Robots That Rove

 Hans P. Moravec
 Robotics Institute
 Carnegie-Mellon University
 Pittsburgh, PA 15213

 August, 1985


The most consistently interesting stories are those about journeys,
and the most fascinating organisms are those that move from place to
place. I think these observations are more than idiosyncrasies of
human psychology, but illustrate a fundamental principle. The world at
large has great diversity, and a traveller constantly encounters novel
circumstances, and is consequently challenged to respond in new
ways. Organisms and mechanisms do not exist in isolation, but are
systems with their environments, and those on the prowl in general
have a richer environment than those rooted to one place.

Mobility supplies danger along with excitement. Inappropriate actions
or lack of well-timed appropriate ones can result in the demise of a
free roamer, say over the edge of a cliff, far more easily than of a
stationary entity for whom particular actions are more likely to have
fixed effects.

Challenge combines with opportunity in a strong selection pressure
that drives an evolving species that happens to find itself in a
mobile way of life in certain directions, directions quite different
from those of stationary organisms. The last billion years on the
surface of the earth has seen a grand experiment exploring these
pressures. Besides the fortunate consequence of our own existence,
some universals are apparent from the results to date and from the
record. In particular, intelligence seems to follow from mobility.

I believe the same pressures are at work in the technological
evolution of robots, and that, by analogy, mobile robots are the most
likely route to solutions to some of the most vexing unsolved problems
on the way to true artificial intelligence - problems such as how to
program common sense reasoning and learning from sensory
experience. This opportunity carries a price - programs to control
mobile robots are more difficult to get right than most - the robot is
free to search the diverse world looking for just the combination that
will mess up your plan. There's still a long way to go, but perhaps my
experiences thus far pursuing this line of thought will convince you
as they have me. Among the conclusions that surprised me is that
future intelligent robots will of necessity be more like animals and
humans that I used to believe, for instance they will exhibit
recognizable emotions and human irrationalities. On to cases.

@heading(Mobility and Intelligence in Nature)

Two billion years ago our unicelled ancestors parted genetic company
with the plants. By accident of energetics and heritage, large plants
now live their lives fixed in place. Awesomely effective in their own
right, the plants have no apparent inclinations towards intelligence;
a piece of negative evidence that supports my thesis that mobility is
a parent of this trait.

Animals bolster the argument on the positive side, except for the
immobile minority like sponges and clams that support it on the
negative.

A billion years ago, before brains or eyes were invented, when the
most complicated animals were something like hydras, double layers of
cells with a primitive nerve net, our progenitors split with the
invertebrates. Now both clans have intelligent members.  Cephalopods
are the most intellectual invertebrates. Most mollusks are sessile
shellfish, but octopus and squid are highly mobile, with big brains
and excellent eyes. Evolved independently of us, they are
different. The optic nerve connects to the back of the retina, so
there is no blind spot. The brain is annular, a ring around the
esophagus. The green blood is circulated by a systemic heart
oxygenating the tissues and two gill hearts moving depleted
blood. Hemocyanin, a copper doped protein related to hemoglobin and
chlorophyll, carries the oxygen.

Octopus and their relatives are swimming light shows, their surfaces
covered by a million individually controlled color changing cells. A
cuttlefish placed on a checkerboard can imitate the pattern, a fleeing
octopus can make deceiving seaweed shapes coruscate backward along its
body. Photophores of deep sea squid, some with irises and lenses,
generate bright multicolored light. Since they also have good vision,
there is a potential for high bandwidth communication.

Their behavior is mammal like. Octopus are reclusive and shy, squid
are occasionally very aggressive. Small octopus can learn to solve
problems like how to open a container of food. Giant squid, with large
nervous systems, have hardly ever been observed except as
corpses. They might be as clever as whales.

Birds are vertebrates, related to us through a 300 million year old,
probably not very bright, early reptile. Size-limited by the dynamics
of flying, some are intellectually comparable to the highest mammals.

The intuitive number sense of crows and ravens extends to seven,
compared to three or four for us. Birds outperform all mammals except
higher primates and the whales in "learning set" tasks, where the idea
is to generalize from specific instances. In mammals generalization
depends on cerebral cortex size.  In birds forebrain regions called
the Wulst and the hyperstriatum are critical, while the cortex is
small and unimportant.

Our last common ancestor with the whales was a primitive rat-like
mammal alive 100 million years ago. Some dolphin species have body and
brain masses identical to ours, and have had them for more
generations. They are as good as us at many kinds of problem solving,
and can grasp and communicate complex ideas. Killer whales have brains
five times human size, and their ability to formulate plans is better
than the dolphins', who they occasionally eat. Sperm whales, though
not the largest animals, have the world's largest brains.
Intelligence may be an important part of their struggle with large
squid, their main food. Elephant brains are three times human
size. Elephants form matriarchal tribal societies and exhibit complex
behavior. Indian domestic elephants learn over 500 commands, and form
voluntary mutual benefit relationships with their trainers, exchanging
labor for baths. They can solve problems such as how to sneak into a
plantation at night to steal bananas, after having been belled
(answer: stuff mud into the bells). And they do have long memories.

