It's not clear that the people with the best priors lost -- the people with a great deal of prior information (the specialists) are also often still using SAS/e

It's not clear that the people with the best priors lost -- the people with a great deal of prior information (the specialists) are also often still using SAS/etc. and linear regression / classical neural nets / etc. Many of the people doing Predictive Analytics (A term I hate) in large organizations have spent 20 years gaining domain knowledge and not keeping up on state-of-the-art methods (there are only so many hours in the day). I mean, hell, Andrew Gelman's multi-level modeling book is considered pretty advanced to this day, which is hard to understand as a machine learning person. The challenge with black-box models is "how to extend them" -- reasons I still have a great deal of faith in the power of Bayesian methods and the utility of joint inference. I think modern methods have really commoditized the predict-y-from-x problem, but there's a lot more to it than that. The future, in my opinion, is letting you specify more, richer, prior knowledge, to solve more interesting problems. That's why I work with bayesian nonparametrics, and am excited about probabilistic programming: http://probabilistic-programming.org/wiki/NIPS*2012_Workshop http://probabilistic-programming.org/wiki/NIPS*2012_Workshop