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Will the mind ever be fully explained by neuroscience? A good discussion from IAI, capably chaired by Barry C. Smith.

Raymond Tallis puts intentionality at the centre of the question of the mind (quite rightly, I think). Neuroscience will never explain meaning or the other forms of intentionality, so it will never tell us about essential aspects of the mind.

Susanna Martinez-Conde says we should not fear reductive explanation. Knowing how an illusion works can enhance our appreciation rather than undermining it. Our brains are designed to find meanings, and will do so even in a chaotic world.

Markus Gabriel says we are not just a pack of neurons – trivially, because we are complete animals, but more interestingly because of the contents of our mind – he broadly agrees that intentionality is essential. London is not contained in my head, so aliens could not decipher from my neurons that I was thinking I was in London. He adds the conceot of geist – the capacity to live according to a conception of ourselves as a certain kind of being – which is essential to humanity, but relies on our unique mental powers.

Martinez-Conde points out that we can have the experience of being in London without in fact being there; Tallis dismisses such ‘brain in a vat’ ideas; for the brain to do that it must have had real experiences and there must be scientists controlling what happens in the vat. The mind is irreducibly social.

My sympathies are mainly with Tallis, but against him it can be pointed out that while neuroscience has no satisfactory account of intentionality, he hasn’t got one either. While the subject remains a mystery, it remains possible that a remarkable new insight that resolves it all will come out of neuroscience. The case against that possibility, I think, rests mainly on a sense of incredulity: the physical is just not the sort of thing that could ever explain the mental. We find this in Brentano of course, and perhaps as far back as Leibniz’s mill, or in the Cartesian point that mental things have no extension. But we ought to admit that this incredulity is really just an intuition, or if you like, a failure to be able to imagine. It puzzles me sometimes that numbers, those extensionless abstract concepts, can nevertheless drive the behaviour of a computer. But surely it would be weird to say they don’t, or that how computers do arithmetic must remain an unfathomable mystery.

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Ian McEwan’s latest book Machines Like Me has a humanoid robot as a central character. Unfortunately I don’t think he’s a terrifically interesting robot; he’s not very different to a naïve human in most respects, except for certain unlikely gifts; an ability to discuss literature impressively and an ability to play the stock market with steady success. No real explanation for these superpowers is given; it’s kind of assumed that direct access to huge volumes of information together with a computational brain just naturally make you able to do these things. I don’t think it’s that easy, though in fairness these feats only resemble the common literary trick where our hero’s facility with languages or amazingly retentive memory somehow makes him able to perform brilliantly at tasks that actually require things like insight and originality.

The robot is called Adam; twenty-five of these robots have been created, twelve Adams and thirteen Eves, on the market for a mere £86,000 each. This doesn’t seem to make much commercial sense; if these are prototypes you wouldn’t sell them; if you’re ready to market them you’d be gearing up to make thousands of them, at least. Surely you’d charge more, too – you could easily spend £86k on a fancy new car. But perhaps prices are misleading, because we are in an alternate world.

This is perhaps the nub of it all. The prime difference here is that in the world of the novel, Alan Turing did not die, and was mainly responsible for a much faster development of computers and IT. Plausible humanoid robots have appeared in 1982. This seems to me an unhelpful contribution to the myth of Turing as ‘Mr Computer’. It’s sadly plausible that if he had lived longer he would have had more to contribute; but most likely in other mathematical fields, not in the practical development of the computer, where many others played key roles (as they did at Bletchley). If you ask me, John Von Neumann was more than capable of inventing computers on his own, and in fact in the real postwar world they developed about as fast as they could have done whether Turing was alive or not. McEwan nudges things along a bit more by having Tesla around to work on silicon chips (!) and he brings Demis Hassabis back a bit so he can be Turing’s collaborator (Hassabis evidently doomed to work on machine learning whenever he’s born). This is all a bit silly, but McEwan enjoys it enough to have advanced IT in Exocet missiles give victory to Argentina in the Falklands war, with consequences for British politics which he elaborates in the background of the story. It’s a bit odd that Argentina should get an edge from French IT when we’re being asked to accept that the impeccably British ‘Sir’ Alan Turing was personally responsible for the great technical leap forward which has been made, but it’s pointless to argue over what it is ultimately not much more than fantasy.

Turing appears in the novel, and I hate the way he’s portrayed. One of McEwan’s weaknesses, IMO, is his reverence for the British upper class, and here he makes Sir Alan into the sort of grandee he admires; a lordly fellow with a large house in North London who summons people when he wants information, dismisses them when he’s finished, and hands out moral lectures. Obviously I don’t know what Turing was really like, but to me his papers give the strong impression of an unassuming man of distinctly lower middle class origins; a far more pleasant person than the arrogant one we get in the book.

