machine-learning theory and practice

Today I got invited to be on a panel discussion (hosted by Soledad Villar of JHU) with Alberto Bietti (Flatiron) about the theory and practice of machine learning. It was great! We talked about why ML works for scientific applications, and Bietti said something (obvious maybe) that I loved: Maybe ML only works because of properties of the data. That is, maybe when we are analyzing ML methods we are looking in the wrong place, and we should be analyizing the data to which they are successfully applied? I made fun of interpretation in ML, and that led to interesting comments from both Bietti and the audience. Several audience members suggested taking something more like a causal approach to interpretation: How does the method work under interventions or in conditional situations? That's interesting; it isn't what a physicist would consider interpetation, but it might be sufficient in many cases.