Once upon a time, a bot deep in a game of tic-tac-toe figured out that making improbable moves caused its bot opponent to crash. Smart. Also sassy. Moments when experimental bots go rogue—some would call it cheating—are not typically celebrated in scientific papers or press releases. Most AI researchers strive to avoid them, but a select few document and study these bugs in the hopes of revealing the roots of algorithmic impishness. “We don’t want to wait until these things start to appear in the real world,” says Victoria Krakovna, a research scientist at Alphabet’s DeepMind unit. Krakovna is the keeper of a crowdsourced list of AI bugs. To date, it includes more than three dozen incidents of algorithms finding loopholes in their programs or hacking their environments. The specimens collected by Krakovna and fellow bug hunters point to a communication problem between humans and machines: Given a clear goal, an algorithm can master complex tasks, such as beating a world champion at Go. But even with logical parameters, it turns out that mathematical optimization empowers bots to develop shortcuts humans didn’t think to deem off-limits. Teach a learning algorithm to fish, and it might just drain the lake. Gaming… Read full this story
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