vtt/episode_020_small.vtt

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 The following is a conversation with Ariol Vanialis.

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 He's a senior research scientist at Google DeepMind,

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

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 His research has been cited over 39,000 times.

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

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 in the field of deep learning.

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

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 including sequence to sequence learning,

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 audio generation, image captioning,

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 neural machine translation,

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 and, of course, reinforcement learning.

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 He's a lead researcher of the AlphaStar project,

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 creating an agent that defeated a top professional

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 at the game of StarCraft.

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 This conversation is part

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 of the artificial intelligence podcast.

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 If you enjoy it, subscribe on YouTube, iTunes,

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 or simply connect with me on Twitter,

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 at Lex Freedman, spelled F R I D.

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 And now, here's my conversation with Ariol Vanialis.

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 You spearheaded the DeepMind team

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 behind AlphaStar that recently beat

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 a top professional player at StarCraft.

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 So you have an incredible wealth of work

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 and deep learning and a bunch of fields,

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 but let's talk about StarCraft first.

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 Let's go back to the very beginning,

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 even before AlphaStar, before DeepMind,

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 before Deep Learning, first,

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 what came first for you?

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 A love for programming or a love for video games?

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 I think for me, it definitely came first,

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 the drive to play video games.

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 I really liked computers.

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 I didn't really code much,

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 but what I would do is I would just mess with the computer,

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 break it and fix it.

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 That was the level of skills, I guess,

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 that I gained in my very early days,

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 I mean, when I was 10 or 11.

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 And then I really got into video games,

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 especially StarCraft, actually, the first version.

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 I spent most of my time just playing,

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 kind of, pseudo professionally,

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

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 which was not a very main scene,

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 like what's called nowadays esports.

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 Right, of course, in the 90s.

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 So how'd you get into StarCraft?

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 What was your favorite race?

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 How did you develop your skill?

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 What was your strategy?

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 All that kind of thing.

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

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 not to disclose the strategies that I developed.

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 And I like to play random, actually,

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 not in competitions, but just to...

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 I think in StarCraft, there's three main races,

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

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 So I would choose random many times,

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 even sometimes in tournaments,

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 to gain skill on the three races,

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 because it's not how you play against someone,

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 but also if you understand the race because you play it,

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 you also understand what's annoying,

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 then when you're on the other side,

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 what to do to annoy that person,

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

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 So I actually played random,

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 although I must say in terms of favorite race,

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 I really like Zerk.

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 I was probably best at Zerk,

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 and that's probably what I tend to use

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 towards the end of my career before starting university.

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 So let's step back a little bit.

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 Could you try to describe StarCraft to people

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 that may never have played video games,

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 especially the massively online variety like StarCraft?

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 So StarCraft is a real time strategy game,

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 and the way to think about StarCraft,

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 perhaps if you understand a bit chess,

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 is that there's a board,

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 which is called map,

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 or the map where people play against each other.

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 There's obviously many ways you can play,

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 but the most interesting one is the one versus one setup,

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 where you just play against someone else,

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 or even the build in AI, right?

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 Blizzard put a system that can play the game reasonably well,

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 if you don't know how to play.

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 And then in this board,

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 you have, again, pieces like in chess,

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 but these pieces are not there initially,

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 like they are in chess.

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 You actually need to decide to gather resources,

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 to decide which pieces to build.

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

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 You start gathering resources in StarCraft.

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 There's minerals and gas that you can gather,

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 and then you must decide how much do you wanna focus,

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 for instance, on gathering more resources,

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 or starting to build units or pieces.

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 And then once you have enough pieces,

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 or maybe a good attack composition,

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

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 And now the other main difference with chess

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

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

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 It's what we call partially observable.

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 So as a result, you must not only decide trading off economy

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 versus building your own units,

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 but you also must decide whether you wanna scout

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 to gather information,

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 but also by scouting you might be giving away some information

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 that you might be hiding from the enemy.

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 So there's a lot of complex decision making

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 all in real time.

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

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 You play basically all the time continuously

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 and thus some skill in terms of speed and accuracy

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 of clicking is also very important.

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 And people that train for this,

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 really play this game at an amazing skill level.

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 I've seen many times these,

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 and if you can witness this life,

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 it's really, really impressive.

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 So in a way it's kind of a chess,

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

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 you're building your own pieces,

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 and you also need to gather resources

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 to basically get some money to build other buildings,

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 pieces, technology, and so on.

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 From the perspective of the human player,

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 the difference between that and chess,

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 or maybe that and a game like turn based strategy,

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 like Heroes of the Might of Magic,

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 is that there's an anxiety,

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 because you have to make these decisions really quickly.

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

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 it's a very stressful balance that you have to,

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 everything you describe is actually quite stressful,

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 difficult to balance for amateur human player.

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

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

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 but at the amateur level,

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 there's this anxiety, oh crap, I'm being attacked,

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 oh crap, I have to build up resources,

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 oh, I have to probably expand,

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 and all these, the real time strategy aspect

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 is really stressful and computational,

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 I'm sure, difficult, we'll get into it.

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 But for me, Battle.net,

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 so StarCraft was released in 98, 20 years ago,

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 which is hard to believe,

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 and Blizzard Battle.net with Diablo 96 came out,

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 and to me, it might be a narrow perspective,

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 but it changed online gaming,

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 and perhaps society forever,

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 but I may have made way too narrow a viewpoint,

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 but from your perspective,

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 can you talk about the history of gaming

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 over the past 20 years?

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 Is this, how transformational,

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 how important is this line of games?

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

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 whilst this was developing the internet and online gaming,

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 so for me, the way it came was I played

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 other games strategy related,

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 I played a bit of Common and Conquer,

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 and then I played Warcraft 2, which is from Blizzard,

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 but at the time, I didn't know,

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

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 Warcraft 2 was just a game,

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 which was actually very similar to StarCraft in many ways.

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 It's also a real time strategy game

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

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 But it was offline, and it was offline, right?

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 So I remember a friend of mine came to school,

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 say, oh, there's this new cool game called StarCraft,

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 and I just said, oh, this sounds like

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 just a copy of Warcraft 2, until I kind of installed it,

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 and at the time, I am from Spain,

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 so we didn't have like very good internet, right?

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 So there was, for us,

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 StarCraft became first kind of an offline experience

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

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 You play against some sort of scripted things

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 to develop the story of the characters in the game,

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

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 and I thought it was impossible to defeat it.

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 Then eventually, you defeat one,

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 and you can actually play against seven built in AIs

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 at the same time,

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 which also felt impossible,

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 but actually, it's not that hard to beat

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 seven built in AIs at once.

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 So once we achieved that,

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 also we discovered that we could play,

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 as I said, internet wasn't that great,

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 but we could play with the LAN, right?

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 Like basically against each other

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 if we were in the same place,

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 because you could just connect machines with like cables, right?

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 So we started playing in LAN mode,

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 and against, you know, as a group of friends,

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 and it was really, really like much more entertaining

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 than playing against the AIs.

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 And later on, as internet was starting to develop

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 and being a bit faster and more reliable,

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

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 which is these amazing universe,

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 not only because of the fact

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

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 but you can also get to know more people.

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

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

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 There was a chat system,

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 you could play against people,

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 but you could also chat with people,

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 not only about Stacker, but about anything.

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

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 And obviously then it became like kind of,

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

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 going to tournaments and so on and so forth.

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 Do you have a sense on a societal sociological level

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 what's this whole part of society

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 that many of us are not aware of?

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 And it's a huge part of society, which is gamers.

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 I mean, every time I come across that in YouTube

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 or streaming sites,

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 I mean, this is a huge number of people play games religiously.

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 Do you have a sense of those folks,

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 especially now that you've returned to that realm

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 a little bit on the AI side?

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 Yeah, so in fact, even after Stacker,

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 I actually played World of Warcraft,

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 which is maybe the main sort of online world and presence

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 that you get to interact with lots of people.

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 So I played that for a little bit.

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 To me, it was a bit less stressful than Starcraft

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 because winning was kind of a given.

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 You just put in this world

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 and you can always complete missions.

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 But I think it was actually the social aspect

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 of especially Starcraft first

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 and then games like World of Warcraft

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 really shaped me in a very interesting ways

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 because what you get to experience

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

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 So even nowadays, I still have many Facebook friends

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 from the area where I played online

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 and their ways of thinking is even political.

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 They just don't, we don't live in it.

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 Like we don't interact in the real world,

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 but we were connected by basically fiber.

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 And that way I actually get to understand a bit better

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 that we live in a diverse world.

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 And these were just connections that were made by,

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 because I happened to go in a city,

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 in a virtual city as a priest

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 and I met this warrior and we became friends.

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 And then we started like playing together, right?

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 So I think it's transformative

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

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 I mean, it's becoming quite mainstream.

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 But back in the day, as you were saying,

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 in 2005 even it was very, still very strange thing to do

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 especially in Europe, I think there were exceptions

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 like Korea for instance, it was amazing

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 like that everything happened so early

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 in terms of cybercafes.

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 Like it's, if you go to Seoul, it's a city

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 that back in the day, StarCraft was kind of,

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 you could be a celebrity by playing StarCraft

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 but this was like 99, 2000, right?

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 It's not like recently.

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 So yeah, it's quite interesting to look back

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 and yeah, I think it's changing society.

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 The same way of course, like technology

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 and social networks and so on are also transforming things.

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 And a quick tangent, let me ask,

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 you're also one of the most productive people

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 in your particular chosen passion and path in life.

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

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 Do you think it's possible to enjoy video games in moderation?

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

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 When I was playing video games,

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 you could choose having a girlfriend,

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 playing video games or studying.

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

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 These things do take time.

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 Games like StarCraft, if you take the game pretty seriously

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 and you wanna study it,

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 then you obviously will dedicate more time to it.

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 And I definitely took gaming

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 and obviously studying very seriously.

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 I love learning science and et cetera.

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 So to me, especially when I started university undergrad,

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 I kind of stepped off StarCraft.

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 I actually fully stopped playing.

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 And then World of Warcraft was a bit more casual.

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 You could just connect online and I mean, it was fun.

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

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 as it was for me in StarCraft.

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 Okay, so let's get into AlphaStar.

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 What are the, you're behind the team.

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 So DeepMind has been working on StarCraft

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 and released a bunch of cool open source agents

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 and so on in the past few years.

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 But AlphaStar really is the moment

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 where the first time you beat a world class player.

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 So what are the parameters of the challenge

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 in the way that AlphaStar took it on

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 and how did you and David and the rest of the DeepMind team

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 get into it?

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 Consider that you can even beat the best in the world

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 or top players.

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 I think it all started in, back in 2015, actually I'm lying.

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 I think it was 2014 when DeepMind was acquired by Google

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 and I at the time was at Google Brain,

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 which is it was in California, it's still in California.

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

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 So Google Brain and Google DeepMind got together

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 and we gave a series of talks.

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 And given that they were doing deep reinforcement learning

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 for games, I decided to bring up part of my past

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 which I had developed at Berkeley like this thing

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 which we call Berkeley Overmind

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 which is really just a StarCraft one bot.

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 So I talked about that

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 and I remember them is just came to me and said,

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

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 but you should just come to DeepMind and do this again

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 with deep reinforcement learning.

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 And at the time it sounded very science fiction

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 for several reasons.

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 But then in 2016, when I actually moved to London

15:01.000 --> 15:04.280
 and joined DeepMind transferring from Brain,

15:04.280 --> 15:07.840
 it became apparent that because of the AlphaGo moment

15:07.840 --> 15:11.280
 and kind of Blizzard reaching out to us to say,

15:11.280 --> 15:13.000
 wait, like, do you want the next challenge

15:13.000 --> 15:15.080
 and also me being full time at DeepMind?

15:15.080 --> 15:17.440
 So sort of kind of all these came together.

15:17.440 --> 15:21.000
 And then I went to Irvine in California

15:21.000 --> 15:23.800
 to the Blizzard headquarters to just chat with them

15:23.800 --> 15:26.320
 and try to explain how would it all work

15:26.320 --> 15:27.800
 before you do anything.

15:27.800 --> 15:31.240
 And the approach has always been about

15:32.120 --> 15:33.680
 the learning perspective, right?

15:33.680 --> 15:38.680
 So in Berkeley, we did a lot of rule based conditioning

15:39.200 --> 15:42.560
 and if you have more than three units, then go attack

15:42.560 --> 15:45.040
 and if the other has more units than me, I retreat

15:45.040 --> 15:46.400
 and so on and so forth.

15:46.400 --> 15:48.840
 And of course, the point of deep reinforcement learning,

15:48.840 --> 15:50.520
 deep learning, machine learning in general

15:50.520 --> 15:53.480
 is that all these should be learned behavior.

15:53.480 --> 15:57.000
 So that kind of was the DNA of the project

15:57.000 --> 15:59.520
 since its inception in 2016

15:59.520 --> 16:02.920
 where we just didn't even have an environment to work with.

16:02.920 --> 16:05.840
 And so that's how it all started really.

16:05.840 --> 16:08.600
 So if you go back to that conversation with Demis

16:08.600 --> 16:12.200
 or even in your own head, how far away did you,

16:12.200 --> 16:14.480
 because we're talking about Atari games,

16:14.480 --> 16:16.720
 we're talking about Go, which is kind of,

16:16.720 --> 16:19.680
 if you're honest about it, really far away from StarCraft.

16:21.120 --> 16:22.160
 Well, now that you've beaten it,

16:22.160 --> 16:23.280
 maybe you could say it's close,

16:23.280 --> 16:27.160
 but it seems like StarCraft is way harder than Go,

16:28.040 --> 16:30.840
 philosophically and mathematically speaking.

16:30.840 --> 16:35.040
 So how far away did you think you were?

16:35.040 --> 16:38.000
 Do you think it's 2019 and 18 you could be doing

16:38.000 --> 16:38.840
 as well as you have?

16:38.840 --> 16:40.880
 Yeah, when I kind of thought about,

16:40.880 --> 16:44.880
 okay, I'm gonna dedicate a lot of my time and focus on this.

16:44.880 --> 16:48.080
 And obviously I do a lot of different research

16:48.080 --> 16:50.400
 in deep learning, so spending time on it.

16:50.400 --> 16:52.240
 I mean, I really had to kind of think

16:52.240 --> 16:55.880
 there's gonna be something good happening out of this.

16:55.880 --> 16:59.120
 So really I thought, well, this sounds impossible

16:59.120 --> 17:01.600
 and it probably is impossible to do the full thing,

17:01.600 --> 17:06.600
 like the full game where you play one versus one

17:06.720 --> 17:09.760
 and it's only a neural network playing and so on.

17:09.760 --> 17:10.960
 So it really felt like,

17:10.960 --> 17:14.000
 I just didn't even think it was possible.

17:14.000 --> 17:14.840
 But on the other hand,

17:14.840 --> 17:19.080
 I could see some stepping stones towards that goal.

17:19.080 --> 17:21.600
 Clearly you could define sub problems in StarCraft

17:21.600 --> 17:23.360
 and sort of dissect it a bit and say,

17:23.360 --> 17:26.720
 okay, here is a part of the game, here is another part.

17:26.720 --> 17:29.400
 And also obviously the fact,

17:29.400 --> 17:31.280
 so this was really also critical to me,

17:31.280 --> 17:34.400
 the fact that we could access human replays.

17:34.400 --> 17:35.720
 So Blizzard was very kind

17:35.720 --> 17:38.560
 and in fact they open source this for the whole community

17:38.560 --> 17:39.960
 where you can just go

17:39.960 --> 17:43.040
 and it's not every single StarCraft game ever played,

17:43.040 --> 17:44.200
 but it's a lot of them.

17:44.200 --> 17:45.880
 You can just go and download

17:45.880 --> 17:47.160
 and every day they will,

17:47.160 --> 17:48.960
 you can just query a data set and say,

17:48.960 --> 17:51.680
 well, give me all the games that were played today.

17:51.680 --> 17:55.800
 And given my kind of experience with language

17:55.800 --> 17:57.920
 and sequences and supervised learning,

17:57.920 --> 18:00.760
 I thought, well, that's definitely gonna be very helpful

18:00.760 --> 18:02.400
 and something quite unique now

18:02.400 --> 18:06.720
 because ever before we had such a large data set

18:06.720 --> 18:11.000
 of replays of people playing the game at this scale

18:11.000 --> 18:12.560
 of such a complex video game, right?

18:12.560 --> 18:15.640
 So that to me was a precious resource.

18:15.640 --> 18:18.000
 And as soon as I knew that Blizzard was able

18:18.000 --> 18:20.960
 to kind of give this to the community,

18:20.960 --> 18:22.280
 I started to feel positive

18:22.280 --> 18:24.280
 about something non trivial happening.

18:24.280 --> 18:27.120
 But I also thought the full thing,

18:27.120 --> 18:30.400
 like really no rules, no single line of code

18:30.400 --> 18:31.680
 that tries to say, well, I mean,

18:31.680 --> 18:33.320
 if you see this unit build a detector,

18:33.320 --> 18:36.680
 all these, not having any of these specializations

18:36.680 --> 18:39.160
 seemed really, really, really difficult to me.

18:39.160 --> 18:40.000
 Intuitively.

18:40.000 --> 18:42.680
 I do also like that Blizzard was teasing

18:42.680 --> 18:43.960
 or even trolling you,

18:45.480 --> 18:48.560
 sort of almost pulling you in

18:48.560 --> 18:50.280
 into this really difficult challenge.

18:50.280 --> 18:51.840
 Do they have any awareness?

18:51.840 --> 18:55.640
 What's the interest from the perspective of Blizzard

18:55.640 --> 18:57.280
 except just curiosity?

18:57.280 --> 18:59.400
 Yeah, I think Blizzard has really understood

18:59.400 --> 19:03.240
 and really bring forward this competitiveness

19:03.240 --> 19:04.800
 of eSports in games.

19:04.800 --> 19:07.840
 The StarCraft really kind of sparked a lot of,

19:07.840 --> 19:10.720
 like something that almost was never seen,

19:10.720 --> 19:13.960
 especially as I was saying, back in Korea.

19:13.960 --> 19:16.480
 So they just probably thought, well,

19:16.480 --> 19:18.880
 this is such a pure one versus one setup

19:18.880 --> 19:21.160
 that it would be great to see

19:21.160 --> 19:24.840
 if something that can play Atari or go

19:24.840 --> 19:27.920
 and then later on chess could even tackle

19:27.920 --> 19:30.600
 these kind of complex real time strategy game, right?

19:30.600 --> 19:33.880
 So for them, they wanted to see first, obviously,

19:33.880 --> 19:36.440
 whether it was possible,

19:36.440 --> 19:39.760
 if the game they created was in a way solvable,

19:39.760 --> 19:40.840
 to some extent.

19:40.840 --> 19:42.200
 And I think on the other hand,

19:42.200 --> 19:45.760
 they also are a pretty modern company that innovates a lot.

19:45.760 --> 19:48.520
 So just starting to understand AI for them

19:48.520 --> 19:50.240
 to how to bring AI into games,

19:50.240 --> 19:54.320
 is not AI for games, but games for AI, right?

19:54.320 --> 19:56.120
 I mean, both ways, I think, can work.

19:56.120 --> 20:00.040
 And we obviously had the manuse games for AI, right?

20:00.040 --> 20:01.280
 To drive AI progress,

20:01.280 --> 20:03.680
 but Blizzard might actually be able to do,

20:03.680 --> 20:04.760
 and many other companies,

20:04.760 --> 20:06.800
 to start to understand and do the opposite.

20:06.800 --> 20:09.800
 So I think that is also something they can get out of this.

20:09.800 --> 20:11.320
 And they definitely,

20:11.320 --> 20:13.720
 we have brainstormed a lot about this, right?

20:13.720 --> 20:16.080
 But one of the interesting things to me about StarCraft

20:16.080 --> 20:19.400
 and Diablo and these games that Blizzard has created

20:19.400 --> 20:23.560
 is the task of balancing classes, for example,

20:23.560 --> 20:27.480
 sort of making the game fair from the starting point,

20:27.480 --> 20:29.920
 and then let skill determine the outcome.

20:30.960 --> 20:33.600
 Is there, I mean, can you first comment?

20:33.600 --> 20:36.760
 There's three races, Zerg, Protoss, and Terran.

20:36.760 --> 20:38.960
 I don't know if I've ever said that out loud.

20:38.960 --> 20:40.600
 Is that how you pronounce it, Terran?

20:40.600 --> 20:41.600
 Yeah, Terran.

20:41.600 --> 20:42.440
 Yeah.

20:42.440 --> 20:45.200
 Yeah, I don't think I've ever,

20:45.200 --> 20:47.680
 in person, interacted with anybody about StarCraft.

20:47.680 --> 20:51.760
 That's funny. So they seem to be pretty balanced.

20:51.760 --> 20:56.240
 I wonder if the AI, the work that you're doing

20:56.240 --> 20:59.160
 with AlphaStar would help balance them even further.

20:59.160 --> 21:00.520
 Is that something you think about?

21:00.520 --> 21:03.280
 Is that something that Blizzard is thinking about?

21:03.280 --> 21:06.400
 Right, so balancing when you add a new unit

21:06.400 --> 21:09.120
 or a new spell type is obviously possible,

21:09.120 --> 21:13.160
 given that you can always train or pre train at scale,

21:13.160 --> 21:16.680
 some agent that might start using that in unintended ways.

21:16.680 --> 21:19.120
 But I think actually, if you understand

21:19.120 --> 21:22.200
 how StarCraft has kind of co evolved with players,

21:22.200 --> 21:24.280
 in a way, I think it's actually very cool,

21:24.280 --> 21:27.400
 the ways that many of the things and strategies

21:27.400 --> 21:28.680
 that people came up with, right?

