The Age of Robots

June 1993 by

Hans Moravec

Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
(412) 268-3829

(8500 words)




Serious attempts to build thinking machines began after the second world war. One line of research, called Cybernetics, used simple electronic circuitry to mimic small nervous systems, and produced machines that could learn to recognize simple patterns, and turtle-like robots that found their way to lighted recharging hutches. An entirely different approach, named Artificial Intelligence (AI), attempted to harness the apparently prodigious power of post-war computers--able to do the arithmetical work of thousands of mathematicians--to more interesting kind of thinking. And indeed, by 1965, computers ran programs that proved theorems in logic and geometry, solved calculus problems and played good games of checkers. In the early 1970s, AI research groups at MIT and Stanford University attached television cameras and robot arms to their computers, so their "thinking" programs could begin to collect their information directly from the real world.



By comparing the edge and motion detecting circuitry in the four layers of nerve cells in the retina, the best understood major circuit in the human nervous system, with similar processes developed for "computer vision" systems that allow robots in research and industry to see, I've estimated that it would take a billion computations per second (the power of an average supercomputer) to produce the same results at the same speed as a human retina. By extrapolation, to emulate a whole brain takes ten trillion arithmetic operations per second, or ten thousand supercomputers worth.



First-Generation Universal Robots

Timeframe: 2000-2010
Processing power: 1,000 MIPS (1993 supercomputer -- Reptile-class)
Distinguishing feature: General-purpose perception, manipulation and mobility


1,000 MIPS (Millions of Instructions Per Second) is just enough computing power for a moving robot to maintain a coarse map of its surroundings and use it for locating itself relative to trained itineraries and to plan and control driving. When not traveling, there is power enough to construct a fine map of a manipulator workspace, to locate particular objects and to plan and control arm motions. When not occupied with its unique robotic functions, the robot should share with personal computers of its time the ability to communicate over wireless networks, to generate and interpret spoken sentences and to generate and read printed text. Programs for specific applications--many obtained via high-speed networks--will orchestrate these basics to accomplish useful tasks.

Universal robots will find their first uses in factories, warehouses and offices, where they will be more versatile than the older generation of robots they replace. Because of their breadth of applicability, their numbers should grow rapidly, and their costs decline. Eventually they will become cheap enough for some households, extending the utility of personal computers from a few tasks in the data world to many in the physical world. Perhaps a program for housecleaning will be included with each robot, as word-processing programs were shipped with early personal computers.


A first-generation robot will have the brain power of a reptile, but most application programs will be so hard pressed to accomplish their primary functions that they will endow the robot with the personality of a washing machine.

Second-Generation Universal Robots

Timeframe: 2010-2020
Processing power: 30,000 MIPS (Mammal-class)
Distinguishing feature: Accommodation learning

First-generation robots will be rigid slaves to inflexible programs, relentless in pursuing their tasks--or repeating their errors. Their programs will contain the frozen results of learning done on bigger computers under human supervision. Except for specialized episodes like recording a new cleaning route or the location of work objects, they will be incapable of learning new skills or adapting to unanticipated circumstances--even modest alterations of behavior will require new programming, probably from the original software suppliers.

Second-generation robots, with thirty times the processing power, will be more adaptable, because they can do some learning onboard. The fundamental idea in adaptive learning is to "close the loop" on behavior: to evaluate each action's effect in a given context to enhance the process that generated the action. In the simplest technique, a behavioral alternative that succeeds becomes more likely to be invoked in similar circumstances, while an alternative that fails becomes less probable. Faster statistical-learning approaches like neural nets repeatedly tweak behavior-control parameters to nudge actual responses closer to an ideal. Programs for second-generation robots will use many such learning techniques, creating new abilities--and new pitfalls.

