François Chollet: Keras, Deep Learning, and the Progress of AI | Lex Fridman Podcast #38

The following is a conversation with Francois Chollet. He's the creator of Keras, which is an open source deep learning library that is designed to enable fast, user friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into the TensorFlow main code base a while ago. Meaning if you want to create, train and use neural networks, probably the easiest and most popular option is to use Keras inside TensorFlow. Aside from creating an exceptionally useful and popular library, Francois is also a world class AI researcher and software engineer at Google. And he's definitely an outspoken if not controversial personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This is the Artificial Intelligence podcast. If you enjoy it, subscribe on YouTube, give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter lexfriedman spelled F R I D M A N. And now here's my conversation with Francois Chollet.

You're known for not sugarcoating your opinions and speaking your mind about ideas in AI, especially on Twitter. That's one of my favorite Twitter accounts. So what's one of the more controversial ideas you've expressed online and gotten some heat for? How do you pick?

How do I pick? Yeah. No, I think if you have. If you go through the trouble of maintaining a Twitter account, you might as well speak your mind, you know. Otherwise, what's even the point? Having a Twitter account, it's like having a nice car and just leave it in the garage. Yeah. So what's one thing for which I got a lot of pushback? Perhaps that time I wrote something about the idea of intelligence explosion and I was questioning the idea and the reasoning behind this idea, and I got a lot of pushback on that. I got a lot of flack for it. So, yeah, so intelligence explorer explosion. I'm sure you're familiar with the idea, but it's the idea that if you were to build general AI problem solving algorithms, well, the problem of building such an AI that itself is a problem that could be solved by your AI, and maybe it could be solved better than what humans can do, your AI could start tweaking its own algorithm, could start being a better version of itself and so on iteratively in a recursive fashion. And so you would end up with an AI with exponentially increasing intelligence.

That's right.

And I was basically questioning this idea, first of all, because the notion of intelligence explosion uses an implicit definition of intelligence that doesn't sound quite right to me. It considers intelligence as a property of a brain that you can consider in isolation, like the height of a building, for instance. But that's not really what intelligence is. Intelligence emerges from the interaction between a brain, a body, like embodied intelligence, and an environment. And if you're missing one of these pieces, then you can actually define intelligence anymore. So just tweaking a brain to make it smarter and smarter doesn't actually make any sense to me.

So first of all, you're crushing the dreams of many people. Right. So there's. Let's look at, like, Sam Harris, actually, a lot of physicists, Max Tegmark, people who think, you know, the universe is an information processing system. Our brain is kind of an information processing system. So what's the theoretical limit? Like? It doesn't make sense that there should be some. It seems naive to think that our own brain is somehow the limit of the capabilities and this information just. I'm playing devil's advocate here, this information processing system. And then if you just scale it, if you're able to build something that's on par with the brain, you just. The process that builds it just continues and it will improve exponentially. So that. That's the logic that's used actually by almost everybody that is worried about superhuman intelligence.

Yeah.

So you're. You're trying to make. So most people who are skeptical of that are kind of like, this doesn't. Their thought process. This doesn't feel right. Like, that's for me as well. So I'm more like, it doesn't. The whole thing is shrouded in mystery where you can't really say anything concrete. But you could say, this doesn't feel right. This doesn't feel like that's how the brain works. And you're trying to, with your blog post and now making it a little more explicit. So one idea is that the brain exists alone. It exists within the environment. So you can't exponentially.

You.

You would have to somehow exponentially improve the environment and the brain together. Almost. Yeah. In order to create something that's much smarter in some kind of. Of course, we don't have a definition of intelligence.

That's correct. That's correct. I don't think if you look at very smart people today, even humans, not Even talking about AIs, I don't think their brain and the performance of their brain is the bottleneck to their expressed intelligence, to their achievements. You cannot just tweak one part of this system, like of this brain, body, environment, system, and Expect capabilities like what emerges out of this system to just explode exponentially. Because anytime you improve one part of a system with many interdependencies like this, there's a new bottleneck that arises. And I don't think even today for very smart people, their brain is not the bottleneck to the sort of problems they can solve. In fact, many very smart people today, they're not actually solving any big scientific problems. They're not Einstein, they're like Einstein but the patent clerk days. Einstein became Einstein because this was a meeting of a genius with a big problem at the right time. But maybe this meeting could have never happened and then Einstein would have just been a patent clerk. In fact, many people today are probably genius level smart, but you wouldn't know because they're not really expressing any of that.

