AlphaZero

[triplecheck]

@Vlad ^

Are you talking about Go or Chess?

There's a paper on the ArXiv on AlphaZero Chess.

There's a paper in Nature on AlphaZero Go.

In the Nature Go paper there appears to be a mass of games in the Extended Data section of the online version of the paper. If the Arxiv paper eventually turns up in Nature after a few months refereeing, I would surmise there would be all the Stockfish games in the Extended Data. Has it been submitted to Nature? I don't know what you mean by "seem to publish".

[Vlad]

I was reading the link provided by Kevin with Ken Regan's view. Many of his comments indicated that there is only limited number of games provided and as a result it is very hard for Ken to make any conclusions. There is also mentioning of a paper in Nature. Quite possibly you are right and that paper is related to Go.

My general frustration is that their approach does not seem to be very scientific. I would be extremely happy if somebody else was interested in reading/commenting my papers. What exactly do they gain by hiding most of the games? Somebody can copy their approach? Highly unlikely. More likely is that they are worried that somebody will find a problem with their approach.

[TheARBChessSys]

How Stockfish 8 Could Have Drawn Each Game Against Google Deep Mind AI AlphaZero...

https://www.youtube.com/watch?v=ZGypfNUXM2U

[TheARBChessSys]

International Master Erik Kislik looks through all of AlphaZero’s published wins against Stockfish and demonstrates how Stockfish could have drawn in all of them. With stronger hardware, the latest Stockfish version, a bigger hash table and tablebases, probably only two or three out of the ten games should have been lost.

In game 1: 31. Kg2 would have prevented …Bh3, when White’s king would have been safe enough to defend. Here is my analysis: http://view.chessbase.com/cbreader/2...e18859335.html
Note that Kg2 is a human move to stop an obvious threat.

In game 2: 38. Rg1! would have defended. This is a human move, intending to play f2-f4 and obtain a natural pawn break. White needs to seek counterplay immediately or else he will lose the c4 pawn: http://view.chessbase.com/cbreader/2...e18902765.html

In game 3: Stockfish had a draw in hand with 22. …Nh5: http://view.chessbase.com/cbreader/2...382065231.html
and even later on in the game likely could have drawn with perfect play with 25. …Rad8 or 49. …Kf8.

In game 4: Stockfish had a drawn endgame after 52. …Rf1 down a pawn: http://view.chessbase.com/cbreader/2...e19249290.html

In game 5, 17. …Qd8 would have defended, rather than having to put the queen on the terrible h7 square. Qd8 is obviously the more human defense: http://view.chessbase.com/cbreader/2...e19274188.html

In game 6, 18. …Be6 would have defended, although 20. …Nc5 was also fine: http://view.chessbase.com/cbreader/2...e19294936.html

In game 7: Stockfish could have drawn by 39. …Rdd8, planning to play …Kf7 and cover the h4 pawn by …Rh8 when needed. It lost this position most likely due to lack of depth. I checked this position at depth 58 on the December 11th Stockfish development version and confirmed that this a draw. With over 12 million tablebase hits, I got a score of +.5, yet no plan to make progress: http://view.chessbase.com/cbreader/2...e19347212.html

In game 8: 33. …Qf7 would have held the position. Black’s kingside is sufficiently solid to withstand any attempts to break it down: http://view.chessbase.com/cbreader/2...384743783.html

In game 9: Stockfish could have defended with 28. …Qe7! and then …Bh6-xg5, drawing and nullifying all of White’s previously enterprising play, emphasizing that White’s concept of Kxd2-e3 was not particularly special after all because Black’s position was just too solid: http://view.chessbase.com/cbreader/2...e19414698.html

In game 10, the human 27. …Bxe4 would have drawn, illustrating that the deep sacrifice played by AlphaZero was only sufficient for equality and not more. 27. …Bg6? was a very bad move, permanently weakening Black’s king: http://view.chessbase.com/cbreader/2...e19451171.html

In short, my suggested game-saving improvements are:

Game 1: 31. Kg2, Game 2: 38. Rg1, game 3: 22. ...Nh5, game 4: 52. ...Rf1, game 5: 17. ...Qd8, game 6: 18. ...Be6, game 7: 39. ...Rdd8, game 8: 33. ...Qf7, game 9: 28. ...Qe7, game 10: 27. ...Bxe4. Only games 5 and 6 were losing in the early middlegame, and that was due to poor queen placement. Games 1, 2, 4, 7, 8, 9 and 10 should have been drawn under TCEC conditions, and perhaps 2 or 3 of the other games as well. The games were fantastic to see and I really hope we get to see more of them under any conditions.

[triplecheck]

Noting that China has more or less cloned AlphaZero and produced a self-learning Go program that beat China's best Go player - while giving a handicap!

http://www.abc.net.au/news/2018-01-2...-in-ai/9357048

[FM_Bill]

The AlphaZero - StockFish was an unfair match for all the reasons given. Even a small different in hardware can lead to a huge difference in results. For example an engine playing itself at twice the speed, would crush it.

Nonetheless, an impressive resulr for the AlphaZero team and the approach has some major advantages (and diadvantages)
over conventional chess engines.

To clarify the 80,000 nodes v 70,000,000, its like ly the random playouts by AZ (random games from the currently analysed position) are not counted. Each playout could average 100 moves or more. If all 80,000 nodes have 800 playouts, thats 80,000 x 800 x 100 = 6,400,000,000.

[triplecheck]

To clarify the 80,000 nodes v 70,000,000, its like ly the random playouts by AZ (random games from the currently analysed position) are not counted. Each playout could average 100 moves or more. If all 80,000 nodes have 800 playouts, thats 80,000 x 800 x 100 = 6,400,000,000.

Muddify?

There are no playouts.
None.

In the training phase, they do 800 tree searches from a given game position. These are done in parallel. Each one needs 4 (model 1) TPUs. So they use 3200 TPUs just to do that.
The tree searches do not go the end of the game.

In playing against Stockfish (playing now not training), they use 4 (model 2) TPUs, so they can do 1 (that's ONE) tree search. That generates 80000 nodes in the tree every second.

Why so few? Because every time they reach a position, they have to do a passive evaluation of it. So (simplifying) does Stockfish. Stockfish's evaluation counts 3 points for a bishop (whatever), 9 for a queen, but with special values for B+N v. R+P, and different again whether it's B+N v. R+P in the middlegame or endgame, and subtracts points for knight-on-the-edge-brings-woe and loads of other things. It's big.

But AlphaZero's evaluation function is HUGE (AlphaGo Zero's had 46 million parameters). It calculates it by running the input data through a really big neural net (not the biggest ever, but it's up there). It uses a lot of parallel processing in the TPUs to calculate it, but it's still slow. It's also very very good.

* TPU = Tensor Processing Unit, a proprietary Google chip designed to support neural net software. About equivalent to 2 of NVIDIA's top-of-the-line graphic cards, which similarly do massive parallel processing. The earliest AlphaGo versions ran on GPUs but when TPUs became available, they switched to those.

*AlphaGo Zero is confusingly not the same as AlphaZero Go ( the latest one).

[Kevin Bonham]

Further developments in Alpha Zero, which seem to have addressed criticisms of the initial results:

https://deepmind.com/blog/alphazero-...-shogi-and-go/

There is also a paper in Science:

https://deepmind.com/documents/260/a...o_preprint.pdf

[MichaelBaron]

GM Sadler's book on Alpha is apparently an interesting read.