Why Robots Won't Rule the World

Last updated 25 November 2000 .


click on image to enlarge. Graph by courtesy of Intel.

The Mathematics of Doom

Moore's Law

In 1965 Gordon Moore, then of Fairchild (later to found Intel) predicted that the amount of transistors packable into a chip would double every year. This was a little optimistic. It has since then turned out to be closer to 18 months, a change Moore agrees with. It affects both computer processors and their associated on-board memory, i.e., the two components most responsible for what we think of as ``computer power''.

Amdahl's Constant

In computer architecture there is a ratio between processor speed and memory which provides optimum performance in terms of ``bang per buck''. If processor speed is expressed in instructions per second, and memory is expressed in bytes, then Amdahl (founder of Amdahl Corporation, a computer manufacturer) has suggested that the optimum ratio here is one, i.e., a 20 MIPS (million instructions per second) processor is best allied with 20 Megabytes of memory. Roughly speaking, on average this does seem to have been the case. The exact number is not important. What is important is there is some simple association between processor speed and the amount of memory that the processor can conveniently exploit. Since memory is produced by the same technology as are processor chips, then Moore's law applies to computer power in general, in terms of the entire package of processor and memory.

click on image to enlarge. Graph by courtesy of Hans Moravec.

Moravec's Extension

Computer technology analysts predict that the current technology on which computers are based has another ten to twenty years left before it hits fundamental physical limits beyond which no further progress in miniaturisation is possible. What then? In fact, as Moravec has shown, Moore's Law can be projected backwards since before the dawn of ``silicon chips'', right back through the earlier computer and calculator technologies of clockwork, relays, electronic valves (US tubes), and discrete transistors. Moravec also normalises the data to ``processing power per $1000 (1997)'' to produce a ``bang per buck'' version of Moore's Law. When the data is plotted it can be seen that Moore's Law has leapt seamlessly from technology to technology, always finding a new one before the old one ran out of steam. So, if this trend persists, we can expect it to leap again, and again, and again.

Forever? It turns out that we needn't worry about forever, because something very interesting indeed happens in the next few decades. Within a few decades $1000 (1997) will be able to buy a computer with the processing power of the human brain, according to our current best estimates of what that is. Such is the magic of this kind of exponential growth of computer power (doubling every 18 months) that it doesn't matter if we have underestimated the power of the human brain by a factor of 10, 100, or even 1,000. In the extreme case of an underestimate of 1,000 we simply have to wait another fifteen years for the requisite computer power to arrive. And we only have to wait another ten years for these $1,000 computers to be 100 times more powerful than a human brain.

Would you prefer to wait until the computers were as powerful as the summed brain power of the entire planetary human population of six billion people? You will just have to wait another 50 years.

In short, we have somehow managed to get ourselves onto a technological escalator which will produce cheap computers of superhuman processing power within a few to several decades.

Will Moore's Second Law Stop It?

Unless of course Moore's Second Law stops it. Moore's Second Law concerns the cost of the fabrication plant needed for each new density of silicon chip. Once more this is an exponential law, but this time the cost of the plant increases linearly with respect to miniaturisation, i.e., exponentially with respect to time. So as Moore's First Law is making computers cheaper, Moore's Second Law is increasing the cost of tooling up for the production run, which is increasing the cost of computers. At the moment the First Law is winning, but soon the Second Law will catch up, and the price per transistor will bottom out. There will then be no economic incentive to making transistors smaller, so the progress of Moore's First Law will stop. According to Ross [Ross 1995] this will happen between 2003 and 2005. Given that particular technology of course. Perhaps by then we will have switched to optical or nanocomputing.

These arguments about what and when might stop the progress of Moore's First Law become increasingly speculative and technical. I don't wish to consider these arguments, because what is really interesting is the conclusion some people, such as Professors Moravec and Warwick, draw from this sustained progress, and that is that when computers have exceeded the human brain in computing power, they (in the form of robots or whatever) will be more intelligent than us, and will take over the planet. Moravec thinks this will be a good thing, because we will be handing the torch of future civilisation over to our ``children''[Moravec 1998]. Warwick thinks this may not be so good, because they may snatch it from us before we are willing to hand it over, and we won't be able to stop them, because they'll be too clever and too technologically advanced [Warwick 1998]. De Garis thinks there will be a terrible world civil war between those people who are on the side of the robots and those who are against them [de Garis 2001]. Kurzweil thinks that we human beings can start to participate in this increase in intelligence by having microscopic nanocomputers link themselves into our own brains [Kurzweil 2000].

The Flawed Assumptions

What all these predictions have in common is two assumptions, often felt to be so obvious that they are barely even stated. The first is that this increase in computer processing power will automatically mean an increase in the intelligence of whatever is using these computers for brains. The second is that superintelligent machines will automatically want to take over the world.

