This article is available in electronic form at:
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/2000/Cerebrum.html
Robots, Re-Evolving Mind
Hans Moravec
Carnegie Mellon University
Robotics Institute
December 2000
A Slide Presentation of this Article
Computers have permeated everyday life and are worming their way into
our gadgets, dwellings, clothes, even bodies. But if pervasive
computing soon automates most of our informational needs, it will
leave untouched a vaster number of essential physical tasks.
Construction, protection, repair, cleaning, transport and so forth
will remain in human hands.
Robot inventors in home, university and industrial laboratories have
tinkered with the problem for most of the century. While mechanical
bodies adequate for manual work can be built, artificial minds for
autonomous servants have been frustratingly out of reach, despite
the arrival of powerful computers.
The first electronic computers in the 1950s did the work of thousands
of clerks. But when those superhuman behemoths were programmed to
reason, they merely matched single human beginners in razor-narrow
tasks. Programmed to control robot eyes and arms, they took pathetic
hours to unreliably find and grasp a few wooden blocks. The situation
did not improve substantially for decades.
But things are changing. Robot tasks wildly impossible in the 1970s
and 1980s began to work experimentally in the 1990s. Robots mapped and
navigated unfamiliar office suites, and robot vehicles drove
themselves, mostly unaided, across entire countries. Vision
systems locate textured objects and track and analyze faces in real
time. Personal computers recognize text and speech. Why suddenly
now?
Trick of Perspective
The short answer is that, after decades at about one MIPS (million
instructions (or calculations) per second), computer power available
to research robots shot through 10, 100 and now 1,000 MIPS starting
about 1990 (Figure 1). This
deserves explanation because the cost-effectiveness of computing rose
steadily all those decades (Figure 2). In 1960 computers were a new
and mysterious factor in the cold war, and even outlandish
possibilities like Artificial Intelligence warranted significant
investment. In the early 1960s AI programs ran on the era's
supercomputers, similar to those used for physical simulations by
weapons physicists and meteorologists. By the 1970s the promise of AI
had faded, and the effort limped for a decade on old hardware. In
contrast, weapons labs upgraded repeatedly to new supercomputers. In
the 1980s, departmental computers gave way to smaller project
computers then to individual workstations and personal computers.
Prices fell at each transition, but power per machine stayed about the
same. Only after 1990 did prices stabilize and power grow.
It was a common opinion in the AI labs that, with the right program,
readily available computers could encompass any human skill. The
position seemed obvious in the 1950s, when computers did the work of
thousands, and defensible in the 1970s, as inference and game-playing
programs performed at modest human levels. The upstart subfields of
of computer vision and robotics, however had a different impression.
On one MIPS computers, single images crammed memory, simply scanning
them consumed seconds, and serious image analysis took hours. Human
vision performed far more elaborate functions many times a
second.
It's easy to explain the discrepancy in hindsight. Computers do
arithmetic using as few gates and switching operations as possible.
Human calculation, by contrast, is a laboriously learned, ponderous,
awkward, unnatural behavior. Tens of billions of neurons in our
vision and motor systems strain to analogize and process a digit a
second. If our brain were rewired into 10 billion arithmetic
circuits, each doing 100 calculations a second, by a mad computer
designer with a future surgical tool, we'd outcompute early computers
a millionfold, and the illusion of computer power would be exposed.
Robotics, in fact, was such an expose.
Though spectacular underachievers at the wacky new stunt of longhand
calculation, we are veteran overachievers at perception and
navigation. Our ancestors, across hundreds of millions of years,
prevailed by being frontrunners in the competition to find food,
escape danger and protect offspring. Existing robot-controlling
computers are far too feeble to match the resulting prodigious
perceptual inheritance. But by how much?
