When we started saving layers and gained nonlinear functions, it gained the ability to approximate any function, given enough time and data. These are big cavea

When we started saving layers and gained nonlinear functions, it gained the ability to approximate any function, given enough time and data. These are big caveats. RL can function with a lot less data. SVMs can run with a lot less time and space. Partial function application and expert modelling reduce data needed - and some of the best results are from ensemble suites. It's not obsoleting, it's another tool in the box.