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Historical Perspective: It All Sounds So Good .
Probably everybody has heard of Artificial Intelligence (AI for
short), but relatively few people have a really good idea of what
the term really means. For most people, AI is associated with
artifacts like the Hal 9000 Computer in the movie 2001: A Space
odyssey. Such images are the product of Hollywood, rather
than the kind of thing which actually happens in the research
labs of the world today. My purpose here is to introduce a few
of the basic ideas behind AI, and to try and offer a means by
which people can come to grips with the current state of the art
in the field.
Roughly speaking, Artificial Intelligence is the study of man-made
computational devices and systems which can be made to act in
a manner which we would be inclined to call intelligent. The birth
of the field can be traced back to the early 1950s. Arguably,
the first significant event in the history of AI was the publication
of a paper entitled "Computing Machinery and Intelligence"
by the British Mathematician Alan Turing.
In this paper, Turing argued that if a machine could past a certain
test (which has become known as the 'Turing test') then we would
have grounds to say that the computer was intelligent. The Turing test
involves a human being (known as the 'judge') asking questions
via a computer terminal to two other entities, one of which is
a human being and the other of which is a computer. If the judge
regularly failed to correctly distinguish the computer from the
human, then the computer was said to have passed the test. In
this paper Turing also considered a number of arguments for, and
objections to, the idea that computers could exhibit intelligence.
It is commonly believed that AI was born as a discipline at a
conference called "The Dartmouth Summer research Project on Artificial Intelligence",
organized by amongst others, John McCarthy
and Marvin Minsky.
At this conference a system known as LOGIC THEORIST was demonstrated
by Alan Newell and Herb Simon.
LOGIC THEORIST was a system which discovered proofs to theorems
in symbolic logic. The significance of this system was that, in
the words of Feigenbaum and Feldman (1963: p. 108) LOGIC THEORIST
the first foray by artificial intelligence into
high-order intellectual processes." This initial success
was rapidly followed by a number of other systems which could
perform apparently intelligent tasks. For example, a system known
was able to mechanize aspects of the scientific reasoning found
in organic chemistry. Another program, known as "MYCIN",
was able to interactively diagnose infectious diseases.
The fundamental strategy which lay behind all these successes
led to the proposal of what is known as the Physical Symbol Systems
Hypothesis, by Newell and Simon in 1976. The Physical Symbol System
Hypothesis amounts to a distillation of the theory which lay behind
much of the work which had gone on up until that date and was
proposed as a general scientific hypothesis. Newell and Simon
(1976: p. 41) wrote;
"A physical symbol system has the necessary and sufficient
means for general intelligent action."
Although there has been a great deal of controversy about exactly
how this hypothesis should be interpreted, there are two important
conclusions which have been drawn from it. The first conclusion
is that computers are physical symbol systems, in the relevant
sense, and thus there are grounds (should the hypothesis be correct)
to believe that they should be able to exhibit intelligence. The
second conclusion is that, as we humans also are intelligent,
we too must be physical symbol systems and thus are in a significant
sense, similar to computers.
Current Perspective: The Problems and the Successes
With all these apparently positive results and interesting theoretical
work, a fairly obvious question seems to be 'Where are the intelligent
machines, like the HAL 9000'? Although there have been many impressive
successes in the field, there have also been a number of significant
problems which AI research has run into. As yet, there is no HAL
9000 and realistically, it will be a good while before such systems
become available, if indeed they ever prove to be possible at
The early successes in AI led researchers in the field to be wildly
optimistic. Unfortunately, the optimism was somewhat misplaced.
For example, in 1957 Simon predicted that it would take only ten
years for a computer to be the world's chess champion. Of course,
this particular feat was not accomplished until this year, by
the Deep Blue system.
There are deeper problems which AI has run into however.
For most people, if they know that President Clinton is in Washington,
then they also know that President Clinton's right knee is also
in Washington. This may seem like a trivial fact, and indeed it
is for humans, but it is not trivial when it comes to AI systems.
In fact, this is an instance of what has come to be known as 'The
Common Sense Knowledge Problem'. A computational system only knows
what it has been explicity told. No matter what the capacities
of a computational system, if that system knows that President
Clinton was in Washington, but doesn't know that his left knee
is there too, then the system will not appear to be too clever.
Of course, it is perfectly possible to tell a computer that if
a person is in one place, then their left knee is in the same
place, but this is only the beginning of the problem. There are
a huge number of similar facts which would also need to be programmed
in. For example, we also know that if President Clinton is in
Washington, then his hair is also in Washington, his lips are
in Washington and so on. The difficulty, from the perspective
of AI, is to find a way to capture all these facts. The Problem
of Common Sense Knowledge is one of the main reasons why we do
not have as yet the intelligent computers predicted by science
fiction, like the HAL 9000.
The Problem of Common Sense Knowledge runs very deep in AI. For
example, it would be very difficult for a computer to pass the
Turing test, if it lacked the kind of knowledge described above.
