
2
SPEAKER_00
How to do it precisely is not, that s learned.
2
SPEAKER_00
But the desire to move around and stand up, that s sort of hardwired.
2
SPEAKER_00
It s very simple to hardwire this kind of stuff.
2
SPEAKER_01
Oh, like the desire to, well, that s interesting.
2
SPEAKER_01
You re hardwired to want to walk.
2
SPEAKER_01
There s gotta be a deeper need for walking.
2
SPEAKER_01
I think it was probably socially imposed by society that you need to walk all the other bipedal.
2
SPEAKER_00
Like a lot of simple animals that would probably walk without ever watching any other members of the species.
2
SPEAKER_01
It seems like a scary thing to have to do, because you suck at bipedal walking at first.
2
SPEAKER_01
It seems crawling is much safer, much more like, why are you in a hurry?
2
SPEAKER_00
Well, because you have this thing that drives you to do it, you know, which is sort of part of the sort of human development.
2
SPEAKER_00
Is that understood, actually, what?
2
SPEAKER_01
Not entirely, no.
2
SPEAKER_01
What s the reason to get on two feet?
2
SPEAKER_01
It s really hard.
2
SPEAKER_01
Like, most animals don t get on two feet.
2
SPEAKER_00
Why not?
2
SPEAKER_00
Well, they get on four feet.
2
SPEAKER_00
You know, many mammals get on four feet.
2
SPEAKER_01
Yeah, they do.
2
SPEAKER_01
Very quickly.
2
SPEAKER_00
Some of them extremely quickly.
2
SPEAKER_01
But I don t, you know, like, from the last time I ve interacted with a table, that s much more stable than a thing on two legs.
2
SPEAKER_01
It s just a really hard problem.
2
SPEAKER_00
Yeah, I mean, birds have figured it out with two feet.
2
SPEAKER_01
Well, technically, we can go into ontology.
2
SPEAKER_01
They have four.
2
SPEAKER_01
I guess they have two feet.
2
SPEAKER_01
They have two feet.
2
SPEAKER_01
Chickens.
2
SPEAKER_00
You know, dinosaurs have two feet, many of them.
2
SPEAKER_01
Allegedly.
2
SPEAKER_01
I m just now learning that T-Rex was eating grass, not other animals.
2
SPEAKER_01
T-Rex might have been a friendly pet.
2
SPEAKER_01
What do you think about, I don t know if you looked at the test for general intelligence that Francois Chollet put together.
2
SPEAKER_01
I don t know if you got a chance to look at that kind of thing.
2
SPEAKER_01
What s your intuition about how to solve an IQ type of test?
2
SPEAKER_00
I don t know, I think it s so outside of my radar screen that it s not really relevant, I think, in the short term.
2
SPEAKER_01
Well, I guess one way to ask another way, perhaps more closer to your work, is how do you solve MNIST with very little example data?
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SPEAKER_00
That s right.
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SPEAKER_00
And that s the answer to this, probably, is self-supervised learning.
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SPEAKER_00
Just learn to represent images and then learning, you know, to recognize handwritten digits on top of this will only require a few samples.
2
SPEAKER_00
And we observe this in humans, right?
2
SPEAKER_00
You show a young child a picture book with a couple of pictures of an elephant and that s it.
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SPEAKER_00
The child knows what an elephant is.
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SPEAKER_00
And we see this today with practical systems that we, you know, we train image recognition systems with
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SPEAKER_00
enormous amounts of images, either completely self-supervised or very weakly supervised.
2
SPEAKER_00
For example, you can train a neural net to predict whatever hashtag people type on Instagram, right?
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SPEAKER_00
And you can do this with billions of images because there s billions per day that are showing up.
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SPEAKER_00
So the amount of training data there is essentially unlimited.
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SPEAKER_00
And then you take the output representation, you know, a couple of layers down from the output of what the system learned and feed this as input to a classifier for any object in the world that you want.
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SPEAKER_00
And it works pretty well.
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SPEAKER_00
So that s transfer learning, or weakly supervised transfer learning.
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SPEAKER_00
People are making very, very fast progress using self-supervised learning for this kind of scenario as well.
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SPEAKER_00
And, you know, my guess is that that s going to be the future.
2
you.
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SPEAKER_01
for self-supervised learning, how much cleaning do you think is needed for filtering malicious signal, or what s a better term?
2
SPEAKER_01
But like a lot of people use hashtags on Instagram to get like good SEO that doesn t fully represent the contents of the image.
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SPEAKER_01
Like they ll put a picture of a cat and hashtag it with like science, awesome, fun, I don t know, all kinds, why would you put science?
2
SPEAKER_01
That s not very good SEO.
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SPEAKER_00
The way my colleagues who worked on this project at Facebook, now Meta AI, a few years ago dealt with this is that they only selected something like 17,000 tags that could
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SPEAKER_00
correspond to kind of physical things or, or situations like, you know, that has some visual content.
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SPEAKER_00
Um, so, you know, you wouldn t have like hash TBT or anything like that.
3
SPEAKER_01
Oh, so they, they keep a very select set of hashtags is what you re saying.
3
SPEAKER_00
Yeah.
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SPEAKER_00
Okay.
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SPEAKER_00
But it s still, it s still on the order of, uh, you know, 10 to 20,000.
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SPEAKER_00
So it s fairly large.
3
SPEAKER_01
Okay.
3
SPEAKER_01
Can you tell me about data augmentation?
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SPEAKER_01
What the heck is data augmentation?
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SPEAKER_01
And how is it used, maybe contrast of learning for video?
3
SPEAKER_01
What are some cool ideas here?
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SPEAKER_00
First, data augmentation is the idea of artificially increasing the size of your training set by distorting the images that you have in ways that don t change the nature of the image.
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SPEAKER_00
You do MNIST, you can do data augmentation on MNIST, and people have done this since the 1990s.
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SPEAKER_00
You take an MNIST digit and you shift it a little bit or you change the size or rotate it, skew it,
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SPEAKER_00
you know, etc.
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SPEAKER_01
Add noise.
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SPEAKER_00
Add noise, etc.
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SPEAKER_00
And it works better.
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SPEAKER_00
If you train a supervised classifier with augmented data, you re going to get better results.
3
SPEAKER_00
Now, it s become really interesting over the last couple of years because a lot of self-supervised learning techniques to pre-train vision systems are based on data augmentation.
3
SPEAKER_00
and the basic techniques is originally inspired by techniques that I worked on in the early 90s and Geoff Hinton worked on also in the early 90s.
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SPEAKER_00
They were sort of parallel work.
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SPEAKER_00
I used to call this Siamese networks.
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SPEAKER_00
So basically you take two identical copies of the same network, they share the same weights,
3
SPEAKER_00
and you show two different views of the same object.
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SPEAKER_00
Either those two different views may have been obtained by data augmentation, or maybe it s two different views of the same scene from a camera that you moved, or at different times, or something like that, right?
3
SPEAKER_00
Or two pictures of the same person, things like that.
3
SPEAKER_00
And then you train this neural net, those two identical copies of this neural net, to produce an output representation, a vector, in such a way that the representation for those two images are as close to each other as possible, as identical to each other as possible, right?
3
SPEAKER_00
Because you want the system to basically learn a function that will be invariant, that will not change, whose output will not change when you transform those inputs in those particular ways, right?
3
SPEAKER_00
So that s easy to do.
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SPEAKER_00
What s complicated is how do you make sure that when you show two images that are different, the system will produce different things.
3
SPEAKER_00
Because if you don t have a specific provision for this, the system will just ignore the input.
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SPEAKER_00
When you train it, it will end up ignoring the input and just produce a constant vector that is the same for every input.
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SPEAKER_00
That s called a collapse.
3
SPEAKER_00
Now, how do you avoid collapse?
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SPEAKER_00
There are two ideas.
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SPEAKER_00
One idea that I proposed in the early 90s with my colleagues at Bell Labs, Jane Bromley and a couple of other people,
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SPEAKER_00
which we now call contrastive learning, which is to have negative examples, right?
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SPEAKER_00
So you have pairs of images that you know are different, and you show them to the network, and those two copies, and then you push the two output vectors away from each other, and it will eventually guarantee that things that are semantically similar produce similar representations, and things that are different produce different representations.
3
SPEAKER_00
We actually came up with this idea for a project of doing signature verification.
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SPEAKER_00
So we would collect signatures from multiple signatures on the same person, and then train a neural net to produce the same representation, and then force the system to produce different
3
SPEAKER_00
representation for different signatures.
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SPEAKER_00
The problem was proposed by people from what was a subsidiary of AT&T at the time called NCR.
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SPEAKER_00
They were interested in storing a representation of the signature on the 80 bytes of the magnetic strip of a credit card.
3
SPEAKER_00
So we came up with this idea of having a neural net with 80 outputs.
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SPEAKER_00
that we quantized on bytes so that we could encode the signature.
3
SPEAKER_01
And that encoding was then used to compare whether the signature matches or not.
3
SPEAKER_00
That s right.
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SPEAKER_00
So then you would sign, it would run through the neural net, and then you would compare the output vector to whatever is stored on your card.
3
SPEAKER_00
Did it actually work?
3
SPEAKER_00
It worked, but they ended up not using it.
3
SPEAKER_00
Because nobody cares, actually.
3
SPEAKER_00
I mean, the American financial payment system is incredibly lax in that respect compared to Europe.
3
SPEAKER_01
What s the purpose of signatures anyway?
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SPEAKER_00
Nobody looks at them, nobody cares.
3
SPEAKER_00
So that s contrastive learning, right?
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SPEAKER_00
So you need positive and negative pairs.
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SPEAKER_00
And the problem with that is that
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SPEAKER_00
You know, even though I had the original paper on this, I m actually not very positive about it because it doesn t work in high dimension.
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SPEAKER_00
If your representation is high dimensional, there s just too many ways for two things to be different.
3
SPEAKER_00
And so you would need lots and lots and lots of negative pairs.
3
SPEAKER_00
So there is a particular implementation of this, which is relatively recent, from actually the Google Toronto group, where Geoff Hinton is the senior member there.
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SPEAKER_00
It s called SimClear, S-I-M-C-L-A-R.
3
SPEAKER_00
And it s basically a particular way of implementing this idea of contractive learning, the particular objective function.
3
SPEAKER_00
Now, what I m much more enthusiastic about these days is non-contrasting methods, so other ways to guarantee that the representations will be different for different inputs.
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SPEAKER_00
And it s actually based on an idea that Geoff Hinton proposed in the early 90s with his student at the time, Sue Becker.
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SPEAKER_00
And it s based on the idea of maximizing the mutual information between the outputs of the two systems.
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SPEAKER_00
You only show positive pairs.
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SPEAKER_00
You only show pairs of images that you know are somewhat similar.
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SPEAKER_00
And you train the two networks to be informative.
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SPEAKER_00
but also to be as informative of each other as possible.
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SPEAKER_00
So basically one representation has to be predictable from the other, essentially.
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SPEAKER_00
And he proposed that idea, had a couple of papers in the early 90s, and then nothing was done about it for decades.
3
SPEAKER_00
And I kind of revived this idea together with my postdocs at FAIR, particularly a postdoc called Stefan Duny, who s now a junior professor in Finland at the University of Aalto.
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SPEAKER_00
We came up with something that we called Barlow twins, and it s a particular way of maximizing the information content of a vector using some hypotheses.
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SPEAKER_00
And we have kind of another version of it that s more recent now called VICREG, V-I-C-R-E-G.
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SPEAKER_00
That means variance, invariance, covariance, regularization.
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SPEAKER_00
And it s the thing I m the most excited about in machine learning in the last 15 years.
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SPEAKER_00
I mean, I m really, really excited about this.
3
SPEAKER_01
what kind of data augmentation is useful for that non-contrastive learning method?
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SPEAKER_01
Are we talking about, does that not matter that much?
3
SPEAKER_01
It seems like a very important part of the step.
3
SPEAKER_01
How you generate the images that are similar but sufficiently different.
3
SPEAKER_00
Yeah, that s right.
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SPEAKER_00
It s an important step and it s also an annoying step because you need to have that knowledge of what data augmentation you can do that do not change the nature of the object.
3
SPEAKER_00
And so the standard scenario, which a lot of people working in this area are using, is you use the type of distortion.
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SPEAKER_00
So basically you do geometric distortion.
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SPEAKER_00
So one basically just shifts the image a little bit.
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SPEAKER_00
It s called cropping.
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SPEAKER_00
Another one kind of changes the scale a little bit.
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SPEAKER_00
Another one kind of rotates it.
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SPEAKER_00
Another one changes the colors.
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SPEAKER_00
You can do a shift in color balance or something like that.
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SPEAKER_00
Saturation.
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SPEAKER_00
Another one sort of blurs it.
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SPEAKER_00
Another one adds noise.
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SPEAKER_00
So you have like a catalog of kind of standard things and people try to use the same ones for different algorithms so that they can compare.
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SPEAKER_00
But some algorithms, some self-supervised algorithm actually can deal with much bigger
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SPEAKER_00
like more aggressive data augmentation, and some don t.
