The following is a conversation with Ariel Vinales.

He’s a senior research scientist at Google DeepMind,

and before that, he was at Google Brain and Berkeley.

His research has been cited over 39,000 times.

He’s truly one of the most brilliant and impactful minds

in the field of deep learning.

He’s behind some of the biggest papers and ideas in AI,

including sequence to sequence learning,

audio generation, image captioning,

neural machine translation,

and, of course, reinforcement learning.

He’s a lead researcher of the AlphaStar project,

creating an agent that defeated a top professional

at the game of StarCraft.

This conversation is part

of the Artificial Intelligence podcast.

If you enjoy it, subscribe on YouTube, iTunes,

or simply connect with me on Twitter at Lex Friedman,

spelled F R I D.

And now, here’s my conversation with Ariel Vinales.

You spearheaded the DeepMind team behind AlphaStar

that recently beat a top professional player at StarCraft.

So you have an incredible wealth of work

in deep learning and a bunch of fields,

but let’s talk about StarCraft first.

Let’s go back to the very beginning,

even before AlphaStar, before DeepMind,

before deep learning first.

What came first for you,

a love for programming or a love for video games?

I think for me, it definitely came first

the drive to play video games.

I really liked computers.

I didn’t really code much, but what I would do is

I would just mess with the computer, break it and fix it.

That was the level of skills, I guess,

that I gained in my very early days,

I mean, when I was 10 or 11.

And then I really got into video games,

especially StarCraft, actually, the first version.

I spent most of my time

just playing kind of pseudo professionally,

as professionally as you could play back in 98 in Europe,

which was not a very main scene

like what’s called nowadays eSports.

Right, of course, in the 90s.

So how’d you get into StarCraft?

What was your favorite race?

How did you develop your skill?

What was your strategy?

All that kind of thing.

So as a player, I tended to try to play not many games,

not to kind of disclose the strategies

that I kind of developed.

And I like to play random, actually,

not in competitions, but just to…

I think in StarCraft, there’s three main races

and I found it very useful to play with all of them.

And so I would choose random many times,

even sometimes in tournaments,

to gain skill on the three races

because it’s not how you play against someone,

but also if you understand the race because you played,

you also understand what’s annoying,

then when you’re on the other side,

what to do to annoy that person,

to try to gain advantages here and there and so on.

So I actually played random,

although I must say in terms of favorite race,

I really liked Zerg.

I was probably best at Zerg

and that’s probably what I tend to use

towards the end of my career before starting university.

So let’s step back a little bit.

Could you try to describe StarCraft

to people that may never have played video games,

especially the massively online variety like StarCraft?

So StarCraft is a real time strategy game.

And the way to think about StarCraft,

perhaps if you understand a bit chess,

is that there’s a board which is called map

or the map where people play against each other.

There’s obviously many ways you can play,

but the most interesting one is the one versus one setup

where you just play against someone else

or even the built in AI, right?

Blizzard put a system that can play the game

reasonably well if you don’t know how to play.

And then in this board, you have again,

pieces like in chess,

but these pieces are not there initially

like they are in chess.

You actually need to decide to gather resources

to decide which pieces to build.

So in a way you’re starting almost with no pieces.

You start gathering resources in StarCraft.

There’s minerals and gas that you can gather.

And then you must decide how much do you wanna focus

for instance, on gathering more resources

or starting to build units or pieces.

And then once you have enough pieces

or maybe like attack, a good attack composition,

then you go and attack the other side of the map.

And now the other main difference with chess

is that you don’t see the other side of the map.

So you’re not seeing the moves of the enemy.

It’s what we call partially observable.

So as a result, you must not only decide

trading off economy versus building your own units,

but you also must decide whether you wanna scout

to gather information, but also by scouting,

you might be giving away some information

that you might be hiding from the enemy.

So there’s a lot of complex decision making

all in real time.

There’s also unlike chess, this is not a turn based game.

You play basically all the time continuously

and thus some skill in terms of speed

and accuracy of clicking is also very important.

And people that train for this really play this game

at an amazing skill level.

I’ve seen many times these

and if you can witness this life,

it’s really, really impressive.

So in a way, it’s kind of a chess

where you don’t see the other side of the board,

you’re building your own pieces

and you also need to gather resources

to basically get some money to build other buildings,

pieces, technology and so on.

From the perspective of a human player,

the difference between that and chess

or maybe that and a game like turn based strategy

like Heroes of Might and Magic is that there’s an anxiety

because you have to make these decisions really quickly.

And if you are not actually aware of what decisions work,

it’s a very stressful balance.

Everything you describe is actually quite stressful,

difficult to balance for an amateur human player.

I don’t know if it gets easier at the professional level,

like if they’re fully aware of what they have to do,

but at the amateur level, there’s this anxiety.

Oh crap, I’m being attacked.

Oh crap, I have to build up resource.

Oh, I have to probably expand.

And all these, the time,

the real time strategy aspect is really stressful

and computationally I’m sure difficult.

We’ll get into it.

But for me, Battle.net,

so StarCraft was released in 98, 20 years ago,

which is hard to believe.

And Blizzard Battle.net with Diablo in 96 came out.

And to me, it might be a narrow perspective,

but it changed online gaming and perhaps society forever.

Yeah.

But I may have made way too narrow viewpoint,

but from your perspective,

can you talk about the history of gaming

over the past 20 years?

Is this, how transformational,

how important is this line of games?

Right, so I think I kind of was an active gamer

whilst this was developing, the internet, online gaming.

So for me, the way it came was I played other games,

strategy related, I played a bit of Common and Conquer,

and then I played Warcraft II, which is from Blizzard.

But at the time, I didn’t know,

I didn’t understand about what Blizzard was or anything.

Warcraft II was just a game,

which was actually very similar to StarCraft in many ways.

It’s also real time strategy game

where there’s orcs and humans, so there’s only two races.

But it was offline.

And it was offline, right?

So I remember a friend of mine came to school,

say, oh, there’s this new cool game called StarCraft.

And I just said, oh, this sounds like

just a copy of Warcraft II, until I kind of installed it.

And at the time, I am from Spain,

so we didn’t have very good internet, right?

So there was, for us,

StarCraft became first kind of an offline experience

where you kind of start to play these missions, right?

You play against some sort of scripted things

to develop the story of the characters in the game.

And then later on, I start playing against the built in AI,

and I thought it was impossible to defeat it.

Then eventually you defeat one

and you can actually play against seven built in AIs

at the same time, which also felt impossible.

But actually, it’s not that hard to beat

seven built in AIs at once.

So once we achieved that, also we discovered that

we could play, as I said, internet wasn’t that great,

but we could play with the LAN, right?

Like basically against each other

if we were in the same place

because you could just connect machines with like cables,

right?

So we started playing in LAN mode

and as a group of friends,

and it was really, really like much more entertaining

than playing against AIs.

And later on, as internet was starting to develop

and being a bit faster and more reliable,

then it’s when I started experiencing Battle.net,

which is this amazing universe,

not only because of the fact

that you can play the game against anyone in the world,

but you can also get to know more people.

You just get exposed to now like this vast variety of,

it’s kind of a bit when the chats came about, right?

There was a chat system.

You could play against people,

but you could also chat with people,

not only about Stalker, but about anything.

And that became a way of life for kind of two years.

And obviously then it became like kind of,

it exploded in me in that I started to play more seriously,

going to tournaments and so on and so forth.

Do you have a sense on a societal, sociological level,

what’s this whole part of society

that many of us are not aware of

and it’s a huge part of society, which is gamers.

I mean, every time I come across that in YouTube

or streaming sites, I mean,

this is the huge number of people play games religiously.

Do you have a sense of those folks,

especially now that you’ve returned to that realm

a little bit on the AI side?

Yeah, so in fact, even after Stalker,

I actually played World of Warcraft,

which is maybe the main sort of online worlds

or in presence that you get to interact

with lots of people.

So I played that for a little bit.

It was to me, it was a bit less stressful than StarCraft

because winning was kind of a given.

You just put in this world

and you can always complete missions.

But I think it was actually the social aspect

of especially StarCraft first

and then games like World of Warcraft

really shaped me in a very interesting ways

because what you get to experience

is just people you wouldn’t usually interact with, right?

So even nowadays, I still have many Facebook friends

from the area where I played online

and their ways of thinking is even political.

They just, we don’t live in,

like we don’t interact in the real world,

but we were connected by basically fiber.

And that way I actually get to understand a bit better

that we live in a diverse world.

And these were just connections that were made by,

because, you know, I happened to go in a city

in a virtual city as a priest and I met this warrior

and we became friends

and then we start like playing together, right?

So I think it’s transformative

and more and more and more people are more aware of it.

I mean, it’s becoming quite mainstream,

but back in the day, as you were saying in 2000, 2005,

even it was very, still very strange thing to do,

especially in Europe.

I think there were exceptions like Korea, for instance,

it was amazing that everything happened so early

in terms of cybercafes, like if you go to Seoul,

it’s a city that back in the day,

StarCraft was kind of,

you could be a celebrity by playing StarCraft,

but this was like 99, 2000, right?

It’s not like recently.

So yeah, it’s quite interesting to look back

and yeah, I think it’s changing society.

The same way, of course, like technology

and social networks and so on are also transforming things.

And a quick tangent, let me ask,

you’re also one of the most productive people

in your particular chosen passion and path in life.

And yet you’re also appreciate and enjoy video games.

Do you think it’s possible to do,

to enjoy video games in moderation?

Someone told me that you could choose two out of three.

When I was playing video games,

you could choose having a girlfriend,

playing video games or studying.

And I think for the most part, it was relatively true.

These things do take time.

Games like StarCraft,

if you take the game pretty seriously

and you wanna study it,

then you obviously will dedicate more time to it.

And I definitely took gaming

and obviously studying very seriously.

I love learning science and et cetera.