The point can be illustrated by considering the case of ELIZA.
is an AI system designed by Weizenbaum in 1966 which was supposed
to emulate a psychotherapist. There are many variants of this
software these days, quite a few of which can be downloaded.
Although in some senses ELIZA can be quite impressive, it doesn't
take much to get the system confused, or off track. It becomes
clear very quickly that the system is far from intelligent.
There have been a number of responses to The Problem of Common
Sense Knowledge within the AI research community. One strategy
is to attempt to build systems which are only designed to operate
in limited domains. This is the strategy which lies behind the
a modern day competition based upon a limited version of the Turing
test. Some recent entries to this contest, such as the TIPS
system are really quite impressive, when compared to ELIZA.
Another more ambitious strategy has been adopted by AI researcher
Lenat and his colleagues have been working for a number of years
on a system which is known as CYC.
The goal of the CYC project is to develop a large computational
database and search tools which enables AI systems to access all
the knowledge which makes up common sense. The CYC project tries
to meet the Problem of Common Sense Knowledge head on. At the
current time, the results of the project are just beginning to
emerge. It is not yet clear whether the massive effort has been
Other researchers have adopted a different tack to try and deal
with the problem. They reason that human being have common sense,
because of the vast wealth of experiences which we have as we
grow up and learn. They prefer to attempt to deal with the Problem
of Common Sense by adopting a machine learning
strategy. Perhaps, if a computer could learn, in a manner similar
to a human being, the it too would develop common sense. This
strategy is still being pursued and it is too early to tell if
it will be successful.
Another problem which AI research has run into is that tasks which
are hard for human beings, like mathematics, or playing chess,
turn out to be quite easy for computers. On the other hand, tasks
which human beings find easy, like learning to navigate through
a room full of furniture, or recognizing faces, computers find
comparatively hard to do. This has inspired some researchers to
try and develop systems which have (at least superficially) brain-like
properties. The research based upon this strategy has come to
be known as the field of Artificial Neural Networks
(also called Connectionism),
and is currently one of the major specialist sub-areas within
AI. On interesting aspect of Artificial Neural Networks is that
many of these systems also learn, thereby incorporating some of
the advantages of the machine learning strategy to solving the
Common Sense Knowledge Problem. Artificial Neural Network systems
have been successful at solving many problems, such as those involving
pattern recognition, which have proved hard for other approaches.
It is important to realize though that not everybody accepts the
premises which AI research operates under. The whole project of
AI has come under sharp criticisms from time to time. One well-known
critic is Herbert Dreyfus.
He has argued on a variety of grounds that the whole enterprise
of AI is doomed to failure, as it makes assumptions about the
world and minds which are not tenable, when critically assessed.
Another well-known critic of AI is John Searle.
Searle has proposed an argument based on a thought experiment,
known as the Chinese Room argument.
This argument purports to show that the goal of building intelligent
machines is not possible. Even though this argument was originally
published in the 1980s, it is still a hot topic of discussion
on internet newsgroups such as comp.ai.philosophy.
Whether the critics of AI are correct or not, only time will tell.
However, there have been two important sets of consequences which
have arisen since the initial inception of the field. The first
of these has been the birth of a new and exciting academic discipline
which has come to be known as 'Cognitive Science'.
Cognitive Science shares with AI the fundamental premise that,
in some sense, mental activity is computational in nature. The
goal of Cognitive Science though is different from that of AI.
Cognitive scientists set themselves the goal of unraveling the
mysteries of the human mind. This is no small task, given that
the human brain is the most complicated device known to mankind.
For example, even when various simplifying assumptions are made,
it seem highly likely that the number of distinct possible states
of a single human brain is actually greater than the number of
atoms of the Universe! Nonetheless, the lessons learned and progress
made in the pursuit of the goal of AI, along with progress in
other disciplines, seem to show that project of Cognitive Science
is viable, though hard to attain.
The second set of consequences which have arisen from the study
of AI are perhaps a little less obvious. There are many programs
and systems around today which make use of the fruits of AI research.
Although we do not have a HAL 9000 as yet, many of the early goals
of AI have been achieved, albeit not in a single grand system.
Perhaps the saddest thing though is that AI seldom gets credit
for its contribution to other areas. There is a saying in academic
circles that "The best fruits of AI, become plain old computer
science". As we learn to do more and more, what was once
almost miraculous, becomes mundane. Now that the goal of a really
fine chess playing computer has been realized, it is likely that
this too will no longer thrill or surprise us. However, there
are still many challenging and exciting frontiers to be conquered
within AI. There are also numerous thorny questions which need
to be thought through. In the articles which follow this one,
I will try and introduce some of the fascinating work which is
being done in AI, so that the contribution of this research program
to the world as we know it will be better known and understood.
© István S. N. Berkeley Ph.D. 1997. All
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