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Abstract:
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Human intelligence is increasingly being challenged by the artificial one it created. We are confused-and troubled-by what AI can, should, or will do, or even by its meaning (Michael Jordan, Harvard DS Review). Performance-driven methods are becoming more popular, be they labeled as AI, or ML, or DS. Yet, procedures without theoretical insights on how, why, and when they work are a frustration of our profession. Deep learning without deep understanding highlights the dilemma. Are we out of depth, out of imagination, or simply out of breath? How do we cultivate and inspire more “deep minds’’ for our profession, to turn our collective frustration into fruition? Where is our “3-Body Problem” to push beyond our current asymptopia for imagination? Or if 3 is too small a number for the Big-Data frenzy, what are our “Hilbert’s 23 Problems” to refuel our deep (re)search of principles? You don’t need a deep mind to decipher my title; but to form a 2020 vision to realize what it implies, we need a theoretical revolution no smaller than the calculus revolution. I dare say that such a revolution is well under way. The question remains: do you want to be Newton or Leibnitz?
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