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\ctrline{AI, WHERE IT HAS BEEN AND WHERE IT IS GOING}
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\ctrline{Arthur L. Samuel}
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\ctrline{Computer Science Department, Stanford University}
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A recent article on Expert Computer Systems carried a banner headline that
said, and I quote, ``Artificial In\-tel\-li\-gence is no longer science theory'',
end of quote.  This statement carries the clear implication that the
science of Artificial Intelligence is now a closed book and there is no
need for further research.  I am sure that the writer of this headline had
no such thoughts but I fear that there is a growing tendency to believe
the implication.  The main thrust of my talk today will be to deny this
implication and to urge that more rather than less attention be given to
AI research.

It is with a profound feeling of personal inadequacy that I address you
today on this subject.  While it is true that I have worked in Artificial
Intelligence, most of my work was done a long time ago when the total
amount of knowledge in this field was much less than it is today.  It may
well be that my concerns are, in part, colored by my lack of complete
familiarity with the work that is now being done.  I agreed to talk only
because it seemed to me that I should be able to view the field of AI
research from a perspective that is denied a newer worker in the field.
I can not hope to say much about AI that you do not already know but perhaps
the retelling of old ideas will stimulate your thinking.

In sharing my perspective with you, it seemed that it might be worthwhile
for me to review some of the earlier history of AI and to recall what our
thoughts were at some earlier time as to where AI research was going.  By
comparing where AI is today with our earlier expectations we might be able
to get a clearer view of what still remains to be done.  Then, perhaps,
we might make some new predictions as to where AI will be at some future
time, say for the year 2000.

Recalling where AI has been is relatively easy for me to do, since it has
been my good fortune to have been involved with the modern digital
computer and with AI almost from the start.  Recalling earlier
expectations and earlier events comes rather close to reminiscing and I
hate to start reminiscing as this is supposed to be a mark of age.

You know, of course, that there are three ways to tell if a person is
getting old.  In the first place, the older person likes to reminisce. I
must plead guilty on this count but then I have always had this failing.
Secondly, old people tend to forget things.  And then there is a third
way---but I have forgotten what it is.

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From one point of view, AI started in 1834 or shortly thereafter, when
Charles Babbage suggested the possibility of having his Analytical Engine
play chess. It was not until the 1940's, with the emergence of the modern
digital computer, that we saw a renewal of interest in Artificial
Intelligence.  There were, of course, numerous attempts at making
automata of various sorts but most of the time the aim was to fool the
public rather than to get machines to exhibit behavior which if done by
humans would be assumed to involve the use of intelligence. So we can date
the emergence of AI as a discipline to some time in the mid 1940's.

Through a fortuitous combination of circumstances, I became interested in
AI at its very beginning, not in 1834, I hasten to say, but in 1947, when
the availability or soon to be realized availability of the modern digital
computer made AI something more than idle speculation.

Actually, my involvement in computing began even earlier, in 1924 at MIT
when Vannevar Bush got me interested in his Differential Analyser. When I
first was introduced to this device, it consisted of a single stage
using a modified watt-hour meter as the integrating device, although Bush
was even then planning to use a Kelvin Integrating Disk for a
second stage.

One of my first assignments was to see what could be done in the way of
using the differential analyser to solve the non-linear equations in
exterior ballistics, which, at the time were solved by the elaborate hand
process of numerical integration using log tables and the old-fashioned
desk calculator.  Trying to solving these equations to the desired
ac\-curacy on the Differential Analyser, which was then capable of perhaps
5\%\ accuracy, seemed quite hopeless to me.  I felt that this problem
required the accuracy of a digital solution and that maybe we should be
working on a digital computer. Yes, digital computers were being talked
about in 1924 although the distinction between computers and calculators
was not then as clear as it later became.  Bush, 
with his usual enthusiasm,
soon talked me out of any such revolutionary ideas and set me to work,
with a few hints as to how the ballistic problem might be solved.

