Fears about robot overlords are (perhaps) premature

In "Artificial Intelligence: A Guide for Thinking Humans," Melanie Mitchell, a computer science professor at Portland State University, tells the story, one of many, of a graduate student who had seemingly trained a computer network to classify photographs according to whether they did or did not contain an animal. When the student looked more closely, however, he realized that the network was not recognizing animals but was instead putting images with blurry backgrounds in the "contains an animal" category. The nature photos that the network had been trained on typically featured both an animal in focus in the foreground and a blurred background. The machine had discovered a correlation between animal photos and blurry backgrounds. Mitchell notes that these types of misjudgments are not unusual in the field of AI. "The machine learns what it observes in the data rather than what you (the human) might observe," she explains.