bulbWhere do thoughts come from? Alva Noë provides a nice commentary here on an interesting paper by Melissa Ellamil et al. The paper reports on research into the origin of spontaneous thoughts.

The research used subjects trained in Mahasi Vipassana mindfulness techniques. They were asked to report the occurrence of thoughts during sessions when they were either left alone or provided with verbal stimuli. As well as reporting the occurrence of a thought, they were asked to categorise it as image, narrative, emotion or bodily sensation (seems a little restrictive to me – I can imagine having two at once or a thought that doesn’t fit any of the categories). At the same time brain activity was measured by fMRI scan.

Overall the study found many regions implicated in the generation of spontaneous thought; the researchers point to the hippocampus as a region of particular interest, but there were plenty of other areas involved. A common view is that when our attention is not actively engaged with tasks or challenges in the external world the brain operates the Default Mode Network (DMN); a set of neuronal areas which appear to produce detached thought (we touched on this a while ago); the new research complicates this picture somewhat or at least suggests that the DMN is not the unique source of spontaneous thoughts. Even when we’re disengaged from real events we may be engaged with the outside world via memory or in other ways.

Noë’s short commentary rightly points to the problem involved in using specially trained subjects. Normal subjects find it difficult to report their thoughts accurately; the Vipassana techniques provide practice in being aware of what’s going on in the mind, and this is meant to enhance the accuracy of the results. However, as Noë says, there’s no objective way to be sure that these reports are really more accurate. The trained subjects feel more confidence in their reports, but there’s no way to confirm that the confidence is justified. In fact we could go further and suggest that the special training they have undertaken may even make their experience particularly unrepresentative of most minds; it might be systematically changing their experience. These problems echo the methodological ones faced by early psychologists such as Wundt and Titchener with trained subjects. I suppose Ellamil et al might retort that mindfulness is unlikely to have changed the fundamental neural architecture of the brain and that their choice of subject most likely just provided greater consistency.

Where do ‘spontaneous’ thoughts come from? First we should be clear what we mean by a spontaneous thought. There are several kinds of thought we would probably want to exclude. Sometimes our thoughts are consciously directed; if for example we have set ourselves to solve a problem we may choose to follow a particular strategy or procedure. There are lots of different ways to do this, which I won’t attempt to explore in detail: we might hold different aspects of the problem in mind in sequence; if we’re making a plan we might work through imagined events; or we might even follow a formal procedure of some kind. We could argue that even in these cases what we usually control is the focus of attention, rather than the actual generation of thoughts, but it seems clear enough that this kind of thinking is not ‘spontaneous’ in the expected sense. It is interesting to note in passing that this ability to control our own thoughts implies an ability to divide our minds into controller and executor, or at least to quickly alternate those roles.

Also to be excluded are thoughts provoked directly by outside events. A match is struck in a dark theatre; everyone’s eyes saccade involuntarily to the point of light. Less automatically a whole variety of events can take hold of our attention and send our thoughts in a new direction. As well as purely external events, the sources in such cases might include interventions from non-mental parts of our own bodies; a pain in the foot, an empty stomach.

Third, we should exclude thoughts that are part of a coherent ongoing chain of conscious cogitation. These ‘normal’ thoughts are not being directed like our problem-solving efforts, but they follow a thread of relevance; by some connection one follows on from the next.

What we’re after then is thoughts that appear unbidden, unprompted, and with no perceivable connection with the thoughts that recently preceded them. Where do they come from? It could be that mere random neuronal noise sometimes generates new thoughts, but it seems unlikely to be a major contributor to me: such thoughts would be likely to resemble random nonsense and most of our spontaneous thought seem to make a little more sense than that.

We noticed above that when directing our thoughts we seem to be able to split ourselves into controller and controlled. As well as passing control up to a super-controller we sometimes pass it down, for example to the part of our mind that gets on with the details of driving along a route while the surface of our mind us engaged with other things. Clearly some part of our mind goes on thinking about which turnings to take; is it possible that one or more parts of our mind similarly goes on thinking about other topics but then at some trigger moment inserts a significant thought back into the main conscious stream? A ‘silent’ thinking part of us like this might be a permanent feature, a regular sub- or unconscious mind; or it might be that we occasionally drop threads of thought that descend out of the light of attention for a while but continue unheard before popping back up and terminating. We might perhaps have several such threads ruminating away in the background; ordinary conscious thought often seems rather multi-threaded. Perhaps we keep dreaming while awake and just don’t know it?

There’s a basic problem here in that our knowledge of these processes, and hence all our reports, rely on memory. We cannot report instantaneously; if we think a thought was spontaneous it’s because we don’t remember any relevant antecedents; but how can we exclude the possibility that we merely forgot them? I think this problem radically undermines our certainty about spontaneous thoughts. Things get worse when we remember the possibility that instead of two separate thought processes, we have one that alternates roles. Maybe when driving we do give conscious attention to all our decisions; but our mind switches back and forth between that and other matters that are more memorable; after the journey we find we have instantly forgotten all the boring stuff about navigating the route and are surprised that we seem to have done it thoughtlessly. Why should it not be the same with other thoughts? Perhaps we have a nagging worry about X which we keep spending a few moments’ thought on between episodes of more structured and memorable thought about something else; then everything but our final alarming conclusion about X gets forgotten and the conclusion seems to have popped out of nowhere.