3
SPEAKER_00
So that kind of makes the whole thing difficult.
3
SPEAKER_00
But that s the kind of distortions we re talking about.
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SPEAKER_00
And so you train with those distortions, and then you chop off the last layer or couple layers of the network, and you use the representation as input to a classifier.
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SPEAKER_00
You train the classifier on ImageNet, let s say.
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SPEAKER_00
or whatever, and measure the performance.
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SPEAKER_00
And interestingly enough, the methods that are really good at eliminating the information that is irrelevant, which is the distortions between those images, do a good job at eliminating it.
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SPEAKER_00
And as a consequence, you cannot use the representations in those systems for things like object detection and localization, because that information is gone.
3
SPEAKER_00
So the type of data augmentation you need to do depends on the tasks you want eventually the system to solve.
3
SPEAKER_00
And the type of data augmentation, standard data augmentation that we use today, are only appropriate for object recognition or image classification.
3
SPEAKER_01
They re not appropriate for things like... Can you help me out to understand why the localization... So you re saying it s just not good at the negative, like at classifying the negative, so that s why it can t be used for the localization?
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SPEAKER_00
No, it s just that you train the system, you give it an image, and then you give it the same image shifted and scaled, and you tell it that s the same image.
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SPEAKER_00
So the system basically is trained to eliminate the information about position and size.
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SPEAKER_00
So now you want to use that to figure out where an object is and what size it is.
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SPEAKER_01
Like a bounding box, to be able to actually, okay.
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SPEAKER_01
It can still find the object in the image, it s just not very good at finding the exact boundaries of that object.
3
SPEAKER_01
Interesting.
3
SPEAKER_01
which, you know, that s an interesting sort of philosophical question.
3
SPEAKER_01
How important is object localization anyway?
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SPEAKER_01
We re like obsessed by measuring like image segmentation, obsessed by measuring perfectly knowing the boundaries of objects when arguably that s not that essential to understanding what are the contents of the scene.
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SPEAKER_00
On the other hand, I think evolutionarily, the first vision systems in animals were basically all about localization, very little about recognition.
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SPEAKER_00
In the human brain, you have two separate pathways for recognizing the nature of a scene or an object and localizing objects.
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SPEAKER_00
You use the first pathway called the ventral pathway for telling what you re looking at,
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SPEAKER_00
The other pathway, the dorsal pathway, is used for navigation, for grasping, for everything else.
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SPEAKER_00
Basically, a lot of the things you need for survival are localization and detection.
3
SPEAKER_01
Is similarity learning or contrastive learning or these non-contrastive methods the same as understanding something?
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SPEAKER_01
Just because you know a distorted cat is the same as a non-distorted cat, does that mean you understand what it means to be a cat?
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SPEAKER_00
To some extent.
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SPEAKER_00
I mean, it s a superficial understanding, obviously.
3
SPEAKER_01
But what is the ceiling of this method, do you think?
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SPEAKER_01
Is this just one trick on the path to doing self-supervised learning, or can we go really, really far?
3
SPEAKER_00
I think we can go really far.
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SPEAKER_00
So if we figure out how to use techniques of that type, perhaps very different, but of the same nature, to train a system from video, to do video prediction essentially, I think we ll have a path towards, I wouldn t say unlimited, but a path towards some level of physical common sense in machines.
3
SPEAKER_00
I also think that that ability to learn how the world works from a high throughput channel like vision is a necessary step towards real artificial intelligence.
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SPEAKER_00
In other words, I believe in grounded intelligence.
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SPEAKER_00
I don t think we can train a machine to be intelligent purely from text.
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SPEAKER_00
because I think the amount of information about the world that s contained in text is tiny compared to what we need to know.
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SPEAKER_00
So for example, let s, and you know, people have attempted to do this for 30 years, right?
3
SPEAKER_00
The psych project and things like that, right?
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SPEAKER_00
So basically kind of writing down all the facts that are known and hoping that some sort of common sense will emerge.
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SPEAKER_00
I think it s basically hopeless.
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SPEAKER_00
But let me take an example.
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SPEAKER_00
You take an object.
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SPEAKER_00
I describe a situation to you.
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SPEAKER_00
I take an object, I put it on the table and I push the table.
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SPEAKER_00
It s completely obvious to you that the object will be pushed with the table, right?
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SPEAKER_00
Because it s sitting on it.
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SPEAKER_00
There s no text in the world, I believe, that explains this.
3
SPEAKER_00
And so, if you train a machine as powerful as it could be, you know, your GPT 5000, or whatever it is, it s never gonna learn about this.
3
SPEAKER_00
That information is just not present in any text.
3
SPEAKER_01
Well, the question, like with the Psych Project, the dream, I think, is to have like 10 million, say, facts like that.
3
SPEAKER_01
that give you a head start, like a parent guiding you.
3
SPEAKER_01
Now, we humans don t need a parent to tell us that the table will move, sorry, the smartphone will move with the table, but we get a lot of guidance in other ways, so it s possible that we can give it a quick shortcut.
3
SPEAKER_00
What about a cat?
3
SPEAKER_00
A cat knows that.
3
SPEAKER_01
No, but they evolved, so.
3
SPEAKER_00
No, they learn like us.
3
SPEAKER_01
Sorry, the physics of stuff?
3
SPEAKER_01
Well, yeah, so you re saying it s, so you re putting a lot of intelligence onto the nurture side, not the nature.
3
SPEAKER_01
We seem to have, you know, there s a very inefficient, arguably, process of evolution that got us from bacteria to who we are today.
3
SPEAKER_01
Started at the bottom, now we re here, so.
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SPEAKER_01
The question is how fundamental is that, the nature of the whole hardware, and then is there any way to shortcut it if it s fundamental?
3
SPEAKER_01
If it s not, if it s most of intelligence, most of the cool stuff we ve been talking about is mostly nurture, mostly trained, we figure it out by observing the world.
3
SPEAKER_01
We can form that big, beautiful, sexy background model that you re talking about just by sitting there.
3
SPEAKER_01
then okay, then you need to, then maybe, it is all supervised learning all the way down.
3
SPEAKER_01
It s all supervised learning, so.
3
SPEAKER_00
Whatever it is that makes human intelligence different from other animals, which a lot of people think is language and logical reasoning and this kind of stuff, it cannot be that complicated because it only popped up in the last million years.
3
SPEAKER_00
And it only involves less than 1% of a genome, which is the difference between human genome and chimps or whatever.
3
SPEAKER_00
So it can be that complicated.
3
SPEAKER_00
It can be that fundamental.
3
SPEAKER_00
I mean, most of the complicated stuff already exists in cats and dogs and certainly primates, non-human primates.
3
SPEAKER_01
Yeah, that little thing with humans might be just something about social interaction and ability to maintain ideas across like a collective of people.
3
SPEAKER_01
It sounds very dramatic and very impressive, but it probably isn t mechanistically speaking.
3
SPEAKER_00
It is, but we re not there yet.
3
SPEAKER_00
Like, you know, we have, I mean, this is number 634, you know, in the list of problems we have to solve.
3
SPEAKER_01
So basic physics of the world is number one.
3
SPEAKER_01
What do you, just a quick tangent on data augmentation.
3
SPEAKER_01
So a lot of it is.
3
SPEAKER_01
hard-coded versus learned.
3
SPEAKER_01
Do you have any intuition that maybe there could be some weird data augmentation, like generative type of data augmentation, like doing something weird to images, which then improves the similarity learning process?
3
SPEAKER_01
So not just kind of dumb, simple distortions, but by you shaking your head, just saying that even simple distortions are enough.
3
SPEAKER_00
I think no, I think data augmentation is a temporary necessary evil.
3
SPEAKER_00
So what people are working on now is two things.
3
SPEAKER_00
One is the type of self-supervised learning
3
SPEAKER_00
trying to translate the type of self-supervised learning people use in language, translating these two images, which is basically a denoising autoencoder method.
3
SPEAKER_00
So you take an image, you block, you mask some parts of it, and then you train some giant neural net to reconstruct the parts that are missing.
3
SPEAKER_00
And
3
SPEAKER_00
Until very recently, there were no working methods for that.
3
SPEAKER_00
All the autoencoder type methods for images weren t producing very good representation.
3
SPEAKER_00
But there s a paper now coming out of
4
SPEAKER_00
fair group at Emmanuel Park that actually works very well.
4
SPEAKER_00
So that doesn t require the documentation, that requires only masking.
4
SPEAKER_01
only masking for images.
4
SPEAKER_01
Okay.
4
SPEAKER_00
Right.
4
SPEAKER_00
So you mask part of the image and you train a system, which, you know, in this case is a transformer because you can, you can, the transformer represents the image as a non-overlapping patches.
4
SPEAKER_00
So it s easy to mask patches and things like that.
4
SPEAKER_01
Okay.
4
SPEAKER_01
Then my question transfers to that problem, the masking, like why should the mask be a square or rectangle?
4
SPEAKER_00
So it doesn t matter.
4
SPEAKER_00
Like, you know, I think we re going to come up probably in the future with sort of, you know, ways to mess that are, you know, kind of random, essentially.
4
SPEAKER_01
I mean, they are random already, but... No, no, but like something that s challenging.
4
SPEAKER_01
like optimally challenging.
4
SPEAKER_01
So like, I mean, maybe it s a metaphor that doesn t apply, but you re, it seems like there s an data augmentation or masking, there s an interactive element with it.
4
SPEAKER_01
Like you re almost like playing with an image.
4
SPEAKER_01
And like, it s like the way we play with an image in our minds.
4
SPEAKER_00
No, but it s like dropout.
4
SPEAKER_00
It s like Boston machine training.
4
SPEAKER_00
You, you know, every, every, every time you see a percept,
4
SPEAKER_00
You also, you can perturb it in some way.
4
SPEAKER_00
And then the principle of the training procedure is to minimize the difference of the output or the representation between the clean version and the corrupted version, essentially, right?
4
SPEAKER_00
And you can do this in real time, right?
4
SPEAKER_00
So, you know, most of the machines work like this, right?
4
SPEAKER_00
You show a percept, you tell the machine that s a good combination of activities or your input neurons.
4
SPEAKER_00
And then you either let them go their merry way without clamping them to values, or you only do this with a subset.
4
SPEAKER_00
And what you re doing is you re training the system so that the stable state of the entire network is the same regardless of whether it sees the entire input or whether it sees only part of it.
4
SPEAKER_00
You know, denoising autoencoder method is basically the same thing, right?
4
SPEAKER_00
You re training a system to reproduce the input, the complete input, and filling the blanks, regardless of which parts are missing.
4
SPEAKER_00
And that s really the underlying principle.
4
SPEAKER_00
And you could imagine sort of, even in the brain, some sort of neural principle where, you know, neurons kind of oscillate, right?
4
SPEAKER_00
So they take their activity and then temporarily they kind of shut off.
4
SPEAKER_00
to force the rest of the system to basically reconstruct the input without their help.
4
SPEAKER_00
I mean, you can imagine more or less biologically possible processes.
4
SPEAKER_01
Something like that.
4
SPEAKER_01
And I guess with this denoising autoencoder and masking and data augmentation, you don t have to worry about being super efficient.
4
SPEAKER_01
You can just do as much as you want and get better over time.
4
SPEAKER_01
Because I was thinking, like, you might want to be clever about the way you do all these procedures, you know, but that s only if it s somehow costly to do every iteration, but it s not really.
4
SPEAKER_00
Not really.
4
SPEAKER_00
Maybe.
4
SPEAKER_00
And then there is, you know, data augmentation without explicit data augmentation.
4
SPEAKER_00
is data augmentation by weighting, which is the sort of video prediction.
4
SPEAKER_00
You re observing a video clip, observing the continuation of that video clip, and you try to learn a representation using those joint embedding architectures in such a way that the representation of the future clip is easily predictable from the representation of the observed clip.
4
SPEAKER_01
Do you think YouTube has enough raw data from which to learn how to be a cat?
4
SPEAKER_00
I think so.
4
SPEAKER_01
So the amount of data is not the constraint.
4
SPEAKER_00
No, it would require some selection, I think.
4
SPEAKER_00
some selection of maybe the right type of data.
4
SPEAKER_01
You need some- Don t go down the rabbit hole of just cat videos.
4
SPEAKER_01
You might need to watch some lectures or something.
4
SPEAKER_01
How meta would that be if it like watches lectures about intelligence and then learns, watches your lectures at NYU and learns from that how to be intelligent?
4
SPEAKER_00
I don t think that would be enough.
4
SPEAKER_01
What s your, do you find multimodal learning interesting?
4
SPEAKER_01
We ve been talking about visual language, like combining those together, maybe audio, all those kinds of things.
4
SPEAKER_00
There s a lot of things that I find interesting in the short term, but are not addressing the important problem that I think are really kind of the big challenges.
4
SPEAKER_00
So I think, you know, things like multitask learning, continual learning, you know,
4
SPEAKER_00
adversarial issues.
4
SPEAKER_00
I mean, those have great practical interests in the relatively short term, possibly, but I don t think they re fundamental.
