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Lectures to
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Tomorrow’s
Technology and
You, 9e
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s
Technology
and You 9/e
Chapter 15
Is Artificial Intelligence Real?
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Objectives
 Explain the two 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 can’t—do
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Alan Turing, Military Intelligence, and
Intelligent Machines
 Alan M. Turing was a British mathematician who designed the world’s first
operational electronic digital computer during the 1940s:
 Turing effectively launched the field of AI (artificial intelligence) with a
1950 paper called “Computing Machinery and Intelligence.”
 In 1952 he was professionally and socially devastated when he was
arrested and injected with hormones for violation of British
antihomosexuality laws.
 The 41-year-old genius apparently committed suicide in 1954, years
before the government made his wartime heroics public.
 Four decades after his death, Turing’s work still has relevance to
computer scientists, mathematicians, and philosophers.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
 Can machines think?
 The Turing test:
 Test involves two people and a computer.
 One person, the interrogator, sits at a
terminal and types questions.
 The questions can be about anything—
math, science, politics, sports,
entertainment, art, human relationships,
emotions, anything.
 As answers to the questions appear on the
screen, the interrogator attempts to guess
whether those answers were typed by the
other person or generated by the
computer.
– According to Turing, by repeatedly
fooling interrogators into thinking it
is a person, a computer can
demonstrate intelligent behavior. If
it acts intelligently, it is intelligent.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
 Can machines think? (cont.)
 The Turing test (cont.):
 Turing did not intend this test to be the only way to
demonstrate machine intelligence; he pointed out that a
machine could fail and still be intelligent.
 Even so, Turing believed that machines would be able to
pass his test by the turn of the century.
 So far no computer has come close, in despite nearly 60
years of AI research.
 While some people still cling to the Turing test to define
artificial intelligence, most AI researchers favor less
stringent definitions.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
 What is artificial intelligence?
 Many computer scientists believe that if a task is easy to
perform with a computer, it can’t be an example of
artificial intelligence.
 A more recent textbook definition reflects this point of view:
 Artificial intelligence is the study of how to make
computers do things
at which, at the moment, people are better.—Elaine Rich,
in Artificial Intelligence
 Artificial intelligence is the study of the computations that
make it possible to perceive, reason, and act.—Patrick Henry
Winston, in Artificial Intelligence
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
What is artificial intelligence? (cont.)



Two common approaches to AI
Use computers to simulate human mental processes
 Three inherent problems:
• Most people have trouble knowing and describing how they do
things.
• There are vast differences between the capabilities of the
human brain and those of a computer.
• Even the most powerful supercomputers can’t approach the
brain’s ability to perform parallel processing.
 The best way to do something with a machine is often very
different from the way people would do it.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
 What is artificial intelligence? (cont.)
 A second approach to AI involves designing intelligent
machines independent of the way people think.
 This is a more common approach.
 Human intelligence is just one possible kind of
intelligence.
 A machine’s method for solving a problem might be
different from the human method, but no less
intelligent. Many problems are far too complex to
solve all at once.
 Break these problems into smaller problems that
are easier to solve.
 Create programs that can function intelligently
when confined to limited domains.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
 Opening games
 One of the first popular domains for AI
research was the checkerboard.
 Some AI techniques are still used today in a
variety of applications:
 Searching: Looking ahead to the
possibilities generated by each potential
move
• The staggering number of
decision points makes bruteforce searching impractical.
• Searching is generally guided
by a planned strategy and by
rules known as heuristics.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
 Opening games (cont.)
 Some AI techniques are still used today in a variety of applications (cont.):
 Heuristics: Rule of thumb
• Heuristics guide us toward judgments that experience tells us are likely to be true.
• For example, in checkers, “Keep checkers in the king’s row as long as possible.”
 Pattern recognition: identifying recurring patterns in input data
• The goal of pattern recognition is understanding or categorizing
that input.
• The best human chess and checkers players remember thousands of critical board
patterns and know the best strategies for playing when those patterns or similar
patterns appear.
• Game-playing programs recognize recurring patterns, too, but not nearly as well as
people do.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Thinking about Thinking Machines
Opening games (cont.)
 Some AI techniques are still used today in a
variety of applications (cont.):
Machine learning: learn from
experience
» If a move pays off, a learning program is
more likely to use that move (or similar
moves) in future games.
Most AI researchers have moved on to
more interesting and practical
applications.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Natural-Language Communication
 Machine translation traps
 One early project attempted to create a program that could
translate scientific papers between English and Russian.
A parsing program (parser) would analyze sentence
structure.
Identify each word by its part of speech
Look up each word in a translation dictionary and
substitute the appropriate word
 Today, programs such as Babel Fish use machine learning
algorithms help to improve machine translation.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Natural-Language Communication