So to me, especially when I started university undergrad,

I kind of step off StarCraft.

I actually fully stopped playing.

And then World of Warcraft was a bit more casual.

You could just connect online.

And I mean, it was fun.

But as I said, that was not as much time investment

as it was for me in StarCraft.

Okay, so let’s get into AlphaStar.

What are the, you’re behind the team.

So DeepMind has been working on StarCraft

and released a bunch of cool open source agents

and so on the past few years.

But AlphaStar really is the moment

where the first time you beat a world class player.

So what are the parameters of the challenge

in the way that AlphaStar took it on

and how did you and David

and the rest of the DeepMind team get into it?

Consider that you can even beat the best in the world

or top players.

I think it all started back in 2015.

Actually, I’m lying.

I think it was 2014 when DeepMind was acquired by Google.

And I at the time was at Google Brain,

which was in California, is still in California.

We had this summit where we got together, the two groups.

So Google Brain and Google DeepMind got together

and we gave a series of talks.

And given that they were doing

deep reinforcement learning for games,

I decided to bring up part of my past,

which I had developed at Berkeley,

like this thing which we call Berkeley OverMind,

which is really just a StarCraft one bot, right?

So I talked about that.

And I remember Demis just came to me and said,

well, maybe not now, it’s perhaps a bit too early,

but you should just come to DeepMind

and do this again with deep reinforcement learning, right?

And at the time it sounded very science fiction

for several reasons.

But then in 2016, when I actually moved to London

and joined DeepMind transferring from Brain,

it became apparent that because of the AlphaGo moment

and kind of Blizzard reaching out to us to say,

wait, like, do you want the next challenge?

And also me being full time at DeepMind,

so sort of kind of all these came together.

And then I went to Irvine in California,

to the Blizzard headquarters to just chat with them

and try to explain how would it all work

before you do anything.

And the approach has always been

about the learning perspective, right?

So in Berkeley, we did a lot of rule based conditioning

and if you have more than three units, then go attack.

And if the other has more units than me,

I retreat and so on and so forth.

And of course, the point of deep reinforcement learning,

deep learning, machine learning in general

is that all these should be learned behavior.

So that kind of was the DNA of the project

since its inception in 2016,

where we just didn’t even have an environment to work with.

And so that’s how it all started really.

So if you go back to that conversation with Demis

or even in your own head, how far away did you,

because we’re talking about Atari games,

we’re talking about Go, which is kind of,

if you’re honest about it, really far away from StarCraft.

In, well, now that you’ve beaten it,

maybe you could say it’s close,

but it’s much, it seems like StarCraft

is way harder than Go philosophically

and mathematically speaking.

So how far away did you think you were?

Do you think it’s 2019 and 18

you could be doing as well as you have?

Yeah, when I kind of thought about,

okay, I’m gonna dedicate a lot of my time

and focus on this.

And obviously I do a lot of different research

in deep learning.

So spending time on it, I mean,

I really had to kind of think

there’s gonna be something good happening out of this.

So really I thought, well, this sounds impossible.

And it probably is impossible to do the full thing,

like the full game where you play one versus one

and it’s only a neural network playing and so on.

So it really felt like,

I just didn’t even think it was possible.

But on the other hand,

I could see some stepping stones towards that goal.

Clearly you could define sub problems in StarCraft

and sort of dissect it a bit and say,

okay, here is a part of the game, here’s another part.

And also obviously the fact,

so this was really also critical to me,

the fact that we could access human replays, right?

So Blizzard was very kind.

And in fact, they open source these for the whole community

where you can just go

and it’s not every single StarCraft game ever played,

but it’s a lot of them you can just go and download.

And every day they will,

you can just query a data set and say,

well, give me all the games that were played today.

And given my kind of experience with language

and sequences and supervised learning,

I thought, well, that’s definitely gonna be very helpful

and something quite unique now,

because ever before we had such a large data set of replays,

of people playing the game at this scale

of such a complex video game, right?

So that to me was a precious resource.

And as soon as I knew that Blizzard

was able to kind of give this to the community,

I started to feel positive

about something non trivial happening.

But I also thought the full thing, like really no rules,

no single line of code that tries to say,

well, I mean, if you see this unit, build a detector,

all these, not having any of these specializations

seemed really, really, really difficult to me.

Intuitively.

I do also like that Blizzard was teasing

or even trolling you,

sort of almost, yeah, pulling you in

into this really difficult challenge.

Do they have any awareness?

What’s the interest from the perspective of Blizzard,

except just curiosity?

Yeah, I think Blizzard has really understood

and really bring forward this competitiveness

of esports in games.

The StarCraft really kind of sparked a lot of,

like something that almost was never seen,

especially as I was saying, back in Korea.

So they just probably thought,

well, this is such a pure one versus one setup

that it would be great to see

if something that can play Atari or Go

and then later on chess could even tackle

these kind of complex real time strategy game, right?

So for them, they wanted to see first,

obviously whether it was possible,

if the game they created was in a way solvable

to some extent.

And I think on the other hand,

they also are a pretty modern company that innovates a lot.

So just starting to understand AI for them

to how to bring AI into games

is not AI for games, but games for AI, right?

I mean, both ways I think can work.

And we obviously at DeepMind use games for AI, right?

To drive AI progress,

but Blizzard might actually be able to do

and many other companies to start to understand

and do the opposite.

So I think that is also something

they can get out of these.

And they definitely, we have brainstormed a lot

about these, right?

But one of the interesting things to me

about StarCraft and Diablo

and these games that Blizzard has created

is the task of balancing classes, for example.

Sort of making the game fair from the starting point

and then let skill determine the outcome.

Is there, I mean, can you first comment,

there’s three races, Zerg, Protoss and Terran.

I don’t know if I’ve ever said that out loud.

Is that how you pronounce it?

Terran?

Yeah, Terran.

Yeah.

Yeah, I don’t think I’ve ever in person interacted

with anybody about StarCraft, that’s funny.

So they seem to be pretty balanced.

I wonder if the AI, the work that you’re doing

with AlphaStar would help balance them even further.

Is that something you think about?

Is that something that Blizzard is thinking about?

Right, so balancing when you add a new unit

or a new spell type is obviously possible

given that you can always train or pre train at scale

some agent that might start using that in unintended ways.

But I think actually, if you understand

how StarCraft has kind of co evolved with players,

in a way, I think it’s actually very cool

the ways that many of the things and strategies

that people came up with, right?

So I think we’ve seen it over and over in StarCraft

that Blizzard comes up with maybe a new unit

and then some players get creative

and do something kind of unintentional

or something that Blizzard designers

that just simply didn’t test or think about.

And then after that becomes kind of mainstream

in the community, Blizzard patches the game

and then they kind of maybe weaken that strategy

or make it actually more interesting

but a bit more balanced.

So these kind of continual talk between players

and Blizzard is kind of what has defined them actually

in actually most games in StarCraft

but also in World of Warcraft, they would do that.

There are several classes and it would be not good

that everyone plays absolutely the same race and so on, right?

So I think they do care about balancing of course

and they do a fair amount of testing

but it’s also beautiful to also see

how players get creative anyways.

And I mean, whether AI can be more creative at this point,

I don’t think so, right?

I mean, it’s just sometimes something so amazing happens.

Like I remember back in the days,

like you have these drop ships that could drop the rivers

and that was actually not thought about

that you could drop this unit

that has this what’s called splash damage

that would basically eliminate

all the enemies workers at once.

No one thought that you could actually put them

in really early game, do that kind of damage

and then things change in the game.

But I don’t know, I think it’s quite an amazing

exploration process from both sides,

players and Blizzard alike.

Well, it’s almost like a reinforcement learning exploration

but the scale of humans that play Blizzard games

is almost on the scale of a large scale

deep mind RL experiment.

I mean, if you look at the numbers,

I mean, you’re talking about, I don’t know how many games

but hundreds of thousands of games probably a month.

Yeah.

I mean, so it’s almost the same as running RL agents.

What aspect of the problem of Starcraft

do you think is the hardest?

Is it the, like you said, the imperfect information?

Is it the fact they have to do longterm planning?

Is it the real time aspects?

We have to do stuff really quickly.

Is it the fact that a large action space

so you can do so many possible things?

Or is it, you know, in the game theoretic sense

there is no Nash equilibrium

or at least you don’t know what the optimal strategy is

because there’s way too many options.

Right.

Is there something that stands out as just like the hardest

the most annoying thing?

So when we sort of looked at the problem

and start to define like the parameters of it, right?

What are the observations?

What are the actions?

It became very apparent that, you know,

the very first barrier that one would hit in Starcraft

would be because of the action space being so large

and as not being able to search like you could in chess

or go even though the search space is vast.

The main problem that we identified

was that of exploration, right?

So without any sort of human knowledge or human prior,

if you think about Starcraft

and you know how deep reinforcement learnings algorithm

work which is essentially by issuing random actions

and hoping that they will get some wins sometimes

so they could learn.

So if you think of the action space in Starcraft

almost anything you can do in the early game is bad

because any action involves taking workers

which are mining minerals for free.

That’s something that the game does automatically

sends them to mine.

And you would immediately just take them out of mining

and send them around.

So just thinking how is it gonna be possible

to get to understand these concepts

but even more like expanding, right?

There’s these buildings you can place

in other locations in the map to gather more resources

but the location of the building is important

and you have to select a worker,

send it walking to that location, build the building,

wait for the building to be built

and then put extra workers there so they start mining.

That feels like impossible if you just randomly click

to produce that state, desirable state

that then you could hope to learn from

because eventually that may yield to an extra win, right?

So for me, the exploration problem

and due to the action space

and the fact that there’s not really turns,

there’s so many turns because the game essentially

takes that 22 times per second.

I mean, that’s how they could discretize sort of time.

Obviously you always have to discretize time

but there’s no such thing as real time

but it’s really a lot of time steps

of things that could go wrong.

