The introduction to Artificial Intelligence follows. Additional
information about this book, along with access to software, is available
The Intelligent Computer
This book is about the field that has come to be called
Artificial Intelligence. In this chapter, you learn how to define Artificial Intelligence, and
you learn how the book is arranged. You get a feeling for why
Artificial Intelligence is important, both as a branch of engineering and as a kind
of science. You learn about some successful applications of
Artificial Intelligence. And finally, you learn about criteria you can use
to determine whether work in Artificial Intelligence is
The Field and the Book
There are many ways to define the field of Artificial
Intelligence. Here is one:
Artificial Intelligence is ...
the study of the
computations that make it possible to perceive, reason, and act.
From the perspective of this definition, Artificial Intelligence
differs from most of psychology because of the greater
emphasis on computation, and Artificial Intelligence differs
from most of computer science because of the emphasis on
perception, reasoning, and action.
From the perspective of goals, Artificial Intelligence can be
viewed as part engineering, part science:
- The engineering goal of Artificial Intelligence is to
solve real-world problems using Artificial Intelligence as an
armamentarium of ideas about representing knowledge, using
knowledge, and assembling systems.
- The scientific goal of Artificial Intelligence is to
determine which ideas about representing knowledge, using
knowledge, and assembling systems explain various sorts of
This Book Has Three Parts
To make use of Artificial Intelligence, you need a basic understanding of how knowledge can
be represented and what methods can make use of that knowledge.
Accordingly, in Part I of this book, you learn about basic
representations and methods. You also learn, by way of vision
and language examples, that the basic representations and methods
have a long reach.
Next, because many people consider learning to be the sine qua
non of intelligence, you learn, in Part II, about a rich variety
of learning methods. Some of these methods involve a great deal
of reasoning; others just dig regularity out of data, without any
analysis of why the regularity is there.
Finally, in Part III, you focus directly on visual perception
and language understanding, learning not only about perception
and language per se, but also about ideas that have been
a major source of inspiration for people working in other
subfields of Artificial Intelligence.
The Long-Term Applications Stagger the Imagination
As the world grows more complex, we must use our material and
human resources more efficiently, and to do that, we need
high-quality help from computers. Here are a few possibilities:
- In farming, computer-controlled robots should control pests,
prune trees, and selectively harvest mixed crops.
- In manufacturing, computer-controlled robots should do the
dangerous and boring assembly, inspection, and maintenance jobs.
- In medical care, computers should help practitioners with
diagnosis, monitor patients' conditions, manage treatment, and
- In household work, computers should give advice on cooking and
shopping, clean the floors, mow the lawn, do the laundry, and
perform maintenance chores.
- In schools, computers should understand why their
students make mistakes, not just react to errors. Computers should
act as superbooks, displaying planetary orbits and playing
musical scores, thus helping students to understand physics and
The Near-Term Applications Involve New Opportunities
Many people are under the false impression that the commercial
goal of Artificial Intelligence must be to save money by replacing human workers.
But in the commercial world, most people are more enthusiastic
about new opportunities than about decreased cost. Moreover, the
task of totally replacing a human worker ranges from difficult to
impossible because we do not know how to endow computers with all
the perception, reasoning, and action abilities that people
Nevertheless, because intelligent people and intelligent
computers have complementary abilities, people and computers can
realize opportunities together that neither can realize alone.
Here are some examples:
- In business, computers can help us to locate pertinent
information, to schedule work, to allocate resources, and to discover
salient regularities in databases.
- In engineering, computers can help us to develop more effective control
strategies, to create better designs, to explain past decisions, and
to identify future risks.
Artificial Intelligence Sheds New Light on Traditional Questions
Artificial Intelligence complements the traditional perspectives of psychology,
linguistics, and philosophy. Here are several reasons why:
Note that wanting to make computers be intelligent is not
the same as wanting to make computers simulate
intelligence. Artificial Intelligence excites people who want to uncover
principles that must be exploited by all intelligent information
processors, not just by those made of neural tissue instead of
electronic circuits. Consequently, there is neither an obsession
with mimicking human intelligence nor a prejudice against using
methods that seem involved in human intelligence. Instead, there
is a new point of view that brings along a new methodology and
leads to new theories.
