The following is a conversation with David Silver, who leads the Reinforcement Learning

Research Group at DeepMind, and was the lead researcher on AlphaGo, AlphaZero, and CoLED,

the AlphaStar and MuZero efforts, and a lot of important work in reinforcement learning

in general.

I believe AlphaZero is one of the most important accomplishments in the history of artificial

intelligence, and David is one of the key humans who brought AlphaZero to life together

with a lot of other great researchers at DeepMind.

He's humble, kind, and brilliant.

We were both jet lagged, but didn't care and made it happen.

It was a pleasure and truly an honor to talk with David.

This conversation was recorded before the outbreak of the pandemic.

For everyone feeling the medical, psychological, and financial burden of this crisis, I'm

sending love your way.

Stay strong, or in this together, we'll beat this thing.

This is the Artificial Intelligence Podcast.

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And now here's my conversation with David Silver.

What was the first program you ever written and what programming language do you remember?

I remember very clearly, yeah, my parents brought home this BBC model B microcomputer.

It was just this fascinating thing to me.

I was about seven years old and couldn't resist just playing around with it.

So I think first program ever was writing my name out in different colors and getting

it to loop and repeat that.

And there was something magical about that, which just led to more and more.

How did you think about computers back then?

The magical aspect of it, that you can write a program and there's this thing that you

just gave birth to that's able to create visual elements and live in its own.

Or did you not think of it in those romantic notions?

Was it more like, oh, that's cool.

I can solve some puzzles.

It was always more than solving puzzles.

It was something where there was this limitless possibilities once you have a computer in

front of you.

You can do anything with it.

I used to play with Lego with the same feeling.

You can make anything you want out of Lego, but even more so with a computer.

You're not constrained by the amount of kit you've got.

And so I was fascinated by it and started pulling out the user guide and the advanced

user guide and then learning.

So I started in basic and then later 6502.

My father also became interested in this machine and gave up his career to go back to school

and study for a master's degree in artificial intelligence, funnily enough, at Essex University

when I was seven.

So I was exposed to those things at an early age.

He showed me how to program in Prologue and do things like querying your family tree.

And those are some of my earliest memories of trying to figure things out on a computer.

Those are the early steps in computer science programming, but when did you first fall in

love with artificial intelligence or with the ideas, the dreams of AI?

I think it was really when I went to study at university.

So I was an undergrad at Cambridge and studying computer science.

And I really started to question, you know, what really are the goals?

What's the goal?

Where do we want to go with computer science?

And it seemed to me that the only step of major significance to take was to try and recreate

something akin to human intelligence.

If we could do that, that would be a major leap forward.

And that idea certainly wasn't the first to have it, but it, you know, nestled within

me somewhere and became like a bug, you know, I really wanted to crack that problem.

So you thought it was like, you had a notion that this is something that human beings can

do, that it is possible to create an intelligent machine?

Well, I mean, unless you believe in something metaphysical, then what are our brains doing?

Well, at some level, their information processing systems, which are able to take whatever information

is in there, transform it through some form of program and produce some kind of output,

which enables that human being to do all the amazing things that they can do in this incredible

world.

So, so then do you remember the first time you've written a program that, because you

also had an interest in games, do you remember the first time you were in a program that

beat you in a game?

That or beat you at anything sort of achieved super David Silver level performance?

So I used to work in the games industry.

So for five years, I programmed games for my first job.

So it was an amazing opportunity to get involved in a startup company.

And so I was involved in building AI at that time.

And so for sure, there was a sense of building handcrafted what people used to call AI in

the games industry, which I think is not really what we might think of as AI in its fuller

sense, but something which is able to take actions in a way which makes things interesting

and challenging for the human player.

And at that time, I was able to build these handcrafted agents, which in certain limited

cases could do things which were able to do better than me, but mostly in these kind of

twitch like scenarios where they were able to do things faster or because they had some

pattern which was able to exploit repeatedly.

I think if we're talking about real AI, the first experience for me came after that when

I realized that this path I was on wasn't taking me towards, it wasn't dealing with

that bug which I still had inside me to really understand intelligence and try and solve it.

Everything people were doing in games was short term fixes rather than long term vision.

And so I went back to study for my PhD, which was finally enough trying to apply reinforcement

learning to the game of Go.

And I built my first Go program using reinforcement learning, a system which would by trial and

error play against itself and was able to learn which patterns were actually helpful

to predict whether it was going to win or lose the game and then choose the moves that

led to the combination of patterns that would mean that you're more likely to win.

And that system, that system beat me.

And how did that make you feel?

Make me feel good.

I mean, it's a mix of a sort of excitement and was there a tinge of sort of like almost

like a fearful awe?

You know, it's like in 2001 Space Odyssey kind of realizing that you've created something

that's achieved human level intelligence in this one particular little task.

And in that case, I suppose the neural networks weren't involved.

There were no neural networks in those days.

This was pre deep learning revolution, but it was a principled self learning system based

on a lot of the principles which people still use in deep reinforcement learning.

How did I feel?

I think I found it immensely satisfying that a system which was able to learn from first

principles for itself was able to reach the point that it was understanding this domain

better than I could and able to outwit me.

I don't think it was a sense of awe.

It was a sense that satisfaction that something I felt should work had worked.

So to me, Alpha Go, and I don't know how else to put it, but to me, Alpha Go and Alpha

Go Zero mastering the game of Go is, again, to me, the most profound and inspiring moment

in the history of artificial intelligence.

So you're one of the key people behind this achievement.

And I'm Russian, so I really felt the first sort of seminal achievement when deep blue

be Garakasparov in 1997.

So as far as I know, the AI community at that point largely saw the game of Go as unbeatable

in AI using the sort of the state of the art to brute force methods, search methods.

Even if you consider at least the way I saw it, even if you consider arbitrary exponential

scaling of compute, Go would still not be solvable, hence why it was thought to be impossible.

So given that the game of Go was impossible to master, when was the dream for you?

You just mentioned your PhD thesis of building the system that plays Go.

When was the dream for you that you could actually build a computer program that achieves

world class, not necessarily beats the world champion, but achieves that kind of level

of playing Go?

First of all, thank you.

That was very kind words.

And funnily enough, I just came from a panel where I was actually in a conversation with

Garakasparov and Murray Campbell, who was the author of Deep Blue.

And it was their first meeting together since the match, so that just occurred yesterday.

So I'm literally fresh from that experience.

So these are amazing moments when they happen, but where did it all start?

Well, for me, it started when I became fascinated in the game of Go.

So Go, for me, I've grown up playing games, I've always had a fascination in board games.

I played chess as a kid, I played Scrabble as a kid.

When I was at university, I discovered the game of Go, and to me, it just blew all of

those other games out of the water, it was just so deep and profound in its complexity

with endless levels to it.

What I discovered was that I could devote endless hours to this game, and I knew in

my heart of hearts that no matter how many hours I would devote to it, I would never

become a grandmaster.

Or there was another path, and the other path was to try and understand how you could get

some other intelligence to play this game better than I would be able to.

And so even in those days, I had this idea that, what if it was possible to build a program

that could crack this?

And as I started to explore the domain, I discovered that this was really the domain

where people felt deeply that if progress could be made in Go, it would really mean

a giant leap forward for AI.

It was the challenge where all other approaches had failed.

This is coming out of the era you mentioned, which was in some sense the golden era for

the classical methods of AI, like heuristic search.

In the 90s, they all fell one after another, not just chess with deep blue, but checkers,

batgammon, Othello.

There were numerous cases where systems built on top of heuristic search methods with these

high performance systems had been able to defeat the human world champion in each of

those domains.

And yet in that same time period, there was a million dollar prize available for the game

of Go, for the first system to be a human professional player.

And at the end of that time period, at year 2000, when the prize expired, the strongest

Go program in the world was defeated by a nine year old child.

When that nine year old child was giving nine free moves to the computer at the start of

the game to try and even things up.

And computer Go expert beat that same strongest program with 29 handicap stones, 29 free moves.

So that's what the state of affairs was when I became interested in this problem in around

2003 when I started working on computer Go.

There was nothing.

There was just very, very little in the way of progress towards meaningful performance

again at anything approaching human level.

And so people, it wasn't through lack of effort, people who tried many, many things.

And so there was a strong sense that something different would be required for Go than had

been needed for all of these other domains where AI had been successful.

And maybe the single clearest example is that Go, unlike those other domains, had this kind

of intuitive property that a Go player would look at a position and say, hey, here's this

mess of black and white stones.

But from this mess, oh, I can predict that this part of the board has become my territory,

this part of the board has become your territory, and I've got this overall sense that I'm going

to win and that this is about the right move to play.

