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Even powerful computers, like those that guide self-driving cars, can be tricked into mistaking random scribbles for trains, fences, or school buses. It was commonly believed that people couldn't see how those images trip up computers, but in a new study, Johns Hopkins University researchers show most people actually can.
The findings suggest modern computers may not be as different from humans as we think, demonstrating how advances in artificial intelligence continue to narrow the gap between the visual abilities of people and machines. The research appears today in the journal Nature Communications.
"Most of the time, research in our field is about getting computers to think like people," says senior author Chaz Firestone, an assistant professor in Johns Hopkins' Department of Psychological and Brain Sciences. "Our project does the opposite—we're asking whether people can think like computers."
Image Caption: Do you see what AI sees? Computers mistook the above images for (from left) a digital clock, a crossword puzzle, a king penguin, and an assault rifle.
What's easy for humans is often hard for computers. Artificial intelligence systems have long been better than people at doing math or remembering large quantities of information, but for decades humans have had an advantage at recognizing everyday objects such as dogs, cats, tables, or chairs. Recently, however, "neural networks" that mimic the brain have approached the human ability to identify objects, leading to technological advances supporting self-driving cars, facial recognition programs, and AI systems that help physicians spot abnormalities in radiological scans.
But even with these technological advances, there's a critical blind spot: It's possible to purposely make images that neural networks cannot correctly see. And these images, called adversarial or fooling images, are a big problem. Not only could they be exploited by hackers and cause security risks, but they suggest that humans and machines are actually seeing images very differently.
In some cases, all it takes for a computer to call an apple a car is reconfiguring a pixel or two. In other cases, machines see armadillos or bagels in what looks like meaningless television static.
"These machines seem to be misidentifying objects in ways humans never would," Firestone says. "But surprisingly, nobody has really tested this. How do we know people can't see what the computers did?"
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