Effing the ineffable:
An engineering approach to consciousness

Steve Grand

Abstract

The Easy Problem of ‘as’

Nevertheless, there are other means to achieve understanding, besides abstract symbolic description. After all, even apparently trivial concepts are often inaccessible to verbal explanation. I find myself completely unable to define the simple word ‘as’, for example, and yet I am able to use it accurately in novel contexts and can interpret its use by others, so therefore can be said to understand it. My understanding of ‘as’ is demonstrated operationally.

In linguistic terms, an operational understanding involves the ability to use or interpret a word or expression successfully in a wide variety of contexts. For an engineer, on the other hand, an operational understanding is demonstrated by the ability to build something from first principles and make it work. If I can build an internal combustion engine without slavishly copying an existing example, then I can legitimately be said to understand how one works, even if I am quite incapable of expressing that understanding in words or equations.

An operational definition of consciousness might exist if we were able to build an artificial brain (attached to a body, so that it could perceive, act and learn), which reasonably sceptical people were willing to regard as conscious (that is, they should be just as willing to ascribe consciousness to the machine as they are to ascribe it to other human beings). Of course, simply emulating consciousness, by building a highly sophisticated puppet designed to fool the observer, should not be sufficient, and here we see a danger inherent in the famous Turing Test for intelligence [Turing 1950], since it turns out to be quite easy to fool most of the people for at least some of the time.

Failing the Turing Test isn’t hard, but nothing should ever really be regarded as having passed it, since it would need to put on a convincing performance for an indefinite period if we are to differentiate between true general intelligence and a finite set of pre-programmed responses. So for an artificial brain to be able to show that we have obtained an operational understanding of consciousness, it should also be built according to a rational set of principles, which are accessible to observers, and should implicitly demonstrate how those principles give rise to the behaviour that is regarded as conscious. In other words, it must encapsulate a theory at some level, even if the totality of that theory can’t be abstracted from its implementation and described in words or simple equations. In particular, the conscious-seeming behaviour must demonstrably emerge from the mechanism of the machine, rather than be explicitly implemented in it, just as a model of an internal combustion engine would be expected to drive itself, not be turned by a hidden electric motor.

I shall return to the distinction between emulation and replication in a moment. For now, I simply want to assert that synthesis is a genuine, powerful method for achieving understanding, and a respectable alternative to analysis, despite its low status in the reductionistic toolkit of science. Strictly, synthesis of an apparently conscious machine would only constitute an answer to Chalmers’ Easy Problems, of course – a description of the mechanism by which consciousness arises, rather than an explanation of the nature of subjectivity. Nevertheless, it may be as close as one can get, and it may still demonstrate a higher understanding in an operational sense, in a way that a written theory can’t do.

At the very least, attempts to synthesise consciousness artificially might offer an answer to a Fairly Hard Problem, which lies somewhere between those set by Chalmers. Theories of consciousness (or at least heartfelt beliefs about it) tend to lie at one extreme or the other of the organisational scale. To some, myself included, consciousness is clearly a product of high levels of organisation: it is an emergent phenomenon that perhaps did not exist anywhere in the universe until relatively recently and could not exist at all until the necessary precursors had arisen. To me, a conscious mind is a perfectly real entity in its own right and not some kind of illusion, but it is nevertheless an invention of a creative universe. Yet for many, consciousness belongs at the very opposite end of the scale, as something fundamental – something inherent in the very fabric of the universe. Overt Cartesian dualism lies at this extreme, as does the science fiction notion of a ‘life force’ and Roger Penrose’s rather more sophisticated retreat into the magic of quantum mechanics [Penrose 1989]. Consciousness is clearly not a property of medium levels of organisation, but at which of these two extremes does it really lie?

