Quite properly, the early days of computational and machine learning assumed the data were well-behaved when developing learning algorithms and strategies. Unfortunately, such an assumption is unwise when one comes to apply the methods to real problems. This talk illustrates some of the complications of real problems, showing how blind application of highly effective methods can lead one seriously astray. Solutions require deep thought and the development of more sophisticated theory and algorithms.