A.I. researchers are making progress on a long-term goal: giving their programs the kind of knowledge we take for granted.

Afew years ago, a computer scientist named Yejin Choi gave a presentation at an artificial-intelligence conference in New Orleans. On a screen, she projected a frame from a newscast where two anchors appeared before the headline “cheeseburger stabbing.” Choi explained that human beings find it easy to discern the outlines of the story from those two words alone. Had someone stabbed a cheeseburger? Probably not. Had a cheeseburger been used to stab a person? Also unlikely. Had a cheeseburger stabbed a cheeseburger? Impossible. The only plausible scenario was that someone had stabbed someone else over a cheeseburger. Computers, Choi said, are puzzled by this kind of problem. They lack the common sense to dismiss the possibility of food-on-food crime.

For certain kinds of tasks—playing chess, detecting tumors—artificial intelligence can rival or surpass human thinking. But the broader world presents endless unforeseen circumstances, and there A.I. often stumbles. Researchers speak of “corner cases,” which lie on the outskirts of the likely or anticipated; in such situations, human minds can rely on common sense to carry them through, but A.I. systems, which depend on prescribed rules or learned associations, often fail.

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