## ABSTRACT

The theoretical work by Weiner and others on the spectral analysis of stationary time series penetrated statistics following Tukey's heuristic work on estimation of the spectrum. In refereeing papers for NIPS the author was struck by the growing emphasis on mathematical theory. Mathematical theory is not critical to the development of machine learning. In machine learning, the current panacea is a sigmoid network fitted using backpropagation. The pi-method, for approximating functions using noisy data, was suggested by results in mathematical approximation theory. In spite of intense activity, none of the work has had any effect on the day-to-day practice of statistics, or even on present-day theory. The useful theories was not meant to be inclusive, but even a more inclusive list would be very short. A possible reason is that it is difficult to formulate reasonable analytic models for complex data.