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What is Artificial Intelligence?

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.

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What is Artificial Intelligence?

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  1. 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

  2. Elements of AI • Natural Language Processing • Robotics • Perceptive Systems (Vision) • Expert Systems

  3. 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

  4. Decision Tree

  5. Depth First Search

  6. Breath First Search

  7. 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

  8. Expert System Successes • XCON - configures systems for DEC • Prospector - an mining expert • MYCIN - infectious blood diseases • EMYCIN - Empty MYCIN

  9. 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

  10. 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

  11. 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.

  12. An Artificial Neural Network w w w w w w Inputs Output Hidden

  13. 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

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