Four principles of pattern recognition in decentralized systems

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Melanie Mitchell

Portland State University and Santa Fe Institute


Modern-day computers have been programmed to do some remarkable things that most people would consider to be "intelligent": beat grandmasters at chess, diagnose diseases, crack cryptographic codes, and predict trends in financial markets, to name a few examples. So what is lacking in today's artificial intelligence systems?

Most practitioners agree that the missing link is sophisticated pattern recognition, both sensory and conceptual. The ability to recognize abstract patterns is a core feature of human intelligence; the most surprising result coming out of artificial intelligence is how hard human-level pattern recognition has turned out to be for computers. While computer programs have beaten world chess champions and are better than Ph.D.s at doing probabilistic inference, they still cannot interpret visual or auditory data , or perceive abstract similarities, with anywhere near the ability of a two-year-old child. This is the paradox behind AI pioneer Marvin Minsky's dictum, "easy things are hard".

Sophisticated pattern recognition is not limited to the brain. In this talk I will describe the mechanisms underlying pattern recognition in three different biological systems, and abstract four general principles that I claim are key to adaptive pattern recognition in decentralized systems such as these. These principles deal with the representation and transmission of information, the essential role of randomness, the importance of fine-grained parallel architectures, and the interplay of bottom-up and top-down processes in all such systems. Finally, I will describe how these principles are inspiring new efforts to develop artificial intelligence systems that can achieve robust and fluid pattern recognition and learning.

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