The vertebrate retina is understood well enough to be a kind of
Rosetta stone roughly relating nervous tissue to computation. Besides
light detectors, the retina contains edge- and motion-detecting
circuitry, packed into a little tenth-millimeter-thick,
two-centimeter-wide patch that reports on a million image regions in
parallel about ten times a second via the optic nerve. In robot
vision, similar detections, well coded, each require the execution of
a few hundred computer instructions, making the retina's 10 million
detections per second worth over 1,000 MIPS. In a risky extrapolation
that must serve until something better emerges, it would take about
50,000 MIPS to functionally imitate a rat-brain's gram of neural
tissue, and almost 100 million MIPS (or 100 trillion instructions per
second) to emulate the 1,500 gram human brain. PCs in 1999 matched
insect nervous systems, but fell short of the human retina and a
goldfish's 0.1 gram brain. They were a millionfold too weak to do the
job of a human brain.
While dispiriting to artificial intelligence pioneers, the deficit
does not warrant abandoning their goals. Computer power for a given
price roughly doubled each year in the 1990s, after doubling every 18
months in the 1980s, and every two years prior. Two or three decades
more at the present pace would close the millionfold gap. Better
yet, sufficiently useful robots don't need full human-scale
brainpower.
Re-Evolving Mind
The incremental growth of computer power suggests an incremental
approach to developing robot intelligence, probably an accelerated
parallel to the evolution of biological intelligence that's its
model. Unlike other approaches, this path demands no great theories
or insights (helpful though they can be): natural intelligence evolved
in small steps through a chain of viable organisms, artificial
intelligence can do the same. Nature performed evolutionary experiments
at an approximately steady rate, even when evolved traits such as
brain complexity grew exponentially. Similarly, a steady
engineering effort should be able to support exponentially
growing robot complexity (especially as ever more of the design
search is delegated to increasingly powerful machines).
The journey will be much easier the second time around: we have a guide,
with directions and distances, in the history of vertebrate nervous
systems.
General industrial development, in electronics, materials,
manufacturing and elsewhere has already carried us well along the
road. A series of notable experimental animal-like robots built using
their day's best techniques read like mileposts along the road to
intelligence.
Around 1950, Bristol University psychologist William Grey Walter, a
pioneer brainwave researcher, built eight electronic "Tortoises", each
with a scanning phototube eye and two vacuum tube amplifiers driving
relays that switched steering and drive motors (Figure 3). Unprecedently lifelike,
they danced around a lighted recharging hutch until batteries ran low,
then entered. But simple bacteria show equally engaging
tropisms.
In the early 1960s the Johns Hopkins University Applied Physics Lab
built the corridor cruising, wall-outlet-recharging "Beast" (Figure 4). Using specialized
wall-ranging sonars, outlet-seeking photocell optics and a
wallplate-feeling arm, all orchestrated by several dozen transistors,
the Beast's multiple coordinated behaviors resemble a large nucleated
cell's, for instance a bacteria-hunting Paramecium.
Big mobile robots radio-controlled by huge computers appeared around
1970 at Stanford University and nearby SRI. While a Tortoise's
actions followed directly from two or three light and touch
discriminations, and the Beast's simply from a few dozen signals,
Stanford's "Cart" (Figure 5a) and
SRI's "Shakey" (Figure 5b) used TV
images with thousands of pixels to choose actions after millions of
calculations. The Cart adapted and predicted to track dirty white
lines in ambient light. Shakey, more ambitiously but less reliably,
identified and reasoned about large blocks. The advent of
multicellular animals with nervous systems in the Cambrian explosion
550 million years ago blew the lid on biological behavioral
complexity. The introduction of computer control did the same for
robots.
By 1980 a slightly faster computer and a more complex program allowed
Stanford's Cart, using stereoscopic vision, to sparsely map and
negotiate obstacle courses, taking five hours to cover 30 meters (Figure 1, first panel). In
complexity and speed the performance was sluglike.
Several research robots in the early 1990s navigated and
two-dimensionally mapped corridor networks in real time (Figure 1, second panel). Some
optimized their interpretations of sensor data in learning
processes. Onboard and offboard 10 MIPS microprocessors conferred
brainpower like the tiniest fish, or middling insects.
In 2000 a guppylike thousand MIPS and hundreds of megabytes of memory
enabled our robots to build dense, almost photorealistic 3D maps of
their surroundings (Figure 1,
third panel). Navigation techniques built around this core spatial
awareness will suffice, I believe, to guide mobile utility robots
reliably through unfamiliar surroundings, suiting them for jobs in
hundreds of thousands of industrial locations and eventually hundreds
of millions of homes. Such abilities have so long eluded that only a
few dozen small research groups pursue them. But the number of robot
developers will balloon once a vigorous commercial industry emerges.
