Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google's DeepMind group's conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute. But what do you do when theory breaks down? Two researchers at Carnegie Mellon University and Facebook went back to the drawing board to solve "heads-up no-limit Texas hold'em," the most popular form of multiplayer poker in the world. Theory isn't computable for this form of the card game, so they designed some elegant search strategies for their computer program, "Pluribus," to beat the best human players in 10,000 hands of poker.
Beating expert poker players differs from past AI successes against human competitors in games such as Jeopardy and Go. Researchers behind a poker-playing AI system called DeepStack say it's the first algorithm to have ever beaten poker pros in heads-up no-limit Texas hold'em. The claim, if verified, would mark a major milestone in the development of artificial-intelligence systems. Beating expert poker players differs from past AI successes against human competitors in games such as Jeopardy and Go because each player's hand provides only an incomplete picture about the state of play and requires a program to navigate tactics, such as bluffing, based on asymmetrical information. DeepStack is the work of a collaboration between researchers at the University of Alberta and two Czech universities, who say in a new non-peer reviewed paper that it's the "first computer program to beat professional poker players in heads-up no-limit Texas hold'em".
Carnegie Mellon's No-Limit Texas Hold'em software made short work of four of the world's best professional poker players in Pittsburgh at the grueling "Brains vs. Artificial Intelligence" poker tournament. Poker now joins chess, Jeopardy, go, and many other games at which programs outplay people. But poker is different from all the others in one big way: players have to guess based on partial, or "imperfect" information. "Chess and Go are games of perfect information," explains Libratus co-creator Noam Brown, a Ph.D. candidate at Carnegie Mellon. "All the information in the game is available for both sides to see.
Don't simply "all in" if there's a bot at your Texas hold'em poker table, because Facebook and Carnegie Mellon University's new Pluribus AI system just beat five human pros at the same time -- including a couple of World Series of Poker Champs. AI models had already bettered human poker pros one-on-one, but Pluribus's success in a six-player game signals a huge leap in ability. Texas hold'em is one of the most popular poker variants that involves game theory, gambling, and strategy. To win the game, each play must assemble the best five cards from any combination of two "hole cards" dealt face down to each player and five community cards dealt face up. Players can choose to check, bet, call, raise, and fold.
In yet another episode of man versus machine, an artificial intelligence developed by Carnegie Mellon University has been absolutely dismantling a team of professional poker players, accumulating a staggering lead of almost $800,000. The showdown takes place as part of the "Brains vs. Artificial Intelligence" competition which pits a group of four poker pros against the crafty supercomputer Libratus in a heads-up game of No-Limit Texas Hold'em slated to continue for 120,000 hands. Since January 11 when the contest initially kicked off, the players have now passed the midway point of the the race, having completed almost 65,000 hands in total. What is more intriguing is that so far Libratus has managed to keep an impressive lead over its human opponents, stacking up a profit of $794,392. While the seasoned players originally underestimated the AI, Jimmy Chou, who is one of the pros, told CMU that they've all come to regard the machine as a tremendously tough rival, noting the computer's ability to continuously improve its game.