The wave of neural network engines that AlphaZero inspired have impacted chess preparation, opening theory, and middlegame concepts. We can see this impact most clearly at the elite level because top grandmasters prepare openings and get ideas by working with modern engines. For instance, Carlsen cited AlphaZero as a source of inspiration for his remarkable play in 2019.
Neural network engines like AlphaZero learn from experience by developing patterns through numerous games against itself (known as self-play reinforcement learning) and understanding which ideas work well in different types of positions. This pattern recognition ability suggests that they are especially strong in openings and strategic middlegames where long-term factors must be assessed accurately. In these areas of chess, their experience allows them to steer the game towards positions that provide relatively high probabilities of winning.
A table of four selected engines is provided below.
Chess Engines
Engine |
Type |
Description |
Stockfish 8 |
Classical |
Relies on hard-wired rules and brute-force calculation of variations. |
AlphaZero |
Neural network |
DeepMind’s revolutionary AI engine used self-play reinforcement learning to train a neural network. |
Leela Chess Zero (Lc0) |
Neural network |
Launched in 2018 as an open-source project to follow the footsteps of AlphaZero. |
Stockfish 12
(and newer versions) |
Hybrid |
Utilizes classical searching algorithms as well as a neural network. |
The hybrid Stockfish engine aims to get the best of both types of AI: the calculation speed of classical engines and the strategic understanding of neural networks. Practice has shown that this approach is a very effective one because it consistently evaluates all types of positions accurately, from strategic middlegames to messy complications.
These two articles introduce a few concepts that the newer (i.e., neural network and hybrid) engines have influenced. Please note that the game annotations are based on work I did for my book, The AI Revolution in Chess, where I analyzed the impact of AI engines.
Clash of Styles
One of the biggest differences in understanding between older and newer engines can be found in strategic middlegames which involve long-term improvements by one side. As shown in many of the AlphaZero – Stockfish games, the older engines sometimes fail to see dangers due to their limited foresight. Relying solely on move-by-move calculation is not always enough to solve problems against the strongest opponents. This is because neural network engines excel at slowly building up pressure, making small improvements to optimize their winning chances, before gradually preparing the decisive breakthrough.
In the following game, the older engines believe that the opening outcome is quite satisfactory for Black, while the newer ones strongly disagree. Grischuk sides with the opinion of the neural network engines and understands that White’s long-term initiative is both practically and objectively extremely difficult for Black to handle.
