This paper describes how the performance of AI machines tends
to improve at the same pace that AI researchers get access to
faster hardware. The processing power and memory capacity necessary
to match general intellectual performance of the human brain
are estimated. Based on extrapolation of past trends and on examination
of technologies under development, it is predicted that the required
hardware will be available in cheap machines in the 2020s.
Brains, Eyes and Machines
Computers have far to go to match human strengths, and our
estimates will depend on analogy and extrapolation. Fortunately,
these are grounded in the first bit of the journey, now behind
us. Thirty years of computer vision reveals that 1 MIPS can extract
simple features from real-time imagery--tracking a white line
or a white spot on a mottled background. 10 MIPS can follow complex
gray-scale patches--as smart bombs, cruise missiles and early
self-driving vans attest. 100 MIPS can follow moderately unpredictable
features like roads--as recent long NAVLAB trips demonstrate.
1,000 MIPS will be adequate for coarse-grained three-dimensional
spatial awareness--illustrated by several mid-resolution stereoscopic
vision programs, including my own. 10,000 MIPS can find three-dimensional
objects in clutter--suggested by several "bin-picking"
and high-resolution stereo-vision demonstrations, which accomplish
the task in an hour or so at 10 MIPS. The data fades there--research
careers are too short, and computer memories too small, for significantly
more elaborate experiments.
There are considerations other than sheer scale. At 1 MIPS
the best results come from finely hand-crafted programs that
distill sensor data with utmost efficiency. 100-MIPS processes
weigh their inputs against a wide range of hypotheses, with many
parameters, that learning programs adjust better than the overburdened
programmers. Learning of all sorts will be increasingly important
as computer power and robot programs grow. This effect is evident
in related areas. At the close of the 1980s, as widely available
computers reached 10 MIPS, good optical character reading (OCR)
programs, able to read most printed and typewritten text, began
to appear. They used hand-constructed "feature detectors"
for parts of letter shapes, with very little learning. As computer
power passed 100 MIPS, trainable OCR programs appeared that could
learn unusual typestyles from examples, and the latest and best
programs learn their entire data sets. Handwriting recognizers,
used by the Post Office to sort mail, and in computers, notably
Apple's Newton, have followed a similar path. Speech recognition
also fits the model. Under the direction of Raj Reddy, who began
his research at Stanford in the 1960s, Carnegie Mellon has led
in computer transcription of continuous spoken speech. In 1992
Reddy's group demonstrated a program called Sphinx II on a 15-MIPS
workstation with 100 MIPS of specialized signal-processing circuitry.
Sphinx II was able to deal with arbitrary English speakers using
a several-thousand-word vocabulary. The system's word detectors,
encoded in statistical structures known as Markov tables, were
shaped by an automatic learning process that digested hundreds
of hours of spoken examples from thousands of Carnegie Mellon
volunteers enticed by rewards of pizza and ice cream. Several
practical voice-control and dictation systems are sold for personal
computers today, and some heavy users are substituting larynx
for wrist damage.
More computer power is needed to reach human performance,
but how much? Human and animal brain sizes imply an answer, if
we can relate nerve volume to computation. Structurally and functionally,
one of the best understood neural assemblies is the retina of
the vertebrate eye. Happily, similar operations have been developed
for robot vision, handing us a rough conversion factor.
The retina is a transparent, paper-thin layer of nerve tissue
at the back of the eyeball on which the eye's lens projects an
image of the world. It is connected by the optic nerve, a million-fiber
cable, to regions deep in the brain. It is a part of the brain
convenient for study, even in living animals because of its peripheral
location and because its function is straightforward compared
with the brain's other mysteries. A human retina is less than
a centimeter square and a half-millimeter thick. It has about
100 million neurons, of five distinct kinds. Light-sensitive
cells feed wide spanning horizontal cells and narrower bipolar
cells, which are interconnected by whose outgoing fibers bundle
to form the optic nerve. Each of the million ganglion-cell axons
carries signals from a amacrine cells, and finally ganglion cells,
particular patch of image, indicating light intensity differences
over space or time: a million edge and motion detections. Overall,
the retina seems to process about ten one-million-point images
It takes robot vision programs about 100 computer instructions
to derive single edge or motion detections from comparable video
images. 100 million instructions are needed to do a million detections,
and 1,000 MIPS to repeat them ten times per second to match the
The 1,500 cubic centimeter human brain is about 100,000 times
as large as the retina, suggesting that matching overall human
behavior will take about 100 million MIPS of computer power.
