DeepMind's artificial intelligence programme has beaten a top-level chess computer programme after learning the game in just four hours without human help.
The Google owned tech company's machine learning algorithm, which last year won a landmark game of Go against a human player in what has been described as a "superhuman performance", was applied to the game by researchers.
"Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case," they said in a paper published by Cornell University in the US but which has yet to be peer reviewed.
AlphaZero, the programme based on Go-winning AlphaGo, triumphed over the chess-playing engine Stockfish within just four hours and outperformed Elmo, a similar engine but for shogi, in just two.
"The game of chess represented the pinnacle of AI research over several decades. State-of-the-art programs are based on powerful engines that search many millions of positions, leveraging handcrafted domain expertise and sophisticated domain adaptations," said researchers.
"AlphaZero is a generic reinforcement learning algorithm – originally devised for the game of Go – that achieved superior results within a few hours, searching a thousand times fewer positions, given no domain knowledge except the rules of chess."
Edison Investment Research analyst Richard Windsor said: "This should make training of algorithms in the future easier, quicker and cheaper than they are today which is why this is yet another very significant advance that has been made by DeepMind."
The UK company, acquired by Google in 2014 for a blockbuster £400m, has tripled its spending on AI experts in the highly competitive field according to its latest annual accounts filed in October.