And that intuitive sense of judgment of being able to evaluate what's going on in a position,

it was pivotal to humans being able to play this game and something that people had no

idea how to put into computers.

So this question of how to evaluate a position, how to come up with these intuitive judgments

was the key reason why Go was so hard in addition to its enormous search space and the reason

why methods which had succeeded so well elsewhere failed in Go.

And so people really felt deep down that in order to crack Go, we would need to get something

akin to human intuition.

And if we got something akin to human intuition, we'd be able to solve many, many more problems

in AI.

So for me, that was the moment where it's like, okay, this is not just about playing

the game of Go.

This is about something profound.

And it was back to that bug which had been itching me all those years.

This is the opportunity to do something meaningful and transformative and I guess a dream was

born.

That's a really interesting way to put it.

So almost this realization that you need to find formulate Go as a kind of a prediction

problem versus a search problem was the intuition.

I mean, maybe that's the wrong crude term, but to give it the ability to kind of intuit

things about positional structure of the board.

Now, okay, but what about the learning part of it?

Did you have a sense that you have to, that learning has to be part of the system?

Again, something that hasn't really as far as I think, except with TD Gammon and the

90s with RL a little bit, hasn't been part of those daily art game playing systems.

So I strongly felt that learning would be necessary and that's why my PhD topic back

then was trying to apply reinforcement learning to the game of Go.

And not just learning of any type, but I felt that the only way to really have a system

to progress beyond human levels of performance wouldn't just be to mimic how humans do it,

but to understand for themselves.

And how else can a machine hope to understand what's going on, except through learning?

If you're not learning, what else are you doing?

Well, you're putting all the knowledge into the system and that just feels like something

which decades of AI have told us is maybe not a dead end, but certainly has a ceiling

to the capabilities.

It's known as the knowledge acquisition bottleneck that the more you try to put into something,

the more brittle the system becomes.

And so you just have to have learning.

You have to have learning.

That's the only way you're going to be able to get a system which has sufficient knowledge

in it, millions and millions of pieces of knowledge, billions, trillions of a form that

can actually apply for itself and understand how those billions and trillions of pieces

of knowledge can be leveraged in a way which will actually lead it towards its goal without

conflict or other issues.

Yeah.

I mean, if I put myself back in that time, I just wouldn't think like that without a good

demonstration of RL.

I would think more in the symbolic AI, like the not learning, but sort of a simulation

of knowledge base, like a growing knowledge base, but it would still be sort of pattern

based, like basically have little rules that you kind of assemble together into a large

knowledge base.

Well, in a sense, that was the state of the art back then.

So if you look at the Go programs which had been competing for this prize I mentioned,

they were an assembly of different specialized systems, some of which used huge amounts of

human knowledge to describe how you should play the opening, how you should all the different

patterns that were required to play well in the game of Go, end game theory, combinatorial

game theory, and combined with more principled search based methods, which we're trying

to solve for particular sub parts of the game, like life and death, connecting groups together,

all these amazing sub problems that just emerge in the game of Go, there were different pieces

all put together into this collage, which together would try and play against a human.

And although not all of the pieces were handcrafted, the overall effect was nevertheless still

brittle and it was hard to make all these pieces work well together.

And so really, what I was pressing for and the main innovation of the approach I took

was to go back to first principles and say, well, let's back off that and try and find

a principled approach where the system can learn for itself.

Just from the outcome, like, you know, learn for itself, if you try something, did that

help or did it not help?

And only through that procedure can you arrive at knowledge which is verified, the system

has to verify it for itself, not relying on any other third party to say this is right

or this is wrong.

And so that principle was already very important in those days that unfortunately we were missing

some important pieces back then.

So before we dive into maybe discussing the beauty of reinforcement learning, let's take

a step back, we kind of skipped it a bit, but the rules of the game of Go, the elements

of it perhaps contrasting to chess that sort of you really enjoyed as a human being and

also that make it really difficult as a AI machine learning problem.

So the game of Go has remarkably simple rules, in fact, so simple that people have speculated

that if we were to meet alien life at some point that we wouldn't be able to communicate

with them, but we would be able to play a game of Go with them, probably have discovered

the same ruleset, so the game is played on a 19 by 19 grid and you play on the intersections

of the grid and the players take turns.

And the aim of the game is very simple, it's to surround as much territory as you can as

many of these intersections with your stones and to surround more than your opponent does.

And the only nuance to the game is that if you fully surround your opponent's piece,

then you get to capture it and remove it from the board and it counts as your own territory.

Now from those very simple rules, immense complexity arises, there's kind of profound

strategies in how to surround territory, how to kind of trade off between making solid

territory yourself now, compared to building up influence that will help you acquire territory

later in the game, how to connect groups together, how to keep your own groups alive, which patterns

of stones are most useful compared to others.

There's just immense knowledge and human Go players have played this game for, it was

discovered thousands of years ago and human Go players have built up this immense knowledge

base over the years.

It's studied very deeply and played by something like 50 million players across the world, mostly

in China, Japan and Korea, where it's an important part of the culture, so much so that it's

considered one of the four ancient arts that was required by Chinese scholars.

There's a deep history there.

But there's interesting quality, so if I were to compare it to chess, chess is in the same

way as it is in Chinese culture for Go, and chess in Russia is also considered one of

the sacred arts.

So if we contrast Go with chess, there's interesting qualities about Go, maybe you can correct

me if I'm wrong, but the evaluation of a particular static board is not as reliable, like you

can't, in chess you can kind of assign points to the different units, and it's kind of a

pretty good measure of who's winning, who's losing.

It's not so clear to do some Go.

Yeah, so in the game of Go, you find yourself in a situation where both players have played

the same number of stones, actually captures at strong level of play happen very rarely,

which means that at any moment in the game you've got the same number of white stones

and black stones, and the only thing which differentiates how well you're doing is this

intuitive sense of where are the territories ultimately going to form on this board?

And if you look at the complexity of a real Go position, it's mind boggling that question

of what will happen in 300 moves from now when you see just a scattering of 20 white

and black stones intermingled, and so that challenge is the reason why position evaluation

is so hard in Go compared to other games.

In addition to that, it has an enormous search space, so there's around 10 to 170 positions

in the game of Go, that's an astronomical number, and that search space is so great

that traditional heuristic search methods that were so successful and things like Deep

Blue and chess programs just kind of fall over in Go.

So at which point did reinforcement learning enter your life, your research life, your way

of thinking?

We just talked about learning, but reinforcement learning is a very particular kind of learning,

one that's both philosophically sort of profound, but also one that's pretty difficult to get

to work as if we look back in the early days.

So when did that enter your life and how did that work progress?

So I had just finished working in the games industry at this startup company, and I took

a year out to discover for myself exactly which path I wanted to take, I knew I wanted

to study intelligence, but I wasn't sure what that meant at that stage, I really didn't

feel I had the tools to decide on exactly which path I wanted to follow.

So during that year, I read a lot, and one of the things I read was Sutton and Bartow,

the sort of seminal textbook on an introduction to reinforcement learning, and when I read

that textbook, I just had this resonating feeling that this is what I understood intelligence

to be.

And this was the path that I felt would be necessary to go down to make progress in AI.

So I got in touch with Rich Sutton and asked him if he would be interested in supervising

me on a PhD thesis in computer go, and he basically said that if he's still alive he'd

be happy to, but unfortunately he'd been struggling with very serious cancer for some years, and

he really wasn't confident at that stage that he'd even be around to see the end event.

But fortunately that part of the story worked out very happily, and I found myself out there

in Alberta, they've got a great games group out there with a history of fantastic work

in board games as well, as Rich Sutton, the father of RL, so it was the natural place

for me to go in some sense to study this question.

And the more I looked into it, the more strongly I felt that this wasn't just the path to progress

in computer go, but really this was the thing I'd been looking for.

This was really an opportunity to frame what intelligence means, like what are the goals

of AI in a single, clear problem definition such that if we're able to solve that clear

single problem definition, in some sense we've cracked the problem of AI.

So to you, reinforcement learning ideas, at least sort of echoes of it, would be at the

core of intelligence.

Is it the core of intelligence?

And if we ever create a human level intelligence system, it would be at the core of that kind

of system.

Let me say it this way, that I think it's helpful to separate out the problem from the solution.

So I see the problem of intelligence, I would say it can be formalized as the reinforcement

learning problem, and that that formalization is enough to capture most if not all of the

things that we mean by intelligence, that they can all be brought within this framework

and gives us a way to access them in a meaningful way that allows us as scientists to understand

intelligence and us as computer scientists to build them.

