This training process was also remarkably efficient: Pluribus was created in just eight days using a 64-core server equipped with less than 512GB of RAM. This is a common technique for AI training, with the system able to learn the game through trial and error playing hundreds of thousands of hands against itself. Pluribus was trained in just eight days for an estimated cost of just $150įirst, they taught Pluribus to play poker by getting it to play against copies of itself - a process known as self-play. To create a program capable of rising to this challenge, Brown and his colleague Tuomas Sandholm, a professor at CMU, deployed a few crucial strategies. Credit: Facebookīack in 2015, a machine learning system beat human pros at two-player Texas Hold ‘em, but upping the number of opponents to five increases the complexity significantly. “Limping” is one strategy used by some human players that the AI eventually discarded. This makes it easier, in some senses, to train an AI on. But all the information is available to see, and the game only has two possible outcomes for players: win or lose. The game of Go famously has more possible board combinations than atoms in the observable universe, making it a huge challenge for AI to map out what move to make next. Not only is the information needed to win hidden from players (making it what’s known as an “imperfect-information game”), it also involves multiple players and complex victory outcomes. Although machine learning has already reached superhuman levels in board games like chess and Go, and computer games like Starcraft II and Dota, six-person no-limit Texas Hold ‘em represents, by some measures, a higher benchmark of difficulty.

In a paper published in Science, the scientists behind Pluribus say the victory is a significant milestone in AI research. It’s really hard to pin him down on any kind of hand,” Chris Ferguson, a six-time World Series of Poker champion and one of the 12 pros drafted against the AI, said in a press statement. “Pluribus is a very hard opponent to play against. “we’re at a superhuman level and that’s not going to change.”

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