[_0w8t]

Recently I was involved in calibrating a thermal infrared camera at work. A colleague out of curiosity tried to use machine learning and ended up with model containing hundreds of parameters (weights). Yet it was not better than a trivial model using a Planck integral (based on simple assumption about how things worked) and a linear regression (to account for systematic errors), 2 parameters in total. And the simple model completely ignored time dependencies assuming thermal stabilization which could be accounted using a couple of extra parameters based on a typical solution of heat transfer equation. Initially it was puzzling as I thought that Planck integral should be easily modeled with basic blocks of ML models. But then I realized that Planck function in our case was integrated over a complex profile of an infrared filter and may not be something that is easy to capture within ML.

[2sk21]

ML is a blunt instrument in such situations. I think your example illustrates the point of the original article very nicely.