McEwan doesn’t give us any great insight into how Adam comes to have human-like behaviour (and surely human-like consciousness). His fellow robots are prone to a sort of depression which leads them to a form of suicide; we’re given the suggestion that they all find it hard to deal with human moral ambiguity, though it seems to me that humans in their position (enslaved to morally dubious idiots) might get a bit depressed too. As the novel progresses, Adam’s robotic nature seems to lose McEwan’s interest anyway, as a couple of very human plots increasingly take over the story.

McEwan got into trouble for speaking dismissively of science fiction; is Machines Like Me SF? On a broad reading I’d say why not? – but there is a respectable argument to be made for the narrower view. In my youth the genre was pretty well-defined. There were the great precursors; Jules Verne, H.G. Wells, and perhaps Mary Shelley, but SF was mainly the product of the American pulp magazines of the fifties and sixties, a vigorous tradition that gave rise to Asimov, Clarke, and Heinlein at the head of a host of others. That genre tradition is not extinct, upheld today by, for example, the beautiful stories of Ted Chiang.

At the same time, though, SF concepts have entered mainstream literature in a new way. The Time Traveller’s Wife, for example, obviously makes brilliant use of an SF concept, but does so in the service of a novel which is essentially a love story in the literary mainstream of books about people getting married which goes all the way back to Pamela. There’s a lot to discuss here, but keeping it brief I think the new currency of SF ideas comes from the impact of computer games. The nerdy people who create computer games read SF and use SF concepts; but even non-nerdy people play the games, and in that way they pick up the ideas, so that novelists can now write about, say, a ‘portal’ and feel confident that people will get the idea pretty readily; a novel that has people reliving bits of their lives in an attempt to get them right (like The Seven Deaths Of Evelyn Hardcastle) will not get readers confused the way it once would have done. But that doesn’t really make Evelyn Hardcastle SF.

I think that among other things this wider dispersal of a sort of SF-aware mentality has led to a vast improvement in the robots we see in films and the like. It used to be the case that only one story was allowed: robots take over. Latterly films like Ex Machina or Her have taken a more sophisticated line; the TV series Westworld, though back with the take-over story, explicitly used ideas from Julian Jaynes.

So, I think we can accept that Machines Like Me stands outside the pure genre tradition but benefits from this wider currency of SF ideas. Alas, in spite of that we don’t really get the focus on Adam’s psychology that I should have preferred.

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Eddy Nahmias recently reported on a ground-breaking case in Japan where a care-giving robot was held responsible for agreeing to a patient’s request for a lethal dose of drugs. Such a decision surely amounts to a milestone in the recognition of non-human agency; but fittingly for a piece published on 1 April, the case was in fact wholly fictional.

However, the imaginary case serves as an introduction to some interesting results from the experimental philosophy Nahmias has been prominent in developing. The research – and I take it to be genuine – aims not at clarifying the metaphysics or logical arguments around free will and responsibility, but at discovering how people actually think about those concepts.

The results are interesting. Perhaps not surprisingly, people are more inclined to attribute free will to robots when told that the robots are conscious. More unexpectedly, they attach weight primarily to subjective and especially emotional conscious experience. Free will is apparently thought to be more a matter of having feelings than it is of neutral cognitive processing.

Why is that? Nahmias offers the reasonable hypothesis that people think free will involves caring about things. Entities with no emotions, it might be, don’t have the right kind of stake in the decisions they make. Making a free choice, we might say, is deciding what you want to happen; if you don’t have any emotions or feelings you don’t really want anything, and so are radically disqualified from an act of will. Nahmias goes on to suggest, again quite plausibly, that reactive emotions such as pride or guilt might have special relevance to the social circumstances in which most of our decisions are made.

I think there’s probably another factor behind these results; I suspect people see decisions based on imponderable factors as freer than others. The results suggest, let’s say, that the choice of a lover is a clearer example of free will than the choice of an insurance policy; that might be because the latter choice has a lot of clearly calculable factors to do with payments versus benefits. It’s not unreasonable to think that there might be an objectively correct choice of insurance policy for me in my particular circumstances, but you can’t really tell someone their romantic inclinations are based on erroneous calculations.

I think it’s also likely that people focus primarily on interesting cases, which are often instances of moral decisions; those in turn often involve self-control in the face of strong desires or emotions.

Another really interesting result is that while philosophers typically see freedom and responsibility as two sides of the same coin, people’s everyday understanding may separate the two. It looks as though people do not generally distinguish all that sharply between the concepts of being causally responsible (it’s because of you it happened, whatever your intentions) and morally responsibly (you are blameworthy and perhaps deserve punishment). So, although people are unwilling to say that corporations or unconscious robots have free will, they are prepared to hold them responsible for their actions. It might be that people generally are happier with concepts such as strict liability than moral philosophers mainly are; or of course, we shouldn’t rule out the possibility that people just tend to suffer some mild confusion over these issues.

Thought-provoking stuff, anyway, and further evidence that experimental philosophy is a tool we shouldn’t reject.

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Can there be minds within minds? I think not.