Wow, that's brilliant. So we can think of the world, Earth, but also the universe as just as a space of problems. So all these problems and tasks are roaming it of various difficulty. And there's agents, creatures like ourselves and animals and so on that are also roaming it. And then you get coupled with a problem and then you solve it. But without that coupling you can't demonstrate your quote unquote intelligence.

Exactly. Intelligence is the meeting of great problem solving capabilities with a great problem. And if you don't have the problem, you, you don't really express any intelligence. All you're left with is potential intelligence, like the performance of your brain or how high your IQ is, which in itself is just a number. Right.

So you mentioned problem solving capacity. What do you think of as problem solving capacity? Can you try to define intelligence? Like what does it mean to be more or less intelligent? Is it completely coupled to a particular problem or is there something a little bit more universal?

Yeah, I do believe all intelligence is specialized intelligence, even human intelligence has some degree of generality. Well, all intelligent systems have some degree of generality, but they're always specialized in one category of problems. So the human intelligence is specialized in the human experience. And that shows at various levels, that shows in some prior knowledge that's innate, that we have at birth knowledge about things like agents, goal driven behavior, visual priors about what makes an object, priors about time and so on. That shows also in the way we learn. For instance, it's very, very easy for us to pick up language. It's very, very easy for us to learn certain things because we are basically hard coded to learn them. And we are specialized in solving certain kinds of problems. And we are quite useless when it comes to Other kinds of problems. For instance, we are not really designed to handle very long term problems. We have no capability of seeing the very long term. We don't have very much working memory.

So how do you think about long term? You think long term planning. We're talking about scale of years, millennia. What do you mean by long term? We're not very good.

Well, human intelligence is specialized in the human experience. And human experience is very short. One lifetime is short. Even within one lifetime. We have a very hard time envisioning things on a scale of years. Like it's very difficult to project yourself at a scale of five years, at a scale of 10 years and so on. We can solve only fairly narrowly scoped problems. So when it comes to solving bigger problems, larger scale problems, we are not actually doing it on an individual level. So it's not actually our brain doing it. We have this thing called civilization, right, which is itself a sort of problem solving system, a sort of artificial intelligence system. And it's not running on one brain. It's running on a network of brains. In fact, it's running on much more than a network of brains. It's running on a lot of infrastructure like books and computers and the Internet and human institutions and so on. And that is capable of handling problems on a much greater scale than any individual human. If you look at computer science, for instance, that's an institution that solves problems and it is superhuman. It operates on a greater scale. It can solve much bigger problems than an individual human could. And science itself, science as a system, as an institution, is a kind of artificially intelligent problem solving algorithm. That is superhuman.

Yeah, it's at least computer science is like a theorem prover At a scale of thousands, maybe hundreds of thousands of human beings at that scale, what do you think is intelligent agent? So there's us humans at the individual level. There is millions, maybe billions of bacteria in our skin. There is. That's at the smaller scale. You can even go to the particle level as systems that behave, you can say intelligently in some ways. And then you can look at Earth as a single organism. You can look at our galaxy and even the universe as a single organism. Do you think, how do you think about scale and defining intelligent systems? And we're here at Google, there is millions of devices doing computation in a distributed way. How do you think about intelligence in terms of scale?

You can always characterize anything as a system. I think people who talk about things like intelligence explosion tend to focus on one agent. Is basically one brain. Like one brain considered in isolation, like a brain, a jar that's controlling a body in a very top to bottom kind of fashion. And that body is pursuing goals into an environment. So it's a very hierarchical view. You have the brain at the top of the pyramid, then you have the body just plainly receiving orders, and then the body is manipulating objects in environment and so on. So everything is subordinate to this one thing, this epicenter, which is the brain. But in real life, intelligent agents don't really work like this, right? There is no strong delimitation between the brain and the body. To start with, you have to look not just at the brain, but at the nervous system. But then the nervous system and the body are not really two separate entities. So you have to look at an entire animal as one agent. But then you start realizing as you observe an animal over any length of time, that a lot of the intelligence of an animal is actually externalized. That's especially true for humans. A lot of our intelligence is externalized. When you write down some notes, that is externalized intelligence. When you write a computer program, you are externalizing cognition. So it's externalized in books, it's externalized in computers, the Internet, in other humans, it's externalized in language and so on. So there is no hard delimitation of what makes an intelligent agent. It's all about context.

Okay, but AlphaGo is better at go than the best human player. You know, there's levels of skill here. So do you think there's such a ability as such a concept as a intelligence explosion in a specific task and then. Well, yeah. Do you think it's possible to have a category of tasks on which you do have something like an exponential growth of ability to solve that particular problem?

