Poker computer cashes in its chips
Alan Du’s never met a poker adversary with such a stonecold demeanour. It’s the fifth day that the venture capitalist and World Series of Poker veteran has gone up against this opponent — and the losses are stacking up.
His rival is literally inhuman, Du conceded. That’s because he went up against 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.
Housed within a supercomputing centre near Carnegie Mellon University in Pittsburgh, its name, 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 five days. On Monday, at a conference centre on China’s Hainan island, the final 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, a seed investor who became the first mainland Chinese to win a WSOP gold bracelet in Las Vegas last year, had tried to prevail where the pros had fallen short by employing an understanding of AI.
Unlike the players in the January matchup 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.
The latest AI exhibition, organized by Sinovation Ventures and Hainan’s government, didn’t generate quite the same buzz as last year’s matchup between Google DeepMind’s AlphaGo and Korean master Lee Sedol in Seoul. Perhaps that’s because even casual observers are becoming accustomed to seeing AI software upstage humans.
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 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 and cybersecurity, he said.
“People have a misunderstanding of what computers and people are each good at. People think that bluffing is very human — it turns out that’s not true,” said Noam Brown, a co-developer of Libratus. “A computer can learn from experience that if it has a weak hand and it bluffs, it can make more money.”