And that definitely felt a priori like the hardest.

You mentioned many good ones.

I think partial observability

and the fact that there is no perfect strategy

because of the partial observability.

Those are very interesting problems.

We start seeing more and more now

in terms of as we solve the previous ones

but the core problem to me was exploration

and solving it has been basically kind of the focus

and how we saw the first breakthroughs.

So exploration in a multi hierarchical way.

So like 22 times a second exploration

has a very different meaning than it does

in terms of should I gather resources early

or should I wait or so on.

So how do you solve the longterm?

Let’s talk about the internals of AlphaStar.

So first of all, how do you represent the state

of the game as an input?

How do you then do the longterm sequence modeling?

How do you build a policy?

What’s the architecture like?

So AlphaStar has obviously several components

but everything passes through what we call the policy

which is a neural network.

And that’s kind of the beauty of it.

There is, I could just now give you a neural network

and some weights.

And if you fed the right observations

and you understood the actions the same way we do

you would have basically the agent playing the game.

There’s absolutely nothing else needed

other than those weights that were trained.

Now, the first step is observing the game

and we’ve experimented with a few alternatives.

The one that we currently use mixes both spatial

sort of images that you would process from the game

that is the zoomed out version of the map

and also a zoomed in version of the camera

or the screen as we call it.

But also we give to the agent the list of units

that it sees more of as a set of objects

that it can operate on.

That is not necessarily required to use it.

And we have versions of the game that play well

without this set vision that is a bit not like

how humans perceive the game.

But it certainly helps a lot

because it’s a very natural way to encode the game

is by just looking at all the units that there are.

They have properties like health, position, type of unit

whether it’s my unit or the enemies.

And that sort of is kind of the summary

of the state of the game,

that list of units or set of units

that you see all the time.

But that’s pretty close to the way humans see the game.

Why do you say it’s not, isn’t that,

you’re saying the exactness of it is not similar to humans?

The exactness of it is perhaps not the problem.

I guess maybe the problem if you look at it

from how actually humans play the game

is that they play with a mouse and a keyboard and a screen

and they don’t see sort of a structured object

with all the units.

What they see is what they see on the screen, right?

So.

Remember that there’s a, sorry to interrupt,

there’s a plot that you showed with camera base

where you do exactly that, right?

You move around and that seems to converge

to similar performance.

Yeah, I think that’s what I,

we’re kind of experimenting with what’s necessary or not,

but using the set.

So, actually, if you look at research in computer vision,

where it makes a lot of sense to treat images

as two dimensional arrays,

there’s actually a very nice paper from Facebook.

I think, I forgot who the authors are,

but I think it’s part of Caming’s group.

And what they do is they take an image,

which is this two dimensional signal,

and they actually take pixel by pixel

and scramble the image as if it was just a list of pixels.

Crucially, they encode the position of the pixels

with the X, Y coordinates.

And this is just kind of a new architecture,

which we incidentally also use in StarCraft

called the Transformer,

which is a very popular paper from last year,

which yielded very nice result in machine translation.

And if you actually believe in this kind of,

oh, it’s actually a set of pixels,

as long as you encode X, Y, it’s okay,

then you could argue that the list of units that we see

is precisely that,

because we have each unit as a kind of pixel, if you will,

and then their X, Y coordinates.

So in that perspective, we, without knowing it,

we use the same architecture that was shown

to work very well on Pascal and ImageNet and so on.

So the interesting thing here is putting it in that way

it starts to move it towards

the way you usually work with language.

So what, and especially with your expertise

and work in language,

it seems like there’s echoes of a lot of

the way you would work with natural language

in the way you’ve approached AlphaStar.

Right.

What’s, does that help

with the longterm sequence modeling there somehow?

Exactly, so now that we understand

what an observation for a given time step is,

we need to move on to say,

well, there’s going to be a sequence of such observations

and an agent will need to, given all that it’s seen,

not only the current time step, but all that it’s seen, why?

Because there is partial observability.

We must remember whether we saw a worker going somewhere,

for instance, right?

Because then there might be an expansion

on the top right of the map.

So given that, what you must then think about is

there is the problem of given all the observations,

you have to predict the next action.

And not only given all the observations,

but given all the observations

and given all the actions you’ve taken,

predict the next action.

And that sounds exactly like machine translation where,

and that’s exactly how kind of I saw the problem,

especially when you are given supervised data

or replays from humans,

because the problem is exactly the same.

You’re translating essentially a prefix of observations

and actions onto what’s going to happen next,

which is exactly how you would train a model to translate

or to generate language as well, right?

Do you have a certain prefix?

You must remember everything that comes in the past

because otherwise you might start having noncoherent text.

And the same architectures we’re using LSTMs

and transformers to operate on across time

to kind of integrate all that’s happened in the past.

Those architectures that work so well in translation

or language modeling are exactly the same

than what the agent is using to issue actions in the game.

And the way we train it, moreover, for imitation,

which is step one of AlphaStar is,

take all the human experience and try to imitate it,

much like you try to imitate translators

that translated many pairs of sentences

from French to English say,

that sort of principle applies exactly the same.

It’s almost the same code, except that instead of words,

you have a slightly more complicated objects,

which are the observations and the actions

are also a bit more complicated than a word.

Is there a self play component then too?

So once you run out of imitation?

Right, so indeed you can bootstrap from human replays,

but then the agents you get are actually not as good

as the humans you imitated, right?

So how do we imitate?

Well, we take humans from 3000 MMR and higher.

3000 MMR is just a metric of human skill

and 3000 MMR might be like 50% percentile, right?

So it’s just average human.

What’s that?

So maybe quick pause, MMR is a ranking scale,

the matchmaking rating for players.

So it’s 3000, I remember there’s like a master

and a grand master, what’s 3000?

So 3000 is pretty bad.

I think it’s kind of goals level.

It just sounds really good relative to chess, I think.

Oh yeah, yeah, no, the ratings,

the best in the world are at 7,000 MMR.

So 3000, it’s a bit like Elo indeed, right?

So 3,500 just allows us to not filter a lot of the data.

So we like to have a lot of data in deep learning

as you probably know.

So we take these kind of 3,500 and above,

but then we do a very interesting trick,

which is we tell the neural network

what level they are imitating.

So we say, this replay you’re gonna try to imitate

to predict the next action for all the actions

that you’re gonna see is a 4,000 MMR replay.

This one is a 6,000 MMR replay.

And what’s cool about this is then we take this policy

that is being trained from human,

and then we can ask it to play like a 3000 MMR player

by setting a beat saying, well, okay,

play like a 3000 MMR player

or play like a 6,000 MMR player.

And you actually see how the policy behaves differently.

It gets worse economy if you play like a goal level player,

it does less actions per minute,

which is the number of clicks or number of actions

that you will issue in a whole minute.

And it’s very interesting to see

that it kind of imitates the skill level quite well.

But if we ask it to play like a 6,000 MMR player,

we tested, of course, these policies to see how well they do.

They actually beat all the built in AIs

that Blizzard put in the game,

but they’re nowhere near 6,000 MMR players, right?

They might be maybe around goal level, platinum, perhaps.

So there’s still a lot of work to be done for the policy

to truly understand what it means to win.

So far, we only asked them, okay, here is the screen.

And that’s what’s happened on the game until this point.

What would the next action be if we ask a pro to now say,

oh, you’re gonna click here or here or there.

And the point is experiencing wins and losses

is very important to then start to refine.

Otherwise the policy can get loose,

can just go off policy as we call it.

That’s so interesting that you can at least hope eventually

to be able to control a policy

approximately to be at some MMR level.

That’s so interesting, especially given that you have

ground truth for a lot of these cases.

Can I ask you a personal question?

What’s your MMR?

Well, I haven’t played StarCraft II, so I am unranked,

which is the kind of lowest league.

So I used to play StarCraft, the first one.

But you haven’t seriously played StarCraft II.

So the best player we have at DeepMind is about 5,000 MMR,

which is high masters.

It’s not at grand master level.

Grand master level will be the top 200 players

in a certain region like Europe or America or Asia.

But for me, it would be hard to say.

I am very bad at the game.

I actually played AlphaStar a bit too late and it beat me.

I remember the whole team was, oh, Oreo, you should play.

And I was, oh, it looks like it’s not so good yet.

And then I remember I kind of got busy

and waited an extra week and I played

and it really beat me very badly.

Was that, I mean, how did that feel?

Isn’t that an amazing feeling?

That’s amazing, yeah.

I mean, obviously I tried my best

and I tried to also impress my,

because I actually played the first game.

So I’m still pretty good at micromanagement.

The problem is I just don’t understand StarCraft II.

I understand StarCraft.

And when I played StarCraft,

I probably was consistently like for a couple of years,

top 32 in Europe.

So I was decent, but at the time we didn’t have

this kind of MMR system as well established.

So it would be hard to know what it was back then.

So what’s the difference in interface

between AlphaStar and StarCraft

and a human player in StarCraft?

Is there any significant differences

between the way they both see the game?

I would say the way they see the game,

there’s a few things that are just very hard to simulate.

The main one perhaps, which is obvious in hindsight

is what’s called cloaked units, which are invisible units.

So in StarCraft, you can make some units

that you need to have a particular kind of unit

to detect it.

So these units are invisible.

If you cannot detect them, you cannot target them.

So they would just destroy your buildings

or kill your workers.

But despite the fact you cannot target the unit,

there’s a shimmer that as a human you observe.

I mean, you need to train a little bit,

you need to pay attention,

but you would see this kind of space time distortion

and you would know, okay, there are, yeah.

Yeah, there’s like a wave thing.

Yeah, it’s called shimmer.

Space time distortion, I like it.

That’s really like, the Blizzard term is shimmer.

Shimmer, okay.

And so these shimmer professional players

actually can see it immediately.

