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CS 2710, ISSP 2610 Foundations of Artificial Intelligence

This course provides an introduction to AI, covering various applied areas such as game playing, speech processing, expert reasoning, and robotics. It explores the synergy between AI, philosophy, psychology, linguistics, and other disciplines. The course delves into the engineering and scientific goals of AI, including the Turing Test and Eliza, assessing the intelligence of machines. It examines the challenges ahead, such as lack of adaptability and context-switching issues. With in-class discussion questions and examples, students will gain insights into the complexities and possibilities of artificial intelligence.

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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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