4
SPEAKER_00
Active learning, even to some extent reinforcement learning, I think those things will become either obsolete or useless or easy
4
SPEAKER_00
once we figured out how to do self-supervised representation learning or predictive world models.
4
SPEAKER_00
I think that s what the entire community should be focusing on.
4
SPEAKER_00
At least people who are interested in fundamental questions or really pushing the envelope of AI towards the next stage.
4
SPEAKER_00
But of course, there s a huge amount of very interesting work to do in practical questions that have short-term impact.
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SPEAKER_01
Well, you know, it s difficult to talk about the temporal scale, because all of human civilization will eventually be destroyed, because the sun will die out, and even if Elon Musk is successful in multi-planetary colonization across the galaxy, eventually the entirety of it will just become giant black holes, and it s gonna heat up the universe.
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But what I m saying is then, that logic can be used to say it s all meaningless.
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I m saying all that to say that multitask learning might be, you re calling it practical or pragmatic or whatever, that might be the thing that achieves something very akin to intelligence.
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while we re trying to solve the more general problem of self-supervised learning of background knowledge.
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SPEAKER_01
So the reason I bring that up, maybe one way to ask that question, I ve been very impressed by what Tesla Autopilot team is doing.
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SPEAKER_01
I don t know if you ve gotten a chance to glance at this particular one example of
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multitask learning, where they re literally taking the problem, like, I don t know, Charles Darwin studying animals, they re studying the problem of driving and asking, okay, what are all the things you have to perceive?
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And,
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the way they re solving it is one, there s an ontology where you re bringing that to the table, so you re formulating a bunch of different tasks, it s like over 100 tasks or something like that that they re involved in driving, and then they re deploying it and then getting data back from people that run into trouble, and they re trying to figure out do we add tasks, do we, like we focus on each individual task separately.
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SPEAKER_01
In fact, half, so I would say, I would classify Andrej Karpathy s talk in two ways.
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So one was about doors,
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and the other one about how much ImageNet sucks.
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He kept going back and forth on those two topics, which ImageNet sucks, meaning you can t just use a single benchmark.
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You have to have a giant suite of benchmarks to understand how well your system actually works.
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SPEAKER_00
I agree with him.
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SPEAKER_00
He s a very sensible guy.
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SPEAKER_00
Now,
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Okay, it s very clear that if you re faced with an engineering problem that you need to solve in a relatively short time, particularly if you have Elon Musk breathing down your neck, you re going to have to take shortcuts, right?
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SPEAKER_00
You might think about the fact that the right thing to do and the long-term solution involves some fancy self-supervisioning, but you have
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SPEAKER_00
you know, it must be sitting on your neck.
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And, you know, this involves, you know, human lives.
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And so you have to basically just do the systematic engineering and, you know, fine-tuning and refinements and trial and error and all that stuff.
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SPEAKER_00
There s nothing wrong with that.
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That s called engineering.
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That s called, you know,
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putting technology out in the world and you have to kind of ironclad it before you do this.
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So much for grand ideas and principles.
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SPEAKER_00
But I m placing myself sort of some
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you know, upstream of this, you know, quite a bit upstream of this.
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Your Plato, think about platonic forms.
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You re, you re speaking- Not platonic, because eventually I want that stuff to get used, but it s okay if it takes five or 10 years for the community to realize this is the right thing to do.
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I ve, I ve done this before.
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SPEAKER_00
It s been the case before that, you know, I ve made that, that case.
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I mean, if you look back in the mid 2000, for example, and you ask yourself the question, okay, I want to recognize cars or faces or whatever.
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SPEAKER_00
you know, I can use convolutional nets or I can use sort of more conventional kind of computer vision techniques, you know, using interest point detectors or SIFT, dense SIFT features and, you know, sticking an SVM on top.
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SPEAKER_00
At that time, the datasets were so small that those methods that used more hand engineering worked better than ConvNets.
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SPEAKER_00
There was just not enough data for ConvNets and ConvNets were a little slow with the kind of hardware that was available at the time.
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SPEAKER_00
And there was a sea change basically when datasets became bigger and GPUs became available.
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two of the main factors that basically made people change their mind.
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SPEAKER_00
You can look at the history of all sub-branches of AI or pattern recognition, and there s a similar trajectory followed by techniques where people start by engineering the head out of it,
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SPEAKER_00
You know, be it optical character recognition, speech recognition, computer vision, like image recognition in general, natural language understanding, like, you know, translation, things like that, right?
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SPEAKER_00
You start to engineer the hell out of it.
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you start to acquire all the knowledge, the prior knowledge you know about image formation, about the shape of characters, about morphological operations, about feature extraction, Fourier transforms, Wernicke moments, whatever.
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People have come up with thousands of ways of representing images so that they could be easily classified afterwards.
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Same for speech recognition.
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There is
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know, two decades for people to figure out a good front end to pre-process a speech signal so that, you know, the information about what is being said is preserved, but most of the information about the identity of the speaker is gone.
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SPEAKER_00
You know, Kestrel coefficients or whatever, right?
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SPEAKER_00
And same for text, right?
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SPEAKER_00
You do named entity recognition and you parse and you
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You do tagging of the parts of speech and you do this sort of tree representation of clauses and all that stuff right before you can do anything.
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So that s how it starts, right?
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Just engineer the hell out of it.
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And then you start having data.
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And maybe you have more powerful computers.
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Maybe you know something about statistical learning.
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SPEAKER_00
So you start using machine learning, and it s usually a small sliver on top of your kind of handcrafted system where you extract features by hand.
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SPEAKER_00
And nowadays, the standard way of doing this is that you train the entire thing end-to-end with a deep learning system, and it learns its own features.
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SPEAKER_00
And speech recognition systems nowadays, OCR systems, are completely end-to-end.
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SPEAKER_00
It s some giant neural net that takes raw waveforms
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and produces a sequence of characters coming out.
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SPEAKER_00
And it s just a huge neural net, right?
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SPEAKER_00
There s no Markov model, there s no language model that is explicit other than, you know, something that s ingrained in the sort of neural language model, if you want.
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Same for translation, same for all kinds of stuff.
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SPEAKER_00
So you see this continuous evolution from, you know, less and less handcrafting and more and more learning.
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And I think
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SPEAKER_00
I mean, it s tree biology as well.
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SPEAKER_01
So, I mean, we might disagree about this, maybe not.
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SPEAKER_01
This one little piece at the end, you mentioned active learning.
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It feels like active learning, which is the selection of data, and also the interactivity, needs to be part of this giant neural network.
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SPEAKER_01
You cannot just be an observer to do self-supervised learning.
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SPEAKER_01
You have to, well, self-supervised learning is just a word, but I would, whatever this giant stack of a neural network that s automatically learning, it feels, my intuition is,
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that you have to have a system, whether it s a physical robot or a digital robot, that s interacting with the world and doing so in a flawed way and improving over time in order to form the self-supervised learning.
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SPEAKER_01
Well, you can t just give it a giant sea of data.
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SPEAKER_00
Okay, I agree and I disagree.
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SPEAKER_00
I agree in the sense that I think
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SPEAKER_00
I agree in two ways.
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SPEAKER_00
The first way I agree is that if you want, and you certainly need, a causal model of the world that allows you to predict the consequences of your actions, to train that model, you need to take actions.
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You need to be able to act in a world and see the effect.
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for you to learn causal models of the world.
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Right?
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So that s not obvious because you can observe others.
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You can observe others.
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And you can infer that they re similar to you and then you can learn from that.
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SPEAKER_00
Yeah, but then you have to kind of hardware that part, right?
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SPEAKER_00
And you know, mirror neurons and all that stuff, right?
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SPEAKER_00
And it s not clear to me how you would do this in a machine.
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SPEAKER_00
So I think the action part would be necessary for having causal models of the world.
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SPEAKER_00
The second reason it may be necessary, or at least more efficient, is that active learning basically goes for the jiggler of what you don t know.
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SPEAKER_00
There s obvious areas of uncertainty about your world and about how the world behaves.
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SPEAKER_00
And you can resolve this uncertainty by systematic exploration of that part that you don t, you don t know.
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SPEAKER_00
And if you know that you don t know, then, you know, it makes you curious.
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SPEAKER_00
You kind of look into situations that, and, uh, you know, across the animal, uh, world, different species at different levels of curiosity, depending on how they re built.
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SPEAKER_00
Right.
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SPEAKER_00
So, you know, cats and rats are incredibly curious, uh, dogs, not so much, I mean, less.
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SPEAKER_01
Yeah.
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SPEAKER_01
So it could be useful to have that kind of curiosity.
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SPEAKER_00
So it d be useful, but curiosity just makes the process faster.
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SPEAKER_00
It doesn t make the process exist.
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SPEAKER_00
So what process, what learning process is it that active learning makes more efficient?
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SPEAKER_00
And I m asking that first question, you know,
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SPEAKER_00
you know, we haven t answered that question yet.
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SPEAKER_00
So, you know, I worry about active learning once this question is.
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SPEAKER_01
So it s the more fundamental question to ask.
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SPEAKER_01
And if active learning or interaction increases the efficiency of the learning, see sometimes it becomes very different if the increase is several orders of magnitude, right?
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SPEAKER_01
Like.
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That s true.
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But fundamentally it s still the same thing and building up the intuition about how to,
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in a self-supervised way to construct background models, efficient or inefficient, is the core problem.
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SPEAKER_01
What do you think about Yoshua Bengio s talking about consciousness and all of these kinds of concepts?
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SPEAKER_00
Okay, I don t know what consciousness is.
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SPEAKER_00
But- It s a good opener.
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SPEAKER_00
And to some extent, a lot of the things that are said about consciousness remind me of the questions people were asking themselves in the 18th century or 17th century when they discovered that, you know, how the eye works and the fact that the image at the back of the eye was upside down, right?
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SPEAKER_00
Because you have a lens.
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And so on your retina, the image that forms is an image of the world, but it s upside down.
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How is it that you see right side up?
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SPEAKER_00
And with what we know today in science, we realize this question doesn t make any sense or is kind of ridiculous in some way, right?
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SPEAKER_00
So I think a lot of what is said about consciousness is of that nature.
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SPEAKER_00
Now, that said, there s a lot of really smart people for whom I have a lot of respect who are talking about this topic, people like David Chalmers, who is a colleague of mine at NYU.
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I have kind of an unorthodox folk speculative hypothesis.
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about consciousness.
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We re talking about the study of a world model, and I think our entire prefrontal cortex basically is the engine for our world model.
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But when we are attending at a particular situation, we re focused on that situation.
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We basically cannot attend to anything else.
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That seems to suggest that we basically have only one
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world model engine in our prefrontal cortex.
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That engine is configurable to the situation at hand.
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So we are building a box out of wood, or we are driving down the highway playing chess.
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We basically have a single model of the world that we re configuring to the situation at hand, which is why we can only attend to one task at a time.
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SPEAKER_01
Now, if there is a task that we do repeatedly, it goes from the sort of deliberate reasoning using model of the world and prediction and perhaps something like model predictive control, which I was talking about earlier, to something that is more subconscious that becomes automatic.
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So I don t know if you ve ever played against a chess grandmaster.
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I get wiped out in 10 plies, right?
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And I have to think about my move for 15 minutes.
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SPEAKER_01
And the person in front of me, the Grandmaster, would just react within seconds, right?
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SPEAKER_01
He doesn t need to think about it.
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SPEAKER_01
That s become part of the subconscious because it s basically just pattern recognition at this point.
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SPEAKER_01
Same, you know, the first few hours you drive a car, you re really attentive, you can t do anything else, and then after 20, 30 hours of practice, 50 hours, you know, it s subconscious, you can talk to the person next to you, you know, things like that, right?
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SPEAKER_01
Unless the situation becomes unpredictable, and then you have to stop talking.
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So that suggests you only have one model in your head, and
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it might suggest the idea that consciousness basically is the module that configures this world model of yours.
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You need to have some sort of executive kind of overseer that configures your world model for the situation at hand.
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SPEAKER_01
And that leads to kind of the really curious concept that consciousness is not a consequence of the power of our minds, but of the limitation of our brains.
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That because we have only one world model, we have to be conscious.
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If we had as many world models as
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there are situations we encounter, then we could do all of them simultaneously and we wouldn t need this sort of executive control that we call consciousness.
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SPEAKER_00
Yeah, interesting, and somehow maybe that executive controller, I mean, the hard problem of consciousness, there s some kind of chemicals in biology that s creating a feeling, like it feels to experience some of these things.
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SPEAKER_00
That s kind of like the hard question is what the heck is that and why is that useful?
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SPEAKER_00
Maybe the more pragmatic question, why is it useful to feel like this is really you experiencing this versus just like information being processed?
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SPEAKER_00
It could be just a very nice side effect of the way we evolved that s just very useful to feel a sense of ownership to the decisions you make, to the perceptions you make, to the model you re trying to maintain.
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SPEAKER_00
Like you own this thing, and it s the only one you got, and if you lose it, it s gonna really suck.
