The second theme Is based around the observation that a computer Is an intrinsically dualistic entity, with its physical set-up designed so as not to interfere tit its logical set-up, which executes the computation. The brain is trendier. When analyses empirically at several trendier levels (cellular, molecular), it appears that there Is no satisfactory way to separate a physical brain model (or algorithm, or representation), from a physical Implementation substrate.
When program and implementation are inseparable and thus interfere with each other, a dualistic point- of-view is impossible. Forced by empiricism into a monistic perspective, the birdbrains appears as neither embodied by or embedded In physical reality, but rather as Identical to physical reality. This perspective has Implications for the future of science and society. I will approach these from a negative point-of-view, by critiquing some of our millennial culture’s popular projected futures.
Keywords: art Call intelligence; neuroscience; cyclic systems; dualism; science Action The ‘Net- heads’ will have been passed on the way by the ‘Worlds’, digital mechanical life- forms which will Crust ease human life by performing all mundane tasks, but will shortly after become so much more Intelligent than the unchanged us that they will practically become ‘spiritual machines’, which may or may not use shells altruism to iced to be benign towards the human animals, and If we are lucky, they will continue to serve us, something Like digital Bodhisattva.
Back in the cybernetic, boundaries between individuals will break down, and transmute life-forms will appear, analogously to the emergence of multicultural life in the ocean. Implanted Into robot spaceships, these life-forms will lumber into space like the Crust amphibious Cash lumbered onto the land. A long time after this, perhaps after a few galactic wars (in which the ‘Dark Side’ may be bribery Ritter with but not joined forever), the universe will be one huge Internet, matter everywhere drawn into the recess of computational living. The extremer of this is called the Omega Point. A Canal twist Is that since the Omega Point does not join the Dark Side, again possibly using game theoretic reasoning, it will decide to be benign and resurrect everyone who ever lived and give them what they most desire. This is called the Jude- Christian heaven by Tippler (1995). Other references used in constructing this version of future history are Gibson (19861 Moravia (1990) and Skuzzier (1999). ) l. INTRODUCTION In this paper I will survey the recent history, current status and future prospects of artificial Intelligence (AAA) and neuroscience.
I will attempt to relate the social motivations and potential impact of the Cells concerned on society at large. 2. THE Formalities over, and given that the Millennium is a significant enough social phenomenon that it colors popular impressions of the future of science, it is worth looking at what impressions a person of the year 2000 might have formed from late twentieth century popular science books, science Action books and Clams, and even from the science pages of newspapers. Such a person might be forgiven for thinking that the future will be something like this.
Anna-robots will perform all molecular repairs in our bodies, making us actively immortal. Highly engineered drugs, perhaps the descendants of Approach and Ecstasy, will take care of emotional disorders, as a side-eject solving all social problems, so everyone will be happy (Canals). That’s for the nostalgic minority who cling to living in the primitive biological form. More cyber-aware individuals will have downloaded themselves into the ‘Net’ and will exist like a William Gibson character in a global computer network which is capable of providing all protagonists with the most fantastic entertainment.
Many global problems will be solved with the demographic move to the ‘Net’, problems such as population, food, transportation and energy. Phil. T ranks. R. Soc. London. B (1999) 354, 201 “2020 These amazing developments are the almost inevitable consequences of the merging of the digital and the organic worlds, on the threshold of which we are now standing. Cellophane and laptop computers are only the beginning. We might call this future the bio-informational age, in keeping with its millennial timing, and the smoothness with which it mixes in with elements of Engage philosophy. 013 & 1999 The Royal Society 2014 A. J. Sell Levels and loops: the future of artificial intelligence and neuroscience side is statistics and signal processing. This is perhaps what makes it such a great Geld to work in. Symbolic AAA was thus subverted by a shift to statistical learning theories. It was also subverted in two other directions by the emergence of the Gelds of artificial life (Longboat 1997) and behavior-based robotics (Irking 1998) (or situated agents).
Artificial life (or life) is subassembly in that it implicitly assumes that intelligence is just the complex end of a simulate life process. A living system and its environment are typically simulated together, often using genetic algorithms and population dynamics to simulate evolution. Behavior-based robotics attempts bravely to deal with the perceptual-motor loop of a robot in a real environment, rejecting both the life simulated worlds and the mainstream AAA notion of a representation of the world.
