Source: The New Yorker, Apr 2017
The most powerful element in these clinical encounters, I realized, was not knowing that or knowing how—not mastering the facts of the case, or perceiving the patterns they formed. It lay in yet a third realm of knowledge: knowing why.
nowing why—asking why—is our conduit to every kind of explanation, and explanation, increasingly, is what powers medical advances.
“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.
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?
The word “diagnosis,” he reminded me, comes from the Greek for “knowing apart.” Machine-learning algorithms will only become better at such knowing apart—at partitioning, at distinguishing moles from melanomas. But knowing, in all its dimensions, transcends those task-focussed algorithms. In the realm of medicine, perhaps the ultimate rewards come from knowing together.