To my mind the ‘fundamentalists’ don’t have a leg to stand on, for simple philosophical reasons. One only has to ask them whether they think their elemental Cartesian goo is simple or complex in structure. If it is complex, then it is surely a machine, since consciousness is a property of its organisation, not its substance. But why add the complication of a whole new class of non-physical machines when we haven’t yet determined the limits of physical ones? If, on the other hand, they think their fundamental force or elemental field is unstructured and shapeless, as Descartes clearly envisaged it, then how do they explain the fact that it has complex properties? How can a shapeless, sizeless, timeless ectoplasm vary from place to place and moment to moment? Why does it give rise to intricate behaviour? A force can push, but it can’t choose to push one minute and pull the next, and then do something quite different at other times. Moreover, if consciousness resides in some universal goo, then why did it have to wait around until the evolution of complex brains before its effects became apparent? Why does it require such specialised (and delicate) equipment?

Despite the absurdity of it, many people cling on to essentially Cartesian views, even if they don’t admit to it, but success at creating artificial consciousness would certainly tip the balance in favour of the emergent viewpoint, since no fairy dust would have been added to achieve the result. Failure, of course, would prove nothing.

Returning to the more practical but still extremely important and challenging ‘Easy Problem’ of the mechanism that underlies conscious thought, I contend that synthesis might not simply be a useful alternative to analysis, but may prove to be the only way in which we might come to understand the brain. The brain may be genuinely irreducible. It might be incapable of abstraction into a simple model without losing something essential, and its basic principles of operation might not be deducible from its parts. Neuroscience tries to understand the brain by taking it to pieces, and although this is a vital part of any attempt to understand things, such reductionism may not be sufficient in itself to give us the answer. At its worst, this is like trying to understand how a car works by melting one down and examining the resulting pool of molten metal. It isn’t steel that makes a car go; it is the arrangement of steel, and the principles that underlie this arrangement. Taking complex non-linear systems to pieces to see how they work often leaves one with a large pile of trees but no sign of the wood.

It may be that the brain has no basic operating principles at all, of course. To many observers the brain, even the notably rather regular structure of the cerebral cortex, is an amazingly complicated hotchpotch. They see the brain as an ad hoc structure patched together by evolution, in which each functional unit has its own unique way of doing things. Explicitly or implicitly, many people also regard the brain as a hard-wired structure (despite overwhelming evidence to the contrary), and their focus is therefore on the wiring, rather than the self-organising processes that give rise to the wiring. In truth, the latter may be relatively simple even though the former is undoubtedly complex, more on which below.

This attitude to the undeniable complexity of the brain reminds me strongly of the way people interpreted the physical universe, before Isaac Newton. The way that planets move in their orbits seemed to have nothing in common with the way heat flows through a material or a bird stays in the air – each seemed to require its own unique explanation. And then along came Newton with three simple observations: if you push something it will move until something stops it; the harder you push it, the faster it will go; and when you kick something your toe hurts because the thing kicks you back just as hard. Newton’s three laws of motion brought dramatic coherence to the world. Suddenly (or at least as suddenly as the implications began to dawn on people), many aspects of the physical universe became amenable to the same basic explanation. I suggest that one day someone will uncover similar basic operating principles for the brain, and as the relevance of these principles emerges, so the brain’s manifold and apparently disparate, inexplicable properties will all start to make sense.

I should emphasise that the above statement is not intended as a defence of ultra-reductionism. The existence of elegant, fundamental principles of operation does not ‘explain away’ the brain or consciousness – the principles are absolutely no substitute for their implementation, as I hope to amplify in the following section.

I submit that trying to build a brain from scratch is more likely to uncover these basic operating principles than taking brains to pieces, or studying vision in isolation from hearing, or cortex in isolation from thalamus. As an engineer, one is faced directly with the fundamental questions – the basic architecture comes first, and the details follow on from this. To start with the fine details and try to work backwards towards their common ground can quickly become futile, as the facts proliferate and the inevitable need for specialisation causes people’s descriptive grammars to diverge rather than converge.