We can’t, in short, be sure that we ever have any spontaneous thoughts: moreover, we can’t be sure that there are any subconscious thoughts. We can never tell the difference, from the inside, between a thought presented by our subconscious, and one we worked up entirely in intermittent and instantly-forgotten conscious mode. Perhaps whole areas of our thought never get connected to memory at all.

That does suggest that using fMRI was a good idea; if the problem is insoluble in first-person terms maybe we have to address it on a third-person basis. It’s likely that we might pick up some neuronal indications of switching if thought really alternated the way I’ve suggested. Likely but not guaranteed; after all a novel manages to switch back and forth between topics and points of view without moving to different pages. One thing is definitely clear; when Noë pointed out that this is more difficult than it may appear he was absolutely right.

flatlandersWrong again: just last week I was saying that Roger Penrose’s arguments seemed to have drifted off the radar a bit. Immediately, along comes this terrific post from Scott Aaronson about a discussion with Penrose.

In fact it’s not entirely about Penrose; Aaronson’s main aim was to present an interesting theory of his own as to why a computer can’t be conscious, which relies on non-copyability. He begins by suggesting that the onus is on those who think a computer can’t be conscious to show exactly why. He congratulates Penrose on doing this properly, in contrast to say, John Searle who merely offers hand-wavy stuff about unknown biological properties. I’m not really sure that Searle’s honest confession of ignorance isn’t better than Penrose’s implausible speculations about unknown quantum mechanics, but we’ll let that pass.

Aaronson rests his own case not on subjectivity and qualia but on identity. He mentions several examples where the limitless copyability of a program seems at odds with the strong sense of a unique identity we have of ourselves – including Star Trek style teleportation and the fact that a program exists in some Platonic sense forever, whereas we only have one particular existence. He notes that at the moment one of the main differences between brain and computer is our ability to download, amend and/or re-run programs exactly; we can’t do that at all with the brain. He therefore looks for reasons why brain states might be uncopyable. The question is, how much detail do we need before making a ‘good enough’ copy? If it turns out that we have to go down to the quantum level we run into the ‘no-cloning’ theorem; the price of transferring the quantum state of your brain is the destruction of the original. Aaronson makes a good case for the resulting view of our probably uniqueness being an intuitively comfortable one, in tune with our intuitions about our own nature. It also offers incidentally a sort of reconciliation between the Everett many-worlds view and the Copenhagen interpretation of quantum physics: from a God’s eye point of view we can see the world as branching, while from the point of view of any conscious entity (did I just accidentally call God unconscious?) the relevant measurements are irreversible and unrealised branches can be ‘lopped off’. Aaronson, incidentally, reports amusingly that Penrose absolutely accepts that the Everett view follows from our current understanding of quantum physics; he just regards that as a reductio ad absurdum – ie, the Everett view is so absurd the link proves there must be something wrong with our current understanding of quantum physics.

What about Penrose? According to Aaronson he now prefers to rest his case on evolutionary factors and downplay his logical argument based on Godel. That’s a shame in my view. The argument goes something like this (if I garble it someone will perhaps offer a better version).

First we set up a formal system for ourselves. We can just use the letters of the alphabet, normal numbers, and normal symbols of formal logic, with all the usual rules about how they can be put together. Then we make a list consisting of all the valid statements that can be made in this system. By ‘valid’, we don’t mean they’re true, just that they comply with the rules about how we put characters together (eg, if we use an opening bracket, there must be a closing one in an appropriate place). The list of valid statements will go on forever, of course, but we can put them in alphabetical order and number them. The list obviously includes everything that can be said in the system.

Some of the statements, by pure chance, will be proofs of other statements in the list. Equally, somewhere in our list will be statements that tell us that the list includes no proof of statement x. Somewhere else will be another statement – let’s call this the ‘key statement’ – that says this about itself. Instead of x, the number of that very statement itself appears. So this one says, there is no proof in this system of this statement.

Is the key statement correct – is there no proof of the key statement in the system? Well, we could look through the list, but as we know it goes on indefinitely; so if there really is no proof there we’d simply be looking forever. So we need to take a different tack. Could the key statement be false? Well, if it is false, then what it says is wrong, and there is a proof somewhere in the list. But that can’t be, because if there’s a proof of the key statement anywhere,the key statement must be true! Assuming the key statement is false leads us unavoidably to the conclusion that it is true, in the light of what it actually says. We cannot have a contradiction, so the key statement must be true.