They understand it very well,

but it’s still something that requires

certain amount of attention

and it’s kind of a bit annoying to deal with.

Whereas for AlphaStar, in terms of vision,

it’s very hard for us to simulate sort of,

oh, are you looking at this pixel in the screen and so on?

So the only thing we can do is,

there is a unit that’s invisible over there.

So AlphaStar would know that immediately.

Obviously still obeys the rules.

You cannot attack the unit.

You must have a detector and so on,

but it’s kind of one of the main things

that it just doesn’t feel there’s a very proper way.

I mean, you could imagine, oh, you don’t have hypers.

Maybe you don’t know exactly where it is,

or sometimes you see it, sometimes you don’t,

but it’s just really, really complicated to get it

so that everyone would agree,

oh, that’s the best way to simulate this, right?

It seems like a perception problem.

It is a perception problem.

So the only problem is people, you ask,

oh, what’s the difference between

how humans perceive the game?

I would say they wouldn’t be able to tell a shimmer

immediately as it appears on the screen,

whereas AlphaStar in principle sees it very sharply, right?

It sees that the bit turned from zero to one,

meaning there’s now a unit there,

although you don’t know the unit,

or you know that you cannot attack it and so on.

So that from a vision standpoint,

that probably is the one that is kind of the most obvious one.

Then there are things humans cannot do perfectly,

even professionals, which is they might miss a detail,

or they might have not seen a unit.

And obviously as a computer,

if there’s a corner of the screen that turns green

because a unit enters the field of view,

that can go into the memory of the agent, the LSTM,

and persist there for a while,

and for however long is relevant, right?

And in terms of action,

it seems like the rate of action from AlphaStar

is comparative, if not slower than professional players,

but it’s more precise is what I read.

So that’s really probably the one that is causing us

more issues for a couple of reasons, right?

The first one is StarCraft has been an AI environment

for quite a few years.

In fact, I mean, I was participating

in the very first competition back in 2010.

And there’s really not been a kind of a very clear set

of rules how the actions per minute,

the rate of actions that you can issue is.

And as a result, these agents or bots that people build

in a kind of almost very cool way,

they do like 20,000, 40,000 actions per minute.

Now, to put this in perspective,

a very good professional human

might do 300 to 800 actions per minute.

They might not be as precise.

That’s why the range is a bit tricky to identify exactly.

I mean, 300 actions per minute precisely

is probably realistic.

800 is probably not, but you see humans doing a lot of actions

because they warm up and they kind of select things

and spam and so on just so that when they need,

they have the accuracy.

So we came into this by not having kind of a standard way

to say, well, how do we measure whether an agent is

at human level or not?

On the other hand, we had a huge advantage,

which is because we do imitation learning,

agents turned out to act like humans

in terms of rate of actions, even

precisions and imprecisions of actions

in the supervised policy.

You could see all these.

You could see how agents like to spam click, to move here.

If you played especially Diablo, you wouldn’t know what I mean.

I mean, you just like spam, oh, move here, move here,

move here.

You’re doing literally like maybe five actions

in two seconds, but these actions are not

very meaningful.

One would have sufficed.

So on the one hand, we start from this imitation policy

that is at the ballpark of the actions per minutes of humans

because it’s actually statistically

trying to imitate humans.

So we see these very nicely in the curves

that we showed in the blog post.

There’s these actions per minute,

and the distribution looks very human like.

But then, of course, as self play kicks in,

and that’s the part we haven’t talked too much yet,

but of course, the agent must play against itself to improve,

then there’s almost no guarantees

that these actions will not become more precise

or even the rate of actions is going to increase over time.

So what we did, and this is probably

the first attempt that we thought was reasonable,

is we looked at the distribution of actions

for humans for certain windows of time.

And just to give a perspective, because I guess I mentioned

that some of these agents that are programmatic,

let’s call them.

They do 40,000 actions per minute.

Professionals, as I said, do 300 to 800.

So what we looked is we look at the distribution

over professional gamers, and we took reasonably high actions

per minute, but we kind of identify certain cutoffs

after which, even if the agent wanted to act,

these actions would be dropped.

But the problem is this cutoff is probably set a bit too high.

And what ends up happening, even though the games,

and when we ask the professionals and the gamers,

by and large, they feel like it’s playing humanlike,

there are some agents that developed maybe slightly

too high APMs, which is actions per minute,

combined with the precision, which

made people start discussing a very interesting issue, which

is, should we have limited these?

Should we just let it lose and see what cool things

it can come up with?

Right?

Interesting.

So this is in itself an extremely interesting

question, but the same way that modeling the shimmer

would be so difficult, modeling absolutely all the details

about muscles and precision and tiredness of humans

would be quite difficult.

So we’re really here kind of innovating

in this sense of, OK, what could be maybe

the next iteration of putting more rules that

makes the agents more humanlike in terms of restrictions?

Yeah, putting constraints that.

More constraints, yeah.

That’s really interesting.

That’s really innovative.

So one of the constraints you put on yourself,

or at least focused in, is on the Protoss race,

as far as I understand.

Can you tell me about the different races

and how they, so Protoss, Terran, and Zerg,

how do they compare?

How do they interact?

Why did you choose Protoss?

Yeah, in the dynamics of the game seen

from a strategic perspective.

So Protoss, so in StarCraft there are three races.

Indeed, in the demonstration, we saw only the Protoss race.

So maybe let’s start with that one.

Protoss is kind of the most technologically advanced race.

It has units that are expensive but powerful.

So in general, you want to kind of conserve your units

as you go attack.

And then you want to utilize these tactical advantages

of very fancy spells and so on and so forth.

And at the same time, they’re kind of,

people say they’re a bit easier to play perhaps.

But that I actually didn’t know.

I mean, I just talked now a lot to the players

that we work with, TLO and Mana, and they said, oh yeah,

Protoss is actually, people think,

is actually one of the easiest races.

So perhaps the easier, that doesn’t

mean that it’s obviously professional players

excel at the three races.

And there’s never a race that dominates

for a very long time anyway.

So if you look at the top, I don’t know, 100 in the world,

is there one race that dominates that list?

It would be hard to know because it depends on the regions.

I think it’s pretty equal in terms of distribution.

And Blizzard wants it to be equal.

They wouldn’t want one race like Protoss

to not be representative in the top place.

So definitely, they tried it to be balanced.

So then maybe the opposite race of Protoss is Zerg.

Zerg is a race where you just kind of expand and take over

as many resources as you can, and they

have a very high capacity to regenerate their units.

So if you have an army, it’s not that valuable in terms

of losing the whole army is not a big deal as Zerg

because you can then rebuild it.

And given that you generally accumulate

a huge bank of resources, Zergs typically

play by applying a lot of pressure,

maybe losing their whole army, but then rebuilding it

quickly.

So although, of course, every race, I mean, there’s never,

I mean, they’re pretty diverse.

I mean, there are some units in Zerg that

are technologically advanced, and they do

some very interesting spells.

And there’s some units in Protoss that are less valuable,

and you could lose a lot of them and rebuild them,

and it wouldn’t be a big deal.

All right, so maybe I’m missing out.

Maybe I’m going to say some dumb stuff, but summary

of strategy.

So first, there’s collection of a lot of resources.

That’s one option.

The other one is expanding, so building other bases.

Then the other is obviously building units

and attacking with those units.

And then I don’t know what else there is.

Maybe there’s the different timing of attacks,

like do I attack early, attack late?

What are the different strategies that emerged

that you’ve learned about?

I’ve read that a bunch of people are super happy

that you guys have apparently, that Alpha Star apparently

has discovered that it’s really good to,

what is it, saturate?

Oh yeah, the mineral line.

Yeah, the mineral line.

Yeah, yeah.

And that’s for greedy amateur players like myself.

That’s always been a good strategy.

You just build up a lot of money,

and it just feels good to just accumulate and accumulate.

So thank you for discovering that and validating all of us.

But is there other strategies that you discovered

that are interesting, unique to this game?

Yeah, so if you look at the kind of,

not being a StarCraft II player,

but of course StarCraft and StarCraft II

and real time strategy games in general are very similar.

I would classify perhaps the openings of the game.

They’re very important.

And generally I would say there’s two kinds of openings.

One that’s a standard opening.

That’s generally how players find sort of a balance

between risk and economy and building some units early on

so that they could defend,

but they’re not too exposed basically,

but also expanding quite quickly.

So this would be kind of a standard opening.

And within a standard opening,

then what you do choose generally is

what technology are you aiming towards?

So there’s a bit of rock, paper, scissors

of you could go for spaceships

or you could go for invisible units

or you could go for, I don’t know,

like massive units that attack against certain kinds

of units, but they’re weak against others.

So standard openings themselves have some choices

like rock, paper, scissors style.

Of course, if you scout and you’re good

at guessing what the opponent is doing,

then you can play as an advantage

because if you know you’re gonna play rock,

I mean, I’m gonna play paper obviously.

So you can imagine that normal standard games

in StarCraft looks like a continuous rock, paper,

scissors game where you guess what the distribution

of rock, paper, and scissors is from the enemy

and reacting accordingly to try to beat it

or put the paper out before he kind of changes his mind

from rock to scissors,

and then you would be in a weak position.

So, sorry to pause on that.

I didn’t realize this element

because I know it’s true with poker.

I know I looked at Labratus.

So you’re also estimating trying to guess the distribution,

trying to better and better estimate the distribution

of what the opponent is likely to be doing.

Yeah, I mean, as a player,

you definitely wanna have a belief state

over what’s up on the other side of the map.

And when your belief state becomes inaccurate,

when you start having that serious doubts,

whether he’s gonna play something that you must know,

that’s when you scout.

You wanna then gather information, right?

Is improving the accuracy of the belief

or improving the belief state part of the loss

that you’re trying to optimize?

Or is it just a side effect?

It’s implicit, but you could explicitly model it,

and it would be quite good at probably predicting

what’s on the other side of the map.