The train of thought I’m pursuing here started in a conversation with a friend (let’s call him Fidel) who somehow manages to remain not only an orthodox member of the Church of England, but one who is apparently quite untroubled by any reservations, doubts, or issues about the theology involved. Now of course we don’t all see Christianity the same way. Maybe Fidel sees it differently from me. For many people (I think) religion seems to be primarily a social organisation of people with a broadly similar vision of what is good, derived mainly from the teachings of Jesus. To me, and I suspect to most people who are likely to read this, it’s primarily a set of propositions, whose truth, falsity, and consistency is the really important matter. To them it’s a club, to us it’s a theory. I reckon the martyrs and inquisitors who formed the religion, who were prepared to die or kill over formal assent to a point of doctrine, were actually closer to my way of thinking on this, but there we are.

Be that as it may, my friend cunningly used the problems (or mysteries) of his religion as a weapon against me. You atheists are so complacent, he said, you think you’ve got it all sorted out with your little clockwork science universe, but you don’t appreciate the deep mysteries, beyond human understanding. There are more things in heaven and earth…
But that isn’t true at all, I said. If you think current physics works like clockwork, you haven’t been paying attention. And there are lots of philosophical problems where I have only reasonable guesses at the answer, or sometimes, even on some fundamental points, little idea at all. Why, I said injudiciously, I don’t understand at all what reality itself even is. I can sort of see that to be real is to be part of a historical process characterised by causality, but what that really means and why there is anything like that, what the hell is really going on with it…? Ah, said Fidel, what a confession! Well, when you’re ready to learn about reality, you know where to come…

I don’t, though. The trouble is, I don’t think Christianity really has any answers for me on this or many other metaphysical points. Maybe it’s just my ignorance of theology talking here, but it seems to me just as Christianity tells us that people are souls and then falls largely silent on how souls and spirits work and what they are, it tells us that God made the world and withholds any useful details of how and what. I know that Buddhism and Taoism tell us pretty clearly that reality is an illusion; that seems to raise other issues but it’s a respectable start. The clearest Christian answer I can come up with is Berkeley’s idealism; that is, that to be real is to be within the mind of God; the world is whatever God imagines or believes it to be.

That means that we ourselves exist only because we are among the contents of God’s mind. Yet we ourselves are minds, so that requires it to be true that minds can exist within minds (yes, at last I am getting to the point). I don’t think a mind can exist within another mind. The simplest way to explain is perhaps as follows; a thought that exists within a mind, that was generated by that mind, belongs to that mind. So if I am sustaining another mind by my thoughts, all of its thoughts are really generated by me, and of course they are within my mind. So they remain my thoughts, the secondary mind has none that are truly its own – and it doesn’t really exist. In the same way, either God is thinking my thoughts for me – in which case I’m just a puppet – or my thoughts are outside his mind, in which case my reality is grounded in something other than the Divine mind.

That might help explain why God would give us free will, and so on; it looks as if Berkeley must have been perfectly wrong: in fact reality is exactly the quality possessed by those things that are outside God’s mind. Anyway, my grip of theology is too weak for my thoughts on the matter to be really worth reading (so I owe you an apology); but the idea of minds within minds arises in AI related philosophy, too; perhaps in relation to Nick Bostrom’s argument that we are almost certainly part of a computer simulation. That argument rests on the idea that future folk with advanced computing tech will produce perfect simulations of societies like their own, which will themselves go on to generate similar simulations, so that most minds, statistically, are likely to be simulated ones. If minds can’t exist within other minds, might we be inclined to doubt that they could arise in mind-like simulations?

Suppose for the sake of argument that we have a conscious mind that is purely computational; its mind arises from the computations it performs. Why should such a program not contain, as some kind of subroutine or something, a distinct process that has the same mind-generating properties? I don’t think the answer is obvious, and it will depend on your view of consciousness. For me it’s all about recognition; a conscious mind is a process whose outputs are conditioned by the recognition of future and imagined entities. So I would see two alternatives; either the computational mind we supposed to exist has one locus of recognition, or two. If it has one, the secondary mind can only be a puppet; if there are two, then whatever the computational relationship, the secondary process is independent in a way that means it isn’t truly within the primary mind.

That doesn’t seem to give me the anti-Bostrom argument I thought might be there, and let’s be honest, the notion of a ‘locus of recognition’ could possibly be attacked. If God were doing my thinking, I feel it would be a bit sharper than this…

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Amanda Sharkey has produced a very good paper on robot ethics which reviews recent research and considers the right way forward – it’s admirably clear, readable and quite wide-ranging, with a lot of pithy reportage. I found it comforting in one way, as it shows that the arguments have had a rather better airing to date than I had realised.

To cut to the chase, Sharkey ultimately suggests that there are two main ways we could respond to the issue of ethical robots (using the word loosely to cover all kinds of broadly autonomous AI). We could keep on trying to make robots that perform well, so that they can safely be entrusted with moral decisions; or we could decide that robots should be kept away from ethical decisions. She favours the latter course.