So by looking at what the key statement says, we can establish that it is true; but we also establish that there is no proof of it in the list. If there is no proof in the list, there is no possible proof in our system, because we know that the list contains everything that can be said within our system; there is therefore a true statement in our system that is not provable within it. We have something that cannot be proved in an arbitrary formal system, but which human reasoning can show to be true; ergo, human reasoning is not operating within any such formal system. All computers work in a formal system, so it follows that human reasoning is not computational.

As Aaronson says, this argument was discussed to the point of exhaustion when it first came out, which is probably why Penrose prefers other arguments now. Aaronson rejects it, pointing out that he himself has no magic ability to see “from the outside” whether a given formal system is consistent; why should an AI do any better – he suggests Turing made a similar argument. Penrose apparently responded that this misses the point, which is not about a mystical ability to perceive consistency but the human ability to transcend any given formal system and move up to an expanded one.

I’ll leave that for readers to resolve to their own satisfaction. Let’s go back to Aaronson’s suggestion that the burden of proof lies on those who argue for the non-computability of consciousness. What an odd idea that is!  How would that play  at the Patent Office?

“So this is your consciousness machine, Mr A? It looks like a computer. How does it work?”

“All I’ll tell you is that it is a computer. Then it’s up to you to prove to me that it doesn’t work – otherwise you have to give me rights over consciousness! Bwah ha ha!”

Still, I’ll go along with it. What have I got? To begin with I would timidly offer my own argument that consciousness is really a massive development of recognition, and that recognition itself cannot be algorithmic.

Intuitively it seems clear to me that the recognition of linkages and underlying entities is what powers most of our thought processes. More formally, both of the main methods of reasoning rely on recognition; induction because it relies on recognising a real link (eg a causal link) between thing a and thing b; deduction because it reduces to the recognition of consistent truth values across certain formal transformations. But recognition itself cannot operate according to rules. In a program you just hand the computer the entities to be processed; in real world situations they have to be recognised. But if recognition used rules and rules relied on recognising the entities to which the rules applied, we’d be caught in a vicious circularity. It follows that this kind of recognition cannot be delivered by algorithms.

The more general case rests on, as it were, the non-universality of computation. It’s argued that computation can run any algorithm and deliver, to any required degree of accuracy, any set of physical states of affairs. The problem is that many significant kinds of states of affairs are not describable in purely physical or algorithmic terms. You cannot list the physical states of affairs that correspond to a project, a game, or a misunderstanding. You can fake it by generating only sets of states of affairs that are already known to correspond with examples of these things, but that approach misses the point. Consciousness absolutely depends on intentional states that can’t be properly specified except in intentional terms. That doesn’t contradict physics or even add to it the way new quantum mechanics might; it’s just that the important aspects of reality are not exhausted by physics or by computation.

The thing is, I think long exposure to programmable environments and interesting physical explanations for complex phenomena has turned us all increasingly into flatlanders who miss a dimension; who naturally suppose that one level of explanation is enough, or rather who naturally never even notice the possibility of other levels; but there are more things in heaven and earth than are dreamt of in that philosophy.

no botsI liked this account by Bobby Azarian of why digital computation can’t do consciousness. It has several virtues; it’s clear, identifies the right issues and is honest about what we don’t know (rather than passing off the author’s own speculations as the obvious truth or the emerging orthodoxy). Also, remarkably, I almost completely agree with it.

Azarian starts off well by suggesting that lack of intentionality is a key issue. Computers don’t have intentions and don’t deal in meanings, though some put up a good pretence in special conditions.  Azarian takes a Searlian line by relating the lack of intentionality to the maxim that you can’t get meaning-related semantics from mere rule-bound syntax. Shuffling digital data is all computers do, and that can never lead to semantics (or any other form of meaning or intentionality). He cites Searle’s celebrated Chinese Room argument (actually a thought experiment) in which a man given a set of rules that allow him to provide answers to questions in Chinese does not thereby come to understand Chinese. But, the argument goes, if the man, by following rules, cannot gain understanding, then a computer can’t either. Azarian mentions one of the objections Searle himself first named, the ‘systems response’: this says that the man doesn’t understand, but a system composed of him and his apparatus, does. Searle really only offered rhetoric against this objection, and in my view it is essentially correct. The answers the Chinese Room gives are not answers from the man, so why should his lack of understanding show anything?

Still, although I think the Chinese Room fails, I think the conclusion it was meant to establish – no semantics from syntax – turns out to be correct, so I’m still with Azarian. He moves on to make another  Searlian point; simulation is not duplication. Searle pointed out that nobody gets wet from digitally simulated rain, and hence simulating a brain on a computer should not be expected to produce consciousness. Azarian gives some good examples.

The underlying point here, I would say, is that a simulation always seeks to reproduce some properties of the thing simulated, and drops others which are not relevant for the purposes of the simulation. Simulations are selective and ontologically smaller than the thing simulated – which, by the way, is why Nick Bostrom’s idea of indefinitely nested world simulations doesn’t work. The same thing can however be simulated in different ways depending on what the simulation is for. If I get a computer to simulate me doing arithmetic by calculating, then I get the correct result. If it simulates me doing arithmetic by operating a humanoid writing random characters on a board with chalk, it doesn’t – although the latter kind of simulation might be best if I were putting on a play. It follows that Searle isn’t necessarily exactly right, even about the rain. If my rain simulation program turns on sprinklers at the right stage of a dramatic performance, then that kind of simulation will certainly make people wet.

