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Since IBM's Deep Blue defeated World Chess Champion Garry Kasparov in their 1997 match, chess engines have only increased dramatically in strength and understanding. Today, the best chess engines are an almost incomprehensible 1,000 Elo points stronger than Deep Blue was at that time. A quick Google search for terms such as "Magnus Carlsen versus Stockfish" turns up numerous threads asking if humans can compete against today's top chess engines. The broad consensus seems to be that the very best humans might secure a few draws with the white pieces, but in general, they would lose the vast majority of games and would have no hope of winning any games. I see no reason to disagree with this consensus. Despite the clear superiority of engines, there ARE positions which chess engines don't (and possibly can't) understand that are quite comprehensible for human players.

In the game between chess and artificial intelligence, Google DeepMind's researchers have made yet another move, this time teaming up with ex-chess world champion Vladimir Kramnik to design and trial new AI-infused variants of the game. With the objective of improving the design of balanced sets of game rules, the research team set out to discover the best tweaks they could possibly give to the centuries-old board game, in an ambitious effort to refresh chess dynamics thanks to AI. The scientists used AlphaZero, an adaptive learning system that can teach itself new rules from scratch and achieve superhuman levels of play, to test the outcomes of nine different chess variants that they pre-defined with Kramnik's help. For each variant, AlphaZero played tens of thousands of games against itself, analyzing every possible move for any given chessboard condition, and generating new strategies and gameplay patterns. Kramnik and the researchers then assessed what games between human players might look like if these variants were adopted, to find out whether different sets of rules might improve the game.

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top flight players know by rote.

Chess was supposed to be a bastion of human ingenuity, an art machines would never conquer. The smarter they get, the more threatened we feel. We, too, are getting smarter, and computers are a big reason why. We certainly needed the challenge. Chess computers, in particular, have exposed our complacency.

We describe a preliminary investigation into learning a Chess player's style from game records. The method is based on attempting to learn features of a player's individual evaluation function using the method of temporal differences, with the aid of a conventional Chess engine architecture. Some encouraging results were obtained in learning the styles of two recent Chess world champions, and we report on our attempt to use the learnt styles to discriminate between the players from game records by trying to detect who was playing white and who was playing black. We also discuss some limitations of our approach and propose possible directions for future research. The method we have presented may also be applicable to other strategic games, and may even be generalisable to other domains where sequences of agents' actions are recorded.