Current machine learning is largely based upon statistics.
Training data is viewed as evidence for and against the various hypothesized concepts.
The member of the hypothesis space that is most probable, given the training data, is the desired output concept. Machine learning would seem to hold great promise for future computer applications. With it, an application could adapt to its deployment context. With such training data, software might tune itself to its user, networks might adjust their routing policies to maximize effective bandwidth, automatically users might be confidently recognized by their peculiar typing and mouse use rather than by today's problematic password login procedures. Unfortunately, statistics assures us that such benefits cannot be realized using current machine learning approaches.