Siddhartha Mukherjee in The New Yorker:
Explanations run shallow and deep. You have a red blister on your finger because you touched a hot iron; you have a red blister on your finger because the burn excited an inflammatory cascade of prostaglandins and cytokines, in a regulated process that we still understand only imperfectly. Knowing why—asking why—is our conduit to every kind of explanation, and explanation, increasingly, is what powers medical advances. Hinton spoke about baseball players and physicists. Diagnosticians, artificial or human, would be the baseball players—proficient but opaque. Medical researchers would be the physicists, as removed from the clinical field as theorists are from the baseball field, but with a desire to know “why.” It’s a convenient division of responsibilities—yet might it represent a loss? “A deep-learning system doesn’t have any explanatory power,” as Hinton put it flatly. A black box cannot investigate cause. Indeed, he said, “the more powerful the deep-learning system becomes, the more opaque it can become. As more features are extracted, the diagnosis becomes increasingly accurate. Why these features were extracted out of millions of other features, however, remains an unanswerable question.” The algorithm can solve a case. It cannot build a case.
Yet in my own field, oncology, I couldn’t help noticing how often advances were made by skilled practitioners who were also curious and penetrating researchers. Indeed, for the past few decades, ambitious doctors have strived to be at once baseball players and physicists: they’ve tried to use diagnostic acumen to understand the pathophysiology of disease. Why does an asymmetrical border of a skin lesion predict a melanoma? Why do some melanomas regress spontaneously, and why do patches of white skin appear in some of these cases? As it happens, this observation, made by diagnosticians in the clinic, was eventually linked to the creation of some of the most potent immunological medicines used clinically today. (The whitening skin, it turned out, was the result of an immune reaction that was also turning against the melanoma.) The chain of discovery can begin in the clinic. If more and more clinical practice were relegated to increasingly opaque learning machines, if the daily, spontaneous intimacy between implicit and explicit forms of knowledge—knowing how, knowing that, knowing why—began to fade, is it possible that we’d get better at doing what we do but less able to reconceive what we ought to be doing, to think outside the algorithmic black box?