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| Melanie Mitchell
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| Portland State University and Santa Fe Institute
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| Abstract:
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| Modern-day computers have been programmed to do some remarkable things
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| that most people would consider to be "intelligent": beat grandmasters
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| at chess, diagnose diseases, crack cryptographic codes, and predict
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| trends in financial markets, to name a few examples. So what is
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| lacking in today's artificial intelligence systems?
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| Most practitioners agree that the missing link is sophisticated
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| pattern recognition, both sensory and conceptual. The ability to
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| recognize abstract patterns is a core feature of human intelligence;
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| the most surprising result coming out of artificial intelligence is
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| how hard human-level pattern recognition has turned out to be for
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| computers. While computer programs have beaten world chess champions
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| and are better than Ph.D.s at doing probabilistic inference, they
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| still cannot interpret visual or auditory data , or perceive abstract
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| similarities, with anywhere near the ability of a two-year-old child.
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| This is the paradox behind AI pioneer Marvin Minsky's dictum of "easy
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| things are hard".
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| Sophisticated pattern recognition is not limited to the brain. In
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| this talk I will describe the mechanisms underlying pattern
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| recognition in three different biological systems, and abstract four
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| general principles that I claim are key to adaptive pattern
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| recognition in decentralized systems such as these. These principles
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| deal with the representation and transmission of information, the
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| essential role of randomness, the importance of fine-grained parallel
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| architectures, and the interplay of bottom-up and top-down processes
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| in all such systems. Finally, I will describe how these principles
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| are inspiring new efforts to develop artificial intelligence systems
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| that can achieve robust and fluid pattern recognition and learning.
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| Back to [http://www.santafe.edu/events/workshops/index.php/Workshop_Program Program]
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