Is Artificial Intelligence Real?

1. Is Artificial Intelligence Real? 13

2. Learning Objectives • Explain what artificial intelligence means • Explain the tow basic approaches of artificial intelligence research • Describe several hard problems that artificial intelligence research has not yet been able to solve • Describe several practical applications of artificial intelligence • Explain what robots are and give several examples illustrating what they can – and cannot - to  2001 Prentice Hall

3. Chapter Outline “A machine may be deemed intelligent when it can pass for a human being in a blind test.” Alan Turing • Thinking About Thinking Machines • Natural-Language Communication • Knowledge Bases and Expert Systems • Pattern Recognition: Making Sense of the World • The Robot Revolution • AI Implications and Ethical Questions  2001 Prentice Hall

4. Thinking Machines • Can machines think? • To answer that question,we must explore: • Definitions of intelligence • The Turing test • What artificialintelligence (AI) is  2001 Prentice Hall

5. Definitions of Intelligence • Some definitions of intelligence include: • Ability to learn from experience • Power of thought • Ability to reason • Ability to perceive relations • Power of insight • Ability to use tools • Intuition  2001 Prentice Hall

6. The Turing Test In 1950, British mathematician Alan Turing proposed a test to determine if a machine had intelligence  2001 Prentice Hall

7. What Is Artificial Intelligence? • Artificial intelligence is the study of: • …ideas which enable computers to do the things that make people intelligent. Patrick Henry Winston • …how to make computers do things at which, at the moment, people are better.Elaine Rich • …the computations that make it possible to perceive, reason, and act.Patrick Henry Winston  2001 Prentice Hall

8. Two Approaches to AI • Simulate Human Mental Processes • Design Non-human Mental Processes  2001 Prentice Hall

9. Designing Intelligent Machines • Some branches of AI research include: • Games • Natural Languages • Knowledge Basesand Expert Systems • Pattern Recognition • Neural Networks • Robotics  2001 Prentice Hall

10. Opening Games • Simple games have limited domains. This allows AI researchers to develop strategies for: • Searching possible moves • Heuristics (“rules of thumb”) • Recognizing patterns (new or old one?) • Machine learning (machine becomes a better player over time)  2001 Prentice Hall

11. Natural-Language Communication • AI researchers would like to develop a machine that understands the words spoken by a person (natural language) • Challenges to developing this kind of machine come from: • Machine Translation Traps • Conversation without Communication • Nonsense and Common Sense  2001 Prentice Hall

12. Machine Translation Traps • Required a “parsing program” to break down words from one language and convert them into another • The meaning was lost in the translation. • For example: • Out of sight, out of mind =Invisible idiot • The spirit is willing, but the flesh is weak =The wine is agreeable, but the meat is rotten  2001 Prentice Hall

13. Conversation without Communication • AI researchers attempted to “converse” with a machine using the software program ELIZA • ELIZA had a limited natural language vocabulary • In order to communicate with humans, ELIZA had to ask and be asked questions  2001 Prentice Hall

14. Patient: I need some help, that much seems certain WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP ELIZA: Conversation without Communication • However, ELIZA had no understanding of what was being communicated  2001 Prentice Hall

15. Nonsense and Common Sense “Bill sings to Sara, Sara sings to Bill. Perhaps they will do other dangerous things together. They may eat lamb or stroke each other. They may chant of their difficulties and their happiness. They have love but they also have typewriters. That is interesting.” A poem by RACTER • AI researchers attempted to learn more about natural languages by using the program RACTER to write a book • However, despite a large and perfect English language vocabulary, RACTER’s book was nonsense • Machines are good at syntax but cannot compete with humans at semantics  2001 Prentice Hall

16. Knowledge Bases and Expert Systems • Machines are good at storing and retrieving facts and figures • People are good at storing and manipulating knowledge • Knowledge bases contain facts and a system of rules for determining the changing relationship between those facts  2001 Prentice Hall

17. Knowledge Bases and Expert Systems • Expert systemsare softwareprogramsdesigned toreplicate humandecision-makingprocesses  2001 Prentice Hall

18. Examples of Expert Systems • Medicine: medical facts and knowledge have been entered into an expert system to aid physicians in diagnosingtheir patients  2001 Prentice Hall

19. Examples of Expert Systems • Factories: expert systems are used to locate parts, tools, and techniques for the assembly of many kinds of products • Financial: automation of banking functions and transactions is being done by many expert systems  2001 Prentice Hall

20. Expert Systems in Perspective • An expert system can: • Help train new employees • Reduce the number of human errors • Take care of routine tasks so workers can focus on more challenging jobs • Provide expertise when no experts are available  2001 Prentice Hall