Computer chess bolsters this yardstick. Deep Blue, the chess
machine that bested world chess champion Garry Kasparov in 1997,
used specialized chips to process chess moves at a the speed
equivalent to a 3 million MIPS universal computer (see Figure
3-4). This is 1/30 of the estimate for total human performance.
Since it is plausible that Kasparov, probably the best human
player ever, can apply his brainpower to the strange problems
of chess with an efficiency of 1/30, Deep Blue's near parity
with Kasparov's chess skill supports the retina-based extrapolation.
The most powerful experimental supercomputers in 1998, composed
of thousands or tens of thousands of the fastest microprocessors
and costing tens of millions of dollars, can do a few million
MIPS. They are within striking distance of being powerful enough
to match human brainpower, but are unlikely to be applied to
that end. Why tie up a rare twenty-million-dollar asset to develop
one ersatz-human, when millions of inexpensive original-model
humans are available? Such machines are needed for high-value
scientific calculations, mostly physical simulations, having
no cheaper substitutes. AI research must wait for the power to
become more affordable.
If 100 million MIPS could do the job of the human brain's
100 billion neurons, then one neuron is worth about 1/1,000 MIPS,
i.e., 1,000 instructions per second. That's probably not enough
to simulate an actual neuron, which can produce 1,000 finely
timed pulses per second. Our estimate is for very efficient programs
that imitate the aggregate function of thousand-neuron assemblies.
Almost all nervous systems contain subassemblies that big.
The small nervous systems of insects and other invertebrates
seem to be hardwired from birth, each neuron having its own special
predetermined links and function. The few-hundred-million-bit
insect genome is enough to specify connections of each of their
hundred thousand neurons. Humans, on the other hand, have 100
billion neurons, but only a few billion bits of genome. The human
brain seems to consist largely of regular structures whose neurons
are trimmed away as skills are learned, like featureless marble
blocks chiseled into individual sculptures. Analogously, robot
programs were precisely hand-coded when they occupied only a
few hundred thousand bytes of memory. Now that they've grown
to tens of millions of bytes, most of their content is learned
from example. But there is a big practical difference between
animal and robot learning. Animals learn individually, but robot
learning can be copied from one machine to another. For instance,
today's text and speech understanding programs were painstakingly
trained over months or years, but each customer's copy of the
software is "born" fully educated. Decoupling training
from use will allow robots to do more with less. Big computers
at the factory--maybe supercomputers with 1,000 times the power
of machines that can reasonably be placed in a robot--will process
large training sets under careful human supervision, and distill
the results into efficient programs and arrays of settings that
are then copied into myriads of individual robots with more modest
Programs need memory as well as processing speed to do their
work. The ratio of memory to speed has remained constant during
computing history. The earliest electronic computers had a few
thousand bytes of memory and could do a few thousand calculations
per second. Medium computers of 1980 had a million bytes of memory
and did a million calculations per second. Supercomputers in
1990 did a billion calculations per second and had a billion
bytes of memory. The latest, greatest supercomputers can do a
trillion calculations per second and can have a trillion bytes
of memory. Dividing memory by speed defines a "time constant,"
roughly how long it takes the computer to run once through its
memory. One megabyte per MIPS gives one second, a nice human
interval. Machines with less memory for their speed, typically
new models, seem fast, but unnecessarily limited to small programs.
Models with more memory for their speed, often ones reaching
the end of their run, can handle larger programs, but unpleasantly
slowly. For instance, the original Macintosh was introduced in
1984 with 1/2 MIPS and 1/8 megabyte, and was then considered
a very fast machine. The equally fast "fat Mac" with
1/2 megabyte ran larger programs at tolerable speed, but the
1 megabyte "Mac plus" verged on slow. The four megabyte
"Mac classic," the last 1/2 MIPS machine in the line,
was intolerably slow, and was soon supplanted by ten-times-faster
processors in the same enclosure. Customers maintain the ratio
by asking "would the next dollar be better spent on more
speed or more memory?"