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SPEAKER_00
And so you should really send the brain some signals about it.
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SPEAKER_00
What ideas do you believe might be true that most or at least many people disagree with you with?
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SPEAKER_00
Let s say in the space of machine learning.
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SPEAKER_01
depends who you talk about.
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SPEAKER_01
But I think, so certainly there is a bunch of people who are nativists, right, who think that a lot of the basic things about the world are kind of hardwired in our minds.
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Things like, you know, the world is three-dimensional, for example.
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Is that hardwired?
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Things like, you know, object permanence.
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Is it something that we learn, you know, before the age of three months or so, or are we born with it?
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And there are wide disagreements among the cognitive scientists for this.
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SPEAKER_01
I think those things are actually very simple to learn.
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SPEAKER_01
Is it the case that the oriented edge detectors in V1 are learned or are they hardwired?
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SPEAKER_01
I think they are learned.
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SPEAKER_01
They might be learned before birth because it s really easy to generate signals from the retina that actually will train edge detectors.
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SPEAKER_01
And again, those are things that can be learned within minutes of opening your eyes, right?
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SPEAKER_01
I mean, since the 1990s, we have algorithms that can learn oriented edge detectors completely unsupervised with the equivalent of a few minutes of real time.
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SPEAKER_01
So those things have to be learned.
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SPEAKER_01
There s also those MIT experiments where you kind of plug the optical nerve on the auditory cortex of a baby ferret, right?
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SPEAKER_01
And that auditory cortex becomes a visual cortex, essentially.
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SPEAKER_01
So, you know, clearly there s learning taking place there.
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SPEAKER_01
So, you know, I think a lot of what people think are so basic that you need to be hardwired, I think a lot of those things are learned because they re easy to learn.
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SPEAKER_00
So you put a lot of value in the power of learning.
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SPEAKER_00
What kind of things do you suspect might not be learned?
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SPEAKER_00
Is there something that could not be learned?
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SPEAKER_01
So your intrinsic drives are not learned.
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SPEAKER_01
They are the things that make humans human or make cats different from dogs, right?
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SPEAKER_01
It s the basic drives that are kind of hardwired in our basal ganglia.
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SPEAKER_01
I mean, there are people who are working on this kind of stuff.
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SPEAKER_01
It s called intrinsic motivation in the context of reinforcement learning.
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SPEAKER_01
So these are objective functions where the reward doesn t come from the external world.
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SPEAKER_01
It s computed by your own brain.
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SPEAKER_01
Your own brain computes whether you re happy or not, right?
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SPEAKER_01
It measures your degree of comfort or incomfort.
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SPEAKER_01
And because it s your brain computing this, presumably it knows also how to estimate gradients of this, right?
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SPEAKER_01
So it s easier to learn when your objective is intrinsic.
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SPEAKER_01
So that has to be hardwired.
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SPEAKER_01
The critic that makes long-term prediction of the outcome, which is the eventual result of this, that s learned.
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SPEAKER_01
and perception is learned and your model of the world is learned.
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SPEAKER_01
But let me take an example of why the critic, I mean, example of how the critic may be learned, right?
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SPEAKER_01
If I come to you, I reach across the table and I pinch your arm, right?
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SPEAKER_01
Complete surprise for you.
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SPEAKER_01
You would not have expected this from me.
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SPEAKER_00
I was expecting that the whole time, but yes, right.
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SPEAKER_00
Let s say for the sake of the story, yes.
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SPEAKER_01
Okay, your basal ganglia is going to light up because it s going to hurt, right?
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SPEAKER_01
And now your model of the world includes the fact that I may pinch you if I approach my... Don t trust humans.
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SPEAKER_01
Right.
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SPEAKER_01
my hand to your arm so if I try again you re going to recoil and that s your critic, your predictor of your ultimate pain system that predicts that something bad is going to happen and you recoil to avoid it.
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SPEAKER_00
So even that can be learned.
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SPEAKER_01
That is learned, definitely.
5
SPEAKER_01
This is what allows you also to define sub-goals, right?
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SPEAKER_01
So the fact that you re a school child, you wake up in the morning and you go to school, and it s not because you necessarily like waking up early and going to school, but you know that there is a long-term objective you re trying to optimize.
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SPEAKER_00
So, Ernest Becker, I m not sure if you re familiar with him, the philosopher, he wrote the book Denial of Death, and his idea is that one of the core motivations of human beings is our terror of death, our fear of death.
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SPEAKER_00
That s what makes us unique from cats.
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SPEAKER_00
Cats are just surviving, they do not have a deep, like a cognizance, introspection that
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SPEAKER_00
over the horizon is the end.
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SPEAKER_00
And he says that, I mean, there s a terror management theory, there s just all these psychological experiments that show basically this idea that all of human civilization, everything we create is kind of trying to forget if, even for a brief moment, that we re going to die.
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SPEAKER_00
When do you think humans understand that they re going to die?
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SPEAKER_00
Is it learned early on also?
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SPEAKER_01
I don t know at what point, I mean, it s a question, like, you know, at what point do you realize that, you know, what death really is?
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SPEAKER_01
And I think most people don t actually realize what death is, right?
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SPEAKER_01
I mean, most people believe that you go to heaven or something, right?
5
SPEAKER_00
So the pushback on that, what Ernest Becker says, and Sheldon Solomon, all of those folks, and I find those ideas a little bit compelling, is that there is moments in life, early in life, a lot of this fun happens early in life, when you are,
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SPEAKER_00
when you do deeply experience the terror of this realization and all the things you think about about religion, all those kinds of things that we kind of think about more like teenage years and later, we re talking about way earlier.
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SPEAKER_01
No, it s like seven or eight years, something like that, yeah.
5
SPEAKER_00
You realize,
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SPEAKER_00
Holy crap, this is like the mystery, the terror, like it s almost like you re a little prey, a little baby deer sitting in the darkness of the jungle, the woods, looking all around you, there s darkness full of terror.
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SPEAKER_00
I mean, that realization says, okay, I m gonna go back in the comfort of my mind where there is a deep meaning, where there is a maybe like pretend I m immortal in however way
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SPEAKER_00
however kind of idea I can construct to help me understand that I m immortal.
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SPEAKER_00
Religion helps with that.
5
SPEAKER_00
You can delude yourself in all kinds of ways, like lose yourself in the busyness of each day, have little goals in mind, all those kinds of things, to think that it s gonna go on forever.
5
SPEAKER_00
And you kinda know you re gonna die, yeah, and it s gonna be sad, but you don t really understand that you re going to die.
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SPEAKER_00
And so that s their idea.
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SPEAKER_00
And I find that compelling because it does seem to be a core unique aspect of human nature that we re able to think that we re able to really understand that this life is finite.
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SPEAKER_00
That seems important.
5
SPEAKER_01
There s a bunch of different things there.
5
SPEAKER_01
So first of all, I don t think there is a qualitative difference between us and cats in the term.
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SPEAKER_01
I think the difference is that we just have a better long-term
5
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ability to predict in the long term.
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SPEAKER_01
And so we have a better understanding of how the world works, so we have a better understanding of finiteness of life and things like that.
5
SPEAKER_00
So we have a better planning engine than cats?
5
SPEAKER_01
Yeah.
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SPEAKER_01
Okay.
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SPEAKER_00
But what s the motivation for planning that far?
5
SPEAKER_01
Well, I think it s just a side effect of the fact that we have just a better planning engine because it makes us, as I said, the essence of intelligence is the ability to predict.
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SPEAKER_01
And so because we re smarter,
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SPEAKER_01
As a side effect, we also have this ability to make predictions about our own future existence or lack thereof.
5
SPEAKER_01
You say religion helps with that.
5
SPEAKER_01
I think religion hurts, actually.
5
SPEAKER_01
It makes people worry about what s gonna happen after their death, et cetera.
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SPEAKER_01
If you believe that you just don t exist after death, it solves completely the problem, at least.
5
SPEAKER_00
You re saying if you don t believe in God, you don t worry about what happens after death?
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SPEAKER_00
I don t know.
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SPEAKER_01
You want to worry about this life because that s the only one you have.
5
SPEAKER_00
I think it s, well, I don t know.
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SPEAKER_00
If I were to say what Ernest Becker says, and I would say I agree with him more than not, is you do deeply worry.
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SPEAKER_00
If you believe there s no God, there s still a deep worry of the mystery of it all.
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SPEAKER_00
How does that make any sense, that it just ends?
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SPEAKER_00
I don t think we can truly understand that this ride, I mean, so much of our life, the consciousness, the ego,
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SPEAKER_00
is invested in this being.
5
SPEAKER_01
And then... Science keeps bringing humanity down from its pedestal.
5
SPEAKER_01
And that s another example of it.
5
SPEAKER_00
That s wonderful, but for us individual humans, we don t like to be brought down from a pedestal.
5
SPEAKER_00
You re saying like... I m fine with that.
5
SPEAKER_00
But see, you re fine with it because, well, so what Ernest Becker would say is you re fine with it because that s just a more peaceful existence for you, but you re not really fine, you re hiding from it.
5
SPEAKER_00
In fact, some of the people that experience the deepest trauma earlier in life, they often, before they seek extensive therapy, will say that I m fine.
5
SPEAKER_00
It s like when you talk to people who are truly angry, how are you doing?
5
SPEAKER_00
I m fine.
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SPEAKER_00
The question is what s going on?
5
SPEAKER_01
I had a near-death experience.
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SPEAKER_01
I had a very bad motorbike accident when I was 17.
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SPEAKER_01
But that didn t have any impact on my reflection on that topic.
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So I m basically just playing a bit of a devil s advocate, pushing back and wondering, is it truly possible to accept death?
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And the flip side that s more interesting, I think, for AI and
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robotics, how important is it to have this as one of the suite of motivations, is to not just avoid falling off the roof or something like that, but
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ponder the end of the ride.
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If you listen to the Stoics, it s a great motivator.
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It adds a sense of urgency.
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So maybe to truly fear death or be cognizant of it might give a deeper meaning and urgency to the moment, to live fully.
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Maybe I don t disagree with that.
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I mean, I think what motivates me here is, you know, knowing more about human nature.
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I mean, I think human nature and human intelligence is a big mystery.
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It s a scientific mystery.
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in addition to philosophical and etc., but I m a true believer in science.
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I do have a belief that for complex systems like the brain and the mind, the way to
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understand it is to try to reproduce it with artifacts that you build, because you know what s essential to it when you try to build it.
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I ve used this analogy before with you, I believe.
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The same way we only started to understand aerodynamics when we started building airplanes, and that helped us understand how birds fly.
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I think there s a similar process here where we don t have a full theory of intelligence, but building intelligent artifacts will help us perhaps develop some underlying theory that encompasses not just artificial implements but also
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human and biological intelligence in general.
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So you re an interesting person to ask this question about sort of all kinds of different other intelligent entities or intelligences.
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What are your thoughts about kind of like the Turing or the Chinese room question?
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If we create an AI system that exhibits a lot of properties of intelligence and consciousness,
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how comfortable are you thinking of that entity as intelligent or conscious?
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So you re trying to build now systems that have intelligence and there s metrics about their performance, but that metric is external.
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So are you okay calling a thing intelligent or are you going to be like most humans and be once again unhappy to be brought down from a pedestal of consciousness slash intelligence?
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No, I ll be very happy to
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understand more about human nature, human mind and human intelligence through the construction of machines that have similar abilities.
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And if a consequence of this is to bring down humanity one notch down from its already low pedestal, I m just fine with it.
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That s just the reality of life.
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So I m fine with that.
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Now, you were asking me about opinions I have that a lot of people may disagree with.
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I think...
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If we think about the design of an autonomous intelligence system, so assuming that we are somewhat successful at some level of getting machines to learn models of the world, predictive models of the world, we build intrinsic motivation objective functions to drive the behavior of that system.
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The system also has perception modules that allows it to estimate the state of the world, and then have some way of figuring out a sequence of actions to optimize a particular objective.
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If it has a critic of the type that I was describing before, the thing that makes you recoil your arm the second time I try to pinch you, intelligent autonomous machine
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will have emotions.
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I think emotions are an integral part of autonomous intelligence.
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If you have an intelligent system that is driven by intrinsic motivation, by objectives, if it has a critic that allows it to predict in advance whether the outcome of a situation is going to be good or bad, it s going to have emotions.
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It s going to have fear.
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when it predicts that the outcome is going to be bad and something to avoid, it s going to have elation when it predicts it s going to be good.
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If it has drives to relate with humans, you know, in some ways, the way humans have, you know, it s going to be social, right?
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And so it s going to have emotions about attachment and things of that type.
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So I think, you know, the sort of
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sci-fi thing where, you know, you see commander data, like having an emotion chip that you can turn off, right?
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I think that s ridiculous.
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So, I mean, here s the difficult philosophical social question.
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Do you think there will be a time, like a civil rights movement for robots where, okay, forget the movement, but a discussion like the Supreme Court
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that particular kinds of robots, you know, particular kinds of systems deserve the same rights as humans because they can suffer just as humans can, all those kinds of things.
