Oriol Vinyals: DeepMind AlphaStar, StarCraft, Language, and Sequences
嘉宾：Oriol Vinyals
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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?
§02Transcript · 08:53–17:38
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
§03Transcript · 17:38–26:12
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
§04Transcript · 26:12–34:53
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.
§05Transcript · 34:53–43:41
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,
§06Transcript · 43:41–52:22
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.
§07Transcript · 52:22–1:01:04
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,
§08Transcript · 1:01:04–1:10:23
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,
§09Transcript · 1:10:23–1:19:59
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
§10Transcript · 1:19:59–1:29:33
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
§11Transcript · 1:29:33–1:38:59
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
§12Transcript · 1:38:59–1:46:00
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.