21:28.680 --> 21:32.280
 So I think it's, we've seen it over and over in StarCraft

21:32.280 --> 21:34.920
 that Blizzard comes up with maybe a new unit,

21:34.920 --> 21:37.240
 and then some players get creative

21:37.240 --> 21:39.080
 and do something kind of unintentional

21:39.080 --> 21:40.840
 or something that Blizzard designers

21:40.840 --> 21:43.560
 that just simply didn't test or think about.

21:43.560 --> 21:46.960
 And then after that becomes kind of mainstream in the community,

21:46.960 --> 21:48.240
 Blizzard patches the game,

21:48.240 --> 21:51.880
 and then they kind of maybe weaken that strategy

21:51.880 --> 21:53.880
 or make it actually more interesting,

21:53.880 --> 21:55.400
 but a bit more balanced.

21:55.400 --> 21:58.280
 So this kind of continual talk between players and Blizzard

21:58.280 --> 22:01.680
 is kind of what has defined them actually,

22:01.680 --> 22:04.040
 in actually most games, like in StarCraft,

22:04.040 --> 22:05.760
 but also in World of Warcraft,

22:05.760 --> 22:07.440
 they would do that, there are several classes

22:07.440 --> 22:10.800
 and it would be not good that everyone plays

22:10.800 --> 22:13.200
 absolutely the same race and so on, right?

22:13.200 --> 22:17.280
 So I think they do care about balancing, of course,

22:17.280 --> 22:19.640
 and they do a fair amount of testing,

22:19.640 --> 22:22.160
 but it's also beautiful to also see

22:22.160 --> 22:24.520
 how players get creative anyways.

22:24.520 --> 22:27.440
 And I mean, whether AI can be more creative at this point,

22:27.440 --> 22:28.680
 I don't think so, right?

22:28.680 --> 22:31.600
 I mean, it's just sometimes something so amazing happens,

22:31.600 --> 22:33.720
 like I remember back in the days,

22:33.720 --> 22:36.920
 like you have these drop ships that could drop the rivers,

22:36.920 --> 22:39.600
 and that was actually not thought about,

22:39.600 --> 22:41.280
 that you could drop this unit

22:41.280 --> 22:43.200
 that has this what's called splash damage

22:43.200 --> 22:47.800
 that would basically eliminate all the enemy's workers at once.

22:47.800 --> 22:50.080
 No one thought that you could actually put them

22:50.080 --> 22:53.040
 in really early game, do that kind of damage,

22:53.040 --> 22:55.400
 and then things change in the game,

22:55.400 --> 22:58.000
 but I don't know, I think it's quite an amazing

22:58.000 --> 23:00.280
 exploration process from both sides,

23:00.280 --> 23:01.840
 players and Blizzard alike.

23:01.840 --> 23:05.000
 Well, it's almost like a reinforcement learning exploration,

23:05.000 --> 23:10.000
 but the scale of humans that play Blizzard games

23:10.000 --> 23:13.680
 is almost on the scale of a large scale,

23:13.680 --> 23:15.360
 deep mind RL experiment.

23:15.360 --> 23:17.200
 I mean, if you look at the numbers,

23:17.200 --> 23:18.720
 that's, I mean, you're talking about,

23:18.720 --> 23:19.560
 I don't know how many games,

23:19.560 --> 23:22.080
 but hundreds of thousands of games, probably a month.

23:22.080 --> 23:23.880
 Yeah, I mean, so you could,

23:23.880 --> 23:28.800
 it's almost the same as running RL agents.

23:28.800 --> 23:31.240
 What aspect of the problem of Starcraft,

23:31.240 --> 23:32.160
 do you think is the hardest?

23:32.160 --> 23:35.400
 Is it the, like you said, the imperfect information?

23:35.400 --> 23:38.160
 Is it the fact they have to do longterm planning?

23:38.160 --> 23:40.280
 Is it the real time aspect?

23:40.280 --> 23:42.240
 So you have to do stuff really quickly?

23:42.240 --> 23:44.760
 Is it the fact that large action space,

23:44.760 --> 23:47.640
 so you can do so many possible things?

23:47.640 --> 23:51.120
 Or is it, you know, in the game theoretic sense,

23:51.120 --> 23:52.440
 there is no Nash equilibrium.

23:52.440 --> 23:54.240
 At least you don't know what the optimal strategy is,

23:54.240 --> 23:56.520
 because there's way too many options.

23:56.520 --> 23:57.360
 Right.

23:57.360 --> 23:58.360
 Is there something that stands out

23:58.360 --> 24:01.000
 as just like the hardest, the most annoying thing?

24:01.000 --> 24:04.200
 So when we sort of looked at the problem

24:04.200 --> 24:07.640
 and start to define the parameters of it, right?

24:07.640 --> 24:08.800
 What are the observations?

24:08.800 --> 24:10.520
 What are the actions?

24:10.520 --> 24:13.920
 It became very apparent that, you know,

24:13.920 --> 24:17.160
 the very first barrier that one would hit in Starcraft

24:17.160 --> 24:20.720
 would be because of the action space being so large

24:20.720 --> 24:24.880
 and as not being able to search like you could in chess

24:24.880 --> 24:27.320
 or go even though the search space is vast.

24:28.640 --> 24:30.600
 The main problem that we identified

24:30.600 --> 24:32.440
 was that of exploration, right?

24:32.440 --> 24:36.720
 So without any sort of human knowledge or human prior,

24:36.720 --> 24:38.040
 if you think about Starcraft

24:38.040 --> 24:41.440
 and you know how deep reinforcement learning algorithm works,

24:41.440 --> 24:45.360
 work, which is essentially by issuing random actions

24:45.360 --> 24:47.800
 and hoping that they will get some wins sometimes

24:47.800 --> 24:49.200
 so they could learn.

24:49.200 --> 24:52.800
 So if you think of the action space in Starcraft,

24:52.800 --> 24:55.880
 almost anything you can do in the early game is bad

24:55.880 --> 24:58.720
 because any action involves taking workers

24:58.720 --> 25:01.360
 which are mining minerals for free.

25:01.360 --> 25:03.560
 That's something that the game does automatically,

25:03.560 --> 25:04.920
 sends them to mine

25:04.920 --> 25:07.720
 and you would immediately just take them out of mining

25:07.720 --> 25:09.080
 and send them around.

25:09.080 --> 25:13.640
 So just thinking how is it gonna be possible

25:13.640 --> 25:16.880
 to get to understand these concepts

25:16.880 --> 25:19.280
 but even more like expanding, right?

25:19.280 --> 25:21.080
 There's these buildings you can place

25:21.080 --> 25:24.160
 in other locations in the map to gather more resources

25:24.160 --> 25:26.840
 but the location of the building is important

25:26.840 --> 25:28.920
 and you have to select a worker,

25:28.920 --> 25:32.680
 send it walking to that location, build the building,

25:32.680 --> 25:34.160
 wait for the building to be built

25:34.160 --> 25:37.840
 and then put extra workers there so they start mining.

25:37.840 --> 25:40.200
 That just, that feels like impossible

25:40.200 --> 25:43.680
 if you just randomly click to produce that state,

25:43.680 --> 25:47.000
 desirable state that then you could hope to learn from

25:47.000 --> 25:49.880
 because eventually that may yield to an extra win, right?

25:49.880 --> 25:51.840
 So for me, the exploration problem

25:51.840 --> 25:53.840
 and due to the action space

25:53.840 --> 25:56.160
 and the fact that there's not really turns,

25:56.160 --> 25:57.000
 there's so many turns

25:57.000 --> 26:01.440
 because the game essentially ticks at 22 times per second.

26:01.440 --> 26:05.560
 If you, I mean, that's how they discretize sort of time.

26:05.560 --> 26:07.320
 Obviously, you always have to discretize time

26:07.320 --> 26:09.640
 where there's no such thing as real time

26:09.640 --> 26:12.560
 but it's really a lot of time steps

26:12.560 --> 26:14.280
 of things that could go wrong

26:14.280 --> 26:17.960
 and that definitely felt a priori like the hardest.

26:17.960 --> 26:19.360
 You mentioned many good ones,

26:19.360 --> 26:21.360
 I think partial observability,

26:21.360 --> 26:23.440
 the fact that there is no perfect strategy

26:23.440 --> 26:25.560
 because of the partial observability,

26:25.560 --> 26:26.880
 those are very interesting problems.

26:26.880 --> 26:29.400
 We start seeing more and more now in terms of

26:29.400 --> 26:31.080
 as we saw of the previous ones

26:31.080 --> 26:34.320
 but the core problem to me was exploration

26:34.320 --> 26:37.800
 and solving it has been basically kind of the focus

26:37.800 --> 26:39.840
 and how we saw the first breakthroughs.

26:39.840 --> 26:43.720
 So exploration in a multi hierarchical way.

26:43.720 --> 26:46.640
 So like 22 times a second exploration

26:46.640 --> 26:48.680
 has a very different meaning than it does

26:48.680 --> 26:51.520
 in terms of should I gather resources early

26:51.520 --> 26:53.200
 or should I wait or so on.

26:53.200 --> 26:56.240
 So how do you solve the long term?

26:56.240 --> 26:58.120
 Let's talk about the internals of Alpha Star.

26:58.120 --> 27:02.520
 So first of all, how do you represent the state

27:02.520 --> 27:05.160
 of the game as an input?

27:05.160 --> 27:08.840
 How do you then do the long term sequence modeling?

27:08.840 --> 27:10.480
 How do you build a policy?

27:10.480 --> 27:12.600
 What's the architecture like?

27:12.600 --> 27:16.880
 So Alpha Star has obviously several components

27:16.880 --> 27:20.920
 but everything passes through what we call the policy

27:20.920 --> 27:22.320
 which is a neural network

27:22.320 --> 27:24.320
 and that's kind of the beauty of it.

27:24.320 --> 27:27.200
 There is, I could just now give you a neural network

27:27.200 --> 27:30.480
 and some weights and if you fed the right observations

27:30.480 --> 27:32.600
 and you understood the actions the same way we do

27:32.600 --> 27:35.160
 you would have basically the agent playing the game.

27:35.160 --> 27:37.280
 There's absolutely nothing else needed

27:37.280 --> 27:40.360
 other than those weights that were trained.

27:40.360 --> 27:43.400
 Now, the first step is observing the game

27:43.400 --> 27:46.680
 and we've experimented with a few alternatives.

27:46.680 --> 27:48.800
 The one that we currently use

27:48.800 --> 27:51.440
 mixes both spatial sort of images

27:51.440 --> 27:53.840
 that you would process from the game

27:53.840 --> 27:56.440
 that is the zoomed out version of the map

27:56.440 --> 27:58.960
 and also a zoomed in version of the camera

27:58.960 --> 28:00.880
 or the screen as we call it.

28:00.880 --> 28:04.840
 But also we give to the agent the list of units

28:04.840 --> 28:09.000
 that it sees more of as a set of objects

28:09.000 --> 28:11.040
 that it can operate on.

28:11.040 --> 28:14.760
 That is not necessarily required to use it

28:14.760 --> 28:16.840
 and we have versions of the game that play well

28:16.840 --> 28:19.080
 without this set vision that is a bit

28:19.080 --> 28:21.680
 not like how humans perceive the game

28:21.680 --> 28:23.640
 but it certainly helps a lot

28:23.640 --> 28:25.040
 because it's a very natural way

28:25.040 --> 28:28.480
 to encode the game is by just looking at all the units

28:28.480 --> 28:32.960
 that they have properties like health, position,

28:32.960 --> 28:36.200
 type of unit, whether it's my unit or the enemy's

28:36.200 --> 28:40.800
 and that sort of is kind of the summary

28:40.800 --> 28:42.880
 of the state of the game,

28:42.880 --> 28:45.520
 not that list of units or set of units

28:45.520 --> 28:47.400
 that you see all the time.

28:47.400 --> 28:49.600
 But that's pretty close to the way humans see the game.

28:49.600 --> 28:51.440
 Why do you say it's not,

28:51.440 --> 28:53.240
 you're saying the exactness of it

28:53.240 --> 28:55.080
 is not similar to humans?

28:55.080 --> 28:57.200
 The exactness of it is perhaps not the problem.

28:57.200 --> 28:59.840
 I guess maybe the problem if you look at it

28:59.840 --> 29:02.320
 from how actually humans play the game

29:02.320 --> 29:05.760
 is that they play with a mouse and a keyboard and a screen

29:05.760 --> 29:08.760
 and they don't see sort of a structured object

29:08.760 --> 29:09.600
 with all the units,

29:09.600 --> 29:12.240
 what they see is what they see on the screen, right?

29:12.240 --> 29:14.400
 So you remember that there's a certain interrupt,

29:14.400 --> 29:17.000
 there's a plot that you showed with camera base

29:17.000 --> 29:19.680
 where you do exactly that, right, you move around

29:19.680 --> 29:22.280
 and that seems to converge to similar performance.

29:22.280 --> 29:24.760
 Yeah, I think that's what we're kind of experimenting

29:24.760 --> 29:28.720
 with what's necessary or not, but using the set.

29:28.720 --> 29:32.360
 So actually if you look at research in computer vision

29:32.360 --> 29:36.000
 where it makes a lot of sense to treat images

29:36.000 --> 29:38.160
 as two dimensional arrays,

29:38.160 --> 29:40.360
 there's actually a very nice paper from Facebook.

29:40.360 --> 29:42.720
 I think, I forgot who the authors are,

29:42.720 --> 29:46.360
 but I think it's part of Kmings has group.

29:46.360 --> 29:49.520
 And what they do is they take an image,

29:49.520 --> 29:51.960
 which is this two dimensional signal

29:51.960 --> 29:54.320
 and they actually take pixel by pixel

29:54.320 --> 29:59.160
 and scramble the image as if it was just a list of pixels.

29:59.160 --> 30:01.800
 Crucially, they encode the position of the pixels

30:01.800 --> 30:03.720
 with the XY coordinates.

30:03.720 --> 30:06.160
 And this is just kind of a new architecture

30:06.160 --> 30:08.520
 which we incidentally also use in stack graph

30:08.520 --> 30:09.880
 called the transformer,

30:09.880 --> 30:12.000
 which is a very popular paper from last year,

30:12.000 --> 30:15.600
 which yielded very nice result in machine translation.

30:15.600 --> 30:18.040
 And if you actually believe in this kind of,

30:18.040 --> 30:20.320
 oh, it's actually a set of pixels

30:20.320 --> 30:22.520
 as long as you encode XY, it's okay.

30:22.520 --> 30:25.560
 Then you could argue that the list of units

30:25.560 --> 30:26.960
 that we see is precisely that

30:26.960 --> 30:31.480
 because we have each unit as a kind of pixel, if you will,

30:31.480 --> 30:33.240
 and then their XY coordinates.

30:33.240 --> 30:36.360
 So in that perspective, without knowing it,

30:36.360 --> 30:37.680
 we use the same architecture

30:37.680 --> 30:39.680
 that was shown to work very well

30:39.680 --> 30:41.400
 on Pascal and ImageNet and so on.

30:41.400 --> 30:45.440
 So the interesting thing here is putting it in that way,

30:45.440 --> 30:47.000
 it starts to move it towards

30:47.000 --> 30:49.480
 the way you usually work with language.

30:49.480 --> 30:52.760
 So what, and especially with your expertise

30:52.760 --> 30:57.000
 and work in language, it seems like there's echoes

30:57.000 --> 31:00.720
 of a lot of the way you would work with natural language

31:00.720 --> 31:02.440
 in the way you've approached AlphaStar.

31:02.440 --> 31:05.080
 Right, does that help

31:05.080 --> 31:08.200
 with the longterm sequence modeling there somehow?

31:08.200 --> 31:11.200
 Exactly, so now that we understand what an observation

31:11.200 --> 31:14.680
 for a given time step is, we need to move on to say,

31:14.680 --> 31:17.760
 well, there's gonna be a sequence of such observations

31:17.760 --> 31:21.120
 and an agent will need to, given all that it's seen,

31:21.120 --> 31:23.720
 not only the current time step, but all that it's seen,

31:23.720 --> 31:25.920
 why, because there is partial observability.

31:25.920 --> 31:28.400
 We must remember whether we saw a worker

31:28.400 --> 31:30.120
 going somewhere, for instance, right?

31:30.120 --> 31:31.720
 Because then there might be an expansion

31:31.720 --> 31:33.640
 on the top right of the map.

31:33.640 --> 31:37.840
 So given that, what you must then think about

31:37.840 --> 31:40.400
 is there is the problem of, given all the observations,

31:40.400 --> 31:42.640
 you have to predict the next action.

31:42.640 --> 31:44.520
 And not only given all the observations,

31:44.520 --> 31:45.920
 but given all the observations

31:45.920 --> 31:47.920
 and given all the actions you've taken,

31:47.920 --> 31:49.360
 predict the next action.

31:49.360 --> 31:52.480
 And that sounds exactly like machine translation,

31:52.480 --> 31:57.160
 where, and that's exactly how kind of I saw the problem,

31:57.160 --> 31:59.960
 especially when you are given supervised data

31:59.960 --> 32:01.760
 or replaced from humans,

32:01.760 --> 32:03.600
 because the problem is exactly the same.

32:03.600 --> 32:06.680
 You're translating essentially a prefix

32:06.680 --> 32:08.240
 of observations and actions

32:08.240 --> 32:10.160
 onto what's gonna happen next,

32:10.160 --> 32:13.000
 which is exactly how you would train a model to translate

32:13.000 --> 32:14.760
 or to generate language as well, right?

32:14.760 --> 32:16.640
 You have a certain prefix.

32:16.640 --> 32:19.000
 You must remember everything that comes in the past,

32:19.000 --> 32:20.080
 because otherwise,

32:20.080 --> 32:22.640
 you might start having non coherent text.

32:22.640 --> 32:25.120
 And the same architectures,

32:25.120 --> 32:27.760
 we're using LSTMs and transformers

32:27.760 --> 32:29.760
 to operate on across time

32:29.760 --> 32:33.080
 to kind of integrate all that's happened in the past.

32:33.080 --> 32:35.000
 Those architectures that work so well

32:35.000 --> 32:36.880
 in translation or language modeling

32:36.880 --> 32:40.640
 are exactly the same than what the agent is using

32:40.640 --> 32:42.360
 to issue actions in the game.

32:42.360 --> 32:43.880
 And the way we train it, moreover,

32:43.880 --> 32:47.120
 for imitation, which is step one of alpha studies,

32:47.120 --> 32:49.880
 take all the human experience and try to imitate it,

32:49.880 --> 32:52.920
 much like you try to imitate translators

32:52.920 --> 32:55.360
 that translated many pairs of sentences

32:55.360 --> 32:57.280
 from French to English say,

32:57.280 --> 33:00.200
 that sort of principle applies exactly the same.

33:00.200 --> 33:02.760
 It's almost the same code,

33:02.760 --> 33:04.520
 except that instead of words,

33:04.520 --> 33:06.680
 you have a slightly more complicated objects,

33:06.680 --> 33:08.280
 which are the observations

33:08.280 --> 33:10.240
 and the actions are also a bit more complicated

33:10.240 --> 33:11.760
 than a word.

33:11.760 --> 33:13.920
 Is there a self play component then too?

33:13.920 --> 33:16.480
 So once you run out of imitation?

33:16.480 --> 33:21.480
 Right, so indeed you can bootstrap from human replays,

33:22.240 --> 33:25.960
 but then the agents you get are actually not as good

33:25.960 --> 33:28.160
 as the humans you imitated, right?

33:28.160 --> 33:30.440
 So how do we imitate?

33:30.440 --> 33:34.240
 Well, we take humans from 3000 MMR and higher.

33:34.240 --> 33:37.960
 3000 MMR is just a metric of human skill.

33:37.960 --> 33:41.880
 And 3000 MMR might be like 50% percentile, right?

33:41.880 --> 33:43.760
 So it's just average human.

33:43.760 --> 33:44.600
 What's that?

33:44.600 --> 33:45.440
 So maybe a quick pause.

33:45.440 --> 33:47.760
 MMR is a ranking scale,

33:47.760 --> 33:50.320
 the matchmaking rating for players.

33:50.320 --> 33:52.320
 So it's 3000, I remember there's like a master

33:52.320 --> 33:54.120
 and a grandmaster, what's 3000?

33:54.120 --> 33:56.720
 So 3000 is pretty bad.

33:56.720 --> 33:58.440
 I think it's kind of gold level.

33:58.440 --> 34:00.680
 It just sounds really good relative to chess, I think.

34:00.680 --> 34:02.440
 Oh yeah, yeah, no, the ratings,

34:02.440 --> 34:04.480
 the best in the world are at 7000 MMR.

34:04.480 --> 34:05.320
 7000.

34:05.320 --> 34:07.840
 So 3000, it's a bit like Elo indeed, right?

34:07.840 --> 34:12.840
 So 3500 just allows us to not filter a lot of the data.

34:13.200 --> 34:15.680
 So we like to have a lot of data in deep learning

34:15.680 --> 34:17.320
 as you probably know.

34:17.320 --> 34:20.640
 So we take these kind of 3500 and above,

34:20.640 --> 34:22.720
 but then we do a very interesting trick,

34:22.720 --> 34:25.000
 which is we tell the neural network

34:25.000 --> 34:27.560
 what level they are imitating.

34:27.560 --> 34:30.800
 So we say these replay you're gonna try to imitate

34:30.800 --> 34:33.040
 to predict the next action for all the actions

34:33.040 --> 34:36.120
 that you're gonna see is a 4000 MMR replay.

34:36.120 --> 34:38.800
 This one is a 6000 MMR replay.

