| There's a ML group at Fermilab just outside Chicago working on ML applications in high energy physics and astrophysics. https://computing.fnal.gov/machine-learning/ One of the "AI" applications I remember seeing -- potentially applicable outside physics -- involved using CNNs to read a 2D graph (as in graphical plot, not G = (V,E)) in order to visually detect certain patterns/aberration. (probably many physics groups around the world are doing the same) At first glance this sounds kind of silly and trivial -- one might say, why not just detect those patterns from the data arrays directly? Instead of from a bitmap image of a plot of the data? Unfortunately some patterns are contextual. A trained human eye can detect them easily, while writing a foolproof mathematical algorithm is difficult: e.g. it has to pick out the pattern, apply a bunch of exclusion rules etc. (One instance of this, for example, is an old mechanic telling you what's going on under the hood just from listening the vibrations of a car, while a traditional DSP algorithm might not be able to do it as reliably because it hasn't seen all the patterns and contexts in which those sounds arise.) This is a domain where neural networks/transfer learning really shines. It can capture "intuition" by learning the surrounding context, rather than relying on handcrafted features. So Fermilab has an AI algorithm that looks at millions of graphs via a CNN, which replicates the work of thousands of human physicists looking for patterns. We've already seen examples of this in radiology. |