Chapter 14

1. Chapter 14 Is Artificial Intelligence Real?

2. Topics • Thinking machines – the concept and the controversy • How computers play (and win) games • Computers that Speak and translate human language • Expert systems and robots at work

3. Thinking about Thinking Machines • If you ask 10 people to define intelligence, you’re likely to get 10 different answers, including some of these: • The ability to learn from experience • The power of thought • The ability to reason • The ability to perceive relations • The power of insight • The ability to use tools • Intuition

4. Can Machines Think? A machine may be deemed intelligent when it can pass for a human being in a blind test. —Alan Turing

5. What Is Artificial Intelligence These definitions commonly appear in today’s popular press: Artificial intelligence is the study of ideas which enable computers to do the things that make people seem intelligent. —Patrick Henry Winston, in Artificial Intelligence Artificial intelligence is the study of how to make computers do things at which, at the moment, people are better. Artificial intelligence is the study of the computations that make it possible to perceive, reason, and act. —Patrick Henry Winston, in Artificial Intelligence

6. What Is Artificial Intelligence Two common approaches to AI include: • One approach attempts to use computers to simulate human mental processes. For example, an AI expert might ask people to describe how they solve a problem and attempt to capture their answers in a software model. • The second, more common, approach to AI involves designing intelligent machine independent of the way people think. According to this approach, human intelligence is just one possible kind of intelligence.

7. Opening Games • Much of the early AI work focused on games because they were easy to represent in the computer’s digital memory, they had clearly defined rules, and the goals were unmistakable. • Game researchers could focus on the concrete question “How can I create a program that wins consistently?”

8. Opening Games • AI techniques developed by game researchers are still used today in a variety of applications. These techniques include: • Searching • Heuristics • Pattern Recognition • Machine Learning

9. Natural Language Communication • Since the earliest days of computing, scientists have dreamed of machines that could communicate in natural languages like English, Russian, and Japanese.

10. Natural Language Communication • Types of Natural language applications include: • Machine Translation Traps • Conversation without communication • Nonsense and common sense

11. Machine Translation Traps • A parsing program (or parser) analyzes sentence structure and identify each word according to whether it was a subject, verb, or other part of speech; another program would look up each word in a translation dictionary and substitute the appropriate word.

12. Conversation without communication • Similar lessons emerged from Joseph Weizenbaum’s work with ELIZA, one of the first software programs to converse in a limited form of natural language.

13. Nonsense and common sense • Every language has a syntax—a set of rules for constructing sentences from words. In a programming language, the syntax rules are exact and unambiguous. • However, natural-language parsing programs have to deal with rules that are vague, ambiguous, and contradictory. “Time flies like an arrow.”

14. Knowledge Bases and Expert System • The human brain isn’t particularly good at storing and recalling facts, but excels at manipulating knowledge. • Computers, on the other hand, are better at handling data than knowledge. • Consequently, Artificial intelligence researchers have developed techniques for representing knowledge in computers. The computer can’t tell you the emotional story. It can give you the exact mathematical design, but what’s missing is the eyebrows. —Frank Zappa

15. Knowledge Bases and Expert System • Knowledge Bases contain a system of rules for determining and changing the relationship among those facts. Facts stored in a database are rigidly organized in categories; ideas stored in a knowledge base can be reorganized as new information changes their relationships.

16. Knowledge Bases and Expert System • An expert system is a software program designed to replicate the decision-making process of a human expert. At the foundation of every expert system is a knowledge base representing ideas from a specific field of expertise.

17. Expert Systems in Action • The first successful expert systems were developed around medical knowledge bases. • The business community has been more enthusiastic than the medical community in its use of expert systems. Some examples of expert systems in action include: • American Express uses an expert system to automate the process of checking for fraud and misuses of its no-limit credit card. • Blue Cross/Blue Shield of Virginia an expert system automates insurance claim processing.

18. Expert Systems in Perspective • From the following examples it should be clear that expert systems offer many advantages. An expert system can perform these tasks: • Help train new employees • Reduce the number of human errors • Take care of routine tasks so workers can focus on more challenging job • Provide expertise when no experts are available • Preserve the knowledge of experts after those experts leave an organization.

19. Pattern Recognition:Making Sense of the World • Pattern recognitioninvolves identifying recurring patterns in input data with the goal of understanding or categorizing that input. • Applications include: • Image Analysis • Optical Character Recognition • Automatic Speech Recognition • Talking Computers • Neural Networks

20. Pattern Recognition:Making Sense of the World • Image analysisis the process of identifying objects and shapes in a photograph, drawing, video, or other visual image.

21. Pattern Recognition:Making Sense of the World • Optical character recognition (OCR) software locates and identifies printed characters embedded in images—it “reads” text. This is no small task for a machine, given the variety of typefaces and styles in use today.

22. Pattern Recognition:Making Sense of the World • Automatic speech recognition systemsuse pattern recognition techniques similar to those used by vision and OCR systems, including these: • Segmentation of input sound patterns into individual words and phonemes • Expert rules for interpreting sounds • Context “experts” for dealing with ambiguous sounds • Learning from a human trainer

23. Pattern Recognition:Making Sense of the World • Many computer applications speak like humans by playing prerecorded digitized speech and other digitized sounds stored in memory or disk.

24. Pattern Recognition:Making Sense of the World • Neural networks (or neural nets) are distributed, parallel computing systems inspired by the structure of the human brain. Instead of a single, complex CPU, a neural network uses a network of a few thousand simpler processors called neurons.

25. The Robot Revolution • A robot is a computer-controlled machine designed to perform specific manual tasks. A robot’s central processor might be a microprocessor embedded in the robot’s shell, or it might be a supervisory computer that controls the robot from a distance.

26. The Robot Revolution • Robots offer several advantages: • Robots can work 24 hours a day, 365 days a year, without vacations, strikes, sick leave, or coffee breaks. • Robots are effective at doing repetitive jobs in which bored, tired people are prone to make errors and have accidents. • Robots are ideal for jobs that are dangerous, uncomfortable, or impossible for human workers.

27. AI Implications andEthical Questions • Experts believe that scientists will eventually create artificial beings that are more intelligent than their creators ….. a prospect with staggering implications.