And so in that sense, I feel that it gives us a path, maybe not the only path, but a

path towards AI.

And so do I think that any system in the future that's solved AI would have to have RL within

it?

Well, I think if you ask that, you're asking about the solution methods.

I would say that if we have such a thing, it would be a solution to the RL problem.

Now, what particular methods have been used to get there?

Well, we should keep an open mind about the best approaches to actually solve any problem.

And the things we have right now for reinforcement learning, maybe I believe they've got a lot

of legs, but maybe we're missing some things.

Maybe there's going to be better ideas.

I think we should keep, let's remain modest, and we're at the early days of this field,

and there are many amazing discoveries ahead of us.

For sure.

The specifics, especially of the different kinds of RL approaches currently, there could

be other things that fall into the very large umbrella of RL.

But if it's okay, can we take a step back and ask the basic question of what is, do

you, reinforcement learning?

So reinforcement learning is the study and the science and the problem of intelligence

in the form of an agent that interacts with an environment.

So the problem you're trying to solve is represented by some environment, like the world in which

that agent is situated.

And the goal of RL is clear, that the agent gets to take actions.

Those actions have some effect on the environment, and the environment gives back an observation

to the agent saying, you know, this is what you see or sense.

And one special thing which it gives back is called the reward signal, how well it's

doing in the environment.

And the reinforcement learning problem is to simply take actions over time so as to maximize

that reward signal.

So a couple of basic questions, what types of RL approaches are there?

So I don't know if there's a nice brief inwards way to paint a picture of sort of value based,

model based, policy based reinforcement learning.

Yeah.

So now if we think about, okay, so there's this ambitious problem definition of RL.

It's really, you know, it's truly ambitious.

It's trying to capture and encircle all of the things in which an agent interacts with

an environment and say, well, how can we formalize and understand what it means to crack that?

Now let's think about the solution method.

Well, how do you solve a really hard problem like that?

Well, one approach you can take is to decompose that very hard problem into pieces that work

together to solve that hard problem.

And so you can kind of look at the decomposition that's inside the agent's head, if you like,

and ask, well, what form does that decomposition take?

And some of the most common pieces that people use when they're kind of putting this system,

the solution method together, some of the most common pieces that people use are whether

or not that solution has a value function.

That means is it trying to predict, explicitly trying to predict how much reward it will get

in the future?

Does it have a representation of a policy?

That means something which is deciding how to pick actions.

Is that decision making process explicitly represented?

And is there a model in the system?

Is there something which is explicitly trying to predict what will happen in the environment?

And so those three pieces are, to me, some of the most common building blocks.

And I understand the different choices in RL as choices of whether or not to use those

building blocks when you're trying to decompose the solution.

Should I have a value function represented?

Should I have a policy represented?

Should I have a model represented?

And there are combinations of those pieces and, of course, other things that you could

add into the picture as well.

But those three fundamental choices give rise to some of the branches of RL with which

we are very familiar.

And so those, as you mentioned, there is a choice of what's specified or modeled explicitly.

And the idea is that all of these are somehow implicitly learned within the system.

So it's almost a choice of how you approach a problem.

Do you see those as fundamental differences or are these almost like small specifics,

like the details of how you solve the problem, but they're not fundamentally different from

each other?

I think the fundamental idea is maybe at the higher level.

The fundamental idea is the first step of the decomposition is really to say, well,

how are we really going to solve any kind of problem where you're trying to figure out

how to take actions and just from this stream of observations, you know, you've got some

agent situated in its sensory motor stream and getting all these observations in, getting

to take these actions.

And what should it do?

How can you even broach that problem?

You know, maybe the complexity of the world is so great that you can't even imagine how

to build a system that would understand how to deal with that.

And so the first step of this decomposition is to say, well, you have to learn.

The system has to learn for itself.

And so note that the reinforcement learning problem doesn't actually stipulate that you

have to learn.

Like you could maximize your rewards without learning, it would just say wouldn't do a

very good job of it.

So learning is required because it's the only way to achieve good performance in any sufficiently

large and complex environment.

So that's the first step.

And so that step gives commonality to all of the other pieces, because now you might

ask, well, what should you be learning?

What does learning even mean?

You know, in this sense, you know, learning might mean, well, you're trying to update

the parameters of some system, which is then the thing that actually picks the actions.

And those parameters could be representing anything, they could be parametrizing a value

function or a model or a policy.

And so in that sense, there's a lot of commonality in that whatever is being represented there

is the thing which is being learned and it's being learned with the ultimate goal of maximizing

rewards.

But the way in which you decompose the problem is really what gives the semantics to the

whole system.

You're trying to learn something to predict well, like a value function or a model, or

you're learning something to perform well, like a policy.

And the form of that objective is kind of giving the semantics to the system.

And so it really is, at the next level down, a fundamental choice.

And we have to make those fundamental choices as system designers or enable our algorithms

to be able to learn how to make those choices for themselves.

So then the next step you mentioned, the very first thing you have to deal with is can you

even take in this huge stream of observations and do anything with it?

So the natural next basic question is what is the, what is deeper enforcement learning

and what is this idea of using neural networks to deal with this huge incoming stream?

So amongst all the approaches for reinforcement learning, deep reinforcement learning is

one family of solution methods that tries to utilize powerful representations that are

offered by neural networks to represent any of these different components of the solution,

of the agent.

Like whether it's the value function or the model or the policy, the idea of deep learning

is to say, well, here's a powerful toolkit that's so powerful that it's universal in

the sense that it can represent any function and it can learn any function.

And so if we can leverage that universality, that means that whatever we need to represent

for our policy or for our value function for a model, deep learning can do it.

So that deep learning is one approach that offers us a toolkit that has no ceiling to

its performance, that as we start to put more resources into the system, more memory and

more computation and more data, more experience, more interactions with the environment, that

these are systems that can just get better and better and better at doing whatever the

job is they've asked them to do.

Whatever we've asked that function to represent, it can learn a function that does a better

and better job of representing that knowledge, whether that knowledge be estimating how well

you're going to do in the world, the value function, whether it's going to be choosing

what to do in the world, the policy, or whether it's understanding the world itself, what's

going to happen next, the model.

Nevertheless, the fact that neural networks are able to learn incredibly complex representations

that allow you to do the policy, the model or the value function is at least to my mind

exceptionally beautiful and surprising.

Was it surprising to you?

Can you still believe it works as well as it does?

Do you have good intuition about why it works at all and works as well as it does?

I think let me take two parts to that question.

I think it's not surprising to me that the idea of reinforcement learning works because

in some sense, I feel it's the only thing which can ultimately.

I feel we have to address it and there must be success as possible because we have examples

of intelligence.

It must at some level be able to possible to acquire experience and use that experience

to do better in a way which is meaningful to environments of the complexity that humans

can deal with.

It must be.

Am I surprised that our current systems can do as well as they can do?

I think one of the big surprises for me and a lot of the community is really the fact

that deep learning can continue to perform so well despite the facts that these neural

networks that they're representing have these incredibly nonlinear kind of bumpy surfaces

which to our kind of low dimensional intuitions make it feel like surely you're just going

to get stuck and learning will get stuck because you won't be able to make any further progress.

Yet, the big surprise is that learning continues and these what appear to be local optima turn

out not to be because in high dimensions when we make really big neural nets, there's always

a way out and there's a way to go even lower and then you're still not in a local optima

because there's some other pathway that will take you out and take you lower still.

No matter where you are, learning can proceed and do better and better and better without

bound.

That is a surprising and beautiful property of neural nets which I find elegant and beautiful

and somewhat shocking that it turns out to be the case.

As you said, which I really like, to our low dimensional intuitions, that's surprising.

Yeah.

We're very tuned to working within a three dimensional environment and so to start to

visualize what a billion dimensional neural network surface that you're trying to optimize

over, what that even looks like is very hard for us and so I think that really if you try

to account for essentially the AI winter where people gave up on neural networks, I think

it's really down to that lack of ability to generalize from low dimensions to high dimensions

because back then we were in the low dimensional case, people could only build neural nets

with 50 nodes in them or something and to imagine that it might be possible to build

a billion dimensional neural net and it might have a completely different qualitatively

different property was very hard to anticipate and I think even now we're starting to build

the theory to support that and it's incomplete at the moment but all of the theory seems

to be pointing in the direction that indeed this is an approach which truly is universal

both in its representational capacity which was known but also in its learning ability

which is surprising.

It makes one wonder what else we're missing due to our low dimensional intuitions that

will seem obvious once it's discovered.