Apes are our 10 million year cousins. Chimps and gorillas can learn to
use tools and to communicate in human sign languages at a retarded
level. Chimps have one third, and gorillas one half, human brainsize.

Animals exhibiting near-human behavior have hundred billion neuron
nervous systems. Imaging vision alone requires a billion. The smartest
insects have a million brain cells, while slugs and worms make do with
a thousand, and sessile animals with a hundred. The portions of
nervous systems for which tentative wiring diagrams have been
obtained, including nearly all of the large neuroned sea slug,
Aplysia, the flight controller of the locust and the early stages of
vertebrate vision, reveal neurons configured into efficient, clever,
assemblies.

@heading(Mobility and Intelligence around the Lab)

The twenty year old modern robotics effort can hardly hope to rival
the billion year history of large life on earth in richness of example
or profundity of result. Nevertheless the evolutionary pressures that
shaped life are already palpable in the robotics labs. I'm lucky
enough to have participated in some of this activity and to have
watched more of it at first hand, and so will presume to interpret the
experience.

The first serious attempts to link computers to robots involved
hand-eye systems, wherein a computer-interfaced camera looked down at
a table where a mechanical manipulator operated. The earliest of these
(ca. 1965) were built while the small community of artificial
intelligence researchers was still flushed with the success of the
original AI programs - programs that almost on the first try played
games, proved mathematical theorems and solved problems in narrow
domains nearly as well as humans. The robot systems were seen as
providing a richer medium for these thought processors. Of course, a
few minor new problems did come up.

A picture from a camera can be represented in a computer as a
rectangular array of numbers, each representing the shade of gray or
the color of a point in the image. A good quality picture requires a
million such numbers.  Identifying people, trees, doors, screwdrivers
and teacups in such an undifferentiated mass of numbers is a
formidable problem - the first programs did not attempt it.  Instead
they were restricted to working with bright cubical blocks on a dark
tabletop; a caricature of a toddler learning hand-eye
co-ordination. In this simplified environment computers more powerful
than those that had earlier aced chess, geometry and calculus
problems, combined with larger, more developed, programs were able to
sometimes, with luck, correctly locate and grab a block.

The general hand-eye systems have now mostly evolved into experiments
to study smaller parts of the problem, for example dynamics or force
feedback, or into specialized systems aimed at industrial
applications. Most arm systems have special grippers, special sensors,
and vision systems and controllers that work only in limited
domains. Economics favors this, since a fixed arm, say on an assembly
line, repetitively encounters nearly identical conditions. Methods
that handle the frequent situations with maximum efficiency beat more
expensive general methods that deal with a wide range of circumstances
that rarely arise, while performing less well on the common cases.

Shortly after cameras and arms were attached to computers, a few
experiments with computer controlled mobile robots were begun. The
practical problems of instrumenting and keeping operational a remote
controlled, battery powered, camera and video transmitter toting
vehicle compounded the already severe practical problems with hand-eye
systems, and conspired to keep many potential players out of the game.

The earliest successful result was SRI's Shakey (ca. 1970). Although
it existed as a sometimes functional physical robot, Shakey's primary
impact was as a thought experiment. Its creators were of the first
wave "reasoning machine" branch of AI, and were interested primarily
in applying logic based problem solving methods to a real world
task. Control and seeing were treated as system functions of the robot
and relegated mostly to staff engineers and undergraduates. Shakey
physically ran very rarely, and its blocks world based vision system,
which reqired that its environment contain only clean walls and a few
large smooth prismatic objects, was coded inefficiently and ran very
slowly, taking about an hour to find a block and a ramp in a simple
scene.  Shakey's most impressive performance, physically executed only
piecemeal, was to "push the block" in a situation where it found the
block on a platform. The sequence of actions included finding a wedge
that could serve as a ramp, pushing it against the platform, then
driving up the ramp onto the platform to push the block off.

The problems of a mobile robot, even in this constrained an
environment inspired and required the development of a powerful,
effective, still unmatched, system STRIPS that constructed plans for
robot tasks. STRIPS' plans were constructed out of primitive robot
actions, each having preconditions for applicability and consequences
on completion. It could recover from unexpected glitches by
incremental replanning. The unexpected is a major distinguishing
feature of the world of a mobile entity, and is one of the
evolutionary pressures that channels the mobile towards intelligence.

Mobile robots have other requirements that guide the evolution of
their minds away from solutions seemingly suitable for fixed
manipulators. Simple visual shape recognition methods are of little
use to a machine that travels through a cluttered three dimensional
world. Precision mechanical control of position can't be achieved by a
vehicle that traverses rough ground. Special grippers don't pay off
when many different and unexpected objects must be handled.  Linear
algorithmic control systems are not adequate for a rover that often
encounters surprises in its wanderings.