What is the problem with robots making ethical decisions? One point is that they lack the ability to understand the very complex background to human situations. At present they are certainly nowhere near a human level of understanding, and it can reasonably be argued that the prospects of their attaining that level of comprehension in the foreseeable future don’t look that good. This is certainly a valid and important consideration when it comes to, say, military kill-bots, which may be required to decide whether a human being is hostile, belligerent, and dangerous. That’s not something even humans find easy in all circumstances. However, while absolutely valid and important, it’s not clear that this is a truly ethical concern; it may be better seen as a safety issue, and Sharkey suggests that that applies to the questions examined by a number of current research projects.

A second objection is that robots are not, and may never be, ethical agents, and so lack the basic competence to make moral decisions. We saw recently that even Daniel Dennett thinks this is an important point. Robots are not agents because they lack true autonomy or free will and do not genuinely have moral responsibility for their decisions.

I agree, of course, that current robots lack real agency, but I don’t think that matters in the way suggested. We need here the basic distinction between good people and good acts. To be a good person you need good motives and good intentions; but good acts are good acts even if performed with no particular desire to do good, or indeed if done from evil but confused motives. Now current robots, lacking any real intentions, cannot be good or bad people, and do not deserve moral praise or blame; but that doesn’t mean they cannot do good or bad things. We will inevitably use moral language in talking about this aspect of robot behaviour just as we talk about strategy and motives when analysing the play of a chess-bot. Computers have no idea that they are playing chess; they have no real desire to win or any of the psychology that humans bring to the contest; but it would be tediously pedantic to deny that they do ‘really’ play chess and equally absurd to bar any discussion of whether their behaviour is good or bad.

I do give full weight to the objection here that using humanistic terms for the bloodless robot equivalents may tend to corrupt our attitude to humans. If we treat machines inappropriately as human, we may end up treating humans inappropriately as machines. Arguably we can see this already in the arguments that have come forward recently against moral blame, usually framed as being against punishment, which sounds kindly, though it seems clear to me that they might also undermine human rights and dignity. I take comfort from the fact that no-one is making this mistake in the case of chess-bots; no-one thinks they should keep the prize money or be set free from the labs where they were created. But there’s undoubtedly a legitimate concern here.

That legitimate concern perhaps needs to be distinguished from a certain irrational repugnance which I think clearly attaches to the idea of robots deciding the fate of humans, or having any control over them. To me this very noticeable moral disgust which arises when we talk of robots deciding to kill humans, punish them, or even constrain them for their own good, is not at all rational, but very much a fact about human nature which needs to be remembered.

The point about robots not being moral persons is interesting in connection with another point. Many current projects use extremely simple robots in very simple situations, and it can be argued that the very basic rule-following or harm prevention being examined is different in kind from real ethical issues. We’re handicapped here by the alarming background fact that there is no philosophical consensus about the basic nature of ethics. Clearly that’s too large a topic to deal with here, but I would argue that while we might disagree about the principles involved (I take a synthetic view myself, in which several basic principles work together) we can surely say that ethical judgements relate to very general considerations about acts. That’s not necessarily to claim that generality alone is in itself definitive of ethical content (it’s much more complicated than that), but I do think it’s a distinguishing feature. That carries the optimistic implication that ethical reasoning, at least in terms of cognitive tractability, might not otherwise be different in kind from ordinary practical reasoning, and that as robots become more capable of dealing with complex tasks they might naturally tend to acquire more genuine moral competence to go with it. One of the plausible arguments against this would be to point to agency as the key dividing line; ethical issues are qualitatively different because they require agency. It is probably evident from the foregoing that I think agency can be separated from the discussion for these purposes.

If robots are likely to acquire ethical competence as a natural by-product of increasing sophistication, then do we need to worry so much? Perhaps not, but the main reason for not worrying, in my eyes, is that truly ethical decisions are likely to be very rare anyway. The case of self-driving vehicles is often cited, but I think our expectations must have been tutored by all those tedious trolley problems; I’ve never encountered a situation in real life where a driver faced a clear cut decision about saving a bus load of nuns at the price of killing one fat man. If a driver follows the rule; ‘try not to crash, and if crashing is unavoidable, try to minimise the impact’, I think almost all real cases will be adequately covered.

A point to remember is that we actually do often make rules about this sort of thing which a robot could follow without needing any ethical sense of its own, so long as its understanding of the general context was adequate. We don’t have explicit rules about how many fat men outweigh a coachload of nuns just because we’ve never really needed them; if it happened every day we’d have debated it and made laws that people would have to know in order to pass their driving test. While there are no laws, even humans are in doubt and no-one can say definitively what the right choice is; so it’s not logically possible to get too worried that the robot’s choice in such circumstances would be wrong.

