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Introduction to Artificial Intelligence

Introduction to Artificial Intelligence. Prof. Kathleen McKeown 722 CEPSR, 939-7118 TAs: Kapil Thadani 724 CEPSR, 939-7120 Phong Pham TA Room. Today. What is artificial intelligence anyway? Requirements and assignments for class Examples of AI systems. What is intelligence?.

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Introduction to Artificial Intelligence

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  1. Introduction to Artificial Intelligence Prof. Kathleen McKeown 722 CEPSR, 939-7118 TAs: Kapil Thadani 724 CEPSR, 939-7120 Phong Pham TA Room

  2. Today • What is artificial intelligence anyway? • Requirements and assignments for class • Examples of AI systems

  3. What is intelligence? • Intelligence • “The ability to learn and solve problems” (Webster’s Dictionary) • The ability to think and act rationally • Goal in artificial intelligence • Build and understand intelligent systems/agents

  4. 2001

  5. Definitions

  6. Systems that think like humans versus • Systems that act like humans

  7. Systems that think rationally versus • Systems that act rationally

  8. Different Approaches to AI • Building exact models of human cognition • The view from psychology and cognitive science • The logical thought approach • Emphasis on correct inference • Building rational agents • Agent: something that perceives and acts • Emphasis on developing systems to match or exceed human performance, often in limited domains

  9. Class focus • Systems that act • Like humans • Rationally

  10. Core areas Knowledge representation Reasoning/inference Machine learning Perception Vision Natural language Robotics Uncertainty Probabilistic approaches General algorithms Search Planning Constraint satisfaction Applications Game playing AI and education Distributed agents Decision theory Electronic commerce Auctions Reasoning with symbolic data AI is a smorgasbord of topics

  11. Core areas Knowledge representation Reasoning/inference Machine learning Perception Vision Natural language Robotics Uncertainty Probabilistic approaches General algorithms Search Planning Constraint satisfaction Applications Game playing AI and education Distributed agents Decision theory Electronic commerce Auctions Reasoning with symbolic data AI is a smorgasbord of topics

  12. AI used to be • Expert systems • Medical expert systems – diagnosis • Computer systems design • Theorem proving/software verification • Inheritance, class-based systems

  13. AI is interdisciplinary • Psychology • Cognitive Science • Linguistics • Neuroscience • Economics • Philosophy • Physics

  14. What will we study in the course?

  15. Assignments • 2 programming assignments • Search (1.5 weeks) • Game playing (3.5 weeks) • Tournament • 1 light programming/using tool plus paper (3 weeks) – machine learning • 1 purely written assignment (1 week) • Each programming assignment has written questions too

  16. Grading • 45% homeworks – homeworks are important. You can’t pass without doing them. • 5% class participation • Notes will be posted on the web • There will be board work in addition to slides. The slides don’t tell the whole story. • Class is a social experience – there will be discussion • End of Class Questions • 20% midterm • 30% final

  17. Undergrad vs. MS • Separate grading curves • Separate game tournaments • MS students picked to raise discussion issues; undergrads expected to respond

  18. Reading • Chapters from the required text: Artificial Intelligence: A Modern Approach, Russell and Norvig, 2003. Columbia University Bookstore. • Selected papers. Watch for papers on reserve. • Will be posted on the Reading Section of the web

  19. Other AI Classes this semester • 4701 NLP (Hirschberg) • 4731 Computer Vision (Nayar) • 4737 Biometrics (Belhumeur) • 6733 3D Photography (Allen) • 6998 Section 4 Search Engine Technology (Radev)

  20. Some Examples • Natural language processing • Question answering on the web • Automatic news summarization • Robotics • Robocup soccer • Roomba: robotics meets the real world • Vision • Modeling the real world

  21. Machine Learning • Learning to play pool • Talking robots

  22. Today’s Assignment • Fill out on courseworks • Survey worth 5 points towards total homework grade • Answer the following questions • UNI: • Degree: BA BS MS PhD non-degree • Year at Columbia (e.g., freshman, sophomore, junior, senior, 1st year MS, etc): • Major: • Why are you taking this class? • What do you want to get out of the class? • What programming languages do you know?

  23. End of Class Questions

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