Replay and check the LiveBook here |
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1.d4 Nf6 2.c4 e6 3.Nf3 d5 4.g3 Be7 5.Bg2 0-0 6.0-0 dxc4 7.Qc2 b5 8.a4 8.Ne5!? c6 9.a4 Nd5 10.Nc3 f6 11.Nf3 Qd7 8...b4 8...Bb7!? 9.axb5 a6 10.bxa6 Nxa6 11.Qxc4 Bd5 12.Qd3 Nb4 13.Rxa8 Qxa8 9.Nfd2 c6 9...Nd5! 10.Nxc4 c5 11.dxc5 Ba6! 12.Ne3 Nd7 13.Nxd5 exd5 14.c6 Rc8 15.Bf4 Nc5 10.Nxc4 Qxd4 11.Rd1 Qc5 12.Be3 Qh5 13.Nbd2 Ng4 13...Nd5?! 14.Nb3 Nxe3 15.Nxe3 a6 15...a5 16.Nc4 16.Nc4 Ra7 17.Rac1 c5 18.Nba5 Qg5 19.h4! Qf6 20.Qe4 Rc7 21.Rd3 g5 22.h5 Qg7 23.h6 14.Nf3 Nxe3 15.Nxe3 a5 15...a6?! 16.Nc4 a5 17.Nfe5 Ra7 18.Rac1 c5 19.Qe4 Ra6 20.Nd2! Rd8 21.Nb3 16.Nd4 Ba6 17.Rac1 Rc8 18.Bf3 Qg6 18...Qe5 19.Ng4! Qc7 20.Qb3 Ra7 21.Be4! Bf8 22.Nxc6 Nxc6 23.Rxc6 Qb8 24.Qe3 Rxc6 25.Bxc6 Rc7 26.Ne5± 19.Be4 Qh5 20.Bf3 Qg6 21.Be4 Qh5 22.Kg2! 22.Qd2?! Bf6 23.Nxc6 Nxc6 24.Bxc6 Rab8 25.Bf3 Qe5 22.Rd2 Ra7 23.Rcd1 g6 24.Nb3 24.Bd3 Bg5 25.Qc5 Rac7 24...Qe5 25.Bd3 Bb7 26.Nc4 Qc7 22...Ra7 23.h4 g6 24.f4 Qh6 24...c5 25.Bf3 Bb7 26.b3 Bxf3+ 27.Nxf3 Qh6 28.Nc4 Qf8 29.h5 25.Nb3?! 25.Ng4! Qg7 26.Ne5 Rac7 27.Bf3 h5 27...Bf6 28.Qc5 Bxe5 29.fxe5 Nd7 30.Qxa5 Bb7 28.Qe4 Bf6 29.Rc5± 25...Kh8? 25...c5! 26.Nxa5 26.Nc4 Qf8 27.Nbxa5 Bd8 26...Bxe2 27.Qxe2 Rxa5 26.Bd3! 26.Nxa5 f5 27.Bf3 e5! 26...Bb7 27.Nc4 c5+ 28.Be4 Ba6 29.Nbxa5 Qf8 30.Bf3 Rd8 31.h5 Bf6 31...g5 32.f5 32.Rxd8 Qxd8 33.Rd1 Rd7 34.Rxd7 Nxd7 35.h6! Nb6 36.Ne5 Bxe5 37.Nc6 Nc4 38.Nxd8 Ne3+ 39.Kf2 Nxc2 40.Nxf7+ Kg8 41.Nxe5 c4 42.Bg4 Nd4 43.Ke1 Kf8 44.Kd1 Ke7 45.e3 Nb3 46.Nc6+ Kf6 47.Nxb4 Bb7 48.Be2 Na5 49.Kd2 Nb3+ 50.Kc3 Nc5 51.a5 Ne4+ 52.Kxc4 Nxg3 53.Bd3 g5 54.fxg5+ 1–0
- Start an analysis engine:
- Try maximizing the board:
- Use the four cursor keys to replay the game. Make moves to analyse yourself.
- Press Ctrl-B to rotate the board.
- Drag the split bars between window panes.
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Grischuk,A | 2772 | Nakamura,H | 2761 | 1–0 | 2019 | E05 | Moscow FIDE GP | 3.2 |
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Opening Developments
Perhaps the most popularized idea of the neural network engines is the h-pawn advance, where White pushes h4-h5-h6 (or Black pushes …h5-h4-h3) to cramp the opponent’s kingside by taking away some key squares. The idea itself is not at all new, but the newer engines have a much greater appreciation for it than the older ones. This has led to many new ideas in openings such as the Grunfeld, where the fianchettoed bishop on g7 can be targeted by an h-pawn attack. Tying back to the theme of long-term improvements, neural network engines understand the problems that it creates for the opponent in the long run.
Our next game surveys a cutting-edge approach against the Grunfeld. Its sharp rise in popularity from 2019 onwards coincides with the widespread use of neural network engines at the top level.
Replay and check the LiveBook here |
Please, wait...