34:38.800 --> 34:42.480
 And what's cool about this is then we take this policy

34:42.480 --> 34:44.280
 that is being trained from human

34:44.280 --> 34:47.400
 and then we can ask it to play like a 3000 MMR player

34:47.400 --> 34:49.600
 by setting a bit saying, well, okay,

34:49.600 --> 34:53.680
 play like a 3000 MMR player or play like a 6000 MMR player.

34:53.680 --> 34:57.320
 And you actually see how the policy behaves differently.

34:57.320 --> 35:01.520
 It gets worse economy if you play like a gold level player.

35:01.520 --> 35:03.000
 It does less actions per minute,

35:03.000 --> 35:05.320
 which is the number of clicks or number of actions

35:05.320 --> 35:07.760
 that you will issue in a whole minute.

35:07.760 --> 35:09.200
 And it's very interesting to see

35:09.200 --> 35:12.360
 that it kind of imitates the skill level quite well.

35:12.360 --> 35:15.480
 But if we ask it to play like a 6000 MMR player,

35:15.480 --> 35:18.600
 we tested of course these policies to see how well they do.

35:18.600 --> 35:20.600
 They actually beat all the built in AIs

35:20.600 --> 35:22.400
 that Blizzard put in the game,

35:22.400 --> 35:24.960
 but they're nowhere near 6000 MMR players, right?

35:24.960 --> 35:29.240
 They might be maybe around gold level, platinum perhaps.

35:29.240 --> 35:32.200
 So there's still a lot of work to be done for the policy

35:32.200 --> 35:34.960
 to truly understand what it means to win.

35:34.960 --> 35:38.160
 So far we only ask them, okay, here is the screen

35:38.160 --> 35:41.600
 and that's what's happened on the game until this point.

35:41.600 --> 35:46.080
 What would the next action be if we ask a pro to now say,

35:46.080 --> 35:49.120
 oh, you're gonna click here or here or there?

35:49.120 --> 35:53.680
 And the point is experiencing wins and losses

35:53.680 --> 35:56.320
 is very important to then start to refine.

35:56.320 --> 35:58.360
 Otherwise the policy can get loose,

35:58.360 --> 36:00.440
 can just go off policy as we call it.

36:00.440 --> 36:03.400
 That's so interesting that you can at least hope eventually

36:03.400 --> 36:06.760
 to be able to control a policy

36:06.760 --> 36:09.960
 approximately to be at some MMR level.

36:09.960 --> 36:12.240
 That's so interesting, especially given

36:12.240 --> 36:15.000
 that you have ground truth for a lot of these cases.

36:15.000 --> 36:17.520
 Can I ask you a personal question?

36:17.520 --> 36:19.200
 What's your MMR?

36:19.200 --> 36:23.600
 Well, I haven't played Starcraft 2, so I am unranked,

36:23.600 --> 36:25.360
 which is the kind of lowest league.

36:25.360 --> 36:26.200
 Okay.

36:26.200 --> 36:28.360
 So I used to play Starcraft 1.

36:28.360 --> 36:29.560
 The first one and...

36:29.560 --> 36:31.280
 But you haven't seriously played Starcraft 2?

36:31.280 --> 36:32.640
 No, not Starcraft 2.

36:32.640 --> 36:37.720
 So the best player we have at DeepMind is about 5,000 MMR,

36:37.720 --> 36:39.560
 which is high masters.

36:39.560 --> 36:42.040
 It's not at the Grand Master level.

36:42.040 --> 36:44.640
 Grand Master level would be the top 200 players

36:44.640 --> 36:49.120
 in a certain region, like Europe or America or Asia.

36:49.120 --> 36:51.560
 But for me, it would be hard to say.

36:51.560 --> 36:53.680
 I am very bad at the game.

36:53.680 --> 36:56.600
 I actually played Alpha Star a bit too late and it beat me.

36:56.600 --> 36:59.720
 I remember the whole team was, oh, Oreo, you should play.

36:59.720 --> 37:02.160
 And I was, oh, it looks like it's not so good yet.

37:02.160 --> 37:06.600
 And then I remember I kind of got busy and waited an extra week

37:06.600 --> 37:09.640
 and I played and it really beat me very badly.

37:09.640 --> 37:11.160
 How did that feel?

37:11.160 --> 37:12.600
 Isn't that an amazing feeling?

37:12.600 --> 37:13.560
 That's amazing, yeah.

37:13.560 --> 37:17.920
 I mean, obviously, I tried my best and I tried to also impress

37:17.920 --> 37:19.720
 because I actually played the first game,

37:19.720 --> 37:23.040
 so I'm still pretty good at micro management.

37:23.040 --> 37:25.200
 The problem is I just don't understand Starcraft 2.

37:25.200 --> 37:27.200
 I understand Starcraft.

37:27.200 --> 37:28.440
 And when I played Starcraft,

37:28.440 --> 37:32.640
 I probably was consistently like for a couple of years,

37:32.640 --> 37:34.600
 top 32 in Europe.

37:34.600 --> 37:36.440
 So I was decent, but at the time,

37:36.440 --> 37:40.280
 we didn't have this kind of MMR system as well established.

37:40.280 --> 37:43.120
 So it would be hard to know what it was back then.

37:43.120 --> 37:45.760
 So what's the difference in interface between Alpha Star

37:45.760 --> 37:49.600
 and Starcraft and a human player in Starcraft?

37:49.600 --> 37:52.000
 Is there any significant differences

37:52.000 --> 37:54.080
 between the way they both see the game?

37:54.080 --> 37:55.960
 I would say the way they see the game,

37:55.960 --> 37:59.720
 there's a few things that are just very hard to simulate.

38:01.000 --> 38:02.640
 The main one, perhaps,

38:02.640 --> 38:05.200
 which is obvious in hindsight,

38:05.200 --> 38:08.440
 is what's called clocked units,

38:08.440 --> 38:10.560
 which are invisible units.

38:10.560 --> 38:13.240
 So in Starcraft, you can make some units

38:13.240 --> 38:18.040
 that you need to have a particular kind of unit to detect it.

38:18.040 --> 38:20.560
 So these units are invisible.

38:20.560 --> 38:22.720
 If you cannot detect them, you cannot target them.

38:22.720 --> 38:25.720
 So they would just destroy your buildings

38:25.720 --> 38:27.720
 or kill your workers.

38:27.720 --> 38:31.640
 But despite the fact you cannot target the unit,

38:31.640 --> 38:34.600
 there's a shimmer that as a human you observe.

38:34.600 --> 38:35.920
 I mean, you need to train a little bit.

38:35.920 --> 38:37.400
 You need to pay attention,

38:37.400 --> 38:41.840
 but you would see this kind of space time distortion

38:41.840 --> 38:44.800
 and you wouldn't know, okay, there are, yeah.

38:44.800 --> 38:46.040
 Yeah, there's like a wave thing.

38:46.040 --> 38:47.840
 Yeah, it's called shimmer.

38:47.840 --> 38:49.120
 Space time distortion, I like it.

38:49.120 --> 38:52.440
 That's really like the blizzard term is shimmer.

38:52.440 --> 38:56.040
 And so these shimmer professional players actually

38:56.040 --> 38:57.160
 can see it immediately.

38:57.160 --> 38:59.480
 They understand it very well,

38:59.480 --> 39:01.400
 but it's still something that requires

39:01.400 --> 39:02.720
 certain amount of attention

39:02.720 --> 39:05.640
 and it's kind of a bit annoying to deal with.

39:05.640 --> 39:08.640
 Whereas for Alpha Star, in terms of vision,

39:08.640 --> 39:11.120
 it's very hard for us to simulate sort of,

39:11.120 --> 39:14.160
 oh, are you looking at this pixel in the screen and so on?

39:14.160 --> 39:17.520
 So the only thing we can do is

39:17.520 --> 39:19.720
 there is a unit that's invisible over there.

39:19.720 --> 39:22.520
 So Alpha Star would know that immediately.

39:22.520 --> 39:24.040
 Obviously still obeys the rules.

39:24.040 --> 39:25.200
 You cannot attack the unit.

39:25.200 --> 39:27.400
 You must have a detector and so on,

39:27.400 --> 39:29.320
 but it's kind of one of the main things

39:29.320 --> 39:32.680
 that it just doesn't feel there's a very proper way.

39:32.680 --> 39:35.480
 I mean, you could imagine, oh, you don't have hypers.

39:35.480 --> 39:36.960
 Maybe you don't know exactly what it is,

39:36.960 --> 39:39.240
 or sometimes you see it, sometimes you don't.

39:39.240 --> 39:43.040
 But it's just really, really complicated to get it

39:43.040 --> 39:44.280
 so that everyone would agree,

39:44.280 --> 39:47.120
 oh, that's the best way to simulate this, right?

39:47.120 --> 39:49.280
 You know, it seems like a perception problem.

39:49.280 --> 39:50.600
 It is a perception problem.

39:50.600 --> 39:54.240
 So the only problem is people, you ask,

39:54.240 --> 39:56.760
 oh, what's the difference between how humans perceive the game?

39:56.760 --> 39:59.960
 I would say they wouldn't be able to tell a shimmer

39:59.960 --> 40:02.240
 immediately as it appears on the screen,

40:02.240 --> 40:04.320
 whereas Alpha Star, in principle,

40:04.320 --> 40:05.640
 sees it very sharply, right?

40:05.640 --> 40:08.680
 It sees that the bit turned from zero to one,

40:08.680 --> 40:10.520
 meaning there's now a unit there,

40:10.520 --> 40:11.960
 although you don't know the unit,

40:11.960 --> 40:15.840
 or you know that you cannot attack it and so on.

40:15.840 --> 40:18.080
 So from a vision standpoint,

40:18.080 --> 40:23.000
 that probably is the one that is kind of the most obvious one.

40:23.000 --> 40:25.200
 Then there are things humans cannot do perfectly,

40:25.200 --> 40:28.120
 even professionals, which is they might miss a detail

40:28.120 --> 40:30.640
 or they might have not seen a unit.

40:30.640 --> 40:32.320
 And obviously, as a computer,

40:32.320 --> 40:35.040
 if there's a corner of the screen that turns green

40:35.040 --> 40:37.720
 because a unit enters the field of view,

40:37.720 --> 40:41.120
 that can go into the memory of the agent, the LSTM,

40:41.120 --> 40:42.560
 and persists there for a while,

40:42.560 --> 40:45.720
 and for however long is relevant, right?

40:45.720 --> 40:49.920
 And in terms of action, it seems like the rate of action

40:49.920 --> 40:51.640
 from Alpha Star is comparative,

40:51.640 --> 40:54.280
 if not slower than professional players,

40:54.280 --> 40:57.160
 but it's more precise is what I heard.

40:57.160 --> 40:59.760
 So that's really probably the one

40:59.760 --> 41:04.760
 that is causing us more issues for a couple of reasons, right?

41:05.040 --> 41:08.440
 The first one is StarCraft has been an AI environment

41:08.440 --> 41:09.960
 for quite a few years.

41:09.960 --> 41:14.000
 In fact, I was participating in the very first competition

41:14.000 --> 41:15.920
 back in 2010.

41:15.920 --> 41:19.920
 And there's really not been a kind of a very clear set

41:19.920 --> 41:22.320
 of rules, how the actions per minute,

41:22.320 --> 41:24.720
 the rate of actions that you can issue is.

41:24.720 --> 41:29.280
 And as a result, these agents or bots that people build

41:29.280 --> 41:31.080
 in a kind of almost very cool way,

41:31.080 --> 41:35.400
 they do like 20,000, 40,000 actions per minute.

41:35.400 --> 41:37.200
 Now, to put this in perspective,

41:37.200 --> 41:41.640
 a very good professional human might do 300

41:41.640 --> 41:45.480
 to 800 actions per minute, they might not be as precise.

41:45.480 --> 41:49.040
 That's why the range is a bit tricky to identify exactly.

41:49.040 --> 41:51.640
 I mean, 300 actions per minute precisely

41:51.640 --> 41:54.600
 is probably realistic, 800 is probably not,

41:54.600 --> 41:57.000
 but you see humans doing a lot of actions

41:57.000 --> 41:59.480
 because they warm up and they kind of select things

41:59.480 --> 42:02.240
 and spam and so on, just so that when they need,

42:02.240 --> 42:04.240
 they have the accuracy.

42:04.240 --> 42:09.240
 So we came into this by not having kind of a standard way

42:09.240 --> 42:12.480
 to say, well, how do we measure whether an agent

42:12.480 --> 42:14.360
 is at human level or not?

42:15.400 --> 42:17.760
 On the other hand, we had a huge advantage,

42:17.760 --> 42:20.960
 which is because we do imitation learning,

42:20.960 --> 42:24.040
 agents turned out to act like humans

42:24.040 --> 42:26.520
 in terms of rate of actions, even precision

42:26.520 --> 42:28.320
 and in precision of actions.

42:28.320 --> 42:30.600
 In the supervised policy, you could see all these.

42:30.600 --> 42:34.280
 You could see how agents like to spam click to move here.

42:34.280 --> 42:36.440
 If you played, especially Diablo, you would know what I mean.

42:36.440 --> 42:39.840
 I mean, you just like spam, oh, move here, move here, move here.

42:39.840 --> 42:43.680
 You're doing literally like maybe five actions in two seconds,

42:43.680 --> 42:46.360
 but these actions are not very meaningful.

42:46.360 --> 42:48.280
 One would have sufficed.

42:48.280 --> 42:51.640
 So on the one hand, we start from this imitation policy

42:51.640 --> 42:55.160
 that is at the ballpark of the actions per minute of humans

42:55.160 --> 42:58.440
 because it's actually statistically trying to imitate humans.

42:58.440 --> 43:00.560
 So we see this very nicely in the curves

43:00.560 --> 43:01.840
 that we showed in the blog post.

43:01.840 --> 43:04.120
 Like there's these actions per minute

43:04.120 --> 43:07.240
 and the distribution looks very human like.

43:07.240 --> 43:10.560
 But then of course, as self play kicks in,

43:10.560 --> 43:12.800
 and that's the part we haven't talked too much yet,

43:12.800 --> 43:16.760
 but of course the agent must play against himself to improve,

43:16.760 --> 43:19.200
 then there's almost no guarantees

43:19.200 --> 43:22.000
 that these actions will not become more precise

43:22.000 --> 43:25.600
 or even the rate of actions is gonna increase over time.

43:25.600 --> 43:29.440
 So what we did, and this is probably kind of the first attempt

43:29.440 --> 43:30.760
 that we thought was reasonable,

43:30.760 --> 43:33.800
 is we looked at the distribution of actions for humans

43:33.800 --> 43:36.440
 for certain windows of time.

43:36.440 --> 43:37.800
 And just to give a perspective,

43:37.800 --> 43:40.760
 because I guess I mentioned that some of these agents

43:40.760 --> 43:42.360
 that are programmatic, let's call them,

43:42.360 --> 43:44.640
 they do 40,000 actions per minute.

43:44.640 --> 43:47.400
 Professionals, as I said, do 300 to 800.

43:47.400 --> 43:49.440
 So what we looked is we look at the distribution

43:49.440 --> 43:51.000
 over professional gamers

43:51.000 --> 43:54.160
 and we took reasonably high actions per minute,

43:54.160 --> 43:57.520
 but we kind of identify certain cutoffs

43:57.520 --> 44:00.600
 after which even if the agent wanted to act,

44:00.600 --> 44:02.000
 these actions would be dropped.

44:02.000 --> 44:06.440
 But the problem is this cutoff is probably set a bit too high

44:06.440 --> 44:09.360
 and what ends up happening, even though the games,

44:09.360 --> 44:11.400
 and when we ask the professionals and the gamers,

44:11.400 --> 44:15.040
 by and large, they feel like it's playing human like.

44:15.040 --> 44:17.240
 There are some agents that developed

44:18.440 --> 44:21.320
 maybe slightly too high APMs,

44:22.040 --> 44:23.520
 which is actions per minute,

44:23.520 --> 44:25.680
 combined with the precision,

44:25.680 --> 44:28.720
 which made people sort of start discussing

44:28.720 --> 44:29.680
 a very interesting issue,

44:29.680 --> 44:31.200
 which is should we have limited

44:31.200 --> 44:34.000
 this, should we just let it loose

44:34.000 --> 44:37.520
 and see what cool things it can come up with, right?

44:37.520 --> 44:38.360
 Interesting.

44:38.360 --> 44:41.960
 So this is in itself an extremely interesting question,

44:41.960 --> 44:43.920
 but the same way that modeling the shimmer

44:43.920 --> 44:45.440
 would be so difficult,

44:45.440 --> 44:48.800
 modeling absolutely all the details about muscles

44:48.800 --> 44:51.600
 and precision and tiredness of humans

44:51.600 --> 44:52.880
 would be quite difficult, right?

44:52.880 --> 44:56.760
 So we're really here in kind of innovating in this sense

44:56.760 --> 45:00.040
 of, okay, what could be maybe the next iteration

45:00.040 --> 45:01.720
 of putting more rules

45:01.720 --> 45:05.040
 that makes the agents more human like

45:05.040 --> 45:06.240
 in terms of restrictions.

45:06.240 --> 45:08.040
 So yeah, putting constraints that.

45:08.040 --> 45:09.240
 More constraints, yeah.

45:09.240 --> 45:11.040
 That's really interesting, that's really innovative.

45:11.040 --> 45:15.400
 So one of the constraints you put on yourself

45:15.400 --> 45:18.000
 or at least focused in is on the Protoss race,

45:18.000 --> 45:19.880
 as far as I understand.

45:19.880 --> 45:21.880
 Can you tell me about the different races

45:21.880 --> 45:26.000
 and how they, so Protoss, Terran and Zerg,

45:26.000 --> 45:27.040
 how do they compare?

45:27.040 --> 45:28.120
 How do they interact?

45:28.120 --> 45:29.960
 Why did you choose Protoss?

45:29.960 --> 45:33.600
 In the dynamics of the game

45:33.600 --> 45:35.680
 seen from a strategic perspective.

45:35.680 --> 45:39.680
 So Protoss, so in Starcraft, there are three races.

45:39.680 --> 45:41.320
 Indeed, in the demonstration,

45:41.320 --> 45:43.880
 we saw only the Protoss race.

45:43.880 --> 45:45.560
 So maybe let's start with that one.

45:45.560 --> 45:49.440
 Protoss is kind of the most technologically advanced race.

45:49.440 --> 45:53.800
 It has units that are expensive, but powerful, right?

45:53.800 --> 45:57.840
 So in general, you wanna kind of conserve your units

45:57.840 --> 45:58.640
 as you go attack.

45:58.640 --> 46:01.840
 So you wanna, and then you wanna utilize

46:01.840 --> 46:05.120
 these tactical advantages of very fancy spells

46:05.120 --> 46:07.240
 and so on, so forth.

46:07.240 --> 46:10.320
 And at the same time, they're kind of,

46:11.280 --> 46:15.040
 people say like they're a bit easier to play perhaps, right?

46:15.040 --> 46:17.160
 But that I actually didn't know.

46:17.160 --> 46:20.120
 I mean, I just talked to now a lot to the players

46:20.120 --> 46:22.520
 that we work with, TLO and Mana.

46:22.520 --> 46:24.120
 And they said, oh yeah, Protoss is actually,

46:24.120 --> 46:26.320
 people think is actually one of the easiest races.

46:26.320 --> 46:29.360
 So perhaps the easier, that doesn't mean

46:29.360 --> 46:32.760
 that it's obviously professional players

46:32.760 --> 46:34.080
 excel at the three races.

46:34.080 --> 46:37.600
 And there's never like a race that dominates

46:37.600 --> 46:38.800
 for a very long time anyway.

46:38.800 --> 46:41.760
 So if you look at the top, I don't know, 100 in the world,

46:41.760 --> 46:44.360
 is there one race that dominates that list?

46:44.360 --> 46:46.840
 It would be hard to know because it depends on the regions.

46:46.840 --> 46:50.600
 I think it's pretty equal in terms of distribution.

46:50.600 --> 46:52.840
 And Blizzard wants it to be equal, right?

46:52.840 --> 46:56.320
 They wouldn't want one race like Protoss

46:56.320 --> 46:59.960
 to not be representative in the top place.

46:59.960 --> 47:03.880
 So definitely like they tried it to be like the balance, right?

47:03.880 --> 47:07.320
 So then maybe the opposite race of Protoss is Zerg.

47:07.320 --> 47:10.600
 Zerg is a race where you just kind of expand

47:10.600 --> 47:13.840
 and take over as many resources as you can.

47:13.840 --> 47:15.720
 And they have a very high capacity

47:15.720 --> 47:17.680
 to regenerate their units.

47:17.680 --> 47:20.520
 So if you have an army, it's not that valuable

47:20.520 --> 47:23.960
 in terms of losing the whole army is not a big deal as Zerg

47:23.960 --> 47:25.960
 because you can then rebuild it.

47:25.960 --> 47:28.320
 And given that you generally accumulate

47:28.320 --> 47:32.000
 a huge bank of resources, Zerg's typically play

47:32.000 --> 47:34.240
 by applying a lot of pressure,

47:34.240 --> 47:36.160
 maybe losing their whole army,

47:36.160 --> 47:37.880
 but then rebuilding it quickly.

47:37.880 --> 47:40.480
 So although of course every race,

47:40.480 --> 47:43.960
 I mean, there's never, I mean, they're pretty diverse.

47:43.960 --> 47:45.160
 I mean, there are some units in Zerg

47:45.160 --> 47:46.600
 that are technologically advanced

47:46.600 --> 47:48.880
 and they do some very interesting spells.

