AI Unit1b.ppt
- 1. Artificial Intelligence: An Introduction Definition of AI Foundations of AI History of AI Advanced Techniques
- 2. General Goal Artificial Intelligence is an attempt to understand and build intelligent devices. It covers activities such as: perception understanding reasoning prediction representing knowledge The field is relatively young (name was coined in 1956). Exciting applications: playing chess proving theorems writing poetry medical diagnosis robotics
- 3. Definitions of AI No single universal definition currently exists for artificial intelligence. Some accepted definitions: a) “The effort to make computers think…” b) “The study of the design of intelligent agents…” c) “The study of mental faculties through …computational models.” Dilemma: acting humanly vs acting rationally. hypothesis and experiments. mathematics and engineering.
- 4. Acting Humanly The Turing Test. In 1950 Alan Turing proposed an interesting test to decide if a machine qualifies as intelligent or not: hide a computer and a person from an interrogator; the computer is considered intelligent if the interrogator cannot decide if the answers are provided by the human or the machine. Interrogator Person Computer
- 5. Acting Humanly To pass the Turing test, a computer needs to display the following abilities: Natural language processing Knowledge representation Automated Reasoning Machine Learning Computer Vision Robotics Related field: Cognitive Science Goal: construct theories of how the human mind works through computer models.
- 6. Acting Rationally Old View. Originally dominated by the “logic” approach. The goal is to build intelligent agents using mathematical logic. Disadvantage: hard to deal with uncertainty. Modern View. More current view is to build rational agents. Agents are autonomous, perceive, adapt, change goals and deal with uncertainty. It is easier to evaluate and more general. The focus of this course is on Rational Agents.
- 7. Artificial Intelligence: An Introduction Definition of AI Foundations of AI History of AI Advanced Techniques
- 8. Connection With Other Disciplines Philosophy. Do we follow rules to draw conclusions? How does mind arise from a physical brain? How to represent knowledge? Mathematics. Formal rules to draw conclusions (logic) What can be computed? Incompleteness theorem Intractability NP-completeness Probability (Bayes rule)
- 9. Connection With Other Disciplines Economics. Decision Theory Make decision to maximize payoff Probability and Utility theories. Sometimes playing random is best. Neuroscience. Study of the brain. Structure of neurons Cognitive processes Compare chips to neurons in terms of processing.
- 10. Connection With Other Disciplines Psychology. How do we think and act? Cognitive science Computer Engineering. How do we make computers more efficient? Performance doubles every approx. 18 months Control Theory. Design systems that maximize an objective function over time. Linguistics. Connection between language and thought.
- 11. Artificial Intelligence: An Introduction Definition of AI Foundations of AI History of AI Advanced Techniques
- 12. Origins McCulloch and Pitts (1943) Model of Artificial Neurons. Donald Hebb (1949) Hebbian Learning Conference at Dartmouth (1956) McCarthy, Minsky, Shannon, Nathaniel, Samuel (IBM), Solomonoff, Newell and Simon. Newell and Simon General Problem Solver
- 13. Origins
- 14. John McCarthy •Born in Boston 1927 •Became full professor at Stanford in 1962 (until his retirement in 2000). •Coined the term Artificial Intelligence (AI) •Developed the language LISP •Supported Mathematic Logic for AI
- 15. Marvin Minsky • Born in New York 1927 • MIT Faculty since 1958 •Winner of the Turing Award in 1969 •Wrote the book “Perceptrons”. • Member of the National Academy of Engineering and National Academy of Sciences.
- 16. Blocks Worlds
- 17. Later on… The knowledge problem. “the spirit is willing but the flesh is weak” “The vodka is good but the meat is rotten” US government funding was cancelled (1966) Minksy and Papert Book Perceptron (1969) Knowledge based-methods (1969-79) Buchanan with DENDRAL (molecular info. from a mass spectrometer) Expert Systems MYCIN (diagnose blood infections)
- 18. AI becomes Industry (1980 – today) More expert systems. Systems using Prolog. After 1988 companies suffered. The return of Neural Networks Hopfield (1982) AI becomes Science neats beat scruffies Data Mining Bayesian Networks Robotics Computer Vision Artificial General Intelligence Universal algorithm for learning and acting in any environment.
- 19. Data Mining Selection Target Data Preprocessing Data Preprocessed Data Transformation Transformed Data Patterns Data Mining Interpretation & Evaluation Knowledge Knowledge Discovery and Data Mining
- 20. Artificial Intelligence: An Introduction Definition of AI Foundations of AI History of AI Advanced Techniques
- 21. Techniques • Autonomous planning and scheduling. • A remote agent generated high-level goals in space • Game playing • IBM Deep Blue defeated Garry Kasparov • Autonomous control • ALVINN: trained to steer a car to follow a lane. • Diagnosis • Performing at a level of experts in medical diagnosis • Logistic Planning • Plans generated in hours (rather than weeks) • Robotics • Surgeons use robots assistants in microsurgery • Language understanding and problem solving
- 22. Techniques Image copied from Wikipedia, the free encyclopedia.
- 23. ALVINN
- 24. Application: Robotics Honda Robots are on the top of the list for achievements. Watch some videos at (look at the Honda Humanoid Robot) General Info: Search for “Honda Robot” on the web. A recent video on Asimov