Artificial Intelligence

1. Artificial Intelligence Plymouth State College

2. 2001: A Space Odyssey Plymouth State College

3. What is Intelligence? • No good definition • We think of it as being “human” • More than the ability to do one task well • More than manipulating symbols Plymouth State College

4. Computers • Calculate quickly and accurately • Relieve us of tedious tasks • Help us to do some tasks better • Can entertain us (Games) • Can provide much information (Internet) Plymouth State College

5. Artificial Intelligence • What is AI? • Group of related technologies used for development machines to emulate human-like qualities Plymouth State College

6. Artificial Intelligence • Types of AI • Virtual reality • Robotics • Natural language processing • Fuzzy logic • Expert systems • Neural networks • Genetic algorithms Plymouth State College

7. Artificial Intelligence • Some early experiments failed • A.I. scientists ridiculed Plymouth State College

8. Game Playing • Early days of AI - Researchers thought that teaching computers to play games such as chess would enable them to understand something about human intelligence. • Found it easy to have computers play games. • Found it difficult to go beyond game playing and into the realm of human intelligence. Plymouth State College

9. Easy Computer Problems Plymouth State College

10. Difficult Computer Problems Plymouth State College

11. “Human” qualities • Emotion • Motivation • Deception Plymouth State College

12. Computer “Intelligence” Plymouth State College

13. Computer Control Plymouth State College

14. What is Intelligence—Artificial or Not? • The search for intelligence: • Plato (400 BC) - This Greek philosopher believed that ethereal spirits were rained down from heaven and entered the body. • Aristotle (Plato’s student) - The heart must contain the soul and the brain’s function was to cool the blood. • Galen - Treated fallen gladiators with spinal cord injuries. Noted that feeling lost in certain limbs sometimes came back. • Galvani - Used Benjamin Franklin’s findings about static electricity to show that static electricity stimulated the nerves causing a frog to jump. • Subsequently - Human nervous system found to be a complex network of billions of neurons. Plymouth State College

15. What is Intelligence—Artificial or Not? • Maillardet’s Automaton (1805): • Object having human form. • Disguised as a young boy. • Machine containing levers, ratchets, cams and other mechanical devices. • Could draw several complex images. • Because it had human form and could draw complex images, a certain feeling of intelligence was ascribed to the machine. Plymouth State College

16. Artificial Life • What is artificial life? • A field of study that deals with computer instructions that try to simulate human responses • What English mathematician and computer pioneer created a test in 1950 to determine computer intelligence? • Alan Turing Plymouth State College

17. What is Intelligence—Artificial or Not? • Alan Turing (1912 - 1954) • Proposed a test - Turing’s Imitation Game • Tests the intelligence of the computer. • Attempts to see of a person (Interrogator) can tell the difference between a human and a computer in answers to questions. • If the interrogator can’t tell the difference, the computer is considered to have intelligence. ? Plymouth State College

18. What is Intelligence—Artificial or Not? • Claude Shannon’s comparison of the human brain and the computer: • Difference in size: The brain has a million more parts. • Difference in structural organization: The seemingly random local structure of nerve networks differ vastly from the precise wiring of a computer. • Differences in reliability: The brain can operate reliably for decades. • Differences in logical organization: The brain is largely self-organizing. Digital computers do only a few narrowly defined tasks well. • Differences in input-output equipment: Brain is designed with input organs and output muscles and glands. Computers operate in an abstract environment of numbers and operations on numbers. Plymouth State College

19. Fundamental Concepts in Artificial Intelligence • Rule-based or Expert systems - Consists of rules of the form IF (condition) THEN (action). • IF (it is raining AND you must go outside) • THEN (put on your raincoat) Plymouth State College

20. Expert Systems Components • Knowledge Base • “Inference Engine” • User Interface Plymouth State College

21. Expert Systems • Knowledge of experts • Understand question (Input) • Lookup facts and rules (Storage) • Make decision (Processing) • Display decision (Output) Plymouth State College

22. Expert Systems • Expert systems are commercially the most successful domain in Artificial Intelligence. • IF (some condition) THEN (some action) • These programs mimic the experts in whatever field. • Auto mechanic Telephone networking • Cardiologist Delivery routing • Organic compounds Professional auditor • Mineral prospecting Manufacturing • Infectious diseases Pulmonary function • Diagnostic internal medicine Weather forecasting • VAX computer configuration Battlefield tactician • Engineering structural analysis Space-station life support • Audiologist Civil law Plymouth State College

23. Expert Systems • Harold Cohen created an expert system called AAORN to create art. Early drawings by AARON Plymouth State College

24. Expert Systems • Intelligent Agents: • Computerized agents that might... • respond to verbal commands as if it were human. • be a personal assistant that would access electronic communications. • take phone calls. • make appointments. • locate individuals by phone. • find research material. Plymouth State College

