A new AI wins at go. Can a similar AI win at chess? Can it achieve pure self-training? One of the more popular questions asked on this site regards the prospect of a purely self-trained chess AI.

Today, ChessBase is distracted from its coverage of the FIDE Candidates tournament to report that a new AI is, for the first time, beating a leading master of the rather different game of go, which had resisted chess-style AIs for many years. An initial reading of the report suggests that the new go AI is unlike chess AIs, but is more nearly a general-game-playing (GGP) AI. ChessBase's article does not however use the term GGP, but does seem to say that the go AI can win at simple video games.

Is there any reason such a winning go AI could not with minor adjustments also win at chess? If so, does such an AI show promise to achieve the pure self-training the several excellent answers to the earlier question have earlier discussed, which at that time was not yet possible? Why or why not?

I suspect that no really complete, fully informed answer to my question is yet available, so even a partial answer based on related expertise would be appreciated.

For additional reference, see also this related question and answers.

UPDATE

When the above question was first posted five days ago and when some of the fine answers below were given, the first news regarding the victory of the go AI had just appeared. Since then, additional information and commentary have emerged.

Particularly interesting since then has been a quite readable, five-sided roundtable discussion in which one Jonathan Schaeffer remarks:


  Learning from human games helps accelerate the program’s learning. AlphaGo could learn to become a strong player on its own, without using the human games. The learning process would just take longer.


According to the roundtable's host, Schaeffer is "[c]omputer science professor at the University of Alberta and the man who solved checkers"; so, presumably, he might be qualified to comment.

For further information, here is the record of another, open discussion, many of whose participants seem better informed than usual.  The discussion took place during the match.

Further update, a year and a half later: commenter @MarkS. writes:


  This is just a comment because it's about Go, not Chess, but AlphaGo Zero achieved "pure self-training" just from being told who won (and not the final score) and is stronger and vastly more efficient than the AI that beat Lee Sedol. For more information, see deepmind.com/blog/alphago-zero-learning-scratch
Well, well, well! DeepMind have published a paper in which they say they have programmed and trained a neural network computer to beat Stockfish.

With 1 minute thinking time per move their AlphaZero computer beat Stockfish by +25, =25, -0 with white and +3,=47,0- as black.

They "trained" 3 separate computers to play chess, shogi and Go and beat their silicon rivals convincingly.

Here is how the paper describes the training and evaluation -


  Self-play games are generated by using the latest parameters for this
  neural network, omitting the evaluation step and the selection of best
  player.
  
  AlphaGo Zero tuned the hyper-parameter of its search by Bayesian
  optimisation. In AlphaZero we reuse the same hyper-parameters for all
  games without game-specific tuning. The sole exception is the noise
  that is added to the prior policy to ensure exploration; this is
  scaled in proportion to the typical number of legal moves for that
  game type.
  
  Like AlphaGo Zero, the board state is encoded by spatial planes based
  only on the basic rules for each game. The actions are encoded by
  either spatial planes or a flat vector, again based only on the basic
  rules for each game (see Methods).
  
  We applied the AlphaZero algorithm to chess, shogi, and also Go.
  Unless otherwise specified, the same algorithm settings, network
  architecture, and hyper-parameters were used for all three games. We
  trained a separate instance of AlphaZero for each game. Training
  proceeded for 700,000 steps (mini-batches of size 4,096) starting from
  randomly initialised parameters, using 5,000 first-generation TPUs to
  generate self-play games and 64 second-generation TPUs to train the
  neural networks. Further details of the training procedure are
  provided in the Methods.
  
  Figure 1 shows the performance of AlphaZero during self-play
  reinforcement learning, as a function of training steps, on an Elo
  scale (10). In chess, AlphaZero outperformed Stockfish after just 4
  hours (300k steps); in shogi, AlphaZero outperformed Elmo after less
  than 2 hours (110k steps); and in Go, AlphaZero outperformed AlphaGo
  Lee (29) after 8 hours (165k steps).
  
