Indeed. ML seems a great part, "Magic Function Machines". People make features that can be tracked, and more features, and more features. Then they splatter th

Indeed. ML seems a great part, "Magic Function Machines". People make features that can be tracked, and more features, and more features. Then they splatter them against the wall, and hope for the best. The best, are things like "Tell what type of bird it is"... But in the end, we have no clue HOW it got those results. Just that the Mystery Magic Function + data = results. I can certainly see a middle path of being able to train a learning function, and then interrogating said function for the underlying things that make it true. Once we understand the primitives, then we can make ideal functions that do X, and do it well. Because right now, learning functions do it rather well but with exceptional overhead. And they're hard to tune without recrunching the whole dataset.