No free lunch theorem

If we train an decision system to work well for some set of data, there is (in principle) another set of data for which it will perform poorly. This has never seemed to us much more than a mathematical nicety. Yes, you can pick points for which it works poorly, but practical pattern recognition works well most of the time for animals and machines.