The best evidence about nervous system memory puts most of
it in the synapses connecting the neurons. Molecular adjustments
allow synapses to be in a number of distinguishable states, lets
say one byte's worth. Then the 100-trillion-synapse brain would
hold the equivalent 100 million megabytes. This agrees with our
earlier estimate that it would take 100 million MIPS to mimic
the brain's function. The megabyte/MIPS ratio seems to hold for
nervous systems too! The contingency is the other way around:
computers are configured to interact at human time scales, and
robots interacting with humans seem also to be best at that ratio.
On the other hand, faster machines, for instance audio and video
processors and controllers of high-performance aircraft, have
many MIPS for each megabyte. Very slow machines, for instance
time-lapse security cameras and automatic data libraries, store
many megabytes for each of their MIPS. Flying insects seem to
be a few times faster than humans, so may have more MIPS than
megabytes. As in animals, cells in plants signal one other electrochemically
and enzymatically. Some plant cells seem specialized for communication,
though apparently not as extremely as animal neurons. One day
we may find that plants remember much, but process it slowly
(how does a redwood tree manage to rebuff rapidly evolving pests
during a 2,000 year lifespan, when it took mosquitoes only a
few decades to overcome DDT?).
With our conversions, a 100-MIPS robot, for instance Navlab,
has mental power similar to a 100,000-neuron housefly. The following
figure rates various entities.
MIPS and Megabytes. to mimic their behavior. Note the scale.
Entities rated by the computational power and memory of the smallest
universal computer needed is logarithmic on both axes: each vertical
division represents a thousandfold increase in processing power,
and each horizontal division a thousandfold increase in memory
size. Universal computers can imitate other entities at their
location in the diagram, but the more specialized entities cannot.
A 100-million-MIPS computer may be programmed not only to think
like a human, but also to imitate other similarly-sized computers.
But humans cannot imitate 100-million-MIPS computers--our general-purpose
calculation ability is under a millionth of a MIPS. Deep Blue's
special-purpose chess chips process moves like a 3-million-MIPS
computer, but its general-purpose power is only a thousand MIPS.
Most of the non-computer entities in the diagram can't function
in a general-purpose way at all. Universality is an almost magical
property, but it has costs. A universal machine may use ten or
more times the resources of one specialized for a task. But if
the task should change, as it usually does in research, the universal
machine can be reprogrammed, while the specialized machine must
By our estimate, today's very biggest supercomputers are within
a factor of a hundred of having the power to mimic a human mind.
Their successors a decade hence will be more than powerful enough.
Yet, it is unlikely that machines costing tens of millions of
dollars will be wasted doing what any human can do, when they
could instead be solving urgent physical and mathematical problems
nothing else can touch. Machines with human-like performance
will make economic sense only when they cost less than humans,
say when their "brains" cost about $1,000. When will
that day arrive?
The expense of computation has fallen rapidly and persistently
for a century. Steady improvements in mechanical and electromechanical
calculators before World War II had increased the speed of calculation
a thousandfold over hand calculation. The pace quickened with
the appearance of electronic computers during the war--from 1940
to 1980 the amount of computation available at a given cost increased
a millionfold. Vacuum tubes were replaced by transistors, and
transistors by integrated circuits, whose components became ever
smaller and more numerous. During the 1980s microcomputers reached
the consumer market, and the industry became more diverse and
competitive. Powerful, inexpensive computer workstations replaced
the drafting boards of circuit and computer designers, and an
increasing number of design steps were automated. The time to
bring a new generation of computer to market shrank from two
years at the beginning of the 1980s to less than nine months.
The computer and communication industries grew into the largest
Computers doubled in capacity every two years after the war,
a pace that became an industry given: companies that wished to
grow sought to exceed it, companies that failed to keep up lost
business. In the 1980s the doubling time contracted to 18 months,
and computer performance in the late 1990s seems to be doubling
every 12 months.
