09 October 2017

The New Yorker: “A.I. versus M.D.”

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.

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.

Siddhartha Mukherjee

Interesting overview of the promises and challenges of applying deep-learning techniques in medical diagnosis. There are certainly high expectations in the area, most notably IBM’s Watson, but recent reporting shows it is still far from fulfilling the promises. As I (and others) commented before, the big challenge for AI in any area is evolving past basic pattern recognition (factual knowledge) to a deeper understanding of the world, knowing how and why things work.

A.I. vs. M.D. cover

I’m interested in magnifying human ability, Thrun said, when I asked him about the impact of such systems on human diagnosticians. Look, did industrial farming eliminate some forms of farming? Absolutely, but it amplified our capacity to produce agricultural goods. Not all of this was good, but it allowed us to feed more people. The industrial revolution amplified the power of human muscle. When you use a phone, you amplify the power of human speech. You cannot shout from New York to California—Thrun and I were, indeed, speaking across that distance—and yet this rectangular device in your hand allows the human voice to be transmitted across three thousand miles. Did the phone replace the human voice? No, the phone is an augmentation device. The cognitive revolution will allow computers to amplify the capacity of the human mind in the same manner. Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful. Thrun insists that these deep-learning devices will not replace dermatologists and radiologists. They will augment the professionals, offering them expertise and assistance.

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