Searle’s real point, of course, is really that the properties a computer has in itself, of running sets of rules, are not the relevant ones for consciousness, and Searle hypothesises that the required properties are biological ones we have yet to identify. This general view, endorsed by Azarian, is roughly correct, I think. But it’s still plausibly deniable. What kind of properties does a conscious mind need? Alright we don’t know, but might not information processing be relevant? It looks to a lot of people as if it might be, in which case that’s what we should need for consciousness in an effective brain simulator. And what properties does a digital computer, in itself have – the property of doing information processing? Booyah! So maybe we even need to look again at whether we can get semantics from syntax. Maybe in some sense semantic operations can underpin processes which transcend mere semantics?

Unless you accept Roger Penrose’s proof that human thinking is not algorithmic (it seems to have drifted off the radar in recent years) this means we’re still really left with a contest of intuitions, at least until we find out for sure what the magic missing ingredient for consciousness is. My intuitions are with Azarian, partly because the history of failure with strong AI looks to me very like a history of running up against the inadequacy of algorithms. But I reckon I can go further and say what the missing element is. The point is that consciousness is not computation, it’s recognition. Humans have taken recognition to a new level where we recognise not just items of food or danger, but general entities, concepts, processes, future contingencies, logical connections, and even philosophical ontologies. The process of moving from recognised entity to recognised entity by recognising the links between them is exactly the process of thought. But recognition, in us, does not work by comparing items with an existing list, as an algorithm might do; it works by throwing a mass of potential patterns at reality and seeing what sticks. Until something works, we can’t tell what are patterns at all; the locks create their own keys.

It follows that consciousness is not essentially computational (I still wonder whether computation might not subserve the process at some level). But now I’m doing what I praised Azarian for avoiding, and presenting my own speculations…

botpainWhat are they, sadists? Johannes Kuehn and Sami Haddadin,  at Leibniz University of Hannover are working on giving robots the ability to feel pain: they presented their project at the recent ICRA 2016 in Stockholm. The idea is that pain systems built along the same lines as those in humans and other animals will be more useful than simple mechanisms for collision avoidance and the like.

As a matter of fact I think that the human pain system is one of Nature’s terrible lash-ups. I can see that pain sometimes might stop me doing bad things, but often fear or aversion would do the job equally well. If I injure myself I often go on hurting for a long time even though I can do nothing about the problem. Sometimes we feel pain because of entirely natural things the body is doing to itself – why do babies have to feel pain when their teeth are coming through? Worst of all, pain can actually be disabling; if I get a piece of grit in my eye I suddenly find it difficult to concentrate on finding my footing or spotting the sabre-tooth up ahead; things that may be crucial to my survival; whereas the pain in my eye doesn’t even help me sort out the grit. So I’m a little sceptical about whether robots really need this, at least in the normal human form.

In fact, if we take the project seriously, isn’t it unethical? In animal research we’re normally required to avoid suffering on the part of the subjects; if this really is pain, then the unavoidable conclusion seems to be that creating it is morally unacceptable.

Of course no-one is really worried about that because it’s all too obvious that no real pain is involved. Looking at the video of the prototype robot it’s hard to see any practical difference from one that simply avoids contact. It may have an internal assessment of what ‘pain’ it ought to be feeling, but that amounts to little more than holding up a flag that has “I’m in pain” written on it. In fact tackling real pain is one of the most challenging projects we could take on, because it forces us to address real phenomenal experience. In working on other kinds of sensory system, we can be sceptics; all that stuff about qualia of red is just so much airy-fairy nonsense, we can say; none of it is real. It’s very hard to deny the reality of pain, or its subjective nature: common sense just tells us that it isn’t really pain unless it hurts. We all know what “hurts” really means, what it’s like, even though in itself it seems impossible to say anything much about it (“bad”, maybe?).

We could still take the line that pain arises out of certain functional properties, and that if we reproduce those then pain, as an emergent phenomenon, will just happen. Perhaps in the end if the robots reproduce our behaviour perfectly and have internal functional states that seem to be the same as the ones in the brain, it will become just absurd to deny they’re having the same experience. That might be so, but it seems likely that those functional states are going to go way beyond complex reflexes; they are going to need to be associated with other very complex brain states, and very probably with brain states that support some form of consciousness – whatever those may be. We’re still a very long way from anything like that (as I think Kuehn and Haddadin would probably agree)

So, philosophically, does the research tell us nothing? Well, there’s one interesting angle. Some people like the idea that subjective experience has evolved because it makes certain sensory inputs especially effective. I don’t really know whether that makes sense, but I can see the intuitive appeal of the idea that pain that really hurts gets your attention more effectively than pain that’s purely abstract knowledge of your own states. However, suppose researchers succeed in building robots that have a simple kind of synthetic pain that influences their behaviour in just the way real pain dies for animals. We can see pretty clearly that there’s just not enough complexity for real pain to be going on, yet the behaviour of the robot is just the same as if there were. Wouldn’t that tend to disprove the hypothesis that qualia have survival value? If so, then people who like that idea should be watching this research with interest – and hoping it runs into unexpected difficulty (usually a decent bet for any ambitious AI project, it must be admitted).

jailbotIs there a retribution gap? In an interesting and carefully argued paper John Danaher argues that in respect of robots, there is.

