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CS 2710, ISSP 2610 Foundations of Artificial Intelligence

CS 2710, ISSP 2610 Foundations of Artificial Intelligence. introduction. Outline. Course information and syllabus Introduction to AI. 4 Views of AI. Basic Framework. Getting computers to do the right thing based on their circumstances and what they know. Applied Areas of AI.

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CS 2710, ISSP 2610 Foundations of Artificial Intelligence

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  1. CS 2710, ISSP 2610Foundations of Artificial Intelligence introduction

  2. Outline • Course information and syllabus • Introduction to AI

  3. 4 Views of AI

  4. Basic Framework Getting computers to do the right thing based on their circumstances and what they know.

  5. Applied Areas of AI • Game playing • Speech and language processing • Expert reasoning and theorem proving • Planning and scheduling • Vision • Robotics

  6. Playing chess Driving on the highway Mowing the lawn Answering questions Recognizing speech Diagnosing diseases Translating languages Some Examples

  7. AI is a synergy among… • Philosophy: Can a machine think? What are knowledge and belief? Logic and reasoning… • psychology and cognitive science: problem solving skills… • Linguistics: syntax, semantics, pragmatics…

  8. Synergy Among… • Computer science and engineering: complexity theory, algorithms, logic and inference, programming languages, system building,… • Mathematics, physics: statistical modeling, complex systems, chaos, game theory,… • Economics: decision theory,… • Neurobiology: how does the brain process information?...

  9. What’s involved in intelligence? • Ability to interact with the real world • Perceive, understand, and act • Reasoning and planning • Modeling external world • Problem solving, planning, decision making • Learning and adaptation

  10. Goals in AI • Engineering goal: solve real-world problems. Build systems that exhibit intelligent behavior • Scientific goal: To understand what kinds of computational mechanisms and knowledge are needed for modeling intelligent behavior

  11. Turing Test (1950) • Interrogator asks questions of two agents who are out of sight and hearing. One is person the other is a computer. • If the interrogator can’t reliably distinguish between human and computer, then the computer is deemed “intelligent”

  12. Eliza (Joseph Weizenbaum in the last 60s) • Takes the role of a psychoanalyst in a psychiatric interview. • Sample dialog and modern Turning test

  13. Turing Test • Pros: Objective evaluation. Focus on behavior (how could we evaluate whether a computer thinks like a human?) • Cons: as much a test of the judge as it is of the machine; promotes development of artificial con artists (Newel and Simon 1976). But….

  14. Passing the Test • Free conversation is very hard • But people are prone towards attributing human qualities to technology

  15. Implications • Whether or not we set out to build intelligent interactive agents, people expect computers to act like people

  16. Challenges Ahead • Systems lack generality and adaptability • They can’t easily switch contexts • Key problems: knowledge acquisition, lack of commonsense knowledge, lack of sufficient data, what aspects of context are relevant?

  17. Example • Information extraction example: consider brittleness and what we could do about it

  18. In-Class Discussion Questions • This file

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