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AI beats human poker master, again

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Artificial intelligence (AI) continues to mature and with that maturity comes the capability of beating human poker master by huge margins.

The latest AI victory came in China where World Series of Poker veteran Alan Du lost to “Lengpudashi:” – an updated version of the Libratus artificial intelligence program that achieved a major milestone by besting four of the world’s best poker pros in January.

The program is housed within a supercomputing center near Carnegie Mellon University in Pittsburgh. The name of the program is intended to resemble its English moniker, fittingly translates into “cold poker master.”

Du and five team members played 36,000 hands against the machine over the course of five days. On Monday, at a resort conference center on China’s Hainan island, the final point-based score was announced: the AI won by a landslide.

Poker’s complex betting strategies and the element of bluffing make it particularly intriguing to AI researchers. A player also decides to bet, bluff or fold without ever seeing the opponent’s full hand — a different kind of challenge than games like chess or Go, in which all the pieces are clearly visible on a playing board.

Du had tried to prevail where the pros had fallen short by employing an understanding of AI. Unlike the players in the January match-up who drew upon years of professional experience, Du’s Chinese team, which included a former Oracle engineer and startup entrepreneurs, attempted to apply their knowledge of machine intelligence and game theory to counter the machine’s moves. It wasn’t enough.

Tuomas Sandholm, a professor of computer science at Carnegie Mellon, has been honing the research underlying Libratus since 2004, honing its ability to make decisions in situations with imperfect information. The point of training AI to win at games like chess, Go, and poker isn’t for the sake of games themselves, but because controlled environments help computers hone strategic decision-making. Those reasoning skills can then be applied to real-world problems such as business, finance, and cybersecurity, he said.

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