A powerful amateur Go player has defeated a high-level AI system after exploiting a weakness discovered by a second computer.
Kellin Pelrine, a player who is one level below the top amateur ranking, beat the machine by taking advantage of a previously unknown vulnerability that had been identified by another computer. But the head-to-head battle in which he won 14 of 15 games was made without direct computer support. It’s a rare Go win for humans since AlphaGo’s win in 2016, which helped lay a solid foundation for the current AI popularity.
Pelrine’s victory was made possible by a research company called FAR AI, which developed a program to check KataGo for weaknesses. After playing over a million games, it was able to find a weakness that could be exploited by a decent amateur player. Pelrine used the same method to beat Leela Zero, another top Go AI.
Here’s how it works: The goal is to create a large “loop(圈)” of stones(棋子) to surround an opponent’s group in a circle, and then distract the computer by making moves in other areas of the board. Even when his group was almost surrounded, the computer didn’t notice the strategy. “As a human, it would be pretty easy to spot,” Pelrine said, as the encircling checkers(环绕的棋子) stand out clearly on the game board. The bug shows that AI systems can’t really “think” beyond their training, so they often do things that look incredibly stupid to humans.
The discovery of a weakness in some of the most advanced Go-playing machines points to a fundamental weakness in the deep-learning systems that underpin(支撑) today’s most advanced AI, said Stuart Russell, a computer science professor at the University of California, Berkeley. The systems can “understand” only specific situations they have been exposed to in the past and are unable to generalize in a way that humans find easy, he added. “It shows once again we’ve been too quick to assume that machines have superhuman levels of intelligence.”
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