If a first-generation robot working in your kitchen runs into trouble--say, failing to complete a key step because a portion of the workspace is awkwardly small--you have to option of abandoning the task, changing its environment, or somehow obtaining altered software that accomplishes the problematic step in a different way. A second-generation robot will make a number of false starts, but most probably will find its own solution, adjust to its home in thousands of more subtle ways, and gradually improve its performance. While a first-generation robot's personality is determined entirely by the sequence of operations in the application program it runs at the moment, a second-generation robot's character is more a product of the suite of conditioning programs it hosts. The conditioning system might, in time, censor an entire application program, if it gave consistently negative results.

Second-generation robots of 2010 will have onboard computers as powerful as the supercomputers that learned for first-generation machines in 2000. But by 2010, supercomputers will be proportionally more powerful (about 3,000,000 MIPS), and will themselves play a background role for the second-generation. The many individual programs of a conditioning suite--each responding to some specific stimulus--interact with one another and with the robot's control programs and environment in ways that will be far too entangled to anticipate accurately. It would be possible to evaluate particular suites by trying them out in robots--the acid test in any case--but that would be a slow and dangerous way to sift a large number of rough candidates--some would certainly behave in unexpected ways that could damage the robot, or even endanger the testers.

Faster and safer initial screenings might be done in factory supercomputer simulations of robots in action. To be of value, simulations would have to be good models, predicting accurately such things as the probability that a given grip can lift a particular object, or that a vision module can find a given something in particular clutter. Simulating the everyday world in full physical detail will still be beyond computer capacity in 2010, but it should be possible to approximate the results by generalizing data collected from actual robots: essentially to learn from the working experience of real robots how everyday things behave. A large systematic collection effort under human supervision will probably be necessary lest there be too many gaps or distortions. A proper simulator would contain at least thousands of learned models for various basic interactions (call them interaction primitives), in what amounts to a robotic version of common-sense physics.

Third-Generation Universal Robots

Timeframe: 2020-2030
Processing power: 1,000,000 MIPS (Primate-class)
Distinguishing feature: World modeling

Adaptive second-generation robots will find jobs everywhere, and may become the largest industry on earth. But teaching them new skills, whether by writing programs or through training, will be very tedious. A third generation of universal robot, with onboard computers as powerful as the supercomputers that optimized second-generation programs, will learn much faster because they do much of the trial and error in fast simulation rather than slow and dangerous physicality. Once again, a process done by human-supervised supercomputers at the factory in one robot generation will be improved and installed directly onboard the next generation, and once again new opportunities and new problems will arise.

With a simulator onboard, it becomes possible for a robot to maintain a running account of the actual events going on around it--to simulate its world in real time. Doing so requires that almost everything the robot senses be recognized for the kind of object it is, so that the proper models of interaction can called up. Recognizing arbitrary objects by sight is as difficult as knowing how they will interact: it will require modules specially trained for each kind of thing (call them perception primitives). Some perception primitives may already have been developed for second-generation factory simulators, to help automate the tedious job of creating simulations of robot workspaces, but an additional effort to fill gaps and systematize them will surely be necessary to prepare them for fully automatic use in the third generation. Perception primitives will allow a robot's three-dimensional map of a room to be transformed into a working model, as each object is identified and linked with its proper interaction primitives.

A continuously updated simulation of self and surroundings gives a robot interesting abilities. By running the simulation slightly faster than real time, the robot can preview what it is about to do, in time to alter its intent if the simulation predicts it will turn out badly--a kind of consciousness. On a larger scale, before undertaking a new task, the robot can simulate it many times, with conditioning system engaged, learning from the simulated experiences as it would from physical ones. Consequently well trained for the task, it would likely succeed the first time it attempted it physically--unlike a second-generation machine, which must make all its mistakes out in real life. When it has some spare time, the robot can replay previous experiences, and try variations on them, perhaps learning ways to improve future performance. A sufficiently advanced third-generation robot, whose simulation extends to other agents--robots and people--would be able to observe a task being done by someone else, and formulate a program for doing the task itself: it could imitate.