Echoing Gibson (1979) in his famous debate with Marry (1982) (Bruce & Green 1990), the ‘agents’-literature focuses on complex behavior coming from simple mechanisms operating in tight coupling with a complex environment, in contrast to Mar’s emphasis on the afterward computation of a structural foundation such as that given to neural networks and statistical machine learning by mathematics. This makes it hard to Judge progress or assess methodology in these Gelds.
However, on the other side, neural networks that learn both sensory perceptions and motor actions in an environment are extremely rare, and for a good reason: it is dizzily to build a statistical model of an environment when the system’s perceptions are transformed into actions that arc the statistics of the input. Furthermore, what should such an acting system do? There is an obvious goal for a feed-forward perceptual system: build a probability distribution of what happens.
The hidden symmetries (dependencies, redundancies) in this distribution are the hidden structure of the world. But in this cyclic case, when the world is at least partly constructed by the actions of the system, the shape of this distribution is action dependent Г¶ the system gets to partly choose what symmetries exist, and the notion of a hidden set of privileged symmetries is under threat. This is post-modernism for statisticians.
At this point, most people would abandon informational, or unsupervised, goals and appeal to one of the many specific goals which a robot system might have, such as to CNN food or recharge the batteries. While these are no doubt important, they do have an air of arbitrariness about them that makes us uneasy: we are familiar enough with the Ex. Of goals in our personal experiences to desire something more invariant to underlay action selection. 5. QUESTIONS CURRENTLY LATENT IN ARTIFICIAL INTELLIGENCE 3. THE CURRENT JOB OF SCIENCE It’s a giddy picture indeed, but how much of it, if any, will come true ?
If none of it is going to happen, it would be very helpful if science could tell us why, so that we could get on with living our real future. The ductility for science is that the prospect of a bioinformatics future, with its cyber, translational themes causes us to ask questions concerning individuality, consciousness, mind and machine, exactly those questions which science has had least success in framing. AAA and neuroscience are the Gelds that come closest in engineering and biology to framing such questions.
Scratch the surface of many AAA researchers and neuroscience (perhaps quite usuriously) and you may And someone who started Ii by asking ‘What are we The answers to this question are not that numerous. Either we are machines, in which case AAA should be possible and neuroscience should be able to work out the algorithm (or algorithms) that the brain is running, or we are something else, in which case both projects will fail in their ultimate goals, which is not to say they will not achieve great things along the way. One of the great things that they might achieve is an exact picture of their own limits. ) Either way, by examining the history ND current state of AAA and neuroscience and by identifying the issues beneath the surface of these Gelds, we may gather some sense of what are the important themes playing along science’s internal frontier (disregarding for now how trendier this frontier looks from outside). 4. HISTORY AND STATE OF ARTIFICIAL INTELLIGENCE Ass’s ultimate purpose is to build a robot that lives in the world with a computer for a be captured in digital computation.
The Crust attempts to produce AAA in the sass involved writing facts and rules into the machine using various quasi-logical languages. In the sass this became less popular. Rule-based systems were seen as non-robust: they could not adapt well to small changes in circumstances. Also, every fact had to be programmed in by a human. This led people to think that real- numbered, ‘subassembly’ systems were needed, and these systems had to be able to learn facts (or learn something) themselves, Just by observing data. Historically, this view carried within it the cybernetics view of the sass.
It was one short step from this shift to statistical theories. The short step was called neural networks (Hacking 999); it started in 1984 (Armlets & McClellan 1986) and it is not over yet. An interdisciplinary Geld with a higher than average tolerance for speculation and free- wheeling enquiry, neural networks were popular with students and military founders, and often regarded with frustration by other disciplines that shared a border. As the Geld became more rigorous, it reestablished its connections with mainstream AAA, through common interests in statistical machine learning.
T scenically speaking, the Geld of neural networks is countless. The empirical side is neuroscience; the theoretical Phil. T ranks. R. Soc. London. B (1999) Here we have identified two questions which lie beneath the surface of the pluralistic AAA of today. The Crust question, to rephrase, asks why we do not have a mathematical theory of the personification cycle. Of course there is work on active perception, on concentrators coordinate systems, and engineering Levels and loops: the future of artificial intelligence and neuroscience A. J. Bell department robotics is full of mathematics.
