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Artificial Intelligence

Artificial Intelligence

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Artificial Intelligence

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  1. Artificial Intelligence

  2. Definition: Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.

  3. According to this test, a computer could be considered to be thinking only when a human interviewer, conversing with both an unseen human being and an unseen computer, could not determine which is which. The Turing Test

  4. Artificial Real Items Airplanes Birds Silk Flowers Flowers Artificial Snow Snow More on AI

  5. - Expert Systems - Natural Language Processor - Speech Recognition - Robotics - Computer Vision - Intelligent Computer-Aided Instruction - Data Mining - Genetic Algorithms AI Major Areas

  6. Artificial vs. Natural (Human) Intelligence

  7. 1. AI is permanent 2. AI offers ease of duplication 3. AI can be less expensive than natural intelligenc 4. AI is consistent 5. AI can be documented AI Advantages

  8. 1. Natural intelligence is creative. 2. Natural intelligence uses sensory experience directly, whereas most AI systems must work with symbolic input. 3. Human reasoning is able to make use at all times of a very wide context experience and bring that to bear on individual problems, where as AI systems typically gain their power by having a very narrow domain. Natural Intelligence Advantages

  9. - Recognize and formulate the problem - Solve the problem fairly quickly - Explain the solution - Learn from experience - Restructure knowledge - Break rules - Determine relevance - Degrade gracefully Characteristics of a Human Experts

  10. It is estimated that a world-class expert, such as a chess grandmaster, has 50,000 to 100,000 chunks of heuristic information about his/her specialty. On the average, it takes at least 10 years to acquire 50,000 rules. What Do Experts Know?

  11. Expert Systems

  12. 1. Knowledge Acquisition 2. Knowledge Base 3. Inference Engine 4. User Interface 5. Explanation Facility 6. Knowledge Refining System Expert Systems Components

  13. Different Categories of Expert Systems Category Problem Addressed Interpretation Inferring situation description from observations Prediction Inferring likely consequences of given situations Diagnosis Inferring systems malfunctions from observations Design Configuring objects under constraints Planning Developing plans to achieve goals Monitoring Comparing observations to plan vulnerabilities Debugging Prescribing remedies for malfunctions Repair Executing a plan to administer a prescribed remedy Control Interpreting, predicting, repairing, and monitoring system behavior

  14. What Tasks Are ES Right For? - Payroll, Inventory - Simple Tax Returns - Database Management - Mortgage Computation - Regression Analysis - Facts are Known - Expertise is Cheap Too Easy - Use Conventional Software

  15. What Tasks Are ES Right For? - Diagnosing and Troubleshooting - Analyzing Diverse Data - Production Scheduling - Equipment Layout - Advise on Tax Shelter - Facts are known but not precisely - Expertise is expensive but available Just Right

  16. What Tasks Are ES Right For? - Designing New Tools - Stock Market Forecast - Discovering New Principles - Common Sense Problems - Requires Innovation or Discovery - Expertise is not available Too Hard - Requires Human Intelligence

  17. - Knowledge is not always readily available. - Expertise is hard to extract from humans. - ES work well only in a narrow domain. - The approach of each expert to problem under consideration may be different, yet correct. Problems and Limitations of Expert Systems

  18. - The task does not require common sense. - The task requires only cognitive, not physical, skills. - There is an expert who is willing to cooperate. - The experts involved can articulate their methods of problem solving. - The task is not too difficult. - The task is well understood, and is defined clearly. - The task definition is fairly stable. - Problem must be well bounded and narrow. Necessary Requirements for ES Development

  19. - The solution to the problem has a high payoff. - The ES can capture scarce human expertise so it will not be lost. - The expertise is needed in many locations. - The expertise is needed in hostile or hazardous environment. - The system can be used for training. - The ES is more dependable and consistent than human expert. Justification for ES Development

  20. A. Financial Feasibility Cost of system development Cost of maintenance Payback period Cash flow analysis B. Technical Feasibility Interface requirements Network issues Availability of data and knowledge Security of confidential knowledge Knowledge representation scheme Hardware/software availability Hardware/software compatibility Feasibility Study

  21. C. Operational Feasibility Availability of human resources Priority compare to other projects Implementation issues Management and user support Availability of experts Availability of knowledge engineers More on Feasibility Study