Google's AlphaGo team has been working on chess

[pkappler]

Today is a big day in computer chess:

https://arxiv.org/abs/1712.01815
https://arxiv.org/pdf/1712.01815.pdf

[Modern Times]

Incredible:

In chess, AlphaZero outperformed Stockfish after just 4 hours

[Xann]

Incredible:

In chess, AlphaZero outperformed Stockfish after just 4 hours

Time is misleading in DeepMind's papers, as they use thousands of "computers" (not even commercially available). Money would be a better measure.

[xcombelle]

Money would be a better measure.

The AlphaZero training system costed $ 4 millions of hardware. (figures given for alpha go zero, don't have source under hand)

[smatovic]

Money would be a better measure.

Or maybe games for training.....

NeuroChess 120 000
Giraffe (est.): 10 000 000
AlphaZero Chess: 44 000 000

--
Srdja

[sasachess]

Evaluation speed:
AlphaZero 80K
Stockfish 70.000K

What?!

[Fulvio]

Today is a big day in computer chess:

https://arxiv.org/abs/1712.01815
https://arxiv.org/pdf/1712.01815.pdf

"Instead of a handcrafted evaluation function and move ordering heuristics, AlphaZero utilises a deep neural network (p,v) = fθ(s) with parameters θ.
This neural network takes the board position s as an input and outputs a vector of move probabilities p with components pa = Pr(a|s) for each action a, and a scalar value v estimating the expected outcome z from position s"

This seems normal to me.

"Instead of an alpha-beta search with domain-specific enhancements, AlphaZero uses a general-purpose Monte-Carlo tree search (MCTS) algorithm. Each search consists of a series of simulated games of self-play that traverse a tree from root to leaf. Each simulation proceeds by selecting in each state a move with low visit count, high move probability and high value" [emphasis mine]

This is interesting. If I understand it correctly, it basically goes deeper only after reaching a high level of hash table hits.


"AlphaZero vs Stockfish: 25 win for AlphaZero, 25 draw, 0 loss (each program was given 1 minute of thinking time per move, strongest skill level using 64 threads and a hash size of 1GB)"

This is sci-fi. I do not have a 64 core machine but on my pc Stockfish do not sacrifice a Knight for 2 pawns:
1.e4 e5 2.Nf3 Nc6 3.Bb5 Nf6 4.d3 Bc5 5.Bxc6 dxc6 6.O-O Nd7 7.Nbd2 O-O 8.Qe1 f6 9.Nc4 Rf7 10.a4 Bf8 11.Kh1 Nc5 12.a5 Ne6 13.Ncxe5?

[Guenther]

Today is a big day in computer chess:

https://arxiv.org/abs/1712.01815
https://arxiv.org/pdf/1712.01815.pdf

"Instead of a handcrafted evaluation function and move ordering heuristics, AlphaZero utilises a deep neural network (p,v) = fθ(s) with parameters θ.
This neural network takes the board position s as an input and outputs a vector of move probabilities p with components pa = Pr(a|s) for each action a, and a scalar value v estimating the expected outcome z from position s"

This seems normal to me.

"Instead of an alpha-beta search with domain-specific enhancements, AlphaZero uses a general-purpose Monte-Carlo tree search (MCTS) algorithm. Each search consists of a series of simulated games of self-play that traverse a tree from root to leaf. Each simulation proceeds by selecting in each state a move with low visit count, high move probability and high value" [emphasis mine]

This is interesting. If I understand it correctly, it basically goes deeper only after reaching a high level of hash table hits.


"AlphaZero vs Stockfish: 25 win for AlphaZero, 25 draw, 0 loss (each program was given 1 minute of thinking time per move, strongest skill level using 64 threads and a hash size of 1GB)"

This is sci-fi. I do not have a 64 core machine but on my pc Stockfish do not sacrifice a Knight for 2 pawns:
1.e4 e5 2.Nf3 Nc6 3.Bb5 Nf6 4.d3 Bc5 5.Bxc6 dxc6 6.O-O Nd7 7.Nbd2 O-O 8.Qe1 f6 9.Nc4 Rf7 10.a4 Bf8 11.Kh1 Nc5 12.a5 Ne6 13.Ncxe5?

The paper is very interesting. Nevertheless selecting only wins and stripping off all game infos from the pgn might do for non-chess scientists,
but here it is quite useless and remains doubtful.

I hope there is more to come with more details for the games and the setup.

[mar]

While this is indeed incredible, show me how it beats SF dev with good book and syzygy on equal hardware in a 1000 game match.

Alternatively winning next TCEC should do

[Daniel Shawul]

Most of us here suspected that this could happen once Giraffe showed it can beat Stockfish's eval.

Just the fact that the new approch to chess programming worked incredibly well is fantastic even if it didn't beat the best.

Daniel