Though they will be able to adapt, imitate and create simple programs of their own, third-generation robots will still rely on externally supplied programs to do complicated jobs. Since their motor and perceptual functions will be quite sophisticated, and their memories and potential skills large, it will be possible to write wonderfully elaborate control programs for them, accomplishing large jobs, with nuances within nuances. It will be increasingly difficult for human programmers to keep track of the many details and interactions. Fortunately, the task can be largely automated. Shakey, the first computer-controlled mobile robot, developed at SRI in the late 1960s, had at its heart a reasoning program called STRIPS (STanford Research Institute Problem Solver) that expressed the robot's situation and capabilities as sentences of symbolic logic, and solved for the sequence of actions that achieved a requested result as a proof of a mathematical theorem. In 1969, on computers with a mere 0.1 MIPS, neither the theorem prover nor the sensory processing which provided its input could handle the complexity of realistic situations, and Shakey was limited to maneuvering around a few blocks. Nevertheless, the idea was sound: given a correct description of the initial and desired state of the world, and enough time and space to work, a theorem prover will find an absolutely correct solution, of whatever generality, subtlety and deviousness is required, if one exists at all. By the time of the third universal robot generation, supercomputers will provide 100,000,000 MIPS, and (thanks to continuing progress in the top-down Artificial Intelligence industry) programs will exist which will be able to STRIPS-like reasoning with real world richness. So factory supercomputers in 2025 will accept complex goals (find a sequence of robot actions which assembles the robot described in the following design database), and compile them via theorem provers into wonderfully intricate control programs for third-generation robots, which will, in turn adapt them to their actual circumstances.

Fourth-Generation Universal Robots

Timeframe: 2030-2040
Processing power: 3,000,000 MIPS (Human-class)
Distinguishing feature: Reasoning


Fourth-generation universal robots will have computers powerful enough to simultaneously simulate the world, and reason about the simulation. Like the factory supercomputers of the third-generation, fourth generation robots will be able to devise ultra-sophisticated robot programs, for other robots or for themselves. Because of another gift from the Artificial Intelligence industry, they will also be able to understand natural languages. While the original language understanders will probably use a verbal common-sense database similar to the one being developed by the Cyc project, where the meaning of words is defined in reference only to other words, in a fourth-generation robot some concepts and statements will be understood more deeply, through the action of the simulator. When someone tells the robot "the water is running in the bathtub" the robot can update its simulation of the world to include flow into the unseen tub, where a simulated extrapolation would indicate an undesirable overflow later, and so motivate the robot to go to turn off the tap. A purely verbal representation might accomplish the same thing if it included the statements such as "A filling bathtub will overflow if its water is not shut off," but a modest number of general principles in a simulator, interacting in combinations, can provide the equivalent information of an indefinite number of sentences. Similarly a reasoning program, making inferences about physical things, might be enhanced by a simulator: candidate inferences would rejected if they failed in a parallel simulation of a typical case, and, conversely, persistent coincidences in the simulation could suggest statements that can be proved--the robot would be visualizing as it listened, spoke and reasoned. A modest but very successful version of such an approach was used in one of the earliest Artificial Intelligence programs, a geometry theorem prover by Herbert Gelernter in 1959. Starting with the postulates and rules of inference in Euclid's "Elements," Gelernter's program proved some of the theorems, using algebraic "diagrams" to eliminate false directions in the proofs. Before attempting to prove two triangles congruent in a certain construction, for instance, the program would generate an example of the construction, using random numbers for the unspecified quantities, and measure the resulting triangles. If they were not sufficiently similar--within the precision of the arithmetic--the program abandoned that approach and tried something else.

Simulator-augmented language understanding and reasoning may be so effective in robots that it will be adopted for use in plain computer programs, "grounding" them in the physical world via the experiences of the robots that tuned the simulators. In time the distinction between robot controllers and disembodied reasoners will diminish, and reasoning programs will sometimes link to robot bodies to interact physically with the world, and robot minds will sometimes retire into large computers, to do some intense thinking off-line.