Conversation without communication
 Joseph Weizenbaum, an MIT professor, designed ELIZA
in the 1960s to simulate the role of a therapist in a
typed conversation with a patient.
 An ELIZA session can easily deteriorate into nonsense
dialog laced
with grammatical errors and inappropriate responses.
 ELIZA doesn’t pass the Turing test.
Nonsense and common sense
 Part of the problem with natural-language
communications is the massive vocabulary of natural
languages.
 Natural-language parsing programs have to deal with
rules that are vague, ambiguous, and occasionally
contradictory.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Natural-Language Communication
 Nonsense and common sense (cont.)
 Computers are far more successful dealing with naturallanguage syntax than with semantics—the underlying meaning
of words and phrases.
 Computers lack what we call common sense—
the wealth of knowledge and understanding
about the world that people share.
 The most successful natural-language
applications limit the domain so that virtually all
relevant information can be fed to the system.
 Natural-language processing has come a long way since ELIZA’s
early conversations.
 Computers still can’t pass the Turing test, but they can at least
fool people sometimes.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Knowledge Bases and Expert Systems
Knowledge bases
 AI researchers continue to develop techniques for
representing knowledge in computers.
 A knowledge base contains a system of rules for
determining and changing the relationship among facts in
a database.
Facts stored in a database are rigidly organized into
categories.
Ideas stored in a knowledge base can be reorganized
as new information changes their relationships.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Knowledge Bases and Expert Systems
 Artificial expert
An expert system is designed to replicate the decision-making process
of a human expert.
It requires a knowledge base representing ideas from a specific field of
expertise that is constructed by a user, an expert, or a knowledge
engineer—a specialist who converts the words and actions of experts into
a knowledge base.
Some new expert systems can grow their own knowledge bases while
observing human decision makers doing their jobs.
For most expert systems, the process is still human intensive.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Knowledge Bases and Expert Systems
 Artificial experts (cont.)
 A complete expert system also includes:
 A human interface, which enables the user to interact with
the system
 An inference engine, which puts the user input together
with the knowledge base, applies logical principles, and
produces the requested expert advice
 Roles of expert systems
 Aid experts by providing automated data analysis and
informed second opinions
 Support nonexperts by providing advice based on
judgments of one or more experts
 Function within narrow, carefully defined domains
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Knowledge Bases and Expert Systems
Expert systems in action
The MYCIN medical expert system outperformed many human
experts in diagnosing diseases.
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 uses an expert system to
automate insurance claim processing.
Boeing Company factory workers use an expert system to locate
the right parts, tools, and techniques for assembling airplane
electrical connectors.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Knowledge Bases and Expert Systems
Expert systems in perspective
 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 jobs
Provide expertise when no experts are available
Preserve the knowledge of experts after those experts leave
an organization
Combine the knowledge of several experts
Make knowledge available to more people
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Knowledge Bases and Expert Systems
 Expert systems in perspective (cont.)
 Even with a knowledge base, an expert system isn’t the
machine equivalent of a human expert.
 Clearly, knowledge engineers can’t use rules to teach
computers all they need to know to perform useful,
intelligent functions outside narrow domains.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Pattern Recognition: Making Sense of the World
 Pattern recognition involves identifying reoccurring patterns in
input data.
 Image analysis
 Image analysis is the process of identifying objects and
shapes in a photograph, drawing, video, or other visual
image.
 Image analysis is used for everything from colorizing classic
motion pictures to piloting cruise missiles.
 Today’s PCs are capable of running image-processing
software with practical applications.
 Example: Security programs enable PCs with video
cameras to recognize faces of valid users with a high
degree of reliability.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Chapter 15
Pattern Recognition: Making Sense of the World
 Optical character recognition
 OCR (Optical character recognition) software locates
and identifies printed characters embedded in images,
thereby “reading” text.
 State-of-the-art OCR programs use several techniques:
 Segmentation of a page into pictures, text blocks,
and (eventually) individual characters
 Scaled-down expert system technology for
recognizing the underlying rules that distinguish
letters
 Context “experts” to help identify ambiguous letters
by their context
 Learning from actual examples and feedback from a
human trainer
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Chapter 15
Pattern Recognition: Making Sense of the World
Optical character recognition (cont.)
 Today’s best programs can achieve up to 99 percent
accuracy; they can perform even better under optimal
circumstances.
 OCR technology also can be applied to handwritten text
but not as reliably.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Pattern Recognition: Making Sense of the World
 Automatic speech recognition
 Automatic speech recognition systems use pattern
recognition techniques similar to those used by vision and
OCR systems, including:
 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
 Voice recognition systems with speaker independence—
the ability to recognize speech without being trained—are
becoming more common, making speech recognition
practical for more applications.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Pattern Recognition: Making Sense of the World
 Talking computers
 Many computer applications speak like humans by playing
prerecorded digitized speech (along with other digitized
sounds) stored in memory or on disk.
 Recorded speech won’t work for applications in which the text
to be spoken is unpredictable—such as a talking word
processor—because all the sounds must be prerecorded.
 These types of applications require text-to-speech
conversion—the creation of synthetic speech by
converting text files into phonetic sounds.
 With speech synthesis software or hardware, PCs
can recite anything you can type but with voices that
sound artificial and robotic.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Pattern Recognition: Making Sense of the World
 Neural networks
 Neural networks (or neural nets): distributed, parallel computing
systems
 Inspired by the structure of the human brain
 Uses a network of a few thousand simpler processors called neurons
 Not programmed in the usual way—they’re trained
 Learns patterns by trial and error, just as the brain does
 Optimistic researchers hope that neural networks may someday provide
hearing for the deaf and eyesight for the blind.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
The Robot Revolution
 What is a robot?
 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.
 The processor is functionally identical to the processor
found
in a computer.
 The most important hardware differences between robots and
other computers are the input and output peripherals.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
The Robot Revolution
 Steel-collar workers
 From a management point of view, robots offer several
advantages:
 Robots save labor costs.
 Robots can work 24 hours a day, 365 days a year,
without vacations,
strikes, sick leave, or coffee breaks.
 Robots can also improve quality and increase
productivity.
• They’re especially effective at doing repetitive jobs.
 Robots are ideal for jobs that are dangerous,
uncomfortable, or impossible
for human workers.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
AI Implications and Ethical Questions