21. Expert Systems in Perspective • Preserve the knowledge of experts after those experts leave an organization • Combine the knowledge of several experts • Make knowledge available to more people  2001 Prentice Hall

22. Pattern Recognition: Making Sense of the World • Pattern recognition involves identifying recurring patterns in input data with the goal of understanding or categorizing that input • Image Analysisidentifies objectsand shapes  2001 Prentice Hall

23. Pattern Recognition: Making Sense of the World • Optical Character Recognition (OCR) identifies words and numbers • the page image must be scanned into the computer’s memory • OCR software identifies the text and converts documents into editable text • Handwritten text is difficult to read and the results are not as reliable as typewritten text  2001 Prentice Hall

24. Pattern Recognition: Making Sense of the World • Speech Recognition identifies spoken words • SpeechSynthesisgeneratessyntheticspeech  2001 Prentice Hall

25. Neural Networks • Neural networks are distributed, parallel computing systems based on the structure of the human brain • A neural network consists of thousands of microprocessors called neurons • A neural network learns by trial and error, just as the brain does  2001 Prentice Hall

26. Neural Networks • Concepts arerepresented aspatterns of activityamong neurons • A neural net canstill function if partof it is destroyed  2001 Prentice Hall

27. The Robot Revolution • The word robot comes from the Czech word for forced labor • Today’s robots combine many AI technologies, including: • Vision, hearing, pattern recognition, knowledge engineering, expert decision making, natural language understanding, and speech  2001 Prentice Hall

28. The Robot Revolution • While a computer performs mental tasks, a robotis a computer-controlledmachinedesigned todo manualtasks  2001 Prentice Hall

29. What Is a Robot? • A robot differs from other computers in its input and output peripherals • Robot input includes sensors(heat, light, motion) • Robotic output isusually sent to joints,arms, or other moving parts  2001 Prentice Hall

30. What Is a Robot? • These peripherals make robots ideally suited for: • Saving labor costs (robots can work 24 hours a day) • Improving the quality and productivity of repetitive jobs • Hazardous or uncomfortable jobs  2001 Prentice Hall

31. Steel-Collar Workers • Despite sophisticated input and output devices, robots still cannot compete with humans for jobs requiring exceptional perceptual or fine-motor skills • But for people who earn their living doing manual labor, robots are a threat • Displaced workers are not limited to factories  2001 Prentice Hall

32. AI Implications and Ethical Questions “There are certain tasks which computers ought not [to] be made to do, independent of whether computers can be made to do them.” Joseph Weizenbaum  2001 Prentice Hall

33. AI Implications andEthical Questions • In the future, we are likely to see products with embedded AI • Some futurists predict that silicon-based intelligence will replace human intelligence • Whether AI becomes embedded in products or evolves into a new form of intelligent life, what becomes of human values?  2001 Prentice Hall

34. Selected Answers to Review Questions (Q) What are the disadvantages of the approach to AI that attempts to simulate human intelligence? What is the alternative? (A) Using AI to simulate human intelligence has three problems. The first is that most people have trouble knowing and describing how they do things. Human intelligence includes unconscious thought, instant insight, and other mental processes that are difficult and even impossible to understand or describe.   2001 Prentice Hall

35. Selected Answers to Review Questions (A)--- Continued The second problem is that even the most powerful supercomputers don’t have the brain’s ability of parallel processing, breaking a complex job into smaller and simpler jobs and completing those jobs simultaneously, The third problem is that a human’s way doing something is not usually the best way for a computer to do it. The alternative is to create programs that can function intelligently when confined to limited domain.  2001 Prentice Hall

36. Selected Answers to Review Questions (Q) What is the relationship between syntax and semantics? Can you construct a sentence that follows the rules of English syntax but has nonsense semantics? (A) Syntax is a set of rules for constructing sentences from words. Semantics is the underlying meaning of words and phrases. Yes, noun, verbs, and so forth can be strung together using correct syntax without creating a meaningful sentence.  2001 Prentice Hall

37. Selected Answers to Review Questions (Q) What is a knowledge base? What is an expert system? How are the two related? (A) A knowledge base is a database that also contains a system of rules for determining and changing the relationship between facts stored. An expert system is a program that replicates the decision-making process of a human expert. A knowledge base is the foundation of an expert system. This knowledge base represents ideas from a specific field of expertise.  2001 Prentice Hall

38. Selected Answers to Review Questions (Q) What are some of the problems that make machine vision so challenging? (A) Machine vision means identifying objects and shapes in a scene. Of course scenes can be complicated by shadows, obscuring objects, lighting changes, movement, and so forth. For humans, identifying what we see is generally effortless. For computers, it becomes a monumental task of pattern recognition and inference.  2001 Prentice Hall