The Short Run (early 2000s)

As the industrial revolution gathered steam two centuries ago, it destroyed cottage industries and concentrated wealth in he hands of factory owners--the capitalists. Millions of displaced home workers competed for too few jobs tending the new machines. It took difficult political readjustments to equalize the benefits of cheaper, more plentiful goods, but gradually laborers' hours were halved, creating need for more workers, and so bidding up salaries. Though it increases communal wealth, each increment in automation threatens a similar unpleasant transient, as it displaces one group of workers with fewer doing different tasks. If the new required skills are common, mass competition for the few jobs drives down salaries. If the skills are rare, scarcity encourages high pay and long hours. Either way, some work excessively while others are jobless--and it takes slow changes in the social contract and in education to level the load.

Though work hours will decline, they cannot be the final answer to rising productivity. In the next century inexpensive but capable robots will displace human labor so broadly that the average workday would have to plummet to practically zero to keep everyone usefully employed. Already, much labor services more questionable needs--gargantuan government bureaucracies, cosmetic medicine, mass entertainment, and speculative writing, to give a few examples. In time almost all humans may work to amuse other humans, while robots run competitive primary industries, like food production and manufacturing. There is a problem with this picture. The "service economy" functions today because many humans willing to buy services work in the primary industries, and so return money to the service providers, who in turn use it to buy life's essentials. As the pool of humans in the primary industries evaporates, the return channel chokes off--efficient, no-nonsense robots will not engage in frivolous consumption. Money will accumulate in the industries, enriching any people still remaining there, and become scarce among the service providers. Prices for primary products will plummet, reflecting both the reduced costs of production, and the reduced means of the consumers. In the ridiculous extreme, no money would flow back, and the robots would fill warehouses with essential goods which the human consumers could not buy.

The scenario above is incomplete. Not all individuals involved in productive enterprises actually work there. Stockholders, having once contributed capital to a thriving enterprise, may collect dividends indefinitely. Workers can be replaced by something more efficient, but in the present legal system, owners remain unless they sell out. Even with total automation, human business proprietors will continue to profit, and so be able to patronize the service providers. An analogous situation existed in classical and feudal times, where an impoverished, overworked majority of slaves or serfs played the role of robots, and land ownership played the role of capital. In between the serfs and the lords, a working population struggled to make a living from secondary sources, often by performing services for the privileged. The most prestigious and prosperous commoners sold high quality products and services directly to the gentry (as in the proud line still seen in Britain, By Appointment to Her Majesty). A larger number lived less well by trading with other townspeople.

It is unlikely that a future majority of service-providing "commoners" with more free time, communications and democracy than today, would tolerate being lorded over by a minority of non-working hereditary capitalists: they would vote to change the system. The trend in the social democracies has been to equalize income by raising the standards of the poorest as high as the economy can bear--in the age of robots, that minimum will be very high. In the early 1980s James Albus, head of the automation division of the then National Bureau of Standards, suggested that the negative effects of total automation could be avoided by giving all citizens stock in trusts that owned automated industries, making everyone a capitalist. Those who chose to squander their birthright could work for others, but most would simply live off their stock income. Even today, the public indirectly owns a majority of the capital in the country, through compounding private pension funds. In the United States, universal coverage could be achieved through the social security system. Social security was originally presented as a pension fund that accumulated wages for retirement, but in practice it transfers income from workers to retirees. The system will probably be subsidized from general taxes in coming decades, when too few workers are available to support the post World War II "baby boom." Incremental expansion of such a subsidy would let money from robot industries, collected as corporate taxes, be returned to the general population as pension payments. By gradually lowering the retirement age towards birth, most of the population would eventually be supported. The money could be distributed under other names, but calling it a pension is meaningful symbolism: we are describing the long, comfortable retirement of the entire original-model human race.