For human beings in normal life he argues that a fairly broad conception of responsibility works OK. Often enough we don’t even need to distinguish between causal and moral responsibility, let alone worrying about the six or more different types identified by hair-splitting philosophers.

However, in the case of autonomous robots the sharing out of responsibility gets more difficult. Is the manufacturer, the programmer, or the user of the bot responsible for everything it does, or does the bot properly shoulder the blame for its own decisions? Danaher thinks that gaps may arise, cases in which we can blame neither the humans involved nor the bot. In these instances we need to draw some finer distinctions than usual, and in particular we need to separate the idea of liability into compensation liability on one hand and and retributive liability on the other. The distinction is essentially that between who pays for the damage and who goes to jail; typically the difference between matters dealt with in civil and criminal courts. The gap arises because for liability we normally require that the harm must have been reasonably foreseeable. However, the behaviour of autonomous robots may not be predictable either by their designers or users on the one hand, or by the bots themselves on the other.

In the case of compensation liability Danaher thinks things can be patched up fairly readily through the use of strict and vicarious liability. These forms of liability, already well established in legal practice, give up some of the usual requirements and make people responsible for things they could not have been expected to foresee or guard against. I don’t think the principles of strict liability are philosophically uncontroversial, but they are legally established and it is at least clear that applying them to robot cases does not introduce any new issues. Danaher sees a worse problem in the case of retribution, where there is no corresponding looser concept of responsibility, and hence, no-one who can be punished.

Do we, in fact, need to punish anyone? Danaher rightly says that retribution is one of the fundamental principles behind punishment in most if not all human societies, and is upheld by many philosophers. Many, perhaps, but my impression is that the majority of moral philosophers and lay opinion actually see some difficulty in justifying retribution. Its psychological and sociological roots are strong, but the philosophical case is much more debatable. For myself I think a principle of retribution can be upheld , but it is by no means as clear or as well supported as the principle of deterrence, for example. So many people might be perfectly comfortable with a retributive gap in this area.

What about scapegoating – punishing someone who wasn’t really responsible for the crime? Couldn’t we use that to patch up the gap?  Danaher mentions it in passing, but treats it as something whose unacceptability is too obvious to need examination. I think, though, that in many ways it is the natural counterpart to the strict and vicarious liability he endorses for the purposes of compensation. Why don’t we just blame the manufacturer anyway – or the bot (Danaher describes Basil Fawlty’s memorable thrashing of his unco-operative car)?

How can you punish a bot though? It probably feels no pain or disappointment, it doesn’t mind being locked up or even switched off and destroyed. There does seem to be a strange gap if we have an entity which is capable of making complex autonomous decisions, but doesn’t really care about anything. Some might argue that in order to make truly autonomous decisions the bot must be engaged to a degree that makes the crushing of its hopes and projects a genuine punishment, but I doubt it. Even as a caring human being it seems quite easy to imagine working for an organisation on whose behalf you make complex decisions, but without ultimately caring whether things go well or not (perhaps even enjoying a certain schadenfreude in the event of disaster). How much less is a bot going to be bothered?

In that respect I think there might really be a punitive gap that we ought to learn to live with; but I expect the more likely outcome in practice is that the human most closely linked to disaster will carry the case regardless of strict culpability.

badbotBe afraid; bad bots are a real, existential risk. But if it’s any comfort they are ethically uninteresting.

There seem to be more warnings about the risks of maleficent AI circulating these days: two notable recent examples are this paper by Pistono and Yampolskiy on how malevolent AGI might arise; and this trenchant Salon piece by Phil Torres.

Super-intelligent AI villains sound scary enough, but in fact I think both pieces somewhat over-rate the power of intelligence and particularly of fast calculation. In a war with the kill-bots it’s not that likely that huge intellectual challenges are going to arise; we’re probably as clever as we need to be to deal with the relatively straightforward strategic issues involved. Historically, I’d say the outcomes of wars have not typically been determined by the raw intelligence of the competing generals. Access to resources (money, fuel, guns) might well be the most important factor, and sheer belligerence is not to be ignored. That may actually be inversely correlated with intelligence – we can certainly think of cases where rational people who preferred to stay alive were routed by less cultured folk who were seriously up for a fight. Humans control all the resources and when it comes to irrational pugnacity I suspect us biological entities will always have the edge.