The continued evolution of robotkind will then become a driver rather
than a mere beneficiary of general technical development.
How do biological and technological development rates compare? The
very simple to the very complex can be found in both realms, and new
designs are as likely to increase simplicity as complexity. But the
simple end of the range is crammed with competitors, while the complex
limit is the beginning of an endless unexplored design space.
Organisms or products that are slightly more complex than any before
sometimes succeed in the ecology or the marketplace, and thus raise
the upper limit. Paleontological and historical records can be
scanned for this upper limit, which mostly rises over time. There are
reversals in both records: notably mass extinctions and civilization
collapses. But, after a recovery period, progress resumes, often
faster than before. If complex entities succumb to disaster, many of
their component innovations may yet survive somewhere. Classical
learning weathered the collapse of Roman civilization in the remote
Islamic world. Some inactive DNA sequences seem to be archives of
ancestral traits. Extincted large organisms may leave much of their
heritage behind in smaller relatives, who can rapidly "re-evolve" size
and complex adaptations by simple mutations in regulator genes. The
rexpression of old good ideas in odd combinations often initiates an
explosion of innovation. Such happened culturally in the Renaissance
and biologically in the Paleocene, when birds and mammals ran riot in
the post-dinosaur world.
Though creative explosions, catastrophic losses and stagnant periods
in both realms, and varying investment scales in different technical
projects disturb the trends, let us compare the growth of biggest
nervous systems since the Cambrian with the information capacity of
common big computers since World War II. Wormlike animals with
perhaps a few hundred neurons evolved early in the Cambrian, over 570
million years ago. The first electromechanical computers, with a few
hundred bits of telephone relay storage, were built around 1940.
Earliest vertebrates, very primitive fish with nervous systems
probably smaller than the modern hagfish's, perhaps 100,000 neurons,
appeared about 470 million years ago. Computers acquired 100,000 bits
of rotating magnetic memory by 1955. Amphibians with perhaps a
salamander's few million neurons crawled out of the water 370 million
years ago. Computers with millions of bits of magnetic core memory
were available by 1965. By 1975, many computer core memories had
exceeded 10 million bits and by 1985 100 million bits was common,
though large mainframe computers were being largely displaced by small
workstations and even smaller personal computers. Small mammals
showed up about 220 million years ago, with brains ranging to several
hundred million neurons, while enormous dinosaurs around them bore
brains with several billion neurons, a situation that changed only
slowly until the sudden extinction of the dinosaurs 65 million years
ago. Our small primate ancestors arose soon after, with brains
ranging to several billion neurons. Larger computer systems had
several billion by 1995. Hominid apes with twenty billion neuron
brains appeared about 30 million years ago. In 2,000, some ambitious
personal computer owners equipped their systems tens of billions of bits
of RAM. Humans have approximately 100 billion neurons. 100 billion
bits of RAM will be standard in computers within five years.
Plot these juxtaposed geologic and recent dates against one another
(the alignment of bits to neurons is arbitrary, and can be shifted
with affecting the slope) and you will discover that large computer's
capacities grew each decade about as much as the large nervous systems
grew every hundred million years. We seem to be re-evolving mind (in
a fashion) at ten million times the original speed!
Earning a Living
Commercial mobile robots, that must perform reliably, have tended to
use techniques about a decade after they first appeared
experimentally. The smartest ones, barely insectlike at 10 MIPS, have
found few jobs. A paltry ten thousand work worldwide, and companies
that made them are struggling or defunct (robot manipulators have a
similar story). The largest class, Automatic Guided Vehicles (AGVs),
transport materials in factories and warehouses. Most follow buried
signal-emitting wires and detect endpoints and collisions with
switches, techniques introduced in the 1960s. It costs hundreds of
thousands of dollars to install guide wires under concrete floors, and
the routes are then fixed, making the robots economical only for
large, exceptionally stable factories. Some robots made possible by
the advent of microprocessors in the 1980s track softer cues, like
patterns in tiled floors, and use ultrasonics and infrared proximity
sensors to detect and negotiate their way around obstacles.