I do nevertheless have some sympathy with Sharkey’s reservations. I don’t think we should hold off from trying to create ethical robots though; we should go on, not because we want to use the resulting bots to make decisions, but because the research itself may illuminate ethical questions in ways that are interesting (a possibility Sharkey acknowledges). Since on my view we’re probably never really going to need robots with a real ethical sense, and on the other hand if we did, there’s a good chance they would naturally have developed the required competence, this looks to me like a case where we can have our cake and eat it (if that isn’t itself unethical).

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Artificial General Intelligence – human-style consciousness in machines – is unnecessary, says Daniel Dennett, in an interesting piece that makes several unexpected points. His thinking seems to have moved on in certain respects, though I think the underlying optimism about digital cognition is still there.

He starts out by highlighting some dangers that arise even with the non-conscious kinds of AI we have already. Recent developments make it easy to fake very realistic video of recognisable people doing or saying whatever we want. We can imagine, Dennett says ‘… a return to analog film-exposed-to-light, kept in “tamper-proof” systems until shown to juries, etc.’ Well, I think that scenario will remain imaginary. These are not completely new concerns; similar ones go back to the people who in the last century used to say ‘the camera cannot lie’ and were so often proven wrong. Actually the question of whether a piece of evidence is digital or analog is pretty well irrelevant; but its history, whether it could have been interfered with, that bit about tamper-proof containers, has always been a concern and will no doubt remain so in one form or another (I remember the special cassette recorders once used by the authorities to interview suspects, which automatically made a copy for the interviewee and could not erase or rewind the tape).

I think his concerns have a more solid foundation though, when he goes on to say that there is now some danger of people mistaking simple AI for the kind of conscious entity they can trust. People do sometimes seem willing to be convinced rather easily that a machine has a mind of its own. That tendency in itself is also not exactly new, going back to Weizenbaum’s simple chat program ELIZA (as Dennett says); but these days serious discussion is beginning about topics like robot rights and robot morality. No reason why we shouldn’t discuss them – I think we should – but the idea that they are issues of immediate practical concern seems radically premature. Still, I’m not that worried. It’s true that some people will play along with a chatbot in the Loebner contest, or pretend Siri or Alexa is a thinking being, but I think anyone who is even half-serious about it can easily tell the difference. Dennett suggests we might need licensed operators trained to be hard-nosed and unsympathetic to AIs (‘an ugly talent, reeking of racism’ – !!!), but I don’t think it’s going to be that bad.

Dennett emphasises that current AI lacks true agency and calls on the creators of new systems to be more upfront about the fact that their humanising touches are fakery and even ‘false advertising’. I have the impression that Dennett would once have considered agency, as a concept, a little fuzzy round the edges, a matter of explanatory stances and optimality rather than a clear reality whose sharp edges needed to be strongly defended. Years ago he worked with Ray Brooks on Cog, a deliberately humanoid robot they hoped would attain consciousness (it all seemed so easy back then…) and my impression is that the strategy then had a large element of ‘fake it till you make it’. But hey, I wouldn’t criticise Dennett for allowing his outlook to develop in the light of experience.

On to the two main points. Dennett says we don’t need conscious AI because there is plenty of natural human consciousness around; what we need is intelligent tools, somewhat like oracles perhaps (given the history of real oracles that might be a dubious comparison – is there a single example of an oracle that was both clear and helpful? In The Golden Ass there’s a pair of soothsayers who secretly give the same short verse to every client; in the end they’re forced to give up the profitable business, not by being rumbled, but by sheer boredom.)

I would have thought that there were jobs a conscious AI could do for us. Consciousness allows us to reflect on future and imagined contingencies, spot relevant factors from scratch, and rise above the current set of inputs. Those ought to be useful capacities in a lot of routine planning and management (I can’t help thinking they might be assets for self-driving vehicles); yes, humans could do it all, but it’s boring, detailed, and takes too long. I reckon, if there’s a job you’d give a slave, it’s probably right for a robot.

The second main point is that we ought to be wary of trusting conscious AIs because they will be invulnerable. Putting them in jail is meaningless because they live in boxes anyway; they can copy themselves and download backups, so they don’t die; unless we build in some pain function, there are really no sanctions to underpin their morality.

This is interesting because Dennett by and large assumes that future conscious AIs would be entirely digital, made of data; but the points he makes about their immortality and generally Platonic existence implicitly underline how different digital entities are from the one-off reality of human minds. I’ve mentioned this ontological difference before, and it surely provides one good reason to hesitate before assuming that consciousness can be purely digital. We’re not just data, we’re actual historical entities; what exactly that means, whether something to do with Meinongian distinctions between existence and subsistence, or something else entirely, I don’t really think anyone knows, frustrating as that is.

Finally, isn’t it a bit bleak to suggest that we can’t trust entities that aren’t subject to the death penalty, imprisonment, or other punitive sanctions? Aren’t there other grounds for morality? Call me Pollyanna, but I like to think of future conscious AIs proving irrefutably for themselves that virtue is its own reward.