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Well, perhaps, perhaps not.
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Like imagine that humans were, that you could,
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you know, die and be restored.
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Like, you know, you could be sort of, you know, be 3D reprinted and, you know, your brain could be reconstructed in its finest details.
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Our ideas of rights will change in that case.
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If you can always just, there s always a backup you could always restore.
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Maybe like the importance of murder will go down one notch.
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That s right.
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But also the, your, you know, desire to do dangerous things like, you know, skydiving or, you know,
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or, you know, race car driving, you know, car racing or that kind of stuff, you know, would probably increase.
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Or, you know, airplane aerobatics or that kind of stuff, right?
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It would be fine to do a lot of those things or explore, you know, dangerous areas and things like that.
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It would kind of change your relationship.
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So now it s very likely that robots would be like that because, you know, they ll be based on perhaps technology that is somewhat similar to today s technology and you can always have a backup.
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So it s possible, I don t know if you like video games, but there s a game called Diablo.
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My sons are huge fans of this.
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Yes.
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In fact, they made a game that s inspired by it.
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Awesome, like built a game?
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My three sons have a game design studio between them, yeah.
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That s awesome.
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They came out with a game.
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They just came out with a game?
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Last year, no, this was last year, early last year, about a year ago.
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That s awesome.
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But so in Diablo, there s something called hardcore mode, which if you die, there s no, you re gone.
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Right.
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That s it.
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And so it s possible with AI systems,
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for them to be able to operate successfully and for us to treat them in a certain way, because they have to be integrated in human society, they have to be able to die, no copies allowed.
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In fact, copying is illegal.
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It s possible with humans as well, like cloning will be illegal, even when it s possible.
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But cloning is not copying, right?
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I mean, you don t reproduce the mind of the person and experience.
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It s just a delayed twin.
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But then it s, but we were talking about with computers that you will be able to copy.
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You will be able to perfectly save, pickle the mind state.
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And it s possible that that will be illegal because that goes against, that will destroy the motivation of the system.
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Okay, so let s say you have a domestic robot sometime in the future.
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And the domestic robot comes to you somewhat pre-trained, it can do a bunch of things, but it has a particular personality that makes it slightly different from the other robots because that makes them more interesting.
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And then because it s lived with you for five years, you ve grown some attachment to it and vice versa, and it s learned a lot about you.
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Or maybe it s not a household robot.
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Maybe it s a virtual assistant that lives in your augmented reality glasses or whatever.
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The horror movie type thing.
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And that system, to some extent, the intelligence in that system
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is a bit like your child or maybe your PhD student, in the sense that there s a lot of you in that machine now, right?
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And so if it were a living thing, you would do this for free if you want, right?
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If it s your child, your child can then live his or her own life.
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And the fact that they learn stuff from you doesn t mean that you have any ownership of it, right?
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But if it s a robot,
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that you ve trained, perhaps you have some intellectual property claim about- Intellectual property?
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Oh, I thought you meant like a permanence value in the sense that s part of use in- Well, there is permanence value, right?
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So you would lose a lot if that robot were to be destroyed and you had no backup, you would lose a lot.
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You would lose a lot of investment.
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You know, kind of like a person dying, you know, that a friend of yours dying or a coworker or something like that.
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But also you have intellectual property rights in the sense that that system is fine-tuned to your particular existence.
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So that s now a very unique instantiation of that original background model, whatever it was, that arrived.
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And then there are issues of privacy, right?
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Because now imagine that that robot has its own kind of volition and decides to work for someone else.
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Or kind of, you know, thinks life with you is sort of untenable or whatever.
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Now, all the things that that system learned from you, can you delete all the personal information that that system knows about you?
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I mean, that would be kind of an ethical question.
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Can you erase the mind of an intelligent robot to protect your privacy?
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You can t do this with humans.
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You can ask them to shut up, but you don t have complete power over them.
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Can t erase humans.
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Yeah, it s the problem with the relationships, you know?
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You break up, you can t erase the other human.
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With robots, I think it ll have to be the same thing with robots, that risk, that there has to be some risk to our interactions to truly experience them deeply, it feels like.
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So you have to be able to lose your robot friend and that robot friend to go tweeting about how much of an asshole you are.
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But then are you allowed to murder the robot to protect your private information if the robot decides to leave?
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I have this intuition that for robots with certain, it s almost like regulation.
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If you declare your robot to be, let s call it sentient or something like that, this robot is designed for human interaction, then you re not allowed to murder these robots.
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It s the same as murdering other humans.
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Well, but what about you do a backup of the robot that you preserve on a hard drive or the equivalent in the future?
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That might be illegal.
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It s like piracy is illegal.
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But it s your own robot, right?
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But you can t, you don t... But then you can wipe out his brain.
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So this robot doesn t know anything about you anymore, but you still have, technically it s still in existence because you backed it up.
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And then there ll be these great speeches at the Supreme Court by saying, oh sure, you can erase the mind of the robot just like you can erase the mind of a human.
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We both can suffer.
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There ll be some epic like Obama type character with a speech that we, like the robots and the humans are the same.
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We can both suffer, we can both hope, we can both, all of those kinds of things, raise families, all that kind of stuff.
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It s interesting for these, just like you said, emotion seems to be a fascinatingly powerful aspect of human interaction, human-robot interaction, and if they re able to exhibit emotions, at the end of the day, that s probably going to have us deeply consider
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human rights, like what we value in humans, what we value in other animals.
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That s why robots and AI is great.
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It makes us ask really good questions.
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The hard questions, yeah.
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You asked about the Chinese room type argument.
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Is it real?
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If it looks real.
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I think the Chinese room argument is the ridiculous one.
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So for people who don t know, Chinese room is, you can, I don t even know how to formulate it well, but basically, you can mimic the behavior of an intelligent system by just following a giant algorithm code book that tells you exactly how to respond in exactly each case, but is that really intelligent?
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It s like a giant lookup table.
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When this person says this, you answer this.
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When this person says this, you answer this.
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And if you understand how that works, you have this giant, nearly infinite lookup table, is that really intelligence?
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Because intelligence seems to be a mechanism that s much more interesting and complex than this lookup table.
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I don t think so.
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So the real question comes down to, do you think you can mechanize intelligence in some way, even if that involves learning?
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And the answer is, of course, yes.
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There s no question.
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There s a second question then, which is, assuming you can reproduce intelligence in different hardware than biological hardware, like computers, can you match human intelligence in all the domains in which humans are intelligent?
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Is it possible, right?
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So this is the hypothesis of a strong AI.
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The answer to this, in my opinion, is an unqualified yes.
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This will happen at some point.
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There s no question that machines at some point will become more intelligent than humans in all domains where humans are intelligent.
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This is not for tomorrow.
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It s going to take a long time, regardless of what Elon and others have claimed or believed.
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This is a lot harder than many of those guys think it is.
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And many of those guys who thought it was simpler than that, five years ago, now think it s hard because it s been five years and they realize it s gonna take a lot longer.
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That includes a bunch of people at DeepMind, for example.
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But- Oh, interesting.
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I haven t actually touched base with the DeepMind folks, but some of it, Elon or Demis Hassabis, I mean, sometimes in your role, you have to kind of,
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create deadlines that are nearer than farther away, to kind of create an urgency, because you have to believe the impossible is possible in order to accomplish it.
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And there s, of course, a flip side to that coin, but it s a weird, you can t be too cynical if you want to get something done.
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Absolutely, I agree with that.
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But I mean, you have to inspire people to work on sort of ambitious things.
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So, you know, it s certainly a lot harder than we believe, but there s no question in my mind that this will happen.
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SPEAKER_00
And now, you know, people are kind of worried about what does that mean for humans?
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SPEAKER_00
They are going to be brought down from their pedestal, you know, a bunch of notches with that.
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SPEAKER_00
And, you know, is that going to be good or bad?
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SPEAKER_00
I mean, it s just going to give more power, right?
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SPEAKER_00
It s an amplifier for human intelligence, really.
6
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So speaking of doing cool, ambitious things, FAIR, the Facebook AI Research Group, has recently celebrated its eighth birthday.
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Or maybe you can correct me on that.
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Looking back, what has been the successes, the failures, the lessons learned from the eight years at FAIR?
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And maybe you can also give context of where does the newly minted meta-AI fit into how does it relate to FAIR?
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SPEAKER_00
Right.
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So let me tell you a bit about the organization of all this.
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Yeah, FAIR was created almost exactly eight years ago.
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It wasn t called FAIR yet.
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It took that name a few months later.
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And at the time I joined Facebook, there was a group called the AI group that had about 12 engineers and a few scientists, like, you know, 10 engineers and two scientists or something like that.
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I ran it for three and a half years as a director.
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you know, hired the first few scientists and kind of set up the culture and organized it, you know, explain to the Facebook leadership what fundamental research was about and how it can work within industry and how it needs to be open and everything.
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SPEAKER_00
And I think it s been an unqualified success in the sense that FAIR has simultaneously produced, you know, top level research and advanced the science and the technology, provided tools, open source tools like PyTorch and many others.
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SPEAKER_00
But at the same time, it has had a direct or mostly indirect impact on Facebook at the time, now Meta, in the sense that a lot of systems that Meta is built around now are based on research projects that started at FAIR.
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SPEAKER_00
And so if you were to take out, you know, deep learning out of Facebook services now and meta more generally, I mean, the company would literally crumble.
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SPEAKER_00
I mean, it s completely built around AI these days and it s really essential to the operations.
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So what happened after three and a half years is that I changed role.
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I became chief scientist.
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So I m not doing day-to-day management of a fair anymore.
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and more of a kind of, you know, think about strategy and things like that.
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And I carry my, I conduct my own research.
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I have, you know, my own kind of research group working on self-supervised learning and things like this, which I didn t have time to do when I was director.
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SPEAKER_00
So now FAIR is run by Joël Pinot and Antoine Borde, together, because FAIR is kind of split in two now.
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There s something called FAIR Labs, which is sort of,
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bottom-up, science-driven research and FairXL, which is slightly more organized for bigger projects that require a little more kind of focus and more engineering support and things like that.
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SPEAKER_00
So Joël needs FairLab and Antoine Bordeaux needs FairXL.
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SPEAKER_00
Where are they located?
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SPEAKER_00
It s delocalized all over.
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So there s no question that the leadership of the company believes that this was a very worthwhile investment.
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SPEAKER_00
And what that means is that
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It s there for the long run.
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SPEAKER_00
If you want to talk in these terms, which I don t like, there s a business model, if you want, where FAIR, despite being a very fundamental research lab, brings a lot of value to the company, mostly indirectly through other groups.
6
SPEAKER_00
Now, what happened three and a half years ago when I stepped down was also the creation of Facebook AI, which was basically a larger organization that covers FAIR, so FAIR is included in it, but also has other organizations that are
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SPEAKER_00
focused on applied research or advanced development of AI technology that is more focused on the products of the company.
6
SPEAKER_01
So less emphasis on fundamental research.
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SPEAKER_00
Less fundamental, but it s still research.
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SPEAKER_00
I mean, there s a lot of papers coming out of those organizations and the people are awesome and wonderful to interact with.
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SPEAKER_00
But it serves as kind of a
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a way to kind of scale up, if you want, sort of AI technology, which, you know, may be very experimental and sort of lab prototypes into things that are usable.
6
SPEAKER_01
So FAIR is a subset of meta-AI.
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SPEAKER_01
Is FAIR become like KFC?
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SPEAKER_01
It ll just keep the F. Nobody cares what the F stands for.
6
SPEAKER_00
We ll know soon enough, probably by the end of 2021.
6
SPEAKER_01
I think this is not a giant change, MAIR, FAIR.
6
SPEAKER_00
Well, maybe it doesn t sound too good, but the brand people are kind of deciding on this and they ve been hesitating for a while now and they tell us they re going to come up with an answer as to whether FAIR is going to change name or whether we re going to change just the meaning of the F. Oh, that s a good call.
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SPEAKER_01
I would keep FAIR and change the meaning of the F. That would be my preference.
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SPEAKER_00
I would turn the F into fundamental.
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SPEAKER_00
Oh, that s good.
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SPEAKER_01
Fundamental AI research.
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SPEAKER_01
Oh, that s really good.
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SPEAKER_00
Yeah, yeah.
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SPEAKER_00
Within meta-AI.
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SPEAKER_00
So this would be- Meta-fair.
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SPEAKER_00
Sort of meta-fair.
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SPEAKER_00
Yeah.
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SPEAKER_00
But, you know, people would call it fair, right?
6
SPEAKER_01
Yeah, exactly.
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SPEAKER_01
I like it.
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SPEAKER_01
And now meta-AI.
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SPEAKER_00
Meta is part of the reality lab.
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SPEAKER_00
The new Facebook is called Meta and it s kind of divided into Facebook, Instagram, WhatsApp.
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SPEAKER_00
Reality Lab.
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SPEAKER_00
Reality Lab is about AR, VR, telepresence, communication technology and stuff like that.
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SPEAKER_00
You can think of it as a combination of new products and technology part of Meta.