I think both of these assumptions fail to hold. The first fails to hold because intelligence is more, much more than just raw processing power, although high intelligence requires a large amount of processing power. It's like supposing that just because you've fitted a rocket engine to the roof rack of your motor car you will now be able to get to work faster.

The second assumption is due to what I call the Weizenbaum Illusion, our human tendency to over-interpret things which behave in some ways as though they might have minds. It's a modern version of the same illusion which tempted early man to think that thunder meant the Gods were angry. It leaps over what I call the contraption/creature chasm, i.e., it imagines that a superintelligent machine will not just be a useful contraption, it will be a creature, with all that that entails about hostility towards something which threatens its dinner. However, just as no amount of extra linguistic ``intelligence'' in your word processor will cause it to try to electrocute you because of your poor grammar, so no amount of extra intelligence in your domestic robot will cause it to demand that you do the washing up for it, because it has more important things to do, and you are its inferior and therefore obliged to serve it. Why not? Because it will not be a creature, with all that creaturehood implies of survival, competitiveness, territoriality, etc..

The Bad Effects of Silly Ideas

Some people argue that these predictions of the coming supremacy of robots are just too silly, and that even bothering to refute such silly ideas is a waste of time. They are just entertaining science fiction horror stories which happen to have caught the attention of the popular media. However, some of those who are putting forward these allegedly silly ideas are very clever knowledgeable men who have spent a great deal of time in related research, such as Moravec, and have spent time dealing seriously with the obvious objections to their ideas. Despite the reluctance many academics and scientists feel for engaging in debates which have moved into the public arena, I think these arguments deserve to be taken seriously for two reasons. Firstly they raise some important issues relevant to artificial life and artificial intelligence. Secondly these ideas can influence public policy and Government research funding strategies. Even in the brief history of artificial intelligence research, only about fifty years old, it is possible that this has happened twice already.

The First Time, the Lighthill Report

The first time happened in the early 1970s. In the UK the Government commissioned a special report from the Science & Engineering Research Council (the infamous Lighthill Report) which damned AI and recommended withdrawal of research funding. The same kind of official doubts which the Lighthill Report made explicit in the UK lay less explicitly behind a less extreme slow down in research funding in the US. This is sometimes referred to generically as the ``first AI winter''. The basic problem was an annoyed backlash by the research funding agencies in response to the the failure of the optimistic predictions of the early AI pioneers. For example, in 1958 Newell and Simon predicted that computers would by 1970 be capable of composing classical music, discovering important new mathematical theorems, playing chess at grandmaster level, and understanding and translating spoken language [Newell & Simon 1958].

Was the Second Time the Alvey Initiative?

The second time may have happened in the mid 1980s, in the last stages of defining the Alvey Initiative, the UK's answer to the threat of the Japanese Fifth Generation Project. The Alvey Report, on which the Initiative was based, recommended putting a lot of money into AI research, which they renamed Knowledge Based Systems so as not to confuse Members of Parliament who might have remembered the Lighthill Report of little more than a decade earlier which had told them what rubbish AI was. It was rumoured in some of the UK national press of the time that Margaret Thatcher watched Professor Fredkin being interviewed on a late night TV science programme. Fredkin explained that superintelligent machines were destined to surpass the human race in intelligence quite soon, and that if we were lucky they might find human beings interesting enough to keep us around as pets. The rumour is that Margaret Thatcher decided that the ``artificial intelligentsia'' whom she was just proposing to give lots of research funds under the Alvey Initiative were seriously deranged. Her answer was to increase the amount of industrial support required by a research project in order to be eligible for Alvey funding, hoping thereby to counterbalance their deranged flights of fancy with industrial common sense. I have so far been unable to substantiate this rumour. Fredkin did say that on British TV at the right time, and there were last minute unexpected increases in the amount of industrial support required for Alvey eligibility.

Will the Third Time be the Albery Committee?

In a letter in New Scientist of 30/1/99 Nicholas Albery of the Institute of Social Inventions sought support for their petition:

``In view of the likelihood that early in the next millennium computers and robots will be developed with a capacity and complexity greater than that of the human brain, and with the potential to act malevolently towards humans, we, the undersigned, call on politicians and scientific associations to establish an international commission to monitor and control the development of artificial intelligence systems.''

This petition was a direct result of a brainstorming session at the Institute of Social Inventions in April 1998 introduced by Professor Kevin Warwick on the basis of his 1997 book The March of the Machines (an earlier edition of In the Mind of the Machine) in which he predicted robots (or superintelligent machines of some kind) forcibly taking over from the human race within the next 50 years.

