If someone showed you a single character from an unfamiliar alphabet and asked you to copy it onto a sheet of paper, you could probably do it. A computer, though, would be stumped—even if it were equipped with state-of-the-art deep-learning algorithms such as those that Google uses to categorize photographs. These machine-learning systems require training on enormous sets of data to make even rudimentary distinctions between images. That may be fine for machines in the post office that sort letters by zip code. But for subtler problems, such as translating between languages on the fly, an approach learned from a handful of examples would be much more efficient.