The most advanced industrial mobile robots to date, developed since
the late 1980s, are guided by occasional navigational markers, for
instance laser-sensed bar codes, and by preexisting features like
walls, corners and doorways. The hard-hat labor of laying guide wires
is replaced by programming carefully tuned for each route segment.
The small companies who developed the robots discovered many
industrial customers eager to automate transport, floor cleaning,
security patrol and other routine jobs. Alas, most buyers lost
interest as they realized that installation and route changing
required time-consuming and expensive work by experienced route
programmers of precarious availability. Technically successful, the
robots fizzled commercially. But in failure they revealed the
essentials for success.
First one needs reasonably-priced physical vehicles to do various
jobs. Fortunately existing AGVs, fork lift trucks, floor scrubbers
and other industrial machines designed for human riders or to follow
wires can be adapted for autonomy. Second, the customer should be
able, unassisted, to rapidly put a robot to work where needed. Floor
cleaning and most other mundane tasks cannot bear the cost, time and
uncertainty of expert installation. Third, the robots must work for
at least six months between missteps. Customers routinely rejected
robots that, after a month of flawless operation, wedged themselves in
corners, wandered away lost, rolled over employees' feet or fell
down stairs. Six months, however, earned the machines a sick
day.
Robots exist that work faultlessly for years, perfected by a repeated
process that fixes the most frequent failures, revealing successively
rarer problems that are corrected in turn. Alas, the reliability has
been achieved only for prearranged routes. Insectlike 10 MIPS is just
enough to track a few hand-picked landmarks on each path segment.
Such robots are easily confused by minor surprises like shifted bar
codes or blocked corridors, not unlike ants on scent trails or moths
guided by the moon, who can be trapped by circularized trails or
streetlights. (Unlike plodding robots, though, insects routinely take
lethal risks, and thus have more interesting, if short, lives.)
A Sense of Space
Robots that chart their own routes emerged from laboratories
worldwide in the mid 1990s, as microprocessors reached 100 MIPS. Most
build two-dimensional maps from sonar or laser rangefinder scans to
locate and route themselves, and the best seem able to navigate office
hallways sometimes for days between confusions. To date they fall far short of
the six-month commercial criterion. Too often different locations in
coarse 2D maps resemble one another, or the same location, scanned at
different heights, looks different, or small obstacles or awkward
protrusions are overlooked. But sensors, computers and techniques are
improving, and success is in sight.
My small laboratory is in the race. In the 1980s we devised a way to
distill large amounts of noisy sensor data into reliable maps by
accumulating statistical evidence of emptiness or occupancy in each
cell of a grid representing the surroundings. The approach worked
well in 2D, and guides some of the robots mentioned above.
Three-dimensional maps, a thousand times richer, promised to be even
better, but for years seemed computationally out of reach. In 1992 we
found economies of scale and other tricks that reduced 3D grid costs a
hundredfold, and now have a test program that accumulates thousands of
measurements from stereoscopic camera glimpses to map a room's volume
down to centimeter-scale. With 1,000 MIPS the program digests over a
glimpse per second, adequate for slow indoor travel. A thousand MIPS
is just appearing in high-end personal computers. In a few years it
will be found in smaller, cheaper computers fit for robots, and we've
begun an intensive three-year project to develop a prototype
commercial product. Highlights are an automatic learning processes to
optimize hundreds of evidence-weighing parameters, programs to find
clear paths, locations, floors, walls, doors and other objects in the
3D maps, and sample application programs orchestrating the basic
skills into tasks like delivery, floor cleaning and patrol. The
initial testbed is a small mobile robot with trinocular cameras.
Inexpensive digital camera chips promise to be the cheapest way to get
the millions of measurements needed for dense maps.
As a first commercial product, we plan a basketball-sized
"navigation head" for retrofit onto existing industrial vehicles. It
would have multiple stereoscopic cameras, 1,000 MIPS, generic mapping,
recognition and control software, an application-specific program, and
a hardware connection to vehicle power, controls and sensors.
