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Unveiling Artificial Intelligence: From Expert Systems to Neural Networks

Explore the realms of AI, from expert systems capturing human knowledge to intricate neural networks mimicking the brain's complexity. Unravel the magic behind machine intelligence and its evolution over time.

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Unveiling Artificial Intelligence: From Expert Systems to Neural Networks

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Presentation Transcript

  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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