25. Fundamental Concepts in Artificial Intelligence • For any of these models of the human knowledge system to work, it must be able to make use of this knowledge in three different ways: • Knowledge acquisition - Must be some way of putting information or knowledge into the system. • Knowledge retrieval - Must be able to find knowledge when it is wanted or needed. • Reasoning with knowledge - Must be able to use that knowledge through “thinking” or reasoning. Plymouth State College

26. Fundamental Concepts in Artificial Intelligence • Knowledge retrieval (by searching): • Brute-force search - Searching all possible moves, and then selecting the best. • Looking for a museum in a small town example: • Drive around, down every street, until you find one! • Heuristic search - Uses rules of thumb, intuition. (The solution is not always guaranteed.) • Looking for a museum in a small town example: • Look for the museum down the town’s main street (museums are usually on the “main street” in small towns!) Plymouth State College

27. Fundamental Concepts in Artificial Intelligence • Machine learning: Writing intelligent computer programs that are capable of learning. • Example: Teaching a computer to play a game. The more the computer plays, the more strategies it will learn. • Common sense: • The computer must be able to make inferences from the knowledge base. • Answers to problems might not be listed. • The computer will need to come up with its own answers! • This has been a very difficult area in Artificial Intelligence. Plymouth State College

28. Pattern Recognition • Humans have the ability to understand or recognize the relationship among various parts of patterns in: visual object, sound patterns, smells, and taste. • Pattern recognition using computers has been applied in many areas including: • Robot vision • Speech recognition • Fingerprint identification • Handwriting identification • Optical character recognition (OCR) • Weather data analysis and satellite data analysis Plymouth State College

29. Pattern Recognition • Speech-pattern recognition: • Problems - Accents, continuous speech, words that sound the same but are spelled differently. • He walked to the store. • He walked to this door. • Some commercial products - Some commercial products do recognize continuous speech. (not yet perfect) Plymouth State College

30. Game Playing • Major problem in game playing: How to make the moves? • Involves: • Processing or searching many moves (see game tree) • By adding heuristics (rules of thumb / intuition), decisions can be made by the computer as to what is the best move. Plymouth State College

31. Game Playing • Chess • MacHack (1974) by Richard Greenblatt of MIT - First program to compete in a chess tournament. • Deep Blue (1996) by IBM - Garry Kasparov, world-champion chess player, won over Deep Blue 4 points to 2. • Deep Blue (1997) by IBM - Garry Kasparov conceded victory to Deep Blue, 3.5 points to 2.5. • The fastest chess-playing program processes 200,000 positions per second. • (Thousands of moves more than a human is capable of.) • Still, the best chess players beat most computer programs. Plymouth State College

32. Natural Language Communication • The semantics (meaning of the words) is the goal in communication with the computer. • Eliza (1965) by Joseph Weizenbaum of MIT • Simulation of a therapist mirroring a patient’s conversation in a positive manner. • The program fooled people into believing it was a therapist. Plymouth State College

33. Natural Language Communication • All men are alike. • IN WHAT WAY • They’re always bugging us about something or other. • CAN YOU THINK OF A SPECIFIC EXAMPLE • Well, my boyfriend made me come here. • YOUR BOYFRIEND MADE YOU COME HERE • He says I’m depressed much of the time. • I AM SORRY TO HEAR YOU ARE DEPRESSED • It’s true, I am unhappy. • DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY • I need some help, that much seems certain. • WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP • Perhaps I could learn to get along with my mother. • TELL ME MORE ABOUT YOUR FAMILY • Eliza conversation Plymouth State College

34. Natural Language Communication • Early failures in natural language translation • Translation of English to Russian: • Typed in English (sentence to be translated...): • The spirit is willing, but the flesh is weak. • Translated by the program into Russian: • The vodka is strong, but the meat is rotten. • Translation programs have come a long way since then! Plymouth State College

35. Neural Networks • Neuron: Basic building-block of the brain. • There are several specialized types, but all have the same basic structure: • The basic structure of an animal neuron. Plymouth State College

36. Neural Networks • Artificial models of the brain are of two distinct types: • Electronic: Has electronic circuits that act like neurons. • Software: This version runs a program on the computer that simulates the action of the neurons. Plymouth State College

37. Neural Networks • Neural Network: • A collection of neurons which are interconnected. • The output of one connects to several others with different strength connections. • Initially, neural networks have no knowledge. (All information is learned from experience using the network.) Neuron 1 Input 1 Input 2 Input 3 Output from Neuron 1 Output from Neuron 2 Neuron 2 Plymouth State College

38. Fuzzy Logic • Probability that a statement is true • Combined with other AI technologies • Washing Machine • Variable speed limits Plymouth State College

39. Finding Information • Intelligent agent • Software that performs work tasks • Example: monster.com Plymouth State College

40. Next Week Your PowerPoint presentation is due You will be able to present it in class for extra credit Plymouth State College

41. Exam in Two Weeks Chapters 7 & 8 from the Textbook Lectures since Exam 2

42. Final Exam Week Final exam is scheduled Tue. & Thu. 5:00 – 6:15 You may take it on either day Plymouth State College