7
SPEAKER_01
Is that where the touch sensing for robots, I saw that you were posting about, that s- But touch sensing for robot is part of FAIR, actually.
7
SPEAKER_01
That s a FAIR project.
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SPEAKER_00
Oh, it is?
7
SPEAKER_00
Okay, cool.
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SPEAKER_00
Yeah.
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SPEAKER_00
This is also the, no, but there is the other way, the haptic glove, right?
7
SPEAKER_00
Yes.
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SPEAKER_00
That has like- That s more reality lab.
7
SPEAKER_00
That s reality lab research.
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SPEAKER_01
Reality lab research.
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SPEAKER_01
But by the way, the touch sensors is super interesting, like integrating that modality into the whole,
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SPEAKER_01
sensing suite is very interesting.
7
SPEAKER_01
So what do you think about the metaverse?
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SPEAKER_01
What do you think about this whole, this whole kind of expansion of the view of the role of Facebook and meta in the world?
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SPEAKER_00
Well, metaverse really should be thought of as the next step in the internet, right?
7
SPEAKER_00
Sort of trying to kind of, you know, make the experience more compelling of, you know, being,
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SPEAKER_00
connected either with other people or with content.
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SPEAKER_00
And, you know, we are evolved and trained to evolve in, you know, 3D environments where, you know, we can see other people, we can talk to them when we re near them, or, you know, and other people who are far away can t hear us, you know, things like that, right?
7
SPEAKER_00
So there s a lot of social conventions that exist in the real world that we can try to transpose.
7
SPEAKER_00
Now, what is going to be eventually the
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SPEAKER_00
How compelling is it going to be?
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SPEAKER_00
Is it going to be the case that people are going to be willing to do this if they have to wear a huge pair of goggles all day?
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SPEAKER_00
Maybe not.
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SPEAKER_01
But then again, if the experience is sufficiently compelling, maybe so.
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SPEAKER_00
Or if the device that you have to wear is just basically a pair of glasses, and technology makes sufficient progress for that, AR is a much easier concept to grasp, that you re gonna have augmented reality glasses that basically contain some sort of virtual assistant that can help you in your daily lives.
7
SPEAKER_01
But at the same time, with AR, you have to contend with reality.
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SPEAKER_01
With VR, you can completely detach yourself from reality, so it gives you freedom.
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SPEAKER_01
It might be easier to design worlds in VR.
7
SPEAKER_00
Yeah, but you can imagine the metaverse being a mix, right?
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SPEAKER_00
Or like you can have objects that exist in the metaverse that pop up on top of the real world or only exist in virtual reality.
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SPEAKER_01
Okay, let me ask the hard question.
7
SPEAKER_00
because all of this was easy.
7
SPEAKER_01
This was easy.
7
SPEAKER_01
The Facebook, now Meta, the social network, has been painted by the media as net negative for society, even destructive and evil at times.
7
SPEAKER_01
You ve pushed back against this, defending Facebook.
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SPEAKER_01
Can you explain your defense?
7
SPEAKER_00
Yeah, so the description, the company that is being described in some media is not the company we know when we work inside.
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And, you know, it could be claimed that a lot of employees are uninformed about what really goes on in the company.
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But, you know, I m a vice president.
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I mean, I have a pretty good vision of what goes on.
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I don t know everything, obviously.
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I m not involved in everything, but certainly not in decision about content moderation or anything like this.
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But I have some decent vision of what goes on.
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And this evil that is being described, I just don t see it.
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And then, you know, I think there is an easy story to buy, which is that, you know, all the bad things in the world and, you know, the reason your friend believe crazy stuff, you know, there s an easy scapegoat, right, in social media in general, Facebook in particular.
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have to look at the data.
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Is it the case that Facebook, for example, polarizes people politically?
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Are there academic studies that show this?
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Is it the case that teenagers think of themselves less if they use Instagram more?
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Is it the case that people get more riled up against opposite sides in a debate or political opinion if they are more on Facebook or if they are less?
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Study after study,
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show that none of this is true.
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This is independent studies by academics.
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They re not funded by Facebook or Meta.
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You know, studied by Stanford, by some of my colleagues at NYU, actually, with whom I have no connection.
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You know, there s a study recently, they paid people, I think it was in
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in former Yugoslavia.
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I m not exactly sure in what part, but they paid people to not use Facebook for a while in the period before the anniversary of the Srebrenica massacres.
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So people get riled up, like, should we have a memorial kind of celebration for it or not?
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So they paid a bunch of people to not use Facebook for a few weeks.
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It turns out that
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those people ended up being more polarized than they were at the beginning.
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And the people who were more on Facebook were less polarized.
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There s a study from Stanford of economists at Stanford that tried to identify the causes of increasing polarization in the US.
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And it s been going on for 40 years before Mark Zuckerberg was born, continuously.
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And so if there is a cause, it s not Facebook or social media.
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So you could say if social media just accelerated, but no, I mean, it s basically a continuous,
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evolution by some measure of polarization in the US.
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And then you compare this with other countries like the West half of Germany, because you can t go 40 years in the East side, or Denmark or other countries.
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And they use Facebook just as much, and they re not getting more polarized, they re getting less polarized.
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So if you want to look for a causal relationship there,
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You can find a scapegoat, but you can t find a cause.
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Now, if you want to fix the problem, you have to find the right cause.
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And what riles me up is that people now are accusing Facebook of bad deeds that are done by others, and those others are, we re not doing anything about them.
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And by the way, those others include the owner of the Wall Street Journal in which all of those papers were published.
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So I should mention that I m talking to Shrepp, Mike Shrepp on this podcast, and also Mark Zuckerberg, and probably these are conversations I can have with them, because it s very interesting.
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To me, even if Facebook has some measurable negative effect,
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You can t just consider that in isolation.
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You have to consider about all the positive ways it connects us.
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So every technology.
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You can t just say there s an increase in division.
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Yes, probably Google search engine has created increase in division, but you have to consider about how much information are brought to the world.
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Like, I m sure Wikipedia created more division if you just look at the division, but you have to look at the full context of the world, and did it make a better world?
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SPEAKER_00
And you have to- I mean, the printing press has created more division, right?
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Exactly.
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So, you know, when the printing press was invented, the first books that were printed were things like the Bible, and that allowed people to read the Bible by themselves, not get the message uniquely from priests.
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in Europe, and that created the Protestant movement and 200 years of religious persecution and wars.
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So that s a bad side effect of the printing press.
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Social networks aren t being nearly as bad as the printing press, but nobody would say the printing press was a bad idea.
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SPEAKER_01
Yeah, a lot of it is perception, and there s a lot of different incentives operating here.
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Maybe a quick comment, since you re one of the top leaders at Facebook and at Meta, sorry, that s in the tech space, I m sure Facebook involves a lot of incredible technological
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challenges that need to be solved.
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A lot of it probably is in the computer infrastructure, the hardware.
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I mean, it s just a huge amount.
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Maybe can you give me context about how much of Schrepp s life is AI and how much of it is low-level compute?
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How much of it is flying all around doing business stuff?
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And the same with Zuckerberg, Mark Zuckerberg.
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They really focus on AI.
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Certainly in the run-up of the creation affair, and for at least a year after that, if not more, Mark was very, very much focused on AI and was spending quite a lot of effort on it.
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That s his style.
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When he gets interested in something, he reads everything about it.
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know, he read some of my papers, for example, before I joined.
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Um, and, uh, so he, he learned a lot about it, right.
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SPEAKER_00
Um, and, uh, uh, you know, Shrepp was really to it also.
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I mean, Shrepp is really kind of, um, you know, has,
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something I ve tried to preserve also despite my not so young age, which is a sense of wonder about science and technology.
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And he certainly has that.
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He s also a wonderful person, I mean, in terms of, as a manager, dealing with people and everything.
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Mark also, actually.
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I mean, they re very human people.
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In the case of Mark, it s shockingly human, you know, given his trajectory.
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SPEAKER_00
I mean, the personality of him that is painted in the press, it s just completely wrong.
7
SPEAKER_01
Yeah.
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But you have to know how to play the press.
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So that s, I put some of that responsibility on him too.
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You have to, it s like,
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You know, like the director, the conductor of an orchestra, you have to play the press and the public in a certain kind of way where you convey your true self to them.
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If there s a depth of kindness.
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It s hard, and it s probably not the best at it, so yeah.
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You have to learn.
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And it s sad to see, and I ll talk to him about it, but Shrepp is slowly stepping down.
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It s always sad to see folks sort of be there for a long time and slowly, I guess time is sad.
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I think he s done the thing he set out to do, and he s got, you know,
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family priorities and stuff like that.
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And I understand, you know, after 13 years or something.
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Which in Silicon Valley is basically a lifetime.
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You know, cause you know, it s dog years.
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SPEAKER_01
So NeurIPS, the conference just wrapped up.
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Let me just go back to something else.
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You posted that the paper you co-authored was rejected from NeurIPS.
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As you said, proudly in quotes, rejected.
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I m a joke.
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SPEAKER_01
Yeah, I know.
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Can you describe this paper and what was the idea in it?
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SPEAKER_01
And also maybe this is a good opportunity to ask what are the pros and cons, what works and what doesn t about the review process?
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SPEAKER_00
Yeah, let me talk about the paper first.
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I ll talk about the review process afterwards.
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The paper is called VicReg.
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So this is, I mentioned that before, variance, invariance, covariance, regularization.
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And it s a technique, a non-contrastive learning technique for what I call joint embedding architecture.
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So Siamese nets are an example of joint embedding architecture.
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So joint embedding architecture is, let me back up a little bit, right?
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So if you want to do self-supervised learning,
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You can do it by prediction.
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So let s say you want to train a system to predict video.
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You show it a video clip and you train the system to predict the continuation of that video clip.
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Now, because you need to handle uncertainty, because there are many continuations that are plausible,
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need to handle this in some way.
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You need to have a way for the system to be able to produce multiple predictions.
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The only way I know to do this is through what s called a latent variable.
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So you have some sort of
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hidden vector of a variable that you can vary over a set or draw from a distribution.
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And as you vary this vector over a set, the output, the prediction, varies over a set of plausible predictions.
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Okay?
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So that s called, I call this a generative latent variable model.
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Got it.
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Okay?
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Now there is an alternative to this, to handle uncertainty.
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And instead of directly predicting the next frames of the clip,
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You also run those through another neural net.
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So you now have two neural nets, one that looks at the initial segment of the video clip, and another one that looks at the continuation during training.
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And what you re trying to do is learn a representation
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of those two video clips that is maximally informative about the video clips themselves, but is such that you can predict the representation of the second video clip from the representation of the first one easily.
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And you can sort of formalize this in terms of maximizing mutual information and some stuff like that, but it doesn t matter.
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What you want is informative representations of the two video clips that are mutually predictable.
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What that means is that there s a lot of details in the second video clip that are irrelevant
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Let s say a video clip consists of a camera panning the scene.
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There s going to be a piece of that room that is going to be revealed, and I can somewhat predict what that room is going to look like, but I may not be able to predict the details of the texture of the ground and where the tiles are ending and stuff like that.
7
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So those are irrelevant details that perhaps my representation will eliminate.
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And so what I need is to train this second neural net in such a way that whenever the continuation video clip varies over all the plausible continuations, the representation doesn t change.
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SPEAKER_01
Got it.
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SPEAKER_01
So it s the, yeah, yeah, got it.
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SPEAKER_01
Over the space of representations, doing the same kind of thing as you re doing with similarity learning.
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Right.
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So these are two ways to handle multimodality in a prediction, right?
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In the first way, you parametrize the prediction with a latent variable, but you predict pixels, essentially, right?
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In the second one, you don t predict pixels, you predict an abstract representation of pixels, and you guarantee that this abstract representation
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has as much information as possible about the input, but sort of, you know, drops all the stuff that you really can t predict, essentially.
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I used to be a big fan of the first approach.
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And in fact, in this paper with Ishan Mishra, this blog post, The Dark Matter of Intelligence, I was kind of advocating for this.
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And in the last year and a half, I ve completely changed my mind.
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I m now a big fan of the second one.
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And it s because of a small collection of algorithms that have been proposed over the last year and a half or so, two years, to do this, including V-Craig, its predecessor called Barlow-Twins, which I mentioned, a method from our friends at DeepMind called BYOL.
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And there s a bunch of others now that kind of work similarly.
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So they re all based on this idea of joint embedding.
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Some of them have an explicit criterion that is an approximation of mutual information.
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Some others at BYOL work, but we don t really know why.
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And there s been like lots of theoretical papers about why BYOL works.
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SPEAKER_00
No, it s not that because we take it out and it still works and blah, blah, blah.
7
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I mean, so there s like a big debate, but
7
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But the important point is that we now have a collection of non-contrastive joint embedding methods, which I think is the best thing since sliced bread.
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So I m super excited about this because I think it s our best shot for techniques that would allow us to kind of build predictive world models and at the same time learn hierarchical representations of the world where what matters about the world is preserved and what is irrelevant is eliminated.
7
SPEAKER_01
By the way, the representations, the before and after, is in the space in a sequence of images, or is it for single images?