As it matures, AI technology finds its way out of the research
lab and into the marketplace.
A growing number of programs and products incorporate
pattern recognition, expert systems, and other AI techniques.
In the near future we’re likely to see more products with
embedded AI, including:

Intelligent word processors that can help writers turn
rough drafts into
polished prose

Smart appliances that can recognize and obey their
owners’ spoken commands

Vehicles that can perform their own diagnostics and, in
many cases, repairs
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Tomorrow’s Technology and You 9/e
Chapter 15
Lesson Summary
 Some AI researchers try to simulate human intelligent behavior, but most
try to design intelligent machines independent of the way people think.
 Successful AI research generally involves working on problems with
limited domains rather than trying to tackle large, open-ended problems.
 AI programs employ a variety of techniques, including searching,
heuristics, pattern recognition, and machine learning, to achieve their
goals.
 AI researchers have developed a variety of schemes for representing
knowledge in computers.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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Chapter 15
Lesson Summary (cont.)
 We’ll also see more distributed intelligence—AI concepts applied
to networks rather than to individual computers.
 A robot is a computer-controlled machine designed to perform
specific manual tasks.
 As robot technology advances, artificial workers will do more
traditional human jobs.
 Despite of the numerous difficulties AI researchers encounter when
trying to produce truly intelligent machines, many experts believe
that people will eventually create artificial beings that are more
intelligent than their creators—a prospect with staggering
implications.
Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall
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