The Revolt of the Machines

This fear of machines revolting and enslaving us is not new. In Samuel Butler's 1901 science fiction novel of the far future, Erewhon, his hero who has been transported to a future time writes of the future civilisation:

``I also questioned them about the museum of old machines, and the cause of the apparent retrogression in all arts, sciences, and inventions. I learnt that about four hundred years previously, the state of mechanical knowledge was far beyond our own, and was advancing with prodigious rapidity, until one of the most learned professors of hypothetics wrote an extraordinary book (from which I propose to give extracts later on), proving that the machines were ultimately destined to supplant the race of man, and to become instinct with a vitality as different from, and superior to, that of animals, as animal to vegetable life.''

The argument of Butler's Professor of Hypothetics [Butler] is basically the same as those advanced by the modern advocates of the view that robots (or other superintelligent machines) will forcibly take over.

  1. Computers will soon be much more powerful than human brains.
  2. Therefore machines with computer brains will be much more intelligent than we are.
  3. By that time they will also control a great deal of our technological and informational infrastructure.
  4. They will therefore naturally want to take over.
  5. We won't be able to stop them.

Will intelligence develop along with processing power?

The first step in the argument is that computers will soon be more powerful than human brains. This is debatable on grounds of technology and economics. I don't propose to deal with that here because I think the more serious and interesting flaws in the argument come later.

The next step in the argument claims that we, or the robots themselves, will be able to organise all that raw computer power in such a way as to provide the robots with a human level of intelligence. The final stage in the argument claims that things which are so much cleverer than us will develop their own purposes, and in pursuit of those purposes will sweep us out of the way or exploit us just as easily as we pushed wolves aside and exploited sheep.

It is true that there are many examples of increases in machine (or robot) capability or ``intelligence'' keeping step with the development of computing power. I argue that this is for a special reason which doesn't hold in general. That reason is that we had earlier developed lots of promising techniques which the computers of the time simply took too long to process.

Vision consumes processing power

For example, in the 1970s we already understood much of the general lines of how to decode the images from stereo cameras to provide a description of the 3D scene in front of the cameras. The problem was that each image consisted of about a million pixels, each of which had to be first compared to its neighbours, and finally, after a lot of deduction of edges and surfaces and volumes, would result in a detailed description of the 3D geometry of the scene. None of the individual calculations was problematic, the problem was simply the millions of times the calculations had to be done. This required either millions of simple computational units running in orchestrated parallel, or one computer running fast enough to do all the work in, say, a second. It is only very recently that enough computer power to do as much calculation as this in under a second has started to come within our reach, the result being visible in many computer games and virtual reality work, as well as the recently greatly improved speeds of robots depending on vision to navigate in the world.

Given the high resolution of the human visual system, and the large amount of our brain power devoted to processing it, machine vision is likely to go on soaking up increases in computer power and returning usefully increased performance for some time to come.

Chess consumes processing power

There are similar examples in abstract thought of the ``intelligent'' kind, such as chess. Let's consider first the very simple game of noughts and crosses. It is not hard for experienced human players to learn how to see right through to the end of this simple game from the start, and once they've done that, they have become perfect players. If we exploit the rapid computation and perfect memory of computers and ignore symmetries and other tricks with which to prune the search space, then to look into every possible board state in noughts and crosses involves a maximum of 362,280 board states. It's easily within the reach of the computational speed and memory of desk top or even palm top computers today to play noughts and crosses by simple exhaustive search and thereby be perfect players. I'm ignoring the fact that there are much easier ways of being a perfect player, because I'm showing the effects that simply increasing the brute force of computers can have on the performance of very simple methods which can be elaborated manifold to produce ``intelligent'' behaviour.

Chess is more complicated. If we assume that the average game involves an average of 40 moves for each player with an average of 33 choices per move, then we get about 10120 (10 followed by 120 zeroes) board states to examine, if we wish to look forward from the start to the very end. Since by current estimates 10120 exceeds the number of atoms in the universe it's clear that we have hit a problem. The required computer would be too big to fit into our universe. That is what scientists refer to as a ``fundamental limitation''. It's not going to be possible for computers to play chess by looking ahead to the end in this simple exhaustive way. They will simply have to do what people do, which is to look ahead as far as they can in the time they have available. Every time we get a more powerful computer the same simple chess program will be able to look further ahead. So all we have to do is to wait for computer power to develop far enough for a simple exhaustive look ahead search to defeat the world chess champion. That hasn't happened yet. Deep Blue, which defeated Kasparov in 1997, used a lot of clever tricks to optimise the search. In effect Kasparov was defeated as two prongs of research and development closed their pincers on him. One prong was the doubling of computer power every 18 months. The second prong was chess programmers devising ever cleverer tricks to prune the search.

If the hardware had developed more slowly the programmers would have come up with a few more tricks and beaten Kasparov with a slower computer and better software. If the hardware had developed more quickly they wouldn't have had time to develop so many clever tricks, but the extra raw computer power would have made up the difference, and Kasparov would have been defeated by a simpler program running in a more powerful computer.

