“At the end of the day, betting $150 is a lot like betting $151,” says Brown. To reduce the number of potential choices that Pluribus needed to consider, the AI grouped similar hands – for example, a king-high flush and queen-high flush – and only considered a few different sizes of bets for a given hand. “What was particularly impressive about this challenge was that the AI played faster and on much less computing power.” “I was pretty amazed that they had made so much progress in just a couple of years,” he says. Les also played against Libratus in 2017. “You really want to push the AI, try everything you can to find a weakness,” says Les. Read more: True AI creativity is coming and will reveal the minds of machines It started as a poker novice with no knowledge of the game, learning the rules over trillions of hands and improving its strategy by reviewing the decisions it made every round.

Pluribus learned to master the game by playing against five copies of itself, an approach that has been used by other AIs to master games such as Go, Dota 2 and StarCraft II. Pluribus’s approach could be applied to situations in cybersecurity, or in having self-driving cars navigate traffic, he says. “If you look at real-world interactions, most of them involve hidden information, multiple participants or both,” says Brown. It also involves hidden information, in which a player has access only to the cards that they see – meaning that an AI has to take into account how it would act with different cards so it isn’t obvious when it has a good hand. There are more possibilities than there are atoms in the universe. Part of what makes poker so difficult for AI to master is the huge number of possible actions to make, says Tristan Cazenave at the Paris Dauphine University.

Read more: AI made from a sheet of glass can recognise numbers just by looking

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