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Is there such a thing as consciousness without content? If so, is that minimal, empty consciousness, in fact, the constant ground underlying all conscious states? Thomas Metzinger launched an investigation of this question in the third Carnap lecture of a year ago; there’s a summary here in a discussion paper, and a fully worked-up paper will appear next year (hat-tip to Tom Clark for drawing this to my attention). The current paper is exploratory in several respects. One possible result of identifying the hypothetical state of Minimal Phenomenal Experience (MPE) would be to facilitate the identification of neural correlates; Metzinger suggests we might look to the Ascending Reticular Arousal System (ARAS), but offers it only as a plausible place-holder which future research might set aside.

More philosophically, the existence of an underlying conscious state which doesn’t represent anything would be a fatal blow to the view that consciousness is essentially representational in character. On that widely-held view, a mental state that doesn’t feature representation cannot in fact be conscious at all, any more than text that contains no characters is really text. The alternative is to think that consciousness is more like a screen being turned on; we see only (let’s say) a blank white expanse, but the basic state, precondition to the appearance of images, is in place, and similarly MPE can be present without ‘showing us’ anything.

There’s a danger here of getting trapped in an essentially irrelevant argument about the difference between representing nothing and not representing anything, but I think it’s legitimate to preserve representationalism (as an option at least) merely by claiming that even a blank screen necessarily represents something, namely a white void. Metzinger prefers to suggest that the MPE represents “the hidden cause of the ARAS- signal”. That seems implausible to me, as it seems to involve the unlikely idea that we all have constantly in mind a hidden thing most of us have never heard of.

Metzinger does a creditable job of considering evidence from mystic experience as well as dreamless sleep. There is considerable support here for the view that when the mind is cleared, consciousness is not lost but purified. Metzinger rightly points to some difficulties with taking this testimony on board. One is the likelihood of what he calls “theory contamination”. Most contemplatives are deeply involved with mystic or scriptural traditions that already tell them what is to be expected. Second is the problem of pinning down a phenomenal experience with no content, which automatically renders it inexpressible or ineffable. Metzinger makes it clear that this is not any kind of magic or supra-scientific ineffability, just the practical methodological issue that there isn’t, as it were, anything to be said about non-existent content. Third we have an issue Metzinger calls “performative self-contradiction”. Reports of what you get when your mind is emptied make clear that the MPE is timeless, placeless, lacking sensory character, and so on. Metzinger is a little disgusted with this; if the experience was timeless, how do you know it happened last Tuesday between noon and ten past? People keep talking about brilliance and white light, which should not be present in a featureless experience!

Here I think he under-rates the power and indeed the necessity of metaphors. To describe lack of content we fall back on a metaphor of blindness, but to be in darkness might imply simple failure of the eyes, so we tend to go for our being blinded by powerful light and the vastness of space. It’s also possible that white is a default product of our neural systems, which when deprived of input are known to produce moire patterns and blobs of light from nowhere. Here we are undoubtedly getting into the classic problems that affect introspection; you cannot have a cleared mind and at the same time be mentally examining your own phenomenal experience. Metzinger aptly likens these problems to trying to check whether the light in the fridge goes off when the door is closed (I once had one that didn’t, incidentally; it gave itself away by getting warm and unhelpfully heating food placed near it). Those are real problems that have been discussed extensively, but I don’t think they need stop the investigation. In a nutshell, William James was right to say that introspection must be retrospection; we examine our experiences afterwards. This perhaps implies that memory must persist alongside MPE, but that seems OK to me. Without expressing it in quite these terms, Metzinger reaches broadly similar conclusions.

Metzinger is mainly concerned to build a minimal model of the basic MPE, and he comes up with six proposed constraints, giving him in effect not a single MPE state but a 6-dimensional space. The constraints are as follows.

• PC1: Wakefulness: the phenomenal quality of tonic alertness.

• PC2: Low complexity of reportable content: an absence of high-level symbolic mental content (i.e., conceptual or propositional thought or mind wandering), but also of perceptual, sensorimotor, of emotional content (as in full-absorption episodes).

• PC3: Self-luminosity: a phenomenal property of MPE typically described as “radiance”, “brilliance”, or the “clear light” of primordial awareness.

• PC4: Introspective availability: we can sometimes actively direct introspective attention

to MPE and we can distinguish possible states by the degree of actually ongoing access.

• PC5: Epistemicity; as MPE is an epistemic relation (“awareness-of”,) if MPE is successfully introspected, then we would predict a distinct phenomenal character of epistemicity or subjective confidence.

• PC6: Transparency/opacity: like all other phenomenal representations, MPE can vary along a spectrum of opacity and transparency.


At first I feared this was building too much on a foundation not yet well established, but against that Metzinger could fairly ask how he could consolidate without building; what we have is acknowledged to be a sketch for now; and in fact there’s nothing that looks obviously out of place to me.

For Metzinger this investigation of minimal experience follows on from earlier explorations of minimal self-awareness and minimal perspective; this might well be the most significant of the three, however. It opens the way to some testable hypotheses and, since it addresses “pure” consciousness offers a head-on attack route on the core problem of consciousness itself. Next year’s paper is surely going to be worth a look.

