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How the AI Revolution Impacted Chess (1/2)


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.

10 Positions Chess Engines Just Don't Understand


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.

The King of the Computer Age


It wasn't easy and it wasn't especially pretty, but world chess champion Magnus Carlsen has successfully defended his crown in what was scheduled to be a 12-game match against world No. 2 Fabiano Caruana. After all 12 of those games were drawn, the victor was decided via a best-of-four series of "rapid chess" contests, in which each player has about 30 minutes to complete all his moves. The Norwegian Carlsen, by far the world's No. 1 player at rapid chess, predictably dominated Caruana, who entered the match ranked only No. 8 in the format, winning the playoff games 3-0 and retaining his title for another two years. What kind of match was it? A bit dull, to be honest, at least until Wednesday's rapid games.

How AI Revolutionised the Ancient Game of Chess


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.

In Just 72 Hours, a Computer Learned How to Beat Nearly Anyone at Chess


For the average player, trying to beat the computer at chess (even when you're just playing on'easy' on your laptop) is a difficult task. But as humans, we take solace in the fact that chess Grand Masters are still able to win against machines. Despite chess engines being capable of searching through 200 million possible moves going against a human player who can only think of maybe five moves per second, the masters still manage to play at the same level as the advanced tech. By evaluating chess moves and having the ability to narrow down the most advantageous avenues of search, thus whittling down the options to just a few, notable possibilities. Computers are unable to this as efficiently as humans, which is why humans still have the upper hand (or at least a somewhat level playing field) when playing against machines.