The solution method, which Bush suggested and which I imple\-mented, was
to write a set of difference equations to account for the departure of an
actual trajectory from a simple parabolic path, and then to solve these
difference equations on the Differential Analyser.  By this method,
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\noindent we were able to achieve an accuracy of something better than
1\%, not the desired accuracy, but near enough to hold out hope that the
desired accuracy could ultimately be obtained.

This solution method was still in use during the war years, on the very
much improved Differential Analysers that were, by then, available.
Interestingly enough, it was the eventual dissatisfaction with this
solution method that led Herman Goldstine to press for government support
of the digital computer work at the University of Pennsylvania.  Contrary
to a popular misconception, the digital computer appeared on the scene too
late to have any effect on the war although there were a few rather
advanced electronic calculators that did do useful work, primarily in the
field of cryptography.

In retrospect, I wonder what would have happened had I been
successful in deflecting Bush from his interest in the Differential
Analyser or alternatively, had we been less successful in applying the
differential analyser to the exterior ballistic problem. Just think what
would have happened, if Bush, with his keen intellect and driving
enthusiasm, had started to work on digital computers in 1924.

A brief note in the Fifty-Year-Ago column of a recent issue of the
Scientific American will serve to point out the way matters stood in 1933.
Let me quote:

``The fundamental ideas of behavioristic and Gestalt psychologies
justify attempts to construct and develop ma\-chines of a new
type---machines that think. Such machines are entirely different from
integraphs, tide calculators and the like to which the term `thinking
machine' is sometimes applied. Unlike the latter they are not designed to
perform with mathematical regularity but can `learn' to vary their actions
under certain conditions.''

The original writer of this note was, of course, thinking about some of
the early work that was then being done on self-organizing systems and not
about digital computers as such but the argument over the relative merits
of these two approaches was already taking shape in 1933.

Yes, we were talking about `thinking machines' in 1933 and even about the
use of electronic devices to solve problems in Artificial Intelligence
although the term Artificial Intelligence had not yet been coined and the
digital computer was still only a dream.  I am sure that our ideas at the
time were hazy in the extreme.  One is apt to remember what one should
have thought at an earlier time and not what one actually did think. On
the other hand, there is the opposing tendency to forget the long gradual
evolution that usually lies behind any new development such, for example,
as the development of the modern digital computer.  I can remember talking
with others at the Bell Laboratories about the need for better computing
facilities and about the feasibility of using the long-life vacuum tubes,
that I had worked on, for the construction of some ill defined machine
that would help me in solving some of my problems.

The war years put a stop to my idle speculation and it was not until 1946
after I had moved to the University of Illinois that I was again able to
think about electronic aids
to calculations and specifically about digital computers.  My 
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\noindent old obsession with solutions of space charge problems with
complex boundary conditions still persisted and I began to agitate for a
digital computer.

Perhaps, it will give you a flavor of conditions in the late 1940's if I tell
you something about our problems at Illinois during this period.
After a lot of delays in getting started we eventually got an appropriation
of $\$$110,000, a lot of money in those days, to build a computer. I was
busy running an Electron Device Laboratory but I did have a lot of
graduate students tumbling all over themselves trying to find suitable
suitable thesis subjects so I put several of them to work on new computer
components, building a cathode ray storage tube, developing a
magnetic core storage scheme, developing some special non-sequencial
adder and multiplier circuits etc.

We were overambitious, and it soon became evident that we did not have
the amount of money that it then took to build the kind of computer that
we wanted. The only solution seemed to be that of putting something
together that was not ideal.  The hope was that we could use this interim
computer to do something spectacular that would induce the University
authorities to give us some more money.

Since I had been making noises about unconventional things that one could
do with a Computer, someone, I think it was Taube, suggested that I was
the logical person to undertake the programming task.  My first thought
was that I should fulfill Babbage's old dream of having it play chess.  I
was disheartened when I found that Claude Shannon had apparently beaten me
to the punch, but then, when I had arranged to meet Claude, I learned that
he was much less far along with the problem that newspaper reports had led
me to believe.  My talk with Claude did awaken me to the difficulties that
chess presented.

