Source: MIT Technology Review, Jan 2016
Google has taken a brilliant and unexpected step toward building an AI with more humanlike intuition, developing a computer capable of beating even expert human players at the fiendishly complicated board game Go.
Go is far more challenging for computers than, say, chess for two reasons: the number of potential moves each turn is far higher, and there is no simple way to measure material advantage. A player must therefore learn to recognize abstract patterns in hundreds of pieces placed across the board. And even experts often struggle to explain why a particular position seems advantageous or problematic.
Michael Bowling, a professor of computer science at the University of Alberta in Canada who recently developed a program capable of beating anyone at heads-up limit poker, was also excited by the achievement. He believes that the approach should indeed prove useful in many areas where machine learning is applied. “A lot of what we would traditionally think of as human intelligence is built around pattern matching,” he says. “And a lot of what we would think of as learning is having seen these patterns in the past, and being able to realize how they connect to a current situation.”