Must read post by David Berreby on why artificial intelligence is already weirdly inhuman.
Artificial intelligence has been conquering hard problems at a relentless pace lately. In the past few years, an especially effective kind of artificial intelligence known as a neural network has equaled or even surpassed human beings at tasks like discovering new drugs, finding the best candidates for a job, and even driving a car. Neural nets, whose architecture copies that of the human brain, can now—usually—tell good writing from bad, and—usually—tell you with great precision what objects are in a photograph. Such nets are used more and more with each passing month in ubiquitous jobs like Google searches, Amazon recommendations, Facebook news feeds, and spam filtering—and in critical missions like military security, finance, scientific research, and those cars that drive themselves better than a person could.
Not knowing why a machine did something strange leaves us unable to make sure it doesn’t happen again.
Neural nets sometimes make mistakes, which people can understand. (Yes, those desks look quite real; it’s hard for me, too, to see they are a reflection.) But some hard problems make neural nets respond in ways that aren’t understandable. Neural nets execute algorithms—a set of instructions for completing a task. Algorithms, of course, are written by human beings. Yet neural nets sometimes come out with answers that are downright weird: not right, but also not wrong in a way that people can grasp. Instead, the answers sound like something an extraterrestrial might come up with.
The problem isn’t just that machines think differently from people. It is that people can’t figure out why.
These oddball results are rare. But they aren’t just random glitches. Researchers have recently devised reliable ways to make neural nets produce such eerily inhuman judgments. That suggests humanity shouldn’t assume our machines think as we do. Neural nets sometimes think differently. And we don’t really know how or why.
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