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1Foundations of Artificial IntelligenceNeural NetworksCS472 – Fall 2007Thorsten JoachimsRestaurant Data SetLimited Expressiveness of Perceptrons•Minsky and Papert (1969) showed certain simple functions cannot be represented (e.g. Boolean XOR). Killed the field! •Mid 80th: Non-linear Neural Networks (Rumelhart et al. 1986)Neural Networks•Rich history, starting in the early forties (McCulloch and Pitts 1943).•Two views:–Modeling the brain–“Just” representation of complex functions(Continuous; contrast decision trees)•Much progress on both fronts.•Drawn interest from: Neuroscience, Cognitive science, AI, Physics, Statistics, and CS/EE.NeuronWhy Neural Nets?Motivation:Solving problems under the constraints similar to those of the brain may lead to solutions to AI problems that would otherwise be overlooked.•Individual neurons operate very slowlymassively parallel algorithms•Neurons are failure-prone devicesdistributed representations•Neurons promote approximate matchingless brittle
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