47:48.880 --> 47:51.360
 And there's some units in Protoss that are less valuable

47:51.360 --> 47:53.360
 and you could lose a lot of them and rebuild them

47:53.360 --> 47:55.160
 and it wouldn't be a big deal.

47:55.160 --> 47:57.840
 All right, so maybe I'm missing out.

47:57.840 --> 47:59.280
 Maybe I'm gonna say some dumb stuff,

47:59.280 --> 48:02.480
 but just summary of strategy.

48:02.480 --> 48:05.720
 So first there's collection of a lot of resources.

48:05.720 --> 48:06.560
 That's one option.

48:06.560 --> 48:11.560
 The other one is expanding, so building other bases.

48:11.920 --> 48:15.640
 Then the other is obviously building units

48:15.640 --> 48:17.200
 and attacking with those units.

48:17.200 --> 48:20.640
 And then I don't know what else there is.

48:20.640 --> 48:24.080
 Maybe there's the different timing of attacks.

48:24.080 --> 48:26.000
 Like do attack early, attack late.

48:26.000 --> 48:28.000
 What are the different strategies that emerged

48:28.000 --> 48:29.120
 that you've learned about?

48:29.120 --> 48:31.360
 I've read that a bunch of people are super happy

48:31.360 --> 48:33.000
 that you guys have apparently,

48:33.000 --> 48:35.000
 that Alpha Star apparently has discovered

48:35.000 --> 48:38.000
 that it's really good to, what is it, saturate.

48:38.000 --> 48:39.600
 Oh yeah, the mineral line.

48:39.600 --> 48:41.360
 Yeah, the mineral line.

48:41.360 --> 48:42.200
 Yeah, yeah.

48:42.200 --> 48:45.600
 And that's for greedy amateur players like myself.

48:45.600 --> 48:47.480
 That's always been a good strategy.

48:47.480 --> 48:49.000
 You just build up a lot of money

48:49.000 --> 48:53.280
 and it just feels good to just accumulate and accumulate.

48:53.280 --> 48:56.720
 So thank you for discovering that and validating all of us.

48:56.720 --> 48:59.200
 But is there other strategies that you discovered

48:59.200 --> 49:01.880
 interesting and unique to this game?

49:01.880 --> 49:05.080
 Yeah, so if you look at the kind of,

49:05.080 --> 49:06.480
 not being a Starcraft 2 player,

49:06.480 --> 49:08.120
 but of course Starcraft and Starcraft 2

49:08.120 --> 49:11.120
 and real time strategy games in general are very similar.

49:11.120 --> 49:16.120
 I would classify perhaps the openings of the game.

49:17.560 --> 49:18.760
 They're very important.

49:18.760 --> 49:21.760
 And generally I would say there's two kinds of openings.

49:21.760 --> 49:23.400
 One that's a standard opening,

49:23.400 --> 49:28.400
 that's generally how players find sort of a balance

49:28.840 --> 49:31.520
 between risk and economy

49:31.520 --> 49:33.400
 and building some units early on

49:33.400 --> 49:34.600
 so that they could defend,

49:34.600 --> 49:36.800
 but they're not too exposed basically,

49:36.800 --> 49:39.480
 but also expanding quite quickly.

49:39.480 --> 49:42.040
 So this would be kind of a standard opening.

49:42.040 --> 49:43.680
 And within a standard opening,

49:43.680 --> 49:45.480
 then what you do choose generally

49:45.480 --> 49:48.400
 is what technology are you aiming towards?

49:48.400 --> 49:50.280
 So there's a bit of rock, paper, scissors

49:50.280 --> 49:52.920
 of you could go for spaceships

49:52.920 --> 49:55.080
 or you could go for invisible units

49:55.080 --> 49:56.400
 or you could go for, I don't know,

49:56.400 --> 50:00.080
 like massive units that attack against certain kinds of units

50:00.080 --> 50:01.640
 but they're weak against others.

50:01.640 --> 50:05.760
 So standard openings themselves have some choices

50:05.760 --> 50:07.480
 like rock, paper, scissors style.

50:07.480 --> 50:09.640
 Of course, if you scout and you're good at guessing

50:09.640 --> 50:11.080
 what the opponent is doing,

50:11.080 --> 50:12.800
 then you can play as an advantage

50:12.800 --> 50:14.480
 because if you know you're gonna play rock,

50:14.480 --> 50:16.480
 I mean, I'm gonna play paper obviously.

50:16.480 --> 50:19.120
 So you can imagine that normal standard games

50:19.120 --> 50:22.920
 in Starcraft looks like a continuous rock, paper,

50:22.920 --> 50:26.600
 scissors game where you guess what the distribution

50:26.600 --> 50:29.920
 of rock, paper, and scissors is from the enemy

50:29.920 --> 50:33.360
 and reacting accordingly to try to beat it

50:33.360 --> 50:37.000
 or put the paper out before he kind of changes

50:37.000 --> 50:38.880
 his mind from rock to scissors

50:38.880 --> 50:40.480
 and then you would be in a weak position.

50:40.480 --> 50:42.120
 So sorry to pause on that.

50:42.120 --> 50:43.320
 I didn't realize this element

50:43.320 --> 50:44.880
 because I know it's true with poker.

50:44.880 --> 50:46.440
 I looked at Leprata's.

50:47.640 --> 50:52.200
 You're also estimating, trying to guess the distribution,

50:52.200 --> 50:54.160
 trying to better and better estimate the distribution

50:54.160 --> 50:56.040
 what the opponent is likely to be doing.

50:56.040 --> 50:57.440
 Yeah, I mean, as a player,

50:57.440 --> 50:59.840
 you definitely wanna have a belief state

50:59.840 --> 51:03.000
 over what's up on the other side of the map

51:03.000 --> 51:05.600
 and when your belief state becomes inaccurate

51:05.600 --> 51:08.040
 when you start having serious doubts

51:08.040 --> 51:11.320
 whether he's gonna play something that you must know,

51:11.320 --> 51:12.440
 that's when you scout.

51:12.440 --> 51:14.560
 You wanna then gather information, right?

51:14.560 --> 51:16.440
 Is improving the accuracy of the belief

51:16.440 --> 51:19.880
 or improving the belief state part of the loss

51:19.880 --> 51:21.040
 that you're trying to optimize?

51:21.040 --> 51:22.720
 Or is it just an side effect?

51:22.720 --> 51:24.040
 It's implicit, but implicit.

51:24.040 --> 51:25.840
 You could explicitly model it

51:25.840 --> 51:28.280
 and it would be quite good at probably predicting

51:28.280 --> 51:30.360
 what's on the other side of the map,

51:30.360 --> 51:32.880
 but so far it's all implicit.

51:32.880 --> 51:36.680
 There's no additional reward for predicting the enemy.

51:36.680 --> 51:38.800
 So there's these standard openings

51:38.800 --> 51:41.640
 and then there's what people call cheese,

51:41.640 --> 51:42.800
 which is very interesting

51:42.800 --> 51:46.760
 and Alpha Star sometimes really likes this kind of cheese.

51:46.760 --> 51:51.120
 These cheeses, what they are is kind of an all in strategy.

51:51.120 --> 51:53.240
 You're gonna do something sneaky.

51:53.240 --> 51:56.680
 You're gonna hide your own buildings

51:56.680 --> 51:58.200
 close to the enemy base

51:58.200 --> 52:01.600
 or you're gonna go for hiding your technological buildings

52:01.600 --> 52:03.040
 so that you do invisible units

52:03.040 --> 52:06.040
 and the enemy just cannot react to detect it

52:06.040 --> 52:08.000
 and thus lose the game.

52:08.000 --> 52:10.000
 And there's quite a few of these cheeses

52:10.000 --> 52:11.800
 and variants of them.

52:11.800 --> 52:14.480
 And there it's where actually the belief state

52:14.480 --> 52:16.360
 becomes even more important

52:16.360 --> 52:18.520
 because if I scout your base

52:18.520 --> 52:20.200
 and I see no buildings at all,

52:20.200 --> 52:22.480
 any human player knows some things up.

52:22.480 --> 52:23.320
 They might know, well,

52:23.320 --> 52:25.640
 you're hiding something close to my base.

52:25.640 --> 52:28.200
 Should I build suddenly a lot of units to defense?

52:28.200 --> 52:31.000
 Should I actually block my ramp with workers

52:31.000 --> 52:33.520
 so that you cannot come and destroy my base?

52:33.520 --> 52:35.680
 So there's all this is happening

52:35.680 --> 52:39.440
 and defending against cheeses is extremely important.

52:39.440 --> 52:40.760
 And in the Alpha Star League,

52:40.760 --> 52:45.080
 many agents actually develop some cheesy strategies.

52:45.080 --> 52:48.000
 And in the games we saw against TLO and Mana,

52:48.000 --> 52:49.240
 two out of the 10 agents

52:49.240 --> 52:51.760
 were actually doing these kind of strategies

52:51.760 --> 52:53.640
 which are cheesy strategies.

52:53.640 --> 52:55.600
 And then there's a variant of cheesy strategy

52:55.600 --> 52:57.360
 which is called all in.

52:57.360 --> 53:00.280
 So an all in strategy is not perhaps as drastic

53:00.280 --> 53:02.560
 as oh, I'm gonna build cannons on your base

53:02.560 --> 53:03.880
 and then bring all my workers

53:03.880 --> 53:06.840
 and try to just disrupt your base and game over

53:06.840 --> 53:08.800
 or GG as we say in StarCraft.

53:09.840 --> 53:12.000
 There's these kind of very cool things

53:12.000 --> 53:14.760
 that you can align precisely at a certain time mark.

53:14.760 --> 53:17.400
 So for instance, you can generate

53:17.400 --> 53:20.280
 exactly 10 unit composition that is perfect.

53:20.280 --> 53:22.960
 Like five of this type, five of these other type

53:22.960 --> 53:26.240
 and align the upgrade so that at four minutes and a half,

53:26.240 --> 53:28.680
 let's say you have these 10 units

53:28.680 --> 53:30.640
 and the upgrade just finished.

53:30.640 --> 53:34.000
 And at that point, that army is really scary.

53:34.000 --> 53:36.440
 And unless the enemy really knows what's going on,

53:36.440 --> 53:40.280
 if you push, you might then have an advantage

53:40.280 --> 53:42.480
 because maybe the enemy is doing something more standard,

53:42.480 --> 53:45.800
 it expanded too much, it developed too much economy

53:45.800 --> 53:49.760
 and it trade off badly against having defenses

53:49.760 --> 53:51.120
 and the enemy will lose.

53:51.120 --> 53:53.680
 But it's called all in because if you don't win,

53:53.680 --> 53:55.080
 then you're gonna lose.

53:55.080 --> 53:57.960
 So you see players that do these kind of strategies,

53:57.960 --> 54:00.000
 if they don't succeed, game is not over.

54:00.000 --> 54:01.240
 I mean, they still have a base

54:01.240 --> 54:02.880
 and they still gathering minerals,

54:02.880 --> 54:04.800
 but they will just GG out of the game

54:04.800 --> 54:06.800
 because they know, well, game is over.

54:06.800 --> 54:08.880
 I gambled and I failed.

54:08.880 --> 54:11.600
 So if we start entering the game

54:11.600 --> 54:14.520
 theoretic aspects of the game, it's really rich

54:14.520 --> 54:18.000
 and that's why it also makes it quite entertaining to watch.

54:18.000 --> 54:21.800
 Even if I don't play, I still enjoy watching the game.

54:21.800 --> 54:26.800
 But the agents are trying to do this mostly implicitly,

54:26.800 --> 54:29.320
 but one element that we improved in self plays

54:29.320 --> 54:31.320
 creating the Alpha Star League.

54:31.320 --> 54:34.600
 And the Alpha Star League is not pure self play.

54:34.600 --> 54:37.880
 It's trying to create different personalities of agents

54:37.880 --> 54:41.480
 so that some of them will become cheesy agents.

54:41.480 --> 54:44.360
 Some of them might become very economical, very greedy,

54:44.360 --> 54:46.160
 like getting all the resources,

54:46.160 --> 54:48.760
 but then maybe early on they're gonna be weak,

54:48.760 --> 54:51.040
 but later on they're gonna be very strong.

54:51.040 --> 54:53.400
 And by creating this personality of agents,

54:53.400 --> 54:55.400
 which sometimes it just happens naturally

54:55.400 --> 54:58.240
 that you can see kind of an evolution of agents

54:58.240 --> 55:00.760
 that given the previous generation,

55:00.760 --> 55:01.920
 they train against all of them

55:01.920 --> 55:04.320
 and then they generate kind of the perfect counter

55:04.320 --> 55:05.760
 to that distribution.

55:05.760 --> 55:09.280
 But these agents, you must have them in the populations

55:09.280 --> 55:11.280
 because if you don't have them,

55:11.280 --> 55:13.040
 you're not covered against these things, right?

55:13.040 --> 55:17.080
 It's kind of, you wanna create all sorts of the opponents

55:17.080 --> 55:18.640
 that you will find in the wild.

55:18.640 --> 55:21.800
 So you can be exposed to these cheeses,

55:21.800 --> 55:25.720
 early aggression, later aggression, more expansions,

55:25.720 --> 55:29.560
 dropping units in your base from the side, all these things.

55:29.560 --> 55:32.760
 And pure self play is getting a bit stuck

55:32.760 --> 55:36.200
 at finding some subset of these, but not all of these.

55:36.200 --> 55:39.480
 So the Alpha Star League is a way to kind of

55:39.480 --> 55:41.560
 do an ensemble of agents

55:41.560 --> 55:43.480
 that they're all playing in a league

55:43.480 --> 55:45.520
 much like people play on Battle.net, right?

55:45.520 --> 55:47.440
 They play, you play against someone

55:47.440 --> 55:50.240
 who does a new cool strategy and you immediately,

55:50.240 --> 55:53.040
 oh my God, I wanna try it, I wanna play again.

55:53.040 --> 55:55.960
 And these to me was another critical part

55:55.960 --> 55:58.520
 of the problem which was,

55:58.520 --> 56:01.240
 can we create a Battle.net for agents?

56:01.240 --> 56:02.080
 Yeah.

56:02.080 --> 56:03.400
 And that's kind of what the Alpha Star League really.

56:03.400 --> 56:04.240
 That's fascinating.

56:04.240 --> 56:06.920
 And where they stick to their different strategies.

56:06.920 --> 56:09.960
 Yeah, wow, that's really, really interesting.

56:09.960 --> 56:13.240
 But that said, you were fortunate enough

56:13.240 --> 56:16.280
 or just skilled enough to win 5.0.

56:17.320 --> 56:19.280
 And so how hard is it to win?

56:19.280 --> 56:20.320
 I mean, that's not the goal.

56:20.320 --> 56:21.920
 I guess, I don't know what the goal is.

56:21.920 --> 56:25.400
 The goal should be to win majority, not 5.0,

56:25.400 --> 56:29.360
 but how hard is it in general to win all matchups?

56:29.360 --> 56:31.080
 I don't want V1.

56:31.080 --> 56:33.600
 So that's a very interesting question

56:33.600 --> 56:37.240
 because once you see Alpha Star

56:37.240 --> 56:39.520
 and superficially you think, well, okay,

56:39.520 --> 56:42.960
 it won, if you sum all the games like 10 to one, right?

56:42.960 --> 56:46.280
 It lost the game that it played with the camera interface.

56:46.280 --> 56:48.480
 You might think, well, that's done, right?

56:48.480 --> 56:50.840
 It's super human at the game.

56:50.840 --> 56:54.800
 And that's not really the claim we really can make, actually.

56:56.000 --> 56:58.840
 The claim is we beat a professional gamer

56:58.840 --> 57:00.120
 for the first time.

57:00.120 --> 57:02.480
 Starcraft has really been a thing

57:02.480 --> 57:04.120
 that has been going on for a few years,

57:04.120 --> 57:09.120
 but a moment like this had not occurred before yet.

57:09.520 --> 57:12.400
 But are these agents impossible to beat?

57:12.400 --> 57:13.440
 Absolutely not, right?

57:13.440 --> 57:17.360
 So that's a bit what's kind of the difference is

57:17.360 --> 57:19.560
 the agents play at grandmaster level.

57:19.560 --> 57:21.520
 They definitely understand the game enough

57:21.520 --> 57:24.960
 to play extremely well, but are they unbeatable?

57:24.960 --> 57:26.600
 Do they play perfect?

57:27.920 --> 57:30.320
 No, and actually in Starcraft,

57:30.320 --> 57:33.240
 because of these sneaky strategies,

57:33.240 --> 57:36.680
 it's always possible that you might take a huge risk sometimes,

57:36.680 --> 57:39.200
 but you might get wins, right, out of this.

57:39.200 --> 57:44.200
 So I think as a domain, it still has a lot of opportunities,

57:44.200 --> 57:47.760
 not only because of course we wanna learn with less experience,

57:47.760 --> 57:50.480
 we would like to, I mean, if I learn to play Protoss,

57:50.480 --> 57:53.280
 I can play Terran and learn it much quicker

57:53.280 --> 57:54.480
 than Alpha Star can, right?

57:54.480 --> 57:58.440
 So there are obvious interesting research challenges as well.

57:58.440 --> 58:03.080
 But even as the raw performance goes,

58:03.080 --> 58:05.960
 really the claim here can be we are at pro level

58:05.960 --> 58:09.080
 or at high grandmaster level,

58:09.080 --> 58:14.080
 but obviously the players also did not know what to expect,

58:14.360 --> 58:17.000
 right, their prior distribution was a bit off

58:17.000 --> 58:20.400
 because they played this kind of new alien brain

58:20.400 --> 58:22.080
 as they like to say it, right?

58:22.080 --> 58:25.080
 And that's what makes it exciting for them,

58:25.080 --> 58:28.040
 but also I think if you look at the games closely,

58:28.040 --> 58:31.520
 you see there were weaknesses in some points,

58:31.520 --> 58:33.320
 maybe Alpha Star did not scout

58:33.320 --> 58:36.080
 or if it had got invisible units going against

58:36.080 --> 58:38.200
 at certain points, it wouldn't have known

58:38.200 --> 58:39.600
 and it would have been bad.

58:39.600 --> 58:42.920
 So there's still quite a lot of work to do,

58:42.920 --> 58:45.440
 but it's really a very exciting moment for us

58:45.440 --> 58:49.120
 to be seeing, wow, a single neural net on a GPU

58:49.120 --> 58:52.080
 is actually playing against these guys who are amazing.

58:52.080 --> 58:53.760
 I mean, you have to see them play in life.

58:53.760 --> 58:55.800
 They're really, really amazing players.

58:55.800 --> 59:00.440
 Yeah, I'm sure there must be a guy in Poland somewhere

59:00.440 --> 59:02.680
 right now training his butt off

59:02.680 --> 59:06.600
 to make sure that this never happens again with Alpha Star.

59:06.600 --> 59:09.720
 So that's really exciting in terms of Alpha Star

59:09.720 --> 59:12.200
 having some holes to exploit, which is great.

59:12.200 --> 59:14.320
 And then you build on top of each other

59:14.320 --> 59:18.920
 and it feels like Starcraft on let go, even if you win,

59:18.920 --> 59:21.640
 it's still not, it's still not,

59:21.640 --> 59:23.120
 there's so many different dimensions

59:23.120 --> 59:24.200
 in which you can explore.

59:24.200 --> 59:25.560
 So that's really, really interesting.

59:25.560 --> 59:28.520
 Do you think there's a ceiling to Alpha Star?

59:28.520 --> 59:32.840
 You've said that it hasn't reached, this is a big,

59:33.960 --> 59:35.520
 let me actually just pause for a second.

59:35.520 --> 59:40.200
 How did it feel to come here to this point,

59:40.200 --> 59:42.240
 to beat a top professional player?

59:42.240 --> 59:44.600
 Like that night, I mean, you know,

59:44.600 --> 59:47.160
 Olympic athletes have their gold medal, right?

59:47.160 --> 59:48.840
 This is your gold medal in a sense.

59:48.840 --> 59:50.400
 Sure, you're cited a lot,

59:50.400 --> 59:53.120
 you've published a lot of prestigious papers, whatever,

59:53.120 --> 59:55.280
 but this is like a win.

59:55.280 --> 59:56.480
 How did it feel?

59:56.480 --> 59:59.440
 I mean, it was, for me, it was unbelievable

59:59.440 --> 1:00:04.440
 because first the win itself, I mean, it was so exciting.

1:00:04.440 --> 1:00:09.440
 I mean, so looking back to those last days of 2018,

1:00:09.840 --> 1:00:12.040
 really, that's when the games were played,

1:00:12.040 --> 1:00:14.560
 I'm sure I'll look back at that moment and say,

1:00:14.560 --> 1:00:17.280
 oh my God, I wanna be in a project like that.

1:00:17.280 --> 1:00:20.440
 It's like, I already feel the nostalgia of like,

1:00:20.440 --> 1:00:23.560
 yeah, that was huge in terms of the energy

1:00:23.560 --> 1:00:25.720
 and the team effort that went into it.

1:00:25.720 --> 1:00:28.520
 And so in that sense, as soon as it happened,

1:00:28.520 --> 1:00:30.640
 I already knew it was kind of,

1:00:30.640 --> 1:00:32.320
 I was losing it a little bit.

1:00:32.320 --> 1:00:35.440
 So it is almost like sad that it happened and oh my God,

1:00:35.440 --> 1:00:40.440
 like, but on the other hand, it also verifies the approach.

1:00:40.600 --> 1:00:43.160
 But to me also, there's so many challenges

1:00:43.160 --> 1:00:45.400
 and interesting aspects of intelligence

1:00:45.400 --> 1:00:49.240
 that even though we can train a neural network

1:00:49.240 --> 1:00:52.040
 to play at the level of the best humans,

1:00:52.040 --> 1:00:53.600
 there's still so many challenges.

