On the contrary, I am expecting ML to evaluate all these seemingly random node weights in neural networks and come up with solid algorithmic explanation of what they really mean in step by step procedural perspective. Deep understanding :)
ML seems to mostly be a parameterisation of a "traditional" algorithm by a bunch of variables-to-be-optimised. How are these "AI techniques" generally applicable to "traditional" algorithm design?
