Archive for the ‘computer science’ Category

Dealing with ideas as squishy as pattern recognition, learning, and analogy making

December 25, 2007

In a previous post I described the differences between induction and deduction: Humans are relatively good at induction and relatively poor at deduction; computers are just the opposite

The discussion of induction vs deduction is quite interesting and relevant to most everyone.  Here are further ideas from the book The Origin of Wealth by Eric Beinhocker:

Deduction only works on very well-defined problems such as chess moves; for deduction to work, the problem cannot have any information missing or ambiguity.  Deduction is thus a powerful method of reasoning, but inherently brittle.

While induction is more error prone, it is also more flexible and better suited for incomplete and ambiguous information that the world throws at us.  It thus makes evolutionary sense that we would be built this way.

Through induction, humans are able to deal with ideas as squishy as pattern recognition, learning, and analogy making.

Note that models of induction featuring pattern recognition and learning have become a staple of computer science research, and many of these models are used in practical applications that range from recognizing the faces of terrorists at airports, to recognizing fraudulent charge patterns on credit cards.