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Chapter 14
Is Artificial Intelligence Real?
 2002 Prentice Hall
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
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Thinking about
Thinking Machines
If you ask 10 people to define intelligence, you’re
likely to get 10 different answers, including some of
these:
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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
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Can Machines Think?
A machine may be
deemed intelligent when
it can
pass for a human being
in a blind test.
—Alan Turing
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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
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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.
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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?”
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Opening Games
AI techniques developed by game researchers are
still used today in a variety of applications. These
techniques include:
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Searching
Heuristics
Pattern Recognition
Machine Learning
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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.
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Natural Language
Communication
Types of Natural language applications include:
 Machine Translation Traps
 Conversation without communication
 Nonsense and common sense
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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.
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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.
“More than iron, more than lead, more
than gold I need electricity. I need it more
than I need lamb or pork or lettuce or
cucumber. I need it for my dreams.”
- Racter
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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.”
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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
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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.
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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.
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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.
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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.
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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.
Applications include:
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Image Analysis
Optical Character Recognition
Automatic Speech Recognition
Talking Computers
Neural Networks
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Pattern Recognition:
Making Sense of the World
Image analysis is the process of identifying
objects and shapes in a photograph, drawing,
video, or other visual image.
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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.
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Pattern Recognition:
Making Sense of the World
Automatic speech recognition systems use
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
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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.
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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.
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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.
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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.
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AI Implications and
Ethical Questions
Experts believe that scientists will eventually
create artificial beings that are more intelligent
than their creators …..
a prospect with staggering implications.
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