Faster than Exponential Growth in Computing Power. The number
of MIPS in $1000 of computer from 1900 to the present. Steady
improvements in mechanical and electromechanical calculators
before World War II had increased the speed of calculation a
thousandfold over manual methods from 1900 to 1940. The pace
quickened with the appearance of electronic computers during
the war, and 1940 to 1980 saw a millionfold increase. The pace
has been even quicker since then, a pace which would make humanlike
robots possible before the middle of the next century. The vertical
scale is logarithmic, the major divisions represent thousandfold
increases in computer performance. Exponential growth would show
as a straight line, the upward curve indicates faster than exponential
growth, or, equivalently, an accelerating rate of innovation.
The reduced spread of the data in the 1990s is probably the result
of intensified competition: underperforming machines are more
rapidly squeezed out. The numerical data for this power curve
are presented in the appendix.
At the present rate, computers suitable for humanlike robots
will appear in the 2020s. Can the pace be sustained for another
three decades? The graph shows no sign of abatement. If anything,
it hints that further contractions in time scale are in store.
But, one often encounters thoughtful articles by knowledgeable
people in the semiconductor industry giving detailed reasons
why the decades of phenomenal growth must soon come to an end.
The keynote for advancing computation is miniaturization:
smaller components have less inertia and operate more quickly
with less energy, and more of them can be packed in a given space.
First the moving parts shrunk, from the gears in mechanical calculators,
to small contacts in electromechanical machines, to bunches of
electrons in electronic computers. Next, the switches' supporting
structure underwent a vanishing act, from thumb-sized vacuum
tubes, to fly-sized transistors, to ever-diminishing flyspecks
on integrated circuit chips. Similar to printed circuits before
them, integrated circuits were made by a photographic process.
The desired pattern was projected onto a silicon chip, and subtle
chemistry used to add or remove the right sorts of matter in
the exposed areas.
In the mid-1970s, integrated circuits, age 15, hit a crisis
of adolescence. They then held ten thousand components, just
enough for an entire computer, and their finest details were
approaching 3 micrometers in size. Experienced engineers wrote
many articles warning that the end was near. Three micrometers
was barely larger than the wavelength of the light used to sculpt
the chip. The number of impurity atoms defining the tiny components
had grown so small that statistical scatter would soon render
most components out of spec, a problem aggravated by a similar
effect in the diminishing number of signaling electrons. Increasing
electrical gradients across diminishing gaps caused atoms to
creep through the crystal, degrading the circuit. Interactions
between ever-closer wires were about to ruin the signals. Chips
would soon generate too much heat to remove, and require too
many external connections to fit. The smaller memory cells were
suffering radiation-induced forgetfulness.
A look at the computer growth graph shows that the problems
were overcome, with a vengeance. Chip progress not only continued,
it sped up. Shorter-wavelength light was substituted, a more
precise way of implanting impurities was devised, voltages were
reduced, better insulators, shielding designs, more efficient
transistor designs, better heat sinks, denser pin patterns and
non-radioactive packaging materials were found. Where there is
sufficient financial incentive, there is a way. In fact, solutions
had been waiting in research labs for years, barely noticed by
the engineers in the field, who were perfecting established processes,
and worrying in print as those ran out of steam. As the need
became acute, enormous resources were redirected to draft laboratory
possibilities into production realities.
In the intervening years many problems were met and solved,
and innovations introduced, but now, nearing a mid-life 40, the
anxieties seem again to have crested. In 1996 major articles
appeared in scientific magazines and major national newspapers
worrying that electronics progress might be a decade from ending.
The cost of building new integrated circuit plants was approaching
a prohibitive billion dollars. Feature sizes were reaching 0.1
micrometers, the wavelength of the sculpting ultraviolet light.
Their transistors, scaled down steadily from 1970s designs, would
soon be so small that electrons would quantum "tunnel"
out of them. Wiring was becoming so dense it would crowd out
the components, and slow down and leak signals. Heat was increasing.
The articles didn't mention that less expensive plants could
make the same integrated circuits, if less cheaply and in smaller
quantities. Scale was necessary because the industry had grown
so large and competitive. Rather than signaling impending doom,
it indicated free-market success, a battle of titans driving
down costs to the users. They also failed to mention new contenders,
waiting on lab benches to step in should the leader fall.