Computer chess started in the 1950s, and by 1970 it was discovered that simply increasing the depth of search correlated fairly linearly with the chess rating scores used to rank human chess players. At that point Kasparov's doom was known to be sealed. Even if no advances in chess programming occurred, the relentless advance of computer power, already well settled on its exponential rails in 1970, would soon produce a machine powerful enough to beat him.

Just as was the case in computer vision, the structure of the problem, and our early understanding of it, meant that we would be able to keep exploiting advances in computer power to produce more and more useful performance to limits well beyond today's computer power. And if it should happen that the continuing development of computer power causes vision processing times to drop under say 50 milliseconds, rendering further improvement unnecessary, we will be able to soak up plenty more power simply by increasing the resolution of the cameras. And if chess playing computers soon become so far ahead of human chess players that we can no longer usefully exploit more computer power, we can simply switch to a game like Go, whose combinatorial explosion makes chess look like noughts and crosses.

Articulated motion consumes processing power

I have given one example in the area of sensory processing, vision, and one in the area of abstract thinking, chess. Let me conclude this part of the argument by giving one example from the area of motion control.

Consider the problem of moving an arm of about the reach, strength, speed, and dexterity of the human arm, without a hand, just the seven joints of the arm. As a consequence of the physics of the problem it turns out that we need to run the basic joint control algorithms in something like 10 milliseconds to get that sort of performance. In the 1980s it wasn't possible to supply that kind of computer power in an economical package in one computer, so all the industrial robot arms of that time used a number of computers, usually dedicating a small computer to each joint, and having an extra one controlling the joint controllers. Today we can easily control an arm of that sophistication with the power of a single modern PC. If you add another arm, then two legs, then balance control, then complex 20-jointed hands, then vision, we soon end up exhausting the power of a modern PC just in trying to reach basic primate sensorimotor control.

In short, there are special areas where the repeated multiplication of simple processes keeps giving better performance. The basic reactive sensorimotor control of a sophisticated humanoid robot is one of those areas and is still well outside the scope of our current PCs. Thus we will be able to continue to soak up the increasing power of computers for quite some time in simply providing more sophisticated sensorimotor control. There are also certain restricted areas of abstract thought, such as chess, where we will also be able to soak up increasing power in producing better performance.

The interesting question is whether all aspects of intelligence will behave in the same way. Are there perhaps some areas of intelligence where we don't have much idea how to use the computer power we already have, and providing more isn't going to help to advance things? If there are, then these areas will advance not with the breathtaking speed with which computer technology is developing, but will be limited by the rather more pedestrian speed of human scientific research into difficult questions.

The deceptive improvement of robot technology in the last 50 years

A robot of the 1950s (Grey Walter's Machina Speculatrix)

During the late 1940s and early 1950s W. Grey Walter, the biologist and cyberneticist, developed his famous Tortoise, Machine Speculatrix, a small simple three-wheeled autonomous mobile robot whose "brain" consisted of a valve, a relay, and a capacitor, but which was capable of apparently complex lifelike behaviour, including feeding (charging) when "hungry", pursuing lights, and flocking behaviour in groups. He added a few more valves to produce a creature capable of learning a conditioned reflex. He saw this as the beginning of a new kind of biology, synthetic biology, which investigated the principles of animal design by trying to make simple artificial creatures. Other cyberneticists of the time, such as Ashby, took this up, but they were severely hampered by the technology of the time -- valves and relays -- which restricted them to creatures with at most a few dozen very simplified "neurons", simpler even than the idealised "neurons" of todays computational neural nets.

A robot of the 2000s (Honda's P3)

Fifty years later there are a number of impressively humanoid-seeming robots around, perhaps the most impressive being Honda's P3. The technology of motors etc. has advanced a bit since Grey Walter's time, but it would have been possible fifty years ago to put together something which was mechanically similar, if a bit cruder. The reason nobody did was the computer technology necessary to control so many motors and sensors in a co-ordinated fashion was not available then. In other words, what at first sight might appear to be a very rapid development of robotic technology is in fact a rapid development of computer technology which has permitted the control of much more complex devices.

The problem of learning

The most important area of Artificial Intelligence in which we are still limited by our understanding rather than computer power is learning. Learning is an essential component of intelligence. Indeed, we often call unintelligent people ``slow learners''. Cognitive psychologists and animal ethologists have been studying learning for a long time, and have come up with rough classification schemes for categorising the different kinds of learning. In Artificial Intelligence many different kinds of learning have been implemented. The first obvious point is that it is not easy to marry the categorisations of the psychologists with those from AI. In other words, those who are looking primarily at what the learning does are modularising the phenomena in quite different ways from those who are looking primarily at the internal mechanisms. The second point is that many of the kinds of learning in AI are very slow compared to comparable biological learning, not in terms of computing speed, but in terms of how much experience is needed for the learning to take place. In other words, they are very ``slow learners'' indeed.