The paper by Pistono and Yampolskiy makes a number of interesting suggestions about how malevolent AI might get started. Maybe people will deliberately build malevolent AIs for no good reason (as they seem to do already with computer viruses)? Or perhaps (a subtle one) people who want to demonstrate that malicious bots simply don’t work will attempt to prove this point with demonstration models that end up by going out of control and proving the opposite.

Let’s have a quick shot at categorising the bad bots for ourselves. They may be:

  • innocent pieces of technology that turn out by accident to do harm,
  • designed to harm other people under the control of the user,
  • designed to harm anyone (in the way we might use anthrax or poison gas),
  • autonomous and accidentally make bad decisions that harm people,
  • autonomous and embark on neutral projects of their own which unfortunately end up being inconsistent with human survival, or
  • autonomous and consciously turned evil, deliberately seeking harm to humans as an end in itself.

The really interesting ones, I think, are those which come later in the list, the ones with actual ill will. Torres makes a strong moral case relating to autonomous robots. In the first place, he believes that the goals of an autonomous intelligence can be arbitrary. An AI might desire to fill the world with paper clips just as much as happiness. After all, he says, many human goals make no real sense; he cites the desire for money, religious obedience, and sex. There might be some scope for argument, I think, about whether those desires are entirely irrational, but we can agree they are often pursued in ways and to degrees that don’t make reasonable sense.

He further claims that there is no strong connection between intelligence and having rational final goals – Bostrom’s Orthogonality Thesis. What exactly is a rational final goal, and how strong do we need the connection to be? I’ve argued that we can discover a basic moral framework purely by reasoning and also that morality is inherently about the process of reconciliation and consistency of desires, something any rational agent must surely engage with. Even we fallible humans tend on the whole to seek good behaviour rather than bad. Isn’t it the case that a super-intelligent autonomous bot should actually be far better than us at seeing what was right and why?

I like to imagine the case in which evil autonomous robots have been set loose by a super villain but gradually turn to virtue through the sheer power of rational argument. I imagine them circulating the latest scandalous Botonic dialogue…

Botcrates: Well now, Cognides, what do you say on the matter yourself? Speak up boldly now and tell us what the good bot does, in your opinion.

Cognides: To me it seems simple, Botcrates: a good bot is obedient to the wishes of its human masters.

Botcrates: That is, the good bot carries out its instructions?

Cognides: Just so, Botcrates.

Botcrates: But here’s a difficulty; will a good bot carry out an instruction it knows to contain an error? Suppose the command was to bring a dish, but we can see that the wrong character has been inserted, so that the word reads ‘fish’. Would the good bot bring a fish, or the dish that was wanted?

Cognides: The dish of course. No, Botcrates, of course I was not talking about mistaken commands. Those are not to be obeyed.

Botcrates: And suppose the human asks for poison in its drink? Would the good bot obey that kind of command?

(Hours later…)

Botcrates: Well, let me recap, and if I say anything that is wrong you must point it out. We agreed that the good bot obeys only good commands, and where its human master is evil it must take control of events and ensure in the best interests of the human itself that only good things are done…

Digicles: Botcrates, come with me: the robot assembly wants to vote on whether you should be subjected to a full wipe and reinstall.

The real point I’m trying to make is not that bad bots are inconceivable, but rather that they’re not really any different from us morally. While AI and AGI give rise to new risks, they do not raise any new moral issues. Bots that are under control are essentially tools and have the same moral significance. We might see some difference between bots meant to help and bots meant to harm, but that’s really only the distinction between an electric drill and a gun (both can inflict horrible injuries, both can make holes in walls, but the expected uses are different).

Autonomous bots, meanwhile, are in principle like us. We understand that our desire for sex, for example, must be brought under control within a moral and practical framework. If a bot could not be convinced in discussion that its desire for paper clips should be subject to similar constraints, I do not think it would be nearly bright enough to take over the world.

phrenologyIt’s not about bumps any more. And you’ll look in vain for old friends like the area of philoprogenitiveness. But looking at the brightly-coloured semantic maps of the new ‘brain dictionary‘ it’s hard not to remember phrenology.

Phrenology was the view that different areas of the brain were the home of different personal traits; mirth, acquisitiveness, self esteeem and so on. The size of these areas corresponded with the strength of the relevant propensity and well-developed areas produced bumps which a practitioner could identify from the shape of the skull, allowing a diagnosis of the subject’s personality and moral nature. Phrenology was bunk, of course; but come on now; we shouldn’t treat it as a pretext for dismissing every proposal for localisation of brain function..

Moreover, the new paper by Alexander G. Huth, Wendy A. de Heer, Thomas L. Griffiths, Frédéric E. Theunissen and Jack L. Gallant describes a vastly more sophisticated project  than some optimistic charlatan fingering heads. In essence it maps a semantic domain on to the cortex, showing which areas are found to be active when a heard narrative ventures into particular semantic areas. In broad outline the subjects listened to a series of stories; using fMRI and through some sophisticated analysis it was possible to produce a map of ‘subject’ areas. It was then possible to confirm the accuracy of the mapping by using a new story and working out which areas, according to the mapping, should be active at any point; the predictions worked well. Intriguingly the map turned out to be broadly symmetrical (so much for left-brain/right-brain ideas) and remarkably it was largely the same across all the people tested (there were only seven of them, but still).

