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FOR ALMOST THREE weeks, Dong Kim sat at a casino in Pittsburgh and played poker against a machine. But Kim wasn’t just any poker player. This wasn’t just any machine. And it wasn’t just any game of poker.
Kim, 28, is among the best players in the world. The machine, built by two computer science researchers at Carnegie Mellon, is an artificially intelligent system that runs on a Pittsburg supercomputer. And for twenty straight days, they played no-limit Texas Hold ‘Em, an especially complex form of poker in which betting strategies play out over dozens of hands.
About halfway through the competition, which ended this week, Kim started to feel like Libratus could see his cards. “I’m not accusing it of cheating,” he said. “It was just that good.” So good, in fact, that it beat Kim and three more of the world’s top human players—a first for artificial intelligence.
Libratus, for one, did not use neural networks. Mainly, it relied on a form of AI known as reinforcement learning, a method of extreme trial-and-error. In essence, it played game after game against itself. Google’s DeepMind lab used reinforcement learning in building AlphaGo, the system that that cracked the ancient game of Go ten years ahead of schedule, but there’s a key difference between the two systems. AlphaGo learned the game by analyzing 30 million Go moves from human players, before refining its skills by playing against itself. By contrast, Libratus learned from scratch.
If that seems unfair, well, it’s how AI works. It’s not just that AI spans many technologies. Humans are so often in the mix, too, actively improving, running, or augmenting the AI. Libratus is indeed a milestone, displaying a breed of AI that could play a role with everything from Wall Street trading to cybersecurity to auctions and political negotiations. “Poker has been one of the hardest games for AI to crack, because you see only partial information about the game state,” says Andrew Ng, who helped found Google’s central AI lab and is now chief scientist at Baidu. “There is no single optimal move. Instead, an AI player has to randomize its actions so as to make opponents uncertain when it is bluffing.”
Libratus did this in the extreme. It would randomize its bets in ways that are well beyond even the best players. And if that didn’t work, Brown’s nighttime algorithm would fill the hole. A finanical trader could work the same way. So could a diplomat. It’s a powerful and rather unsettling proposition: a machine that can out-bluff a human.
originally posted by: SolAquarius
Perhaps organic life and humanity is just a stepping stone in evolution and the next phase is inteligent machines maybe ET doesn't say hello because it's machine based life and is waiting for the rise of the machines on our planet.
I agree with you. Kim is experienced and way above average casino enthusiasts, yet is far away from "Kasparov of the hold em".
originally posted by: slapjacks
Dong Kim one of the best? hahaha no wonder he thought the machine could see his cards, Dong kim is at best an amateur.
originally posted by: SolAquarius
Perhaps organic life and humanity is just a stepping stone in evolution and the next phase is inteligent machines maybe ET doesn't say hello because it's machine based life and is waiting for the rise of the machines on our planet.