The Stanford Cart was a mobile robot built about the same time as
Shakey, on a lower budget. From the start the emphasis of the Cart
project was on low level perception and control rather than planning,
and the Cart was actively used as a physical experimental testbed to
guide the research. Until its retirement in 1980 it (actually the
large mainframe computer that remote controlled it) was programmed to:

@begin(itemize)

Follow a white line in real time using a TV camera mounted at about
eye level on the robot. The program had to find the line in a scene
that contained a lot of extraneous imagery, and could afford to
digitize only a selected portion of the images it processed.

Travel down a road in straight lines using points on the horizon as
references for its compass heading (the cart carried no
instrumentation of any kind other than the TV camera). The program
drove it in bursts of one to ten meters, punctuated by 15 second
pauses to think about the images and plan the next move.

Go to desired destinations about 20 meters away (specified as so many
meters forward and so many to the left) through messy obstacle courses
of arbitrary objects, using the images from the camera to servo the
motion and to detect (and avoid) obstacles in three dimensions. With
this program the robot moved in meter long steps, thinking about 15
@i(minutes) before each one. Crossing a large room or a loading dock
took about five hours, the lifetime of a charge on the Cart's
batteries.

@end(itemize)

The vision, world representation and planning methods that ultimately
worked for the Cart (a number were tried and rejected) were quite
different than the "blocks world" and specialized industrial vision
methods that grew out of the hand-eye efforts. Blocks world vision was
completely inappropriate for the natural indoor and outdoor scenes
encountered by the robot. Much experimentation with the Cart
eliminated several other initially promising approaches that were
insufficiently reliable when fed voluminous and variable data from the
robot. The product was a vision system with a different flavor than
most. It was "low level" in that it did no object modelling, but by
exploiting overlapping redundancies it could map its surroundings in
3D reliably from noisy and uncertain data. The reliability was
necessary because Cart journeys consisted of typically twenty moves
each a meter long punctuated by vision steps, and each step had to be
accurate for the journey to succeed.

At Carnegie-Mellon University we are building on the Cart work with
(so far) four different robots optimized for different parts of the
research.

Pluto was designed for maximum generality - its wheel system is
omnidirectional, allowing motion in any direction while simultaneously
permitting the robot to spin like a skater. It was planned that Pluto
would continue the line of vision research of the Cart and also
support work in close-up navigation with a manipulator (we would like
a fully visually guided procedure that permits the robot to find, open
and pass through a door). The real world has changed our plans. To our
surprise, the problem of controlling the three independently steerable
and driveable wheel assemblies of Pluto is an example of a difficult,
so far unsolved, problem in control of overconstrained systems. We are
working on it, but in the meantime Pluto is nearly immobile.

When the difficulty with Pluto became apparent, we built a simple
robot, Neptune, to carry on the long range vision work. I'm happy to
announce that Neptune is now able to cross a room in under an hour,
five times more quickly than the Cart.

Uranus is the third robot in the CMU line, designed to do well the
things that Pluto has so far failed to do. It will achieve
omnidirectionality through curious wheels, tired with rollers at 45
degrees, that, mounted like four wagon wheels, can travel forward and
backward normally, but that screw themselves sideways when wheels on
opposite sides of the robot are turned in opposite directions.

Our fourth mobile robot is called the Terragator, for terrestrial
navigator, and is designed to travel outdoors for long distances. It
is much bigger than the others, almost as large as a small car, and is
powered by a gasoline generator rather than batteries. We expect to
program it to travel on roads, avoid and recognize outdoor obstacles
and landmarks. Our earlier work makes clear that in order to run at
the speeds we have in mind (a few km/hr) we will need processing
speeds about 100 times faster than our medium size mainframes now
provide. We plan to augment our regular machines with a specialized
computer called an array processor to achieve these rates.

Our ambitions for the new robots (go down the hall to the third door,
go in, look for a cup and bring it back) has created another pressing
need - a computer language in which to concisely specify complex tasks
for the rover, and a hardware and software system to embody it. We
considered something similar to Stanford's AL arm controlling language
from which the commercial languages VAL at Unimation and the more
sophisticated AML at IBM were derived.

Paper attempts at defining the structures and primitives required for
the mobile application revealed that the linear control structure of
these state-of-the-art arm languages was inadequate for a rover. The
essential difference is that a rover, in its wanderings, is regularly
"surprised" by events it cannot anticipate, but with which it must
deal.  This requires that contingency routines be activated in
arbitrary order, and run concurrently. We are experimenting with a
structure where a number of specialist programs communicating via a
common data structure called a blackboard are active at the same time,
some operating sensors, some controlling effectors, some integrating
the results of other modules, and some providing overall direction.
As conditions change the priority of the various modules changes, and
control may be passed from one to another.

@heading(The Psychology of Mobile Robots)

Suppose we ask Uranus, equipped with a controller based on the
blackboard system mentioned in the last section to, in fact, go down
the hall to the third door, go in, look for a cup and bring it
back. This will be implemented as a process that looks very much like
a program written for the arm control languages (that in turn look
very much like Algol, or even Basic), except that the door recognizer
routine would probably be activated separately. Consider the following
caricature of such a program.

