Anyone happen to have a link (or can PM one to me) to a copy that isn't behind a paywall?
From the Deepmind blog post: https://deepmind.com/documents/119/agz_unformatted_nature.pd...
AlphaGo Zero: Learning from scratch
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Re: AlphaGo Zero: Learning from scratch
#12Anyone happen to have a link (or can PM one to me) to a copy that isn't behind a paywall?
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Re: AlphaGo Zero: Learning from scratch
#13Anyone happen to have a link (or can PM one to me) to a copy that isn't behind a paywall?
Re: AlphaGo Zero: Learning from scratch
#14"It uses one neural network rather than two." and "AlphaGo Zero only uses the black and white stones from the Go board as its input, whereas previous versions of AlphaGo included a small number of hand-engineered features." This is amazing! The technology they came up with must be super generic.
It sounds like that's what they are going for. Minimal tuning, generally adaptable. Very interesting.
Re: AlphaGo Zero: Learning from scratch
#15> Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. So technically this version has lost every game it's ever won. Jokes aside, it's pretty interesting to note that they were able to combine the "policy" and "value" net…
https://blog.openai.com/
I believe their DotA 2 AI uses that approach
Re: AlphaGo Zero: Learning from scratch
#16How I wish Marvin Minsky would have stayed alive for one more year and seen this. He would have been so happy!
Re: AlphaGo Zero: Learning from scratch
#17I'm reminded of Eliezer Yudkowski's article "There is no fire Alarm for Artificial General Intelligence." Is this smoke? https://intelligence.org/2017/10/13/fire-alarm/ Yes, this is not an AGI. But the hockey-stick takeoff from defeats some players, to defeats an undefeated world-champion, to defeats the version of itself that beat the world champion 100% of the time is nuts . If this happens in other domains, like f…
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Re: AlphaGo Zero: Learning from scratch
#18I'm reminded of Eliezer Yudkowski's article "There is no fire Alarm for Artificial General Intelligence." Is this smoke? https://intelligence.org/2017/10/13/fire-alarm/ Yes, this is not an AGI. But the hockey-stick takeoff from defeats some players, to defeats an undefeated world-champion, to defeats the version of itself that beat the world champion 100% of the time is nuts . If this happens in other domains, like f…
The core technique of AlphaGo is using tree search as a "policy improvement operator". Tree search doesn't work on most real-world tasks: the "game state" is too complex, there are too many choices, it's hard to predict the full effect of any choice you might make, and there often isn't even a "win" or "lose" state which would let you stop your self-play.
Re: AlphaGo Zero: Learning from scratch
#19I'm reminded of Eliezer Yudkowski's article "There is no fire Alarm for Artificial General Intelligence." Is this smoke? https://intelligence.org/2017/10/13/fire-alarm/ Yes, this is not an AGI. But the hockey-stick takeoff from defeats some players, to defeats an undefeated world-champion, to defeats the version of itself that beat the world champion 100% of the time is nuts . If this happens in other domains, like f…
Games are a joke compare with real life. The number of variables and rules is well defined in games, while in real life it is not. That is why AGI is not coming anytime soon.
Re: AlphaGo Zero: Learning from scratch
#20I'm reminded of Eliezer Yudkowski's article "There is no fire Alarm for Artificial General Intelligence." Is this smoke? https://intelligence.org/2017/10/13/fire-alarm/ Yes, this is not an AGI. But the hockey-stick takeoff from defeats some players, to defeats an undefeated world-champion, to defeats the version of itself that beat the world champion 100% of the time is nuts . If this happens in other domains, like f…
While this is promising, there's a long way to go between this and the other things you mentioned. Go is very well-defined, has an unequivocal objective scoring system that can be run very quickly, and can be simulated in such a way that the system can go through many, many iterations very quickly.
There's no way to train an AI like this for, say, health: We cannot simulate the human body to the level of detail that's required, and we definitely aren't going to be able to do it at the speed required for a system like this for a very long time. Producing a definitive, objective score for a paper clip collection is very difficult if not impossible.
AlphaGo/DeepMind represents a very strong approach to a certain set of well-defined problems, but most of the problems required for a general AI aren't well-defined.