I often wonder when we one day do have AIs which are superhuman in their abilities to

understand the world, what will they think of the algorithms that we developed back now?

Will it be looking back at these days and thinking that will we look back and feel that

these algorithms were naive first steps or will they still be the fundamental ideas which

are used even in 100,000, 10,000 years?

They'll watch back to this conversation and with a smile and do a little bit of a laugh.

My sense is I think just like when we used to think that the sun revolved around the

earth they'll see our systems of today reinforcement learning as too complicated that the answer

was simple all along.

There's something just like you said in the game of Go, I mean I love the systems of like

cellular automata that there's simple rules from which incredible complexity emerges.

So it feels like there might be some very simple approaches.

Just like Rich Sutton says, these simple methods would compute over time seem to prove to be

the most effective.

I 100% agree I think that if we try to anticipate what will generalize well into the future

I think it's likely to be the case that it's the simple clear ideas which will have the

longest legs and which will carry us further into the future.

And nevertheless we're in a situation where we need to make things work today and sometimes

that requires putting together more complex systems where we don't have the full answers

yet as to what those minimal ingredients might be.

So speaking of which, if we could take a step back to Go, what was Mogo and what was the

key idea behind the system?

So back during my PhD on computer Go around about that time there was a major new development

in which actually happened in the context of computer Go and it was really a revolution

in the way that heuristic search was done and the idea was essentially that a position

could be evaluated or a state in general could be evaluated not by humans saying whether

that position is good or not or even humans providing rules as to how you might evaluate

it but instead by allowing the system to randomly play out the game until the end multiple times

and taking the average of those outcomes as the prediction of what will happen.

So for example if you're in the game of Go the intuition is that you take a position

and you get the system to kind of play random moves against itself all the way to the end

of the game and you see who wins and if black ends up winning more of those random games

than white well you say hey this is a position that favors white and if white ends up winning

more of those random games than black then it favors white.

So that idea was known as Monte Carlo search and a particular form of Monte Carlo search

that became very effective and was developed in computer Go first by Remy Coulomb in 2006

and then taken further by others was something called Monte Carlo tree search which basically

takes that same idea and uses that insight to evaluate every node of a search tree is

evaluated by the average of the random playouts from that node onwards.

And this idea was very powerful and suddenly led to huge leaps forward in the strength

of computer Go playing programs and among those the strongest of the Go playing programs

in those days was a program called Mogo which was the first program to actually reach human

master level on small boards nine by nine boards.

And so this was a program by someone called Sylvangeli who's a good colleague of mine

but I worked with him a little bit in those days part of my PhD thesis and Mogo was a

first step towards the latest successes we saw in computer Go but it was still missing

a key ingredient Mogo was evaluating purely by random rollouts against itself and in a

way it's truly remarkable that random play should give you anything at all like why in

this perfectly deterministic game that's very precise and involves these very exact sequences

why is it that random randomization is helpful and so the intuition is that randomization

captures something about the nature of the search tree from a position that you're understanding

the nature of the search tree from that node onwards by using randomization and this was

a very powerful idea.

And I've seen this in other spaces when I talk to Richard Karp and so on, randomized

algorithms somehow magically are able to do exceptionally well and simplifying the problem

somehow makes you wonder about the fundamental nature of randomness in our universe.

It seems to be a useful thing but so from that moment, can you maybe tell the origin

story in the journey of AlphaGo?

Yeah, so programs based on Monte Carlo tree search were a first revolution in the sense

that they led to suddenly programs that could play the game to any reasonable level but

they plateaued, it seemed that no matter how much effort people put into these techniques

they couldn't exceed the level of amateur, Dan level go players.

So strong players but not anywhere near the level of professionals, never mind the world

champion.

And so that brings us to the birth of AlphaGo which happened in the context of a startup

company known as DeepMind where a project was born and the project was really a scientific

investigation where myself and Ajah Huang and an intern Chris Madison were exploring

a scientific question and that scientific question was really, is there another fundamentally

different approach to this key question of go, the key challenge of how can you build

that intuition in?

How can you just have a system that could look at a position and understand what move

to play or how well you're doing in that position, who's going to win?

And so the deep learning revolution had just begun, the systems like ImageNet had suddenly

been won by deep learning techniques back in 2012 and following that it was natural

to ask, well, if deep learning is able to scale up so effectively with images to understand

them enough to classify them, well, why not go, why not take the black and white stones

of the go board and build a system which can understand for itself what that means in terms

of what move to pick or who's going to win the game, black or white?

And so that was our scientific question which we were probing and trying to understand and

as we started to look at it, we discovered that we could build a system, so in fact our

very first paper on AlphaGo was actually a pure deep learning system which was trying

to answer this question and we showed that actually a pure deep learning system with

no search at all was actually able to reach human band level, master level at the full

game of go, 19 by 19 boards.

And so without any search at all, suddenly we had systems which were playing at the level

of the best Monte Carlo tree set systems, the ones with randomized rollouts.

So first of all, sorry to interrupt, but that's kind of a groundbreaking notion that's like

basically a definitive step away from a couple of decades of essentially search dominating

AI.

Yeah.

How did that make you feel, was it surprising from a scientific perspective in general how

to make you feel?

I found this to be profoundly surprising.

In fact, it was so surprising that we had a bet back then and like many good projects,

bets are quite motivating and the bet was whether it was possible for a system based

purely on deep learning, no search at all to beat a Dan level human player.

And so we had someone who joined our team, who was a Dan level player, he came in and

we had this first match against him.

And which side of the bet were you on, by the way, the losing and the winning side?

I tend to be an optimist with the power of deep learning and reinforcement learning.

So the system won and we were able to beat this human Dan level player.

And for me, that was the moment where it was like, okay, something special is afoot

here.

We have a system which without search is able to already just look at this position and

understand things as well as a strong human player.

And from that point onwards, I really felt that reaching the top levels of human play,

you know, professional level, world champion level, I felt it was actually an inevitability

and, and if it was an inevitable outcome, I was rather keen that it would be us that

achieved it.

So we scaled up.

This was something where, you know, so had lots of conversations back then with Dennis

the service at the head of DeepMind, who was extremely excited.

And we made the decision to scale up the project, brought more people on board.

And so AlphaGo became something where we had a clear goal, which was to try and crack this

outstanding challenge of AI to see if we could beat the world's best players.

And this led within the space of not so many months to playing against the European champion

Fan Hui in a match which became, you know, memorable in history as the first time a go

program had ever beaten a professional player.

And at that time, we had to make a judgment as to whether when and whether we should go

and challenge the world champion.

And this was a difficult decision to make again.

We were basing our predictions on our own progress and had to estimate based on the

rapidity of our own progress when we thought we would exceed the level of the human world

champion and we tried to make an estimate and set up a match and that became the AlphaGo

versus LisaDoll match in 2016.

And we should say spoiler alert that AlphaGo was able to defeat LisaDoll.

That's right.

Yeah.

Maybe we could take even a broader view, AlphaGo involves both learning from expert games

and as far as I remember a self played component to where it learns by playing against itself.

But in your sense, what was the role of learning from expert games there?

And in terms of your self evaluation, whether you can take on the world champion, what was

the thing that you're trying to do more of, sort of train more on expert games?

Or was there's now another, I'm asking so many poorly faced questions, but did you have

a hope or dream that self play would be the key component at that moment yet?

So in the early days of AlphaGo, we used human data to explore the science of what deep learning

can achieve.

And so when we had our first paper that showed that it was possible to predict the winner

of the game, that it was possible to suggest moves, that was done using human data.

Oh, solely human data.

Yeah.

And so the reason that we did it that way was at that time we were exploring separately

the deep learning aspect from the reinforcement learning aspect.

That was the part which was new and unknown to me at that time was how far could that

be stretched?

Once we had that, it then became natural to try and use that same representation and

see if we could learn for ourselves using that same representation.

And so right from the beginning, actually, our goal had been to build a system using

self play.

And to us, the human data right from the beginning was an expedient step to help us for pragmatic

reasons to go faster towards the goals of the project than we might be able to starting

solely from self play.

And so in those days, we were very aware that we were choosing to use human data and that

might not be the long term holy grail of AI, but that it was something which was extremely

useful to us.

It helped us to understand the system.

It helped us to build deep learning representations which were clear and simple and easy to use.

And so really, I would say it served a purpose not just as part of the algorithm, but something

which I continue to use in our research today, which is trying to break down a very hard

challenge into pieces which are easier to understand for us as researchers and develop.

So if you use a component based on human data, it can help you to understand the system such

that then you can build the more principled version later that does it for itself.