The wave-like nature of matter at very small scales is a problem
for conventional transistors, which depend on the smooth flow
of masses of electrons. But, it is a property exploited by a
radical new class of components known as single-electron transistors
and quantum dots, which work by the interference of electron
waves. These new devices work better as they grow smaller. At
the scale of today's circuits, the interference patterns are
so fine that it takes only a little heat energy to bump electrons
from crest to crest, scrambling their operation. Thus, these
circuits have been demonstrated mostly at a few degrees above
absolute zero. But, as the devices are reduced, the interference
patterns widen, and it takes ever larger energy to disrupt them.
Scaled to about 0.01 micrometers, quantum interference switching
works at room temperature. It promises more than a thousand times
higher density than today's circuits, possibly a thousand times
the speed, and much lower power consumption, since it moves a
few electrons across small quantum bumps, rather than pushing
them in large masses through resistive material. In place of
much wiring, quantum interference logic may use chains of switching
devices. It could be manufactured by advanced descendants of
today's chip fabrication machinery (Goldhaber-Gordon et al. 1997).
Proposals abound in the research literature, and the industry
has the resources to perfect the circuits and their manufacture,
when the time comes.
Wilder possibilities are brewing. Switches and memory cells
made of single molecules have been demonstrated, which might
enable a volume to hold a billion times more circuitry than today.
Potentially blowing everything else away are "quantum computers,"
in which a whole computer, not just individual signals, acts
in a wavelike manner. Like a conventional computer, a quantum
computer consists of a number of memory cells whose contents
are modified in a sequence of logical transformations. Unlike
a conventional computer, whose memory cells are either 1 or 0,
each cell in a quantum computer is started in a quantum superposition
of both 1 and 0. The whole machine is a superposition of all
possible combinations of memory states. As the computation proceeds,
each component of the superposition individually undergoes the
logic operations. It is as if an exponential number of computers,
each starting with a different pattern in memory, were working
on the problem simultaneously. When the computation is finished,
the memory cells are examined, and an answer emerges from the
wavelike interference of all the possibilities. The trick is
to devise the computation so that the desired answers reinforce,
while the others cancel. In the last several years, quantum algorithms
have been devised that factor numbers and search for encryption
keys much faster than any classical computer. Toy quantum computers,
with three or four "qubits" stored as states of single
atoms or photons, have been demonstrated, but they can do only
short computations before their delicate superpositions are scrambled
by outside interactions. More promising are computers using nuclear
magnetic resonance, as in hospital scanners. There, quantum bits
are encoded as the spins of atomic nuclei, and gently nudged
by external magnetic and radio fields into magnetic interactions
with neighboring nuclei. The heavy nuclei, swaddled in diffuse
orbiting electron clouds, can maintain their quantum coherence
for hours or longer. A quantum computer with a thousand or more
qubits could tackle problems astronomically beyond the reach
of any conceivable classical computer.
Molecular and quantum computers will be important sooner or
later, but humanlike robots are likely to arrive without their
help. Research within semiconductor companies, including working
prototype chips, makes it quite clear that existing techniques
can be nursed along for another decade, to chip features below
0.1 micrometers, memory chips with tens of billions of bits and
multiprocessor chips with over 100,000 MIPS. Towards the end
of that period, the circuitry will probably incorporate a growing
number of quantum interference components. As production techniques
for those tiny components are perfected, they will begin to take
over the chips, and the pace of computer progress may steepen
further. The 100 million MIPS to match human brain power will
then arrive in home computers before 2030.
It may seem rash to expect fully intelligent machines in a
few decades, when the computers have barely matched insect mentality
in a half-century of development. Indeed, for that reason, many
long-time artificial intelligence researchers scoff at the suggestion,
and offer a few centuries as a more believable period. But there
are very good reasons why things will go much faster in the next
fifty years than they have in the last fifty.
The stupendous growth and competitiveness of the computer
industry is one reason. A less appreciated one is that intelligent
machine research did not make steady progress in its first fifty
years, it marked time for thirty of them! Though general computer
power grew a hundred thousand fold from 1960 to 1990, the computer
power available to AI programs barely budged from 1 MIPS during
those three decades.
