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IMHO the wording of point 12 is a silly way to pretend that Alphago has not completely achieved its goal. Human players learn Go using manuals that compile the

by reacweb 10y ago

IMHO the wording of point 12 is a silly way to pretend that Alphago has not completely achieved its goal. Human players learn Go using manuals that compile the experiences of millions of games. The fact that Alphago can use directly huge collections of games is not very relevant for its success.

reacweb 10y ago
BTW, the point 32 is very inspiring. Could an AI imagine experiences to create models to explain the physic of our world. An AI to reinvent QM and relativity from scratch ? Maybe reconcile both ?
afro88 10y ago
There's a difference between needing 100,000,000 games and 50,000 for training to be the best. That difference is learning efficiency and astuteness - a good way of judging AI progress, no?
- daveguy 10y ago
  I think "50,000 games played" puts it a bit too simply. How many games has he watched how many equivalents of a game has he visualized as sequences of moves? If "played" is over 50k I'd be willing to bet "considered" is over 1,000,000.
conjectures 10y ago
> The fact that Alphago can use directly huge collections of games is not very relevant for its success. On the contrary, it is, and the _need_ for such quantities of data is one of the biggest challenges in neural networks. How about 50,000 games + access to manuals ;)
Almaviva 10y ago
We know that AlphaGo uses a Monte Carlo tree search, and presumably contains innovations and refinements to techniques applicable to turn based board games with perfect information and a clear binary win condition. We also know it uses a Deep Learning algorithm to imitate play from the best humans. And presumably, being stronger than any human now, it could also recursively refine and train on its own games. Do we know how important the Deep Learning component is? Would AlphaGo be just as strong or nearly so without that part? I know that over the last few years chess engines have become dramatically more powerful, even on the same hardware, to the tune of hundreds of Elo points. They still stick to the classical techniques though (Alpha-Beta pruning, minimax is still the core). This is not to understate the refinements, but the general ideas are (I think?) only applicable to turn based games with finite moves and a binary mathematical criterion for victory. Is it possible that the improvements AlphaGo has made are mostly of this type, and the Deep Learning parts are not that important to its actual play strength? Or can we totally rule this out? Is there an in depth discussion of this question somewhere?

by reacweb 10y ago

reacweb 10y ago

BTW, the point 32 is very inspiring. Could an AI imagine experiences to create models to explain the physic of our world. An AI to reinvent QM and relativity from scratch ? Maybe reconcile both ?

afro88 10y ago

There's a difference between needing 100,000,000 games and 50,000 for training to be the best. That difference is learning efficiency and astuteness - a good way of judging AI progress, no?

daveguy 10y ago
I think "50,000 games played" puts it a bit too simply. How many games has he watched how many equivalents of a game has he visualized as sequences of moves? If "played" is over 50k I'd be willing to bet "considered" is over 1,000,000.

conjectures 10y ago

> The fact that Alphago can use directly huge collections of games is not very relevant for its success. On the contrary, it is, and the _need_ for such quantities of data is one of the biggest challenges in neural networks. How about 50,000 games + access to manuals ;)

Almaviva 10y ago

We know that AlphaGo uses a Monte Carlo tree search, and presumably contains innovations and refinements to techniques applicable to turn based board games with perfect information and a clear binary win condition. We also know it uses a Deep Learning algorithm to imitate play from the best humans. And presumably, being stronger than any human now, it could also recursively refine and train on its own games. Do we know how important the Deep Learning component is? Would AlphaGo be just as strong or nearly so without that part? I know that over the last few years chess engines have become dramatically more powerful, even on the same hardware, to the tune of hundreds of Elo points. They still stick to the classical techniques though (Alpha-Beta pruning, minimax is still the core). This is not to understate the refinements, but the general ideas are (I think?) only applicable to turn based games with finite moves and a binary mathematical criterion for victory. Is it possible that the improvements AlphaGo has made are mostly of this type, and the Deep Learning parts are not that important to its actual play strength? Or can we totally rule this out? Is there an in depth discussion of this question somewhere?