So as I said, the AlphaGo victory, and I don't think I'm being sort of romanticizing this

notion.

I think it's one of the greatest moments in the history of AI.

So were you cognizant of this magnitude of the accomplishment of the time?

I mean, are you cognizant of it even now?

Because to me, I feel like it's something that we mentioned, what the AGI systems of

the future will look back.

I think they'll look back at the AlphaGo victory as like, holy crap, they figured it out.

This is where it started.

Well thank you again.

I mean, it's funny because I guess I've been working on, I've been working on computer

go for a long time.

So I'd been working at the time of the AlphaGo match on computer go for more than a decade.

And throughout that decade, I'd had this dream of what would it be like to, what would it

be like really to actually be able to build a system that could play against the world

champion.

And I imagined that that would be an interesting moment that maybe, you know, some people might

care about that and that this might be, you know, a nice achievement.

But I think when I arrived in Seoul and discovered the legions of journalists that were following

us around and the hundred million people that were watching the match online live, I realized

that I'd been off in my estimation of how significant this moment was by several orders

of magnitude.

And so there was definitely an adjustment process to realize that this was something

which the world really cared about and which was a watershed moment.

And I think there was that moment of realization, which was also a little bit scary because,

you know, if you go into something thinking it's going to be maybe of interest and then

discover that a hundred million people are watching, it suddenly makes you worry about

whether some of the decisions you've made were really the best ones or the wisest or

were going to lead to the best outcome.

And we knew for sure that there were still imperfections in AlphaGo, which were going

to be exposed to the whole world watching.

And so, yeah, it was, I think, a great experience.

And I feel privileged to have been part of it, privileged to have led that amazing team.

I feel privileged to have been in a moment of history, like you say.

But also lucky that, you know, in a sense, I was insulated from the knowledge of, I think

it would have been harder to focus on the research if the full kind of reality of what

was going to come to pass had been known to me and the team.

I think it was, you know, we were in our bubble and we were working on research and we were

trying to answer the scientific questions.

And then bam, you know, the public sees it.

And I think it was better that way in retrospect.

Were you confident that, I guess, what were the chances that you could get the win?

So just like you said, I'm a little bit more familiar with another accomplishment than

we may not even get a chance to talk to.

I talked to Oriel Villalos about AlphaStar, which is another incredible accomplishment.

But here, you know, with AlphaStar and beating the StarCraft, there was like already a track

record with AlphaGo.

This is like the really first time you get to see reinforcement learning face the best

human in the world.

So what was your confidence like?

What was the odds?

Well, we actually...

Was there a bet?

Funnily enough, there was.

So just before the match, we weren't betting on anything concrete, but we all held out

a hand.

Everyone in the team held out a hand at the beginning of the match.

And the number of fingers that they had out on that hand was supposed to represent how

many games they thought we would win against Lisa Dahl.

And there was an amazing spread in the team's predictions.

But I have to say, I predicted four one.

And the reason was based purely on data.

So I'm a scientist first and foremost.

And one of the things which we had established was that AlphaGo in around one in five games

would develop something which we called a delusion, which was a kind of hole in its

knowledge where it wasn't able to fully understand everything about the position.

And that hole in its knowledge would persist for tens of moves throughout the game.

And we knew two things.

We knew that if there were no delusions that AlphaGo seemed to be playing at a level that

was far beyond any human capabilities.

But we also knew that if there were delusions, the opposite was true.

And in fact, that's what came to pass.

We saw all of those outcomes and Lisa Dahl in one of the games played a really beautiful

sequence that AlphaGo just hadn't predicted.

And after that, it led it into this situation where it was unable to really understand the

position fully and found itself in one of these delusions.

So indeed, four one was the outcome.

So yeah.

And can you maybe speak to it a little bit more?

What were the five games?

What happened, is there interesting things that come to memory in terms of the play of

the human machine?

So I remember all of these games vividly, of course.

Moments like these don't come too often in the lifetime of a scientist.

And the first game was magical because it was the first time that a computer program

had defeated a world champion in this grand challenge of go.

And there was a moment where AlphaGo invaded Lisa Dahl's territory towards the end of the

game.

And that's quite an audacious thing to do.

It's like saying, hey, you thought this was going to be your territory in the game, but

I'm going to stick a stone right in the middle of it and prove to you that I can break it

up.

And Lisa Dahl's face just dropped.

She wasn't expecting a computer to do something that audacious.

The second game became famous for a move known as Move 37.

This was a move that was played by AlphaGo that broke all of the conventions of go.

That the go players were so shocked by this, they thought that maybe the operator had made

a mistake.

They thought there was something crazy going on, and it just broke every rule that go players

are taught from a very young age.

They're just taught, you know, this kind of move called a shoulder hit.

You can only play it on the third line or the fourth line, and AlphaGo played it on

the fifth line.

And it turned out to be a brilliant move and made this beautiful pattern in the middle

of the board that ended up winning the game.

And so this really was a clear instance where we could say computers exhibited creativity,

that this was really a move that was something humans hadn't known about, hadn't anticipated.

And computers discovered this idea.

They were the ones to say, actually, here's a new idea, something new, not in the domains

of human knowledge of the game.

And now the humans think this is a reasonable thing to do, and it's part of go knowledge

now.

The third game, something special happens when you play against a human world champion,

which again, I hadn't anticipated before going there, which is, you know, these players

are amazing.

Lee Siddle was a true champion, 18 time world champion, and had this amazing ability to

probe AlphaGo for weaknesses of any kind.

And in the third game, he was losing, and we felt we were sailing comfortably to victory,

but he managed to, from nothing, stir up this fight and build what's called a double co,

these kind of repetitive positions.

And he knew that historically, no computer go program had ever been able to deal correctly

with double code positions, and he managed to summon one out of nothing.

And so for us, you know, this was a real challenge, like would AlphaGo be able to deal with this,

or would it just kind of crumble in the face of this situation?

And fortunately, it dealt with it perfectly.

The fourth game was amazing in that Lee Siddle appeared to be losing this game, AlphaGo thought

it was winning, and then Lee Siddle did something which I think only a true world champion can

do, which is he found a brilliant sequence in the middle of the game, a brilliant sequence

that led him to really just transform the position, it kind of, he found just a piece

of genius really.

And after that, AlphaGo, its evaluation just tumbled, it thought it was winning this game,

and all of a sudden it tumbled and said, oh, now I've got no chance, and it starts to behave

rather oddly at that point.

In the final game, for some reason, we as a team were convinced having seen AlphaGo in

the previous game suffer from delusions, we as a team were convinced that it was suffering

from another delusion, we were convinced that it was misevaluating the position and

that something was going terribly wrong.

And it was only in the last few moves of the game that we realized that actually, although

it had been predicting it was going to win all the way through, it really was.

And so somehow, you know, it just taught us yet again that you have to have faith in your

systems when they exceed your own level of ability and your own judgment, you have to

trust in them to know better than you, the designer, once you've bestowed in them the

ability to judge better than you can, then trust the system to do so.

So just like in the case of Deep Blue beating Gary Kasparov, so Gary is, I think the first

time he's ever lost actually to anybody, and I mean, there's a similar situation with

Lisa Dahl, it's a tragic, it's a tragic loss for humans, but a beautiful one.

I think that's kind of, from the tragedy, sort of emerges over time, emerges a kind

of inspiring story, but Lisa Dahl recently analysis retirement, I don't know if we can

look too deeply into it, but he did say that even if I become number one, there's an entity

that cannot be defeated.

So what do you think about these words?

What do you think about his retirement from the game ago?

Well, let me take you back first of all to the first part of your comment about Gary Kasparov,

who was actually at the panel yesterday.

He specifically said that when he first lost to Deep Blue, he viewed it as a failure.

He viewed that this had been a failure of his, but later on in his career, he said he'd

come to realize that actually it was a success, it was a success for everyone, because this

marked a transformational moment for AI, and so even for Gary Kasparov, he came to realize

that that moment was pivotal and actually meant something much more than his personal

loss in that moment.

Lisa Dahl, I think, was much more cognizant of that even at the time.

So in his closing remarks to the match, he really felt very strongly that what had happened

in the AlphaGo match was not only meaningful for AI, but for humans as well, and he felt

as a go player that it had opened his horizons and meant that he could start exploring new

things.

It brought his joy back for the game of go because it had broken all of the conventions

and barriers and meant that suddenly anything was possible again.

And so I was sad to hear that he'd retired, but he's been a great world champion over

many, many years, and I think he'll be remembered for that ever more.

He'll be remembered as the last person to beat AlphaGo.

