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How the AI Revolution Impacted Chess (2/2)

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In 2019, Dubov introduced many new ideas in a rare variation of the Tarrasch Defense, which quickly attracted attention at the top level. Several of the world's best players have tried it, including Carlsen who employed it successfully in the 2019 World Rapid and Blitz Championships. Dubov's double-edged opening system is based around concepts that are suggested by the newer engines, including early h-pawn advances and pawn sacrifices for the initiative. Note that both game annotations are based on work I did for my book, The AI Revolution in Chess. At the top level these days, everyone uses neural network (or hybrid) engines.


How AI Revolutionised the Ancient Game of Chess

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I have come to the personal conclusion that while all artists are not chess players, all chess players are artists. Originally called Chaturanga, the game was set on an 8x8 Ashtāpada board and shared two key fundamental features that still distinguish the game today. Different pieces subject to different rules of movement and the presence of a single king piece whose fate determines the outcome. But it was not until the 15th century, with the introduction of the queen piece and the popularization of various other rules, that we saw the game develop into the form we know today. The emergence of international chess competition in the late 19th century meant that the game took on a new geopolitical importance.


The future is here – AlphaZero learns chess

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About three years ago, DeepMind, a company owned by Google that specializes in AI development, turned its attention to the ancient game of Go. Go had been the one game that had eluded all computer efforts to become world class, and even up until the announcement was deemed a goal that would not be attained for another decade! This was how large the difference was. When a public challenge and match was organized against the legendary player Lee Sedol, a South Korean whose track record had him in the ranks of the greatest ever, everyone thought it would be an interesting spectacle, but a certain win by the human. The question wasn't even whether the program AlphaGo would win or lose, but how much closer it was to the Holy Grail goal. The result was a crushing 4-1 victory, and a revolution in the Go world. In spite of a ton of second-guessing by the elite, who could not accept the loss, eventually they came to terms with the reality of AlphaGo, a machine that was among the very best, albeit not unbeatable. It had lost a game after all.


Chess's New Best Player Is A Fearless, Swashbuckling Algorithm

@machinelearnbot

Chess is an antique, about 1,500 years old, according to most historians. As a result, its evolution seems essentially complete, a hoary game now largely trudging along. That's not to say that there haven't been milestones. In medieval Europe, for example, they made the squares on the board alternate black and white. In the 15th century, the queen got her modern powers.1


DeepMind's superhuman AI is rewriting how we play chess

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Since 1997, when IBM's Deep Blue beat world champion and chess legend Garry Kasparov in a six-game match, chess players have accepted that machines are stronger at chess. We have taken some comfort from the fact that we taught these machines how to play. But strangely enough, despite being programmed by humans, traditional chess engines don't play quite like humans. Despite the hand-crafted heuristics, the fundament of an engine's superiority lies in calculation: sifting through vast numbers of moves to find concrete ways to solve a position. Back then, chess grandmasters were hired in to evaluate a series of typical positions and describe the considerations that led to the assessment, and then programmers turned these considerations into ever more sophisticated heuristics.