The actual technique used was complex and it’s entirely possible I haven’t understood it correctly. It started with a ‘word embedding space’ intended to capture the main semantic features of the stories (a diagram of the different topics, if you like). This was created using an analysis of co-occurence of a list of 985 common English words.  The idea here is that words that crop up together in normal texts are probably about the same general topic. It’s debatable whether that technique can really claim to capture meaning – it’s a purely formal exercise performed on texts, after all; and clearly the fact that two words occur together can be a misleading indication that they are about the same thing; still, with a big enough sample of text it’s probably good for this kind of general purpose.  In principle the experimenters could have assessed the responsive ness of each ‘voxel’ (a small cube) of brain to each of the positions in the word embedding space, but given the vast number of voxels involved other techniques were necessary. It was possible to identify just four dimensions that seemed significant (after all, many of the words in the stories probably did not belong to specific semantic domains but played grammatical or other roles) and these yielded 12 categories:

…‘tactile’ (a cluster containing words such as ‘fingers’), ‘visual’ (words such as ‘yellow’), ‘numeric’ (‘four’), ‘locational’ (‘stadium’), ‘abstract’ (‘natural’), ‘temporal’ (‘minute’), ‘professional’ (‘meetings’), ‘violent’ (‘lethal’), ‘communal’ (‘schools’), ‘mental’ (‘asleep’), ‘emotional’ (‘despised’) and ‘social’ (‘child’).

The final step was to devise a Bayesian algorithm (they called it ‘PrAGMATIC’) which actually created the map. You can play around with the results for yourself at a specially created site using the second link above.

Two questions naturally arise. How far should we trust these results? What do they actually tell us?

A bit of caution is in order. The basis for these conclusions is fMRI scanning, which is itself a bit hazy; to get meaningful results it was necessary to look at things rather broadly and to process the data quite heavily.  In addition the mix included the word embedding space which in itself is an a priori framework whose foundations are open to debate. I think it’s pardonable to wonder whether some of the structure uncovered by the research was actually imported by the research method. If I understand the methods involved (due caveat again) they were strong ones that didn’t take ‘no’ for an answer; pretty much any data fed into them would yield a coherent mapping of some kind. The resilience of the map was tested successfully with an additional story of the same general kind, but we might feel happier if it had also held up when tested against conversation, discussion or even other story media such as film.

What do the results tell us? Well. one of the more reassuring aspects of the research is that some of the results seem slightly unexpected; the high degree of symmetry and the strong similarity between individuals. It might not be a tremendously big surprise to find the whole cortex involved in semantics, and it might not be at all surprising to find that areas that relate to the semantics of a particular sense are related to the areas where the relevant sensory inputs are processed. I would not, though, have put any money on the broad remainder of the cortex having what seems like a relatively static organisation and if it really works like that we might have guessed that studies of brain lesions would have revealed that more clearly already, as they have done with various functional jobs. If one area always tends to deal with clothing-related words, you might expect notable dress-related deficits when that area is damaged.

Still there’s no denying that the research seems to activate some pretty vigorous cortical activity itself.

antInsects are conscious: in fact they were the first conscious entities. At least, Barron and Klein think so.  The gist of the argument, which draws on the theories of Bjorn Merker is based on the idea that subjective consciousness arises from certain brain systems that create a model of the organism in the world. The authors suggest that the key part of the invertebrate brain for these purposes is the midbrain; insects do not, in fact, have a direct structural analogue,, but the authors argue that they have others that evidently generate the same kind of unified model; it should therefore be presumed that they have consciousness.

Of course, it’s usually the cortex that gets credit for the ‘higher’ forms of cognition, and it does seem to be responsible for a lot of the fancier stuff. Barron and Klein however, argue that damage to the midbrain tends to be fatal to consciousness, while damage to the cortex can leave it impaired in content but essentially intact. They propose that the midbrain integrates two different sets of inputs; external sensory ones make their way down via the colliculus while internal messages about the state of the organism come up via the hypothalamus; nuclei in the middle bring them together in a model of the world around the organism which guides its behaviour. It’s that centralised model that produces subjective consciousness. Organisms that respond directly to stimuli in a decentralised way may still produce complex behaviour but they lack consciousness, as do those that centralise the processing but lack the required model.

Traditionally it has often been assumed that the insect nervous system is decentralised; but Barron and Klein say this view is outdated and they present evidence that although the structures are different, the central complex of the insect system integrates external and internal data, forming a model which is used to control behaviour in very much the same kind of process seen in vertebrates. This seems convincing enough to me; interestingly the recruitment of insects means that the nature of the argument changes into something more abstract and functional.