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Interesting to see the review of progress and prospects for the science of consciousness produced by Matthias Michel and others, and particularly the survey that was conducted in parallel. The paper discusses funding and other practical issues, but we’re also given a broad view of the state of play, with the survey recording broadly optimistic views and interestingly picking out Global Workspace proposals as the most favoured theoretical approach. However, consciousness science was rated less rigorous than other fields (which I suppose is probably attributable to the interdisciplinary character of the topic and in particular the impossibility of avoiding ‘messy’ philosophical issues).

Michel suggests that the scientific study of consciousness only really got established a few decades ago, after the grip of behaviourism slackened. In practical terms you can indeed start in the mid twentieth century, but that actually overlooks the early structuralist psychologists a hundred years earlier. Wundt is usually credited as the first truly scientific psychologist, though there were others who adopted the same project around the same time. The investigation of consciousness (in the sense of awareness) was central to their work, and some of their results were of real value. Unfortunately, their introspective methods suffered a fatal loss of credibility, which is what precipitated the extreme reaction against consciousness represented by behaviourism, which eventually suffered an eclipse of its own, leaving the way clear for something like a fresh start, the point Michel takes as the real beginning. I think the longer history is worth remembering because it illustrates a pattern in which periods of energetic growth and optimism are followed by dreadful collapses, a pattern still recognisable in the field, perhaps most obviously in AI, but also in the outbreaks of enthusiasm followed by scepticism that have affected research based on fMRI scanning, for example.

In spite of the ‘winters’ affecting those areas, it is surely the advances in technology that have been responsible for the genuine progress recognised by respondents to the survey. Whatever our doubts about scanning, we undeniably know a lot more about neurology now than we did, even if that sometimes serves to reveal new mysteries, like the uncertain function of the newly-discovered ‘rosehip’ neurons. Similarly, though we don’t have conscious robots (and I think almost everyone now has a more mature sense of what a challenge that is), the project of Artificial General Intelligence has reshaped our understanding. I think, for example, that Daniel Dennett is right to argue that exploration of the wider Frame Problem in AI is not just a problem for computer scientists, but tells us about an important aspect of the human mind we had never really noticed before – its remarkable capacity for dealing with relevance and meaning, something that is to the fore in the fascinating recent development of the pragmatics of language, for example.

I was not really surprised to see the Global Workspace theory achieving top popularity in the survey (Bernard Baars perhaps missing out on a deserved hat-tip here); it’s a down-to-earth approach that makes a lot of sense and is relatively easily recruited as an ally of other theoretical insights. That said, it has been around for a while without much in the way of a breakthrough. It was not that much more surprising to see Integrated Information also doing well, though rated higher by non-professionals (Michel shrewdly suggests that they may be especially impressed by the relatively complex mathematics involved).

However, the survey only featured a very short list of contenders which respondents could vote for. The absence of illusionism and quantum theories is acknowledged; myself I would have included at least two schools of sceptical thought; computationalism/functionalism and other qualia sceptics – though it would be easy to lengthen the list. Most surprising, perhaps, is the absence of panpsychism. Whatever you think about it (and regulars will know I’m not a fan), it’s an idea whose popularity has notably grown in recent years and one whose further development is being actively pursued by capable adherents. I imagine the absence of these theories, and others such as mysterianism and the externalism doughtily championed by Riccardo Manzotti and others, is due to their being relatively hard to vindicate neurologically – though supporters might challenge that. Similarly, its robustly scientific neurological basis must account for the inclusion of ‘local recurrence’ – is that the same as recurrent processing?

It’s only fair to acknowledge the impossibility of coming up with a comprehensive taxonomy of views on consciousness which would satisfy everyone. It would be easy to give a list of twenty or more which merely generated a big argument. (Perhaps a good thing to do, then?)

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Could Artificial Intelligence be the tool that finally allows us understand the natural kind?

We’ve talked before about the possibility; this Nautilus piece explains that scientists at the Max Planck Institute Of Neurobiology have come up with a way of using ‘neural’ networks to map, well, neural networks. There has been a continued improvement in our ability to generate images of neurons and their connections, but using those abilities to put together a complete map is a formidable task; the brain has often been described as the most complex object in the universe and drawing up a full schematic of its connections is potentially enough work for a lifetime. Yet that map may well be crucial; recently the idea of a ‘connectome’ roughly equivalent to the ‘genome’ has become a popular concept, one that suggests such a map may be an essential part of understanding the brain and consciousness. The Max Planck scientists have developed an AI, called ‘SyConn’ which tu4ns images into maps automatically with very high accuracy. In principle I suppose this means we could have a complete human connectome in the not-too-distant future.