Checkers was my next choice. It happened that a world champion checker
match was to be held in the neighboring town, Kankakee, the next spring
and it seemed quite reasonable at the time for us to put together some
sort of a computer in a few months and for me to write a checker program
during this same period of time that could challenge and beat the new
world checker champion at the conclusion of this match.  This would give
us the publicity that we needed.  How naive can one be?

So I started to work, writing a checker program for a machine that did not
exist, using an instruction set that I was forced to create as I needed
it, writing directly in octal, and assigning fixed memory locations for my
variables, since the idea of an assembly language and of an assembler had
not yet been invented.  The situation was ideal to highlight the need for
a symbolic notation and why I did not invent one, I will never know.

Finding myself thwarted in my desires to devote all of my time to computer
activities by the teaching load that I had to carry, I decided to leave
University life in 1949 and go with IBM.  I had written a checker program
of sorts but the computer was still a long way from completion.

I had, by this time, become a confirmed hacker so I wanted to get my
checker program to run on an IBM 
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\noindent machine.  The program, of
course, had to be completely rewritten, first for an interim computer
called the Defense Calculator and then for the 701 as soon as the
instruction set for this machine had begun to take shape.  This first 701
version was still written in octal and I was able to get it to run on the
very first experimental model of the 701.  Late, after the idea of an
assembly program had been conceived and implemented, I was faced with the
difficult task of converting a running machine-language program from its
octal form into symbolic notation.  This led me to write a ``Disassembly''
program, which was to do yeoman duties in converting the library of system
and application programs, that had by then been written, from octal to
symbolic form.

IBM, in those days did not take kindly to one of their engineers wasting
company time playing checkers, even if it was against a machine, and so
most of my checker work had to be done on my own time.  I had dressed my
efforts up with a certain amount of respectability by adding a learning
feature to the program but even then it was the use of the program as a
computer test vehicle which could be adjusted to run continuously that got
me the machine time that I needed to try out my experimental learning
routines.

Later, during the 704 days, I can remember a period when there were often
several 704's on the test floor at the same time with test crews working
two shifts a day. The test crews would arrange to let me have all the time
I wanted from 11 PM to 7 AM, sometimes on as many as four machines at the
same time, in return for reports on the machine behavior.

Several events occured along about this time that make it possible for me
to recall the exact time sequence of AI development during these early
years. Two of these events had a significant effect on the course of
future events.

The first event was the Dartmouth Summer Research Project on Artificial
Intelligence that John McCarthy ran during the summer of 1956.
The name Arti\-fi\-cial Intel\-li\-gence was, of course, coined by John in
the process of naming this project.  Only a very few people were
involved, but never again would it be possible for such
a small a group to has such a profound influence on the course of AI
research.  The interchange of ideas that occurred at Dartmouth did a great
deal to establish AI as a viable subject for continuing research.

The second event was the publication in 1963 of the book ``Computers and
Thought'' edited by Feigenbaum and Feldman. This book reprinted twenty
papers by twenty eight authors that the editors thought would summarize or
at least would be typical of the state of the art in some ten aspects of
the general field of artificial intelligence.  Most of you know this book,
I am sure, but I do want to call your attention to two rather significant
features. 

In the first place, most of the papers discussed were what might be
called ``toy'' problems, that is problems that were specifically chosen
because they required the development of new AI principles but were,
never-the-less, suf\-fi\-cient\-ly well defined that one could measure one's
prog\-ress toward meeting some desired goal.  A second feature was the sharp
division of the papers into two distinct categories,
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\noindent the first set that
tried to look at the way that people solved the chosen problem and ape
this action by machine, and the second group that considered the problem
more or less in the abstract and tried to invent machine methods that were
tied to the unique characteristics of the computer.  This book was to
become the bible of the early workers in the field.

It may be instructive to compare a recent publication, ``The Handbook of
Artificial Intelligence'', for which Feigenbaum was again one of the
authors, with this earlier book.  The difference in treatment, and the
difference in size, attest to the fact that AI is growing up.

