The article is weird but also interesting to me at the same time. My background is in more traditional statistical modeling. The adversarial networks paradigm

The article is weird but also interesting to me at the same time. My background is in more traditional statistical modeling. The adversarial networks paradigm is really fascinating to me because it's a new way to think about model fit problems. It may turn out to be the same as some traditional model fit statistics approaches, but they're usually not thought of as competitive, which seems to open up a new way of thinking of it. At some point in the author's discussion of game theory, I began thinking that if DL does go that route, it will eventually rediscover Bayesian statistics, which can be derived from decision-theoretic principles. It seems like many things in DL have parallels in traditional statistical theory, but are expressed in a mechanistic way. I guess my broader point is that I had the opposite reaction as a lot of people: rather than wondering how game theory might be relevant, to me it seemed inevitable, given the important role it plays in basic derivations of major inferential paradigms. Those derivations usually leave the mechanisms of decision theory a black box, and in DL the making the contents of the box is the focus.