But the kind of theory I mean is one that is as universally useful for characterizing cyclic systems as Chanson’s information hurry is for characterizing communications channels, I. E. Feed-forward systems). (Incidentally, maximizing the channel capacity involves Cadging those hidden symmetries we mentioned that exist in the probability distribution of the input. This forms the basic goal of my own favored area of neural networksГ¶unsupervised learning (Hint & Snowiest 1999). ) Implicit in this is the second question. What would we want such a post-Shannon system to do?
What quantity should a personification cycle system maximize, as a feed-forward channel might maximize its capacity? A third question was directed at AAA researchers by Penrose (1989), and by the hostility and controversy it caused, you knew he had hit a weak spot in AAA. Penrose wondered if the fact that the physical substrate of the world, of which relativity and quantum mechanics are our best accounts, might be succinctly trendier from the digital substrate of computers that it would render AAA impossible. Is there something in the quantum that is necessary for mind?
Song AAA-philosophers characterized Penrose position as ‘we don’t understand quantum mechanics and we don’t understand consciousness, so they must be the same thing’. The derision increased when Penrose, to make his hypothesis more specific, proposed, with Stuart Hammier, that quantum consciousness manifests itself through coherent quantum ejects in a network of proteins called misconstrues which form the structural skeleton proposals (which are not crucial to his argument), may miss the validity of Penrose general doubt about the computer: that it is a particularly unusual artifact, being deterministic, discrete time and discrete state.
The whole state of the machine at the digital level may be written down. No natural objects seem to be of this nature. The imputer is really a physical instantiation of a model. We know a model can compute, but can it live or think ? Functionalism (the philosophy of AAA) was based on using the computer metaphor for mind, arguing that the brain was the hardware implementation of the ‘mental program’. But Penrose arguments were really designed to raise doubts about this separation of physical and mental processes. Could the brain be separated from a supposedly Contritely describable mental process running on ” it ?
Since Rene Descartes, the conceptual separation has been there in our language, but is it scientifically really there? Either there is a physical level at which the separation can be performed (analogous to the level of logic gates in computers) or functionalists have to admit that the brain is not a machine. But the failure to detect ‘logic gate level’ halfway up the brain’s reductionism hierarchy may not be the end of the argument for the functionalist, who could still argue that if there is a computer at the bottom, AAA would be possible, at the very least with a computer with the resources of the universe.
The ‘universe-computer’ is a popular fringe-topic in physics, lying behind an riot to CNN a Canine discrete process such as a alular automaton that might underlay the known laws of Phil. T ranks. R. Soc. London. B (1999) 2015 physics. But until someone succeeds in showing this, we might be wiser to stick with R. F. Funnyman, who noted that quantum processes are not in general simulate, even by Turing machines (and who in the process gave rise to the mysterious and unformed Geld known today as quantum computing). The luck (or skill) of scientists is that sometimes they do not have to philosophize to CNN the answer.
They can ask questions of Nature directly. So perhaps this is a good point to survey the history and rent state of neuroscience, because this is the discipline whose empirical project is exactly the Canine description of brain processes. 6. HISTORY AND STATE OF NEUROSCIENCE The early landmarks in post-war neuroscience were the Nobel prize winning work of Hubble & Wishes (1968) for their studies of the receptive Gelds of monkey visual cortical cells, and that of Hodgkin & Huxley (1952) for their uncovering of the mechanism and mathematics of spiking in neurons.
It has grown into a huge Geld with the annual Society of Neuroscience meeting in the USA attracting 30 000 people. The two early Nobel prizes reflect perhaps a natural split in the Geld between those working above or below the level of the cell. Many of the great successes of the sass and sass were at the subculture level, as the molecular biology revolution progressed, and as a result this part of neurobiology was highly empirical and essentially continuous with mainstream cellular, molecular and developmental biology. In this period, the uncovered.
A bewildering array of ion channels, neurotransmitters and neurologists were found to be engaged in the processes of sculpting neural response properties and controlling communication between neurons. From the chemistry of photon absorption by photoreceptor’s, to the chemistry of muscle contraction, the nervous system apparently performed an astonishingly complicated and coordinated series of molecular actions not qualitatively trendier from those in other living cells, but somehow in the brain this molecular dance constituted percept, thought and action.