1.d4 Nf6 2.c4 g6 3.h4!? c5 3...Bg7 4.Nc3 d6 4...d5?! 5.h5 Nxh5 6.cxd5 e6 7.g4! Nf6 8.dxe6 Bxe6 9.e4 Bxg4 10.f3 Be6 11.Bg5 5.e4 Nc6!? 4.d5 Bg7 5.Nc3 d6 6.e4 0-0 6...e6 7.Be2 exd5 8.exd5 Nbd7!? 8...0-0 9.h5 9.Nf3 9.h5 Nxh5 9...Ng4! 9...0-0 10.Bf4 Qb6 11.Qd2 10.h5 Qe7 11.Bg5 Bxc3+ 12.bxc3 f6 13.Bd2 g5 14.0-0 Nge5?! 14...Nde5 15.Nd4 0-0 15.Nd4! 7.Be2 e6 8.h5 exd5 9.exd5 Re8 9...Nbd7 10.h6! Bh8 11.Bg5 Re8 12.Nf3 Qb6 13.Qd2 Ne4 14.Nxe4 Rxe4 15.0-0! Qxb2 16.Rae1 Nf8 17.Bd3 Qxd2 18.Bxd2 Rxe1 19.Rxe1 Bg4 20.Re7 10.h6! 10.hxg6 fxg6 11.Nf3 Nbd7 12.0-0 Ne5 10...Bh8 11.Bg5 Qb6!? 11...Nbd7 12.b3 Ne4 12...Qa5!? 13.Bd2 Qd8 14.Kf1! Ne4 15.Nxe4 Rxe4 16.Rc1 Rh4 17.Rxh4 Qxh4 18.g3 Qd8 18...Qh2 19.Bg4 Nd7 20.Nf3 Qh1+ 21.Ke2 19.Kg2 Nd7 20.Nf3± 13.Nxe4 Rxe4 14.Rc1 14.Kf1! Na6 14...Re8 15.Rc1 Nd7 16.g3 Nf6 17.Bd3 15.Bd3 Rd4? 15...Re8 16.a3! 16.Qe2 Bg4 17.Nf3 Rxd3 18.Re1! 14...Na6 15.Rh4!? 15.Nf3 Qa5+ 16.Qd2 Nb4 17.a4 Nd3+ 18.Kf1 Qxd2 19.Nxd2 Rxe2 20.Kxe2 Nxc1+ 21.Rxc1 Bf5 15...Rxh4 16.Bxh4 Bf5 17.Bg4 17.g4! Bd7 18.Bg3 Re8 19.Kf1 17...Re8+? 17...Bxg4! 18.Qxg4 Nb4 19.Qe2 Nxd5! 20.cxd5? Qb4+ 21.Kf1 Qxh4 18.Kf1 Bxg4 18...Re4 19.f3 Rd4 20.Qe1 Bd3+ 21.Kf2 Be5 22.Bf6! 19.Qxg4 f5 19...Qc7 20.Nf3 20.Qd1 Re4 21.Bg5 Nb4 22.f3 Re8 23.Qd2 Qc7 24.Nh3 Qd7 25.Nf4 b6 26.Ne6 Na6 27.Re1 Be5 28.a3 Rc8 29.f4 Bh8 30.Ng7! Bxg7 31.Re7 Qxe7 32.Bxe7 Bxh6 33.Qe3 Nc7 34.Bxd6 Re8 35.Qh3 1–0
- Start an analysis engine:
- Try maximizing the board:
- Use the four cursor keys to replay the game. Make moves to analyse yourself.
- Press Ctrl-B to rotate the board.
- Drag the split bars between window panes.
- Download&Clip PGN/GIF/FEN/QR Codes. Share the game.
- Games viewed here will automatically be stored in your cloud clipboard (if you are logged in). Use the cloud clipboard also in ChessBase.
- Create an account to access the games cloud.
Paravyan,D | 2629 | Wagner,D | 2590 | 1–0 | 2020 | E73 | Moscow Aeroflot op-A | 3 |
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The clash of chess styles between classical and neural network AI is fascinating to analyze. Many examples on this topic can be found in the famous AlphaZero – Stockfish games and in openings where the engines disagree on the evaluation, such as the Grischuk – Nakamura game. Their disagreement has led to major advancements in all popular openings, as old lines are revised, and new lines supported by modern engines are introduced into high-level practice.
Part 2 will examine another AI-inspired opening and the modern battle between two players armed with ideas from neural network engines.
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