But so far, it’s all implicit.

There’s no additional reward for predicting the enemy.

So there’s these standard openings,

and then there’s what people call cheese,

which is very interesting.

And AlphaStar sometimes really likes this kind of cheese.

These cheeses, what they are is kind of an all in strategy.

You’re gonna do something sneaky.

You’re gonna hide your own buildings

close to the enemy base,

or you’re gonna go for hiding your technological buildings

so that you do invisible units

and the enemy just cannot react to detect it

and thus lose the game.

And there’s quite a few of these cheeses

and variants of them.

And there it’s where actually the belief state

becomes even more important.

Because if I scout your base and I see no buildings at all,

any human player knows something’s up.

They might know, well,

you’re hiding something close to my base.

Should I build suddenly a lot of units to defend?

Should I actually block my ramp with workers

so that you cannot come and destroy my base?

So there’s all this is happening

and defending against cheeses is extremely important.

And in the AlphaStar League,

many agents actually develop some cheesy strategies.

And in the games we saw against TLO and Mana,

two out of the 10 agents

were actually doing these kind of strategies

which are cheesy strategies.

And then there’s a variant of cheesy strategy

which is called all in.

So an all in strategy is not perhaps as drastic as,

oh, I’m gonna build cannons on your base

and then bring all my workers

and try to just disrupt your base and game over,

or GG as we say in StarCraft.

There’s these kind of very cool things

that you can align precisely at a certain time mark.

So for instance,

you can generate exactly 10 unit composition

that is perfect, like five of this type,

five of this other type,

and align the upgrade

so that at four minutes and a half, let’s say,

you have these 10 units and the upgrade just finished.

And at that point, that army is really scary.

And unless the enemy really knows what’s going on,

if you push, you might then have an advantage

because maybe the enemy is doing something more standard,

it expanded too much, it developed too much economy,

and it trade off badly against having defenses,

and the enemy will lose.

But it’s called all in because if you don’t win,

then you’re gonna lose.

So you see players that do these kinds of strategies,

if they don’t succeed, game is not over.

I mean, they still have a base

and they still gathering minerals,

but they will just GG out of the game

because they know, well, game is over.

I gambled and I failed.

So if we start entering the game theoretic aspects

of the game, it’s really rich and it’s really,

that’s why it also makes it quite entertaining to watch.

Even if I don’t play, I still enjoy watching the game.

But the agents are trying to do this mostly implicitly.

But one element that we improved in self play

is creating the Alpha Star League.

And the Alpha Star League is not pure self play.

It’s trying to create a different personalities of agents

so that some of them will become cheesy agents.

Some of them might become very economical, very greedy,

like getting all the resources,

but then being maybe early on, they’re gonna be weak,

but later on, they’re gonna be very strong.

And by creating this personality of agents,

which sometimes it just happens naturally

that you can see kind of an evolution of agents

that given the previous generation,

they train against all of them

and then they generate kind of the perfect counter

to that distribution.

But these agents, you must have them in the populations

because if you don’t have them,

you’re not covered against these things.

You wanna create all sorts of the opponents

that you will find in the wild.

So you can be exposed to these cheeses, early aggression,

later aggression, more expansions,

dropping units in your base from the side, all these things.

And pure self play is getting a bit stuck

at finding some subset of these, but not all of these.

So the Alpha Star League is a way

to kind of do an ensemble of agents

that they’re all playing in a league,

much like people play on Battle.net, right?

They play, you play against someone

who does a new cool strategy and you immediately,

oh my God, I wanna try it, I wanna play again.

And this to me was another critical part of the problem,

which was, can we create a Battle.net for agents?

And that’s kind of what the Alpha Star League really is.

That’s fascinating.

And where they stick to their different strategies.

Yeah, wow, that’s really, really interesting.

But that said, you were fortunate enough

or just skilled enough to win five, zero.

And so how hard is it to win?

I mean, that’s not the goal.

I guess, I don’t know what the goal is.

The goal should be to win majority, not five, zero,

but how hard is it in general to win all matchups

on a one V one?

So that’s a very interesting question

because once you see Alpha Star and superficially

you think, well, okay, it won.

Let’s, if you sum all the games like 10 to one, right?

It lost the game that it played with the camera interface.

You might think, well, that’s done, right?

It’s superhuman at the game.

And that’s not really the claim we really can make actually.

The claim is we beat a professional gamer

for the first time.

StarCraft has really been a thing

that has been going on for a few years,

but a moment like this had not occurred before yet.

But are these agents impossible to beat?

Absolutely not, right?

So that’s a bit what’s kind of the difference is

the agents play at grandmaster level.

They definitely understand the game enough

to play extremely well, but are they unbeatable?

Do they play perfect?

No, and actually in StarCraft,

because of these sneaky strategies,

it’s always possible that you might take a huge risk

sometimes, but you might get wins, right?

Out of this.

So I think that as a domain,

it still has a lot of opportunities,

not only because of course we wanna learn

with less experience, we would like to,

I mean, if I learned to play Protoss,

I can play Terran and learn it much quicker

than Alpha Star can, right?

So there are obvious interesting research challenges

as well, but even as the raw performance goes,

really the claim here can be we are at pro level

or at high grandmaster level,

but obviously the players also did not know what to expect,

right?

Their prior distribution was a bit off

because they played this kind of new like alien brain

as they like to say it, right?

And that’s what makes it exciting for them.

But also I think if you look at the games closely,

you see there were weaknesses in some points,

maybe Alpha Star did not scout,

or if it had invisible units going against

at certain points, it wouldn’t have known

and it would have been bad.

So there’s still quite a lot of work to do,

but it’s really a very exciting moment for us

to be seeing, wow, a single neural net on a GPU

is actually playing against these guys

who are amazing.

I mean, you have to see them play in life.

They’re really, really amazing players.

Yeah, I’m sure there must be a guy in Poland

somewhere right now training his butt off

to make sure that this never happens again with Alpha Star.

So that’s really exciting in terms of Alpha Star

having some holes to exploit, which is great.

And then we build on top of each other

and it feels like StarCraft on let go,

even if you win, it’s still not,

there’s so many different dimensions

in which you can explore.

So that’s really, really interesting.

Do you think there’s a ceiling to Alpha Star?

You’ve said that it hasn’t reached,

you know, this is a big,

wait, let me actually just pause for a second.

How did it feel to come here to this point,

to beat a top professional player?

Like that night, I mean, you know,

Olympic athletes have their gold medal, right?

This is your gold medal in a sense.

Sure, you’re cited a lot,

you’ve published a lot of prestigious papers, whatever,

but this is like a win.

How did it feel?

I mean, it was, for me, it was unbelievable

because first the win itself,

I mean, it was so exciting.

I mean, so looking back to those last days of 2018 really,

that’s when the games were played.

I’m sure I look back at that moment, I’ll say,

oh my God, I want to be in a project like that.

It’s like, I already feel the nostalgia of like,

yeah, that was huge in terms of the energy

and the team effort that went into it.

And so in that sense, as soon as it happened,

I already knew it was kind of,

I was losing it a little bit.

So it is almost like sad that it happened and oh my God,

but on the other hand, it also verifies the approach.

But to me also, there’s so many challenges

and interesting aspects of intelligence

that even though we can train a neural network

to play at the level of the best humans,

there’s still so many challenges.

So for me, it’s also like, well,

this is really an amazing achievement,

but I already was also thinking about next steps.

I mean, as I said, these Asians play Protoss versus Protoss,

but they should be able to play a different race

much quicker, right?

So that would be an amazing achievement.

Some people call this meta reinforcement learning,

meta learning and so on, right?

So there’s so many possibilities after that moment,

but the moment itself, it really felt great.

We had this bet, so I’m kind of a pessimist in general.

So I kind of send an email to the team.

I said, okay, let’s against TLO first, right?

Like what’s gonna be the result?

And I really thought we would lose like five zero, right?

We had some calibration made against the 5,000 MMR player.

TLO was much stronger than that player,

even if he played Protoss, which is his off race.

But yeah, I was not imagining we would win.

So for me, that was just kind of a test run or something.

And then it really kind of, he was really surprised.

And unbelievably, we went to this bar to celebrate

and Dave tells me, well, why don’t we invite someone

who is a thousand MMR stronger in Protoss,

like actual Protoss player,

like that it turned up being Mana, right?

And we had some drinks and I said, sure, why not?

But then I thought, well,

that’s really gonna be impossible to beat.

I mean, even because it’s so much ahead,

a thousand MMR is really like 99% probability

that Mana would beat TLO as Protoss versus Protoss, right?

So we did that.

And to me, the second game was much more important,

even though a lot of uncertainty kind of disappeared

after we kind of beat TLO.

I mean, he is a professional player.

So that was kind of, oh,

but that’s really a very nice achievement.

But Mana really was at the top

and you could see he played much better,

but our agents got much better too.

So it’s like, ah, and then after the first game,

I said, if we take a single game,

at least we can say we beat a game.

I mean, even if we don’t beat the series,

for me, that was a huge relief.

And I mean, I remember the hugging demis.

And I mean, it was really like,

this moment for me will resonate forever as a researcher.

And I mean, as a person,

and yeah, it’s a really like great accomplishment.

And it was great also to be there with the team in the room.

I don’t know if you saw like this.

So it was really like.

I mean, from my perspective,

the other interesting thing is just like watching Kasparov,

watching Mana was also interesting

because he didn’t, he has kind of a loss of words.

I mean, whenever you lose, I’ve done a lot of sports.

You sometimes say excuses, you look for reasons.

And he couldn’t really come up with reasons.

I mean, so with the off race for Protoss,

you could say, well, it felt awkward, it wasn’t,

but here it was just beaten.

And it was beautiful to look at a human being

being superseded by an AI system.

I mean, it’s a beautiful moment for researchers, so.