Deep simulation

But if computer synthesis is ever to succeed in demonstrating that consciousness is an emergent phenomenon with a mechanistic origin, or even if it is only to shed light on the operation of natural brains, certain mistakes and misapprehensions need to be addressed regarding its use. I’ve met at least one prominent neuroscientist who flatly refuses to accept that artificial intelligence is possible at all, due to her insufficiently deep conception of the nature of computers and simulation. Several others of my acquaintance accept that computer simulation is useful for gaining insights into brain function, but insist that, no matter how sophisticated the simulation, it can’t ever really be conscious and can never be more than a pale imitation of the thing it purports to represent. There are good ways and bad ways to approach computer modelling of the mind, and unfortunately many people take the bad route, provoking well deserved, but unnecessarily sweeping, criticism.

I should therefore like to differentiate between two kinds or levels of simulation, ‘shallow’ and ‘deep’, because I think that many people’s objections (and many fundamental mistakes in approach by researchers into artificial intelligence) lie in an unwitting conflation of these two ideas. But to begin with, I need to make some assertions about the nature of reality.

Intuitively, we tend to divide the universe up into substance and form – tangible and intangible. We regard electrons and pot plants as things – hardware – but paintings and computer programs as form – software. Moreover, we tend to assign a higher status to hardware, believing that solid, substantial things are real, while intangible ‘things’ are somehow not. Such a pejorative attitude is deeply embedded in our language: tangible assets are considered better than intangible ones; responsible people are ‘solid’, while things that don’t really matter (!) are ‘immaterial’. But I suggest that this distinction is completely mistaken: substance is really a kind of form; the intangible is no less real than the tangible; hardware is actually a subset of software.

Take an electron, for example. We intuitively think of electrons as little dots, sitting on top of space. But an electron is really (in my view anyway) a propagating disturbance in an electromagnetic field. It persists because it has the property of self-maintenance. If you were to create a set of randomly shaped ‘dents’ in the electromagnetic field, many of them would lack this property and rapidly fade away or change form, but electron-shaped dents would propagate themselves forward indefinitely, switching between a magnetic phase and an electrical one in much the same way that hemlines oscillate up and down as fashionable people try to differentiate themselves from the crowd and the crowd simultaneously does its best to keep up with fashion.

So an electron is not a dot lying on space, it is a self-preserving dimple or vibration of space – it is therefore form, not substance. And if electrons, protons and neutrons are form, then so is a table, and so, in fact, are you. The entire so-called physical universe is composed of different classes of form, each of which has some property that enables it to persist. A living creature is a higher level of form than the atoms with which it is painted. It even has a degree of independence from those atoms – a creature is not a physical object in any trivial sense at all: it is a coherent pattern in space through which atoms merely flow (as they are eaten, become part of the creature and are eventually excreted).

I submit that everything is form, and that simple forms become the pigment and canvas upon which higher persistent forms are able to paint themselves. The universe is an inventive place, and once it had discovered electrons and protons, it became possible for molecules to exist. Molecules allowed the universe to discover life, and life eventually discovered consciousness. Conscious minds are now a stable and persistent class of pattern in the universe, of fundamentally the same nature as all other phenomena. Perhaps there are patterns at even higher levels of organisation, of which we are as oblivious as the individual neurons in our brains must be of our minds.

The relevance of this to computers is that they are also capable of an endless hierarchy of form. Computers are supposed to be the epitome of the distinction between hardware and software, but even here the dichotomy blurs when you consider that the so-called hardware of a computer is actually a pattern drawn in impurities on a silicon chip. Even if you regard the physical silicon as true hardware, the computer itself is really software. But leaving aside metaphysics and starting from what is conventionally regarded as the first level of software – the machine instructions – computers are a wonderful example of a hierarchy of form. At the most trivial level, instructions clump together into subroutines, which give rise to modules, which constitute applications, and so on. The particular class of hierarchy I have in mind is subtly different from this one and has a crucial break in it, but the rough parallel between the hierarchy of form in the ‘real’ universe and that inside the software of a computer should be readily apparent.

