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In artificial intelligence, it's a milestone when a computer program can beat top players at a game like chess. But a game like poker, specifically six-player Texas hold 'em has been too tough for a machine to master - until now. NPR's Merrit Kennedy has the story.
MERRIT KENNEDY, BYLINE: Darren Elias holds four World Poker Tour titles and has won millions of dollars playing the game. So he was a perfect person to test the skills of a poker bot called Pluribus. Actually, he played Texas hold 'em against a whole table of these bots.
DARREN ELIAS: It's just me and then five versions of this AI poker bot, which I would play against every day thousands of hands.
KENNEDY: Pluribus learns by playing against itself over and over and remembering which strategies worked best. And Elias would alert the computer scientists who designed the bot when it made a mistake.
ELIAS: And it was improving very rapidly, where it went from being a mediocre player to basically a world class-level poker player in a matter of days and weeks, which was pretty scary.
KENNEDY: After 5,000 hands, the machine came out ahead of Elias. So scientists tried a different experiment - pitting Pluribus against five professional players at a time. It still won.
NOAM BROWN: If you - if this were for live money, the bot would be winning at a rate of about a thousand dollars an hour.
KENNEDY: That's Noam Brown from Facebook's AI Research Unit. He designed Pluribus with his adviser at Carnegie Mellon University, and their research was published in the journal Science. There are a couple of reasons why multiplayer poker has been a challenge for AI. The information isn't out in the open, like it is in chess, for example. The cards are hidden. And multiple opponents make it a lot tougher for a bot to figure out a winning strategy. As Pluribus taught itself to play, Brown says some of the tactics it came up with were surprising.
BROWN: Because it was developed completely from scratch, without any access to human data, the strategy that it's developed is very different from how humans play poker.
KENNEDY: The bot learned to pick its moments and then make huge bets and bluffs - bigger than most humans would make. Here's Elias, the poker pro.
ELIAS: The bot was not afraid to make these kind of plays often, which is something that humans could probably do a little more.
KENNEDY: And he says the bot was excellent at varying its strategy, even when dealt the exact same hand, so it was very unpredictable. Ultimately, he says Pluribus could spell the end of high-stakes online poker. People might not want to risk a lot of money if they think they might actually be playing against a superhuman bot.
ELIAS: It's humbling and a bit sad, I guess, that - to be defeated by a bot like this so quickly in a game that you dedicated, like, your life to.
KENNEDY: Brown, the developer, says their AI technology could eventually be useful in many situations where there are multiple people involved and a lot of unknown variables, like getting a self-driving car through traffic.
Merrit Kennedy, NPR News.
(SOUNDBITE OF LADY GAGA SONG, "POKER FACE")
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