Game theory <\/p>\n
After the humans' gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but the humans were feeling worn out. When I chatted with Kim and Les in their hotel bar after the penultimate day's play, the mood was understandably somber. <\/p>\n
\"Yesterday, I think, I played really bad,\" Kim said, rubbing his eyes. \"I was pretty upset, and I made a lot of big mistakes. I was pretty frustrated. Today, I cut that deficit in half, but it's still probably unlike for me to win.\" At this point, with so little time left and such a large gap to close, their plan was to blitz through the remaining hands and complete the task in front of them. <\/p>\n
For these world-class players, beating Libratus had gone from being a real possibility to a pipe dream in just a matter of days. It was obvious that the AI was getting better at the game over time, sometimes by leaps and bounds that left Les, Kim, McAulay and Chou flummoxed. It wasn't long before the pet theories began to surface. Some thought Libratus might have been playing completely differently against each of them, and others suspected the AI was adapting to their play styles while they were playing. They were wrong. <\/p>\n
As it turned out, they weren't the only ones looking back at the past day's events to concoct a game plan for the days to come. Every night, after the players had retreated to their hotel rooms to strategize, the basement of the Supercomputing Center continued to thrum. Libratus was busy. Many of us watching the events unfold assumed the AI was spending its compute cycles figuring out ways to counter the players' individual play styles and fight back, but Professor Sandholm was quick to rebut that idea. Libratus isn't designed to find better ways to attack its opponents; it's designed to constantly fortify its defenses. Remember those major Libratus components I mentioned? This is the last, and perhaps most important, one. <\/p>\n
\"All the time in the background, the algorithm looks at what holes the opponents have found in our strategy and how often they have played those,\" Sandholm told me. \"It will prioritize the holes and then compute better strategies for those parts, and we have a way of automatically gluing those fixes into the base strategy.\" <\/p>\n
If the humans leaned on a particular strategy -- like their constant three-bets -- Libratus could theoretically take some big losses. The reason those attacks never ended in sustained victory is because Libratus was quietly patching those holes by using the supercomputer in the background. The Great Wall of Libratus was only one reason the AI managed to pull so far ahead. Sandholm refers to Libratus as a \"balanced\" player that uses randomized actions to remain inscrutable to human competitors. More interesting, though, is how good Libratus was at finding rare edge cases in which seemingly bad moves were actually excellent ones. <\/p>\n
\"It plays these weird bet sizes that are typically considered really bad moves,\" Sandholm explained. These include tiny underbets, like 10 percent of the pot, or huge overbets, like 20 times the pot. Donk betting, limping -- all sorts of strategies that are, according to the poker books and folk wisdom, bad strategies.\" To the players' shock and dismay, those \"bad strategies\" worked all too well. <\/p>\n<\/p>\n
On the afternoon of January 30th, Libratus officially won the second Brains vs AI competition. The final margin of victory: $1,766,250. Each of the players divvied up their $200,000 spoils (Dong Kim lost the least amount of money to Libratus, earning about $75,000 for his efforts), fielded questions from reporters and eventually left to decompress. Not much had gone their way over the past 20 days, but they just might have contributed to a more thoughtful, AI-driven future without even realizing it. <\/p>\n
Through Libratus, Sandholm had proved algorithms could make better, more-nuanced decisions than humans in one specific realm. But remember: Libratus and systems like it are general-purpose intelligences, and Sandholm sees plenty of potential applications. As an entrepreneur and negotiation buff, he's enthusiastic about algorithms like Libratus being used for bargaining and auctions. <\/p>\n
\"When the FCC auctions spectrum licenses, they sell tens of billions of dollars of spectrum per auction, yet nobody knows even one rational way of bidding,\" he said. \"Wouldn't it be nice if you had some AI support?\" <\/p>\n
But there are bigger problems to tackle ones that could affect all of us more directly. Sandholm pointed to developments in cybersecurity, military settings and finance. And, of course, there's medicine. <\/p>\n
\"In a new project, we're steering evolution and biological adaptation to battle viral and bacterial infections,\" he said. \"Think of the infection as the opponent and you're taking sequential actions and measurements just like in a game.\" Sandholm also pointed out that such algorithms could even be used to more helpfully manage diseases like cancer, both by optimizing the use of existing treatment methods and maybe even developing new ones. <\/p>\n
Jason, Dong, Daniel and Jimmy might have lost this prolonged poker showdown, but what Sandholm and his contemporaries have learned in the process could lead to some big wins for humanity. <\/p>\n
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How an AI took down four world-class poker pros - Engadget<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Game theory After the humans' gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but the humans were feeling worn out. Continue reading