Now consider this thought experiment: suppose we took an extremely powerful computer and programmed it with a model of atomic theory. Admittedly there are gaps in our knowledge of atomic behaviour, but our theories are complete enough to predict and explain the existence of higher levels of form, such as molecules and their interactions, so let’s assume that our hypothetical model is sufficiently faithful for the purpose. The computer code itself simply executes the rules that represent the generic properties of electrons, protons and neutrons. To simulate the behaviour of specific particles, we need to provide the simulation with some data, representing their mass, charge and spin, their initial positions and speeds, and hence their relationships to one another. If we were to provide data about the electron and proton in a hydrogen atom, and repeat this data set to make several such atoms, we would expect these simulated atoms to spontaneously rearrange themselves into hydrogen molecules (H₂), purely as a consequence of the mathematics encoded in the computer program. If we were then to add the data describing some oxygen molecules, and give some of them sufficient kinetic energy to start an explosion, we would expect to end up with virtual water.

The computer knows nothing of water. It only knows how electrons shift in their orbitals. But if the database of particles and their relationships was large enough, and our initial model contained the rules for gravity as well as electromagnetism, then the simulated water should flow. Water droplets will form a cloud and rain will fall. If we add trillions of virtual silicate molecules (rock) then the rain will also form rivers and the rivers will cut valleys. Nothing in the code represents the concept of a valley – it is an emergent consequence of the data we fed into our atomic simulation.

Given a powerful enough computer, and a hypothetical scanning device that can extract the necessary configuration data from real objects in our world, we would expect to be able to scan the atoms of a physical clock, feed the numbers into the simulation, and watch the resultant virtual clock tick. Now imagine what would happen if I turned such a scanner on myself. Unless the Dualists are right, it seems to me that I would find myself copied into the simulation. One of me would remain outside, marvelling at what had happened, while the other copy would be startled to find itself inside a virtual world of clocks and rivers, but otherwise perfectly convinced that it was the same person who, a moment earlier, was standing in a computer room. The virtual copy of me would strenuously assert that it was alive and conscious, and who are we to disbelieve it?

Many people do disbelieve it. They insist that the copy of me would just look as if it thought it was conscious. It wouldn’t be real, they say, because it isn’t really made of atoms, it is only numbers. But what evidence is there for this conclusion? I accept that the electrons and protons are not real – they are merely mathematical models that behave as if they were particles. But the molecules, the rivers, the clocks and the people are emergent forms, which have arisen in exactly the same way that ‘real’ atoms, rivers and clocks arise. So what is the difference? An emergent phenomenon is a direct consequence of the interaction of components having certain properties and arranged in a certain relationship with one another. It is the properties and the relationships that give rise to the phenomenon, not the components. To my mind, molecules that arise out of the properties and relationships of simulated particles are just as much molecules as those that arise from so-called real particles.

Whether you accept this metaphysical argument about reality doesn’t much matter. I partly wanted to draw a distinction between code-driven programming and data-driven programming. In the above thought experiment, the simulation became more and more sophisticated, not by adding more rules, but by adding more data – more relationships. Indeed, the total quantity of data hasn’t really increased. In the initial state, the program consisted of a few rules describing atomic theory, and a vast database of particle positions and types, all of which were initially set to zero. To add more sophistication to the model, we simply made some of these values non-zero. We might equally have started out with a database filled entirely with random numbers, in which case I would expect the system to develop of its own accord, discovering new molecular configurations that are stable, and losing those that are not. Eventually, such a model might even discover life. The implications of data-driven programming (which is related to object-oriented programming) are perhaps relevant only to computer scientists, but it has a distinct bearing on computational functionalism in AI, and lies at the heart of many people’s objections to the idea that computers can be intelligent and/or conscious.

My assertion is that while computers themselves can’t be intelligent or conscious, they can create an environment in which intelligence and consciousness can exist. Trying to program a computer to behave intelligently is practically a contradiction in terms – blindly following rules is not normally regarded as intelligent behaviour. But if those blindly followed rules are not actually the rules for intelligent behaviour, but rules for atomic behaviour (or more practically, neural and biochemical behaviour), then genuine intelligence, agency and autonomy can arise from them just as they can arise from the equally deterministic and blind rules that govern ‘real’ atoms or neurons. To think otherwise is dualism.