Head-equipped vehicles with transport or patrol programs could be
taught new routes simply by leading them through once. Floor-cleaning
programs would be shown the boundaries of their work area. Introduced
to a job location, the vehicles would understand their changing
surroundings competently enough to work at least six months without
debilitating mistakes. Ten thousand AGVs, a hundred thousand cleaning
machines and, possibly, a million fork-lift trucks are candidates for
retrofit, and robotization may greatly expand those markets.
Income and experience from spatially-aware industrial robots would
set the stage for smarter yet cheaper ($1,000 rather than $10,000)
consumer products, starting probably with small, patient robot vacuum
cleaners that automatically learn their way around a home, explore
unoccupied rooms and clean whenever needed. I imagine a
machine low enough to fit under some furniture, with an even lower
extendible brush, that returns to a docking station to recharge and
disgorge its dust load. Such machines could open a true mass market
for robots, with a hundred million potential customers.
Evolutionary Speedway
Commercial success will provoke competition and accelerate investment
in manufacturing, engineering and research. Vacuuming robots should
beget smarter cleaning robots with dusting, scrubbing and picking-up
arms, followed by larger multifunction utility robots with stronger,
more dextrous arms and better sensors. Programs will be written to
make such machines pick up clutter, store, retrieve and deliver
things, take inventory, guard homes, open doors, mow lawns, play games
and on. New applications will expand the market and spur further
advancements, when robots fall short in acuity, precision, strength,
reach, dexterity, skill or processing power. Capability, numbers
sold, engineering and manufacturing quality, and cost effectiveness
will increase in a mutually reinforcing spiral. Perhaps by 2010
the process will have produced the first broadly competent
"universal robots," as big as people but with lizardlike
5,000 MIPS minds that can be programmed for almost any simple chore.
Like competent but instinct-ruled reptiles, first-generation
universal robots will handle only contingencies explicitly covered in
their current application programs. Unable to adapt to changing
circumstances, they will often perform inefficiently or not at all.
Still, so much physical work awaits them in businesses, streets,
fields and homes that robotics could begin to overtake pure
information technology commercially.
A second generation of universal robot with a mouselike 100,000 MIPS
will adapt as the first generation does not, and even be trainable.
Besides application programs, the robots would host a suite of
software "conditioning modules" that generate positive and
negative reinforcement signals in predefined circumstances.
Application programs would have alternatives for every step small and
large (grip under/over hand, work in/out doors). As jobs are
repeated, alternatives that had resulted in positive reinforcement
will be favored, those with negative outcomes shunned. With a
well-designed conditioning suite (eg. positive for doing a job fast,
keeping the batteries charged, negative for breaking or hitting
something) a second-generation robot will slowly learn to work
increasingly well.
A monkeylike 5 million MIPS will permit a third generation of robots
to learn very quickly from mental rehearsals in simulations that model
physical, cultural and psychological factors. Physical properties
include shape, weight, strength, texture and appearance of things and
how to handle them. Cultural aspects include a thing's name,
value, proper location and purpose. Psychological factors, applied to
humans and other robots, include goals, beliefs, feelings and
preferences. Developing the simulators will be a huge undertaking
involving thousands of programmers and experience-gathering robots.
The simulation would track external events, and tune its models to
keep them faithful to reality. It should let a robot learn a skill by
imitation, and afford a kind of consciousness. Asked why there are
candles on the table, a third generation robot might consult its
simulation of house, owner and self to honestly reply that it put them
there because its owner likes candlelit dinners and it likes to please
its owner. Further queries would elicit more details about a simple
inner mental life concerned only with concrete situations and people
in its work area.
