This week, Google’s AlphaGo defeated a top-ranked Chinese Go player, Ke Jie, in the first match of a three-game series underway in China. The victory by AlphaGo — created by DeepMind, which Google bought 3 years ago — follows its headline-grabbing triumph over South Korean Go star Lee Sedol last year. Go is a two-player ancient Chinese board game played by more 40 million people worldwide.
This battle of man versus machine naturally strikes the public’s fancy and recalls Deep Blue‘s epic 1997 win over Garry Kasparov, a world chess champion. Deep Blue was an IBM supercomputer that relied on brute force computation. AlphaGo, meanwhile, uses machine learning to get better and faster at avoiding risks to narrowly achieve one thing: winning a game of Go. “Last year, it was still quite humanlike when it played,” Ke said after his loss. “But this year, it became like a god of Go.”
Even while the AI capabilities of AlphaGo surpass those of Deep Blue (or Watson), they share a common limitation. They’re examples of artificial narrow intelligence that come nowhere near the generalized artificial intelligence expected (or feared) by so many. Chess? Check. Self-driving cars? Check. Go? Check. HBO’s Westworld? Not yet, if ever.
AlphaGo’s win shows how far narrow AI has come and of how far away general AI remains. Games two and three are scheduled for today and Saturday and can be watched via live stream.
For AI coverage, send news tips to Khari Johnson and Blair Hanley Frank, and direct guest post submissions to John Brandon. To receive this information in your inbox every Thursday morning, subscribe to AI Weekly — and be sure to bookmark our AI Channel.
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