I mean, after that, we increased the power of the system, and the next version of AlphaGo

beats the other strong human players 60 games to nil.

So what a great moment for him and something to be remembered for.

It's interesting that you spent time at AAAI on a panel with Gary Kasparov.

But I mean, it's almost, I'm just curious to learn the conversations you've had with

Gary because he's also now, he's written a book about artificial intelligence.

He's thinking about AI.

He has kind of a view of it, and he talks about AlphaGo a lot.

What's your sense, arguably, I'm not just being Russian, but I think Gary is the greatest

chess player of all time.

Probably one of the greatest game players of all time.

And you sort of at the center of creating a system that beats one of the greatest players

of all time.

So what is that conversation like?

Is there anything, any interesting digs, any bets, any funny things, any profound things?

So Gary Kasparov has an incredible respect for what we did with AlphaGo, and it's an

amazing tribute coming from him, of all people, that he really appreciates and respects what

we've done.

And I think he feels that the progress which has happened in computer chess, which later

after AlphaGo, we built the AlphaZero system, which defeated the world's strongest chess

programs.

And to Gary Kasparov, that moment in computer chess was more profound than deep blue.

And the reason he believes it mattered more was because it was done with learning and

a system which was able to discover for itself new principles, new ideas, which were able

to play the game in a way which he hadn't always known about, or anyone.

And in fact, one of the things I discovered at this panel was that the current world champion

Magnus Carlsen apparently recently commented on his improvement in performance, and he

attributes it to AlphaZero, that he's been studying the games of AlphaZero, he's changed

his style to play more like AlphaZero.

And it's led to him actually increasing his rating to a new peak.

Yeah, I guess to me, just like to Gary, the inspiring thing is that, and just like you

said with reinforcement learning, reinforcement learning and deep learning, machine learning

feels like what intelligence is.

And you could attribute it to sort of a bitter viewpoint from Gary's perspective, from us

humans perspective, saying that pure search that IBM Deep Blue was doing is not really

intelligence, but somehow it didn't feel like it.

And so that's the magical.

I'm not sure what it is about learning that feels like intelligence, but it does.

So I think we should not demean the achievements of what was done in previous areas of AI.

I think that Deep Blue was an amazing achievement in itself, and that heuristic search of the

kind that was used by Deep Blue had some powerful ideas that were in there.

But it also missed some things.

So the fact that the evaluation function, the way that the chess position was understood,

was created by humans and not by the machine is a limitation, which means that there's

a ceiling on how well it can do.

But maybe more importantly, it means that the same idea cannot be applied in other domains

where we don't have access to the kind of human grandmasters and that ability to kind

of encode exactly their knowledge into an evaluation function.

And the reality is that the story of AI is that most domains turn out to be of the second

type where knowledge is messy, it's hard to extract from experts or it isn't even available.

And so we need to solve problems in a different way.

And I think AlphaGo is a step towards solving things in a way which puts learning as a first

class citizen and says, systems need to understand for themselves how to understand the world,

how to judge the value of any action that they might take within that world in any state

they might find themselves in.

And in order to do that, we make progress towards AI.

Yeah.

So one of the nice things about this, about taking a learning approach to the game of

go or game playing is that the things you learn, the things you figure out are actually

going to be applicable to other problems that are real world problems.

That's ultimately, I mean, there's two really interesting things about AlphaGo.

One is the science of it, just the science of learning, the science of intelligence.

And then the other is, while you're actually learning to figuring out how to build systems

that would be potentially applicable in other applications, medical, autonomous vehicles,

robotics.

And it's just open the door to all kinds of applications.

So the next incredible step, really the profound step is probably AlphaGo Zero.

I mean, it's arguable I kind of see them all as the same place, but really, and perhaps

you were already thinking that AlphaGo Zero is the natural, it was always going to be

the next step, but it's removing the reliance on human expert games for pre training as

you mentioned.

So how big of an intellectual leap was this, that self play could achieve super human level

performance in its own?

And maybe could you also say what is self play, we kind of mentioned it a few times.

So let me start with self play.

So the idea of self play is something which is really about systems learning for themselves,

but in the situation where there's more than one agent.

And so if you're in a game, and the game is played between two players, then self play

is really about understanding that game just by playing games against yourself rather than

against any actual real opponent.

And so it's a way to kind of discover strategies without having to actually need to go out and

play against any particular human player, for example.

The main idea of Alpha Zero was really to try and step back from any of the knowledge

that we put into the system and ask the question, is it possible to come up with a single elegant

principle by which a system can learn for itself all of the knowledge which it requires

to play a game such as Go.

Importantly by taking knowledge out, you not only make the system less brittle in the sense

that perhaps the knowledge you were putting in was just getting in the way and maybe stopping

the system learning for itself, but also you make it more general.

The more knowledge you put in, the harder it is for a system to actually be placed,

taken out of the system in which it's kind of been designed, and placed in some other

system that maybe would need a completely different knowledge base to understand and

perform well.

And so the real goal here is to strip out all of the knowledge that we put in to the

point that we can just plug it into something totally different.

And that to me is really the promise of AI is that we can have systems such as that,

which no matter what the goal is, no matter what goal we set to the system, we can come

up with, we have an algorithm which can be placed into that world, into that environment

and can succeed in achieving that goal.

And then that to me is almost the essence of intelligence if we can achieve that.

And so AlphaZero is a step towards that, and it's a step that was taken in the context

of two player perfect information games like Go and chess.

We also applied it to Japanese chess.

So just to clarify, the first step was AlphaGo Zero.

The first step was to try and take all of the knowledge out of AlphaGo in such a way

that it could play in a fully self discovered way, purely from self play.

And to me, the motivation for that was always that we could then plug it into other domains,

but we saved that until later.

Well, in fact, I mean, just for fun, I could tell you exactly the moment where the idea

for AlphaZero occurred to me, because I think there's maybe a lesson there for researchers

who are kind of too deeply embedded in their research and working 24 sevens, try and come

up with the next idea, which is, it actually occurred to me on honeymoon, and I was at

my most fully relaxed state, really enjoying myself, and just being this, like, the algorithm

for AlphaZero just appeared, and in its full form, and this was actually before we played

against Lisa Dahl, but we just didn't, I think we were so busy trying to make sure we could

beat the world champion, that it was only later that we had the opportunity to step

back and start examining that sort of deeper scientific question of whether this could

really work.

So nevertheless, so self play is probably one of the most profound ideas that represents

to me at least, artificial intelligence.

But the fact that you could use that kind of mechanism to, again, beat world class players,

that's very surprising.

So we kind of, to me, it feels like you have to train in a large number of expert games.

So was it surprising to you?

What was the intuition?

Can you sort of think, not necessarily at that time, even now, what's your intuition?

Why this thing works so well?

Why is it able to learn from scratch?

Well, let me first say why we tried it.

So we tried it both because I feel that it was the deeper scientific question to be asking,

to make progress towards AI.

And also because in general, in my research, I don't like to do research on questions for

which we already know the likely outcome.

I don't see much value in running an experiment where you're 95% confident that you will succeed.

And so we could have tried maybe to take AlphaGo and do something which we knew for sure it

would succeed on.

But much more interesting to me was to try it on the things which we weren't sure about.

And one of the big questions on our minds back then was, could you really do this with

self play alone?

How far could that go?

Would it be as strong?

And honestly, we weren't sure.

It was 50, 50, I think.

If you'd asked me, I wasn't confident that it could reach the same level as these systems,

but it felt like the right question to ask.

And even if it had not achieved the same level, I felt that that was an important direction

to be studying.

And so then, lo and behold, it actually ended up outperforming the previous version of AlphaGo

and indeed was able to beat it by 100 games to zero.

So what's the intuition as to why?

I think the intuition to me is clear that whenever you have errors in a system, as we

did in AlphaGo, AlphaGo suffered from these delusions.

Occasionally, it would misunderstand what was going on in a position and misevaluate

it.

How can you remove all of these errors?

Errors arise from many sources.

For us, they were arising both from starting from the human data, but also from the nature

of the search and the nature of the algorithm itself.

But the only way to address them in any complex system is to give the system the ability to

correct its own errors.

It must be able to correct them.

It must be able to learn for itself when it's doing something wrong and correct for it.

And so it seemed to me that the way to correct delusions was indeed to have more iterations

of reinforcement learning, that no matter where you start, you should be able to correct

those errors until it gets to play that out and understand, oh, well, I thought that I

was going to win in this situation, but then I ended up losing.

That suggests that I was misevaluating something, there's a hole in my knowledge and now the

system can correct for itself and understand how to do better.