Fourth-generation universal robots with a humanlike 100 million MIPS
will be able to abstract and generalize. The first ever AI programs
reasoned abstractly almost as well as people, albeit in very narrow
domains, and many existing expert systems outperform us. But the
symbols these programs manipulate are meaningless unless interpreted
by humans. For instance, a medical diagnosis program needs a human
practitioner to enter a patient's symptoms, and to implement a
recommended therapy. Not so a third-generation robot, whose simulator
provides a two-way conduit between symbolic descriptions and physical
reality. Fourth-generation machines result from melding powerful
reasoning programs to third-generation machines. They may reason
about everyday actions by referring to their simulators like Herbert
Gelernter's 1959 geometry theorem prover examined
analytic-geometry "diagrams" to check special-case examples
before trying to prove general geometric statements. Properly
educated, the resulting robots are likely to become intellectually
formidable.
Passing the Torch
Barring cataclysms, I consider the development of intelligent
machines a near-term inevitability. Every technical step toward
intelligent robots has a
rough evolutionary counterpart, and each is likely to benefit its
creators, manufacturers, and users. Each advance will provide
intellectual rewards, competitive advantages, and increased wealth and
options of all kinds. Each can make the world a nicer place to live.
At the same time, by performing better and cheaper, the robots will
displace humans from essential roles. Rather quickly, they could
displace us from existence. I'm not as alarmed as many by the latter
possibility, since I consider these future machines our progeny,
``mind children'' built in our image and likeness, ourselves in more
potent form. Like biological children of previous generations, they
will embody humanity's best chance for a long-term future. It
behooves us to give them every advantage and to bow out when we can no
longer contribute.
But, as also with biological children, we can probably arrange for a
comfortable retirement before we fade away. Some biological children can
be convinced to care for elderly parents. Similarly, ``tame''
superintelligences could be created and induced to protect and support us,
for a while. Such relationships require advance planning and diligent
maintenance: it's time to pay attention.
It is the ``wild'' intelligences, however, those beyond our
constraints, to whom the future belongs. The available tools for peeking
into that strange future---extrapolation, analogy, abstraction, and
reason---are, of course, totally inadequate.
References and Acknowledgments
Citations, expansions, illustrations and updates on matters discussed
here can be found at the author's web page
http://www.frc.ri.cmu.edu/~hpm
This work has been supported since 1999 by the DARPA Mobile Autonomous
Robot Software program. Prior funding came from the Office of Naval
Research, NASA, Pennsylvania's Ben Franklin program, Daimler-Benz
Research, Thinking Machines Corp., Denning Mobile Robotics Inc., and
Carnegie Mellon University Robotics Institute.
Hans Moravec is a Principal Research Scientist at the Carnegie Mellon
University Robotics Institute, where he has been since 1980.
FIGURES
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Fig 1 for screen:
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/1999/CACM/1980-2000.scr.jpg
Fig 1 for printing:
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/1999/CACM/1980-2000.jpg
Figure 1: Progress in Robot Spatial Awareness: By 1980
the Stanford Cart had (sometimes, slowly) managed to negotiate
obstacle courses by tracking and avoiding the 3D locations of a few
dozen object corners in the route ahead. The top panel shows the
Cart's view of a room, superimposed with red dots marking points its
program has selected and stereoscopically ranged. The consequent 3D
map at the right shows the same points, with diagonal stalks
indicating height, and a planned obstacle-avoiding path. (Labels were
added by hand.) The program updated map and plan each meter of
travel. The sparse maps were barely adequate, and blunders occurred
every few tens of meters.
The second panel shows a dense 2D grid map of 150 meters of corridor
produced in 1993 by a program by Barry Brummitt controlling Carnegie
Mellon's Xavier robot via a remote Sparc 2 workstation. The sensor was
a ring of sonar rangefinders, whose interpretation was automatically
learned. In the map image evidence of occupancy ranges from empty
(black) through unknown (grey) to occupied (white). Regular
indentations marking doors are evident, also bumps where cans, water
coolers, fire extinguishers, poster displays, etc. protrude. The
curvature is dead-reckoning error.
The last panel shows work in progress. As with the Cart, the left
image is a robot's eye view of a scene. The right image, though
resembling a fuzzy photograph, is actually a perspective view of the
occupied cells of a 3D map of the scene, built from about 100,000
range measurements extracted from 20 stereoscopic views similar to the
one on the left. The grid is 256 cells wide by 256 deep by 128 high,
covering 6x6x3 meters. Of the eight million total cells, about
100,000 are occupied. The realistic occupied cell colors are a side
effect of a learning process. The shape of the evidence patterns
corresponding to stereoscopic range values, among other system
parameters, are tuned up automatically to make the best grids. A
candidate grid is evaluated by "projecting" colors from the original
images onto the grid's occupied cells from the appropriate directions.