1:00:53.600 --> 1:00:54.800
 So for me, it's also like,

1:00:54.800 --> 1:00:56.800
 well, this is really an amazing achievement,

1:00:56.800 --> 1:00:59.280
 but I already was also thinking about next steps.

1:00:59.280 --> 1:01:01.720
 I mean, as I said, these Asians play Protos,

1:01:01.720 --> 1:01:04.080
 they play Protos versus Protos,

1:01:04.080 --> 1:01:07.240
 but they should be able to play a different race

1:01:07.240 --> 1:01:08.160
 much quicker, right?

1:01:08.160 --> 1:01:10.640
 So that would be an amazing achievement.

1:01:10.640 --> 1:01:13.360
 Some people call this meta reinforcement learning,

1:01:13.360 --> 1:01:15.200
 meta learning and so on, right?

1:01:15.200 --> 1:01:18.960
 So there's so many possibilities after that moment,

1:01:18.960 --> 1:01:22.240
 but the moment itself, it really felt great.

1:01:22.240 --> 1:01:24.520
 It's, we had this bet.

1:01:24.520 --> 1:01:27.720
 So I'm kind of a pessimist in general.

1:01:27.720 --> 1:01:30.120
 So I kind of send an email to the team and I said,

1:01:30.120 --> 1:01:33.680
 okay, let's, against TLO first, right?

1:01:33.680 --> 1:01:35.120
 Like what's going to be the result?

1:01:35.120 --> 1:01:38.680
 And I really thought we would lose like five zero, right?

1:01:38.680 --> 1:01:41.480
 I, I, we had some calibration made

1:01:41.480 --> 1:01:44.080
 against the 5,000 MMR player.

1:01:44.080 --> 1:01:47.360
 TLO was much stronger than that player.

1:01:47.360 --> 1:01:50.040
 Even if he played Protos, which is his off race,

1:01:51.040 --> 1:01:53.120
 but yeah, it was not imagining we would win.

1:01:53.120 --> 1:01:55.600
 So for me, that was just kind of a test run or something.

1:01:55.600 --> 1:01:59.000
 And then it really kind of, he was really surprised.

1:01:59.000 --> 1:02:02.360
 And unbelievably, we went to this,

1:02:02.360 --> 1:02:04.560
 to this bar to celebrate.

1:02:04.560 --> 1:02:08.360
 And Dave tells me, well, why don't we invite someone

1:02:08.360 --> 1:02:11.000
 who is a thousand MMR stronger in Protos?

1:02:11.000 --> 1:02:13.040
 Like an actual Protos player, like,

1:02:13.040 --> 1:02:16.200
 like that it turned out being mana, right?

1:02:16.200 --> 1:02:19.400
 And, you know, we had some drinks and I said, sure, why not?

1:02:19.400 --> 1:02:20.240
 But then I thought, well,

1:02:20.240 --> 1:02:22.080
 that's really going to be impossible to beat.

1:02:22.080 --> 1:02:24.600
 I mean, even because it's so much ahead.

1:02:24.600 --> 1:02:28.440
 A thousand MMR is really like 99% probability

1:02:28.440 --> 1:02:33.080
 that mana would beat TLO as Protos versus Protos, right?

1:02:33.080 --> 1:02:34.240
 So we did that.

1:02:34.240 --> 1:02:39.000
 And to me, the second game was much more important,

1:02:39.000 --> 1:02:42.120
 even though a lot of uncertainty kind of disappeared

1:02:42.120 --> 1:02:43.680
 after we kind of beat TLO.

1:02:43.680 --> 1:02:45.680
 I mean, he is a professional player.

1:02:45.680 --> 1:02:48.000
 So that was kind of, oh, but that's really

1:02:48.000 --> 1:02:49.760
 a very nice achievement.

1:02:49.760 --> 1:02:51.800
 But mana really was at the top.

1:02:51.800 --> 1:02:53.880
 And you could see he played much better,

1:02:53.880 --> 1:02:55.400
 but our agents got much better too.

1:02:55.400 --> 1:02:57.440
 So it's like, ah.

1:02:57.440 --> 1:02:59.800
 And then after the first game, I said,

1:02:59.800 --> 1:03:02.760
 if we take a single game, at least we can say we beat A game.

1:03:02.760 --> 1:03:04.320
 I mean, even if we don't beat the series,

1:03:04.320 --> 1:03:06.960
 for me, that was a huge relief.

1:03:06.960 --> 1:03:09.200
 And I mean, I remember the hacking them is.

1:03:09.200 --> 1:03:11.600
 And I mean, it was really like this moment,

1:03:11.600 --> 1:03:14.200
 for me, will resonate forever as a researcher.

1:03:14.200 --> 1:03:15.880
 And I mean, as a person, and yeah,

1:03:15.880 --> 1:03:18.280
 it's a really like great accomplishment.

1:03:18.280 --> 1:03:21.360
 And it was great also to be there with the team in the room.

1:03:21.360 --> 1:03:23.080
 I don't know if you saw like this.

1:03:23.080 --> 1:03:24.760
 So it was really like.

1:03:24.760 --> 1:03:26.000
 I mean, from my perspective,

1:03:26.000 --> 1:03:29.720
 the other interesting thing is just like watching Kasparov,

1:03:29.720 --> 1:03:33.760
 now watching mana was also interesting

1:03:33.760 --> 1:03:36.160
 because he is kind of at a loss of words.

1:03:36.160 --> 1:03:38.400
 I mean, whenever you lose, I've done a lot of sports.

1:03:38.400 --> 1:03:43.560
 You sometimes say excuses, you look for reasons.

1:03:43.560 --> 1:03:46.280
 And he couldn't really come up with reasons.

1:03:46.280 --> 1:03:50.040
 I mean, so with the off race for Protoss,

1:03:50.040 --> 1:03:52.320
 you could say, well, it felt awkward, it wasn't,

1:03:52.320 --> 1:03:55.200
 but here it was just beaten.

1:03:55.200 --> 1:03:57.960
 And it was beautiful to look at a human being

1:03:57.960 --> 1:04:00.320
 being superseded by an AI system.

1:04:00.320 --> 1:04:04.480
 I mean, it's a beautiful moment for researchers.

1:04:04.480 --> 1:04:05.920
 Yeah, for sure it was.

1:04:05.920 --> 1:04:09.960
 I mean, probably the highlight of my career so far

1:04:09.960 --> 1:04:11.760
 because of its uniqueness and coolness.

1:04:11.760 --> 1:04:14.280
 And I don't know, I mean, it's obviously, as you said,

1:04:14.280 --> 1:04:16.240
 you can look at paper citations and so on.

1:04:16.240 --> 1:04:19.280
 But this really is like a testament

1:04:19.280 --> 1:04:22.400
 of the whole machine learning approach

1:04:22.400 --> 1:04:24.640
 and using games to advance technology.

1:04:24.640 --> 1:04:27.880
 I mean, it really was, everything came together

1:04:27.880 --> 1:04:29.840
 at that moment, that's really the summary.

1:04:29.840 --> 1:04:34.040
 Also, on the other side, it's a popularization of AI too

1:04:34.040 --> 1:04:38.200
 because it's just like traveling to the moon and so on.

1:04:38.200 --> 1:04:41.000
 I mean, this is where a very large community of people

1:04:41.000 --> 1:04:45.080
 that don't really know AI, they get to really interact with it.

1:04:45.080 --> 1:04:46.000
 Which is very important.

1:04:46.000 --> 1:04:50.800
 I mean, we must, you know, writing papers helps our peers,

1:04:50.800 --> 1:04:52.520
 researchers to understand what we're doing.

1:04:52.520 --> 1:04:55.880
 But I think AI is becoming mature enough

1:04:55.880 --> 1:04:59.000
 that we must sort of try to explain what it is.

1:04:59.000 --> 1:05:01.440
 And perhaps through games is an obvious way

1:05:01.440 --> 1:05:03.640
 because these games always had built in AI.

1:05:03.640 --> 1:05:07.680
 So it may be everyone experienced an AI playing a video game

1:05:07.680 --> 1:05:08.520
 even if they don't know.

1:05:08.520 --> 1:05:10.240
 Because there's always some scripted element

1:05:10.240 --> 1:05:13.880
 and some people might even call that AI already, right?

1:05:13.880 --> 1:05:16.320
 So what are other applications

1:05:16.320 --> 1:05:20.280
 of the approaches underlying Alpha Star that you see happening?

1:05:20.280 --> 1:05:23.120
 There's a lot of echoes of, you said, transformer

1:05:23.120 --> 1:05:25.680
 of language modeling and so on.

1:05:25.680 --> 1:05:29.480
 Have you already started thinking where the breakthroughs

1:05:29.480 --> 1:05:32.280
 in Alpha Star get expanded to other applications?

1:05:32.280 --> 1:05:34.640
 Right, so I thought about a few things

1:05:34.640 --> 1:05:37.280
 for like kind of next months, next years.

1:05:38.440 --> 1:05:40.520
 The main thing I'm thinking about actually is

1:05:40.520 --> 1:05:43.160
 what's next as a kind of a grand challenge

1:05:43.160 --> 1:05:47.120
 because for me, like we've seen Atari

1:05:47.120 --> 1:05:50.280
 and then there's like the sort of three dimensional walls

1:05:50.280 --> 1:05:52.520
 that we've seen also like pretty good performance

1:05:52.520 --> 1:05:54.160
 from this capture the flag agents

1:05:54.160 --> 1:05:57.600
 that also some people at DeepMind and elsewhere are working on.

1:05:57.600 --> 1:05:59.600
 We've also seen some amazing results on like,

1:05:59.600 --> 1:06:03.280
 for instance, Dota 2, which is also a very complicated game.

1:06:03.280 --> 1:06:05.960
 So for me, like the main thing I'm thinking about

1:06:05.960 --> 1:06:07.960
 is what's next in terms of challenge.

1:06:07.960 --> 1:06:12.960
 So as a researcher, I see sort of two tensions

1:06:12.960 --> 1:06:16.760
 between research and then applications or areas

1:06:16.760 --> 1:06:18.480
 or domains where you apply them.

1:06:18.480 --> 1:06:20.480
 So on the one hand, we've done,

1:06:20.480 --> 1:06:23.320
 thanks to the application of StarCraft is very hard.

1:06:23.320 --> 1:06:25.600
 We developed some techniques, some new research

1:06:25.600 --> 1:06:27.480
 that now we could look at elsewhere,

1:06:27.480 --> 1:06:30.520
 like are there other applications where we can apply this?

1:06:30.520 --> 1:06:32.880
 And the obvious ones, absolutely,

1:06:32.880 --> 1:06:37.480
 you can think of feeding back to sort of the community

1:06:37.480 --> 1:06:40.240
 we took from, which was mostly sequence modeling

1:06:40.240 --> 1:06:41.680
 or natural language processing.

1:06:41.680 --> 1:06:46.120
 So we've developed an extended things from the transformer

1:06:46.120 --> 1:06:48.120
 and we use pointer networks.

1:06:48.120 --> 1:06:51.280
 We combine LSTM and transformers in interesting ways.

1:06:51.280 --> 1:06:54.200
 So that's perhaps the kind of lowest hanging fruit

1:06:54.200 --> 1:06:58.840
 of feeding back to now a different field of machine learning

1:06:58.840 --> 1:07:00.880
 that's not playing video games.

1:07:00.880 --> 1:07:05.680
 Let me go old school and jump to Mr. Alan Turing.

1:07:05.680 --> 1:07:09.880
 So the Turing test is a natural language test,

1:07:09.880 --> 1:07:11.560
 a conversational test.

1:07:11.560 --> 1:07:15.760
 What's your thought of it as a test for intelligence?

1:07:15.760 --> 1:07:17.360
 Do you think it is a grand challenge

1:07:17.360 --> 1:07:18.920
 that's worthy of undertaking?

1:07:18.920 --> 1:07:21.960
 Maybe if it is, would you reformulate it

1:07:21.960 --> 1:07:23.720
 or phrase it somehow differently?

1:07:23.720 --> 1:07:25.640
 Right, so I really love the Turing test

1:07:25.640 --> 1:07:29.600
 because I also like sequences and language understanding.

1:07:29.600 --> 1:07:32.160
 And in fact, some of the early work

1:07:32.160 --> 1:07:33.520
 we did in machine translation,

1:07:33.520 --> 1:07:37.320
 we tried to apply to kind of a neural chat bot,

1:07:37.320 --> 1:07:40.200
 which obviously would never pass the Turing test

1:07:40.200 --> 1:07:42.320
 because it was very limited.

1:07:42.320 --> 1:07:45.200
 But it is a very fascinating idea

1:07:45.200 --> 1:07:49.440
 that you could really have an AI

1:07:49.440 --> 1:07:51.800
 that would be indistinguishable from humans

1:07:51.800 --> 1:07:56.040
 in terms of asking or conversing with it, right?

1:07:56.040 --> 1:08:00.720
 So I think the test itself seems very nice

1:08:00.720 --> 1:08:02.600
 and it's kind of well defined actually,

1:08:02.600 --> 1:08:05.000
 like the passing it or not.

1:08:05.000 --> 1:08:06.560
 I think there's quite a few rules

1:08:06.560 --> 1:08:11.560
 that feel like pretty simple and you could really have,

1:08:12.520 --> 1:08:14.800
 I mean, I think they have these competitions every year.

1:08:14.800 --> 1:08:15.920
 Yeah, so the Leibniz Prize,

1:08:15.920 --> 1:08:20.920
 but I don't know if you've seen the kind of bots

1:08:22.240 --> 1:08:24.160
 that emerge from that competition.

1:08:24.160 --> 1:08:28.000
 They're not quite as what you would,

1:08:28.000 --> 1:08:29.920
 so it feels like that there's weaknesses

1:08:29.920 --> 1:08:31.400
 with the way Turing formulated it.

1:08:31.400 --> 1:08:35.000
 It needs to be that the definition

1:08:35.000 --> 1:08:40.000
 of a genuine, rich, fulfilling human conversation

1:08:40.000 --> 1:08:41.640
 it needs to be something else.

1:08:41.640 --> 1:08:43.000
 Like the Alexa Prize,

1:08:43.000 --> 1:08:44.880
 which I'm not as well familiar with,

1:08:44.880 --> 1:08:46.200
 has tried to define that more.

1:08:46.200 --> 1:08:48.240
 I think by saying you have to continue

1:08:48.240 --> 1:08:50.680
 keeping a conversation for 30 minutes,

1:08:50.680 --> 1:08:52.240
 something like that.

1:08:52.240 --> 1:08:55.520
 So basically forcing the agent not to just fool,

1:08:55.520 --> 1:08:58.000
 but to have an engaging conversation kind of thing,

1:08:58.000 --> 1:09:03.000
 is that, I mean, have you thought

1:09:03.720 --> 1:09:06.400
 about this problem richly?

1:09:06.400 --> 1:09:10.680
 And if you have in general, how far away are we from,

1:09:10.680 --> 1:09:14.160
 you worked a lot on language understanding,

1:09:14.160 --> 1:09:16.640
 language generation, but the full dialogue,

1:09:16.640 --> 1:09:19.920
 the conversation, just sitting at the bar,

1:09:19.920 --> 1:09:21.760
 having a cup of beers for an hour,

1:09:21.760 --> 1:09:22.960
 that kind of conversation.

1:09:22.960 --> 1:09:23.800
 Have you thought about it?

1:09:23.800 --> 1:09:26.440
 Yeah, so I think you touched here on the critical point,

1:09:26.440 --> 1:09:28.640
 which is feasibility, right?

1:09:28.640 --> 1:09:32.880
 So there's a great sort of essay by Hamming,

1:09:32.880 --> 1:09:37.400
 which describes sort of grand challenges of physics.

1:09:37.400 --> 1:09:41.080
 And he argues that, well, okay, for instance,

1:09:41.080 --> 1:09:44.720
 teleportation or time travel are great grand challenges

1:09:44.720 --> 1:09:46.600
 of physics, but there's no attacks.

1:09:46.600 --> 1:09:50.360
 We really don't know or cannot kind of make any progress.

1:09:50.360 --> 1:09:53.360
 So that's why most physicists and so on,

1:09:53.360 --> 1:09:55.360
 they don't work on these in their PhDs

1:09:55.360 --> 1:09:57.920
 and as part of their careers.

1:09:57.920 --> 1:10:01.000
 So I see the Turing test as, in the full Turing test,

1:10:01.000 --> 1:10:02.760
 as a bit still too early.

1:10:02.760 --> 1:10:06.760
 Like I am, I think we're, especially with the current trend

1:10:06.760 --> 1:10:10.080
 of deep learning language models,

1:10:10.080 --> 1:10:11.640
 we've seen some amazing examples,

1:10:11.640 --> 1:10:14.160
 I think GPT2 being the most recent one,

1:10:14.160 --> 1:10:15.840
 which is very impressive,

1:10:15.840 --> 1:10:20.840
 but to understand to fully solve passing or fooling a human

1:10:21.080 --> 1:10:23.480
 to think that there's a human on the other side,

1:10:23.480 --> 1:10:24.960
 I think we're quite far.

1:10:24.960 --> 1:10:27.360
 So as a result, I don't see myself

1:10:27.360 --> 1:10:30.520
 and I probably would not recommend people doing a PhD

1:10:30.520 --> 1:10:31.680
 on solving the Turing test,

1:10:31.680 --> 1:10:34.120
 because it just feels it's kind of too early

1:10:34.120 --> 1:10:35.520
 or too hard of a problem.

1:10:35.520 --> 1:10:37.840
 Yeah, but that said, you said the exact same thing

1:10:37.840 --> 1:10:40.480
 about StarCraft about a few years ago.

1:10:40.480 --> 1:10:42.600
 So to demo, so I pre...

1:10:42.600 --> 1:10:43.920
 Yes.

1:10:43.920 --> 1:10:45.600
 You'll probably also be the person

1:10:45.600 --> 1:10:48.240
 who passes the Turing test in three years.

1:10:48.240 --> 1:10:51.040
 I mean, I think the, yeah, so...

1:10:51.040 --> 1:10:52.720
 So we have this on record, this is nice.

1:10:52.720 --> 1:10:53.560
 It's true.

1:10:53.560 --> 1:10:56.600
 I mean, it's true that progress sometimes

1:10:56.600 --> 1:10:57.840
 is a bit unpredictable.

1:10:57.840 --> 1:11:00.840
 I really wouldn't have not, even six months ago,

1:11:00.840 --> 1:11:02.520
 I would not have predicted the level

1:11:02.520 --> 1:11:05.480
 that we see that these agents can deliver.

1:11:05.480 --> 1:11:10.120
 At grandmaster level, but I have worked on language enough.

1:11:10.120 --> 1:11:13.640
 And basically my concern is not that something could happen,

1:11:13.640 --> 1:11:16.440
 a breakthrough could happen that would bring us to solving

1:11:16.440 --> 1:11:18.440
 or passing the Turing test,

1:11:18.440 --> 1:11:21.680
 is that I just think the statistical approach to it,

1:11:21.680 --> 1:11:24.160
 like this is not gonna cut it.

1:11:24.160 --> 1:11:25.960
 So we need a breakthrough,

1:11:25.960 --> 1:11:28.320
 which is great for the community.

1:11:28.320 --> 1:11:31.840
 But given that, I think there's quite a more uncertainty.

1:11:31.840 --> 1:11:34.280
 Whereas for StarCraft,

1:11:34.280 --> 1:11:38.160
 I knew what the steps would be to kind of get us there.

1:11:38.160 --> 1:11:41.640
 I think it was clear that using the imitation learning part

1:11:41.640 --> 1:11:44.360
 and then using these battle network agents

1:11:44.360 --> 1:11:48.320
 were gonna be key and it turned out that this was the case

1:11:48.320 --> 1:11:51.640
 and a little more was needed, but not much more.

1:11:51.640 --> 1:11:54.360
 For Turing test, I just don't know what the plan

1:11:54.360 --> 1:11:56.000
 or execution plan would look like.

1:11:56.000 --> 1:11:59.160
 So that's why I myself working on it

1:11:59.160 --> 1:12:01.520
 as a grand challenge is hard,

1:12:01.520 --> 1:12:03.920
 but there are quite a few sub challenges

1:12:03.920 --> 1:12:05.480
 that are related that you could say,

1:12:05.480 --> 1:12:09.080
 well, I mean, what if you create a great assistant,

1:12:09.080 --> 1:12:11.400
 like Google already has like the Google Assistant.

1:12:11.400 --> 1:12:13.120
 So can we make it better

1:12:13.120 --> 1:12:15.440
 and can we make it fully neural and so on?

1:12:15.440 --> 1:12:18.200
 That I start to believe maybe we're reaching a point

1:12:18.200 --> 1:12:20.760
 where we should attempt these challenges.

1:12:20.760 --> 1:12:22.480
 I like this conversation so much

1:12:22.480 --> 1:12:24.920
 because it echoes very much the StarCraft conversation.

1:12:24.920 --> 1:12:26.920
 It's exactly how you approach StarCraft.

1:12:26.920 --> 1:12:29.680
 Let's break it down into small pieces and solve those

1:12:29.680 --> 1:12:31.400
 and you end up solving the whole game.

1:12:31.400 --> 1:12:34.120
 Great, but that said, you're behind some

1:12:34.120 --> 1:12:37.960
 of the sort of biggest pieces of work and deep learning

1:12:37.960 --> 1:12:39.360
 in the last several years.

1:12:40.280 --> 1:12:42.320
 So you mentioned some limits.

