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I was left unconvinced (reproducing my comment from reddit): AlphaStar had 0 cost to sense the entire map simultaneously, and when they introduced the attentio

I was left unconvinced (reproducing my comment from reddit): AlphaStar had 0 cost to sense the entire map simultaneously, and when they introduced the attention/context switching cost (camera control) in the showmatch, it lost. Also very notable for me in the showmatch was it seemed completely blind and exploitable to some strategies. When AlphaGo played top players, it made a few mistakes, but there wasn't anything obvious that it just couldn't see. Here it just couldn't think of making phoenixes vs. the warp prism harass which shows strategically it isn't near human level yet. It could clearly be exploited by back and forth harassment too (probably has to do with the limited memory those networks have). Finally, DeepMind were just emphasizing average APM, when it clearly reached totally superhuman levels at times -- even a top professional can't execute 900+ flawless apm in a battle that we saw. David Silver was clearly expecting the showmatch to be totally one sided (hence his speech that 'this is another historic victory for AI), but I were left with the opposite impression: that strategically top humans are still ahead in this game. This is not the end of the game for humans yet !

roenxi 8y ago
The original AlphaGo showing (vs Fan Hui) had obvious problems. They weren't catastrophic, it was clearly playing at a professional level stronger than 1d, but it also wasn't obviously superhuman. Lee Sedol could have expected to win his matches, and not been arrogant about it. The gap between that AlphaGo and the AlphaGo that beat Lee Sedol was probably something like 10 years of human improvement compressed into a couple of months. 500 Elo is huge. Even if they still have further to go, comparing this to AlphaGo's progress is reasonable. I'm going to live dangerously and make an assumption - you probably understand Starcraft better than Go, so can see the flaws more easily. Any mistake in Go comes back to something the AI just didn't see.
- andreyk 8y ago
  StarCraft is also a FAR bigger challenge than Go. So much so that even saying AlphaStar is similar to AlphaGo is quite a stretch (see https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ https://deepmind.com/blog/alphastar-mastering-real-time-stra... - the architecture and to some extent training regime are very different). I'd be surprised if they can learn the macro without imitation, unlike in Go ; but we'll see. Would sure be nice if they also released the paper...
- darkmighty 8y ago
  Yea stepping back a little I agree it's a fundamental achievement. It's all about expectations -- I were expecting a Lee Sedol kind of event, and it was more of a Fan Hui (or a bit further) kind of demonstration. A milestone for sure considering how difficult the problem is with various previously unconquered subchallenges (uncertainty, partial observability, massive control space), different but in some ways more difficult than OpenAI's Dota 2 matches. > I'm going to live dangerously and make an assumption - you probably understand Starcraft better than Go, so can see the flaws more easily Correct :) Although I'm not great at either. This comes closer to opening doors to real uncharted territory in robotics and general agency; it trained in crazy low wall-clock time. But now we're this far it makes me question even more the cost, flexibility, adaptability, of the AI less forgivingly. How much did it actually cost to train? Would it be economical to train one of those for every single task? (e.g. in a factory or warehouse setting), Would it be vulnerable to those exploitable strategic weakness in a real world setting?, etc. It's definitely not over yet as far as SC2 goes though :)
YeGoblynQueenne 8y ago
I'm always partial to a good argument for being sceptical of such announcements (I'm a professional skeptic; "researchers", some people call us), but in this case the question is whether it is ever possible to make the comparison fair at all. For example, we can restrict the AI player to perceive the game world in the same way (iiish) as the human player, but we can't really make the human player see the game world in the same way as the AI player. So if the AI player has an inherent advantage because of its superior perception, there's just no way to level the field and compare the human to the AI in a fair manner. Perhaps we just have to accept that a human will never have an advantage against a computer player in a computer game, or that computer games are just not very interesting testbeds for AI, after all (that would be very controversial). On the other hand, it's also useful to remember that previous attempts have been made to develop strong Starcraft players, and they all had access to the same API as the DeepMind player, yet they didn't perform as well. Of course it makes a difference that it's DeepMind who achieved this and they seem to have spared no costs in hardware and training time, unavailable to a smaller team. Perhaps we shouldn't be that surprised to learn that a poweful computer can do better than a human in some cases. But, at the end of the day, the result they got against MaNa shows that it is possible for an AI player to beat a strong human player -fairly or unfairly- in a game that was rightly considered very hard for AI players, under any assumption.
- kilburn 8y ago
  A fair comparison is simple: make the AI play the game using a standard screen, mouse and keyboard (the game was designed to be played this way after all, and non-standard peripherals are not allowed in tournaments by the way). It suddenly seems WAY more challenging for the machine, doesn't it? You could also invent some contraption that interfaces directly with the human's brain to even out on the other front, but this sounds even more futuristic. The average APM limitation is just a publicity stunt. There are similar caps one could apply that would make the challenge much more "fairer". For instance: Max (instead of average) APM or limiting mouse travel distances (no one can get even remotely close to clicking 300 times/minute on alternate sides of the screen with pixel-perfect accuracy AND timing). Finally, the AI only plays 1 of 6 possible match-ups in 1 of infinity possible maps, and the live game has shown severe strategic mistakes (mainly the indecision when getting harassed and the inability to build a phoenix to counter the prism / keep harassing itself with the oracles instead of stupidly watching over the prism with them). It still has a long way to go for me to consider it "superior" to humans regarding intelligence (not just superhuman control). All in all, this is still an impressive achievement though!