I think if you consider specific vertical, it's probably possible to some extent. I also don't think we have to speculate about it because we have real world examples of recursively self improving intelligent systems. For instance, science is a problem solving system, a knowledge generation system, like a system that experiences the world in some sense and then gradually understands it and can act on it. And that system is superhuman. And it is clearly recursively self improving because science feeds into technology. Technology can be used to build better tools, better computers, better instrumentation and so on, which in turn can make science faster. So science is probably the closest thing we have today to a recursively self improving superhuman AI. And you can just observe is scientific progress today exploding, which itself is an interesting question. And you can use that as a basis to try to understand what will happen with a superhuman AI that has science like behavior?

Let me linger on it a little bit more. What is your intuition, why an intelligence explosion is not possible? Like taking the scientific all the scientific revolutions. Why can't we slightly accelerate that process?

So you can absolutely accelerate any problem solving process. So recursive self improvement is absolutely a real thing. But what happens with recursively self improving system is typically not explosion, because no system exists in isolation. And so tweaking one part of the system means that suddenly another part of the system becomes a bottleneck. And if you look at science, for instance, which is clearly recursively self improving, clearly a problem solving system, scientific progress is not actually exploding. If you look at science, what you see is the picture of a system that is consuming an exponentially increasing amount of resources, but it's having a linear output in terms of scientific progress. And maybe that will seem like a very strong claim. Many people are actually saying that scientific progress is exponential. But when they're claiming this, they're actually looking at indicators of resource consumption by science. For instance, the number of papers being published, the number of patents being filed, and so on, which are just completely credited with how many people are working on science today. So it's actually an indicator of resource consumption. But what you should look at is the output is progress in terms of the knowledge that science generates, in terms of the scope and significance of the problems that we solve. And some people have actually been trying to measure that, like Michael Nielsen, for instance. He had a very nice paper, I think that was last year about it. So his approach to measure scientific progress was to look at the timeline of scientific discoveries over the past 150 years. And for each major discovery, ask a panel of experts to rate the significance of the discovery. And if the output of sciences institutions were exponential, you would expect the temporal density of significance to go up exponentially. Maybe because there's a faster rate of discoveries, maybe because the discoveries are increasingly more important. And what actually happens if you plot this temporal density of significance measured in this way, is that you see very much a flat graph. You see a flat graph across all disciplines, across physics, biology, medicine and so on. And it actually makes a lot of sense if you think about it, because think about the progress of physics 110 years ago, right? It was a time of crazy change about the progress of technology 170 years ago, when we started replacing horses with cars, when we started having electricity and so on. It was a time of incredible change. And today is also a time of very Very fast change. But it would be an unfair characterization to say that today technology and science are moving way faster than they did 50 years ago or 100 years ago. And if you do try to rigorously plot the temporal density of the significance, you do see very flat curves.

That's fascinating.

And you can check out the paper that Michael Nielsen had about this idea. And so the way I interpret it is as you make progress in a given field or in a given subfield of science, it becomes exponentially more difficult to make further progress. Like the very first person to work on information theory, if you enter a new field and it's still the very early years, there's a lot of low hanging fruit you can pick.

That's right. Yeah.

But the next generation of researchers is going to have to dig much harder actually to make smaller discoveries, probably larger number of smaller discoveries, and to achieve the same amount of impact, you're going to need a much greater headcount. And that's exactly the picture you're seeing with science, that the number of scientists and engineers is in fact increasing exponentially. The amount of computational resources that are available to science is increasing exponentially and so on. So the resource consumption of science is exponential, but the output in terms of progress, in terms of significance is linear. And the reason why is because, and even though science is regressively self improving, meaning that scientific progress turns into technological progress, which in turn helps science. If you look at computers, for instance, are a product of science, and computers are tremendously useful in speeding up science. The Internet, same thing. The Internet is a technology that's made possible by very recent scientific advances and itself because it enables scientists to network, to communicate, to exchange papers and ideas much faster. It is a way to speed up scientific progress. So even though you're looking at a recursively self improving system, it is consuming exponentially more resources to produce the same amount of problem solving.

Very much so this is a fascinating way to paint it and certainly that holds for the deep learning community.

Right.

If you look at the temporal, what did you call it? The temporal density of significant ideas. If you look at, in deep learning, I think I'd have to think about that. But if you really look at significant ideas in deep learning, there might even be decreasing.

So I do believe the per paper significance is decreasing, but the amount of papers is still today exponentially increasing. So I think if you look at an aggregate, my guess is that you would see linear progress. Linear progress. If you were to sum the significance of all papers, you would see roughly in your Progress. And if you. In my opinion, it is not a coincidence that you're seeing linear progress in science despite exponential resource consumption. I think the resource consumption is dynamically adjusting itself to maintain linear progress. Because we as a community expect linear progress, meaning that if we start investing less and seeing less progress, it means that suddenly there are some lower hanging fruits that become available and someone's going to step up and pick them. Right. So it's very much like a market for discoveries and ideas.

