13-anns - Restaurant Data Set Foundations of Artificial...

Info icon This preview shows pages 1–3. Sign up to view the full content.

1 Foundations of Artificial Intelligence Neural Networks CS472 – Fall 2007 Thorsten Joachims Restaurant Data Set Limited Expressiveness of Perceptrons Minsky and Papert (1969) showed certain simple functions cannot be represented (e.g. Boolean XOR). Killed the field! Mid 80 th : 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. Neuron Why 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 slowly massively parallel algorithms Neurons are failure-prone devices distributed representations Neurons promote approximate matching less brittle
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.