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Artificial Intelligence: Definition

This lecture notes presents a comprehensive overview of Artificial Intelligence (AI) systems, from historical milestones like Deep Blue defeating world chess champion Garry Kasparov to the modern era of web agents and recognition systems. It covers the development phases of AI, including the boom and bust of expert systems and the resurgence of neural networks. The discussion delves into the different approaches to AI, such as acting humanly, thinking humanly, thinking rationally, and acting rationally, highlighting the challenges and limitations in achieving perfect rationality due to computational constraints. The course emphasizes designing rational agents to maximize goal achievement in various environments.

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Artificial Intelligence: Definition

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  1. Lecture Notes Artificial Intelligence: Definition Dae-Won Kim School of Computer Science & Engineering Chung-Ang University

  2. What are AI Systems?

  3. Deep Blue defeated the world chess champion Garry Kasparov in 1997

  4. During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people

  5. Proverb solves crossword puzzles better than most humans

  6. Sony’s AIBO and Honda’s ASIMO

  7. Web Agents & Search engines: Google, Yahoo

  8. Recognition Systems: Speech, Character, Face, Iris, Fingerprint

  9. Virtual Reality and Computer Vision

  10. Potted History of AI 1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turing’s “Computing Machinery and Intelligence” 1950s Early AI programs 1956 Dartmouth meeting: “Artificial Intelligence” adopted 1965 Robinson’s complete algorithm for logical reasoning 1966 AI discovers computational complexity Neural network research almost disappears 1969 Early development of knowledge-based systems 1980 Expert systems industry booms 1988 Expert systems industry busts: “AI Winter” 1985 Neural networks return to popularity 1988 Resurgence of probability, soft computing. 1995 Agents, agents, everywhere … with Data Mining 2000 Bioinformatics powered by Human Genome Project 2003 Human-level AI back on the agenda: challengeable

  11. Some researchers consider AI as one of the four concepts:

  12. 1. Systems that think like humans

  13. 2. Systems that thinkrationally

  14. 3. Systems that act like humans

  15. 4. Systems that actrationally

  16. AI: Acting humanly

  17. Turing (1950): “The Turing Test”

  18. Can machines think?

  19. Can machines behave intelligently?

  20. Turing test is The ‘Imitation’ Game

  21. Predicted that by 2000, a machine might have 30% chance of fooling a lay person for 5 min. In 2014, something has happened. http://www.bbc.com/news/technology-27762088

  22. Problem: Turing test is NOT …

  23. Turing test is NOT reproducible and amendable to mathematical analysis

  24. AI: Thinking humanly

  25. It requires scientific theories of internal activities of the brain

  26. What level of abstraction? “Knowledge” or “circuits”.

  27. How to validate? Requires something

  28. Requires: Cognitive Science Predicting and testing behavior of human subjects (top-down)

  29. Requires: Cognitive Neuroscience Direct identification from neurological data (bottom up)

  30. Problem: Thinking humanly is NOT

  31. Both are distinct from AI in CS The available theories do not explain anything resembling human-level general intelligence.

  32. AI: Thinking rationally

  33. Laws of Thought: “What are correct arguments/thought processes?” by Aristotle

  34. Several Greek schools developed various forms of logic:

  35. Logic: notation and rules of derivation of thoughts

  36. Problem: Thinking rationally is NOT

  37. Not all intelligent behavior is mediated by logical deliberation

  38. AI: Acting rationally

  39. Rational behavior: doing the RIGHT thing

  40. The RIGHT thing: that which is expected to maximize goal achievement, given the available information

  41. An agent is an entity that perceives and acts.

  42. Agents include humans, robots, programs, systems, etc.

  43. This course is about designing rational agents/SWs/programs/platforms.

  44. Abstractly, an agent is a function from percept histories to actions f : P  A

  45. The agent program runs on the physical architecture to produce f

  46. For any given class of tasks and environments, we seek the agent with the best performance.

  47. Problem: Acting rationally is NOT

  48. Computational limitations make perfect rationality unachievable e.g.) NP-hard problems

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