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 co led 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
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.
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and financial burden of this crisis,
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And now, here’s my conversation with David Silver.
What was the first program you’ve 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?
Like 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 sort of 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, you know,
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 know,
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, I 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 they’re 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 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 more 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 fullest sense,
but something which is able to take actions
and 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.
That 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 funny 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 gonna 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?
Made me feel good.
I mean, was there sort of the, yeah,
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 space, 2001 Space Odyssey
kind of realizing that you’ve created something that,
you know, that’s achieved human level intelligence
in this one particular little task.
And in that case, I suppose neural networks
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
are still using 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, AlphaGo, and I don’t know how else to put it,
but to me, AlphaGo and AlphaGo 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 beat Garry Kasparov in 1987.
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
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,
what was the dream for you?
You just mentioned your PhD thesis
of building the system that plays Go.
What 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’s very kind words.
And funnily enough, I just came from a panel
where I was actually in a conversation
with Garry Kasparov 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, 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,
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,
in 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 handicapped 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 very, very little
in the way of progress towards meaningful performance,
again, anything approaching human level.
And so people, it wasn’t through lack of effort,
people had 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 gonna 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 in the 90s with RL a little bit,
hasn’t been part of those state of the art game playing
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 it 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 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 were 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 like 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 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, but 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,
what 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 Go with them.
Probably have discovered the same rule set.
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.
So there’s a deep history there.
But there’s interesting qualities.
So if I sort of compare 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 sort of 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.
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 a 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 kind of 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 the 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 in 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 Saturn and Barto,
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 Saturn
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 Saturn, 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 clear,
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.
It is at 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 gonna 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 kind of ask the basic question
of what is to 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, 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 the 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, 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
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’re 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 a 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 gonna solve any kind of problem
where you’re trying to figure out how to take actions
and just from this stream of observations,
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, 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 parameterizing 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.
Like, are you trying to learn something to predict well,
like a value function or a model?
Are you 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 deep reinforcement 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 or 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 gonna do in the world,
the value function,
whether it’s gonna be choosing what to do in the world,
or whether it’s understanding the world itself,
what’s gonna 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.
Like, 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 think it’s the,
I feel it’s the only thing which can ultimately.
And so I feel we have to address it
and there must be success as possible
because we have examples of intelligence.
And 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 fact 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 gonna get stuck
and learning will get stuck
because you won’t be able to make any further progress.
And 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.
And so no matter where you are,
learning can proceed and do better and better and better
And so 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 the,
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.
And 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?
It’s hard to know.
They’ll watch back to this conversation
and with a smile, maybe a little bit of a laugh.
I mean, 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 really simple approaches,
just like Rich Sutton says, right?
These simple methods with 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 furthest into the future.
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
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 play outs
from that node onwards.
And this idea, when you think about it,
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 Sylvain Gelli,
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.
Why in this perfectly deterministic game
that’s very precise and involves these very exact sequences,
why is it that 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,
talked 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
and 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,
nevermind 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.
I heard of them.
Where a project was born.
And the project was really a scientific investigation
where myself and Adger 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
and 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 gonna win?
And so the deep learning revolution had just begun.
That 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 gonna 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 search 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.
So 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,
with no search at all to beat a down level human player.
And so we had someone who joined our team
who was a down level player.
He came in and we had this first match against him and…
Which side of the bed were you on, by the way?
The losing or 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 down 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,
professional level, world champion level,
I felt it was actually an inevitability.
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,
so I had lots of conversations back then
with Demis Sassabis, 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 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 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 Lee Sedol match in 2016.
And we should say, spoiler alert,
that AlphaGo was able to defeat Lee Sedol.
That’s right, yeah.
So maybe we could take even a broader view.
AlphaGo involves both learning from expert games
and as far as I remember, a self play 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 phrased 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.
A 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 longterm 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 at the time?
I mean, are you cognizant of it even now?
Because to me, I feel like it’s something that would,
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 ComputerGo for a long time.
So I’d been working at the time of the AlphaGo match
on ComputerGo 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 some people might care about that
and that this might be a nice achievement.
But I think when I arrived in Seoul
and discovered the legions of journalists
that were following us around and the 100 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.
