Robots That Rove

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

September 11, 1984

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.

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.

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:

-- 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 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.

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 deg, 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.

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.

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
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
Travelto Door-Location
IF Door-Open THEN Go-Through-Opening
ELSE Open-Door-and-Go-Through
Travelto Start-Location

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.

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 10operations/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.

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?

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 a 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.


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.