At and above the level of the spiking neuron, things were slightly trendier. Lacking the formal structural basis of molecular biology, neuron-level neuroscience focused on the spike trains as signals representing neural information. The discreteness of the spike as an information-carrying unit was matched in biology only by the genetic code. This led to early attempts to characterize the ‘neural code’, attempts that were revived by Viable and co-workers in the sass (Reek et al. 1997). Notably, inevitably, these retorts attempt to characterize neurons as feed-forward information channels. ) Behind these retorts is a faith in the neuron level, certainly as a useful descriptive level, but also as a ‘computing level’ which molecular and biophysical processes exist to implement. Does the goop that we see in the electron cardiographs merely exist to implement ‘the spiking computer’? This is the neuroscience analogue of the functionalist debate in AAA, and I will return to it in h 7(c), after addressing the issue of cycles in neuroscience. 016 Levels and loops: the future of artificial intelligence and neuroscience we may measure their Joint probability distribution p(X, Y), and we could do so by observing X and Y under normal operating conditions, observing a peak in the distribution at equilibrium, and some trajectories corresponding to the stereotypical dynamics of the variables. But in trying to estimate whether X controls Y, experiments often take he form of measuring the conditional distribution p(WAXY) and constructing the Joint distribution through the formula p(X, Y) 0 p(WAXY)p(X).
This latter strategy gives the wrong answer for p(X, Y) because (I) rather than the system controlling p(X), we are controlling it, thus cutting the system at X, and we have, through our choice of independent and dependent variables, imposed on the system a direction (X ! Y) of dependency, with an implied direction of causality that does not exist in nature. There is no doubt that such experiments can still be useful in teasing out dynamic cyclic behavior.
The kinetics of ion channels can be identified with the aid of voltage and current clamping techniques, but there is a recognition in such experiments that the clamped cell is a frozen picture of the true process. This recognition often seems to go missing as the feedback loops get wider (out of sight, out of mind’) and particularly as biology becomes technology. Examples that spring to mind are the widespread prescription of drugs that combat depression by controlling serotonin levels, or attempts to control ecosystems by introducing new species, or, for that industrial model of agriculture.
Anyone seriously studying or modeling metabolism or ecosystems knows the extent to which they are dealing with cycles, but somehow, when the results reach into the area of medicine or its macroscopic equivalent ‘ planet management’, the causal, feed-forward style of thinking is what is presented, particularly to the news media and commercial interests. Anything which does not Ct the feed-forward model is linguistically demoted to the status off ‘side-eject’, to be eliminated if possible. But sidepieces are nature’s way of telling the scientist that all processes are cyclic.
I cannot resist, at this point, discussing the role of biology’s master control node, the genome. Although it is somewhat Ii the subject of AAA and neuroscience, arguments pointing back to the genome as the causal factor behind animal behavior and intelligence are so universal in our culture, that to allow the genome special status outside feedback cycles would be to endorse a control-node mysticism rivaled in shape and form only by that of the monotheistic Anglican bishops who debated so famously with T. H. Huxley. When science became a greater authority on human origins than the church, the transition hid the fact that it was a hang of government without a change in policy. Furthermore, awarding the genome special status allows the present-day church of evolutionary psychology to rampage unchecked and, in my opinion, the wrong lessons are then drawn from biology. ) The genome’s grand cycle with other genomes, mediated through populations of phenotypes is the king of all biological feedback loops. It is a trans-individual (b) Interlude: biology’s master control node 7.
QUESTIONS CURRENTLY LATENT IN NEUROSCIENCE The same problem with cycles presents itself in neuroscience as in AAA, but whereas he primary cycle of concern in AAA was the personification cycle, in neuroscience, the cycles are everywhere. It is interesting that the clearest stories in neuroscience are those which at Crust glance most closely resemble afterward systems. One example is the synapse. The spike arrives at the prescription button, causing vesicles of neurotransmitter to be released, which in turn cause ion channels in the posthypnotic site to open and change the posthypnotic electrical potential.
Another example is the early visual system, starting with the retina and moving through thalamus into early visual cortex. The treatment of this system as a feed-forward channel, despite massive chromatically and acrobatically feedback, has enabled information theoretic learning models the modest success of producing qualitatively correct predictions for the form of the static (Bell & Snowiest 1997) and dynamic (Van Heathen & Van deer Chaff 1998) cortical receptive Gelds that were Crust observed by Hubble & Wishes (1968).
