Journal of Evolution and Technology. 1998. Vol. 1

When will computer hardware match the human brain?
(Received Dec. 1997)

Hans Moravec
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213−3890, USA
net: hpm@cmu.edu
web: http://www.frc.ri.cmu.edu/~hpm/

ABSTRACTThis paper describes how the performance of AI machines tends to improve at the same pacethat 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. Basedon extrapolation of past trends and on examination of technologies under development, it ispredicted 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 andextrapolation. Fortunately, these are grounded in the first bit of the journey, now behind us. Thirty years ofcomputer vision reveals that 1 MIPS can extract simple features from real−time imagery−−tracking a whiteline 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 unpredictablefeatures like roads−−as recent long NAVLAB trips demonstrate. 1,000 MIPS will be adequate forcoarse−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 anhour 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 awide 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 unusualtypestyles 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 asimilar 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 beendeveloped 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 lensprojects an image of the world. It is connected by the optic nerve, a million−fiber cable, to regions deep in thebrain. 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 lessthan 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 areinterconnected by whose outgoing fibers bundle to form the optic nerve. Each of the million ganglion−cellaxons 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 per second.
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 retina.
The 1,500 cubic centimeter human brain is about 100,000 times as large as the retina, suggesting thatmatching overall human behavior will take about 100 million MIPS of computer power. Computer chessbolsters 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 chesswith 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 thefastest 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 scientificcalculations, 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 isenough 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 theyoccupied 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 androbot 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 willallow robots to do more with less. Big computers at the factory−−maybe supercomputers with 1,000 times thepower 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 processors.
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 ofmemory 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 originalMacintosh was introduced in 1984 with 1/2 MIPS and 1/8 megabyte, and was then considered a very fastmachine. 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 theline, 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 morememory?"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 ourearlier 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 tointeract at human time scales, and robots interacting with humans seem also to be best at that ratio. On theother 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, thoughapparently 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 thesmallest universal computer needed is logarithmic on both axes: each vertical division represents a thousandfold increase inprocessing power, and each horizontal division a thousandfold increase in memory size. Universal computers can imitate otherentities 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 machinemust be replaced.

Extrapolation
By our estimate, today's very biggest supercomputers are within a factor of a hundred of having the power tomimic a human mind. Their successors a decade hence will be more than powerful enough. Yet, it is unlikelythat 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 withhuman−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 inmechanical and electromechanical calculators before World War II had increased the speed of calculation athousandfold 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 theindustry became more diverse and competitive. Powerful, inexpensive computer workstations replaced thedrafting 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 on earth.
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 athousandfold over manual methods from 1900 to 1940. The pace quickened with the appearance of electronic computers during thewar, 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 besustained 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 sooncome 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 bunchesof electrons in electronic computers. Next, the switches' supporting structure underwent a vanishing act, fromthumb−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 matterin the exposed areas.
In the mid−1970s, integrated circuits, age 15, hit a crisis of adolescence. They then held ten thousandcomponents, 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, aproblem 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 muchheat to remove, and require too many external connections to fit. The smaller memory cells were sufferingradiation−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 efficienttransistor 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 researchlabs for years, barely noticed by the engineers in the field, who were perfecting established processes, andworrying in print as those ran out of steam. As the need became acute, enormous resources were redirected todraft laboratory possibilities into production realities.
In the intervening years many problems were met and solved, and innovations introduced, but now, nearing amid−life 40, the anxieties seem again to have crested. In 1996 major articles appeared in scientific magazinesand major national newspapers worrying that electronics progress might be a decade from ending. The cost ofbuilding new integrated circuit plants was approaching a prohibitive billion dollars. Feature sizes werereaching 0.1 micrometers, the wavelength of the sculpting ultraviolet light. Their transistors, scaled downsteadily 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 wasincreasing.
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 dependon 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 theiroperation. 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 thana 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 inlarge masses through resistive material. In place of much wiring, quantum interference logic may use chainsof 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 theresources 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. Potentiallyblowing 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 numberof 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 byexternal 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 totake 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.

False Start
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 thirtyof 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, hadsubstituted a paranoia. Artificial Intelligence may not have delivered on its first promise, but what if it were tosuddenly 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 withthe end of the Vietnam war. Artificial Intelligence research was forced to tighten its belt and beg forunaccustomed small grants and contracts from science agencies and industry. The major research centerssurvived, 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 tothe design, the Stanford AI Lab received a 1.5 MIPS KL−10 in the late 1970s from Digital, as a gift.
Funding improved somewhat in the early 1980s, but the number of research groups had grown, and theamount 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−sharedmachines. 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, butpartly 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. Machinesread text, recognize speech, even translate languages. Robots drive cross−country, crawl across Mars, andtrundle 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 ofmajor 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 thelargest computers. Some of these, like pattern analysis for satellite images and other kinds of spying, and inseismic oil exploration, are closely held secrets. Another, though, basks in the limelight. The bestchess−playing computers are so interesting they generate millions of dollars of free advertising for thewinners, 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 havebeen 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 bysupercomputers, or specialized machines whose chess power is comparable. In 1958 IBM had both the firstchecker 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, andthis 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 DeepThought, 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 Labsin 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 asecond. 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 themachine'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 fieldof 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 theintervening 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 firstcompetition 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 amechanical predictability stemming from their undiscriminating but limited lookahead, and absence oflong−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 tohave 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 andintelligence. 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 thatallows the machine to look ahead an average of fourteen half−moves per turn. Unlike some earlier, lesssuccessful, chess programs, Deep Blue was not designed to think like a human, to form abstract strategies orsee 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 chessunderstanding to share Kasparov's deep appreciation of the difference in the quality of its play. I think thisdichotomy 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 numberof 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 supportsthat 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 ittakes 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 todrown the lowlands, driving out human calculators and record clerks, but leaving most of us dry. Now theflood 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 rotememorization? 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 painstakinglyand 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 fromthe 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 thanartificial intelligence (Moravec 1998).
The mental steps underlying good human chess playing and theorem proving are complex and hidden, puttinga mechanical interpretation out of reach. Those who can follow the play naturally describe it instead inmentalistic 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 mechanicalinterpretation. 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.

REFERENCES
Goldhaber−Gordon, D. J. et al. (1997) "Overview of Nanoelectronic Devices", Proceedings of the IEEE, April
1997.
Moravec, H. (1998) Robot, Being: mere machine to transcendent mind. (forthcoming) Oxford University
Press.
Appendix

Open peer commentary
Journal of Transhumanism