Many AI researchers try to build computational models of intelligence in the shallow, explicit way that I rejected above. Psychologists develop computational models of intelligence too, and these models are generally abstract, symbolic and relatively simple. Such abstract models may well be helpful as theories of intelligence, but they don’t constitute intelligence itself. If you were to embed an abstract psychological model of the mind directly into a computer, you would merely have emulated the behaviour of the mind as a shallow simulation, and it would almost certainly fail to capture all of the mind’s features. A real mind emerges out of myriad non-linear interactions, borne of the relationships between the properties of its neurons and neuromodulator molecules. There may be severe limits on how much this can be simplified. It could well be that the basic operating principles of the brain are simple in concept yet not reducible in their implementation, and no matter how convenient it might be to describe these principles in linguistic or mathematical terms (as I will myself later), this doesn’t mean that the abstraction is itself sufficient to implement a mind. An elephant is an animal with a trunk like a snake, ears like fans and legs like tree-trunks. But strapping a cobra and a pair of fans onto four logs doesn’t make an elephant.

To recap: a shallow simulation is a first-order mathematical model of something. It is only an approximation and is merely a sham – something that behaves as if it were the system being modelled. But deep simulations are far more powerful and, I suggest, have a different metaphysical status. Deep simulations are built out of shallow simulations. They are second-order constructs, and their properties are emergent (even if they were fully anticipated and deliberately invoked by their designer). Some phenomena have to be emergent, and mere abstractions of them are no better than a photograph.

To create artificial consciousness, I suspect we can’t just build an abstraction of the brain. We have to build a real brain – something whose implementation level of description is at least one step lower than that of the behaviour we wish it to generate. Nevertheless, it can still legitimately be a virtual brain, because (unless Penrose is right) we don’t need to descend as far as the quantum level, only the neural one. Computers are wonderful machines for creating virtual spaces in which such hierarchical structures may be built. Trying to make computers themselves intelligent is futile, but trying to use computers as a space in which to implement brains from which intelligence emerges is not.

Everybody needs some body

Perhaps the first point is that brains simply don’t work in the absence of bodies. Absurdly futile attempts have been made to imbue computers with such things as natural language understanding, without providing any mechanism through which words might actually carry meaning. It is all very well telling a computer that ‘warm’ means ‘moderately high temperature’, but unless temperature actually has some consequence to the computer, such symbols never find themselves grounded. Bodies and the world in which they are situated provide the grounding for concept hierarchies.

Also, intelligence (if not also consciousness) has more to do with learning to walk and learning to see than it does with playing chess and using language, so having a body and its associated senses is important. People often assume that getting a computer to see is a relatively simple problem, but this is because they are only consciously aware of the products of their visual system, not the raw materials it has to deal with. Point a video camera at a scene and examine an oscilloscope trace of the camera’s output instead of looking at the scene itself and suddenly the problem of interpretation seems a lot harder. A creature with no sensory perception would be unlikely to develop consciousness at all. Moreover, when we consciously imagine a visual scene we do so by utilising our brain’s visual system. So vision and other such ‘primitive’ aspects of the brain are absolutely essential components of the conscious mind, and therefore need to be understood and replicated.

Finally, to get at the fundamental operating principles of the brain, we need to tackle as many aspects of brain function as we can at one time, otherwise we will fail to spot the common level of description that unites them. In any case, interaction between multiple subsystems is important for many aspects of learning. For instance, our developing binocular vision may rely on our ability to reach out and touch things to calibrate its estimates of distance, while at the same time our capacity to reach out and touch things may depend on our having binocular vision.

So for all these reasons, the best approach to understanding the brain through synthesis is probably to build a robot. Such a robot should be as comprehensive as possible, to allow for multi-modal interaction and exploration of as many aspects of perception, cognition and action as possible. In my case I decided to build a humanoid (actually, anthropoid) robot, which I named Lucy (Figure 1). Lucy has eyes, ears, proprioception and a number of other senses, plus a fair number of degrees of freedom in her arms and head. My first attempt was built on a very tight personal budget (whereas her nearest equivalent in academia cost millions of dollars) and hence is rather limited. Recently, thanks to a NESTA Dream Time award, I’ve been able to start work on Lucy MkII (Figure 2), which is considerably more powerful and capable. It is tough for any machine to develop a mind when it can’t see very well and can’t even reach to scratch its own nose, so Lucy MkII’s extra capability will greatly improve my ability to test out ideas over the coming years.