@begin(format)

MODULE Go-Fetch-Cup Wake up Door-Recognizer with instructions ( On
Finding-Door Add 1 to Door-Number Record Door-Location ) Record
Start-Location Set Door-Number to 0 While Door-Number < 3 Wall-Follow
Face-Door IF Door-Open THEN Go-Through-Opening ELSE
Open-Door-and-Go-Through Set Cup-Location to result of Look-for-Cup
Travel to Cup-Location Pickup-Cup at Cup-Location Travel to
Door-Location Face-Door IF Door-Open THEN Go-Through-Opening ELSE
Open-Door-and-Go-Through Travel to Start-Location End

@end(format)

So far so good. We activate our program and Uranus obediently begins
to trundle down the hall counting doors. It correctly recognizes the
first one.  The second door, unfortunately is decorated with some
garish posters, and the lighting in that part of the corridor is poor,
and our experimental door recognizer fails to detect it. The wall
follower, however, continues to operate properly and Uranus continues
on down the hall, its door count short by one. It recognizes door 3,
the one we had asked it to go through, but thinks it is only the
second, so continues. The next door is recognized correctly, and is
open.  The program, thinking it is the third one, faces it and
proceeds to go through.  This fourth door, sadly, leads to the
stairwell, and poor Uranus, unequipped to travel on stairs, is in
mortal danger.

Fortunately there is a process in our concurrent programming system
called Detect-Cliff that is always running and that checks ground
position data posted on the blackboard by the vision processes and
also requests sonar and infrared proximity checks on the ground. It
combines these, perhaps with an a-priori expectation of finding a
cliff set high when operating in dangerous areas, to produce a number
that indicates the likelyhood there is a drop-off in the neighborhood.

A companion process Deal-with-Cliff also running continuously, but
with low priority, regularly checks this number, and adjusts its own
priority on the basis of it. When the cliff probability variable
becomes high enough the priority of Deal-with-Cliff will exceed the
priority of the current process in control, Go-Fetch-Cup in our
example, and Deal-with-Cliff takes over control of the robot. A
properly written Deal-with-Cliff will then proceed to stop or greatly
slow down the movement of Uranus, to increase the frequency of sensor
measurements of the cliff, and to slowly back away from it when it has
been reliably identified and located.

Now there's a curious thing about this sequence of actions. A person
seeing them, not knowing about the internal mechanisms of the robot
might offer the interpretation "First the robot was determined to go
through the door, but then it noticed the stairs and became so
frightened and preoccupied it forgot all about what it had been
doing". Knowing what we do about what really happened in the robot we
might be tempted to berate this poor person for using such sloppy
anthropomorphic concepts as determinination, fear, preoccupation and
forgetfulness in describing the actions of a machine. We could berate
the person, but it would be wrong.

I think the robot came by the emotions and foibles indicated as
honestly as any living animal. An octopus in pursuit of a meal can be
diverted by hints of danger in just the way Uranus was. An octopus
also happens to have a nervous system that evolved entirely
independently of our own vertebrate version. Yet most of us feel no
qualms about ascribing concepts like passion, pleasure, fear and pain
to the actions of the animal.

We have in the behavior of the vertebrate, the mollusc and the robot a
case of convergent evolution. The needs of the mobile way of life have
conspired in all three instances to create an entity that has modes of
operation for different circumstances, and that changes quickly from
mode to mode on the basis of uncertain and noisy data prone to
misinterpretation. As the complexity of the mobile robots increases I
expect their similarity to animals and humans will become even
greater.

Among the natural traits I see in the immediate roving robot horizon
is parameter adjustment learning. A precision mechanical arm in a
rigid environment can usually have its kinematic self-model and its
dynamic control parameters adjusted once permanently. A mobile robot
bouncing around in the muddy world is likely to continuously suffer
insults like dirt buildup, tire wear, frame bends and small mounting
bracket slips that mess up accurate a-priori models. Our present
visual obstacle course software, for instance, has a camera
calibration phase where the robot is parked precisely in front of an
exact grid of spots so that a program can determine a function that
corrects for distortions in the camera optics. This allows other
programs to make precise visual angle measurements in spite of
distortions in the cameras. We have noticed that our present code is
very sensitive to mis-calibrations, and are working on a method that
will continuously calibrate the cameras just from the images perceived
on normal trips through clutter. With such a procedure in place, a
bump that slightly shifts one of the robot's cameras will no longer
cause systematic errors in its navigation. Animals seem to tune most
of their nervous systems with processes of this kind, and such
accomodation may be a precursor to more general kinds of learning.

Perhaps more controversially, I see the begininnings of self awareness
in the robots. All of our control programs have internal
representations, at varying levels of abstraction and precision, of
the world around the robot, and of the robot's position within that
world. The motion planners work with these world models in considering
alternative future actions for the robot.  If our programs had verbal
interfaces we could ask questions that receive answers such as "I
turned right because I didn't think I could fit through the opening on
the left ". As it is we get the same information in the form of
pictures drawn by the programs.

