I am a physicist by training. I have neither an applied math/stats nor a ML/comp sci background, but I am increasingly using these methods. So I am far from an

I am a physicist by training. I have neither an applied math/stats nor a ML/comp sci background, but I am increasingly using these methods. So I am far from an expert, but I also am not coming at this from some start-up perspective. ML-the-buzzword is now penetrating into all parts of STEM research and research funding. Some of the potential applications are exciting, too. My conceptualization of the field is simply that ML is the design of universal function approximators to be used as part of statistical modeling/analysis. The key insight seems to be that a complex set of adapted network architectures, together with stochastic gradient descent are unreasonably effective. Further the effectiveness is a very non-linear function of size. As far as I can tell there is not much known about why this is the case. But as far as I can tell there really isn't anything done with these models that isn't statistical inference.