Now if you take that same idea and trace it back all the way to the beginning, it should

be able to take you from no knowledge, from completely random starting point, all the

way to the highest levels of knowledge that you can achieve in a domain.

And the principle is the same, that if you give, if you bestow a system with the ability

to correct its own errors, then it can take you from random to something slightly better

than random because it sees the stupid things that the random is doing and it can correct

them.

And then it can take you from that slightly better system and understand, well, what's

that doing wrong?

And it takes you on to the next level and the next level and this progress can go on indefinitely.

And indeed, what would have happened if we'd carried on training AlphaGo Zero for longer?

We saw no sign of it slowing down its improvements, or at least it was certainly carrying on to

improve.

And presumably, if you had the computational resources, this could lead to better and better

systems that discover more and more.

So your intuition is fundamentally there's not a ceiling to this process.

One of the surprising things, just like you said, is the process of patching errors.

It's intuitively makes sense that reinforcement learning should be part of that process.

But what is surprising is in the process of patching your own lack of knowledge, you don't

open up other patches.

You keep sort of like there's a monotonic decrease of your weaknesses.

Well, let me back this up.

I think science always should make falsifiable hypotheses.

Yes.

So let me back up this claim with a falsifiable hypothesis, which is that if someone was to,

in the future, take AlphaZero as an algorithm and run it on with greater computational

resources that we had available today, then I would predict that they would be able to

beat the previous system 100 games to zero.

And that if they were then to do the same thing a couple of years later, that that would

beat that previous system 100 games to zero, and that that process would continue indefinitely

throughout at least my human lifetime.

Probably the game of go would set the ceiling.

I mean, the game of go would set the ceiling, but the game of go has 10 to the 170 states

in it.

So the ceiling is unreachable by any computational device that can be built out of the 10 to

the 80 atoms in the universe.

You asked a really good question, which is, do you not open up other errors when you correct

your previous ones?

And the answer is yes, you do.

And so it's a remarkable fact about this class of two player game and also true of single

agent games that essentially progress will always lead you to, if you have sufficient

representational resource like imagine you had, could represent every state in a big

table of the game, then we know for sure that a progress of self improvement will lead all

the way in the single agent case to the optimal possible behavior, and in the two player case

to the minimax optimal behavior that is the best way that I can play knowing that you're

playing perfectly against me.

And so for those cases, we know that even if you do open up some new error that in some

sense you've made progress, you're progressing towards the best that can be done.

So AlphaGo was initially trained on expert games with some self play AlphaGo zero removed

the need to be trained on expert games.

And then another incredible step for me because I just love chess is to generalize that further

to be in Alpha zero to be able to play the game of go beating AlphaGo zero and AlphaGo,

and then also being able to play the game of chess and others.

So what was that step like?

What's the interesting aspects there that required to make that happen?

I think the remarkable observation which we saw with Alpha zero was that actually without

modifying the algorithm at all, it was able to play and crack some of AI's greatest previous

challenges.

In particular, we dropped it into the game of chess.

And unlike the previous systems like Deep Blue, which had been worked on for years and years,

we were able to beat the world's strongest computer chess program convincingly using

a system that was fully discovered by its own from scratch with its own principles.

And in fact, one of the nice things that we found was that in fact, we also achieved the

same result in Japanese chess, a variant of chess where you get to capture pieces and then

place them back down on your own side as an extra piece.

So a much more complicated variant of chess.

And we also beat the world's strongest programs and reached superhuman performance in that

game too.

And it was the very first time that we'd ever run the system on that particular game was

the version that we published in the paper on Alpha zero.

It just worked out of the box, literally no touching it, we didn't have to do anything

and there it was, superhuman performance, no tweaking, no twiddling.

And so I think there's something beautiful about that principle that you can take an

algorithm and without twiddling anything, it just works.

Now to go beyond Alpha zero, what's required?

Alpha zero is just a step.

And there's a long way to go beyond that to really crack the deep problems of AI.

But one of the important steps is to acknowledge that the world is a really messy place.

It's this rich, complex, beautiful, but messy environment that we live in and no one gives

us the rules.

Like no one knows the rules of the world, at least maybe we understand that it operates

according to Newtonian or quantum mechanics at the micro level or according to relativity

at the macro level, but that's not a model that's useful for us as people to operate

in it.

Somehow the agent needs to understand the world for itself in a way where no one tells

it the rules of the game and yet it can still figure out what to do in that world, deal

with this stream of observations coming in, rich sensory input coming in, actions going

out in a way that allows it to reason in the way that Alpha zero can reason in the way

that these go and chess playing programs can reason, but in a way that allows it to take

actions in that messy world to achieve its goals.

And so this led us to the most recent step in the story of AlphaGo, which was a system

called Mu Zero, and Mu Zero is a system which learns for itself even when the rules are

not given to it.

It actually can be dropped into a system with messy perceptual inputs.

We actually tried it in some Atari games, the canonical domains of Atari that have

been used for reinforcement learning, and this system learned to build a model of these

Atari games that was sufficiently rich and useful enough for it to be able to plan successfully.

And in fact, that system not only went on to beat the state of the art in Atari, but

the same system without modification was able to reach the same level of superhuman performance

in Go, Chess, and Shogi that we'd seen in Alpha Zero, showing that even without the

rules, the system can learn for itself just by trial and error, just by playing this game

of Go, and no one tells you what the rules are, but you just get to the end and someone

says, you know, win or loss, or you play this game of chess and someone says win or loss,

or you play a game of breakout in Atari and someone just tells you, you know, your score

at the end.

And the system for itself figures out essentially the rules of the system, the dynamics of the

world, how the world works, and not in any explicit way, but just implicitly enough understanding

for it to be able to plan in that system in order to achieve its goals.

And that's the fundamental process that you have to go through when you're facing in

any uncertain kind of environment that you would in the real world is figuring out the

sort of the rules, the basic rules of the game.

That's right.

So that allows it to be applicable to basically any domain that could be digitized in the

way that it needs to in order to be consumable, sort of in order for the reinforcement learning

framework to be able to sense the environment, to be able to act in the environment, so on.

The full reinforcement learning problem needs to deal with worlds that are unknown and complex

and the agent needs to learn for itself how to deal with that.

And so Musero is a step, a further step in that direction.

One of the things that inspired the general public, and just in conversations I have with

my parents or something with my mom, that just loves what was done is kind of at least

a notion that there was some display of creativity, some new strategies, new behaviors that were

created.

That again has echoes of intelligence.

So is there something that stands out?

Do you see it the same way that there's creativity and there's some behaviors, patterns that

you saw that AlphaZero was able to display that are truly creative?

So let me start by saying that I think we should ask what creativity really means.

So to me, creativity means discovering something which wasn't known before, something unexpected,

something outside of our norms.

And so in that sense, the process of reinforcement learning or the self play approach that was

used by AlphaZero is the essence of creativity.

It's really saying at every stage, you're playing according to your current norms and

you try something.

And if it works out, you say, hey, here's something great, I'm going to start using that.

And then that process, it's like a micro discovery that happens millions and millions of times

over the course of the algorithm's life, where it just discovers some new idea, oh, this

pattern, this pattern's working really well for me, I'm going to start using that.

Oh, now, oh, here's this other thing I can do, I can start to connect these stones together

in this way, or I can start to sacrifice stones or give up on pieces or play shoulder hits

on the fifth line or whatever it is.

The system's discovering things like this for itself continually, repeatedly all the

time.

And so it should come as no surprise to us then, when if you leave these systems going,

that they discover things that are not known to humans, that to the human norms are considered

creative.

And we've seen this several times, in fact, in AlphaGo Zero, we saw this beautiful timeline

of discovery where what we saw was that there are these opening patterns that humans play

called joseki, these are like the patterns that humans learn to play in the corners and

they've been developed and refined over literally thousands of years in the game of Go.

And what we saw was in the course of the training AlphaGo Zero, over the course of the 40 days

that we trained this system, it starts to discover exactly these patterns that human

players play.

And over time, we found that all of the joseki that humans played were discovered by the

system through this process of self play and this sort of essential notion of creativity.

But what was really interesting was that over time, it then starts to discard some of these

in favor of its own joseki that humans didn't know about.

And it starts to say, oh, well, you thought that the Knights move pincer joseki was a great

idea.

But here's something different you can do there, which makes some new variation that

the humans didn't know about.

And actually now the human Go players study the joseki that AlphaGo played and they become

the new norms that are used in today's top level Go competitions.

That never gets old.

And just the first to me, maybe just makes me feel good as a human being that a self

play mechanism that knows nothing about us humans discovers patterns that we humans do.