Yeah, for sure it was.

I mean, probably the highlight of my career so far

because of its uniqueness and coolness.

And I don’t know, I mean, it’s obviously, as you said,

you can look at papers, citations and so on,

but these really is like a testament

of the whole machine learning approach

and using games to advance technology.

I mean, it really was,

everything came together at that moment.

That’s really the summary.

Also on the other side, it’s a popularization of AI too,

because it’s just like traveling to the moon and so on.

I mean, this is where a very large community of people

that don’t really know AI,

they get to really interact with it.

Which is very important.

I mean, we must, you know,

writing papers helps our peers, researchers,

to understand what we’re doing.

But I think AI is becoming mature enough

that we must sort of try to explain what it is.

And perhaps through games is an obvious way

because these games always had built in AI.

So it may be everyone experience an AI playing a video game,

even if they don’t know,

because there’s always some scripted element

and some people might even call that AI already, right?

So what are other applications

of the approaches underlying AlphaStar

that you see happening?

There’s a lot of echoes of, you said,

transformer of language modeling and so on.

Have you already started thinking

where the breakthroughs in AlphaStar

get expanded to other applications?

Right, so I thought about a few things

for like kind of next month, next years.

The main thing I’m thinking about actually is what’s next

as a kind of a grand challenge.

Because for me, like we’ve seen Atari

and then there’s like the sort of three dimensional walls

that we’ve seen also like pretty good performance

from these capture the flag agents

that also some people at DeepMind and elsewhere

are working on.

We’ve also seen some amazing results on like,

for instance, Dota 2, which is also a very complicated game.

So for me, like the main thing I’m thinking about

is what’s next in terms of challenge.

So as a researcher, I see sort of two tensions

between research and then applications or areas

or domains where you apply them.

So on the one hand, we’ve done,

thanks to the application of StarCraft is very hard.

We developed some techniques, some new research

that now we could look at elsewhere.

Like are there other applications where we can apply these?

And the obvious ones, absolutely.

You can think of feeding back to sort of the community

we took from, which was mostly sequence modeling

or natural language processing.

So we’ve developed and extended things from the transformer

and we use pointer networks.

We combine LSTM and transformers in interesting ways.

So that’s perhaps the kind of lowest hanging fruit

of feeding back to now a different field

of machine learning that’s not playing video games.

Let me go old school and jump to Mr. Alan Turing.

So the Turing test is a natural language test,

a conversational test.

What’s your thought of it as a test for intelligence?

Do you think it is a grand challenge

that’s worthy of undertaking?

Maybe if it is, would you reformulate it or phrase it

somehow differently?

Right, so I really love the Turing test

because I also like sequences and language understanding.

And in fact, some of the early work

we did in machine translation, we

tried to apply to kind of a neural chatbot, which obviously

would never pass the Turing test because it was very limited.

But it is a very fascinating idea

that you could really have an AI that

would be indistinguishable from humans in terms of asking

or conversing with it.

So I think the test itself seems very nice.

And it’s kind of well defined, actually,

like the passing it or not.

I think there’s quite a few rules

that feel pretty simple.

And I think they have these competitions every year.

Yes, there’s the Lebner Prize.

But I don’t know if you’ve seen the kind of bots

that emerge from that competition.

They’re not quite as what you would.

So it feels like that there’s weaknesses with the way Turing

formulated it.

It needs to be that the definition

of a genuine, rich, fulfilling human conversation,

it needs to be something else.

Like the Alexa Prize, which I’m not as well familiar with,

has tried to define that more, I think,

by saying you have to continue keeping

a conversation for 30 minutes, something like that.

So basically forcing the agent not to just fool,

but to have an engaging conversation kind of thing.

Have you thought about this problem richly?

And if you have in general, how far away are we from?

You worked a lot on language understanding,

language generation, but the full dialogue,

the conversation, just sitting at the bar

having a couple of beers for an hour,

that kind of conversation.

Have you thought about it?

Yeah, so I think you touched here

on the critical point, which is feasibility.

So there’s a great essay by Hamming,

which describes sort of grand challenges of physics.

And he argues that, well, OK, for instance,

teleportation or time travel are great grand challenges

of physics, but there’s no attacks.

We really don’t know or cannot kind of make any progress.

So that’s why most physicists and so on,

they don’t work on these in their PhDs

and as part of their careers.

So I see the Turing test, in the full Turing test,

as a bit still too early.

Like I think we’re, especially with the current trend

of deep learning language models,

we’ve seen some amazing examples.

I think GPT2 being the most recent one, which

is very impressive.

But to understand to fully solve passing or fooling a human

to think that there’s a human on the other side,

I think we’re quite far.

So as a result, I don’t see myself

and I probably would not recommend people doing a PhD

on solving the Turing test because it just

feels it’s kind of too early or too hard of a problem.

Yeah, but that said, you said the exact same thing

about StarCraft about a few years ago.

Indeed.

To Demis.

So you’ll probably also be the person who passes

the Turing test in three years.

I mean, I think that, yeah.

So we have this on record.

This is nice.

It’s true.

I mean, it’s true that progress sometimes

is a bit unpredictable.

I really wouldn’t have not.

Even six months ago, I would not have predicted the level

that we see that these agents can deliver at grandmaster

level.

But I have worked on language enough.

And basically, my concern is not that something could happen,

a breakthrough could happen that would bring us to solving

or passing the Turing test, is that I just

think the statistical approach to it is not going to cut it.

So we need a breakthrough, which is great for the community.

But given that, I think there’s quite more uncertainty.

Whereas for StarCraft, I knew what the steps would

be to get us there.

I think it was clear that using the imitation learning part

and then using this battle net for agents

were going to be key.

And it turned out that this was the case.

And a little more was needed, but not much more.

For Turing test, I just don’t know

what the plan or execution plan would look like.

So that’s why I myself working on it as a grand challenge

is hard.

But there are quite a few sub challenges

that are related that you could say,

well, I mean, what if you create a great assistant

like Google already has, like the Google Assistant.

So can we make it better?

And can we make it fully neural and so on?

That I start to believe maybe we’re

reaching a point where we should attempt these challenges.

I like this conversation so much because it echoes very much

the StarCraft conversation.

It’s exactly how you approach StarCraft.

Let’s break it down into small pieces and solve those.

And you end up solving the whole game.

Great.

But that said, you’re behind some

of the biggest pieces of work in deep learning

in the last several years.

So you mentioned some limits.

What do you think of the current limits of deep learning?

And how do we overcome those limits?

So if I had to actually use a single word

to define the main challenge in deep learning,

it’s a challenge that probably has

been the challenge for many years.

And it’s that of generalization.

So what that means is that all that we’re doing

is fitting functions to data.

And when the data we see is not from the same distribution,

or even if there are some times that it

is very close to distribution, but because

of the way we train it with limited samples,

we then get to this stage where we just

don’t see generalization as much as we can generalize.

And I think adversarial examples are a clear example of this.

But if you study machine learning and literature,

and the reason why SVMs came very popular

were because they were dealing and they

had some guarantees about generalization, which

is unseen data or out of distribution,

or even within distribution where you take an image adding

a bit of noise, these models fail.

So I think, really, I don’t see a lot of progress

on generalization in the strong generalization

sense of the word.

I think our neural networks, you can always

find design examples that will make their outputs arbitrary,

which is not good because we humans would never

be fooled by these kind of images

or manipulation of the image.

And if you look at the mathematics,

you kind of understand this is a bunch of matrices

multiplied together.

There’s probably numerics and instability

that you can just find corner cases.

So I think that’s really the underlying topic many times

we see when even at the grand stage of Turing test

generalization, if you start passing the Turing test,

should it be in English or should it be in any language?

As a human, if you ask something in a different language,

you actually will go and do some research

and try to translate it and so on.

Should the Turing test include that?

And it’s really a difficult problem

and very fascinating and very mysterious, actually.

Yeah, absolutely.

But do you think if you were to try to solve it,

can you not grow the size of data intelligently

in such a way that the distribution of your training

set does include the entirety of the testing set?

Is that one path?

The other path is totally a new methodology.

It’s not statistical.

So a path that has worked well, and it worked well

in StarCraft and in machine translation and in languages,

scaling up the data and the model.

And that’s kind of been maybe the only single formula that

still delivers today in deep learning, right?

It’s that data scale and model scale really

do more and more of the things that we thought,

oh, there’s no way it can generalize to these,

or there’s no way it can generalize to that.

But I don’t think fundamentally it will be solved with this.

And for instance, I’m really liking some style or approach

that would not only have neural networks,

but it would have programs or some discrete decision making,

because there is where I feel there’s a bit more.

I mean, the best example, I think, for understanding this

is I also worked a bit on, oh, we

can learn an algorithm with a neural network, right?

So you give it many examples, and it’s

going to sort the input numbers or something like that.

But really strong generalization is you give me some numbers

or you ask me to create an algorithm that sorts numbers.

And instead of creating a neural net, which will be fragile

because it’s going to go out of range at some point,

you’re going to give it numbers that are too large, too small,

and whatnot, if you just create a piece of code that

sorts the numbers, then you can prove

that that will generalize to absolutely all the possible

input you could give.

So I think the problem comes with some exciting prospects.

I mean, scale is a bit more boring, but it really works.

And then maybe programs and discrete abstractions

are a bit less developed.

But clearly, I think they’re quite exciting in terms

of future for the field.

Do you draw any insight wisdom from the 80s and expert

systems and symbolic systems, symbolic computing?

Do you ever go back to those reasoning, that kind of logic?

Do you think that might make a comeback?

You’ll have to dust off those books?

Yeah, I actually love actually adding more inductive biases.

To me, the problem really is, what are you trying to solve?

If what you’re trying to solve is so important that try

to solve it no matter what, then absolutely use rules,

use domain knowledge, and then use

a bit of the magic of machine learning

to empower to make the system as the best system that

will detect cancer or detect weather patterns, right?

