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Transcript
What is Artificial Intelligence? • AI is the effort to develop systems that can behave/act like humans. • Turing Test • The problem = unrestricted domains – – – – human intelligence vastly complex and broad associations, metaphors, and analogies common sense conceptual frameworks Elements of AI • • • • Natural Language Processing Robotics Perceptive Systems (Vision) Expert Systems How are Machines Intelligent? • Constrained Heuristic Search – How do you play chess? • first move = 20 possible • second move = 400 possible • 7th move = 1,280,000,000 possible – Depth First vs. Breath First Searching – Ability to Learn Decision Tree Depth First Search Breath First Search Expert Systems • Capture knowledge of an expert. • Represent Knowledge as a – rule base • if then rules – semantic net • hierarchy – frames • shared characteristics, IS-A relationships Expert System Successes • • • • XCON - configures systems for DEC Prospector - an mining expert MYCIN - infectious blood diseases EMYCIN - Empty MYCIN Elements of Expert System Shell • Knowledge Base – rules • Working Memory – facts of current case • Inference Engine – applies rules using current set of facts • Explanation Facility • CLIPS Neural Networks & The Brain • Base on architecture of human brain – – – – – Neurons connected by axons & dendrites 100 billion neurons 1,000 dendrites per neuron 100,000 billion synapses 10 million billion interconnectons per second How a Neuron Works Sending impulses to next level of neurons. Impulses come from other neurons. When sum of inputs reaches a threshold, neuron fires. An Artificial Neural Network w w w w w w Inputs Hidden Output Neural Networks, NN • NNs learn by using a training set and adjusting the weights on each connection. • NNs do not have to be “told” explicit relationship rules. • NNs can work with partial inputs. • NNs cannot explain their results. • NNs can take a long time to train. • A NN demonstration