In the 1950s, the pioneers of AI viewed computers as locomotives
of thought, which might outperform humans in higher mental work
as prodigiously as they outperformed them in arithmetic, if they
were harnessed to the right programs. Success in the endeavor
would bring enormous benefits to national defense, commerce and
government. The promise warranted significant public and private
investment. For instance, there was a large project to develop
machines to automatically translate scientific and other literature
from Russian to English. There were only a few AI centers, but
those had the largest computers of the day, comparable in cost
to today's supercomputers. A common one was the IBM 704, which
provided a good fraction of a MIPS.
By 1960 the unspectacular performance of the first reasoning
and translation programs had taken the bloom off the rose, but
the unexpected launching by the Soviet Union of Sputnik, the
first satellite in 1957, had substituted a paranoia. Artificial
Intelligence may not have delivered on its first promise, but
what if it were to suddenly succeed after all? To avoid another
nasty technological surprise from the enemy, it behooved the
US to support the work, moderately, just in case. Moderation
paid for medium scale machines costing a few million dollars,
no longer supercomputers. In the 1960s that price provided a
good fraction of a MIPS in thrifty machines like Digital Equipment
Corp's innovative PDP-1 and PDP-6.
The field looked even less promising by 1970, and support
for military-related research declined sharply with the end of
the Vietnam war. Artificial Intelligence research was forced
to tighten its belt and beg for unaccustomed small grants and
contracts from science agencies and industry. The major research
centers survived, but became a little shabby as they made do
with aging equipment. For almost the entire decade AI research
was done with PDP-10 computers, that provided just under 1 MIPS.
Because it had contributed to the design, the Stanford AI Lab
received a 1.5 MIPS KL-10 in the late 1970s from Digital, as
Funding improved somewhat in the early 1980s, but the number
of research groups had grown, and the amount available for computers
was modest. Many groups purchased Digital's new Vax computers,
costing $100,000 and providing 1 MIPS. By mid-decade, personal
computer workstations had appeared. Individual researchers reveled
in the luxury of having their own computers, avoiding the delays
of time-shared machines. A typical workstation was a Sun-3, costing
about $10,000, and providing about 1 MIPS.
By 1990, entire careers had passed in the frozen winter of
1-MIPS computers, mainly from necessity, but partly from habit
and a lingering opinion that the early machines really should
have been powerful enough. In 1990, 1 MIPS cost $1,000 in a low-end
personal computer. There was no need to go any lower. Finally
spring thaw has come. Since 1990, the power available to individual
AI and robotics programs has doubled yearly, to 30 MIPS by 1994
and 500 MIPS by 1998. Seeds long ago alleged barren are suddenly
sprouting. Machines read text, recognize speech, even translate
languages. Robots drive cross-country, crawl across Mars, and
trundle down office corridors. In 1996 a theorem-proving program
called EQP running five weeks on a 50 MIPS computer at Argonne
National Laboratory found a proof of a boolean algebra conjecture
by Herbert Robbins that had eluded mathematicians for sixty years.
And it is still only spring. Wait until summer.
The big freeze. From 1960 to 1990 the cost of computers used
in AI research declined, as their numbers dilution absorbed computer-efficiency
gains during the period, and the power available to individual
AI programs remained almost unchanged at 1 MIPS, barely insect
power. AI computer cost bottomed in 1990, and since then power
has doubled yearly, to several hundred MIPS by 1998. The major
visible exception is computer chess (shown by a progression of
knights), whose prestige lured the resources of major computer
companies and the talents of programmers and machine designers.
Exceptions also exist in less public competitions, like petroleum
exploration and intelligence gathering, whose high return on
investment gave them regular access to the largest computers.
The Game's Afoot
A summerlike air already pervades the few applications of
artificial intelligence that retained access to the largest computers.