How much good would it do us, though? It can’t be bad to have a complete map, but there are three big problems. The first is that we can already be pretty sure that connections between neurons are not the whole story. Neurons come in many different varieties, ones that pretty clearly seem to have different functions – but it’s not really clear what they are. They operate with a vast repertoire of neurotransmitters, and are themselves pretty complex entities that may have genuine computational properties all on their own. They are supported by a population of other, non-networked cells that may have a crucial role in how the overall system works. They seem to influence each other in ways that do not require direct connection; through electromagnetic fields, or even through DNA messages. Some believe that consciousness is not entirely a matter of network computation anyway, but resides in quantum or electrical fields; certainly the artificial networks that were originally inspired by biological neurology seem to behave in ways that are useful but quite distinct from those of human cognition. Benjamin Libet thought that if only he could do the experiment, he might be able to demonstrate that a sliver of the brain cut out from its neighbouring tissue but left in situ would continue to do its job. That, surely, is going too far; the brain didn’t grow all those connections with such care for nothing. The connectome may not be the full story, but it has to be important.

The second problem, though, is that we might be going at the problem from the wrong end. A map of the national road network tells us some useful things about trade, but not what it is or, in the deeper sense, how it works. Without those roads, trade would not occur in anything like the same form; blockages and poor connections may hamper or distort it, and in regions isolated by catastrophic, um, road lesions, trade may cease altogether. Of course to understand things properly we should need to know that there are different kinds of vehicle doing different jobs on those roads, that places may be connected by canals and railways as well as roads, and so on. But more fundamentally, if we start with the map, we have no idea what trade really is. It is, in fact, primarily driven and determined by what people want, need, and believe, and if we fall into the trap of thinking that it is wholly determined by the availability of trucks, goods, and roads we shall make a serious mistake.

Third, and perhaps it’s the same problem in different clothes, we still don’t have any clear and accepted idea of how neurology gives rise to consciousness anyway. We’re not anywhere near being able to look at a network and say, yup, that is (or could be) conscious, if indeed it is ever possible to reach such a conclusion.

So do we really even want a map of the connectome? Oh yes…

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We’ve discussed a few times recently how deep learning systems are sometimes inscrutable. They work out how to do things for themselves, and there may be no way to tell what method they are using. I’ve suggested that it’s worse than that; they are likely to be using methods which, though sound, are in principle incomprehensible to human beings. These unknowable methods may well be a very valuable addition to our stock of tools, but without understanding them we can’t be sure they won’t fail; and the worrying thing about that is that unlike humans, who often make small or recoverable mistakes, these systems have a way of failing that is sudden, unforgiving, and catastrophic.

So I was interested to see this short interview with Been Kim, who works for Google Brain (is there a Google Eyes?) has the interesting job of building a system that can explain to humans what the neural networks are up to.

That ought to be impossible, you’d think. If you use the same deep learning techniques to generate the explainer, you won’t be able to understand how it works, and the same worries will recur on another level. The other approach is essentially the old-fashioned one of directly writing an algorithm to do the job; but to write an explicit algorithm to explain system X you surely need to understand system X to begin with?

I suspect there’s an interesting general problem here about the transmission of understanding. Perhaps data can be transferred, but understanding just has to happen, and sometimes, no matter how helpfully the relevant information is transmitted, understanding just fails to occur (I feel teachers may empathise with this).

Suppose the explainer is in place; how do we know that its explanations are correct? We can see over time whether it successfully picks out systems that work reliably and those that are going to fail, but that is only ever going to be a provisional answer. The risk of sudden unexpected failure remains. For real certainty, the only way is for us to validate it by understanding it, and that is off the table.

So is Been Kim wasting her time on a quixotic project – perhaps one that Google has cynically created to reassure the public and politicians while knowing the goal is unattainable? No; her goal is actually a more modest, negative one. Her interpreter is not meant to provide an assurance that a given system is definitely reliable; rather, it is supposed to pick out one’s that are definitely dodgy; and this is much more practical. After all, we may not always understand how a given system executes a particular complex task, but we do know in general how neural networks and deep learning work. We know that the output decisions come from factors in the input data, and the interpreter ought to be able to tell us what factors are being taken into account. Then, using the unique human capacity to identify relevance, we may be able to spot some duds – cases where the system is using a variable that tracks the relevant stuff only unreliable, or where there was some unnoticed problem with the corpus of examples the system learnt from.

Is that OK? Well, in principle there’s the further risk that the system is actually cleverer than we realise; that it is using features (perhaps very complex ones) that actually work fine, but which we’re too dim to grasp. Our best reassurance here is again understanding; if we can see how things seem to be working, we have to be very unlucky to hit a system which is actually superior but just happens, in all the examined cases, to look like a dodgy one. We may not always understand the system, but if we understand something that’s going wrong, we’re probably on firm ground.

Of course, weeding out egregiously unreliable systems does not solve the basic problem of efficient but inscrutable systems. Without accusing Google of a cunning sleight of hand after all, I can well imagine that the legislators and bureaucrats who are gearing up to make rules about this issue might mistake interpreter systems like Kim’s for a solution, require them in all cases, and assume that the job is done and dusted…

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