@heading(So What's Missing?)

There may seem to be a contradiction in the various figures on the
speed of computers. Once billed as "Giant Brains" computers can do
some things, like arithmetic, millions of times faster than human
beings. "Expert systems" doing qualitative reasoning in narrow problem
solving areas run on these computers approximately at human speed. Yet
it took such a computer five hours to simply drive the Cart across a
room, down to an hour for Neptune.  How can such numbers be
reconciled?

The human evolutionary record provides the clue. While our sensory and
muscle control systems have been in development for a billion years,
and common sense reasoning has been honed for probably about a
million, really high level, deep, thinking is little more than a
parlor trick, culturally developed over a few thousand years, which a
few humans, operating largely against their natures, can learn. As
with Samuel Johnson's dancing dog, what is amazing is not how well it
is done, but that it is done at all.

Computers can challenge humans in intellectual areas, where humans
perform inefficiently, because they can be programmed to carry on much
less wastefully.  An extreme example is arithmetic, a function learned
by humans with great difficulty, which is instinctive to
computers. These days an average computer can add a million large
numbers in a second, which is more than a million times faster than a
person, and with no errors. Yet one hundred millionth of the neurons
in a human brain, if reorganized into an adder using switching logic
design principles, could sum a thousand numbers per second. If the
whole brain were organized this way it could do sums one hundred
thousand times faster than the computer.

Computers do not challenge humans in perceptual and control areas
because these billion year old functions are carried out by large
fractions of the nervous system operating as efficiently as the
hypothetical neuron adder above.  Present day computers, however
efficiently programmed, are simply too puny to keep up. Evidence comes
from the most extensive piece of reverse engineering yet done on the
vertebrate brain, the functional decoding of some of the visual system
by D. H. Hubel, T. N. Weisel and colleagues.

 The vertebrate retina's 20 million neurons take signals from a
million light sensors and combine them in a series of simple
operations to detect things like edges, curvature and motion. Then
image thus processed goes on to the much bigger visual cortex in the
brain.

Assuming the visual cortex does as much computing for its size as the
retina, we can estimate the total capability of the system. The optic
nerve has a million signal carrying fibers and the optical cortex is a
thousand times deeper than the neurons which do a basic retinal
operation. The eye can process ten images a second, so the cortex
handles the equivalent of 10,000 simple retinal operations a second,
or 3 million an hour.

An efficient program running on a typical computer can do the
equivalent work of a retinal operation in about two minutes, for a
rate of 30 per hour. Thus seeing programs on present day computers
seem to be 100,000 times slower than vertebrate vision. The whole
brain is about ten times larger than the visual system, so it should
be possible to write real-time human equivalent programs for a machine
one million times more powerful than todays medium sized
computer. Even todays largest supercomputers are about 1000 times
slower than this desiratum. How long before our research medium is
rich enough for full intelligence?

Since the 1950s computers have gained a factor of 1000 in speed per
constant dollar every decade. There are enough developments in the
technological pipeline, and certainly enough will, to continue this
pace for the forseeable future.

The processing power available to AI programs has not increased
proportionately. Hardware speedups and budget increases have been
dissipated on convenience features; operating systems, time sharing,
high level languages, compilers, graphics, editors, mail systems,
networking, personal machines, etc. and have been spread more thinly
over ever greater numbers of users. I believe this hiatus in the
growth of processing power explains the disappointing pace of AI in
the past 15 years, but nevertheless represents a good investment. Now
that basic computing facilities are widely available, and thanks
largely to the initiative of the instigators of the Japanese
Supercomputer and Fifth Generation Computer projects, attention
worldwide is focusing on the problem of processing power for AI.

The new interest in crunch power should insure that AI programs share
in the thousandfold per decade increase from now on. This puts the
time for human equivalence at twenty years. The smallest vertebrates,
shrews and hummingbirds, derive interesting behavior from nervous
systems one ten thousandth the size of a human's, so we can expect
fair motor and perceptual competence in less than a decade. By my
calculations and impressions present robot programs are similar in
power to the control systems of insects.

Some principals in the Fifth Generation Project have been quoted as
planning "man capable" systems in ten years. I believe this more
optimistic projection is unlikely, but not impossible. The fastest
present and nascent computers, notably the Cray X-MP and the Cray 2,
compute at 109 operations/second, only 1000 times too slowly.

As the computers become more powerful and as research in this area
becomes more widespread the rate of visible progress should
accelerate. I think artificial intelligence via the "bottom up"
approach of technological recapitulation of the evolution of mobile
animals is the surest bet because the existence of independently
evolved intelligent nervous systems indicates that there is an
incremental route to intelligence. It is also possible, of course,
that the more traditional "top down" approach will achieve its goals,
growing from the narrow problem solvers of today into the much harder
areas of learning, common-sense reasoning and perceptual acquisition
of knowledge as computers become large and powerful enough, and the
techniques are mastered.  Most likely both approaches will make enough
progress that they can effectively meet somewhere in the middle, for a
grand synthesis into a true artificial sentience.