However, feed-forward processing in the nervous system is the exception rather than the rule, and often what looks feed-forward contains complicated feedback systems at a trendier level of analysis. For example, the spikes of a cortical neuron have now been seen to extend far into the dendrites tree, acting, through plenipotentiary channels, the integration of signals from synapses. This destroys the illusion that the neuron works like a directional ‘neural network’ neuron, performing a weighted sum of its input signals.
Even in the synapse inputs from the brain, the brain controls gaze direction which determines what the retina sees. Although neurotransmitter does not travel backwards across synapses in most neurons, many other molecular signals do, as the extensive and controversial attempts to CNN synaptic Hobbies learning mechanisms in long-term potential have revealed. In abstract, the lack of a theory of cycles in biology can be seen by considering an experiment in which some variable X is changed and some other variable Y is monitored. What is published are the relatively rare cases where some correlation in X and Y is observed.
The temptation then is to say that ‘X controls Y’ and from this to build a model of feed-forward neural information processing (or if X is a chemical, we may market it as a drug to control Y). In nature, things happen directly from in the experiment. X may rise, causing Y to rise, but then increased Y usually causes X to diminish, directly or through some other variables Z. These cycles of positive and negative feedback are universal in biology and cause equilibrium values of X and Y, or stereotypical dynamic behavior to occur.
A neural spike is one example of a transient dynamic caused by positive and negative feedback, where X is the sodium current and Y the potassium current. Slipping into the language of probability theory, if we desire to discover the relationship in nature, of X and Y, Phil. T ranks. R. Soc. London. B (1999) a) Cycles in neuroscience molecular regulation loop, qualitatively similar to those occurring within cells, with cooperation (or symbiosis; Margins & Sang 1995) corresponding to the positive feedback loop and competition for resources corresponding to the negative feedback loop.
Neo-Darwinist, stuck on the negative pole, like to interpret cooperative behavior as ‘shells’ altruism (I’ll scratch your back if you scratch mine). The inverse position, on the positive pole, is to interpret competition for resources as useless greediness (I’ll eat you, but honestly, this is not about me). Y might consider both positions absurd, or you oh might use the latter point of view as an antidote to the dominance of the former in our culture.
The point here is that competition and cooperation have equal status and the process of ‘natural selection’ in which we are judged by an external environment (more biblical parallels) is better viewed as a complex molecular regulation loop like any other. The regulation loop is mediated through phenotypes success, which brings up another loop-denying habit of neo- Darwinist, which is to see the genome as a controller for all aspects of the honey, right down to its specific behavior: DNA as the determining code for an organism.
There must be a particular attraction in this idea for certain authors, because they take great pleasure in outraging people’s common sense by portraying organisms as the helpless puppets of their genes (Adkins 1990). I will not duplicate the riot of the many authors who have attacked the social or behavioral versions of this notion (for example, the preposterous notion that there could be a gene for homelessness, which was actually considered in an editorial in Science), because this would be to attack it at its weakest point. I’d like to attack the notion in its strongest proteins, and not the other way round’.
The central dogma of molecular biology is wrong! Sequences of DNA code for strings of amino acids Г¶ true Г¶ but how these amino acids are assembled into functioning proteins and which parts of the DNA are read in the Crust place are both controlled by proteins, and depend on the state of the cell and its type. It’s as if there was a bookish town (a cell) with a central library (the genome) and people (proteins) who came in to read short sections here and there, share with each other what they had read, and use the knowledge to build and hang the town. Who is controlling here Г¶ the townsfolk or the library? Answer: neither. ) Where did the people in the town come from? If ‘genes make proteins’, then the library made them, but the truth is that they were there all along. The functioning networks of enzymes that set to work on your DNA when you were conceived were already in place in the salty water of your mother’s egg cell. They were Just the latest installment in a continuous epigenetic lineage that stretches back to your primordial metabolic ancestor, a droplet of seawater that accidentally got stuck inside a lipid membrane with a fortuitous set of amino acids.