Some of these, like pattern analysis for satellite images and
other kinds of spying, and in seismic oil exploration, are closely
held secrets. Another, though, basks in the limelight. The best
chess-playing computers are so interesting they generate millions
of dollars of free advertising for the winners, and consequently
have enticed a series of computer companies to donate time on
their best machines and other resources to the cause. Since 1960
IBM, Control Data, AT&T, Cray, Intel and now again IBM have
been sponsors of computer chess. The "knights" in the
AI power graph show the effect of this largesse, relative to
mainstream AI research. The top chess programs have competed
in tournaments powered by supercomputers, or specialized machines
whose chess power is comparable. In 1958 IBM had both the first
checker program, by Arthur Samuel, and the first full chess program,
by Alex Bernstein. They ran on an IBM 704, the biggest and last
vacuum-tube computer. The Bernstein program played atrociously,
but Samuel's program, which automatically learned its board scoring
parameters, was able to beat Connecticut checkers champion Robert
Nealey. Since 1994, Chinook, a program written by Jonathan Schaeffer
of the University of Alberta, has consistently bested the world's
human checker champion. But checkers isn't very glamorous, and
this portent received little notice.
By contrast, it was nearly impossible to overlook the epic
battles between world chess champion Garry Kasparov and IBM's
Deep Blue in 1996 and 1997. Deep Blue is a scaled-up version
of a machine called Deep Thought, built by Carnegie Mellon University
students ten years earlier. Deep Thought, in turn, depended on
special-purpose chips, each wired like the Belle chess computer
built by Ken Thompson at AT&T Bell Labs in the 1970s. Belle,
organized like a chessboard, circuitry on the squares, wires
running like chess moves, could evaluate and find all legal moves
from a position in one electronic flash. In 1997 Deep Blue had
256 such chips, orchestrated by a 32 processor mini-supercomputer.
It examined 200 million chess positions a second. Chess programs,
on unaided general-purpose computers, average about 16,000 instructions
per position examined. Deep Blue, when playing chess (and only
then), was thus worth about 3 million MIPS, 1/30 of our estimate
for human intelligence.
Deep Blue, in a first for machinekind, won the first game
of the 1996 match. But, Kasparov quickly found the machine's
weaknesses, and drew two and won three of the remaining games.
In May 1997 he met an improved version of the machine. That
February, Kasparov had triumphed over a field of grandmasters
in a prestigious tournament in Linares, Spain, reinforcing his
reputation as the best player ever, and boosting his chess rating
past 2800, uncharted territory. He prepared for the computer
match in the intervening months, in part by playing against other
machines. Kasparov won a long first game against Deep Blue, but
lost next day to masterly moves by the machine. Then came three
grueling draws, and a final game, in which a visibly shaken and
angry Kasparov resigned early, with a weak position. It was the
first competition match he had ever lost.
The event was notable for many reasons, but one especially
is of interest here. Several times during both matches, Kasparov
reported signs of mind in the machine. At times in the second
tournament, he worried there might be humans behind the scenes,
feeding Deep Blue strategic insights!
Bobby Fischer, the US chess great of the 1970s, is reputed
to have played each game as if against God, simply making the
best moves. Kasparov, on the other hand, claims to see into opponents'
minds during play, intuiting and exploiting their plans, insights
and oversights. In all other chess computers, he reports a mechanical
predictability stemming from their undiscriminating but limited
lookahead, and absence of long-term strategy. In Deep Blue, to
his consternation, he saw instead an "alien intelligence."
In this paper-thin slice of mentality, a computer seems to
have not only outperformed the best human, but to have transcended
its machinehood. Who better to judge than Garry Kasparov? Mathematicians
who examined EQP's proof of the Robbins conjecture, mentioned
earlier, report a similar impression of creativity and intelligence.
In both cases, the evidence for an intelligent mind lies in the
machine's performance, not its makeup.
Now, the team that built Deep Blue claim no "intelligence"
in it, only a large database of opening and end games, scoring
and deepening functions tuned with consulting grandmasters, and,
especially, raw speed that allows the machine to look ahead an
average of fourteen half-moves per turn. Unlike some earlier,
less successful, chess programs, Deep Blue was not designed to
think like a human, to form abstract strategies or see patterns
as it races through the move/countermove tree as fast as possible.