The third and fourth events, not of earth shaking sig\-nif\-i\-cance, but
important to me as mnemonic aids, were the publication by me of two
papers, the first one contained my assessment of the state of AI in 1962,
and the second made predictions as to where computers would be in
1984.

The first paper was called ``Artificial Intelligence:  A Frontier of
Automation''.  I am tempted to quote from this paper at some length since
many things that I said then could equally well be said today, but I will
forbear.  The paper ends with a statement that ``We are still in the
game-playing stage, but, as we progress in our understanding, it seems
reasonable to assume that these newer techniques will be applied to real
life with increasing frequency and that the effort devoted to games and
other toy problems will decrease.''  I will have more to say about toy
problems later because I have come to attach greater significance to them
than I did in 1962.  

As a minor aside, I revealed my own personal bias between the two
approaches to AI problems by using the phrase ``Bird-Watching'' to head
the section describing the how-people-solve-the-problem approach and by
using the phrase ``Back to Aerodynamics'' to head the section describing
the second approach.  Here again, there has been a softening of my disdain
for inquires as to how people handle problems and in particular as to the
way in which they learn. But more on this later.

I cannot refrain from quoting another excerpt from this paper to wit:
``Progress is being made in machine learning and ...we will be able to
devise better machines or even to program existing ones so that they can
outperform man in most forms of mental activity. In fact, one suspects
that our present machines would be able to do this now were we but smart
enough to write the right kinds of programs.  The limitation is not in the
machine but in man.  Here then is a paradox. In order to make machines
which appear smarter than man, man himself must be smarter than the
machine''.  This, I think, is the proper answer to those people
who decry the way that computers are permeating our society and who
express the fear that the machine will eventually dominate man.

The second paper, published in 1964, and called ``The Banishment of Paper
Work'', was an attempt to predict the state of affairs in 1984.  I want to
review these predictions both to assess our progress and as a prelude to
making some new predictions for the year 2000.

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A few excerpts will give the flavor of these predictions.  In one place I
say ``Given computers that are perhaps 100 to 1000 times as fast as the
present day computers [they now bracket this range],
computers with large memories [and they have gotten large, in spite of von
Neumann's insistence that 1000 words of fast storage was all that would
ever be needed], computers which occupy perhaps one one-hundredth the
volume that they now do [again, just about right], computers that are much
cheaper [the current factor is about 1000] and finally computers that
learn from their experience and which can converse freely with their
masters---what can we predict?''  All of this has become more or less true,
except for the final prediction that computers would learn from their
experience and that they would be able to converse freely with their
masters.

I went on to predict a number of specific things about computers.
I will mention only three:

1. That we would see a dichotomy in the development of very large
computers acting as number crunchers and as data bases with the widespread
use of small personal computers that were quite respectable computers in
their own right but that would also serve as terminals for access to the
central data bases.

2. That all the accounting work of the world would be done by computer,
hence the title of The Banishment of Paper-Work.

3. That process control with the attending automation would have
reached a very high degree of development.

As for my unrealized predictions regarding artificial in\-tel\-li\-gence, I
went on to say that: ``...we have good reason for
predicting that two rather basic problems will have been solved. The first
of these has to do with learning... .The second difficulty resides in the
nature of the instructions which must be given ... in
short one cannot converse with a computer.''  I still believe that I was
right in assessing these two problems as the two most basic problems that
stood in the way of progress but I was completely wrong in my belief that
they would be solved in twenty years.

One can quibble with the soundness of my reasoning when I predicted that
these problems would be solved. In one sense, problems relating to the
real world are never solved, since each new contribution to our
understanding usually leads to the unfolding of facets of the problem that
we did not previously know existed.  This has happened in the
field of AI. What I did mean was that our lack of understanding would no
longer stand in the way of our effective use of machine learning and of
man-computer communications in the application of AI to problems of the
real world. We are still some distance from this goal.

The situation with respect to machine learning is particularly
distressing.  What does The Handbook of Artificial Intelligence have to
say about the matter?  I quote ``But in general, learning is not
noticeable in AI systems.''