This artificial person will have some interesting properties. Its high
level reasoning abilities should be astonishingly better than a
human's - even today's puny systems are much better in some areas -
but its low level perceptual and motor abilities will be comparable to
ours. Most interestingly it will be highly changeable, both on an
individual basis and from one of its generations to the next. And it
will quickly become cheap.

@heading(The Future)

What happens when increasingly cheap machines can replace humans in
any situation? What will I do when a computer can write this article,
and do research, better than me? These questions face some occupations
now. They will affect everybody in a few decades.

By design, machines are our obedient and able slaves. But intelligent
machines, however benevolent, threaten our existence because they are
alternative inhabitants of our ecological niche. Machines merely as
clever as human beings will have enormous advantages in competitive
situations.  Their production and upkeep costs less, so more of them
can be put to work with given resources.  They can be optimized for
their jobs, and programmed to work tirelessly.

Intelligent robots will have even greater advantages away from our
usual haunts. Very little of the known universe is suitable for
unaided humans.  Only by massive machinery can we survive in outer
space, on the surfaces of the planets or on the sea floor. Smaller,
intelligent but unpeopled, devices will be able to do what needs to be
done there more cheaply. The Apollo project put people on the moon for
forty billion dollars. Viking landed machines on Mars for one
billion. If the Viking landers had been as capable as humans, their
multi-year stay would have told us much more about Mars than we found
out about the moon from Apollo.

As if this weren't bad enough, the very pace of technology presents an
even more serious challenge. We evolved with a leisurely 100 million
years between significant changes. The machines are making similar
strides in decades. The rate will quicken further as multitudes of
cheap machines are put to work as programmers and engineers, with the
task of optimizing the software and hardware which makes them what
they are. The successive generations of machines produced this way
will be increasingly smarter and cheaper. There is no reason to
believe that human equivalence represents any sort of upper
bound. When pocket calculators can out-think humans, what will a big
computer be like? We will simply be outclassed.

Then why rush headlong into the intelligent machine era? Wouldn't any
sane human try to delay things as long as possible? The answer is
obvious, if unpalatable on the surface. Societies and economies are as
surely subject to evolutionary pressures as biological
organisms. Failing social systems wither and die, to be replaced by
more successful competitors. Those that can sustain the most rapid
expansion dominate sooner or later.

We compete with each other for the resources of the accessible
universe. If automation is more efficient than hand labor,
organizations and societies which embrace it will be wealthier and
better able to survive in difficult times, and expand in favorable
ones. If the U.S. were to unilaterally halt technological development,
an occasionally fashionable idea, it would soon succumb either to the
military might of the Soviets, or the economic success of its trading
partners. Either way the social ideals that led to the decision would
become unimportant on a world scale.

If, by some evil and unlikely miracle, the whole human race decided to
eschew progress, the long term result would be almost certain
extinction.  The universe is one random event after another. Sooner or
later an unstoppable virus deadly to humans will evolve, or a major
asteroid will collide with the earth, or the sun will go nova, or we
will be invaded from the stars, or a black hole will swallow the
galaxy.

The bigger, more diverse and competent a culture is, the better it can
detect and deal with external dangers. The bigger events happen less
frequently. By growing sufficiently rapidly it has a finite chance of
surviving forever. Even the eventual collapse or heat death of the
universe might be evaded or survived if an entity can restructure
itself properly.

The human race will expand into the solar system soon, and human
occupied space colonies will be part of it. But the economics of
automation will become very persuasive in space even before machines
achieve human competence.

I visualize immensely lucrative self-reproducing robot factories in
the asteroids. Solar powered machines would prospect and deliver raw
materials to huge, unenclosed, automatic processing plants. Metals,
semiconductors and plastics produced there would be converted by
robots into components which would be assembled into other robots and
structural parts for more plants.  Machines would be recycled as they
broke. If the reproduction rate is higher than the wear out rate, the
system will grow exponentially. A small fraction of the output of
materials, components, and whole robots could make someone very, very
rich.

The first space industries will be more conventional. Raw materials
purchased from Earth or from human space settlements will be processed
by human supervised machines and sold at a profit. The high cost of
maintaining humans in space insures that that there will always be
more machinery per person there than on Earth. As machines become more
capable, the economics will favor an ever higher machine/people
ratio. Humans will not necessarily become fewer, but the machines will
multiply faster.

When humans become unnecessary in space industry, the machines'
physical growth rate will climb. When machines reach and surpass
humans in intelligence, the intellectual growth rate will rise
similarly. The scientific and technical discoveries of
super-intelligent mechanisms will be applied to making themselves
smarter still. The machines, looking quite unlike the machines we
know, will explode into the universe, leaving us behind in a
figurative cloud of dust. Our intellectual, but not genetic, progeny
will inherit the universe.  Barring prior claims.

