A Brief Review of Artificial Intelligence Research
The Forties:
The seeds of artificial intelligence research
were sown during World War II when such electronic computers as
the ENIAC, the EDVAC, the Aiken Mark I, the Differential
Analyzer, and a number of analog shipboard fire control computers
made their debut. It's not hard to see how these new
"electronic brains", which could perform arithmetic and
logical calculations orders of magnitude faster than human
"computresses", would fire the imaginations of
engineers and mathematicians. Could we develop thinking machines
which could outperform humans in the mental arena, as labor
saving machinery had already done in the physical domain? As Dr.
Hans Moravec put it,
"One line of
research, called Cybernetics, used analog circuitry to produce
machines that could recognize simple patterns, and turtle-like
robots that found their way to lighted recharging hutches. An
entirely different approach, named Artificial Intelligence (AI)
attempted to duplicate rational human thought in large
computers."
The brain was regarded as a digital computer
but, perhaps, with analog/digital circuits to accommodate control
functions. In the latter 40's and early 50's, there was great
fanfare regarding these prospects, together with concerns over
what "technological unemployment', "automation",
and the new science of cybernetics would do to humanity in a
robot-run world. What would happen when we humans were no longer
the smartest people we knew? An MIT mathematical prodigy, Dr.
Norbert Wiener, wrote books called "Cybernetics", and
"The Human Use of Human Beings", calling for
responsible application of this revolutionary technology. The
science fiction films of the era ("The Day the Earth Stood
Still") had the robot as the master and a specially-created
human emissary as its loyal servant. (Remember Jack Williamson's
"The Humanoids"?) The mind was considered to be a
program which ran in the brain, and it was thought to be only a
matter of a few short years before intelligent machines were
running our factories. This was the era of the first-generation
vacuum tube computers such as the UNIVAC I, and the IBM 650 and
701. It was also the era of patch boards and analog computers.
Feedback systems and servo theory were very much in vogue.
Not to be ignored throughout the whole period
from the forties to the nineties were the continuing studies of
the brain by neurology researchers. These tended to proceed
largely, though not completely, independently of artificial
intelligence research.
The Fifties and Sixties:
This rampant optimism persisted
throughout the 50's and well into the 60's. In 1959, a Cornell
Aeronautical Laboratories psychologist by the name of Dr. Frank
Rosenblatt developed the first artificial neuron-based computer,
called "The Perceptron". It was a 500-neuron,
single-layer neural network and was attached to a 400-photocell
optical array. Another major milestone occurred that year when
Simon and Newell developed a theorem-proving program called
"Logic Theorist" which was able to prove a number of
mathematical theorems. Checkers-playing programs, algebraic
manipulation programs (including symbolic integration and
differential equation solving), language translation, natural
language processing, and () were all under development during
this time. OCR-A and OCR-B typing balls were offered for IBM
Selectric typewriters, and optical character recognition systems
were available to read text printed in those fonts. Simple
wire-following robots that any radio amateur could build were
devised and described in Scientific American. As one writer has
put it, this was a period of "initial intoxication with
cognitive science". (As we shall see in the Section below
concerning the capabilities of the brain, the computers of the
50's were ludicrously slow and small, by a factor of at least
1,000,000 and perhaps closer to 1,000,000,000,000, for the
implementation of human-caliber intelligence.)
In the early sixties, the U.S. Postal Service
mounted a major effort to develop optical character recognition
hardware and software. (The program was oversold at the time but
by now, it has led to advanced optical character recognition
equipment which is in daily use by the Postal Service.) Also, in
the early sixties, Simon and Newell created the General Problem
Solver (GPS) as a generalized theorem proving system. Throughout
the sixties, there was a ferment of activities in all areas of
artificial intelligence. (Digital-to-analog converters probably
weren't fast enough in the sixties to do much with machine
vision.)
However, by the end of the decade, the Postal
Service had discovered how difficult it was to build a machine
that could read addresses on letters. IBM had thrown in the towel
on their Russian language translation program when it became
apparent that a computer couldn't translate language without
understanding it. And computers were too slow by many orders of
magnitude for machine vision, virtual reality, and speech and
handwriting recognition. While they could perform arithmetic and
logical manipulations with great proficiency, they were
light-years away from posing their own problems or understanding
the real world, let alone handling the subtle nuances of
interpersonal relationships.
In 1969, Drs. Marvin Minsky and Seymour Papert
of MIT published a book entitled "Perceptrons" in which
they proved that single layer perceptron networks were, among
other limitations, inherently incapable of performing the
exclusive OR function, and were a dead end. One would think that
their arguments would have been insupportable. After all, the
human brain is a neural network of incredible complexity,
containing tens of billions of neurons and hundreds of trillions
of synapses. But for some reason, they were sufficient to derail
neural network research for 15 years. (The authors would later
explain that neural networks were competitors for research
money.) Such is the power of scientific snobbery.
The Seventies:
In the early 70's, researchers at
Stanford and MIT began mounting TV cameras and manipulators on
wheeled robotic carts and turning them loose in real-world
environments. To quote Dr. Moravec again,
"What a shock!
While the pure reasoning programs did their jobs about as well
and about as fast as a college freshman, the best robot control
programs took hours to find and pick up a few blocks on a table,
and often failed completely, a performance much worse than a
six-month old child. This disparity, between programs that reason
and programs that perceive and act holds to this day. At Carnegie
Mellon University there are two desk-sized computers that can
play chess at grandmaster level, within the top 100 players in
the world, when given their moves on a keyboard. But present-day
robotics could produce only a complex and unreliable machine for
finding and moving normal chess pieces.