  We evaluated the fully trained instances of AlphaZero against
  Stockfish, Elmo and the previous version of AlphaGo Zero (trained for
  3 days) in chess, shogi and Go respectively, playing 100 game matches
  at tournament time controls of one minute per move. AlphaZero and the
  previous AlphaGo Zero used a single machine with 4 TPUs. Stockfish and
  Elmo played at their strongest skill level using 64 threads and a hash
  size of 1GB. AlphaZero convincingly defeated all opponents, losing
  zero games to Stockfish and eight games to Elmo (see Supplementary
  Material for several example games), as well as defeating the previous
  version of AlphaGo Zero (see Table 1).


Their computer used a new form of chip called a "TPU" (Tensor Processing Unit) developed by Google for machine learning tasks.

They also claim their Monte Carlo tree search algorithm is better and more "human like" than the traditional alpha-beta search algorithms -


  We also analysed the relative performance of AlphaZero’s MCTS search
  compared to the state-of-the-art alpha-beta search engines used by
  Stockfish and Elmo. AlphaZero searches just 80 thousand positions per
  second in chess and 40 thousand in shogi, compared to 70 million for
  Stockfish and 35 million for Elmo. AlphaZero compensates for the lower
  number of evaluations by using its deep neural network to focus much
  more selectively on the most promising variations – arguably a more
  “human-like” approach to search, as originally proposed by Shannon.
  Figure 2 shows the scalability of each player with respect to thinking
  time, measured on an Elo scale, relative to Stockfish or Elmo with
  40ms thinking time. AlphaZero’s MCTS scaled more effectively with
  thinking time than either Stockfish or Elmo, calling into question the
  widely held belief that alpha-beta search is inherently superior in
  these domains


Here are some of the games -

[FEN ""]
[White "Stockfish "]
[Black "AlphaZero"]

 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 fxe5 14. Nxe5 Rf6 15. Ng4 Rf7 16. Ne5 Re7 17. a6 c5 18. f4 Qe8 
 19. axb7 Bxb7 20. Qa5 Nd4 21. Qc3 Re6 22. Be3 Rb6 23. Nc4 Rb4 24. b3 a5 
 25. Rxa5 Rxa5 26. Nxa5 Ba6 27. Bxd4 Rxd4 28. Nc4 Rd8 29. g3 h6 30. Qa5 Bc8 
 31. Qxc7 Bh3 32. Rg1 Rd7 33. Qe5 Qxe5 34. Nxe5 Ra7 35. Nc4 g5 36. Rc1 Bg7 
 37. Ne5 Ra8 38. Nf3 Bb2 39. Rb1 Bc3 40. Ng1 Bd7 41. Ne2 Bd2 42. Rd1 Be3 
 43. Kg2 Bg4 44. Re1 Bd2 45. Rf1 Ra2 46. h3 Bxe2 47. Rf2 Bxf4 48. Rxe2 Be5 
 49. Rf2 Kg7 50. g4 Bd4 51. Re2 Kf6 52. e5+ Bxe5 53. Kf3 Ra1 54. Rf2 Re1 
 55. Kg2+ Bf4 56. c3 Rc1 57. d4 Rxc3 58. dxc5 Rxc5 59. b4 Rc3 60. h4 Ke5 
 61. hxg5 hxg5 62. Re2+ Kf6 63. Kf2 Be5 64. Ra2 Rc4 65. Ra6+ Ke7 66. Ra5 Ke6 
 67. Ra6+ Bd6 0-1


Game

 [FEN ""]
 [White "Stockfish "]
 [Black "AlphaZero"]