Or in terms of StarCraft, it also was a very big challenge.

So I was definitely happy that if we

had to cut a corner here and there,

it could have been interesting to do.

And in fact, in StarCraft, we start

thinking about expert systems because it’s a very,

you know, you can define.

I mean, people actually build StarCraft bots by thinking

about those principles, like state machines and rule based.

And then you could think of combining

a bit of a rule based system, but that has also

neural networks incorporated to make it generalize a bit

better.

So absolutely, I mean, we should definitely

go back to those ideas.

And anything that makes the problem simpler,

as long as your problem is important, that’s OK.

And that’s research driving a very important problem.

And on the other hand, if you want to really focus

on the limits of reinforcement learning,

then of course, you must try not to look at imitation data

or to look for some rules of the domain that would help a lot

or even feature engineering, right?

So this is a tension that depending on what you do,

I think both ways are definitely fine.

And I would never not do one or the other

as long as what you’re doing is important

and needs to be solved, right?

Right, so there’s a bunch of different ideas

that you developed that I really enjoy.

But one is translating from image captioning,

translating from image to text, just another beautiful idea,

I think, that resonates throughout your work, actually.

So the underlying nature of reality

being language always, somehow.

So what’s the connection between images and text,

or rather the visual world and the world

of language in your view?

Right, so I think a piece of research that’s been central

to, I would say, even extending into StarGraph

is this idea of sequence to sequence learning,

which what we really meant by that

is that you can now really input anything

to a neural network as the input x.

And then the neural network will learn a function f

that will take x as an input and produce any output y.

And these x and y’s don’t need to be static or features,

like fixed vectors or anything like that.

It could be really sequences and now beyond data structures.

So that paradigm was tested in a very interesting way

when we moved from translating French to English

to translating an image to its caption.

But the beauty of it is that, really,

and that’s actually how it happened.

I changed a line of code in this thing that

was doing machine translation.

And I came the next day, and I saw

how it was producing captions that seemed like, oh my god,

this is really, really working.

And the principle is the same.

So I think I don’t see text, vision, speech, waveforms

as something different as long as you basically

learn a function that will vectorize these into.

And then after we vectorize it, we

can then use transformers, LSTMs, whatever

the flavor of the month of the model is.

And then as long as we have enough supervised data,

really, this formula will work and will keep working,

I believe, to some extent.

Modulo these generalization issues that I mentioned before.

But the task there is to vectorize,

so to form a representation that’s meaningful.

And your intuition now, having worked with all this media,

is that once you are able to form that representation,

you could basically take any things, any sequence.

Going back to StarCraft, is there

limits on the length so that we didn’t really

touch on the long term aspect?

How did you overcome the whole really long term

aspect of things here?

Is there some tricks?

So the main trick, so StarCraft, if you

look at absolutely every frame, you

might think it’s quite a long game.

So we would have to multiply 22 times 60 seconds per minute

times maybe at least 10 minutes per game on average.

So there are quite a few frames.

But the trick really was to only observe, in fact,

which might be seen as a limitation,

but it is also a computational advantage.

Only observe when you act.

And then what the neural network decides

is what is the gap going to be until the next action.

And if you look at most StarCraft games

that we have in the data set that Blizzard provided,

it turns out that most games are actually only,

I mean, it is still a long sequence,

but it’s maybe like 1,000 to 1,500 actions,

which if you start looking at LSTMs, large LSTMs,

transformers, it’s not that difficult, especially

if you have supervised learning.

If you had to do it with reinforcement learning,

the credit assignment problem, what

is it in this game that made you win?

That would be really difficult.

But thankfully, because of imitation learning,

we didn’t have to deal with these directly.

Although if we had to, we tried it.

And what happened is you just take all your workers

and attack with them.

And that is kind of obvious in retrospect

because you start trying random actions.

One of the actions will be a worker

that goes to the enemy base.

And because it’s self play, it’s not

going to know how to defend because it basically

doesn’t know almost anything.

And eventually, what you develop is this take all workers

and attack because the credit assignment issue in a rally

is really, really hard.

I do believe we could do better.

And that’s maybe a research challenge for the future.

But yeah, even in StarCraft, the sequences

are maybe 1,000, which I believe is

within the realm of what transformers can do.

Yeah, I guess the difference between StarCraft and Go

is in Go and Chess, stuff starts happening right away.

So there’s not, yeah, it’s pretty easy to self play.

Not easy, but to self play, it’s possible to develop

reasonable strategies quickly as opposed to StarCraft.

I mean, in Go, there’s only 400 actions.

But one action is what people would call the God action.

That would be if you had expanded the whole search

tree, that’s the best action if you did minimax

or whatever algorithm you would do if you

had the computational capacity.

But in StarCraft, 400 is minuscule.

Like in 400, you couldn’t even click

on the pixels around a unit.

So I think the problem there is in terms of action space size

is way harder.

And that search is impossible.

So there’s quite a few challenges indeed

that make this kind of a step up in terms of machine learning.

For humans, maybe playing StarCraft

seems more intuitive because it looks real.

I mean, the graphics and everything moves smoothly,

whereas I don’t know how to.

I mean, Go is a game that I would really need to study.

It feels quite complicated.

But for machines, kind of maybe it’s the reverse, yes.

Which shows you the gap actually between deep learning

and however the heck our brains work.

So you developed a lot of really interesting ideas.

It’s interesting to just ask, what’s

your process of developing new ideas?

Do you like brainstorming with others?

Do you like thinking alone?

Do you like, what was it, Ian Goodfellow said

he came up with GANs after a few beers.

He thinks beers are essential for coming up with new ideas.

We had beers to decide to play another game of StarCraft

after a week.

So it’s really similar to that story.

Actually, I explained this in a DeepMind retreat.

And I said, this is the same as the GAN story.

I mean, we were in a bar.

And we decided, let’s play a GAN next week.

And that’s what happened.

I feel like we’re giving the wrong message

to young undergrads.

Yeah, I know.

But in general, do you like brainstorming?

Do you like thinking alone, working stuff out?

So I think throughout the years, also, things changed.

So initially, I was very fortunate to be

with great minds like Jeff Hinton, Jeff Dean,

Ilya Sutskever.

I was really fortunate to join Brain at a very good time.

So at that point, ideas, I was just

brainstorming with my colleagues and learned a lot.

And keep learning is actually something

you should never stop doing.

So learning implies reading papers and also

discussing ideas with others.

It’s very hard at some point to not communicate

that being reading a paper from someone

or actually discussing.

So definitely, that communication aspect

needs to be there, whether it’s written or oral.

Nowadays, I’m also trying to be a bit more strategic

about what research to do.

So I was describing a little bit this tension

between research for the sake of research,

and then you have, on the other hand,

applications that can drive the research.

And honestly, the formula that has worked best for me

is just find a hard problem and then

try to see how research fits into it,

how it doesn’t fit into it, and then you must innovate.

So I think machine translation drove sequence to sequence.

Then maybe learning algorithms that had to,

combinatorial algorithms led to pointer networks.

StarCraft led to really scaling up imitation learning

and the AlphaStarLeague.

So that’s been a formula that I personally like.

But the other one is also valid.

And I’ve seen it succeed a lot of the times

where you just want to investigate model based

RL as a research topic.

And then you must then start to think, well,

how are the tests?

How are you going to test these ideas?

You need a minimal environment to try things.

You need to read a lot of papers and so on.

And that’s also very fun to do and something

I’ve also done quite a few times,

both at Brain, at DeepMind, and obviously as a PhD.

So I think besides the ideas and discussions,

I think it’s important also because you start

sort of guiding not only your own goals,

but other people’s goals to the next breakthrough.

So you must really kind of understand this feasibility

also, as we were discussing before,

whether this domain is ready to be tackled or not.

And you don’t want to be too early.

You obviously don’t want to be too late.

So it’s really interesting, this strategic component

of research, which I think as a grad student,

I just had no idea.

I just read papers and discussed ideas.

And I think this has been maybe the major change.

And I recommend people kind of feed forward

to success how it looks like and try to backtrack,

other than just kind of looking, oh, this looks cool.

This looks cool.

And then you do a bit of random work,

which sometimes you stumble upon some interesting things.

But in general, it’s also good to plan a bit.

Yeah, I like it.

Especially like your approach of taking a really hard problem,

stepping right in, and then being

super skeptical about being able to solve the problem.

I mean, there’s a balance of both, right?

There’s a silly optimism and a critical sort of skepticism

that’s good to balance, which is why

it’s good to have a team of people that balance that.

You don’t do that on your own.

You have both mentors that have seen,

or you obviously want to chat and discuss

whether it’s the right time.

I mean, Demis came in 2014.

And he said, maybe in a bit we’ll do StarCraft.

And maybe he knew.

And I’m just following his lead, which is great,

because he’s brilliant, right?

So these things are obviously quite important,

that you want to be surrounded by people who are diverse.

They have their knowledge.

There’s also important to, I mean,

I’ve learned a lot from people who actually have an idea

that I might not think it’s good.

But if I give them the space to try it,

I’ve been proven wrong many, many times as well.

So that’s great.

I think your colleagues are more important than yourself,

I think.

Sure.

Now let’s real quick talk about another impossible problem,

AGI.

Right.

What do you think it takes to build a system that’s

human level intelligence?

We talked a little bit about the Turing test, StarCraft.

All of these have echoes of general intelligence.

But if you think about just something

that you would sit back and say, wow,

this is really something that resembles

human level intelligence.

What do you think it takes to build that?

So I find that AGI oftentimes is maybe not very well defined.

So what I’m trying to then come up with for myself

is what would be a result look like that you would start

to believe that you would have agents or neural nets that

no longer overfeed to a single task,

but actually learn the skill of learning, so to speak.

