It’s easy to look at the amazing advances in information technology and robotics over the last century and be fearful about the future of the American worker. From factory floors to your grocery store checkout, countless jobs once done by humans have been handed over to computers. Budding technologies like driverless cars promise that more of us will lose our jobs to a computer in the generation ahead.

But David Autor, a leading scholar of labor markets at M.I.T., offers a somewhat more sunny way of looking at things. In a paper presented at the annual gathering of central bankers in Jackson Hole, Wyo., on Friday, Mr. Autor argues that even as computers have gotten better at rote tasks, they have progressed far less in applying common sense.

Try to teach a computer how to tell that a picture of a chair is a chair, for example, and it will be befuddled. “Both a toilet and a traffic cone look somewhat like a chair,” Mr. Autor writes, “but a bit of reasoning about their shapes vis-à-vis the human anatomy suggests that a traffic cone is unlikely to make a comfortable seat. Drawing this inference, however, requires reasoning about what an object is ‘for,’ not simply what it looks like,” a skill computers generally still lack.

Machine learning, such as Google Translate or Netflix movie recommendations, is deeply inconsistent, he argues, “uncannily accurate at times, typically, only so-so; and occasionally, unfathomable.”