"In hindsight,
it seems that, in an absolute sense, reasoning is much easier
than perceiving and acting—a position not hard to rationalize
in evolutionary terms. The survival of human beings and their
ancestors has depended for hundreds of millions of years on
seeing and moving in the physical world, and in that competition
large parts of their brains have become efficiently organized for
the task. But we didn't appreciate this monumental skill because
it is shared by every human being and most animals—it is
commonplace. On the other hand, rational thinking, as in chess,
is a newly acquired skill, perhaps less than one hundred thousand
years old. The parts of our brains devoted to it are not well
organized, and, in an absolute sense, we're not very good at it.
But until recently, we had no competition to show us up."1
Image enhancement was a popular topic in the
70's in support of DoD and NASA satellite image analysis and
JPL's successes with Voyager photographs. Intel introduced the
first microprocessor chip: the 8008.
The Eighties:
Another False Dawn for AI
In the mid-80's, artificial intelligence
enjoyed another false dawn. This time, it was rule-based expert
systems, tree searches, and Symbolics Computers. Expert systems
proved hostage to the intuition that so often guides human beings
and that depends upon an overall understanding of the world.
Also, it took too long to enter all the rules into a computer
program. Expert systems still exist but they don't replace
experts. Symbolics Computer Systems soon declared bankruptcy.
Slow Patient Progress Behind the Scenes
In the meantime, slow, patient progress was
underway. Machine vision systems began to be used for assembly
line inspections. Unimation's "Puma" robotic arms were
installed to carry out repetitive assembly line functions. Cheap
embedded microprocessor chips were becoming faster and faster.
The rapidly rising capabilities of personal computers permitted
rapid programming of sophisticated software. Caere's Omnipage
Professional became an increasingly robust optical character
recognition program. Video games became ever more realistic.
Though initially very expensive, trail-blazing speech recognition
systems were developed by Bell Labs, and by many universities and
small companies.
The Resurrection of Neural Networks and Fuzzy Logic
During the 80's, a few "keepers of the
flame" had devised multi-layer neural networks that
circumvented the limitations described by Minsky and Papert.
Fuzzy logic and genetic programming were added to neural
networks, which were embraced with great enthusiasm by the
Japanese. Various kinds of multi-layer neural networks with
back-propagation and sometimes, fuzzy logic, are proving to
possess fascinating and highly useful capabilities in the areas
of pattern recognition and control. The latest release (6.0) of
the Omnipage optical character recognition package incorporates a
neural network to help recognize printed text. There is a great
ferment of activity in this now-highly-fashionable area of
research.
Neural networks and fuzzy logic are hot!!
Genetic programming seems to be receiving less
attention.
The Nineties:
Speech Recognition in 1990:
Computer control by voice command became
available in the early 90's: Dragon Systems for IBM-compatibles
and Voice Navigator for the Macintosh. These early speech
recognition systems were speaker-dependent and had vocabularies
of a few hundred words, spoken one at a time. (In 1991, AT&T
had a laboratory system capable of recognizing continuous speech,
but it required16 parallel, 32-bit digital signal processors.)
Speech Recognition in 1995:
In 1995, IBM began offering a voice dictation
package with their latest PCs. The IBM system is context
sensitive and can distinguish among homonyms. Dragon Systems
recently introduced a 120,000-word, discrete-word speech
recognition system called DragonDictate, while Apple Computer
Company is bundling a speech recognition program called
Voiceprint with its high-end 8500 and 9500 computers. IBM's
"VoiceType" is the most accurate of the speech
dictation systems and includes the ability to examine context and
to distinguish among homonyms. A small company called Speech
Systems, Inc., began offering the first continuous speech,
speaker independent, voice dictation system for personal computer
owners in 1995. These systems aren't yet the kind of Smith Corona
"Voicewriter" that you'll be able to buy from Service
Merchandise sometime within the next ten or fifteen years, but
they'll get there.
Other 1995 Capabilities:
Optical character recognition (OCR) has
improved steadily with the Omnipage Professional series of OCR
packages, coupled with 600 dot-per-inch and higher resolution
scanners. Handwriting recognition has improved rapidly since
Apple Computing introduced the first Newton Personal Digital
Assistant in 1992. Voice synthesis systems are also improving
steadily at AT&T. Facial recognition systems are under
development, together with usable fingerprint identification
packages. Machine vision and industrial robotics systems should
be entering their heyday with cheap multi-gigops processors such
as the Texas Instruments T320C80 entering the marketplace.
These are uniquely-human capabilities which
are not even shared with the rest of the animal kingdom.
Interestingly enough, they are being realized using conventional
computers running conventional software. And these programs will
only improve. The introduction of MMx processing on 80X86
processors in early 1997, coupled with ever-increasing clock
rates, wider data paths, and Intel's 1998 P7 processor, should
afford a 5-to-40-fold jump in implementing these higher human
functions.
However, it is important to distinguish
between systems that perform functions upon command and
self-organizing systems that give commands. What is missing from
this picture are the self-aware, self-organizing, motivated
characteristics of the animal kingdom, so perhaps it is in this
arena that we might gainfully concentrate our efforts.
Two of the most striking areas of computer
progress thus far in the 1990s are the Internet and the advances
which are being made in computer graphics.
Historical Summary
It is clear that early AI researchers hugely
underestimated the computational requirements of artificial
intelligence.
AI research has been hampered by
"Big-Endian" and "Little-Endian" arguments
about whether to concentrate on connectionist (neural net) or
purely cognitive (e.g., theorem proving) approaches to achieving
artificial intelligence. In reality, the two approaches will
probably turn out to be complementary. It is never a good idea in
research to put all one's eggs in one basket.