Each cell in a perfect grid would collect colors from different views
of the same thing in real space. Since most objects show the same
color from different viewpoints, the various colorings of each single
cell would agree with one another. Incorrect extra cells, however,
would intercept many disparate background colors from different points
of view. Conversely, colors of incorrectly missing cells would be
"sprayed" across various background cells, spoiling their uniformity.
The learning program tunes the system to minimize total color
variance. The maps so far are ragged around the edges, and many
promising improvements remain to be tried, but the results are very
encouraging nevertheless. Compare the richness of the 3D maps in the
first and third panels. Both were produced by processing about 20
stereoscopic image sets, the 1980 result on a 1 MIPS DEC KL-10
mainframe computer with 500 kilobytes of memory, the 2000 result on a
1,000 MIPS Macintosh G4 with 500 megabytes of memory.
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Fig 2 for screen (75dpi):
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Fig 2 better resolution (150dpi):
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Fig 2 our best resolution (300dpi):
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Figure 2: AI's Sudden Boil: From 1960 to 1990 the cost of
computers used in AI and robotics research declined from the
equivalent of millions of dollars per computer in 1960 to a few
thousand dollars in 1990. The computer population increased greatly,
but the power available to individual AI programs remained an almost
constant 1 MIPS--less than insect power. Cost per machine stabilized
in 1990, and since then power has doubled yearly, to 1,000 MIPS in
2000. The major visible exception to this pattern is computer chess,
shown by a progression of knights, whose prestige lured major computer
companies into providing access to their most powerful machines, and
researchers into developing chess-specific hardware. (Special-purpose
chess machines are positioned at the minimum general-purpose computer
power that could emulate them. Similarly for the organisms at the
right: each marks the minimum power of a general-purpose computer that
could produce similarly complex behavior, as estimated by the text's
retina to robot vision criterion.)
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Fig 3:
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Figure 3: Grey Walter Tortoise Elsie:
One of eight built, with phototube eye and two vacuum tube amplifiers
driving relays that controlled steering and drive motors. Elsie's
shell, removed for surgery, can be seen in the background. The tortoises
exhibited very lively behavior, for instance dancing near a lighted
recharging hutch until their battery ran low, then enter. Their
simple tropisms resemble bacterial "intelligence".
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Fig 4:
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/2000/revo.slides/1960.jpg
Figure 4: The Hopkins Beast:
Built in the early 1960s, using dozens of transistors, the Johns Hopkins
University Applied Physics Lab's "Beast" wandered white hallways,
centering by sonar, until its batteries ran low. Then it would
seek black wall outlets with special photocell optics, and plug itself
in by feel with its special recharging arm. After feeding, it would
resume patrolling. Much more complex than Elsie, its
deliberate behavior can be compared to a nucleated single-cell
organism like a paramecium or amoeba.
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Fig 5a:
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/2000/revo.slides/1970.jpg
Fig 5b:
http://www.frc.ri.cmu.edu/~hpm/project.archive/robot.papers/2000/revo.slides/1970b.jpg
Figure 5: Stanford's Cart and SRI's Shakey: The
"Stanford Cart" (a) and SRI's "Shakey" (b) were the first mobile
robots to be controlled by computers (large mainframes doing about a
quarter million calculations per second, linked to the robots by
radio). Both used television cameras to see. The Cart could follow
white lines quite reliably, Shakey could find large prismatic objects
somewhat less reliably. Their control complexity was far greater than
Elsie's or the Beast's (lines can be tracked using simple Elsie-like
techniques with ground-mounted lights and photocells, but it takes
complex adaptation and prediction to do it with ambient light from a
high vantage point), and the use of computers to control robots can be
compared to the advent of multicellular animals with nervous systems
in the Cambrian explosion: both events blew the lid on behavioral
complexity in their respective domains.
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