But there's another fundamental part which you're highlighting, which as a hypothesis, as science, or like the space of ideas, any one path you travel down, it gets exponentially more difficult to get new, to develop new ideas. And your sense is that's going to hold across our mysterious universe?

Yes. Well, exponential progress triggers exponential friction. So that if you tweak one part of the system, suddenly some other part becomes a bottleneck. For instance, let's say you develop some device that measures its own acceleration and then it has some engine and it outputs even more acceleration in proportion of its own acceleration, and you drop it somewhere. It's not going to reach infinite speed because it exists in a certain context. So the air around it is going to generate friction and it's going to block it at some top speed. And even if you were to consider the broader context and lift the bottleneck there, like the bottleneck of friction, then some other part of the system would start stepping in and creating exponential friction, maybe the speed of light. And this definitely holds true when you look at the problem solving algorithm that is being run by science as an institution, science as a system. As you make more and more progress despite having this recursive self improvement component, you are encountering exponential friction. The more researchers you have working on different ideas, the more overhead you have in terms of communication across researchers. If you look at, you were mentioning quantum mechanics. Well, if you want to start making significant discoveries today, significant progress in quantum mechanics, there is an amount of knowledge you have to ingest, which is huge. So there's a very large overhead to even start to contribute. There's a large amount of overhead to synchronize across researchers and so on. And of course the significant practical experiments are going to require exponentially expensive equipment because the easier ones have already been run. Right.

So in your senses there is no way escaping, there's no way of escaping this kind of friction with artificial intelligence systems.

Yeah, no, I think science is a very good way to model what would happen with a superhuman recursivity, self improving AI.

That makes sense.

That's my intuition. It's not like a mathematical proof of anything. That's not my point. I'm not trying to prove anything. I'm just trying to make an argument to question the narrative of intelligence explosion, which is quite a dominant narrative. And you do get a lot of pushback if you go against it. Because for many people AI is not just a subfield of computer science, it's more like a belief system. Like this belief that the world is headed towards an event, the singularity, past which AI will go exponential very much and the world will be transformed and humans will become obsolete. And if you go against this narrative, because it is not really a scientific argument, but more of a belief system, it is part of the identity of many people. If you go against this narrative, it's like you're attacking the identity of people who believe in it. It's almost like saying God doesn't exist or something. So you do get a lot of pushback if you try to question his ideas.

First of all, I believe most people, they might not be as eloquent or explicit as you're being, but most people in computer science, or most people who actually have built anything that you could call AI, quote unquote, would agree with you. They might not be describing in the same kind of way. It's more. So the pushback you're getting is from people who get attached to the narrative from not from a place of science, but from a place of imagination.

That's correct. That's correct.

So why do you think that's so appealing? Because the usual dreams that people have when you create a super intelligent system pass a singularity that what people imagine is somehow always destructive. Do you have, if you were put on your psychology hat, why is it so appealing to imagine the ways that all of human civilization will be destroyed?

I think it's a good story. You know, it's a good story and very interestingly it mirrors religious stories, religious mythology. If you look at the mythology of most civilizations, it's about the world being headed towards some final event in which the world will be destroyed and some new world order will arise that will be mostly spiritual, like the apocalypse followed by paradise probably. It's a very appealing story on a fundamental level. And we all need stories. We all need stories to structure the way we see the world, especially at timescales that are beyond our ability to make predictions.

So on a more serious non exponential explosion question, do you think there will be a time when we'll create something like human level intelligence or intelligent systems that will make you sit back and be just surprised at damn how smart this thing is that doesn't require exponential growth or exponential improvement, but what's your sense of the timeline and so on that where you'll be really surprised at certain capabilities and we'll talk about limitations in deep learning. So when do you do you think in your lifetime you'll be really Damn surprised?

Around 2013, 2014? I was many times surprised by the capabilities of deep learning. Actually, that was before we had assessed exactly what deep learning could do and could not do. And it felt like a time of immense potential. And then we started narrowing it down. But I was very surprised. I would say it has already happened.

Was there a moment, there must have been a day in there where your surprise was almost bordering on the belief of the narrative that we just discussed. Was there a moment? Because you've written quite eloquently about the limits of deep learning. Was there a moment that you thought that maybe deep learning is limitless?

No, I don't think I've ever believed this. What was really shocking is that it worked. Right?

It worked at all.

Yeah, yeah.