roenxi 8y ago

The original AlphaGo showing (vs Fan Hui) had obvious problems. They weren't catastrophic, it was clearly playing at a professional level stronger than 1d, but it also wasn't obviously superhuman. Lee Sedol could have expected to win his matches, and not been arrogant about it. The gap between that AlphaGo and the AlphaGo that beat Lee Sedol was probably something like 10 years of human improvement compressed into a couple of months. 500 Elo is huge. Even if they still have further to go, comparing this to AlphaGo's progress is reasonable. I'm going to live dangerously and make an assumption - you probably understand Starcraft better than Go, so can see the flaws more easily. Any mistake in Go comes back to something the AI just didn't see.

andreyk 8y ago
StarCraft is also a FAR bigger challenge than Go. So much so that even saying AlphaStar is similar to AlphaGo is quite a stretch (see https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ https://deepmind.com/blog/alphastar-mastering-real-time-stra... - the architecture and to some extent training regime are very different). I'd be surprised if they can learn the macro without imitation, unlike in Go ; but we'll see. Would sure be nice if they also released the paper...
darkmighty 8y ago
Yea stepping back a little I agree it's a fundamental achievement. It's all about expectations -- I were expecting a Lee Sedol kind of event, and it was more of a Fan Hui (or a bit further) kind of demonstration. A milestone for sure considering how difficult the problem is with various previously unconquered subchallenges (uncertainty, partial observability, massive control space), different but in some ways more difficult than OpenAI's Dota 2 matches. > I'm going to live dangerously and make an assumption - you probably understand Starcraft better than Go, so can see the flaws more easily Correct :) Although I'm not great at either. This comes closer to opening doors to real uncharted territory in robotics and general agency; it trained in crazy low wall-clock time. But now we're this far it makes me question even more the cost, flexibility, adaptability, of the AI less forgivingly. How much did it actually cost to train? Would it be economical to train one of those for every single task? (e.g. in a factory or warehouse setting), Would it be vulnerable to those exploitable strategic weakness in a real world setting?, etc. It's definitely not over yet as far as SC2 goes though :)

YeGoblynQueenne 8y ago

I'm always partial to a good argument for being sceptical of such announcements (I'm a professional skeptic; "researchers", some people call us), but in this case the question is whether it is ever possible to make the comparison fair at all. For example, we can restrict the AI player to perceive the game world in the same way (iiish) as the human player, but we can't really make the human player see the game world in the same way as the AI player. So if the AI player has an inherent advantage because of its superior perception, there's just no way to level the field and compare the human to the AI in a fair manner. Perhaps we just have to accept that a human will never have an advantage against a computer player in a computer game, or that computer games are just not very interesting testbeds for AI, after all (that would be very controversial). On the other hand, it's also useful to remember that previous attempts have been made to develop strong Starcraft players, and they all had access to the same API as the DeepMind player, yet they didn't perform as well. Of course it makes a difference that it's DeepMind who achieved this and they seem to have spared no costs in hardware and training time, unavailable to a smaller team. Perhaps we shouldn't be that surprised to learn that a poweful computer can do better than a human in some cases. But, at the end of the day, the result they got against MaNa shows that it is possible for an AI player to beat a strong human player -fairly or unfairly- in a game that was rightly considered very hard for AI players, under any assumption.

kilburn 8y ago
A fair comparison is simple: make the AI play the game using a standard screen, mouse and keyboard (the game was designed to be played this way after all, and non-standard peripherals are not allowed in tournaments by the way). It suddenly seems WAY more challenging for the machine, doesn't it? You could also invent some contraption that interfaces directly with the human's brain to even out on the other front, but this sounds even more futuristic. The average APM limitation is just a publicity stunt. There are similar caps one could apply that would make the challenge much more "fairer". For instance: Max (instead of average) APM or limiting mouse travel distances (no one can get even remotely close to clicking 300 times/minute on alternate sides of the screen with pixel-perfect accuracy AND timing). Finally, the AI only plays 1 of 6 possible match-ups in 1 of infinity possible maps, and the live game has shown severe strategic mistakes (mainly the indecision when getting harassed and the inability to build a phoenix to counter the prism / keep harassing itself with the oracles instead of stupidly watching over the prism with them). It still has a long way to go for me to consider it "superior" to humans regarding intelligence (not just superhuman control). All in all, this is still an impressive achievement though!