One important thing to mention about Lucy is the extent to which I’ve tried to give her biologically valid sensors and actuators. Brains evolved to receive signals from retinas and talk to muscles. They didn’t evolve to listen to video signals and talk to electric motors. The signals leaving the retina are not a bit like a video picture – they are distorted, blurred (convolved), and represent contrast ratios rather than brightness, amongst other things. These differences between biology and technology might be extremely important. If we are to understand how the brain functions, it is crucial that we try to speak to it in its own language. For that reason, much of Lucy’s computer power is devoted to ‘biologifying’ her various sensors and actuators.

Castles in the air

My own approach is to work from both ends of the problem at once. Neuroscience and psychology have uncovered a dazzling array of symptoms of brain activity: some detailed and some abstract, but all of them bizarre. Somehow, all of these phenomena must contain clues about the underlying mechanism, and the problem is essentially one of finding the level of description that unifies them. For example, it is strange enough that schizophrenics hear voices inside their heads, but who would have guessed that many of them can also tickle themselves? The two features seem at first to be quite unconnected. Yet if you regard both phenomena as disorders of self/non-self determination, everything starts to make sense. The voices in a schizophrenic’s head are presumably his own thoughts, but the vital mechanism that normally masks such self-generated perceptions to prevent them from being confused with external stimuli, has failed. Exactly the same logic explains perfectly why most of us are unable to tickle ourselves while schizophrenics can. It is by trying to find such unified levels of description that we can hope to grasp the essence of what is going on underneath, and see glimpses of the core mechanisms at work.

Simultaneously, one can begin from first principles and a blank sheet of paper and work upwards from there. What does a brain have to do? What are the key engineering problems it has to face? For instance, one problem is the significant time it takes signals to travel from the senses through to the deeper parts of the brain, especially when considerable processing is involved. This observation alone is enough to suggest a raft of possible mechanisms and consequent predictions, upon which evidence from neuroscience can be brought to bear. In fact signal delays are the mainstay of my developing theories of the brain, and why, I think, mammalian brains have evolved in the way that they have.

To use this approach on such an incredibly hard problem requires that one rely on skyhooks, to hold up otherwise unsupportable hypotheses long enough to see where they might lead. Progress depends on following up flaky ideas, stimulated by dubious and possibly absurd interpretations of the evidence. For instance a whole chunk of my present work grew out of a silly line of reasoning about why chemically staining one part of the cortex makes it appear blobby while another area looks stripy. My logic is almost certainly complete nonsense, but it led me on a fruitful path towards a practical mechanism for visual invariance – an otherwise highly mysterious phenomenon.

For this reason I find myself reluctant to record any of my ‘findings’ in a technical context yet, since so many of my ideas are still suspended by skyhooks. This article is meant to be no more than a position paper on behalf of the synthetic approach, not an exposition of a theory of the brain. Nevertheless, it’s worth giving a general overview of the picture of Lucy’s brain that is gradually emerging, since it demonstrates the synthetic method in action (Figure 3), and also shows how counter-establishment an idea can become when it is allowed to follow its own nose. The degree to which Lucy’s brain mirrors natural brains remains to be seen, but all of my ideas are backed up by evidence from neuroscience to a greater or lesser extent, and most have been implemented and shown to work inside the real robot, at least up to a point.