One might question the correctness of this stricture when one notes that
the third volume of this same handbook devotes some 188 pages, out of the
three-volume total of 1300 pages to the subject of ``Learning and
Inductive Inference''. Much of this work is really quite good.  An
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\noindent embarrassingly large amount of space, however, is used to describe some of
my own work, work that was done twenty and more years ago and work that
should have long since been superseded.

The stricture in volume 1 is essentially correct, since it is refering to
the use of AI learning methods in application programs and not to the
research on learning as such, and not also to the transfer of man derived
knowledge to computer programs as is usually done in most Expert Systems
applications.  In fact the stricture appears even more damning when one
considers the large amount of good research work that has been done, and
the extent to which Knowledge Engineering and Expert Systems have been
adopted by practitioners.

A considerable amount of work has been done on the problem of man-machine
communication, and in making use of this work in AI systems, if we
include, under this umbrella, the work on menu-driven techniques, the use
of questioning techniques in which the computer restricts the range of the
human responses by asking questions, the work on character reading, and
the more profound work on speech recognition, and on language
understanding, In spite of all of this work, we are still a long way from
having a complete answer to the problem of man-machine communication.  I
believe that the desired progress in man-machine communication will not be
made until it is treated as a learning problem.

To illustrate my point, let me raise the question as to how a child learns
to talk.  You will note an implication in the very way that we phrase the
question. The question is: How does a child learn to talk?, not, How do we
store in the child's brain all the information that he or she will need in
order to be able to talk?  Children learn to talk willy-nilly, whether
there is a conscious effort on the part of their parents to teach them or
not.  We explain this by saying that children are born with an instinctive
desire to learn, and with an instinctive ability to learn.  Computers do not have
instinctive desires but they are obedient servants and if we tell then to
learn and if we tell them how to learn, they will do it. So all we have to
do is to tell them how, either by wiring this knowledge into their very
structure or by supplying them with the necessary software.  This we have
failed to do.

In fact, I look on learning as the central problem of AI research, and I
believe that our failure to address this problem with enough vigor is the
chief reason why the entire field of AI has failed to come up to my
expectations.  Oh, of course, you will say that my expectations were
highly inflated and based on a gross failure on my part to realize how
difficult the problems really are. The fact remains, that my 1963 AI
predictions are nearly as far from realization today as they were in 1963,
and this in spite of the fact that there has been much more research, and
good research, done on AI in general, than I envisioned possible in
1963.

As an example, I said in 1963 that; ``Computers will perform yet
another function---that of language translation. Not only will we be able
to obtain information from
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\noindent the central files in the language of one's
choice, but auto\-matic trans\-lation via the telephone will also have come
into use---although perhaps not general use, because of the cost and
because of the gradual drift toward a universal language.  It will,
nevertheless, be possible to dial anywhere in the world and converse with
anyone speaking a different language with only a slight transmission delay
to allow for the differences in sentence structure and word ordering
between the languages.''  The drift toward a universal language has been
painfully slow and does not lead to any simple solution because of
linguistic loyalties although English has become the lingua franca in
some situations as evidenced by this
conference that is being conducted in English in a German speaking
country. There is still need for a telephone translation system which
would be technically feasible to implement today if we only knew how.

Perhaps it is time to pause and see where all of this is leading us. 

In the first place, there is an apparent inconsistency in my remarks.  I am
advocating an increase in research effort in the field of machine learning
and yet the deficiencies that I have been citing all seem to be in the
application of learning principles to problems of the real world.  I think
that this can be explained by remarking that the users of AI systems and
particularly the designers of these systems would be anxious to add more
elaborate learning schemes to their systems if they could.  This, they are
not able to do because there seem to be missing links in our
knowledge that it is your business to supply.

Let me see if I can point out some of these missing links.  I will do this
under the four headings used in The Handbook of Artificial Intelligence.
These headings are: 1. Rote learning, 2.Learning by being told, 3.
Learning from examples, and 4.  Learning by analogy.

{\bf \noindent Rote Learning}

Rote learning was one of the first forms to be studied. As the Handbook
points out, rote learning is useful only if it takes less time to
retrieve the learned information  than it does to recompute it.