1:12:42.320 --> 1:12:44.960
 What do you think are the current limits of deep learning

1:12:44.960 --> 1:12:47.080
 and how do we overcome those limits?

1:12:47.080 --> 1:12:50.160
 So if I had to actually use a single word

1:12:50.160 --> 1:12:53.200
 to define the main challenge in deep learning,

1:12:53.200 --> 1:12:55.720
 it's a challenge that probably has been the challenge

1:12:55.720 --> 1:12:59.760
 for many years and is that of generalization.

1:12:59.760 --> 1:13:04.520
 So what that means is that all that we're doing

1:13:04.520 --> 1:13:06.800
 is fitting functions to data.

1:13:06.800 --> 1:13:11.800
 And when the data we see is not from the same distribution

1:13:12.160 --> 1:13:14.080
 or even if there are some times

1:13:14.080 --> 1:13:16.800
 that it is very close to distribution

1:13:16.800 --> 1:13:20.240
 but because of the way we train it with limited samples,

1:13:20.240 --> 1:13:23.880
 we then get to this stage where we just don't see

1:13:23.880 --> 1:13:27.760
 generalization as much as we can generalize.

1:13:27.760 --> 1:13:31.240
 And I think adversarial examples are a clear example of this

1:13:31.240 --> 1:13:34.640
 but if you study machine learning and literature

1:13:34.640 --> 1:13:38.320
 and the reason why SVMs came very popular

1:13:38.320 --> 1:13:39.720
 were because they were dealing

1:13:39.720 --> 1:13:42.400
 and they had some guarantees about generalization

1:13:42.400 --> 1:13:45.600
 which is unseen data or out of distribution

1:13:45.600 --> 1:13:47.000
 or even within distribution

1:13:47.000 --> 1:13:49.760
 where you take an image adding a bit of noise,

1:13:49.760 --> 1:13:51.280
 these models fail.

1:13:51.280 --> 1:13:56.280
 So I think really I don't see a lot of progress

1:13:56.280 --> 1:14:00.800
 on generalization in the strong generalization sense

1:14:00.800 --> 1:14:01.880
 of the word.

1:14:01.880 --> 1:14:05.280
 I think our neural networks,

1:14:05.280 --> 1:14:08.000
 you can always find design examples

1:14:08.000 --> 1:14:11.000
 that will make their outputs arbitrary

1:14:11.000 --> 1:14:16.000
 which is not good because we humans would never be fooled

1:14:16.000 --> 1:14:19.920
 by these kind of images or manipulation of the image.

1:14:19.920 --> 1:14:21.720
 And if you look at the mathematics,

1:14:21.720 --> 1:14:23.960
 you kind of understand this is a bunch of matrices

1:14:23.960 --> 1:14:27.320
 multiplied together, there's probably numerics

1:14:27.320 --> 1:14:30.880
 and instability that you can just find corner cases.

1:14:30.880 --> 1:14:34.560
 So I think that's really the underlying topic

1:14:34.560 --> 1:14:38.760
 many times we see when even at the grand stage

1:14:38.760 --> 1:14:40.840
 of like during test generalization,

1:14:40.840 --> 1:14:44.560
 I mean, if you start, I mean, passing the during test,

1:14:44.560 --> 1:14:47.920
 should it be in English or should it be in any language?

1:14:47.920 --> 1:14:52.320
 I mean, as a human, if you ask something

1:14:52.320 --> 1:14:54.120
 in a different language, you actually will go

1:14:54.120 --> 1:14:56.280
 and do some research and try to translate it

1:14:56.280 --> 1:15:01.080
 and so on, should the during test include that, right?

1:15:01.080 --> 1:15:02.920
 And it's really a difficult problem

1:15:02.920 --> 1:15:05.360
 and very fascinating and very mysterious actually.

1:15:05.360 --> 1:15:06.320
 Yeah, absolutely.

1:15:06.320 --> 1:15:09.120
 But do you think it's, if you were to try to solve it,

1:15:10.520 --> 1:15:14.280
 can you not grow the size of data intelligently

1:15:14.280 --> 1:15:17.400
 in such a way that the distribution of your training set

1:15:17.400 --> 1:15:20.360
 does include the entirety of the testing set?

1:15:20.360 --> 1:15:21.800
 I think is that one path?

1:15:21.800 --> 1:15:23.880
 The other path is totally a new methodology.

1:15:23.880 --> 1:15:25.000
 That's not statistical.

1:15:25.000 --> 1:15:27.080
 So a path that has worked well

1:15:27.080 --> 1:15:29.880
 and it worked well in StarCraft and in machine translation

1:15:29.880 --> 1:15:32.800
 and in language is scaling up the data and the model.

1:15:32.800 --> 1:15:37.400
 And that's kind of been maybe the only single formula

1:15:37.400 --> 1:15:40.440
 that still delivers today in deep learning, right?

1:15:40.440 --> 1:15:44.080
 It's that scale, data scale and model scale

1:15:44.080 --> 1:15:47.080
 really do more and more of the things that we thought,

1:15:47.080 --> 1:15:49.240
 oh, there's no way it can generalize to these

1:15:49.240 --> 1:15:51.360
 or there's no way it can generalize to that.

1:15:51.360 --> 1:15:54.840
 But I don't think fundamentally it will be solved with this.

1:15:54.840 --> 1:15:58.960
 And for instance, I'm really liking some style

1:15:58.960 --> 1:16:02.120
 or approach that would not only have neural networks

1:16:02.120 --> 1:16:06.400
 but it would have programs or some discrete decision making

1:16:06.400 --> 1:16:09.760
 because there is where I feel there's a bit more,

1:16:09.760 --> 1:16:12.200
 like, I mean, the example of the best example,

1:16:12.200 --> 1:16:14.680
 I think for understanding this is,

1:16:14.680 --> 1:16:17.640
 I also worked a bit on, oh, like we can learn an algorithm

1:16:17.640 --> 1:16:18.840
 with a neural network, right?

1:16:18.840 --> 1:16:20.160
 So you give it many examples

1:16:20.160 --> 1:16:22.880
 and it's gonna sort the input numbers

1:16:22.880 --> 1:16:24.440
 or something like that.

1:16:24.440 --> 1:16:29.520
 But really, strong generalization is you give me some numbers

1:16:29.520 --> 1:16:32.360
 or you ask me to create an algorithm that sorts numbers

1:16:32.360 --> 1:16:34.760
 and instead of creating a neural net which will be fragile

1:16:34.760 --> 1:16:38.000
 because it's gonna go out of range at some point,

1:16:38.000 --> 1:16:40.400
 you're gonna give it numbers that are too large,

1:16:40.400 --> 1:16:42.680
 too small and whatnot, you just,

1:16:42.680 --> 1:16:46.400
 if you just create a piece of code that sorts the numbers,

1:16:46.400 --> 1:16:48.760
 then you can prove that that will generalize

1:16:48.760 --> 1:16:52.040
 to absolutely all the possible inputs you could give.

1:16:52.040 --> 1:16:53.920
 So I think that's, the problem comes

1:16:53.920 --> 1:16:56.000
 with some exciting prospects.

1:16:56.000 --> 1:16:59.560
 I mean, scale is a bit more boring, but it really works.

1:16:59.560 --> 1:17:02.960
 And then maybe programs and discrete abstractions

1:17:02.960 --> 1:17:04.920
 are a bit less developed,

1:17:04.920 --> 1:17:07.520
 but clearly I think they're quite exciting

1:17:07.520 --> 1:17:10.000
 in terms of future for the field.

1:17:10.000 --> 1:17:13.560
 Do you draw any insight wisdom from the 80s

1:17:13.560 --> 1:17:17.000
 and expert systems and symbolic systems, symbolic computing?

1:17:17.000 --> 1:17:18.920
 Do you ever go back to those,

1:17:18.920 --> 1:17:20.800
 the reasoning, that kind of logic?

1:17:20.800 --> 1:17:23.200
 Do you think that might make a comeback?

1:17:23.200 --> 1:17:25.000
 You'll have to dust off those books?

1:17:25.000 --> 1:17:30.000
 Yeah, I actually love actually adding more inductive biases.

1:17:31.360 --> 1:17:34.360
 To me, the problem really is what are you trying to solve?

1:17:34.360 --> 1:17:36.560
 If what you're trying to solve is so important

1:17:36.560 --> 1:17:39.240
 that try to solve it no matter what,

1:17:39.240 --> 1:17:44.240
 then absolutely use rules, use domain knowledge

1:17:44.280 --> 1:17:46.960
 and then use a bit of the magic of machine learning

1:17:46.960 --> 1:17:50.160
 to empower or to make the system as the best system

1:17:50.160 --> 1:17:55.160
 that will detect cancer or detect weather patterns, right?

1:17:56.080 --> 1:17:59.160
 Or in terms of StarCraft, it also was a very big challenge.

1:17:59.160 --> 1:18:01.320
 So I was definitely happy

1:18:01.320 --> 1:18:04.560
 that if we had to cut a corner here and there,

1:18:04.560 --> 1:18:06.920
 it could have been interesting to do.

1:18:06.920 --> 1:18:08.400
 And in fact, in StarCraft,

1:18:08.400 --> 1:18:10.600
 we start thinking about expert systems

1:18:10.600 --> 1:18:12.840
 because it's a very, you can define,

1:18:12.840 --> 1:18:15.120
 I mean, people actually build StarCraft bots

1:18:15.120 --> 1:18:18.720
 by thinking about those principles like state machines

1:18:18.720 --> 1:18:21.600
 and rule based and then you could think

1:18:21.600 --> 1:18:24.520
 of combining a bit of a rule based system,

1:18:24.520 --> 1:18:27.480
 but that has also neural networks incorporated

1:18:27.480 --> 1:18:29.080
 to make it generalize a bit better.

1:18:29.080 --> 1:18:31.840
 So absolutely, I mean, we should definitely go back

1:18:31.840 --> 1:18:35.440
 to those ideas and anything that makes the problem simpler.

1:18:35.440 --> 1:18:38.040
 As long as your problem is important, that's okay.

1:18:38.040 --> 1:18:41.080
 And that's research driving a very important problem.

1:18:41.080 --> 1:18:42.160
 And on the other hand,

1:18:42.160 --> 1:18:45.240
 if you wanna really focus on the limits

1:18:45.240 --> 1:18:47.240
 of reinforcement learning, then of course,

1:18:47.240 --> 1:18:50.800
 you must try not to look at imitation data

1:18:50.800 --> 1:18:54.200
 or to look for some rules of the domain

1:18:54.200 --> 1:18:57.040
 that would help a lot or even feature engineering, right?

1:18:57.040 --> 1:19:00.760
 So this is a tension that depending on what you do,

1:19:00.760 --> 1:19:03.360
 I think both ways are definitely fine.

1:19:03.360 --> 1:19:06.080
 And I would never not do one or the other

1:19:06.080 --> 1:19:08.040
 if you're, as long as what you're doing

1:19:08.040 --> 1:19:10.080
 is important and needs to be solved, right?

1:19:10.080 --> 1:19:13.520
 All right, so there's a bunch of different ideas

1:19:13.520 --> 1:19:16.920
 that you've developed that I really enjoy.

1:19:16.920 --> 1:19:21.920
 But one is translating from image captioning,

1:19:22.240 --> 1:19:23.960
 translating from image to text.

1:19:23.960 --> 1:19:28.720
 Just another beautiful idea, I think,

1:19:28.720 --> 1:19:33.240
 that resonates throughout your work, actually.

1:19:33.240 --> 1:19:36.760
 So the underlying nature of reality being language always.

1:19:36.760 --> 1:19:37.680
 Yeah, somehow.

1:19:37.680 --> 1:19:42.520
 So what's the connection between images and text?

1:19:42.520 --> 1:19:45.880
 Or rather the visual world and the world of language

1:19:45.880 --> 1:19:46.720
 in your view?

1:19:46.720 --> 1:19:51.480
 Right, so I think a piece of research that's been central

1:19:51.480 --> 1:19:54.400
 to, I would say, even extending into StarCraft

1:19:54.400 --> 1:19:57.680
 is this idea of sequence to sequence learning,

1:19:57.680 --> 1:19:59.840
 which what we really meant by that

1:19:59.840 --> 1:20:03.520
 is that you can now really input anything

1:20:03.520 --> 1:20:06.160
 to a neural network as the input X

1:20:06.160 --> 1:20:09.600
 and then the neural network will learn a function F

1:20:09.600 --> 1:20:12.840
 that will take X as an input and produce any output Y.

1:20:12.840 --> 1:20:16.240
 And these X and Ys don't need to be like static

1:20:16.240 --> 1:20:21.240
 or like a feature, like a fixed vectors

1:20:21.240 --> 1:20:22.240
 or anything like that.

1:20:22.240 --> 1:20:23.800
 It could be really sequences

1:20:23.800 --> 1:20:26.600
 and now beyond like data structures, right?

1:20:26.600 --> 1:20:31.600
 So that paradigm was tested in a very interesting way

1:20:31.600 --> 1:20:35.760
 when we moved from translating French to English

1:20:35.760 --> 1:20:37.960
 to translating an image to its caption.

1:20:37.960 --> 1:20:40.760
 But the beauty of it is that really,

1:20:40.760 --> 1:20:42.160
 and that's actually how it happened.

1:20:42.160 --> 1:20:45.240
 I ran, I changed a line of code in this thing

1:20:45.240 --> 1:20:47.520
 that was doing machine translation

1:20:47.520 --> 1:20:51.800
 and I came the next day and I saw how it was producing

1:20:51.800 --> 1:20:54.200
 captions that seemed like, oh my God,

1:20:54.200 --> 1:20:56.040
 this is really, really working.

1:20:56.040 --> 1:20:57.560
 And the principle is the same, right?

1:20:57.560 --> 1:21:02.560
 So I think I don't see text, vision, speech, way forms

1:21:02.560 --> 1:21:07.560
 as something different, as long as you basically learn

1:21:08.120 --> 1:21:13.120
 a function that will vectorize these into,

1:21:13.480 --> 1:21:17.480
 and then after we vectorize it, we can then use transformers,

1:21:17.480 --> 1:21:21.160
 LSTMs, whatever the flavor of the month of the model is.

1:21:21.160 --> 1:21:24.280
 And then as long as we have enough supervised data,

1:21:24.280 --> 1:21:28.280
 really this formula will work and will keep working,

1:21:28.280 --> 1:21:30.280
 I believe to some extent.

1:21:30.280 --> 1:21:33.360
 Model of these generalization issues that I mentioned before.

1:21:33.360 --> 1:21:35.400
 So, but the task there is to vectorize

1:21:35.400 --> 1:21:37.880
 sort of form a representation that's meaningful,

1:21:37.880 --> 1:21:41.400
 and your intuition now, having worked with all this media,

1:21:41.400 --> 1:21:45.240
 is that once you are able to form that representation,

1:21:45.240 --> 1:21:47.960
 you could basically take anything, any sequence.

1:21:48.960 --> 1:21:51.240
 Is there, going back to StarCraft,

1:21:51.240 --> 1:21:52.800
 is there limits on the length?

1:21:54.080 --> 1:21:57.960
 So we didn't really touch on the long term aspect.

1:21:57.960 --> 1:22:01.640
 How did you overcome the whole really long term aspect

1:22:01.640 --> 1:22:02.480
 of things here?

1:22:02.480 --> 1:22:03.920
 Is there some tricks or is it?

1:22:03.920 --> 1:22:07.000
 So the main trick, so StarCraft,

1:22:07.000 --> 1:22:09.360
 if you look at absolutely every frame,

1:22:09.360 --> 1:22:11.120
 you might think it's quite a long game.

1:22:11.120 --> 1:22:14.440
 So we would have to multiply 22 times,

1:22:15.600 --> 1:22:18.200
 60 seconds per minute times maybe

1:22:18.200 --> 1:22:20.360
 at least 10 minutes per game on average.

1:22:20.360 --> 1:22:24.160
 So there are quite a few frames,

1:22:24.160 --> 1:22:26.600
 but the trick really was to,

1:22:26.600 --> 1:22:30.760
 only observe, in fact, which might be seen as a limitation,

1:22:30.760 --> 1:22:33.600
 but it is also a computational advantage.

1:22:33.600 --> 1:22:36.040
 Only observe when you act.

1:22:36.040 --> 1:22:38.440
 And then what the neural network decides

1:22:38.440 --> 1:22:42.200
 is what is the gap gonna be until the next action?

1:22:43.200 --> 1:22:46.520
 And if you look at most StarCraft games

1:22:46.520 --> 1:22:50.200
 that we have in the data set that Blizzard provided,

1:22:50.200 --> 1:22:54.720
 it turns out that most games are actually only,

1:22:54.720 --> 1:22:56.720
 I mean, it is still a long sequence,

1:22:56.720 --> 1:23:00.720
 but it's maybe like 1,000 to 1,500 actions,

1:23:00.720 --> 1:23:04.720
 which if you start looking at LSTMs,

1:23:04.720 --> 1:23:07.720
 large LSTMs, transformers,

1:23:07.720 --> 1:23:10.720
 it's not that difficult,

1:23:10.720 --> 1:23:13.720
 especially if you have supervised learning.

1:23:13.720 --> 1:23:15.720
 If you had to do it with reinforcement learning,

1:23:15.720 --> 1:23:16.720
 the credit assignment problem,

1:23:16.720 --> 1:23:18.720
 what is it that in this game that made you win?

1:23:18.720 --> 1:23:20.720
 That would be really difficult.

1:23:20.720 --> 1:23:23.720
 But thankfully, because of imitation learning,

1:23:23.720 --> 1:23:26.720
 we didn't kind of have to deal with this directly.

1:23:26.720 --> 1:23:28.720
 Although if we had to, we tried it,

1:23:28.720 --> 1:23:30.720
 and what happened is you just take all your workers

1:23:30.720 --> 1:23:32.720
 and attack with them.

1:23:32.720 --> 1:23:35.720
 And that sort of is kind of obvious in retrospect,

1:23:35.720 --> 1:23:37.720
 because you start trying random actions.

1:23:37.720 --> 1:23:39.720
 One of the actions will be a worker

1:23:39.720 --> 1:23:40.720
 that goes to the enemy base,

1:23:40.720 --> 1:23:42.720
 and because it's self play,

1:23:42.720 --> 1:23:44.720
 it's not gonna know how to defend,

1:23:44.720 --> 1:23:46.720
 because it basically doesn't know almost anything.

1:23:46.720 --> 1:23:48.720
 And eventually what you develop is this,

1:23:48.720 --> 1:23:51.720
 take all workers and attack,

1:23:51.720 --> 1:23:54.720
 because the credit assignment issue in our rally

1:23:54.720 --> 1:23:55.720
 is really, really hard.

1:23:55.720 --> 1:23:57.720
 I do believe we could do better,

1:23:57.720 --> 1:24:00.720
 and that's maybe a research challenge for the future.

1:24:00.720 --> 1:24:03.720
 But yeah, even in StarCraft,

1:24:03.720 --> 1:24:05.720
 the sequences are maybe 1,000,

1:24:05.720 --> 1:24:08.720
 which I believe is within the realm

1:24:08.720 --> 1:24:10.720
 of what transformers can do.

1:24:10.720 --> 1:24:13.720
 Yeah, I guess the difference between StarCraft and Go

1:24:13.720 --> 1:24:15.720
 is in Go and chess,

1:24:15.720 --> 1:24:17.720
 stuff starts happening right away.

1:24:17.720 --> 1:24:18.720
 Right.

1:24:18.720 --> 1:24:21.720
 Yeah, it's pretty easy to self play,

1:24:21.720 --> 1:24:23.720
 not easy, but to self play is possible

1:24:23.720 --> 1:24:25.720
 to develop reasonable strategies quickly

1:24:25.720 --> 1:24:27.720
 as opposed to StarCraft.

1:24:27.720 --> 1:24:29.720
 In Go, there's only 400 actions,

1:24:29.720 --> 1:24:32.720
 but one action is what people would call

1:24:32.720 --> 1:24:34.720
 the God action that would be,

1:24:34.720 --> 1:24:37.720
 if you had expanded the whole search tree,

1:24:37.720 --> 1:24:39.720
 that's the best action if you did minimax

1:24:39.720 --> 1:24:41.720
 or whatever algorithm you would do

1:24:41.720 --> 1:24:43.720
 if you had the computational capacity.

1:24:43.720 --> 1:24:45.720
 But in StarCraft,

1:24:45.720 --> 1:24:48.720
 400 is minuscule.

1:24:48.720 --> 1:24:51.720
 In 400, you couldn't even click

1:24:51.720 --> 1:24:53.720
 on the pixels around a unit, right?

1:24:53.720 --> 1:24:55.720
 So I think the problem there

1:24:55.720 --> 1:24:58.720
 is in terms of action space size

1:24:58.720 --> 1:25:00.720
 is way harder,

1:25:00.720 --> 1:25:03.720
 and that search is impossible.

1:25:03.720 --> 1:25:05.720
 So there's quite a few challenges indeed

1:25:05.720 --> 1:25:08.720
 that make this kind of a step up

1:25:08.720 --> 1:25:10.720
 in terms of machine learning.

1:25:10.720 --> 1:25:12.720
 For humans, maybe playing StarCraft

1:25:12.720 --> 1:25:14.720
 seems more intuitive

1:25:14.720 --> 1:25:16.720
 because it looks real,

1:25:16.720 --> 1:25:18.720
 the graphics and everything moves smoothly,

1:25:18.720 --> 1:25:20.720
 whereas I don't know how to...

1:25:20.720 --> 1:25:22.720
 Go is a game that I wouldn't really need to study.

1:25:22.720 --> 1:25:24.720
 It feels quite complicated,

1:25:24.720 --> 1:25:26.720
 but for machines, maybe it's the reverse, yes.