These are all good clues that we don't really understand learning very well yet, and the improved computer power of tomorrow is not going to help. We need more research and more ideas.

The speed of research

How long does this kind of research take? A good example can be provided in the area of learning itself. In the 1940s McCullough and Pitts proposed connectionism or neural networks as a feasible architecture modelled after the massive simple neuronal parallelism of the brain [McCulloch & Pitts 1943]. In the 1950s Rosenblatt devised a kind of simple single layer network and a learning algorithm for it, which he called a perceptron [Rosenblatt 1958]. In the 1960s a number of authors showed the fundamental limitations of this kind of learning [Minsky & Papert 1988]. It was clear that a way in which learning could take place in multi-layer networks was required. Although such a method (backpropagation) was developed independently in the 1970s by several researchers, it took until the 1980s before its significance was appreciated and it began to be widely used. It works well, but is a very slow learner. Ways of improving the speed of backpropagation learning is currently a very active research area. In other words, in this one particular subsection of learning research we have made significant progress in, say, fifty years, but are still far from approaching biological speeds of learning.

The modern counterparts of the Professor of Hypothetics like to finesse awkward problems of research and understanding like this by imagining that once the machines become an order of magnitude or two superior to us in intelligence, they will also inevitably take over scientific research from us, and solve all our baffling problems, including those of how to make them even smarter, at mind-boggling speeds. This simply begs the question, however, because the problem is learning, and you can't solve it by supposing the robots have become good enough at learning to discover its solution!

The problem of generality

Another problem which needs to be solved in the development of the superintelligent robot is generality and integration. Suppose a specific human being is a good chess player, speaks three languages, is a medical doctor, and plays tennis, golf, and billiards. All of these are specialised areas in which we have good expectations of being able to produce good artificial competence in a robot, but we couldn't simply put all these together into a robot, add a general purpose conversationalist, a fair quantity of common sense, and have the equivalent intelligence of this human being. The point is that human beings are capable of learning all these things, and many more. The way we devise a robot tennis player or medical diagnostic system is by trying to copy the knowledge and inference methods of a human expert. We haven't the slightest idea how to devise a system which would be capable of learning all these kinds of things, and also capable of inventing completely new games and negotiating their rules.

It can be considered that robotics research began seriously in the 1960s. In the early 1980s a number of robot research laboratories concluded that we now knew enough about most of the component subproblems of controlling assembly robots in the factory, and it was time to aim for an integration of all these component parts into an overall architecture [Lozano-Perez & Brooks 1985]. As it became clear that these attempts were doomed to become increasingly mired in a bog of computational intractability some of researchers, most notably Rodney Brooks (MIT), decided that these programmes were based on a fundamentally flawed architecture, and a new research programme was required to discover and elaborate the principles of a new more appropriate architecture [Brooks 1986]. The loose coalition of roboticists inspired by and controibuting to Brooks's lead soon developed to the point of constituing a new paradigm (in the Kuhnian sense of a new approach based on a new philosophy and a new methodology [Kuhn 1970]) in robotics research [Malcolm et al 1989]. That research programme is now developing a robot modelled after a human torso, known as Cog [Brooks 1997], a sufficiently interesting robot that the philosopher Daniel Dennett has devoted a paper to the question of whether (and in what sense) Cog might one day become conscious [Dennett 1994].

The moral of this story is that it took 25 years of robitics research to discover that the aspects of robotic behaviour being developed were not going to fit together to provide further more general competence. This was for most a completely unexpected result which put most of world's robot manufacturing companies, who were waiting hopefully for the promised robot revolution in manufacturing, out of business. We are now 15 years into the new robotics paradigm, and still taking the measure of the magnitude of the problem which in the early 1980s had simply seemed a question of polishing up the technology and software a bit.

This is one more illustration of the most important lesson which Artificial Intelligence has learned in its 50 years of research: the problem is more complex and difficult and fundamental than you think, even when you take this into account. (This is a generalisation of Hofstadter's Law of software development [Hofstadter 1979]).

How does the Ghost get into the Machine?

There is some confusion over whether it will be necessary for an intelligent robot to be conscious in the way that we think we are in order for the robot to reach our levels of intelligence, and in order for it to pose a threat to us by taking its own independent autonomous decisions, by having its own ``free will''. There are questions here to which we do not yet know the answers. For example, even if it is necessary for us to be conscious in order to be able to do all that we can do, we do not know if that is the only way of reaching that level of performance. If there is another non-conscious way, then a robot need not be conscious in our sense to be more intelligent, and to be a threat to us. On the other hand, it might be the case that consciousness of some kind is an inevitable concomitant of getting all the rest of the machine right, so we won't have to take any special extra steps to provide the machine with consciousness.