 1. e4 e5 2. Nf3 Nc6 3. Bb5 Nf6 4. d3 Bc5 5. Bxc6 dxc6 6. O-O Nd7 7. c3 O-O 
 8. d4 Bd6 9. Bg5 Qe8 10. Re1 f6 11. Bh4 Qf7 12. Nbd2 a5 13. Bg3 Re8 14. Qc2 Nf8 15. c4 c5 16. d5 b6 17. Nh4 g6 18. Nhf3 Bd7 19. Rad1 Re7 20. h3 Qg7 21. Qc3 Rae8 22. a3 h6 23. Bh4 Rf7 24. Bg3 Rfe7 25. Bh4 Rf7 26. Bg3 a4 27. Kh1 Rfe7 28. Bh4 Rf7 29. Bg3 Rfe7 30. Bh4 g5 31. Bg3 Ng6 32. Nf1 Rf7 33. Ne3 Ne7 34. Qd3 h5 35. h4 Nc8 36. Re2 g4 37. Nd2 Qh7 38. Kg1 Bf8 39. Nb1 Nd6 40. Nc3 Bh6 41. Rf1 Ra8 42. Kh2 Kf8 43. Kg1 Qg6 44. f4 gxf3 45. Rxf3 Bxe3+ 46. Rfxe3 Ke7 47. Be1 Qh7 48. Rg3 Rg7 49. Rxg7+ Qxg7 50. Re3 Rg8 51. Rg3 Qh8 52. Nb1 Rxg3 53. Bxg3 Qh6 54. Nd2 Bg4 55. Kh2 Kd7 56. b3 axb3 57. Nxb3 Qg6 58. Nd2 Bd1 59. Nf3 Ba4 60. Nd2 Ke7 61. Bf2 Qg4 62. Qf3 Bd1 63. Qxg4 Bxg4 64. a4 Nb7 65. Nb1 Na5 66. Be3 Nxc4 67. Bc1 Bd7 68. Nc3 c6 69. Kg1 cxd5 70. exd5 Bf5 71. Kf2 Nd6 72. Be3 Ne4+ 73. Nxe4 Bxe4 74. a5 bxa5 75. Bxc5+ Kd7 76. d6 Bf5 77. Ba3 Kc6 78. Ke1 Kd5 79. Kd2 Ke4 80. Bb2 Kf4 81. Bc1 Kg3 82. Ke2 a4 83. Kf1 Kxh4 84. Kf2 Kg4 85. Ba3 Bd7 86. Bc1 Kf5 87. Ke3 Ke6 0-1


White: AlphaZero Black: Stockfish

 [FEN ""]
 [White "AlphaZero "]
 [Black "Stockfish"]

 1. Nf3 Nf6 2. c4 b6 3. d4 e6 4. g3 Ba6 5. Qc2 c5 6. d5 exd5 7. cxd5 Bb7 8. Bg2 Nxd5 9. O-O Nc6 10. Rd1 Be7 11. Qf5 Nf6 12. e4 g6 13. Qf4 O-O 14. e5 Nh5 15. Qg4 Re8 16. Nc3 Qb8 17. Nd5 Bf8 18. Bf4 Qc8 19. h3 Ne7 20. Ne3 Bc6 21. Rd6 Ng7 22. Rf6 Qb7 23. Bh6 Nd5 24. Nxd5 Bxd5 25. Rd1 Ne6 26. Bxf8 Rxf8 27. Qh4 Bc6 28. Qh6 Rae8 29. Rd6 Bxf3 30. Bxf3 Qa6 31. h4 Qa5 32. Rd1 c4 33. Rd5 Qe1+ 34. Kg2 c3 35. bxc3 Qxc3 36. h5 Re7 37. Bd1 Qe1 38. Bb3 Rd8 39. Rf3 Qe4 40. Qd2 Qg4 41. Bd1 Qe4 42. h6 Nc7 43. Rd6 Ne6 44. Bb3 Qxe5 45. Rd5 Qh8 46. Qb4 Nc5 47. Rxc5 bxc5 48. Qh4 Rde8 49. Rf6 Rf8 50. Qf4 a5 51. g4 d5 52. Bxd5 Rd7 53. Bc4 a4 54. g5 a3 55. Qf3 Rc7 56. Qxa3 Qxf6 57. gxf6 Rfc8 58. Qd3 Rf8 59. Qd6 Rfc8 60. a4 1-0