And that actually is a field that I

am fascinated by, which is the learning to learn,

or meta learning, which is about no longer learning

about a single domain.

So you can think about the learning algorithm

itself is general.

So the same formula we applied for AlphaStar or StarCraft,

we can now apply to almost any video game,

or you could apply to many other problems and domains.

But the algorithm is what’s generalizing.

But the neural network, those weights

are useless even to play another race.

I train a network to play very well at Protos versus Protos.

I need to throw away those weights.

If I want to play now Terran versus Terran,

I would need to retrain a network from scratch

with the same algorithm.

That’s beautiful.

But the network itself will not be useful.

So I think if I see an approach that

can absorb or start solving new problems without the need

to kind of restart the process, I

think that, to me, would be a nice way

to define some form of AGI.

Again, I don’t know the grandiose like age.

I mean, should Turing tests be solved before AGI?

I mean, I don’t know.

I think concretely, I would like to see clearly

that meta learning happen, meaning

that there is an architecture or a network that

as it sees new problem or new data, it solves it.

And to make it kind of a benchmark,

it should solve it at the same speed

that we do solve new problems.

When I define you a new object and you

have to recognize it, when you start playing a new game,

you played all the Atari games.

But now you play a new Atari game.

Well, you’re going to be pretty quickly pretty good

at the game.

So that’s perhaps what’s the domain

and what’s the exact benchmark is a bit difficult.

I think as a community, we might need

to do some work to define it.

But I think this first step, I could

see it happen relatively soon.

But then the whole what AGI means and so on,

I am a bit more confused about what

I think people mean different things.

There’s an emotional, psychological level

that like even the Turing test, passing the Turing test

is something that we just pass judgment on as human beings

what it means to be as a dog in AGI system.

Yeah.

What level, what does it mean, what does it mean?

But I like the generalization.

And maybe as a community, we converge

towards a group of domains that are sufficiently far away.

That would be really damn impressive

if it was able to generalize.

So perhaps not as close as Protoss and Zerg,

but like Wikipedia.

That would be a step.

Yeah, that would be a good step and then a really good step.

But then like from StarCraft to Wikipedia and back.

Yeah, that kind of thing.

And that feels also quite hard and far.

But I think as long as you put the benchmark out,

as we discovered, for instance, with ImageNet,

then tremendous progress can be had.

So I think maybe there’s a lack of benchmark,

but I’m sure we’ll find one and the community will then

work towards that.

And then beyond what AGI might mean or would imply,

I really am hopeful to see basically machine learning

or AI just scaling up and helping people

that might not have the resources to hire an assistant

or that they might not even know what the weather is like.

So I think in terms of the positive impact of AI,

I think that’s maybe what we should also not lose focus.

The research community building AGI,

I mean, that’s a real nice goal.

But I think the way that DeepMind puts it is,

and then use it to solve everything else.

So I think we should paralyze.

Yeah, we shouldn’t forget about all the positive things

that are actually coming out of AI already

and are going to be coming out.

Right.

But on that note, let me ask relative

to popular perception, do you have

any worry about the existential threat

of artificial intelligence in the near or far future

that some people have?

I think in the near future, I’m skeptical.

So I hope I’m not wrong.

But I’m not concerned, but I appreciate efforts,

ongoing efforts, and even like whole research

field on AI safety emerging and in conferences and so on.

I think that’s great.

In the long term, I really hope we just

can simply have the benefits outweigh

the potential dangers.

I am hopeful for that.

But also, we must remain vigilant to monitor and assess

whether the tradeoffs are there and we have enough also lead

time to prevent or to redirect our efforts if need be.

But I’m quite optimistic about the technology

and definitely more fearful of other threats

in terms of planetary level at this point.

But obviously, that’s the one I have more power on.

So clearly, I do start thinking more and more about this.

And it’s grown in me actually to start reading more

about AI safety, which is a field that so far I have not

really contributed to.

But maybe there’s something to be done there as well.

I think it’s really important.

I talk about this with a few folks.

But it’s important to ask you and shove it in your head

because you’re at the leading edge of actually what

people are excited about in AI.

The work with AlphaStar, it’s arguably

at the very cutting edge of the kind of thing

that people are afraid of.

And so you speaking to that fact and that we’re actually

quite far away to the kind of thing

that people might be afraid of.

But it’s still worthwhile to think about.

And it’s also good that you’re not as worried

and you’re also open to thinking about it.

There’s two aspects.

I mean, me not being worried.

But obviously, we should prepare for things

that could go wrong, misuse of the technologies

as with any technologies.

So I think there’s always trade offs.

And as a society, we’ve kind of solved this to some extent

in the past.

So I’m hoping that by having the researchers

and the whole community brainstorm and come up

with interesting solutions to the new things that

will happen in the future, that we can still also push

the research to the avenue that I think

is kind of the greatest avenue, which is

to understand intelligence.

How are we doing what we’re doing?

And obviously, from a scientific standpoint,

that is kind of my personal drive of all the time

that I spend doing what I’m doing, really.

Where do you see the deep learning as a field heading?

Where do you think the next big breakthrough might be?

So I think deep learning, I discussed a little of this

before.

Deep learning has to be combined with some form

of discretization, program synthesis.

I think that’s kind of as a research in itself

is an interesting topic to expand and start

doing more research.

And then as kind of what will deep learning

enable to do in the future?

I don’t think that’s going to be what’s going to happen this year.

But also this idea of starting not to throw away all the weights,

that this idea of learning to learn

and really having these agents not having

to restart their weights.

And you can have an agent that is kind of solving or classifying

images on ImageNet, but also generating speech

if you ask it to generate some speech.

And it should really be kind of almost the same network,

but it might not be a neural network.

It might be a neural network with an optimization

algorithm attached to it.

But I think this idea of generalization to new task

is something that we first must define good benchmarks.

But then I think that’s going to be exciting.

And I’m not sure how close we are.

But I think if you have a very limited domain,

I think we can start doing some progress.

And much like how we did a lot of programs in computer vision,

we should start thinking.

I really like a talk that Leon Buto gave at ICML

a few years ago, which is this train test paradigm should

be broken.

We should stop thinking about a training set and a test set.

And these are closed things that are untouchable.

I think we should go beyond these.

And in meta learning, we call these the meta training

set and the meta test set, which is really thinking about,

if I know about ImageNet, why would that network not

work on MNIST, which is a much simpler problem?

But right now, it really doesn’t.

But it just feels wrong.

So I think that’s kind of the, on the application

or the benchmark sites, we probably

will see quite a few more interest and progress

and hopefully people defining new and exciting challenges

really.

Do you have any hope or interest in knowledge graphs

within this context?

So this kind of constructing graph.

So going back to graphs.

Well, neural networks and graphs.

But I mean, a different kind of knowledge graph,

sort of like semantic graphs or those concepts.

Yeah.

So I think the idea of graphs is,

so I’ve been quite interested in sequences first and then

more interesting or different data structures like graphs.

And I’ve studied graph neural networks in the last three

years or so.

I found these models just very interesting

from deep learning sites standpoint.

But then why do we want these models

and why would we use them?

What’s the application?

What’s kind of the killer application of graphs?

And perhaps if we could extract a knowledge graph

from Wikipedia automatically, that

would be interesting because then these graphs have

this very interesting structure that also is a bit more

compatible with this idea of programs and deep learning

kind of working together, jumping neighborhoods

and so on.

You could imagine defining some primitives

to go around graphs, right?

So I think I really like the idea of a knowledge graph.

And in fact, when we started or as part of the research

we did for StarCraft, I thought, wouldn’t it

be cool to give the graph of all these buildings that

depend on each other and units that have prerequisites

of being built by that.

And so this is information that the network

can learn and extract.

But it would have been great to see

or to think of really StarCraft as a giant graph that even

also as the game evolves, you start taking branches

and so on.

And we did a bit of research on these,

nothing too relevant, but I really like the idea.

And it has elements that are something

you also worked with in terms of visualizing your networks.

It has elements of having human interpretable,

being able to generate knowledge representations that

are human interpretable that maybe human experts can then

tweak or at least understand.

So there’s a lot of interesting aspect there.

And for me personally, I’m just a huge fan of Wikipedia.

And it’s a shame that our neural networks aren’t

taking advantage of all the structured knowledge that’s

on the web.

What’s next for you?

What’s next for DeepMind?

What are you excited about for AlphaStar?

Yeah, so I think the obvious next steps

would be to apply AlphaStar to other races.

I mean, that sort of shows that the algorithm works

because we wouldn’t want to have created by mistake something

in the architecture that happens to work for Protoss

but not for other races.

So as verification, I think that’s an obvious next step

that we are working on.

And then I would like to see so agents and players can

specialize on different skill sets that

allow them to be very good.

I think we’ve seen AlphaStar understanding very well

when to take battles and when to not to do that.

Also very good at micromanagement

and moving the units around and so on.

And also very good at producing nonstop and trading off

economy with building units.

But I have not perhaps seen as much

as I would like this idea of the poker idea

that you mentioned, right?

I’m not sure StarCraft or AlphaStar

rather has developed a very deep understanding of what

the opponent is doing and reacting to that

and sort of trying to trick the player to do something else

or that.

So this kind of reasoning, I would like to see more.

So I think purely from a research standpoint,

there’s perhaps also quite a few things

to be done there in the domain of StarCraft.

Yeah, in the domain of games, I’ve

seen some interesting work in even auctions,

manipulating other players, sort of forming a belief state

and just messing with people.

Yeah, it’s called theory of mind, I guess.

Theory of mind, yeah.

So it’s a fascinating.

Theory of mind on StarCraft is kind of they’re

really made for each other.

So that would be very exciting to see those techniques apply

to StarCraft or perhaps StarCraft

driving new techniques, right?

As I said, this is always the tension between the two.

Well, Orel, thank you so much for talking today.

Awesome.

It was great to be here.

Thanks.