- Computer metaphors aid thinking. Work with computers has led
to a rich new language for talking about how to do things and how
to describe things. Metaphorical and analogical use of the
concepts involved enables more powerful thinking about thinking.
- Computer models force precision. Implementing a theory
uncovers conceptual mistakes and oversights that ordinarily
escape even the most meticulous researchers. Major roadblocks
often appear that were not recognized as problems at all before
the cycle of thinking and experimenting began.
- Computer implementations quantify task requirements. Once a
program performs a task, upper-bound statements can be made about
how much information processing the task requires.
- Computer programs exhibit unlimited patience, require no
feeding, and do not bite. Moreover, it is usually simple to
deprive a computer program of some piece of knowledge to
test how important that piece really is. It is almost always
impossible to work with animal brains with the same precision.
Artificial Intelligence Helps Us to Become More Intelligent
Just as psychological knowledge about human information
processing can help to make computers intelligent, theories
derived primarily with computers in mind often suggest useful
guidance for human thinking. Through Artificial Intelligence research, many
representations and methods that people seem to use unconsciously
have been crystallized and made easier for people to deploy
What Artificial Intelligence Can Do
In this section, you learn about representative systems that were enabled
by ideas drawn from Artificial Intelligence. Once you have finished this
book, you will be well on your way toward incorporating the ideas of
Artificial Intelligence into your own systems.
Intelligent Systems Can Help Experts
to Solve Difficult Analysis Problems
During the early days of research in Artificial Intelligence, James R.
Slagle showed that computers can work problems in integral calculus at the
level of college freshmen. Today, programs can perform certain kinds of
mathematical analysis at a much more sophisticated level.
The KAM program, for example, is an expert in nonlinear dynamics, a subject
of great interest to scientists who study the equations that govern complex
Intelligent Systems Can Help Experts to Design New Devices
The utility of intelligence programs in science and engineering
is not limited to sophisticated analysis; many recent programs
have begun to work on the synthesis side as well.
For example, a program developed by Karl Ulrich designs simple
devices and then looks for cost-cutting opportunities to reduce
the number of components. In one experiment, Ulrich's program
designed a device that measures an airplane's rate of descent by
measuring the rate at which air pressure is increasing.
Intelligent Systems Can Learn from Examples
Most learning programs are either experience oriented or data
oriented. The goal of work on experience-oriented learning is
to discover how programs can learn the way people usually do---by
reasoning about new experiences in the light of commonsense
The goal of work on data-oriented learning programs is to develop
practical programs that can mine databases for exploitable
regularities. Among these data-oriented learning programs, the
most well-known is the ID3 system developed by J. Ross
Quinlan. ID3 and its descendants have mined thousands of
databases, producing identification rules in areas ranging from
credit assessment to disease diagnosis.
One typical exercise of the technology, undertaken by Quinlan
himself, was directed at a database containing information on
patients with hypothyroid disease. This information, provided by the
Garvan Institute of Medical Research in Sydney, described each of
several thousand cases of thyroid disease in terms of 7
continuous variables (such as the measured level of
thyroid-stimulating hormone, TSH), and 16 discrete
variables, (such as whether or not the patient had already had
From this sea of data, Quinlan's program extracted three
straightforward rules for classification of hypothyroid disease:
If the patient's TSH level is less than 6.05 units
then the patient's class is negative
If the patient has not had thyroid surgery
the patient's TSH level is greater than 6.05 units
the patient's FTI level is less than 64.5 units
then the patient's class is primary hypothyroid
If the patient is not taking thyroxine
the patient has not had thyroid surgery
the patient's TSH level is greater than 6.05 units
the patient's FTI level is greater than 64.5 units
the patient's TT4 level is less than 150.5 units
then the patient's class is compensated hypothyroid
Evidently, of the seven continuous and 16 discrete variables
available, only five are useful in disease classification.
Intelligent Systems Can Provide Answers to English Questions Using
both Structured Data and Free Text
As the Voyager 2 spacecraft concluded its 12-year grand tour
of the outer planets, it sent back spectacular images of Neptune's
moons and rings, much to the delight of journalists and
scientists gathered to witness the event. In cooperation with
researchers from the Jet Propulsion Laboratory, Boris Katz
invited those journalists and scientists to use his
START system to ask questions about the Neptune
encounter, the Voyager spacecraft, and the Solar system.
