Humans Fold AI Conquers Pokers Final Milestone

Jeremy Hsu writing for Scientific American

So Pluribus instead deployed its depth-limited search, which considers how opponents might choose among only four general betting strategies: the precomputed blueprint, one biased toward folding, another biased toward calling and a fourth biased toward raising. This modified search helps explain why Pluribus’s success in six-player poker required relatively minimal computing resources and memory in comparison with past superhuman achievements in gaming AIs. Specifically, during live poker play, Pluribus ran on a machine with just two central CPUs and 128 gigabytes of memory. “It’s amazing this can be done at all, and second, that it can be done with no $&graphics processing units$& and no extreme hardware,” Sandholm says. By comparison, DeepMind’s famous AlphaGo program used 1,920 CPUs and 280 GPUs during its 2016 matches against top professional Go player Lee Sedol.