🔥 ポーカーでAIが人間に勝利することの、なにが凄まじいのか - ログミーBiz

Such areas could include cybersecurity, financial trading, business negotiations and competitive price setting. Sandholm says the AI could even help in the party primaries for the U. Carnegie Mellon University and Facebook plan to make the Pluribus pseudo code—a detailed explanation of each necessary step in the program—available alongside the published paper, so that other AI researchers can generally reproduce their efforts. presidential election: candidates competing in a packed field could theoretically benefit from AI suggestions on spending just enough advertising money to win in key states, making the most of a limited war chest. But Pluribus ended up using this technique much more frequently. But the team decided not to release the actual code; this would likely facilitate the spread of superhuman poker-playing programs, which could be extremely disruptive to the online poker community and industry. This is a pretty big deal—certainly a notable milestone. Two years later, he has proved himself wrong. But Brown plans to further explore how AI performs in more complex multiplayer scenarios that go beyond card games.{/INSERTKEYS}{/PARAGRAPH} Although Pluribus did not reach a win rate quite as high as Libratus or another two-player poker program called DeepStack, it still notched a very respectable win rate. By Jeremy Hsu on July 11, Credit: Paul Yeung Getty Images During a casino tournament, a poker-playing program called Libratus deftly defeated four professional players in , hands of two-player poker. Sandholm has founded three start-ups, including the companies Strategic Machine and Strategy Robot, that might incorporate this multiplayer AI into the services they offer to business and military clients. For its part, Facebook does not have immediate plans for exploiting the poker-specific Pluribus. Specifically, during live poker play, Pluribus ran on a machine with just two central CPUs and gigabytes of memory. {PARAGRAPH}{INSERTKEYS}Could business, political or military applications come next? Many other poker-playing programs have used similar search features, but doing so with six players would require an impractical amount of computing memory: there are too many scenarios to simulate, based on what cards each player holds, what each believes the other players to have and all the betting decisions that follow. Past AI victories over humans have involved two-player or two-team games such as checkers, chess, Go and two-player no-limit poker. All of these games are zero-sum—they have just one winning side and one losing side. 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. These successes are detailed in a paper published this week in Science. Libratus got around this bottleneck by only using searches in the final two out of four betting rounds—but that solution still required the use of central processing units CPUs in a game with only two players.