Further than this, it might be a mistake to think of consciousness in terms of a single person, brain, or machine. It may be that consciousness arises out of linguistic social interaction, is a kind of ``centre of narrative gravity'' that we acquire through telling each other why we do what we do, and why others do what they do, and in so doing we learn the trick of looking at ourselves as though from the outside, and looking at others as though from the inside.

It may even be the case that the whole notion of consciousness as we currently think we understand it is a ghastly mistake, an artefact of the Cartesian Error of dividing mind from body and creature from world, whereas it might be that mind emerges from the interactive and historical matrix of mind/body/world. If this is the case it no wonder we are baffled when we look at the machinery of brain and find something important strangely absent. As Leibniz put it three hundred years ago:-

It must be confessed, however, that perception, and that which depends upon it, are inexplicable by mechanical causes, that is to say, by figures and motions. Supposing that there were a machine whose structure produced thought, sensation, and perception, we could conceive of it as increased in size with the same proportions until one was able to enter into its interior, as he would into a mill. Now, on going into it he would find only pieces working upon one another, but never would he find anything to explain perception. [Leibniz 1714].

The philosopher Gilbert Ryle suggested that this was a ``category mistake'', akin to the question raised by a foreign potentate to whom one has just shown the University Campus, Halls of Residence, Lecture Theatres, Library, Laboratories, etc., ``Yes, this is all very interesting, but when are you going to show me the The University Itself?''[Ryle 1949].

Books can and have been written on this topic, and recent insights of neurophysiology and artificial intelligence have stirred up philosophy of mind into a very active and interesting research area. I propose to sidestep it here, since there is too much of relevance that we do not yet know, and there are plenty of suggestive clues that at least some of our current ideas are nonsense. It is possible to sidestep it here, since the point of real importance to whether superintelligent robots could threaten us is whether or not they could be creatures, not whether or not they could be conscious, since it is at least possible that there could be such a thing as a dangerous non-conscious creature.

In this connection it is interesting to note that since we are conscious, and prefer to explain our own behaviour, and that of others, in terms of the contents of a conscious mind, we have a habit of ``anthropomorphising'' the behaviour of lesser creatures, and even of complex contraptions. For example, we may interpret the naughty toilet behaviour of our dog as a deliberate act of revenge, or (in a fit of rage against some Microsoft product) the loss of our files to computer malevolence. This kind of ``anthropomorphic metaphor'' has a lot of explanatory convenience, but there is a lot more to it than that. Any hunter of animal prey will benefit from being able to imagine what the prey can see from its point of view, what it therefore thinks, and how therefore to fool it. I don't know if lions imagine what a gazelle is thinking. I do know that human hunters do, and that it is a very useful skill. Consequently evolution may well have wired into our brains a natural propensity to this kind of useful anthropomorphising.

Evolution, of course, could never have ``foreseen'' that we would one day be playing with ``intelligent'' contraptions of our own devising, such as computers and robots, and when considering how they behave we are as likely to anthropomorphise as we are when looking at our dog. This leads to something I call the Weizenbaum Illusion.

The Weizenbaum Illusion

Joseph Weizenbaum of MIT was long ago the author of the famous Eliza program which simulated the rather simple conversational responses of a Rogerian psychotherapist.

You: "I got lost on my way here."

Dr Eliza: "That is interesting that you got lost on your way here. Did your mother ever lose you as a child?"


One day he found his secretary, who knew very well the whole Eliza thing was a bag of plausible tricks with the intelligence of a flea, nevertheless seriously consulting Dr Eliza about the problems she was having with her husband. He was so appalled by this evidence of the gullibility of people in the face of apparently knowledgeable behaviour by computer systems, that he decided that the human race simply lacked the intellectual maturity to be allowed to pursue research into the seductive realms of AI. He thinks if we persist in this kind of research our natural gullibility will cause us to make awful fools of ourselves, with possibly dangerous consequences [Weizenbaum 1977].

I had always thought Weizenbaum had gone rather over the top in his alarmist reaction here. After all, his secretary may simply have seen the ``bag of tricks'' as a useful way of provoking her to think about the issues, much as many people will use horoscopes or Tarot cards without actually believing there is some underlying Cosmic Intelligence guiding the fall of the cards, simply that the chance fall of highly ambiguous cards is one way of provoking a useful kind of brainstorming on the issues. However, faced with the extraordinary claims which otherwise intelligent and well-educated people are prepared to make about the imminent future of robotics, I have changed my mind. I have decided to call this gullible tendency to over-interpret what lies behind the apparently cute or knowledgeable behaviour of artefacts the Weizenbaum Illusion in honour of JW's prescience here.