There is an interesting parallel, where, in the early days, the Mark I
computer at Harvard was constructed and used primarily for the purpose of
computing mathematical tables that were bound into books and offered for
sale.  Most of these books were never sold and those that were sold were
never used because the computational speeds of the, soon to follow, true
electronic computers made it easier to recompute mathematical functions as
needed than to store and retreive them.  We did go through an interesting
transition state in which key tabular values were frequently stored and
used with interpolation routines, but even this use of stored values has
largely disappeared.

So I look on rote learning as ancient history.

{\bf \noindent Learning by Being Told}

Learning by being told also appeared on the AI scene at an early date with
the publication of John McCarthy's paper on advice-taking systems. But
here, future work followed two diverging paths, the one envisioned by
McCarthy in which the system would be able to accept abstract, high-
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\noindent level
advice and turn this advice into rules that would govern its subsequent
behavior, and the second in which the emphasis has been on the development
of tools that make it easy for an expert to transfer his detailed and extensive
knowledge to the system.

This division between the Advice Taker and the more recent work on Expert
Systems may seem unnatural and may even be resented by devotees of the expert
system approach. In support of my view, I can reference a recent rather long
review paper on ``Expert Computer Systems'' that lists some fifty references
and that neither references nor mentions McCarthy's work or any of the much more
recent work typified by the work of Mostow, Hayes-Roth, Klahr, and Burge 
that can be considered as a follow up on McCarthy's original ideas.

By and large, there has been a hiatus in the work on McCarthy's Advice
Taker that is, in my opinion, very unfortunate. There is some work being
done, as previously noted, but not nearly enough.  This is not the place
to try to develop a procedure for rectifying this deficiency, and indeed I
am afraid that I have nothing constructive to offer.

A great many tools have been and are being developed to simplify the task
of transporting specific and sometimes abstractively formulated expertise
from a human expert to a computer program. In fact the entire field of
Knowledge Engineering and of Expert Systems, as commonly understood, is
based on this work.  I certainly do not want to belittle this work but
much of it is application engineering and not research.

{\bf \noindent Learning from Examples}

If we again judge the effort in a field by the number of pages devoted to
the field, in the Handbook of Artificial Intelligence, then certainly
learning from examples is the most extensively studied phase of learning,
with 150 out of 188 pages devoted to it.  So I believe we can conclude
that this form of learning is being given enough attention.

{\bf \noindent Learning by Analogy}

Refering to the Handbook, which is my bible on these matters, we note that it
leaves this form of learning undiscussed  ``...since this area has not received 
much attention.'' So here is a neglected area, that is, if it is important.  I
happen to think that it is.

Perhaps there is a slight misconception here that confuses the issue.  By
and large, people do not often learn by analogy. People reason by analogy
in order to try to make sense out of a strange situation but then, if they
are smart, they try to confirm the correctness of the analogy by some
independent method of evaluation.  If computers are to ape the way that
people learn, they must somehow be given this added ability.

And speaking of the way people learn, this brings me back to a point that
I have been on the verge of making several times during this talk.  When
we speak of research in machine learning methods we are reallly concerned
with a problem in human learning and in how we can best devise experiments
that will make it possible for us to learn how to tell a computer how to
learn.  So we must, after all, look into the way people learn, taking advantage,
when we can,
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\noindent  of the work in the emerging field of Cognitive Science.

Also, there is still a need for continuing work on toy problems, since it
is often quite impossible to control, or even to know about, all of the
variables that are involved when one deals with a real-life problem.

Perhaps I have said enough.  It was my original intention to cite chapter
and verse as to why research in artificial intelligence was in danger of
being neglected, but you already know these reasons and they have even been
discussed in the public press. 

So it is time for me to make my predictions for the year 2000 and to sit down.

As far as computers are concerned, I predict:

1. The present trend toward personal computers will continue and two distinct
types of small computers will be available, a home computer and a much
smaller pocket computer.