There is no stereotypical model of the brain – no specific theory to which all neuroscientists currently subscribe. Nevertheless, there is a distinct underlying paradigm – a set of assumptions and models that are held more or less unwittingly, more or less vociferously by many cognitive scientists and lay people. For the sake of comparison with Lucy’s rather dissident brain architecture, this paradigm can be shamelessly caricatured as follows:

The paradigmatic brain is a factory, in which raw materials flow in from the senses at one end, to be modified and assembled into intentions in the middle, before squirting out the other end as motor signals. Very little traffic of any significance passes in the other direction in this model. The factory is also a fairly passive structure – more like a maze of empty corridors through which nerve signals pass than a room full of noisy conversations that continue even in the absence of input. It is very complicated, and has many highly specialised departments, each with its own unique way of doing things. These specialised structures are hard-wired by evolution to perform operations on the data (as opposed to being consequences of the data themselves). The nerve activity that runs through them is presumed to be finely detailed and precise, more like the signals in a computer than ripples on a wobbly jelly. The system is also taken to be largely reactive, whereby programmed actions are triggered only by incoming sensory signals – any apparently pre-emptive action is dismissed as illusory. In this paradigm, work on the ‘higher’ aspects of thought must wait until all of the complex lower details have been fully understood, and the ‘C-word’ is not to be uttered for at least another century.

Lucy’s brain, on the other hand, is a responsive balancing act – a dynamic equilibrium between two opposing forces, which I’ve christened Yin and Yang. These two signals flow in opposite directions along different pathways in her brain, meeting at many points along the way, and playing an important role in several distinct aspects of the brain’s activity and development. Her brain begins as a simple repeated structure, onto which a mixture of sensory experience and regular, internally generated, ‘test card’ signal patterns write a story of ever-increasing complexity and specialisation. The specialisation in her cerebral cortex is not hard-wired but dynamically created and maintained, much as frail ripples in the sand are defined and held firmly in place by subtle regularities in the movement of waves. This map of specialised areas is essentially a mirror of the world outside – a model, or anti-model, of the world, whose deepest purpose is to undo all the changes that the world wreaks upon Lucy, just as a photographic negative placed over its own positive turns the whole image back to a uniform grey.

Like our own cerebral cortex, Lucy’s is divided into many specialised regions, which in her case at least, have pulled themselves up by their own bootstraps. Nerve activity dances on the surface of these regions in huge sweeping patterns, and it is the shape of these patterns that carry the information. Each dance takes place within a specific coordinate frame: retinal or body coordinates towards the periphery, and more shifting, abstract spaces deeper in. In Lucy, these coordinate frames are (or will be eventually) entirely self-organised. The yin and yang streams carry with them information about other coordinate systems through which they have passed, and the tension between these flows generates a morphing effect, creating new intermediate frames between the fixed peripheral ones. Some of this morphing occurs during development, but it needs to be periodically maintained, and eventually this will happen while Lucy sleeps – an alternation of slow-wave sleep with dream sleep is a crucial component of her self-maintenance. I suspect that this basic mechanism for stopping brains from falling apart is more fundamental, evolutionarily speaking, than the more sophisticated mental processes that it makes possible, so perhaps the Australian aborigines were right: perhaps consciousness arose out of the Dreamtime and we dreamed long before we were awake.

Unlike the largely unidirectional, pipelined structure of the stereotypical brain, Lucy’s neural circuitry is arranged like an inverted tree, with both sensory and motor signals arising from the same leaves. Yin flows in from the leaves towards the trunk, while yang flows outwards to the leaves. Yin invokes yang, and yang invokes yin. The two spark off each other like lightning dancing among clouds. Sensory signals flow inwards in search of something to connect with – something that can make sense of them. Meanwhile, expectation, attention and intention flow outward, looking for confirmation that they are on the right track or are achieving their aims. Each affects the other.

The system is in a perpetual state of tension, between sensory data on the one hand, which tell Lucy what is happening (or since nerve signals travel slowly, what has just happened), and an opposing set of hypotheses or intentions on the other, which provide a context for the interpretation of these sensory signals and proclaim what Lucy expects to happen next, or intends to happen next. The outgoing yang signals constitute a model, forming a set of predictions on different timescales. Incoming yin confirms or denies, pushes and pummels this interpretation of events, and meanwhile the predictions fill in gaps or compensate for delays in the sensory data, creating a running narrative of what is going on and allowing Lucy’s mental world to remain one step ahead of the outside world.

