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The researchers culled tens of thousands of photos from an online-dating site, then used an off-the-shelf computer model to extract users’ facial characteristics—both transient ones, like eye makeup and hair color, and more fixed ones, like jaw shape. Then they fed the data into their own model, which classified users by their apparent sexuality. When shown two photos, one of a gay man and one of a straight man, Kosinski and Wang’s model could distinguish between them eighty-one per cent of the time; for women, its accuracy dropped slightly, to seventy-one per cent.

So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?”