To answer straightforward questions, START accessed a
variety of tables, including a distance table supplied by the
Voyager navigation team and a time-line table supplied by the
Voyager sequencing group. Here are a few representative
START also answered questions by printing out English
text drawn from various space publications, as illustrated by the
following representative questions and answers. Note that the
critical words in the questions---color, talk, and
weather---do not appear in the answers:
- When was Voyager launched?
The Voyager Spacecraft was launched in Florida on
August 20 1977.
- At what time will Voyager lose contact with Earth?
The Voyager Spacecraft will lose the contact with
Earth around 2014.
- How long is the year on Neptune?
Neptune's period of revolution is about 165 Earth
- What color is Neptune?
When you look at a photograph of Neptune, you see the planet's
atmosphere and the tops of clouds. Neptune looks bluish because the
methane in its atmosphere reflects blue and green frequencies of light.
The methane absorbs red and yellow frequencies of
- How does Voyager talk to Earth?
Commands are sent from Earth to one of two
receivers on the Voyager spacecraft. Engineering data and
scientific information are sent to Earth over two channels.
One channel is on all the time, but both channels are on during
encounters with the planets. The radio transmitter on the
Voyager transmits with about 25 watts of power, or with about the
same power as a refrigerator lightbulb. These faint signals are
picked up on Earth by ultrasensitive receivers known as the Deep
- What's the weather on Neptune?
Neptune receives about a thousand times less sunlight than the
Earth. The temperatures there are thus quite low. Typically,
the temperature is about -360 degrees Fahrenheit (-218 degrees
Artificial Intelligence Is Becoming Less Conspicuous, yet
The first applications of Artificial Intelligence were mostly motivated by
the desire of researchers to demonstrate that Artificial Intelligence is of
practical value. Now, as the field is maturing, the development of
applications is motivated increasingly by the desire of business people to
achieve strategic business goals.
One example of a business-motivated application is the Airport
Resource Information System---ARIS---developed by Ascent
Technology, Inc., and used by Delta Airlines to help allocate
airport gates to arriving flights.
Handling the constraints was not the principal challenge faced by
ARIS's developers, however. Other difficult challenges were
posed by the need to provide human decision makers with a
transparent view of current operations, the need to exchange
information with mainframe databases, the need to provide rapid,
automatic recovery from hardware failures, and the need to
distribute all sorts of information to personnel responsible for
baggage, catering, passenger service, crew scheduling, and
aircraft maintenance. Such challenges require considerable skill
in the art of harnessing Artificial Intelligence ideas with those of other
established and emerging technologies.
Criteria for Success
Every field needs criteria for success. To determine if research
work in Artificial Intelligence is successful, you should ask three key questions:
To determine if an application of Artificial Intelligence is successful, you need to
ask additional questions, such as the following:
- Is the task defined clearly?
- Is there an implemented procedure performing the defined task? If
not, much difficulty may be lying under a rug somewhere.
- Is there a set of identifiable regularities or constraints from which
the implemented procedure gets its power? If not, the procedure may
be an ad hoc toy, capable perhaps of superficially impressive
performance on carefully selected examples, but incapable of deeply
impressive performance and incapable of helping you to solve
any other problem.
Throughout this book, you see examples of research and
applications-oriented work that satisfy these criteria: all
perform clearly defined tasks; all involve implemented
procedures; all involve identified regularities or constraints;
and some solve real problems or open up new opportunities.
- Does the application solve a real problem?
- Does the application open up a new opportunity?
- Artificial Intelligence is the study of the computations that
make it possible to perceive, reason, and act.
- The engineering goal of Artificial Intelligence is to solve
real-world problems; the scientific goal of Artificial
Intelligence is to explain various sorts of intelligence.
- Applications of Artificial Intelligence should be judged
according to whether there is a well-defined task, an implemented
program, and a set of identifiable principles.
- Artificial Intelligence can help us to solve difficult, real-world
problems, creating new opportunities in business, engineering,
and many other application areas.
- Artificial Intelligence sheds new light on questions traditionally asked by
psychologists, linguists, and philosophers. A few rays of this
new light can help us to be more intelligent.