Some of these yang signals can be described as attention, some as expectation (sensory hypotheses) and some as intention. What you call them depends only on their context: they are fundamentally the same thing. If a yang signal erupts at a leaf that has control of eye movements, then one might regard this as an attentional signal, swivelling the eyes to focus on a potentially interesting or dangerous stimulus. But in other circumstances it could be viewed as an expectation, turning the eyes to where something is expected to appear next. In both cases we could reinterpret the signal controlling the eyes as an intention to move them – essentially a prediction about where they will soon be pointing.

The fundamental purpose of this interplay between expectation and intention is to maintain Lucy in a contented and comfortable state. If her needs change or the outside world changes, then these flows of yin and yang seek to rebalance themselves, sometimes by changing Lucy’s internal beliefs but usually by changing the world outside. At any moment there will be two distinct but related states in Lucy’s brain, one represented by millions of yin signals (her sensory state) and the other represented by equal numbers of yang signals (her mental state). The system learns to react in such a way as to keep these two states in balance. Lucy’s mental state, her internal narrative about the world, is what I choose to call her imagination. In humans, this is where consciousness resides.

A perfect match between her mental and sensory states is nigh on impossible. Usually her mental state is trying to keep one step ahead of her sensory state, making predictions or issuing intentions according to context, and meanwhile her beliefs may or may not match reality. Different parts of the two systems may be in greater or lesser correspondence. If incoming yin signals are straightforward enough to be able to trigger corresponding yang responses right out at the leaves, without requiring more advanced contextual processing, then the trunk of the tree does not become involved. But since it can never be silent, it freewheels. If this were to happen in a human being, I would say that the person was daydreaming or thinking.

Sometimes daydreams lead to decisions, and decisions lead to actions, and in this case yang signals from the trunk would stretch right out to the leaves and cause change – something we might regard as a voluntary, conscious decision, as opposed to a simpler unconscious response, were it to happen in a human. The outgoing yang pathway is a limited resource. Because of the very broad and diffuse way that Lucy’s nerve signals pass around her cortex (crucial to the way they perform computations), it is only possible for one ‘thought’ at a time to traverse outwards along any given yang path. So when such a yang cascade occupies one of the central trunk routes, Lucy’s whole body is essentially given over to the same thought and no others at that level are possible. Signals that reach right up to the trunk are thus ‘conscious’, because they take over most of her mind, while those that cause more peripheral activity can happen unconsciously, leaving the rest of the neural tree to float in a daydream world of its own or deal with something else.

At the moment, not all of these ideas have been fully implemented, and there is obviously a stupendous amount left to be understood before I can see how to grow a whole brain in this way. Lucy I’s brain, before I dismantled it to begin building Lucy II, had only a hundred thousand neurons anyway – a millionth the size of a human brain. Moreover, despite having developed some really strong intuitions about the basic operating principles that should allow her brain to self-organise from scratch, I’m not yet able to define these rules completely enough that all the messy details sort themselves out, without me having to intervene. I accept that it is probably well beyond the wit of one person to solve all of these problems, but for the moment things are going well and I intend to keep up this absurd hubris, since I think that non-disciplinary, holistically inclined individuals like me might actually have more chance of success than large teams of specialists do. It is very hard for ten people, with ten different mindsets, to examine ten completely different aspects of a problem and yet still see the common ground.

But suppose that I really could solve all of these problems eventually, perhaps twenty years from now. Suppose that I could implement the rules for how my yin and yang circuits interact, in such a way that a complete, neurologically plausible brain develops entirely of its own accord. Will Lucy then be conscious? I simply don’t know. All I can say is that I might have demonstrated an operational understanding of a machine that grows in such a way that it comes to behave as if it were conscious. That may have to be enough.

Meanwhile, work continues...

References

Chalmers, D. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2 (3), pp. 200-19.

Penrose, R. (1989). The Emperor's New Mind. Oxford University Press, NY

Turing, A.M. (1950) Computing machinery and intelligence. Mind, LIX(236):433-460

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Last modified: 06/04/04