2. The pocket computer will be a rather modest sixteen bit computer with,
perhaps, 128 K bytes of a non-volatile memory.  Its chief function will
be to communicate by radio with one's home computer or one's office
computer either directly or via an exchange system when far away. It will,
in effect, be a very smart terminal.  It will also double as a telephone
so that one will be able to speak with anyone any place in the world who
has a phone connection. One will also be able to send voice messages to
those computer systems that can receive such messages although the pocket
computer itself will not be able to receive voice communications.
Video-phone equipment will still be rather bulky and so most people will
not include this facility in their pocket computers.

3. The home computer will be a thirty-two-bit machine and it will have at
least ten million bytes of memory.  This may use a non-volatile storage
medium although such a memory will have to compete with volatile storage
schemes that consume so little power that they will normally be left on at
all times.

This computer will be capable of communicating with the many
large data banks that will be available and with other personal computers,
all, of course, under suitable protocols which attend to such matters as
file integrity and secrecy.  It will also be capable of communicating via
radio with one's pocket computer, as mentioned earlier.

The home computer will be capable of receiving and transmitting pictorial
material as well as textual material, and it will function as a
video-telephone when this is desired. Since there will still be an
occasional need for printed output this computer will produce book quality
output and adequate paper handling facilities will be available, probably
at an extra cost, to enable one to receive printed and pictorial material,
the morning paper perhaps, without having to be present at the computer
when it is arriving.

The tendency will be to leave one's home computer operating at all times
so that it can receive and store messages that may be sent to it and even
acknowledge the receipt of such messages. Most home computers will be
battery powered with a trickle charger so as to be independent of power failures.

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4. Many large centrally located computers with associated large data files
will be available for public use, rather later than I predicted in 1963,
but differing from those predicted for 1984 by the routine use of the
knowledge engineering principals that are currently being used in pres\-ent
day Expert Systems, even augmented by principles yet to be discovered
through future research. These systems will be, in effect, expert systems
for the non-expert.

As I said in 1963, ``One will be able to browse through the fiction
section of the central library, enjoy an evening's light entertainment
viewing any movie that has ever been produced (for a suitable fee, of
course, since Hollywood will still be commercial) or inquire as to the
previous day's production figures for tin in Bolivia---all for the asking
via one's remote terminal''.

But even more than that, these systems will be able to acquire knowledge
and to learn not only from experts but also from their experience in
dealing with the general public.  Information acquired from the public, and
gen\-er\-al\-iza\-tions based on this information, will probably still have to be
approved by a panel of experts before being in\-corp\-or\-at\-ed into the data
base available to the public.

5. Large centrally located computers will still be used for large
calculations but they will have reached a practical limit in size
as set by velocity of light limitations.  The general trend will be
either to partition large problems so that they can be solved in pieces or to
tie many separate machines together on a temporary basis for truly large
problems.
Newer methods of storage will have made it possible to
construct very large memories so that this will not be a bottleneck.

6. I am less sanguine now than I was in 1963, about the prediction that:
``Libraries for books will have ceased to exist in the more advanced
countries except for a few which will be preserved as mu\-se\-ums''
although, I do believe now as I then said ,``and most of the world's
knowledge will be in machine readable form''. 

Having lived with computers for many years, with a terminal in my office
and one in my study at home, I still find printed books to be very
comforting.  I suspect that many people feel this way and I now wonder if
this feeling will somehow keep libraries in existence in spite of the fact
that, and I again quote, ``... the storage problem will make it imperative
that a more condensed form of recording be used,..''  Almost all of the
new knowledge that is being generated, even at the present time, is now in
machine readable form because of the wide spread use of word processing
systems but we may still have a way to go before the machine-readable form
will be the only form in which this knowledge is preserved.

7. I am much less sure as to where AI research will be in the year 2000.
Perhaps I should leave this, as they say in some texts, as an exercise for
the reader. If I let you make the predictions then, perhaps, you will feel
a compulsion to do the necessary research to guarentee that your predictions 
come true. It is up to you to decide what the future will be.

And now having said too much, I will sit down.

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\eject
\end