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SPEAKER_00
It would be either for a single image, for a sequence.
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It doesn t have to be images.
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This could be applied to text.
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This could be applied to just about any signal.
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I m looking at, you know, I m looking for methods that are generally applicable that are not specific to
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to one particular modality.
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It could be audio or whatever.
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Got it.
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So what s the story behind this paper?
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This paper is what, is describing one such method?
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It s this Wittkreig method.
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So this is co-authored, the first author is a student called Adrien Barne, who is a resident PhD student at Fair Paris.
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SPEAKER_01
who is co-advised by me and Jean Ponce, who is a professor at Economie Supérieure, also a research director at INRIA.
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SPEAKER_01
So there s this wonderful program in France where PhD students can basically do their PhD in industry, and that s kind of what s happening here.
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SPEAKER_01
And this paper is a follow-up on this Barlow-Tween paper by my former postdoc, now Stéphane Denis, with Li Jing and Joris Bontart and a bunch of other people from FAIR.
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SPEAKER_01
And one of the main criticisms from reviewers is that Vic Craig is not different enough from Barlow-Tweens.
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SPEAKER_01
But, you know, my impression is that
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It s, you know, Bollytwins with a few bugs fixed, essentially.
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SPEAKER_01
And in the end, this is what people will use.
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SPEAKER_01
But you know, I m used to stuff that I submit being rejected for a while.
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SPEAKER_00
So it might be rejected and actually exceptionally well cited because people use it.
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SPEAKER_01
Well, it s already cited like a bunch of times.
8
SPEAKER_00
So, I mean, the question is then to the deeper question about peer review and conferences.
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SPEAKER_00
I mean, computer science as a field is kind of unique that the conference is highly prized.
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SPEAKER_00
That s one.
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SPEAKER_00
And it s interesting because the peer review process there is similar, I suppose, to journals, but it s accelerated significantly.
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SPEAKER_00
Well, not significantly, but it goes fast.
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SPEAKER_00
And it s a nice way to get stuff out quickly, to peer review it quickly, go to present it quickly to the community.
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SPEAKER_00
So not quickly, but quicker.
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SPEAKER_00
But nevertheless, it has many of the same flaws of peer review, because it s a limited number of people look at it.
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SPEAKER_00
There s bias and the following.
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SPEAKER_00
If you wanna do new ideas, you re gonna get pushback.
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SPEAKER_00
There s self-interested people that kinda can infer who submitted it and kinda be cranky about it, all that kinda stuff.
8
SPEAKER_01
Yeah, I mean, there s a lot of social phenomena there.
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SPEAKER_01
There s one social phenomenon, which is that because the field has been growing exponentially, the vast majority of people in the field are extremely junior.
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SPEAKER_01
So as a consequence, and that s just a consequence of the field growing, right?
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SPEAKER_01
So as the number of, as the size of the field kind of starts saturating, you will have less of that problem of reviewers being very inexperienced.
8
SPEAKER_01
A consequence of this is that, you know, young reviewers... I mean, there s a phenomenon which is that reviewers try to make their life easy, and to make their life easy when reviewing a paper is very simple.
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SPEAKER_01
You just have to find a flaw in the paper, right?
8
SPEAKER_01
So basically, they see their task as finding flaws in papers, and most papers have flaws, even the good ones.
8
SPEAKER_01
And so it s easy to do that.
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SPEAKER_01
Your job is easier as a reviewer if you just focus on this.
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SPEAKER_01
But what s important is, is there a new idea in that paper that is likely to influence?
8
SPEAKER_01
It doesn t matter if the experiments are not that great, if the protocol is, you know,
8
SPEAKER_01
So things like that, as long as there is a worthy idea in it that will influence the way people think about the problem, even if they make it better, eventually, I think that s really what makes a paper useful.
8
SPEAKER_01
And so this combination of social phenomena creates a disease that has plagued other fields in the past, like speech recognition, where basically people chase numbers on
8
SPEAKER_01
on benchmarks.
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SPEAKER_01
And it s much easier to get a paper accepted if it brings an incremental improvement on a sort of mainstream, well-accepted method or problem.
8
SPEAKER_01
And those are, to me, boring papers.
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SPEAKER_01
I mean, they re not useless, right?
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SPEAKER_01
Because industry strives on those kind of progress.
8
SPEAKER_01
But they re not the ones that I m interested in, in terms of new concepts and new ideas.
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SPEAKER_01
So papers that are really trying to strike new advances generally don t make it.
8
SPEAKER_01
Now, thankfully, we have Archive.
8
SPEAKER_00
Archive, exactly, and then there s open review type of situations where you, and then, I mean, Twitter s a kind of open review.
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SPEAKER_00
I m a huge believer that review should be done by thousands of people, not two people.
8
SPEAKER_00
I agree.
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SPEAKER_00
And so archive, do you see a future where a lot of really strong papers, it s already the present, but a growing future where it ll just be archive,
8
SPEAKER_00
and you re presenting an ongoing continuous conference called Twitter slash the internet slash archive sanity.
8
SPEAKER_00
Andre just released a new version.
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SPEAKER_00
So just not, you know, not being so elitist about this particular gating.
8
SPEAKER_01
It s not a question of being elitist or not.
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SPEAKER_01
It s a question of being basically, you know,
8
SPEAKER_01
recommendation and seal of approvals for people who don t see themselves as having the ability to do so by themselves, right?
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SPEAKER_01
And so it saves time, right?
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SPEAKER_01
If you rely on other people s opinion and you trust those people or those groups to evaluate a paper for you,
8
SPEAKER_01
That saves you time because you don t have to scrutinize the paper as much.
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SPEAKER_01
It s brought to your attention.
8
SPEAKER_01
It s the whole idea of a collective recommender system.
8
SPEAKER_01
I actually thought about this a lot about 10, 15 years ago.
8
SPEAKER_01
Because there were discussions at NIPS and we re about to create iClear with Yoshua Bengio.
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SPEAKER_01
And so I wrote a document describing a reviewing system, which basically was, you post your paper on some repository, let s say Archive, or now it could be OpenReview,
8
SPEAKER_01
And then you can form a reviewing entity, which is equivalent to a reviewing board of a journal or a program committee of a conference.
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SPEAKER_01
You have to list the members.
8
SPEAKER_01
And then that group, reviewing entity, can choose to review a particular paper, spontaneously or not.
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SPEAKER_01
There is no exclusive relationship anymore between a paper and a venue or a reviewing entity.
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SPEAKER_01
Any reviewing entity can review any paper.
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SPEAKER_01
or may choose not to.
8
SPEAKER_01
And then, you know, give an evaluation.
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SPEAKER_01
It s not published, not published.
8
SPEAKER_01
It s just an evaluation and a comment, which would be public, signed by the reviewing entity.
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SPEAKER_01
And if it s signed by a reviewing entity, you know, it s one of the members of a reviewing entity.
8
SPEAKER_01
So if the reviewing entity is, you know, Lex Friedman s, you know, preferred papers, right, you know, it s Lex Friedman writing the review.
8
SPEAKER_00
Yes, so for me, that s a beautiful system, I think.
8
SPEAKER_00
But in addition to that, it feels like there should be a reputation system for the reviewers.
8
SPEAKER_01
For the reviewing entities, not the reviewers individually.
8
SPEAKER_00
The reviewing entities, sure.
8
SPEAKER_00
But even within that, the reviewers too, because there s another thing here.
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SPEAKER_00
It s not just the reputation, it s an incentive for an individual person to do great.
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SPEAKER_00
Right now, in the academic setting, the incentive is kind of internal, just wanting to do a good job, but honestly, that s not a strong enough incentive to do a really good job in reading a paper and finding the beautiful amidst the mistakes and the flaws and all that kind of stuff.
8
SPEAKER_00
If you re the person that first discovered a powerful paper,
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SPEAKER_00
and you get to be proud of that discovery, then that gives a huge incentive to you.
8
SPEAKER_01
That s a big part of my proposal, actually, where I describe that as, you know, if your evaluation of papers is predictive of future success, then your reputation should go up as a reviewing entity.
8
SPEAKER_01
So yeah, exactly.
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SPEAKER_01
I mean, I even had a master s student who was a master s student in library science and computer science actually kind of work out exactly how that should work with formulas and everything.
8
SPEAKER_00
So in terms of implementation, do you think that s something that s doable?
8
SPEAKER_01
I mean, I ve been sort of, you know, talking about this to sort of various people like, you know, Andrew McCallum, who started OpenReview.
8
SPEAKER_01
And the reason why we picked OpenReview for iClear initially, even though it was very early for them, is because my hope was that iClear, it was eventually going to kind of inaugurate this type of system.
8
SPEAKER_01
So iClear kept the idea of OpenReviews.
8
SPEAKER_01
So where the reviews are, you know, published with a paper, which I think is very useful.
8
SPEAKER_01
But in many ways that s kind of reverted to kind of more of a conventional type conferences for everything else.
8
SPEAKER_01
And that, I mean, I, I don t run iClear, I m just the president of the foundation, but you know, people who run it should make decisions about how to run it.
8
SPEAKER_01
And I m not going to tell them because they are volunteers and I m really thankful that they do that.
8
SPEAKER_01
So, but I m saddened by the fact that we re not being innovative enough.
8
SPEAKER_00
Yeah, me too.
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SPEAKER_00
I hope that changes, yeah.
8
SPEAKER_00
Because the communication of science broadly, but the communication of computer science ideas is how you make those ideas have impact, I think.
8
SPEAKER_01
Yeah, and I think a lot of this is because people have in their mind kind of an objective, which is fairness for authors.
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SPEAKER_01
and the ability to count points basically and give credits accurately.
8
SPEAKER_01
But that comes at the expense of the progress of science.
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SPEAKER_01
So to some extent, we re slowing down the progress of science.
8
SPEAKER_00
And are we actually achieving fairness?
8
SPEAKER_01
And we re not achieving fairness.
8
SPEAKER_01
We still have biases.
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SPEAKER_01
We re doing a double-blind review, but the biases are still there.
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SPEAKER_01
There are different kinds of biases.
8
SPEAKER_00
You write that the phenomenon of emergence, collective behavior exhibited by a large collection of simple elements in interaction, is one of the things that got you into neural nets in the first place.
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SPEAKER_00
I love cellular automata, I love simple interacting elements and the things that emerge from them.
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Do you think we understand how complex systems can emerge from such simple components that interact simply?
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No, we don t.
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It s a big mystery.
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Also, it s a mystery for physicists.
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It s a mystery for biologists.
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You know, how is it that the universe around us seems to be increasing in complexity and not decreasing?
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I mean, that is a kind of curious property of physics that despite the second law of thermodynamics, we seem to be, you know, evolution and learning and et cetera seems to be kind of, at least locally,
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to increase complexity, not decrease it.
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So perhaps the ultimate purpose of the universe is to just get more complex.
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Have these, I mean, small pockets of beautiful complexity.
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Does that, does cellular automata, do these kinds of emergence of complex systems give you some intuition or guide your understanding of machine learning systems and neural networks and so on?
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Or are these, for you right now, disparate concepts?
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Well, it got me into it.
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I discovered the existence of the Perceptron when I was a college student by reading a book.
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It was a debate between Chomsky and Piaget and Seymour Papert from MIT was kind of singing the praise of the Perceptron in that book.
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And it was the first time I heard about the learning machine.
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So I started digging the literature and I found those books, which were basically transcription of workshops or conferences.
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from the 50s and 60s about self-organizing systems.
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So there was a series of conferences on self-organizing systems and there s books on this.
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Some of them are, you can actually get them at the Internet Archive, you know, the digital version.
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And there are like fascinating articles in there by, there s a guy whose name has been largely forgotten, Heinz von Foerster.
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It was a German physicist who immigrated to the US and worked on self-organizing systems in the 50s.
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And in the 60s, at the University of Illinois Urbana-Champaign, he created the Biological Computer Laboratory, BCL, which was all about neural nets.
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Unfortunately, that was kind of towards the end of the popularity of neural nets, so that lab never kind of strived very much.
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But he wrote a bunch of papers about self-organization and about the mystery of self-organization.
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An example he has is, imagine you are in space, there s no gravity, you have a big box with magnets in it, kind of rectangular magnets with North Pole on one end, South Pole on the other end.
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you shake the box gently, and the magnets will kind of stick to themselves and probably form a complex structure spontaneously.
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That could be an example of self-organization.
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But you have lots of examples.
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Neural nets are an example of self-organization in many respects.
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It s a bit of a mystery what is possible with this.
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Pattern formation in physical systems, in chaotic systems and things like that, the emergence of life, things like that.
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How does that happen?
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It s a big puzzle for physicists as well.
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It feels like understanding the mathematics of emergence in some constrained situations might help us create intelligence, like help us add a little spice to the systems because you seem to be able to, in complex systems with emergence, to be able to get a lot from little.
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And so that seems like a shortcut to get big leaps in performance.
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But there s a missing theoretical concept that we don t have.
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And it s something also I ve been fascinated by since my undergrad days.