It says like an affirmation that we're doing okay as humans.

In this domain and other domains, we figured out it's like the Churchill quote about democracy.

It's the, you know, it sucks, but it's the best one we've tried.

So in general, it's taking a step outside of Go and you have like a million accomplishment

that I have no time to talk about with AlphaStar and so on and the current work.

But in general, this self play mechanism that you've inspired the world with by beating

the world champion Go player.

Do you see that as DC being applied in other domains, do you have sort of dreams and hope

that it's applied in both the simulated environments and the constrained environments of games constrained?

I mean, AlphaStar really demonstrates that you can remove a lot of the constraints, but

nevertheless, it's an individual simulated environment.

Do you have a hope or dream that it starts being applied in the robotics environment

and maybe even in domains that are safety critical and so on and have, you know, have

a real impact in the real world like autonomous vehicles, for example, which seems like a very

far out dream at this point.

So I absolutely do hope and imagine that we will get to the point where ideas just like

these are used in all kinds of different domains.

In fact, one of the most satisfying things as a researcher is when you start to see other

people use your algorithms in unexpected ways.

So in the last couple of years, there have been, you know, a couple of nature papers

where different teams unbeknownst to us took AlphaZero and applied exactly those same algorithms

and ideas to real world problems of huge meaning to society.

So one of them was the problem of chemical synthesis and they were able to beat the state

of the art in finding pathways of how to actually synthesize chemicals, retrochemical synthesis.

And the second paper actually just came out a couple of weeks ago in Nature showed that

in quantum computation, you know, one of the big questions is how to understand the

nature of the function in quantum computation and a system based on AlphaZero beat the state

of the art by quite some distance there again.

So these are just examples.

And I think, you know, the lesson which we've seen elsewhere in machine learning time and

time again is that if you make something general, it will be used in all kinds of ways.

You know, you provide a really powerful tool to society and those tools can be used in

amazing ways.

And so I think we're just at the beginning and for sure, I hope that we see all kinds

of outcomes.

So the other side of the question of reinforcement learning framework is, you know, usually want

to specify a reward function and an objective function.

What do you think about sort of ideas of intrinsic rewards of when we're not really sure about,

you know, if we take, you know, human beings as existence proof that we don't seem to

be operating according to a single reward.

Do you think that there's interesting ideas for when you don't know how to truly specify

the reward, you know, that there's some flexibility for discovering it intrinsically or so on

in the context of reinforcement learning?

So I think, you know, when we think about intelligence, it's really important to be

clear about the problem of intelligence.

And I think it's clearest to understand that problem in terms of some ultimate goal that

we want the system to try and solve for.

And after all, if we don't understand the ultimate purpose of the system, do we really

even have a clearly defined problem that we're solving at all?

Now within that, as with your example for humans, the system may choose to create its

own motivations and sub goals that help the system to achieve its ultimate goal.

And that may indeed be a hugely important mechanism to achieve those ultimate goals.

But there is still some ultimate goal I think the system needs to be measurable and evaluated

against.

And even for humans, I mean, humans, we're incredibly flexible.

We feel that we can, you know, any goal that we're given, we feel we can master to some

degree.

But if we think of those goals really, you know, like the goal of being able to pick

up an object or the goal of being able to communicate or influence people to do things

in a particular way or whatever those goals are, really, they're sub goals really that

we set ourselves.

You know, we choose to pick up the object.

We choose to communicate.

We choose to influence someone else.

And we choose those because we think it will lead us to something, you know, in later on.

We think that that's helpful to us to achieve some ultimate goal.

Now I don't want to speculate whether or not humans as a system necessarily have a singular

overall goal of survival or whatever it is.

But I think the principle for understanding and implementing intelligence is has to be

that if we're trying to understand intelligence or implement our own, there has to be a well

defined problem.

Otherwise, if it's not, I think it's like an admission of defeat that for that to be

hoped for understanding or implementing intelligence, we have to know what we're doing.

We have to know what we're asking the system to do.

Otherwise, if you don't have a clearly defined purpose, you're not going to get a clearly

defined answer.

The ridiculous big question that has to naturally follow is I have to pin you down on this thing

that nevertheless, one of the big silly or big real questions before humans is the meaning

of life is us trying to figure out our own reward function.

And you just kind of mentioned that if you want to build intelligence systems and you

know what you're doing, you should be at least cognizant to some degree of what the reward

function is.

So the natural question is, what do you think is the reward function of human life, the

meaning of life for us humans, the meaning of our existence?

I think I'd be speculating beyond my own expertise, but just for fun, let me do that and say

I think that there are many levels at which you can understand a system and you can understand

something as optimizing for a goal at many levels.

And so you can understand the, let's start with the universe, like does the universe

have a purpose?

It feels like it's just at one level, just following certain mechanical laws of physics

and that that's led to the development of the universe.

But at another level, you can view it as actually, there's the second law of thermodynamics

that says that this is increasing in entropy over time forever.

And now there's a view that's been developed by certain people at MIT that you can think

of this as almost like a goal of the universe, that the purpose of the universe is to maximize

entropy.

So there are multiple levels at which you can understand a system.

The next level down, you might say, well, if the goal is to maximize entropy, well,

how can that be done by a particular system?

And maybe evolution is something that the universe discovered in order to kind of dissipate

energy as efficiently as possible.

And by the way, I'm borrowing from Max Tegmark for some of these metaphors, the physicist.

But if you can think of evolution as a mechanism for dispersing energy, then evolution, you

might say, then becomes a goal, which is if evolution disperses energy by reproducing

as efficiently as possible, what's evolution then?

Well, it's now got its own goal within that, which is to actually reproduce as effectively

as possible.

And now, how does reproduction, how is that made as effective as possible?

Well, you need entities within that that can survive and reproduce as effectively as possible.

And so it's natural that in order to achieve that high level goal, those individual organisms

discover brains, intelligences, which enable them to support the goals of evolution.

And those brains, what do they do?

Well, perhaps the early brains, maybe they were controlling things at some direct level.

Maybe they were the equivalent of preprogrammed systems, which were directly controlling what

was going on and setting certain things in order to achieve these particular goals.

But that led to another level of discovery, which was learning systems, parts of the brain

which were able to learn for themselves and learn how to program themselves to achieve

any goal, and presumably there are parts of the brain where goals are set to parts of

that system and provides this very flexible notion of intelligence that we as humans presumably

have, which is the ability to kind of write the reason we feel that we can achieve any

goal.

So, it's a very long winded answer to say that, you know, I think there are many perspectives

and many levels at which intelligence can be understood.

And at each of those levels, you can take multiple perspectives, you know, you can view

the system as something which is optimizing for a goal, which is understanding it at a

level by which we can maybe implement it and understand it as AI researchers or computer

scientists, or you can understand it at the level of the mechanistic thing which is going

on, that there are these atoms bouncing around in the brain and they lead to the outcome

of that system is not in contradiction with the fact that it's also a decision making

system that's optimizing for some goal and purpose.

I've never heard the description of the meaning of life structured so beautifully in layers,

but you did miss one layer, which is the next step, which you're responsible for, which

is creating the artificial intelligence layer on top of that.

Indeed.

And I can't wait to see, well, I may not be around, but I can't wait to see what the

next layer beyond that will be.

Well, let's just take that argument and pursue it to its natural conclusion.

So the next level indeed is for how can our learning brain achieve its goals most effectively?

Well, maybe it does so by us as learning beings, building a system which is able to solve for

those goals more effectively than we can.

And so when we build a system to play the game of go, when I said that I wanted to build

a system that can play go better than I can, I've enabled myself to achieve that goal of

playing go better than I could by directly playing it and learning it myself.

And so now a new layer has been created, which is systems which are able to achieve goals

for themselves.

And ultimately, there may be layers beyond that where they set subgoals to parts of their

own system in order to achieve those and so forth.

So the story of intelligence I think is a multi layered one and a multi perspective one.

We live in an incredible universe.

David, thank you so much first of all for dreaming of using learning to solve go and

building intelligence systems and for actually making it happen and for inspiring millions

of people in the process.

It's truly an honor.

Thank you so much for talking today.

Okay.

Thank you.

Thanks for listening to this conversation with David Silver.

And thank you to our sponsors masterclass and cash app.

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And now let me leave you some words from David Silver.

My personal belief is that we've seen something of a turning point where we're starting to

understand that many abilities like intuition and creativity that we've previously thought

were in the domain only of the human mind are actually accessible to machine intelligence

as well.

And I think that's a really exciting moment in history.

Thank you for listening and hope to see you next time.