This may not be as bad as it sounds, since the machine civilization
will certainly take along everything we consider important, including
the information in our minds and genes. Real live human beings, and a
whole human community, could be reconstituted if an appropriate
circumstance ever arose.  Since we are biologically committed to
personal death, immortal only through our children and our culture,
shouldn't we rejoice to see that culture become as robust as possible?

@heading(An Alternative)

Some of us have very egocentric world views. We anticipate the
discovery, within our lifetimes, of methods to extend human lifespans,
and look forward to a few eons of exploring the universe. We don't
take kindly to being upstaged by our creations.

The machines' major advantage is their progress rate. Our evolution is
largely cultural, but is tightly constrained by our Darwinianly
evolving biological substrate. Machinery evolves 100% culturally,
culture itself being a rapidly evolving process that feeds on and
accelerates itself. How can we, personally, become full,
unhandicapped, players in this new game?

Genetic engineering is an option. Successive generations of human
beings could be designed by mathematics, computer simulations, and
experimentation, like airplanes and computers are now. But this is
just building robots out of protein. Away from Earth, protein is not
an ideal material. It's stable only in a narrow temperature and
pressure range, is sensitive to high energy disturbances, and rules
out many construction techniques and components.  Anyway, second rate
superhuman beings are just as threatening as first rate ones, of
whatever they're made.

What's really needed is a process that gives an individual all the
advantages of the machines, at small personal cost. Transplantation of
human brains into manufactured bodies has some merit, because the body
can be matched to the environment. It does nothing about the limited
and fixed intelligence of the brain, which the artificial intellects
will surpass.

@heading(Transmigration)

You are in an operating room. A robot brain surgeon is in
attendance. By your side is a potentially human equivalent computer,
dormant for lack of a program to run. Your skull, but not your brain,
is anesthetized. You are fully conscious. The surgeon opens your brain
case and peers inside. Its attention is directed at a small clump of
about 100 neurons somewhere near the surface. It determines the three
dimensional structure and chemical makeup of that clump
non-destructively with high resolution 3D NMR holography, phased array
radio encephalography, and ultrasonic radar. It writes a program that
models the behavior of the clump, and starts it running on a small
portion of the computer next to you. Fine connections are run from the
edges of the neuron assembly to the computer, providing the simulation
with the same inputs as the neurons. You and the surgeon check the
accuracy of the simulation. After you are satisfied, tiny relays are
inserted between the edges of the clump and the rest of the
brain. Initially these leave brain unchanged, but on command they can
connect the simulation in place of the clump. A button which activates
the relays when pressed is placed in your hand. You press it, release
it and press it again.  There should be no difference. As soon as you
are satisfied, the simulation connection is established firmly, and
the now unconnected clump of neurons is removed. The process is
repeated over and over for adjoining clumps, until the entire brain
has been dealt with. Occasionally several clump simulations are
combined into a single equivalent but more efficient program. Though
you have not lost consciousness, or even your train of thought, your
mind (some would say soul) has been removed from the brain and
transferred to a machine.

In a final step your old body is disconnected. The computer is
installed in a shiny new one, in the style, color and material of your
choice. You are no longer a cyborg halfbreed, your metamorphosis is
complete.

For the squeamish there are other ways to work the transfer. The high
resolution brain scan could be done in one fell swoop, without
surgery, and a new you made, "While-U-Wait". Some will object that the
instant process makes only a copy, the real you is still trapped in
the old body (please dispose of properly). This is an understandable
misconception growing from the intimate assocation of a person's
identity with a particular, unique, irreplaceable piece of meat. Once
the possibility of mind transfer is accepted, however, a more mature
notion of life and identity becomes possible. You are not dead until
the last copy is erased; a faithful copy is exactly as good as the
original.

If even the last technique is too invasive for you, imagine a more
psychological approach. A kind of pocket computer (perhaps shaped and
worn like glasses) is programmed with the universals of human
mentality, with your genetic makeup and with whatever details of your
life are conveniently available. It carries a program that makes it an
excellent mimic. You carry this computer with you through the prime of
your life, and it diligently listens and watches, and perhaps monitors
your brainwaves, and learns to anticipate your every move and
response. Soon it is able to fool your friends on the phone with its
convincing imitation of you. When you die it is installed in a
mechanical body and smoothly and seamlessly takes over your life and
responsibilities.

What? Still not satisfied? If you happen to be a vertebrate there is
another option that combines some of the sales features of the methods
above. The vertebrate brain is split into two hemispheres connected by
a very large bundle of nerve fibers called the corpus callosum. When
brain surgery was new it was discovered that severing this connection
between the brain halves cured some forms of epilepsy. An amazing
aspect of the procedure was the apparent lack of side effects on the
patient. The corpus callosum is a bundle far thicker than the optic
nerve or even the spinal cord. Cut the optic nerve and the victim is
utterly blind; sever the spinal cord and the body goes limp. Slice the
huge cable between the hemispheres and nobody notices a thing. Well,
not quite. In subtle experiments it was noted that patients who had
this surgery were unable, when presented with the written word
"brush", for instance, to identify the object in a collection of
others using their left hand. The hand wanders uncertainly from object
to object, seemingly unable to decide which is "brush".  When asked to
do the same task with the right hand, the choice is quick and
unhesitating. Sometimes in the left handed version of the task the
right hand, apparently in exasperation, reaches over to guide the left
to the proper location. Other such quirks involving spatial reasoning
and motor co-ordination were observed.