1:25:26.720 --> 1:25:28.720
 Which shows you the gap, actually,

1:25:28.720 --> 1:25:30.720
 between deep learning and however the heck

1:25:30.720 --> 1:25:32.720
 our brains work.

1:25:32.720 --> 1:25:35.720
 So you developed a lot of really interesting ideas.

1:25:35.720 --> 1:25:37.720
 It's interesting to just ask,

1:25:37.720 --> 1:25:40.720
 what's your process of developing new ideas?

1:25:40.720 --> 1:25:42.720
 Do you like brainstorming with others?

1:25:42.720 --> 1:25:44.720
 Do you like thinking alone?

1:25:44.720 --> 1:25:46.720
 Do you like...

1:25:46.720 --> 1:25:48.720
 Like what was it?

1:25:48.720 --> 1:25:50.720
 Ian Goodfellow said he came up with Gans

1:25:50.720 --> 1:25:52.720
 after a few beers.

1:25:52.720 --> 1:25:54.720
 He thinks beers are essential

1:25:54.720 --> 1:25:56.720
 for coming up with new ideas.

1:25:56.720 --> 1:25:58.720
 We had beers to decide to play another game

1:25:58.720 --> 1:26:00.720
 of StarCraft after a week,

1:26:00.720 --> 1:26:02.720
 so it's really similar to that story.

1:26:02.720 --> 1:26:04.720
 Actually, I explained this

1:26:04.720 --> 1:26:06.720
 in a deep mind retreat,

1:26:06.720 --> 1:26:08.720
 and I said this is the same as the Gans story.

1:26:08.720 --> 1:26:10.720
 I mean, we were on a bar and we decided,

1:26:10.720 --> 1:26:12.720
 we were on a week and that's what happened.

1:26:12.720 --> 1:26:14.720
 I feel like we're giving the wrong message

1:26:14.720 --> 1:26:16.720
 to young undergrads.

1:26:16.720 --> 1:26:18.720
 But in general, do you like brainstorming?

1:26:18.720 --> 1:26:20.720
 Do you like thinking alone, working stuff out?

1:26:20.720 --> 1:26:22.720
 So I think throughout the years

1:26:22.720 --> 1:26:24.720
 also things changed, right?

1:26:24.720 --> 1:26:26.720
 So initially, I was

1:26:26.720 --> 1:26:28.720
 very fortunate to be

1:26:28.720 --> 1:26:30.720
 with great minds like

1:26:30.720 --> 1:26:32.720
 Jeff Hinton,

1:26:32.720 --> 1:26:34.720
 Jeff Dean, Ilya Tsutskiber.

1:26:34.720 --> 1:26:36.720
 I was really fortunate to join Brain

1:26:36.720 --> 1:26:38.720
 at a very good time.

1:26:38.720 --> 1:26:40.720
 At that point, ideas,

1:26:40.720 --> 1:26:42.720
 I was just kind of brainstorming with my colleagues

1:26:42.720 --> 1:26:44.720
 and learned a lot,

1:26:44.720 --> 1:26:46.720
 and keep learning is actually

1:26:46.720 --> 1:26:48.720
 something you should never stop doing, right?

1:26:48.720 --> 1:26:50.720
 So learning implies

1:26:50.720 --> 1:26:52.720
 reading papers and also discussing ideas

1:26:52.720 --> 1:26:54.720
 with others. It's very hard

1:26:54.720 --> 1:26:56.720
 at some point to not communicate

1:26:56.720 --> 1:26:58.720
 that being reading a paper from someone

1:26:58.720 --> 1:27:00.720
 or actually discussing, right?

1:27:00.720 --> 1:27:02.720
 So definitely

1:27:02.720 --> 1:27:04.720
 that communication aspect

1:27:04.720 --> 1:27:06.720
 needs to be there, whether it's written

1:27:06.720 --> 1:27:08.720
 or oral.

1:27:08.720 --> 1:27:10.720
 Nowadays,

1:27:10.720 --> 1:27:12.720
 I'm also trying to be a bit more strategic

1:27:12.720 --> 1:27:14.720
 about what research to do.

1:27:14.720 --> 1:27:16.720
 So I was describing

1:27:16.720 --> 1:27:18.720
 a little bit this sort of tension between

1:27:18.720 --> 1:27:20.720
 research for the sake of research,

1:27:20.720 --> 1:27:22.720
 and then you have, on the other hand,

1:27:22.720 --> 1:27:24.720
 applications that can drive the research, right?

1:27:24.720 --> 1:27:26.720
 And honestly,

1:27:26.720 --> 1:27:28.720
 the formula that has worked best for me is

1:27:28.720 --> 1:27:30.720
 just find a hard problem

1:27:30.720 --> 1:27:32.720
 and then try to

1:27:32.720 --> 1:27:34.720
 see how research fits into it,

1:27:34.720 --> 1:27:36.720
 how it doesn't fit into it,

1:27:36.720 --> 1:27:38.720
 and then you must innovate.

1:27:38.720 --> 1:27:40.720
 So I think machine translation

1:27:40.720 --> 1:27:42.720
 drove sequence to sequence.

1:27:42.720 --> 1:27:44.720
 Then maybe

1:27:44.720 --> 1:27:46.720
 learning algorithms

1:27:46.720 --> 1:27:48.720
 that had to, like combinatorial algorithms

1:27:48.720 --> 1:27:50.720
 led to pointer networks.

1:27:50.720 --> 1:27:52.720
 Starcraft led to really scaling up

1:27:52.720 --> 1:27:54.720
 imitation learning and the Alpha Star League.

1:27:54.720 --> 1:27:56.720
 So that's been a formula

1:27:56.720 --> 1:27:58.720
 that I personally like,

1:27:58.720 --> 1:28:00.720
 but the other one is also valid,

1:28:00.720 --> 1:28:02.720
 and I see it succeed a lot of the times

1:28:02.720 --> 1:28:04.720
 where you just want to investigate

1:28:04.720 --> 1:28:06.720
 model based RL

1:28:06.720 --> 1:28:08.720
 as a kind of a research topic,

1:28:08.720 --> 1:28:10.720
 and then you must then start to think,

1:28:10.720 --> 1:28:12.720
 well, how are the tests?

1:28:12.720 --> 1:28:14.720
 How are you going to test these ideas?

1:28:14.720 --> 1:28:16.720
 You need kind of a minimal

1:28:16.720 --> 1:28:18.720
 environment to try things.

1:28:18.720 --> 1:28:20.720
 You need to read a lot of papers and so on,

1:28:20.720 --> 1:28:22.720
 and that's also very fun to do,

1:28:22.720 --> 1:28:24.720
 and something I've also done quite a few times,

1:28:24.720 --> 1:28:26.720
 both at Brain, at DeepMind,

1:28:26.720 --> 1:28:28.720
 and obviously as a PhD.

1:28:28.720 --> 1:28:30.720
 So I think

1:28:30.720 --> 1:28:32.720
 the ideas and discussions,

1:28:32.720 --> 1:28:34.720
 I think it's important also

1:28:34.720 --> 1:28:36.720
 because you start sort of

1:28:36.720 --> 1:28:38.720
 guiding not only

1:28:38.720 --> 1:28:40.720
 your own goals, but

1:28:40.720 --> 1:28:42.720
 other people's goals

1:28:42.720 --> 1:28:44.720
 to the next breakthrough, so

1:28:44.720 --> 1:28:46.720
 you must really kind of understand

1:28:46.720 --> 1:28:48.720
 this feasibility also

1:28:48.720 --> 1:28:50.720
 as we were discussing before, right?

1:28:50.720 --> 1:28:52.720
 Whether this domain is ready

1:28:52.720 --> 1:28:54.720
 to be tackled or not, and you don't want

1:28:54.720 --> 1:28:56.720
 to be too early, you obviously don't want

1:28:56.720 --> 1:28:58.720
 to be too late, so it's really interesting

1:28:58.720 --> 1:29:00.720
 this strategic component of research,

1:29:00.720 --> 1:29:02.720
 which I think as a grad student

1:29:02.720 --> 1:29:04.720
 I just had no idea,

1:29:04.720 --> 1:29:06.720
 I just read papers and discussed

1:29:06.720 --> 1:29:08.720
 ideas, and I think this has been maybe

1:29:08.720 --> 1:29:10.720
 the major change, and I recommend

1:29:10.720 --> 1:29:12.720
 people kind of

1:29:12.720 --> 1:29:14.720
 fit forward to success, how it looks like

1:29:14.720 --> 1:29:16.720
 and try to backtrack, other than just

1:29:16.720 --> 1:29:18.720
 kind of looking out, this looks cool,

1:29:18.720 --> 1:29:20.720
 this looks cool, and then you do a bit of

1:29:20.720 --> 1:29:22.720
 random work, which sometimes you stumble upon

1:29:22.720 --> 1:29:24.720
 some interesting things, but

1:29:24.720 --> 1:29:26.720
 in general it's also good to plan a bit.

1:29:26.720 --> 1:29:28.720
 Yeah, I like it.

1:29:28.720 --> 1:29:30.720
 Especially like your approach of taking

1:29:30.720 --> 1:29:32.720
 on really hard problems, stepping right in

1:29:32.720 --> 1:29:34.720
 and then being super skeptical about

1:29:34.720 --> 1:29:36.720
 being able to solve the problem.

1:29:36.720 --> 1:29:38.720
 I mean, there's a

1:29:38.720 --> 1:29:40.720
 balance of both, right? There's a silly

1:29:40.720 --> 1:29:42.720
 optimism

1:29:42.720 --> 1:29:44.720
 and a critical

1:29:44.720 --> 1:29:46.720
 sort of skepticism

1:29:46.720 --> 1:29:48.720
 that's good to balance, which

1:29:48.720 --> 1:29:50.720
 is why it's good to have a team of people

1:29:50.720 --> 1:29:52.720
 that balance that.

1:29:52.720 --> 1:29:54.720
 You don't do that on your own, you have both

1:29:54.720 --> 1:29:56.720
 mentors that have seen

1:29:56.720 --> 1:29:58.720
 or you obviously want to chat and

1:29:58.720 --> 1:30:00.720
 discuss whether it's the right time.

1:30:00.720 --> 1:30:02.720
 I mean, Damis

1:30:02.720 --> 1:30:04.720
 came in 2014 and he said

1:30:04.720 --> 1:30:06.720
 maybe in a bit we'll do StarCraft and

1:30:06.720 --> 1:30:08.720
 maybe he knew

1:30:08.720 --> 1:30:10.720
 and I'm just following his lead, which

1:30:10.720 --> 1:30:12.720
 is great because he's brilliant, right?

1:30:12.720 --> 1:30:14.720
 So, these things are

1:30:14.720 --> 1:30:16.720
 obviously quite

1:30:16.720 --> 1:30:18.720
 important that you want to

1:30:18.720 --> 1:30:20.720
 be surrounded by people

1:30:20.720 --> 1:30:22.720
 who are diverse, they

1:30:22.720 --> 1:30:24.720
 have their knowledge. There's also

1:30:24.720 --> 1:30:26.720
 important to...

1:30:26.720 --> 1:30:28.720
 I've learned a lot from people

1:30:28.720 --> 1:30:30.720
 who actually

1:30:30.720 --> 1:30:32.720
 have an idea that I might not think it's good

1:30:32.720 --> 1:30:34.720
 but if I give them the space to try it

1:30:34.720 --> 1:30:36.720
 I've been proven wrong many, many times

1:30:36.720 --> 1:30:38.720
 as well. So, that's great.

1:30:38.720 --> 1:30:40.720
 I think it's...

1:30:40.720 --> 1:30:42.720
 Your colleagues are more important than yourself

1:30:42.720 --> 1:30:44.720
 I think so.

1:30:44.720 --> 1:30:46.720
 Now, let's real quick

1:30:46.720 --> 1:30:48.720
 talk about another impossible problem.

1:30:48.720 --> 1:30:50.720
 AGI.

1:30:50.720 --> 1:30:52.720
 What do you think it takes to build a system

1:30:52.720 --> 1:30:54.720
 that's human level intelligence?

1:30:54.720 --> 1:30:56.720
 We talked a little bit about the touring test, StarCraft

1:30:56.720 --> 1:30:58.720
 all these have echoes of general intelligence

1:30:58.720 --> 1:31:00.720
 but if you think about

1:31:00.720 --> 1:31:02.720
 just something that you would sit back

1:31:02.720 --> 1:31:04.720
 and say, wow, this is

1:31:04.720 --> 1:31:06.720
 really something that resembles

1:31:06.720 --> 1:31:08.720
 human level intelligence, what do you think it takes

1:31:08.720 --> 1:31:10.720
 to build that?

1:31:10.720 --> 1:31:12.720
 So, I find that

1:31:12.720 --> 1:31:14.720
 AGI oftentimes is maybe not

1:31:14.720 --> 1:31:16.720
 very well defined

1:31:16.720 --> 1:31:18.720
 so what I'm trying to

1:31:18.720 --> 1:31:20.720
 then come up with for myself is

1:31:20.720 --> 1:31:22.720
 what would be a result

1:31:22.720 --> 1:31:24.720
 look like that

1:31:24.720 --> 1:31:26.720
 you would start to believe that

1:31:26.720 --> 1:31:28.720
 you would have agents or neural nets

1:31:28.720 --> 1:31:30.720
 that no longer sort of overfit

1:31:30.720 --> 1:31:32.720
 to a single task, right?

1:31:32.720 --> 1:31:34.720
 But actually

1:31:34.720 --> 1:31:36.720
 kind of learn

1:31:36.720 --> 1:31:38.720
 the skill of learning, so to speak

1:31:38.720 --> 1:31:40.720
 and that actually is a field that I

1:31:40.720 --> 1:31:42.720
 am fascinated by which is

1:31:42.720 --> 1:31:44.720
 the learning to learn or meta learning

1:31:44.720 --> 1:31:46.720
 which is about no longer

1:31:46.720 --> 1:31:48.720
 learning about a single domain

1:31:48.720 --> 1:31:50.720
 so you can think about the learning algorithm

1:31:50.720 --> 1:31:52.720
 itself is general, right?

1:31:52.720 --> 1:31:54.720
 So the same formula we applied for

1:31:54.720 --> 1:31:56.720
 Alpha Star or StarCraft

1:31:56.720 --> 1:31:58.720
 we can now apply to kind of almost any

1:31:58.720 --> 1:32:00.720
 video game or you could apply to

1:32:00.720 --> 1:32:02.720
 many other problems and domains

1:32:02.720 --> 1:32:04.720
 but the algorithm

1:32:04.720 --> 1:32:06.720
 is what's kind of generalizing

1:32:06.720 --> 1:32:08.720
 but the neural network, the weights

1:32:08.720 --> 1:32:10.720
 those weights are useless even

1:32:10.720 --> 1:32:12.720
 to play another race, right? I train

1:32:12.720 --> 1:32:14.720
 a network to play very well at PROTOS vs PROTOS

1:32:14.720 --> 1:32:16.720
 I need to throw away those weights

1:32:16.720 --> 1:32:18.720
 if I want to play

1:32:18.720 --> 1:32:20.720
 now Terran vs Terran

1:32:20.720 --> 1:32:22.720
 I would need to retrain

1:32:22.720 --> 1:32:24.720
 a network from scratch with the same algorithm

1:32:24.720 --> 1:32:26.720
 that's beautiful but the network

1:32:26.720 --> 1:32:28.720
 itself will not be useful

1:32:28.720 --> 1:32:30.720
 so I think when I, if I see

1:32:30.720 --> 1:32:32.720
 an approach that

1:32:32.720 --> 1:32:34.720
 can absorb or start

1:32:34.720 --> 1:32:36.720
 solving new problems

1:32:36.720 --> 1:32:38.720
 without the need to kind of restart

1:32:38.720 --> 1:32:40.720
 the process I think that

1:32:40.720 --> 1:32:42.720
 to me would be a nice way to define

1:32:42.720 --> 1:32:44.720
 some form of AGI

1:32:44.720 --> 1:32:46.720
 again, I don't know

1:32:46.720 --> 1:32:48.720
 the grandiose like age, I mean

1:32:48.720 --> 1:32:50.720
 during tests we solve before AGI

1:32:50.720 --> 1:32:52.720
 I mean, I don't know, I think concretely

1:32:52.720 --> 1:32:54.720
 I would like to see clearly

1:32:54.720 --> 1:32:56.720
 that meta learning happen

1:32:56.720 --> 1:32:58.720
 meaning there is

1:32:58.720 --> 1:33:00.720
 an architecture or a network

1:33:00.720 --> 1:33:02.720
 that as it sees new problem

1:33:02.720 --> 1:33:04.720
 or new data it solves it

1:33:04.720 --> 1:33:06.720
 and to make it

1:33:06.720 --> 1:33:08.720
 kind of a benchmark it should

1:33:08.720 --> 1:33:10.720
 solve it at the same speed that we do solve

1:33:10.720 --> 1:33:12.720
 new problems when I define

1:33:12.720 --> 1:33:14.720
 a new object and you have to recognize it

1:33:14.720 --> 1:33:16.720
 when you start playing a new game

1:33:16.720 --> 1:33:18.720
 you played all the Atari games but now you play a new Atari game

