Choong-am Dojang is far from a typical Korean school. Its best pupils will never study history or math, nor will they receive traditional high-school diplomas. The academy, which operates above a bowling alley on a narrow street in northwestern Seoul, teaches only one subject: the game of Go, known in Korean as baduk and in Chinese as wei qi. Each day, Choong-am's students arrive at nine in the morning, find places at desks in a fluorescent-lit room, and play, study, memorize, and review games--with breaks for cafeteria meals or an occasional soccer match--until nine at night. Choong-am, which is the product of a merger between four top Go academies, is currently the biggest of a handful of dojangs in South Korea.
What do the games of chess, Jeopardy!, Go, Texas Hold'em, and StarCraft have in common? In each of these competitive arenas, an AI has resoundingly beat the best human players in the world. These victories are astounding feats of artificial intelligence--yet they've become almost humdrum. At the Allen Institute for Artificial Intelligence (AI2), in Seattle, researchers set out to do something different. Their AllenAI collaborates with a human player in a Pictionary-style drawing and guessing game, which is won through human-AI cooperation.
It is no mystery why poker is such a popular pastime: the dynamic card game produces drama in spades as players are locked in a complicated tango of acting and reacting that becomes increasingly tense with each escalating bet. The same elements that make poker so entertaining have also created a complex problem for artificial intelligence (AI). A study published today in Science describes an AI system called DeepStack that recently defeated professional human players in heads-up, no-limit Texas hold'em poker, an achievement that represents a leap forward in the types of problems AI systems can solve. DeepStack, developed by researchers at the University of Alberta, relies on the use of artificial neural networks that researchers trained ahead of time to develop poker intuition. During play, DeepStack uses its poker smarts to break down a complicated game into smaller, more manageable pieces that it can then work through on the fly.
In conjunction with the Association for the Advancement of Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research.
In March of last year, Google's (Menlo Park, California) artificial intelligence (AI) computer program AlphaGo beat the best Go player in the world, 18-time champion Lee Se-dol, in a tournament, winning 4 of 5 games.1 At first glance this news would seem of little interest to a pathologist, or to anyone else for that matter. After all, many will remember that IBM's (Armonk, New York) computer program Deep Blue beat Garry Kasparov--at the time the greatest chess player in the world--and that was 19 years ago. The rules of the several-thousand-year-old game of Go are extremely simple. The board consists of 19 horizontal and 19 vertical black lines.