Today I listened to a podcast about discussing the current status of AI in health care. One story strikes me: During my time as assistant professor at NYU, we tried ML to classify prostate cancer using Prostate indicator time series, as opposed to classical rule-of-thumb. Guess what? We could not beat the classical rule-of-thumb. In the era of AI+X, everyone in the field of data-driven modeling is claiming they are performing better than the classic old-but-gold folks, who has no distractions from cell phone/internet but only books to read. Everyone is saying their model is faster than classic computing with the same accuracy, given the underlying physics following a smooth relation. However, when it comes to industry practice, still the classical simulation plays the dominant role.
Here is my hypothesis: one must think out of the box, in order to win over the golden-forerunners. One example for the above is to instead of looking at a human-crafted indicator, how about looking at hundreds of indicators? This is related to the popular common thought of "everything can be better learned end-to-end". Here the box is looking at human crafted features. What are the boxes in my field? And how to jump out of them? 1. Classic numerical schemes, turbulence models. If I use fancy ML for just model calibration, one should not expect to outperform existing models very well.
2. Classic engineering cycle of simulation - with multiple physical fidelity, then change-of-design, then do it again.
3. Classic simulation setup. Know what physics that you are dealing with. Then figure out the equations to solve. Go search corresponding solvers or numerical schemes. Run the simulations. Visualize the results to stakeholders.
4. Classical algorithms rely on mesh and simulation is not integrated with experiments.
All in all, do not explore the same direction that old people had gone before. Either you start a completely new game (preferred) or you change the rules (weak). Concluding remarks:
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AuthorShaowu Pan Archives
December 2017
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