Machine Learning Widens the Gap Between Knowledge and Understanding
And gives us the tools for our next evolutionary step
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The program “Deep Patient” doesn’t know that being knocked on the head can make us humans dizzy or that diabetics shouldn’t eat 5-pound Toblerone bars in one sitting. It doesn’t even know that the arm bone is connected to the wrist bone. All it knows is what researchers fed it in 2015: the medical records of 700,000 patients as discombobulated data, with no skeleton of understanding to hang it all on.
Yet, after analyzing the relationships among these blind bits, Deep Patient was not only able to diagnose the likelihood of individual patients developing particular diseases, it was in some instances more accurate than human physicians, including about some diseases that until now have utterly defied predictability.
Deep learning
If you ask your physician why Deep Patient thinks it might be wise for you to start taking statins or undergo preventive surgery, your doctor might not be able to tell you, but not because she’s not sufficiently smart or technical. Deep Patient is a type of artificial intelligence called deep learning (itself a type of machine learning) that finds relationships among pieces of data, knowing nothing about what that data represents.
From this, it assembles a network of information points, each with a weighting that determines how likely the points it’s connected to will “fire,” which in turn affects the points they’re connected to, the way firing a neuron in a brain would. To understand why Deep Patient thinks, say, that there’s a 72 percent chance that a particular patient will develop schizophrenia, a doctor would have to internalize those millions of points and each of their connections and weightings. But there are just too many, and they are in relationships that are too complex. You, as a patient, are, of course, free to reject Deep Patient’s probabilistic conclusions, but you do so at a risk, for the reality is that we use “black box” diagnostic systems that cannot explain their predictions because, in some cases, they are significantly more accurate than human doctors.
This is the future, and not just for medicine. Your phone’s navigation system, type-ahead…