The Medium Run (around 2050)

What happens to people when work becomes passe? Existing retirement communities are probably too sleepy to be a good model--most of the individuals there have completed their life's work, and are of declining vigor and health. Better examples may be the richest Arabian petro-kingdoms, where oil-bought foreign labor plays the role of total automation. In a tradition of tribal sharing shaped by a sparsely-furnished nomadic past, Kuwait, Saudi Arabia and the United Arab Emirates have managed to spread the new wealth broadly among the citizenry in a single generation. Free health care and education, and undemanding government jobs, or outright welfare, secure life's needs, and life expectancies and literacy rates are among the world's highest. Comfort and security mute the stresses of civilization, including the tension between circumscribed Islamic values and the liberties of a wealthy world culture. The societies produce both world-class achievers and criminals, but on average show less driven urgency than many industrialized nations: most of their citizens seem happy to simply live their lives, and stability is endangered only by neighboring countries, where impoverished majorities are less content with the status quo. In numerous smaller examples, wealthy families often produce generations of content, even smug, heirs (as well as exceptions to titillate the tabloids).

Contrary to fears of some enmeshed in civilization's work ethic, our tribal past prepared us well for lives as idle rich. In a good climate and location the hunter-gatherer's lot can be pleasant indeed: an afternoon's outing picking berries or catching fish--what we civilized types would recognize as a recreational weekend--provides life's needs for several days. The rest of the time can be spent with children, socializing or simply resting. Of course, our ancestors had also to survive hard times, and evolution bequeathed us the capacity for desperate measures, including hard work. Civilization turned that extremity into everyday normality, and now stress is the leading cause of disease, and probably triggers some of the ugliest aspects of tribalism. In primates, overpopulation is a common reason for group distress, as nature-provided food and shelter falls short. To survive, a strong tribe may chase away or exterminate a weaker neighbor, or drive out or otherwise eliminate some of its own members: maybe those who smell, look, sound or act differently. Sometimes stressed individuals become accident or disease prone, and die spontaneously, improving the prospects for their relatives--similar considerations could regulate the prevalence of non-reproductive behaviors like homosexuality. City life, absurdly crowded and stressful by tribal-village standards, may inappropriately activate unconscious overpopulation reflexes--the self-destructive emotional vehemence of ethnic strife hardly reflects rational self-interest. It will harder to stir up battle fervor against minorities from the luxurious lassitude of a robot-supported life.

Ultra-conservative Switzerland may be a hint of things to come. Government and commercial institutions perfected through centuries of peace (interrupted only briefly by Napoleon) have given Switzerland unmatched prosperity, stability and security. Most Swiss citizens work, but they do so comfortably, with generous government welfare--and Italian immigrant labor--having lessened the desperation that forces workers elsewhere into unpleasant jobs. Comfortable prosperity has allowed multi-ethnic, multi-religious, multi-language Switzerland, made of 23 fiercely independent Cantons each with its own traditions and history, to peacefully endure the most severe internal differences of opinion, for instance the political fury between German and French factions during the first World War. The average Swiss citizen may resist most major changes (why ruin a good thing?), but Switzerland produces world-class contributors in all fields--if a bit less flamboyance than average. While it gives everyone the opportunity to excel, it lacks the social trauma that drives some other countries. Few Swiss would prefer it otherwise.

Many trends in industrialized countries presage a future of humans supported by a rich robot economy, as our ancestors were supplied by their ecology (call it paradise with plumbing). Technology and global competition are gradually depopulating businesses. Even absent universal robots, increasingly flexible automation is displacing labor in food production and manufacture, while communicating computers are replacing clerks, secretaries, and managers in offices. Jobs that still require human labor are moving to the homes of computer-equipped "tele-commuters" (like this author) who report reduced stress and improved family life. In a ripple effect, smaller work staffs imply less catering, janitorial and maintenance support. In future, as smarter computers, able to handle policy making, public relations, law, engineering and research replace the last telecommuters, and as capable robots displace technicians, janitors, vehicle drivers and construction crews, it will only be common sense for a population to vote itself income from taxes on labor-free but superbly productive industries. Less developed countries might rapidly catch up by offering the same industries location and raw materials at lower tax cost--a trained population will no longer be a requirement.

