One problem in the field of statistics has been that everyone wants to be a theorist. Part of this is envy - the real sciences are based on mathematical theory. In the universities for this century, the glamor and prestige has been in mathematical models and theorems, no matter how irrelevant.I love this quote because it highlights the divide between the practical and the elegant or sophisticated. Data mining and predictive analytics are "low-brow" sciences, empirical, and practical. That doesn't mean that the mathematics aren't important; they are very much so. But while we wait for the elegances of a theory to trickle down to us, we still need solutions.
In courses I teach, one of my objectives is to take the mathematics of the algorithms and translate the practical meaning of what they do into understandable pieces so that practitioners can manipulate learning rates and hidden units, gini and two-ing, radial kernels and polynomials kernels. Understanding backprop isn't important to most practitioners, but understanding how one can improve the performance of backprop is very much a key topic for practitioners.
We need more Breimans to pave the way toward practical innovations in predictive modeling.