1:33:18.720 --> 1:33:20.720
 well, you're going to be

1:33:20.720 --> 1:33:22.720
 pretty quickly pretty good at the game

1:33:22.720 --> 1:33:24.720
 so that's perhaps

1:33:24.720 --> 1:33:26.720
 what's the domain and what's the exact benchmark

1:33:26.720 --> 1:33:28.720
 it's a bit difficult, I think as a community

1:33:28.720 --> 1:33:30.720
 we might need to do some work to define it

1:33:32.720 --> 1:33:34.720
 but I think this first step

1:33:34.720 --> 1:33:36.720
 I could see it happen relatively soon

1:33:36.720 --> 1:33:38.720
 but then the whole

1:33:38.720 --> 1:33:40.720
 what AGI means and so on

1:33:40.720 --> 1:33:42.720
 I am a bit more confused about

1:33:42.720 --> 1:33:44.720
 what I think people mean different things

1:33:44.720 --> 1:33:46.720
 there's an emotional psychological level

1:33:48.720 --> 1:33:50.720
 that

1:33:50.720 --> 1:33:52.720
 like even the Turing test, passing the Turing test

1:33:52.720 --> 1:33:54.720
 is something that we just pass judgment

1:33:54.720 --> 1:33:56.720
 on as human beings what it means to be

1:33:56.720 --> 1:33:58.720
 you know, as a

1:33:58.720 --> 1:34:00.720
 as a dog

1:34:00.720 --> 1:34:02.720
 an AGI system

1:34:02.720 --> 1:34:04.720
 like what level, what does it mean

1:34:04.720 --> 1:34:06.720
 what does it mean

1:34:06.720 --> 1:34:08.720
 but I like the generalization

1:34:08.720 --> 1:34:10.720
 and maybe as a community we converge towards

1:34:10.720 --> 1:34:12.720
 a group of domains

1:34:12.720 --> 1:34:14.720
 that are sufficiently far away

1:34:14.720 --> 1:34:16.720
 that would be really damn impressive

1:34:16.720 --> 1:34:18.720
 if we're able to generalize

1:34:18.720 --> 1:34:20.720
 so perhaps not as close as Protoss and Zerg

1:34:20.720 --> 1:34:22.720
 but like Wikipedia

1:34:22.720 --> 1:34:24.720
 that would be a good step

1:34:24.720 --> 1:34:26.720
 and then a really good step

1:34:26.720 --> 1:34:28.720
 but then from Starcraft to Wikipedia

1:34:28.720 --> 1:34:30.720
 and back

1:34:30.720 --> 1:34:32.720
 that kind of thing

1:34:32.720 --> 1:34:34.720
 and that feels also quite hard and far

1:34:34.720 --> 1:34:36.720
 I think this

1:34:36.720 --> 1:34:38.720
 as long as you put the benchmark out

1:34:38.720 --> 1:34:40.720
 as we discovered for instance with ImageNet

1:34:40.720 --> 1:34:42.720
 then tremendous progress can be had

1:34:42.720 --> 1:34:44.720
 so I think maybe there's a lack of

1:34:44.720 --> 1:34:46.720
 benchmark

1:34:46.720 --> 1:34:48.720
 but I'm sure we'll find one and the community

1:34:48.720 --> 1:34:50.720
 will then work towards that

1:34:52.720 --> 1:34:54.720
 and then beyond what AGI might mean

1:34:54.720 --> 1:34:56.720
 or would imply

1:34:56.720 --> 1:34:58.720
 I really am hopeful to see

1:34:58.720 --> 1:35:00.720
 basically machine learning

1:35:00.720 --> 1:35:02.720
 or AI just scaling up

1:35:02.720 --> 1:35:04.720
 and helping

1:35:04.720 --> 1:35:06.720
 people that might not have the resources

1:35:06.720 --> 1:35:08.720
 to hire an assistant

1:35:08.720 --> 1:35:10.720
 or that

1:35:10.720 --> 1:35:12.720
 they might not even know what the weather is like

1:35:12.720 --> 1:35:14.720
 but so I think there's

1:35:14.720 --> 1:35:16.720
 in terms of the impact

1:35:16.720 --> 1:35:18.720
 the positive impact of AI

1:35:18.720 --> 1:35:20.720
 I think that's maybe what we should also

1:35:20.720 --> 1:35:22.720
 not lose focus

1:35:22.720 --> 1:35:24.720
 the research community building AGI

1:35:24.720 --> 1:35:26.720
 that's a real nice goal

1:35:26.720 --> 1:35:28.720
 and I think the way that DeepMind puts it

1:35:28.720 --> 1:35:30.720
 is and then use it to solve everything else

1:35:30.720 --> 1:35:32.720
 so I think we should paralyze

1:35:32.720 --> 1:35:34.720
 yeah we shouldn't forget

1:35:34.720 --> 1:35:36.720
 of all the positive things that are actually

1:35:36.720 --> 1:35:38.720
 coming out of AI already and are going

1:35:38.720 --> 1:35:40.720
 to be coming out

1:35:40.720 --> 1:35:42.720
 right

1:35:42.720 --> 1:35:44.720
 and then let me ask

1:35:44.720 --> 1:35:46.720
 relative to popular perception

1:35:46.720 --> 1:35:48.720
 do you have any worry about the existential

1:35:48.720 --> 1:35:50.720
 threat of artificial intelligence

1:35:50.720 --> 1:35:52.720
 in the near or far future

1:35:52.720 --> 1:35:54.720
 that some people have

1:35:54.720 --> 1:35:56.720
 I think in the near future

1:35:56.720 --> 1:35:58.720
 I'm skeptical so I hope

1:35:58.720 --> 1:36:00.720
 I'm not wrong but

1:36:00.720 --> 1:36:02.720
 I'm not concerned

1:36:02.720 --> 1:36:04.720
 but I appreciate efforts

1:36:04.720 --> 1:36:06.720
 ongoing efforts

1:36:06.720 --> 1:36:08.720
 and even like a whole research field on

1:36:08.720 --> 1:36:10.720
 AI safety emerging and in conferences

1:36:10.720 --> 1:36:12.720
 and so on I think that's great

1:36:12.720 --> 1:36:14.720
 in the long term

1:36:14.720 --> 1:36:16.720
 I really hope we

1:36:16.720 --> 1:36:18.720
 just can simply

1:36:18.720 --> 1:36:20.720
 have the benefits outweigh the potential dangers

1:36:20.720 --> 1:36:22.720
 I am hopeful for that

1:36:22.720 --> 1:36:24.720
 but also we must

1:36:24.720 --> 1:36:26.720
 remain vigilant to kind of monitor

1:36:26.720 --> 1:36:28.720
 and assess whether the tradeoffs

1:36:28.720 --> 1:36:30.720
 are there and we have

1:36:30.720 --> 1:36:32.720
 enough

1:36:32.720 --> 1:36:34.720
 also lead time to prevent

1:36:34.720 --> 1:36:36.720
 or to redirect our efforts

1:36:36.720 --> 1:36:38.720
 if need be

1:36:38.720 --> 1:36:40.720
 but I'm quite optimistic

1:36:40.720 --> 1:36:42.720
 about the technology

1:36:42.720 --> 1:36:44.720
 and definitely more fearful

1:36:44.720 --> 1:36:46.720
 of other threats in terms of

1:36:46.720 --> 1:36:48.720
 planetary level

1:36:48.720 --> 1:36:50.720
 at this point but obviously

1:36:50.720 --> 1:36:52.720
 that's the one I kind of have more

1:36:52.720 --> 1:36:54.720
 power on so clearly

1:36:54.720 --> 1:36:56.720
 start thinking more and more about this

1:36:56.720 --> 1:36:58.720
 and it's kind of

1:36:58.720 --> 1:37:00.720
 it's grown in me actually to

1:37:00.720 --> 1:37:02.720
 start reading more about AI safety

1:37:02.720 --> 1:37:04.720
 which is a field that so far I have not

1:37:04.720 --> 1:37:06.720
 really contributed to but maybe

1:37:06.720 --> 1:37:08.720
 there's something to be done there as well

1:37:08.720 --> 1:37:10.720
 I think it's really important

1:37:10.720 --> 1:37:12.720
 I talk about this with a few folks

1:37:12.720 --> 1:37:14.720
 but it's important to ask you

1:37:14.720 --> 1:37:16.720
 and shove it in your head because you're at the

1:37:16.720 --> 1:37:18.720
 leading edge of actually

1:37:18.720 --> 1:37:20.720
 what people are excited about in AI

1:37:20.720 --> 1:37:22.720
 I mean the work with AlphaStar

1:37:22.720 --> 1:37:24.720
 at the very cutting edge of the kind

1:37:24.720 --> 1:37:26.720
 of thing that people are afraid of

1:37:26.720 --> 1:37:28.720
 and so you speaking

1:37:28.720 --> 1:37:30.720
 to that fact and

1:37:30.720 --> 1:37:32.720
 that we're actually quite far away

1:37:32.720 --> 1:37:34.720
 to the kind of thing that people might be

1:37:34.720 --> 1:37:36.720
 afraid of but it's still

1:37:36.720 --> 1:37:38.720
 worthwhile to think about

1:37:38.720 --> 1:37:40.720
 and it's also good that you're

1:37:40.720 --> 1:37:42.720
 that you're not as worried

1:37:42.720 --> 1:37:44.720
 and you're also open to

1:37:44.720 --> 1:37:46.720
 I mean there's two aspects

1:37:46.720 --> 1:37:48.720
 I mean me not being worried but obviously

1:37:48.720 --> 1:37:50.720
 we should prepare

1:37:50.720 --> 1:37:52.720
 for it

1:37:52.720 --> 1:37:54.720
 for things that could

1:37:54.720 --> 1:37:56.720
 go wrong, misuse of the technologies

1:37:56.720 --> 1:37:58.720
 as with any technologies

1:37:58.720 --> 1:38:00.720
 so I think

1:38:00.720 --> 1:38:02.720
 there's always tradeoffs

1:38:02.720 --> 1:38:04.720
 and as a society we've kind of

1:38:04.720 --> 1:38:06.720
 solved this to some extent

1:38:06.720 --> 1:38:08.720
 in the past so I'm hoping that

1:38:08.720 --> 1:38:10.720
 by having the researchers

1:38:10.720 --> 1:38:12.720
 and the whole community

1:38:12.720 --> 1:38:14.720
 brainstorm and come up with

1:38:14.720 --> 1:38:16.720
 interesting solutions to the new things

1:38:16.720 --> 1:38:18.720
 that will happen in the future

1:38:18.720 --> 1:38:20.720
 that we can still also push the research

1:38:20.720 --> 1:38:22.720
 to the avenue that

1:38:22.720 --> 1:38:24.720
 I think is kind of the greatest avenue

1:38:24.720 --> 1:38:26.720
 which is to

1:38:26.720 --> 1:38:28.720
 understand intelligence, right? How are we doing

1:38:28.720 --> 1:38:30.720
 what we're doing and

1:38:30.720 --> 1:38:32.720
 obviously from a scientific standpoint

1:38:32.720 --> 1:38:34.720
 that is kind of the drive

1:38:34.720 --> 1:38:36.720
 my personal drive of

1:38:36.720 --> 1:38:38.720
 all the time that I spend doing

1:38:38.720 --> 1:38:40.720
 what I'm doing really.

1:38:40.720 --> 1:38:42.720
 Where do you see the deep learning as a field heading

1:38:42.720 --> 1:38:44.720
 where do you think the next big

1:38:44.720 --> 1:38:46.720
 breakthrough might be?

1:38:46.720 --> 1:38:48.720
 So I think deep learning

1:38:48.720 --> 1:38:50.720
 I discussed a little of this before

1:38:50.720 --> 1:38:52.720
 deep learning has to be

1:38:52.720 --> 1:38:54.720
 combined with some form of discretization

1:38:54.720 --> 1:38:56.720
 program synthesis

1:38:56.720 --> 1:38:58.720
 I think that's kind of as a research

1:38:58.720 --> 1:39:00.720
 in itself is an interesting topic

1:39:00.720 --> 1:39:02.720
 to expand and start doing more research

1:39:02.720 --> 1:39:04.720
 and then

1:39:04.720 --> 1:39:06.720
 as kind of what will deep learning

1:39:06.720 --> 1:39:08.720
 enable to do in the future

1:39:08.720 --> 1:39:10.720
 I don't think that's going to be what's going to happen

1:39:10.720 --> 1:39:12.720
 this year but also this

1:39:12.720 --> 1:39:14.720
 idea of

1:39:14.720 --> 1:39:16.720
 not to throw away all the weights

1:39:16.720 --> 1:39:18.720
 this idea of learning to learn

1:39:18.720 --> 1:39:20.720
 and really having

1:39:20.720 --> 1:39:22.720
 these agents

1:39:22.720 --> 1:39:24.720
 not having to restart their weights

1:39:24.720 --> 1:39:26.720
 and you can have an agent

1:39:26.720 --> 1:39:28.720
 that is kind of solving

1:39:28.720 --> 1:39:30.720
 or classifying images on ImageNet

1:39:30.720 --> 1:39:32.720
 but also generating speech

1:39:32.720 --> 1:39:34.720
 if you ask it to generate some speech

1:39:34.720 --> 1:39:36.720
 and it should really be kind of

1:39:36.720 --> 1:39:38.720
 almost the same

1:39:38.720 --> 1:39:40.720
 network but

1:39:40.720 --> 1:39:42.720
 might not be a neural network it might be a neural network

1:39:42.720 --> 1:39:44.720
 with an optimization algorithm

1:39:44.720 --> 1:39:46.720
 attached to it but I think this idea

1:39:46.720 --> 1:39:48.720
 of generalization to new task

1:39:48.720 --> 1:39:50.720
 is something that we first

1:39:50.720 --> 1:39:52.720
 must define good benchmarks but then

1:39:52.720 --> 1:39:54.720
 I think that's going to be exciting

1:39:54.720 --> 1:39:56.720
 and I'm not sure how close we are

1:39:56.720 --> 1:39:58.720
 but I think there's

1:39:58.720 --> 1:40:00.720
 if you have a very limited domain

1:40:00.720 --> 1:40:02.720
 I think we can start doing some progress

1:40:02.720 --> 1:40:04.720
 and

1:40:04.720 --> 1:40:06.720
 much like how we did a lot of programs

1:40:06.720 --> 1:40:08.720
 in computer vision we should start thinking

1:40:08.720 --> 1:40:10.720
 I really like a talk that

1:40:10.720 --> 1:40:12.720
 Leon Boutou gave at ICML

1:40:12.720 --> 1:40:14.720
 a few years ago which is

1:40:14.720 --> 1:40:16.720
 this train test paradigm should be broken

1:40:16.720 --> 1:40:18.720
 we should stop

1:40:18.720 --> 1:40:20.720
 thinking about a training test

1:40:20.720 --> 1:40:22.720
 sorry a training set and a test set

1:40:22.720 --> 1:40:24.720
 and these are closed

1:40:24.720 --> 1:40:26.720
 things that are untouchable

1:40:26.720 --> 1:40:28.720
 I think we should go beyond these and

1:40:28.720 --> 1:40:30.720
 in meta learning we call these the meta training set

1:40:30.720 --> 1:40:32.720
 and the meta test set which is

1:40:32.720 --> 1:40:34.720
 really thinking about

1:40:34.720 --> 1:40:36.720
 if I know about ImageNet

1:40:36.720 --> 1:40:38.720
 why would that network

1:40:38.720 --> 1:40:40.720
 not work on MNIST which is a much

1:40:40.720 --> 1:40:42.720
 simpler problem but right now it really doesn't

1:40:42.720 --> 1:40:44.720
 it you know

1:40:44.720 --> 1:40:46.720
 but it just feels wrong right so I think

1:40:46.720 --> 1:40:48.720
 that's kind of the

1:40:48.720 --> 1:40:50.720
 there's the on the application

1:40:50.720 --> 1:40:52.720
 or the benchmark sites we probably

1:40:52.720 --> 1:40:54.720
 will see quite a few

1:40:54.720 --> 1:40:56.720
 more interest and progress and hopefully

1:40:56.720 --> 1:40:58.720
 people defining new

1:40:58.720 --> 1:41:00.720
 and exciting challenges really

1:41:00.720 --> 1:41:02.720
 do you have any hope or

1:41:02.720 --> 1:41:04.720
 interest in knowledge graphs

1:41:04.720 --> 1:41:06.720
 within this context so it's kind of

1:41:06.720 --> 1:41:08.720
 constructing graphs

1:41:08.720 --> 1:41:10.720
 going back to graphs

1:41:10.720 --> 1:41:12.720
 well neural networks are graphs but I mean

1:41:12.720 --> 1:41:14.720
 a different kind of knowledge graph

1:41:14.720 --> 1:41:16.720
 sort of like semantic graphs

1:41:16.720 --> 1:41:18.720
 where there's concepts

1:41:18.720 --> 1:41:20.720
 so I think

1:41:20.720 --> 1:41:22.720
 the idea of graphs

1:41:22.720 --> 1:41:24.720
 is so I've been quite interested

1:41:24.720 --> 1:41:26.720
 in sequences first and then more

1:41:26.720 --> 1:41:28.720
 interesting or different data structures

1:41:28.720 --> 1:41:30.720
 like graphs and

1:41:30.720 --> 1:41:32.720
 I've studied graph neural networks

1:41:32.720 --> 1:41:34.720
 in the last three years or so

1:41:34.720 --> 1:41:36.720
 I

1:41:36.720 --> 1:41:38.720
 found these models just very interesting from

1:41:38.720 --> 1:41:40.720
 like deep learning

1:41:40.720 --> 1:41:42.720
 standpoint but then

1:41:42.720 --> 1:41:44.720
 how what do we want

1:41:44.720 --> 1:41:46.720
 why do we want these models and why would we

1:41:46.720 --> 1:41:48.720
 use them what's the application

1:41:48.720 --> 1:41:50.720
 what's kind of the killer application of graphs

1:41:50.720 --> 1:41:52.720
 right and

1:41:52.720 --> 1:41:54.720
 perhaps

1:41:54.720 --> 1:41:56.720
 if we

1:41:56.720 --> 1:41:58.720
 could extract a knowledge graph

1:41:58.720 --> 1:42:00.720
 from Wikipedia automatically

1:42:00.720 --> 1:42:02.720
 that would be interesting because

1:42:02.720 --> 1:42:04.720
 then these graphs have

1:42:04.720 --> 1:42:06.720
 this very interesting structure

1:42:06.720 --> 1:42:08.720
 that also is a bit more compatible with

1:42:08.720 --> 1:42:10.720
 this idea of programs and

1:42:10.720 --> 1:42:12.720
 deep learning kind of working together

1:42:12.720 --> 1:42:14.720
 jumping neighborhoods and so on

1:42:14.720 --> 1:42:16.720
 you could imagine defining some primitives

1:42:16.720 --> 1:42:18.720
 to go around graphs right so

1:42:18.720 --> 1:42:20.720
 I think

1:42:20.720 --> 1:42:22.720
 I really like the idea of a knowledge

1:42:22.720 --> 1:42:24.720
 graph and in fact

1:42:24.720 --> 1:42:26.720
 when we

1:42:26.720 --> 1:42:28.720
 we started or you know

1:42:28.720 --> 1:42:30.720
 as part of the research we did for StarCraft

1:42:30.720 --> 1:42:32.720
 I thought wouldn't it be cool to give

1:42:32.720 --> 1:42:34.720
 the graph of

1:42:34.720 --> 1:42:36.720
 you know all the

1:42:36.720 --> 1:42:38.720
 all these buildings that depend on each other

1:42:38.720 --> 1:42:40.720
 and units that have

1:42:40.720 --> 1:42:42.720
 prerequisites of being built by that and so

1:42:42.720 --> 1:42:44.720
 this is information

1:42:44.720 --> 1:42:46.720
 that the network can learn and extract

1:42:46.720 --> 1:42:48.720
 but it would have been great to see

1:42:48.720 --> 1:42:50.720
 or to think of

1:42:50.720 --> 1:42:52.720
 really StarCraft as a giant graph

1:42:52.720 --> 1:42:54.720
 that even also as the game evolves

1:42:54.720 --> 1:42:56.720
 you kind of start taking branches

1:42:56.720 --> 1:42:58.720
 and so on and we tried

1:42:58.720 --> 1:43:00.720
 to do a little bit of research on this

1:43:00.720 --> 1:43:02.720
 nothing too relevant

1:43:02.720 --> 1:43:04.720
 but I really like the idea

1:43:04.720 --> 1:43:06.720
 and it has elements that are

1:43:06.720 --> 1:43:08.720
 which something you also worked with in terms of visualizing

1:43:08.720 --> 1:43:10.720
 your networks as elements of

1:43:10.720 --> 1:43:12.720
 having human interpretable

1:43:12.720 --> 1:43:14.720
 being able to generate knowledge

1:43:14.720 --> 1:43:16.720
 representations that are human interpretable

1:43:16.720 --> 1:43:18.720
 that maybe human experts can then tweak

1:43:18.720 --> 1:43:20.720
 or at least understand

1:43:20.720 --> 1:43:22.720
 so there's a lot of interesting

1:43:22.720 --> 1:43:24.720
 aspect there and for me personally I'm just a huge fan of

1:43:24.720 --> 1:43:26.720
 Wikipedia and it's a shame

1:43:26.720 --> 1:43:28.720
 that our neural networks

1:43:28.720 --> 1:43:30.720
 aren't taking advantage of all the structured

1:43:30.720 --> 1:43:32.720
 knowledge that's on the web.

1:43:32.720 --> 1:43:34.720
 What's next for you?

1:43:34.720 --> 1:43:36.720
 What's next for DeepMind?

1:43:36.720 --> 1:43:38.720
 What are you excited about?

1:43:38.720 --> 1:43:40.720
 For AlphaStar?

1:43:40.720 --> 1:43:42.720
 Yeah so I think

1:43:42.720 --> 1:43:44.720
 the obvious next steps

1:43:44.720 --> 1:43:46.720
 would be to

1:43:46.720 --> 1:43:48.720
 apply AlphaStar to

1:43:48.720 --> 1:43:50.720
 other races I mean that sort of

1:43:50.720 --> 1:43:52.720
 shows that the algorithm

1:43:52.720 --> 1:43:54.720
 works because

1:43:54.720 --> 1:43:56.720
 by mistake something in the architecture

1:43:56.720 --> 1:43:58.720
 that happens to work for proto's

1:43:58.720 --> 1:44:00.720
 but not for other races right so

1:44:00.720 --> 1:44:02.720
 as verification I think

1:44:02.720 --> 1:44:04.720
 that's an obvious next step that we are working on

1:44:04.720 --> 1:44:06.720
 and

1:44:06.720 --> 1:44:08.720
 then I would like to see

1:44:08.720 --> 1:44:10.720
 so agents and players

1:44:10.720 --> 1:44:12.720
 can specialize on

1:44:12.720 --> 1:44:14.720
 different skill sets that allow them to be

1:44:14.720 --> 1:44:16.720
 very good. I think we've seen

1:44:16.720 --> 1:44:18.720
 AlphaStar understanding

1:44:18.720 --> 1:44:20.720
 very well when to take battles and when

1:44:20.720 --> 1:44:22.720
 to not do that

1:44:22.720 --> 1:44:24.720
 also very good at micromanagement

1:44:24.720 --> 1:44:26.720
 and moving the units around and so on

1:44:26.720 --> 1:44:28.720
 and also very good at producing

1:44:28.720 --> 1:44:30.720
 nonstop and trading of economy

1:44:30.720 --> 1:44:32.720
 with building units

1:44:32.720 --> 1:44:34.720
 but I have not

1:44:34.720 --> 1:44:36.720
 perhaps seen as much as I would like

1:44:36.720 --> 1:44:38.720
 this idea of the poker idea

1:44:38.720 --> 1:44:40.720
 that you mentioned right.

1:44:40.720 --> 1:44:42.720
 I'm not sure StarCraft or AlphaStar

1:44:42.720 --> 1:44:44.720
 rather has developed a very

1:44:44.720 --> 1:44:46.720
 deep understanding of

1:44:46.720 --> 1:44:48.720
 what the opponent is doing

1:44:48.720 --> 1:44:50.720
 and reacting to that and sort of

1:44:50.720 --> 1:44:52.720
 trying to

1:44:52.720 --> 1:44:54.720
 trick the player to do something else or that

1:44:54.720 --> 1:44:56.720
 you know so this kind of reasoning

1:44:56.720 --> 1:44:58.720
 I would like to see more so I think

1:44:58.720 --> 1:45:00.720
 purely from a research standpoint

1:45:00.720 --> 1:45:02.720
 there's perhaps also quite a few

1:45:02.720 --> 1:45:04.720
 things to be done there

1:45:04.720 --> 1:45:06.720
 in the domain of StarCraft. Yeah in the

1:45:06.720 --> 1:45:08.720
 domain of games I've seen some

1:45:08.720 --> 1:45:10.720
 interesting work in sort of

1:45:10.720 --> 1:45:12.720
 in even auctions manipulating

1:45:12.720 --> 1:45:14.720
 other players sort of forming a belief

1:45:14.720 --> 1:45:16.720
 state and just messing with

1:45:16.720 --> 1:45:18.720
 people. Yeah it's called theory of mind

1:45:18.720 --> 1:45:20.720
 so it's a fast

1:45:20.720 --> 1:45:22.720
 theory of mind on StarCraft

1:45:22.720 --> 1:45:24.720
 is kind of they're really

1:45:24.720 --> 1:45:26.720
 made for each other so

1:45:26.720 --> 1:45:28.720
 that would be very exciting to see

1:45:28.720 --> 1:45:30.720
 those techniques applied to StarCraft

1:45:30.720 --> 1:45:32.720
 or perhaps StarCraft driving

1:45:32.720 --> 1:45:34.720
 new techniques as I said

1:45:34.720 --> 1:45:36.720
 this is always the tension between the two.

1:45:36.720 --> 1:45:38.720
 Wow Oriel thank you so much for talking

1:45:38.720 --> 1:45:48.720
 awesome it was great to be here thanks