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Artificial Intelligence

Artificial Intelligence. CIS 479/579 Bruce R. Maxim UM-Dearborn. Overview. What is artificial intelligence (AI)? Why study AI? How can you tell whether a machine or program is intelligent? What kinds of things (if any) can be learned by a machine?

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Artificial Intelligence

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  1. Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

  2. Overview • What is artificial intelligence (AI)? • Why study AI? • How can you tell whether a machine or program is intelligent? • What kinds of things (if any) can be learned by a machine? • When can (should) machines replace human experts?

  3. What is AI? • AI is the study of ideas that enable computers to be intelligent • Intelligence might be defined as the capacity to acquire and apply knowledge • Ai is the part of computer science concerned with design of computer systems that exhibit human intelligence

  4. AI Problem Characteristics • Decomposable to smaller or easier problems • Solution steps can be ignored or undone • Predictable problem universe • Good solutions are obvious • Uses internally consistent knowledge base • Requires lots of knowledge or uses knowledge to constrain solutions • Requires periodic interaction between human and computer

  5. Goals of AI • To make computers more useful by letting them take over dangerous or tedious tasks from human • Understand principles of human intelligence

  6. Physical Symbol Hypothesis(Newell and Simon) • Given enough symbols related to one another • Given collection of operators capable of producing new symbol expressions • You have the necessary & sufficient conditions for intelligent behavior • The problem is can this critical mass be achieved in real-time within the memory capacity of some machine

  7. AI Technique Characteristics • They capture generalizations • Can be understood by domain expert • Easily modified to correct errors or reflect changes in the world view • Used in lots of different situations • Reduce its own size/bulk by narrowing the range of possibilities it must consider at any given time

  8. AI System Taxonomy(easiest to hardest) • Data acquisition • Expert System • Problem Solving • Automatic Programming • Robotic Control • Pattern Recognition • Natural Language Processing • Vision & Scene Analysis

  9. Turing Test • Used as criteria for evaluating success of AI system • Inquisitor, AI System, Human • Inquisitor connected to AI System and Human by “wire” and free to ask either any question • If Inquisitor can not determine which is the Human without guessing AI System is successful

  10. AI Success Criteria • Is the task clearly defined? • Is there an implemented procedure performing the task? • Is there an identifiable set of regularities or constraints that the procedure uses to derive its power?

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