Deep Blue's creators know its quantitative superiority over
other chess machines intimately, but lack the chess understanding
to share Kasparov's deep appreciation of the difference in the
quality of its play. I think this dichotomy will show up increasingly
in coming years. Engineers who know the mechanism of advanced
robots most intimately will be the last to admit they have real
minds. From the inside, robots will indisputably be machines,
acting according to mechanical principles, however elaborately
layered. Only on the outside, where they can be appreciated as
a whole, will the impression of intelligence emerge. A human
brain, too, does not exhibit the intelligence under a neurobiologist's
microscope that it does participating in a lively conversation.
Agony to ecstasy. In forty years, computer chess progressed
from the lowest depth to the highest peak of human chess performance.
It took a handful of good ideas, culled by trial and error from
a larger number of possibilities, an accumulation of previously
evaluated game openings and endings, good adjustment of position
scores, and especially a ten-million-fold increase in the number
of alternative move sequences the machines can explore. Note
that chess machines reached world champion performance as their
(specialized) processing power reached about 1/30 human, by our
brain to computer measure. Since it is plausible that Garry Kasparov
(but hardly anyone else) can apply his brainpower to the problems
of chess with an efficiency of 1/30, the result supports that
retina-based extrapolation. In coming decades, as general-purpose
computer power grows beyond Deep Blue's specialized strength,
machines will begin to match humans in more common skills.
The Great Flood
Computers are universal machines, their potential extends
uniformly over a boundless expanse of tasks. Human potentials,
on the other hand, are strong in areas long important for survival,
but weak in things far removed. Imagine a "landscape of
human competence," having lowlands with labels like "arithmetic"
and "rote memorization", foothills like "theorem
proving" and "chess playing," and high mountain
peaks labeled "locomotion," "hand-eye coordination"
and "social interaction." We all live in the solid
mountaintops, but it takes great effort to reach the rest of
the terrain, and only a few of us work each patch.
Advancing computer performance is like water slowly flooding
the landscape. A half century ago it began to drown the lowlands,
driving out human calculators and record clerks, but leaving
most of us dry. Now the flood has reached the foothills, and
our outposts there are contemplating retreat. We feel safe on
our peaks, but, at the present rate, those too will be submerged
within another half century. I propose (Moravec 1998) that we
build Arks as that day nears, and adopt a seafaring life! For
now, though, we must rely on our representatives in the lowlands
to tell us what water is really like.
Our representatives on the foothills of chess and theorem-proving
report signs of intelligence. Why didn't we get similar reports
decades before, from the lowlands, as computers surpassed humans
in arithmetic and rote memorization? Actually, we did, at the
time. Computers that calculated like thousands of mathematicians
were hailed as "giant brains," and inspired the first
generation of AI research. After all, the machines were doing
something beyond any animal, that needed human intelligence,
concentration and years of training. But it is hard to recapture
that magic now. One reason is that computers' demonstrated stupidity
in other areas biases our judgment. Another relates to our own
ineptitude. We do arithmetic or keep records so painstakingly
and externally, that the small mechanical steps in a long calculation
are obvious, while the big picture often escapes us. Like Deep
Blue's builders, we see the process too much from the inside
to appreciate the subtlety that it may have on the outside. But
there is a non-obviousness in snowstorms or tornadoes that emerge
from the repetitive arithmetic of weather simulations, or in
rippling tyrannosaur skin from movie animation calculations.
We rarely call it intelligence, but "artificial reality"
may be an even more profound concept than artificial intelligence
The mental steps underlying good human chess playing and theorem
proving are complex and hidden, putting a mechanical interpretation
out of reach. Those who can follow the play naturally describe
it instead in mentalistic language, using terms like strategy,
understanding and creativity. When a machine manages to be simultaneously
meaningful and surprising in the same rich way, it too compels
a mentalistic interpretation. Of course, somewhere behind the
scenes, there are programmers who, in principle, have a mechanical
interpretation. But even for them, that interpretation loses
its grip as the working program fills its memory with details
too voluminous for them to grasp.
As the rising flood reaches more populated heights, machines
will begin to do well in areas a greater number can appreciate.
The visceral sense of a thinking presence in machinery will become
increasingly widespread. When the highest peaks are covered,
there will be machines than can interact as intelligently as
any human on any subject. The presence of minds in machines will
then become self-evident.
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