MIT's Kismet ``emotional'' robot face

The Weizenbaum Illusion is not just silliness, it a compelling intuition which has been wired into our brains. This idea was demonstrated very neatly in the science fiction story of the indestructible robot. A physicist bets a roboticist he can't build an indestructible robot. Come the day the physicist is shown a small furry thing running around on a table, given a hammer, and invited to destroy it. The physicist raises the hammer. The furry thing turns over on its back and squeaks piteously. It has big eyes. The physicist finds himself unable to destroy it. Why? Because it displays four simple aspects of baby creaturehood. It runs around. It has big eyes. It is small and defenceless. It turns over when threatened and squeaks piteously. Our brains are wired to recognise that as a baby animal which needs protecting, and the illusion is very compelling indeed.

How much more compelling will be the illusion when ``it'' is clearing the dishes off the table and discussing the day's news with you? How much more compelling it is when an apparent ``face'' interacts behaviourally with you in a manner which suggests a comprehensible interior emotional life, as in MIT AI Lab's Kismet above?

The Intentional Stance

The dangers of the kind of anthropomorphisation going on when we think a conversational computer program ``understands'' us, or a chess playing program is ``trying to trap my queen'', or when we imagine we know what our dog is thinking, used to be avoided by scientists by simply outlawing all this kind of anthropomorphisation and insisting on ``objective'' descriptions of behaviour. This is too crude and procrustean a response, as Dennett explained in The Intentional Stance [Dennett 1989]. Dennett points out that if a system has been designed to achieve certain purposes, such as beating people at chess in the case of a chess program, then it makes sense for us to talk about it ``trying'' to ``occupy the centre'' even if there is no specific representation inside the system of ``trying'' or of ``occupying the centre''. It makes sense because, as the designer intended, that kind of behaviour emerges from the interaction of all the component parts of the system within the historical context of an ongoing game. It's a way of talking about a system at a very high level of abstraction concerning its purposes, or the purposes of its designer, neglecting the actual details of how these purposes are implemented in the system. This level of description, which Dennett calls ``the intentional stance'', i.e., describing the system as though it were specifically intending to achieve these purposes, is a very useful abstract summary of what is going on which helps us to understand what the system is likely to do next.

What Dennett has done with this elucidation of the intentional stance is to explain that there is a definite utility in adopting explanations in these kinds of terms, even in cases where these terms (such as "trying'') don't refer to any specific identifiable internal states or representations within the machine. In other words, he has explained that we can quite properly and usefully describe a machine in these apparently anthropomorphic terms, provided we remember that there is not necessarily an implication that the insides of the machine resemble our insides in any way, or that the terms we are using have any specific correspondences with internal states or representations in the machine. In short, he has licensed us to use anthropomorphic language provided we remember that we are using it in this ``as if'' sense, without taking the ``as if'' to be true. It is a subtle distinction that our human language was never designed to encompass, and never needed to make, until we got around to inventing machines which could process information and simulate fragments of human cognitive behaviour.

What we have done in building computers is to make a machine which is naturally capable of some aspects of human mental behaviour, but not others. Since it has always historically been the case that aspects of mental behaviour were only manifested as part of the complete package of a mind we have this natural tendency to assume that something which shows one aspect of mentality is equipped with the full mental orchestra.

Of course normally when we say that something is ``intelligent'', we are referring to some kind of live biological creature which, being an animal with all that that entails about competitive survival, might be hostile to things which might want to share its dinner. In this case, however, we are referring to some kind of artificial robotic/computer system. This will not have the usual animal complement of competitive survival instincts, etc., unless of course, we had deliberately built them in, or they had somehow developed by accident.

We can rule out deliberately building them in. We are interested in robots, computer technology, etc., because we're interested in building mechanical slaves, and it would be extremely silly to give them the capability of revolting against their masters on purpose.

This is the mistake that our Professor of Hypothetics and his modern followers make. It is very tempting indeed to suppose that something of the right shape and physical capabilities which shows aspects of mental behaviour is in fact some kind of animal, albeit an artificial one. Because of our common biological evolutionary heritage we share with our animal fellows an instinct to survive, an instinct to procreate, territorial behaviour, motivation in terms of pleasure and pain, and so on. No matter how good they are at chess, table tennis, and erudite conversation, a general purpose domestic robot of the future will not share any of this evolutionary heritage with us, and will not by natural default have any of these instincts. Even if it happened to be a thousand times more intelligent than us.

Why a Humanoid Robot Won't Be Dangerous Anyway

We are well used to the Hollywood idea that robots of the future of roughly human form will of course possess superhuman strength, will be able to punch holes in walls, tie knots in rifle barrels, and so on. Of course they'll be much stronger. After all, they're machines, and you can make machines any strength you like. Our factories are full of machines much stronger than we are.