It is harder to make more unsubstantiated assertions in biology than in the area known as ‘origin of life’. But if the ‘genes makes proteins’ debate really comes down to whether there was RNA (code) before proteins Phil. T ranks. R. Soc. London. B (1999) 2017 (metabolism) or proteins before RNA in the Crust protocols (De Duvet 1991), then two factors should be considered: (I) amino-acid chains form much more readily than nucleic-acid chains, and (it) it is more likely that the Crust people wrote the Crust books, than that the Crust books wrote the Crust people.
It is noteworthy that both interpretations and New T stamens theologians believe that ‘in the beginning was the word (logos)’. ) Of course, now it is claimed there were ribosome’s (RNA with the ability to catalyst reactions), but was this metabolism evolving a code, or a code evolving metabolism? The outcome of this debate is not crucial. The intent here is merely to weaken the notion of DNA as a kind of controller of the phenotype. An equally valid (and equally invalid) perspective has the phenotype choosing what is read from the gene and what is done with it.
In reality, the organism and its genes re caught in a cyclic dynamic, and if the organism decides to spend its afternoon in a (real) library, instead of attempting to father children, then you can be sure that the pattern of gene expression will alter accordingly. This argument Acts with our Crust general theme of critiquing feed-forward thinking in AAA and neuroscience. Returning now to the second theme we touched on when discussing AAA, h 5 ended with a consideration of levels of a system and functionalism.
There was a challenge to the functionalist to empirically investigate the brain and identify a level at which the rain could be Contritely ‘written down’, a level analogous to logic gates in computers. The obvious candidate is the neuron level. If we wrote down the sequence of all spikes of all neurons, would that be enough to specify the ‘neural computation’? Do molecular and biophysical processes exist to implement a ‘spiking computer’ at the neuron level ? I believe the answer to these questions is no.
While no specific operation of the computer (unless something goes wrong), this cannot be said at the neuron level of the brain. Molecular and biophysical processes control the sensitivity f neurons to incoming spikes (both synaptic science and posthypnotic responsively), the excitability of the neuron to produce spikes, the patterns of spikes it can produce and the likelihood of new synapses forming (dynamic rewiring), to list only four of the most obvious interferences from the subaltern level.
Furthermore, transsexual volume ejects such as local electric Gelds and the transmigrate discussion of nitric oxide have been seen to incidence, respectively, coherent neural Cringe, and the delivery of energy (blood Owe) to cells, the latter of which directly correlates with rural activity. The list could go on. I believe that anyone who seriously studies neurologists, ion channels or synaptic mechanism and is honest, would have to reject the neuron level as a separate computing level, even while Cadging it to be a useful descriptive level.
Perhaps a physicist or a neural-network theorist, in looking for an easy theory, would still argue that the molecular level is mere implementation detail, but in most cases this is more a result of prejudice, supported by laziness and ignorance. If the molecular level is unimportant for an (c) Levels in neuroscience 2018 Levels and loops: the future of artificial intelligence and neuroscience nature may be trendier. In fact, it is. There are spectacular interferences that violate the separateness of the ‘molecular machine’ level, and they are quantum ejects.
Two examples of this are electron transfer in photosynthesis and the energetic of enzyme interactions (Welch 1986). In both cases, quantum coherence are necessary to explain the science of the reactions. But we don’t even need to go as far down as quantum ejects, because proteins do not end at the edges of the black and red balls of which ball-and-stick molecular models are constructed. Their electrical Gelds extend into the surrounding water molecules, orientating them to form what is called structured water. Structured water is also important in determining how enzyme reactions occur, and how ion channels are selective to certain ions.
T argue that one piece of structured water or one o quantum coherence is a necessary detail in the functional description of the brain would clearly be ludicrous. But if, in every cell, molecules derive systematic functionality from these spectacular processes, if these processes are used all the time, all over the brain, to reject, record and reportage spatial-temporal correlations of molecular Situations, to enhance or diminish the probabilities and specificities of reactions, then we have a situation qualitatively trendier from the logic gate.
The variables lying beneath the level of a molecular ‘gate’ can arc the behavior of the gate, so the functionalist is again frustrated, and the notion of the brain as a molecular ‘computer’ can be viewed as no more than an analogy, and an inaccurate one. T say these things is not to be a ‘ New Age quantum o mystic’. It is to attempt to clearly state empirical observations