Intelligence is not Artificial

by piero scaruffi

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(These are excerpts from my book "Intelligence is not Artificial")

Footnote: The Problem with Pattern Recognition


Neural networks are very good at discovering a pattern and drawing conclusions from it. If all objects of a certain shape are called “apple”, then a new object of approximately that shape is most likely also an “apple”. If people take the umbrella when it rains, and it starts raining, then most likely you too will take an umbrella. The problem with pattern recognition is, quite simply, that it often leads to the wrong conclusions. For example, during World War II the British analyzed the bombers returning from bombing campaigns over Germany and decided to reinforce them where they were taking most bullets. The Hungarian-born statistician Abraham Wald at Columbia University humbly disagreed: those aircrafts were the ones that had survived and returned to base. The aircrafts that had been downed by the enemy did not return to base and therefore were not analyzed to recognize the most important pattern: that bullets in the other parts of the aircraft caused the aircraft to crash. The original conclusion was based on the wrong assumption that German anti-aircraft fire hit only the parts of the aircraft that had bullet holes, when in fact it is logical to assume that anti-aircraft fire hits all parts of the aircraft randomly. This is known as an “observer selection effect”: when analyzing patterns, you are biased by what you see, and you may be ignoring the most important factors because you don’t see them. You need to complement your analysis of the pattern with logical thinking; and logical thinking may show you that the pattern you see is actually misleading. (Trivia: ironically, Wald died in an air crash).

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