The explanation offered is that the callosum indeed is the main
communications channel between the brain hemispheres. It has fibers
running to every part of the cortex on each side. The brain halves,
however, are fully able to function separately, and call on this
channel only when a task involving co-ordination becomes necessary. We
can postulate that each hemisphere has its own priorities, and that
the other can request, but not demand, information or action from it,
and must be able to operate effectively if the other chooses not to
respond, even when the callosum is intact. The left hemisphere handles
language and controls the right side of the body. The right hemisphere
controls the left half of the body, and without the callosum the
correct interpretation of the letters "b r u s h" could not be
conveyed to the controller of the left hand.

But what an opportunity. Suppose we sever your callosum but then
connect a cable to both severed ends leading into an external
computer. If the human brain is understood well enough this external
computer can be programmed to pass, but also monitor the traffic
between the two. Like the personal mimic it can teach itself to think
like them. After a while it can insert its own messages into the
stream, becoming an integral part of your thought processes.  In time,
as your original brain fades away from natural causes, it can smoothly
take over the lost functions, and ultimately your mind finds itself in
the computer. With advances in high resolution scanning it may even be
possible to have this effect without messy surgery - you would just
wear some kind of helmet or headband.

Whatever style you choose, when the process is complete advantages
become apparent. Your computer has a control labelled speed. It had
been set to slow, to keep the simulations synchronized with the old
brain, but now you change it to fast. You can communicate, react and
think a thousand times faster. But wait, there's more!

The program in your machine can be read out and altered, letting you
conveniently examine, modify, improve and extend yourself. The entire
program may be copied into similar machines, giving two or more
thinking, feeling versions of you. You may choose to move your mind
from one computer to another more technically advanced, or more suited
to a new environment.  The program can also be copied to some future
equivalent of magnetic tape.  If the machine you inhabit is fatally
clobbered, the tape can be read into an blank computer, resulting in
another you, minus the experiences since the copy. With enough copies,
permanent death would be very unlikely.

As a computer program, your mind can travel over information
channels. A laser can send it from one computer to another across
great distances and other barriers. If you found life on a neutron
star, and wished to make a field trip, you might devise a way to build
a neutron computer and robot body on the surface, then transmit your
mind to it. Nuclear reactions are a million times quicker than
chemistry, so the neutron you can probably think that much faster.  It
can act, acquire new experiences and memories, then beam its mind back
home.  The original body could be kept dormant during the trip to be
reactivated with the new memories when the return message
arrived. Alternatively, the original might remain active. There would
then be two separate versions of you, with different memories for the
trip interval.

Two sets of memories can be merged, if mind programs are adequately
understood.  To prevent confusion, memories of events would indicate
in which body they happened. Merging should be possible not only
between two versions of the same individual but also between different
persons.  Selective mergings, involving some of the other person's
memories, and not others would be a very superior form of
communication, in which recollections, skills, attitudes and
personalities can be rapidly and effectively shared.

Your new body will be able to carry more memories than your original
biological one, but the accelerated information explosion will insure
the impossibility of lugging around all of civilization's
knowledge. You will have to pick and choose what your mind contains at
any one time. There will often be knowledge and skills available from
others superior to your own, and the incentive to substitute those
talents for yours will be overwhelming. In the long run you will
remember mostly other people's experiences, while memories you
originated will be floating around the population at large. The very
concept of you will become fuzzy, replaced by larger, communal egos.

Mind transferral need not be limited to human beings. Earth has other
species with brains as large, from dolphins, our cephalic equals, to
elephants, whales, and giant squid, with brains up to twenty times as
big.  Translation between their mental representation and ours is a
technical problem comparable to converting our minds into a computer
program. Our culture could be fused with theirs, we could incorporate
each other's memories, and the species boundaries would
fade. Non-intelligent creatures could also be popped into the data
banks.  The simplest organisms might contribute little more than the
information in their DNA. In this way our future selves will benefit
from all the lessons learned by terrestrial biological and cultural
evolution. This is a far more secure form of storage than the present
one, where genes and ideas are lost when the conditions that gave rise
to them change.

Our speculation ends in a super-civilization, the synthesis of all
solar system life, constantly improving and extending itself,
spreading outwards from the sun, converting non-life into mind. There
may be other such bubbles expanding from elsewhere. What happens when
we meet? Fusion of us with them is a possibility, requiring only a
translation scheme between the memory representations. This process,
possibly occuring now elsewhere, might convert the entire universe
into an extended thinking entity, a probable prelude to greater
things.