Western democracies may come to resemble lazier Switzerlands, but with large differences. Big cities will lose their economic advantages, and may begin to evaporate, as individuals, linked to the world by high fidelity communications and served by personal robots, scatter to areas offering more elbow room. Large countries may similarly become less important, as taxes on local industries, and local robot labor, become adequate to supply all human needs. The civilized world may again return to a comfortable tribalism, after a five millennium detour into organized civilization. Countries with traditional tribal structures may simply stay that way, building on their ancestral customs, leapfrogging urbanization altogether, while developed countries foster tribes with customs and beliefs that exceed even today's notion of bizarre.

Tribalism will express itself in entirely novel ways. Over the last two decades, inter linked computer networks have hosted small communities whose members happen to be distributed around the world. In 1993 the informal "Usenet" had about ten million subscribers, carrying on about three thousand specialized discussions on every conceivable topic, some with fifty page-long messages every day. Regular contributors to a particular "newsgroup" soon begin to recognize one another, and develop characteristic interactions, likes and dislikes. They form factions that praise, recruit, condemn and ostracize. When a newsgroup grows too large and noisy, specialized subgroups are formed, reducing the original group's population. In future, the world networks will have much greater capacity, and new abilities, such as language translation. "Tribes of common interest" will share more than written text, perhaps exchanging voice and video, or manifesting themselves in full sensory 3D, in synthesized virtual realities: tribal lands that exist in the minds of computers, in greater number, variety and accessibility than possible in the physical world.

While computer simulations create entirely new worlds, robots will transform physical life. Today, manufactured items are difficult to make, and thus relatively rare and expensive, and we expend great effort in acquiring and defending them--our homes are fortified warehouses of our possessions. Stockpiling will be less appropriate amidst robotic abundance: why hoard fruit in an orchard? Conventional manufacturing methods--molding, casting, milling, assembly--can be robotically orchestrated to make new items fairly quickly. Even better, robotic accuracy and patience can build up solid objects by precisely "painting" various materials, layer upon cross-sectional layer. Such new approaches, refined to molecular resolution (done now modestly in scanning tunneling microscopes) will produce arbitrary solid objects from computer descriptions. Humans may be able to live in uncluttered spaces--in ecological preserves, if they choose--yet have any needed item, perhaps even food or housing, made on the spot, or delivered from small local caches--then disassembled back into into raw materials after use. The most visible technological products remaining may be robots themselves, in various sizes and shapes, and these may lurk unobtrusively until called upon.

Robots that live among humans, providing goods and services, will themselves be consumer products, styled, outfitted and programmed to please the customers. They will be manufactured by very different robots that extract energy and raw materials and perform major engineering, exploration and research projects. Molded by the constraints of the physical world rather than by human whim, these worker machines are likely to become ever more varied in size, shape and function, forming an entire ecology of artificial life that will eventually surpass the existing biosphere in diversity. The first fully automated companies, evolved from existing firms, will be in familiar industrial settings near population centers. As human labor becomes superfluous, economics will dictate cheaper sites, perhaps locations that humans find unpleasant because they are too hot, too cold, too dry, too poisonous, too far underground or too remote.







Humans too will be shaped by the relationship. Robot services will be inexpensive, but not free, and income will be finite. Corporations will operate globally, but taxes will increasingly be assessed on and redistributed on a tribal scale. Tribes that tax too heavily will drive away the corporations, and so eliminate their revenue--like tribes of the past that overburdened their ecology, they will learn modesty of expectation. More subtly, corporations struggling to appeal to consumers will develop and act on increasingly detailed and accurate models of human psychology. The superintelligences, just doing their job, will peer into the workings of human minds--and manipulate them with subtle cues and nudges, like adults redirecting toddlers.



The Long Run (2100 and beyond)