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And it s how you measure complexity.
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So we don t actually have good ways of measuring, or at least we don t have good ways of interpreting the measures that we have at our disposal.
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How do you measure the complexity of something?
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So there s all those things like Kolmogorov, Chaitin, Solomonov, complexity of
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the length of the shortest program that would generate a bit string can be thought of as the complexity of that bit string.
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I ve been fascinated by that concept.
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The problem with that is that that complexity is defined up to a constant, which can be very large.
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There are similar concepts that are derived from Bayesian probability theory, where the complexity of something is the negative log of its probability, essentially.
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And you have a complete equivalence between the two things.
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And there you would think, you know, the probability is something that s well defined mathematically, which means complexity is well defined.
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But it s not true.
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You need to have a model of the distribution.
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You may need to have a prior if you re doing Bayesian inference.
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And the prior plays the same role as the choice of the computer with which you measure your Kolmogorov complexity.
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And so every measure of complexity we have has some arbitrariness in it.
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You know, an additive constant which can be arbitrarily large.
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And so
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Now how can we come up with a good theory of how things become more complex if we don t have a good measure of complexity?
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Which we need for, one way that people study this in the space of biology, the people that study the origin of life or try to recreate life in the laboratory, and the more interesting one is the alien one, is when we go to other planets, how do we recognize that it s life?
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because complexity, we associate complexity, maybe some level of mobility with life.
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We have to be able to have concrete algorithms for measuring the level of complexity we see in order to know the difference between life and non-life.
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And the problem is that complexity is in the eye of the beholder.
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So let me give you an example.
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If I give you an image of the MNIST digits, right, and I flip through MNIST digits, there is obviously some structure to it because local structure, you know, neighboring pixels are correlated across the entire dataset.
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Now imagine that
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I apply a random permutation to all the pixels, a fixed random permutation.
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Now I show you those images.
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They will look, you know, really disorganized to you, more complex.
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In fact, they re not more complex in absolute terms.
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They re exactly the same as originally, right?
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And if you knew what the permutation was, you know, you could undo the permutation.
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Now, imagine I give you special glasses that undo that permutation.
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Now, all of a sudden, what looked
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complicated becomes simple.
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So if you have humans on one end and then another race of aliens that sees the universe with permutation glasses, what we perceive as simple to them is horribly complicated.
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It s probably heat.
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Yeah.
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Heat, yeah.
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Okay.
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And what they perceive as simple to us is random fluctuation.
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It s heat.
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Yeah.
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Truly in the eye of the beholder.
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Depends what kind of glasses you re wearing.
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Depends what kind of algorithm you re running in your perception system.
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So I don t think we ll have a theory of intelligence, self-organization, evolution, things like this, until we have a good handle on a notion of complexity, which we know is in the eye of the beholder.
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Yeah, it s sad to think that we might not be able to detect or interact with alien species because we re wearing different glasses.
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Because their notion of locality might be different from ours.
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Yeah.
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This actually connects with fascinating questions in physics at the moment, like modern physics, quantum physics, like, you know, questions about, like, you know, can we recover the information that s lost in a black hole and things like this, right?
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And that relies on notions of complexity, which, you know, I find is fascinating.
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Can you describe your personal quest to build an expressive electronic wind instrument, EWI?
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What is it?
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What does it take to build it?
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Well, I m a tinkerer.
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I like building things.
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I like building things with combinations of electronics and mechanical stuff.
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I have a bunch of different hobbies, but probably my first one was little, was building model airplanes and stuff like that, and I still do that to some extent.
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But also electronics, I taught myself electronics before I studied it.
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And the reason I taught myself electronics is because of music.
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My cousin was an inspiring electronic musician and he had an analog synthesizer and I was basically modifying it for him and building sequencers and stuff like that for him.
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I was in high school when I was doing this.
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That s the interest in progressive rock, like 80s.
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What s the greatest band of all time according to Yann LeCun?
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I don t know, there s too many of them, but you know, it s a combination of, you know, Mahavishnu Orchestra, Weather Report, Yes, Genesis, you know, Pre-Peter Gabriel, Gentle Giant, you know, things like that.
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Great.
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Okay, so this love of electronics and this love of music combined together.
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Right, so I was actually trained to play Baroque and Renaissance music and I played in an orchestra when I was in high school and first years of college.
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And I played the recorder, cramhorn, a little bit of oboe, you know, things like that.
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So I m a wind instrument player, but I always wanted to play improvised music, even though I don t know anything about it.
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And the only way I figured, you know, short of like learning to play a saxophone was to play electronic wind instruments.
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So they behave, you know, the fingering is similar to a saxophone, but you know, you have wide variety of sound because you control the synthesizer with it.
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So I had a bunch of those, you know, going back to the late 80s from either Yamaha or Akai.
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They re both kind of the main manufacturers of those.
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So they were classically, you know, going back several decades.
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But I ve never been completely satisfied with them because of lack of expressivity.
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And, you know, those things, you know, are somewhat expressive.
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I mean, they measure the breath pressure, they measure the lip pressure and, you know, you have various parameters.
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You can vary it with fingers, but they re not really as expressive as an acoustic instrument, right?
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You hear John Coltrane play two notes.
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And you know it s John Coltrane, you know it s got a unique sound, or Miles Davis, right?
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You can hear it s Miles Davis playing the trumpet because the sound reflects their
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you know, physiognomy, basically the shape of the vocal track kind of shapes the sound.
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So how do you do this with an electronic instrument?
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And I was many years ago, I met a guy called David Wessel.
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He was a professor at Berkeley and created the center for like, you know, music technology there.
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And he was interested in that question.
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And so I kept kind of thinking about this for many years.
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And finally, because of COVID, you know, I was at home.
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I was in my workshop.
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My workshop serves also as my kind of Zoom room and home office.
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This is in New Jersey?
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In New Jersey.
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And I started really being serious about, you know, building my own EOE instrument.
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What else is going on in that New Jersey workshop?
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Is there some crazy stuff you ve built or like left on the workshop floor, left behind?
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A lot of crazy stuff is electronics built with microcontrollers of various kinds and weird flying contraptions.
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So you still love flying.
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It s a family disease.
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My dad got me into it when I was a kid.
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He was building modular planes when he was a kid.
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He was a mechanical engineer.
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He taught himself electronics also, so he built his early radio control systems in the late 60s, early 70s.
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And so that s what got me into, I mean, he got me into kind of, you know, engineering and science and technology.
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Do you also have an interest and appreciation of flight in other forms, like with drones, quadropters, or do you, is it model airplane, the thing that s- You know, before drones were, you know, kind of a consumer product,
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You know, I built my own, you know, with also building a microcontroller with javascopes and accelerometers for stabilization, writing the firmware for it, you know, and then when it became kind of a standard thing you could buy, it was boring, you know, I stopped doing it, it was not fun anymore.
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Yeah, you were doing it before it was cool.
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Yeah.
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What advice would you give to a young person today in high school and college that
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Dreams of doing something big like Yann LeCun, like let s talk in the space of intelligence.
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Dreams of having a chance to solve some fundamental problem in the space of intelligence, both for their career and just in life, being somebody who was a part of creating something special.
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So try to get interested by big questions.
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Things like, you know, what is intelligence?
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What is the universe made of?
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What s life all about?
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Things like that.
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Like even like crazy big questions like, what s time?
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Like nobody knows what time is.
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and then learn basic things, basic methods, either from math, from physics, or from engineering.
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Things that have a long shelf life.
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If you have a choice between learning mobile programming on iPhone or quantum mechanics, take quantum mechanics.
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because you re going to learn things that you have no idea exist.
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You may never be a quantum physicist, but you will learn about path integrals, and path integrals are used everywhere.
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It s the same formula that you use for Bayesian integration and stuff like that.
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So the ideas, the little ideas within quantum mechanics, within some of these kind of more solidified fields, will have a longer shelf life.
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You ll somehow use indirectly in your work.
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Learn classical mechanics, like you learn about Lagrangians, for example.
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which is a hugely useful concept for all kinds of different things.
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Learn statistical physics because all the math that comes out for machine learning basically comes out of what s figured out by statistical physicists in the late 19th, early 20th century.
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So
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And for some of them, actually more recently, by people like Giorgio Parisi, who just got the Nobel Prize for the replica method, among other things.
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It s used for a lot of different things.
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You know, variational inference, that math comes from statistical physics.
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So a lot of those kind of, you know, basic courses, you know, if you do electrical engineering, you take signal processing, you ll learn about Fourier transforms.
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Again, something super useful.
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It s at the basis of things like graph neural nets, which is an entirely new sub area of AI, machine learning, deep learning, which I think is super promising for all kinds of applications.
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Something very promising if you re more interested in applications is the applications of AI, machine learning, and deep learning to science.
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or to science that can help solve big problems in the world.
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I have colleagues at META, at FAIR, who started this project called Open Catalyst, and it s an open project collaborative.
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And the idea is to use deep learning to help design new chemical compounds or materials that would facilitate the separation of hydrogen from oxygen.
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If you can efficiently separate oxygen from hydrogen with electricity, you solve climate change.
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It s as simple as that.
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Because you cover some random desert with solar panels and you have them work all day, produce hydrogen, and then you ship the hydrogen wherever it s needed.
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You don t need anything else.
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You have controllable power that can be transported anywhere.
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So if we have a large-scale, efficient energy storage technology, like producing hydrogen, we solve climate change.
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Here s another way to solve climate change, is figuring out how to make fusion work.
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Now, the problem with fusion is that you make a super-hot plasma, and the plasma is unstable and you can t control it.
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Maybe with deep learning, you can find controllers that will stabilize plasma and make practical fusion reactors.
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I mean, that s very speculative, but it s worth trying because the payoff is huge.
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There s a group at Google working on this led by John Platt.
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So convert as many problems in science and physics and biology and chemistry into a learnable problem and see if a machine can learn it.
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Right.
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I mean, there s properties of, you know, complex materials that we don t understand from first principle, for example.
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Right.
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So, you know, if we could design new, you know, new materials, we could make more efficient batteries, you know, we could make maybe faster electronics.
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I mean, there s a lot of things we can imagine.
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doing lighter materials for cars or airplanes and things like that, maybe better fuel cells.
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I mean, there s all kinds of stuff we can imagine.
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If we had good fuel cells, hydrogen fuel cells, we could use them to power airplanes and transportation wouldn t be, or cars, we wouldn t have a CO2 emission problem for air transportation anymore.
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So there s a lot of those things, I think, where AI, you know, can be used.
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And this is not even talking about all the sort of medicine, biology and everything like that, right?
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You know, like protein folding, you know, figuring out like how can you design your proteins that it sticks to another protein at a particular site, because that s how you design drugs in the end.
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So, you know, deep learning would be useful.
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So those are kind of, you know, would be sort of enormous progress if we could use it for that.
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Here s an example.
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If you take, this is like from recent material physics, you take a monoatomic layer of graphene, right?
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So it s just carbon on an hexagonal mesh and you make this single atom thick.
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You put another one on top, you twist them by some magic number of degrees, three degrees or something.
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it becomes superconductor.
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Nobody has any idea why.
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I want to know how that was discovered, but that s the kind of thing that machine learning can actually discover, these kinds of things.
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Well, maybe not, but there is a hint perhaps that with machine learning, we would train a system to basically be a phenomenological model of some complex emergent phenomenon, which, you know, superconductivity is one of those.
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where this collective phenomenon is too difficult to describe from first principles with the usual sort of reductionist type method.
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But we could have deep learning systems that predict the properties of a system from a description of it after being trained with sufficiently many samples.
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This guy Pascal Fouad at EPFL, he has a startup company where he basically trained a convolutional net essentially to predict the aerodynamic properties of solids.
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And you can generate as much data as you want by just running computational free dynamics, right?
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So you give like a
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a wing airfoil or something, shape of some kind, and you run computational fluid dynamics, you get, as a result, the drag and lift and all that stuff, right?
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And you can generate lots of data, train a neural net to make those predictions.
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And now what you have is a differentiable model of, let s say, drag and lift as a function of the shape of that solid.
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And so you can do background and design.
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You can optimize the shape so you get the properties you want.
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Yeah, that s incredible, that s incredible.
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And on top of all that, probably you should read a little bit of literature and a little bit of history for inspiration and for wisdom.
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Because after all, all of these technologies will have to work in the human world.
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And the human world is complicated.
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Yeah, and this is an amazing conversation.
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I m really honored that you talked with me today.
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Thank you for all the amazing work you re doing at FAIR, at Meta, and thank you for being so passionate after all these years about everything that s going on.
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You re a beacon of hope for the machine learning community.
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And thank you so much for spending your valuable time with me today.
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That was awesome.
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Thanks for having me on.
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SPEAKER_00
That was a pleasure.
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Thanks for listening to this conversation with Yann LeCun.
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To support this podcast, please check out our sponsors in the description.
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And now, let me leave you with some words from Isaac Asimov.
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Your assumptions are your windows on the world.
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Scrub them off every once in a while, or the light won t come in.
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Thank you for listening, and hope to see you next time.