But it’s also a little bit scary
because if you go into something thinking
it’s gonna be maybe of interest
and then discover that 100 million people are watching,
it suddenly makes you worry about
whether some of the decisions you’d 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 gonna 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 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 gonna come to pass
had been known to me and the team.
I think it was, we were in our bubble
and we were working on research
and we were trying to answer the scientific questions
and then bam, 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
that we may not even get a chance to talk to.
I talked to Oriel Venialis about Alpha Star
which is another incredible accomplishment,
but here with Alpha Star and beating the StarCraft,
there was already a track record with AlphaGo.
This is 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 their hand
was supposed to represent how many games
they thought we would win against Lee Sedol.
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 in a 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 Lee Sedol 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, yeah, 4.1 was the outcome.
So yeah, and can you maybe speak to it a little bit more?
What were the five games?
Is there interesting things that come to memory
in terms of the play of the human or the 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 Lee Sedol’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 Lee Sedol’s face just dropped.
He 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 that 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 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 these players are amazing.
Lee Sedol 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 co positions.
And he managed to summon one out of nothing.
And so for us, this was a real challenge.
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 Sedol
appeared to be losing this game.
AlphaGo thought it was winning.
And then Lee Sedol 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.
He kind of 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 started 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, 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 was, I think, the first time he’s ever lost, actually,
And I mean, there’s a similar situation with Lee Sedol.
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 Lee Sedol recently announced his 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,
because 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
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.
Lee Sedol, I think, was much more cognizant of that,
even at the time.
And 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.
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
player 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 Garry Kasparov.
What, I mean, it’s almost, I’m just
curious to learn the conversations you’ve
had with Garry, 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 Garry 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 Garry 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 Garry 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
And he attributed it to AlphaZero,
that he’s been studying the games of AlphaZero.
And 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 Garry,
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 a bitter viewpoint
from Garry’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 eras 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 human grandmasters
and that ability to 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
and 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 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,
I mean, 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 superhuman level
performance in its own?
And maybe could you also say, what is self play?
Kind of mention 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 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 too deeply embedded in their research
and working 24 sevens to 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 bing,
the algorithm for AlphaZero just appeared 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,
But the fact that you could use that kind of mechanism
to, again, beat world class players,
that’s very surprising.
So 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 it’s 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 miss evaluate it.
How can you remove all of these errors?
Errors arise from many sources.
For us, they were arising both 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 miss evaluating 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 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 intuitively makes sense that this is,
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.
So let me back up this claim with a falsifiable hypothesis,
which is that if someone was to, in the future,
take Alpha Zero 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.
Presumably the game of Go would set the ceiling.
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.
And 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 AlphaZero
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
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 AlphaZero,
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,
and we were able to beat
the world’s strongest computer chess program convincingly
using a system that was fully discovered
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 AlphaZero.
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 AlphaZero, what’s required?
AlphaZero 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 AlphaGo or AlphaZero can reason
in the way that these go and chess playing programs
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 MuZero.
And MuZero 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 AlphaZero,
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 win or loss.
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 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.
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 and 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 MuZero is a further step in that direction.
One of the things that inspired the general public
and just in conversations I have like with my parents
or something with my mom that just loves what was done
is kind of at least the 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 gonna 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 gonna start using that.
And 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 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.
Even 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.
That’s just like an affirmation
that we’re doing okay as humans.
We’ve, in this domain and other domains,
we figured out it’s like the Churchill quote
It’s the, you know, it sucks,
but it’s the best one we’ve tried.
So in general, taking a step outside of Go
and you’ve 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,
do you see it being applied in other domains?
Do you have sort of dreams and hopes
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 in a digital simulated environment.
Do you have a hope, a 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,
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,
you 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 subgoals 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 subgoals,
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 later on.
We think 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 there to be hope 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,
because 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
intelligent 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.
Does the universe have a purpose?
Well, 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 this,
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.
There are parts of the brain
which are 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,
the reason we feel that we can achieve any goal.
So it’s a very long winded answer to say that,
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 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.
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 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
And ultimately there may be layers beyond that
where they set sub goals 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 intelligent 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,
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And now let me leave you with 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.