This is, however, a silly generalisation. The machines which are so much stronger than us have external power supplies, such as being plugged into the electricity supply. If we add in the proper creature constraints a different picture emerges. These constraints are that the robot must carry its own power supply around with it, suitable for a few hours of operation without refuelling. And it can't be built just for one purpose, such as tying knots in rifle barrels. It must be general purpose, able to walk up and down stairs, open doors, fetch things out of cupboards, and so on. Roboticists who are trying to build robots with today's technology within these kind of constraints have discovered an uncomfortable truth. Biological muscles, tendons, bones, nerves, etc., are in fact extremely efficient in engineering terms. It is going to be a very demanding task indeed, and one we certainly can't contemplate with today's technology, to make a robot of roughly human size with even a small fraction of a human being's strength, speed, and agility.

Very considerable mechanical engineering development is going to be required to catch up with biological performance. The problem here is that mechanical engineering technology is not developing in the crazy exponential fashion that computers are doing. There are unique reasons for the way computer technology is developing, largely a consequence of the fact that computers are mass produced by a kind of photocopying technique, and perform better the smaller their component parts get. That isn't true of muscles or motors. To apply a large causally effective wallop to the physical world they simply have to be of appropriately large size. If motor cars had partaken of computer-type development the modern motor car would be smaller than a match box, travel at supersonic speeds, and carry more passengers than a railway train on a teaspoon of petrol. In the world of everyday Newtonian physics where size matters this is simply silly.

The point is that the electromechanical bodies of robots will be made of mechanical technology and will have to put up with the much more pedestrian engineering technology rates of development. From that it follows that even if in 50 years a humanoid robot is hundreds of times more intelligent than you, if it is about the same size as you, you will be able to run rings round it and knock it over with one hand behind your back. In fact given the mechanical advantages in terms of size and power of wheels over legs, it is extremely likely that the superintelligent robots of the future will roll around on wheels rather like Daleks, and suffer from the same kind of problems as Daleks had with staircases. If you were a follower of the Dr Who stories involving Daleks, you will remember that staircases were one of the major obstacles in the way of Daleks conquering the Universe. This is a humorous way of illustrating the general point that the kind of physically general purpose body we have, which can easily switch from driving cars to climbing trees, is going to be very difficult indeed to better mechanically, and certainly not within a human lifetime. Indeed, our very general purpose body, which interfaces very well with our tools and weapons, is one of the things that makes us such formidable soldiers and hunters.

The Contraption/Creature Chasm

There are lots of different ways in which superintelligent robots might try to take over the world from us, from a direct assault with automated battlefield weapons, to blackmailing us with a doomsday weapon such as simultaneous meltdown of all nuclear power stations. We don't need to consider the pros and cons of all these different scenarios, because they all trip up over the same fundamental question: Why would they want to do it?

They will not be animals, they will not have a competitive survival instinct, they will not have an instinct to reproduce themselves in competition with us, they will not compete with us for dinner, oil, or electricity. To be very simple about it, they will not object to being switched off. We could of course try to give them these qualities, but that would be rather silly.

We are tempted to imagine that they would be some kind of creature because of the Weizenbaum Illusion that tempts us to suppose that anything with behaviour which is not entirely predictable and seems to display some aspects of mentality must in fact have a fully functioning mind of the usual animal kind. However, our clever technology has enabled us to produce machines which only have some aspects of mentality, such as memory or intelligence, and definitely don't have others, such as feelings and their own purposes. The illusion is fascinating, but they are no more likely to develop mind and imperial ambitions than is the waxwork simulacrum of Napoleon in Madame Tussaud's, no matter how lifelike it looks.

Could they develop these other qualities of mind by accident? One of the favourite scenarios here is accidentally letting the genie of evolution out of the bottle by deliberately setting up evolutionary programmes to improve the design of our robots. This scenario misunderstands the very considerable sophistication of modern biological evolution, which we are only now beginning to appreciate, and which we are very far indeed from understanding. It is true that biological evolution is capable of such ingenious feats of engineering design as making an eye or a wing. It is doubtful that we know enough about evolution yet to be able to set up an artificial evolutionary program which would result in the invention of an eye. Even if we did, the number of small increments that have to be added together to make an eye makes it impossible to do this in less than a very very long time.

Is it not possible that artificial evolution with the assistance of extremely powerful computers might work a great deal faster? It is possible, but in order to happen by accident whatever simple kinds of evolution we started up to improve this or that aspect of the robots would have first to evolve improved methods of evolution. That would take far longer than evolving something as practical and simple and immediately assessable as the eye. We would have centuries if not millennia to see this kind of thing coming.

In sum, we no more need fear our superintelligent contraptions of the future taking over the work from us than we need fear that the ``intelligent'' motor cars of the future will plot to run us over. Of course given superintelligent contraptions we will no doubt be able to magnify our own incompetences and errors to an even greater degree, and I have no doubt that newspapers will soon start reporting human workers being killed by robots. But they will just be accidents, analogous to being struck by lightning, no more evidence of nascent machine malevolence than lightning is of the anger of the gods.

Chris Malcolm 25 November 2000

these comments for some further links and comment on this topic.]


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