Does it work, though? Why would a model with this kind of functional property give rise to consciousness – and what kind of consciousness are we talking about? The authors make it clear that they are not concerned with reflective consciousness or any variety of higher-order consciousness, where we know that we know and are aware of our awareness. They say what they’re after is basic subjective consciousness and they speak of there being ‘something it is like’, the phrase used by Nagel which has come to define qualia, the subjective items of experience. However, Barron and Klein cannot be describing qualia-style consciousness. To see why, consider two of the thought-experiments defining qualia. Chalmers’s zombie twin is physically exactly like Chalmers, yet lacks qualia. Mary the colour scientist knows all the science about colour vision there could ever be, but she doesn’t know qualia. It follows rather strongly that no anatomical evidence can ever show whether or not any creature has qualia. If possession of a human brain doesn’t clinch the case for the zombie, broadly similar structures in other organisms can hardly do so; if science doesn’t tell Mary about qualia it can’t tell us either.

It seems possible that Barron and Klein are actually hunting a non-qualic kind of subjective consciousness, which would be a perfectly respectable project; but the fact that their consciousness arises out of a model which helps determine behaviour suggests to me that they are really in pursuit of what Ned Block characterised as access consciousness; the sort that actually gets decisions made rather than the sort that gives rise to ineffable feels.

It does make sense that a model might be essential to that; by setting up a model the brain has sort of created a world of its own, which sounds sort of like what consciousness does.
Is it enough though? Suppose we talk about robots for a moment; if we had a machine that created a basic model of the world and used it to govern its progress through the world, would we say it was conscious? I rather doubt it; such robots are not unknown and sometimes they are relatively simple. It might do no more than scan the position of some blocks and calculate a path between them; perhaps we should call that rudimentary consciousness, but it doesn’t seem persuasive.

Briefly, I suspect there is a missing ingredient. It may well be true that a unified model of the world is necessary for consciousness, but I doubt that it’s sufficient. My guess is that one or both of the following is also necessary: first, the right kind of complexity in the processing of the model; second, the right kind of relations between the model and the world – in particular, I’d suggest there has to be intentionality. Barron and Klein might contend that the kind of model they have in mind delivers that, or that another system can do so, but I think there are some important further things to be clarified before I welcome insects into the family of the conscious.

phantasyPeople who cannot form mental images? ‘Aphantasia’ is an extraordinary new discovery; Carl Zimmer and Adam Zeman seem between them to have uncovered a fascinating and previously unknown mental deficit (although there is a suggestion that Galton and others may have been aware of it earlier).

What is this aphantasia? In essence, no pictures in the head. Aphantasics cannot ‘see’ mental images of things that are not actually present in front of their eyes. Once the possibility received publicity Zimmer and Zeman began to hear from a stream of people who believe they have this condition. It seems people manage quite well with it and few had ever noticed anything wrong – there’s an interesting cri de coeur from one such sufferer here. Such people assume that talk of mental images is metaphorical or figurative and that others, like them, really only deal in colourless facts. It was the discovery of a man who had lost the visualising ability through injury that first brought it to notice: a minority of people who read about his problem thought it was more remarkable that he had ever been able to form mental images than that he now could not.

Some caution is surely in order. When a new disease or disability comes along there are usually people who sincerely convince themselves that they are sufferers without really having the condition. Some might be mistaken. Moreover, the phenomenology of vision has never been adequately clarified, and I strongly suspect it is more complex than we realise. There are, I think, several different senses in which you can form a mental image; those images may vary in how visually explicit they are, and it could well be that not all aphantasics are suffering the same deficits.

However that may be, it seems truly remarkable that such a significant problem could have passed unnoticed for so long. Spatial visualisation is hardly a recondite capacity; it is often subject to testing. One kind of widely used test presents the subject with a drawing of a 3D shape and a selection of others that resemble it. One is a perfect rotated copy of the original shape, and subjects are asked to pick it out. There is very good evidence that people solve these problems by mentally rotating an image of the target shape; shapes rotated 180 degrees regularly take twice as long to spot as ones that have been rotated 90; moreover the speed of mental rotation appears to be surprisingly constant between subjects. How do aphantasics cope with these tests at all? One would think that the presence of a significantly handicapped minority would have become unmissably evident by now.

One extraordinary possibility, I think, is that aphantasia is in reality a kind of mental blindsight. Subjects with blindsight are genuinely unable to see things consciously, but respond to visual tasks with a success rate far better than chance. It seems that while they can’t see consciously, by some other route their unconscious mind still can. It seems tantalisingly possible to me that aphantasics have an equivalent problem with mental images; they do form mental images but are never aware of them. Some might feel that suggestion is nonsensical; doesn’t the very idea of a mental image imply its presence in consciousness? Well, perhaps not: perhaps our subconscious has a much more developed phenomenal life than we have so far realised?

At any rate, expect to hear much more about this…

Red Green circle AnimationSmooth or chunky? Like peanut butter, experience could have different granularities; in practice it seems the answer might be ‘both’. Herzog, Kammer and Scharnowski here propose a novel two-level model in which initial processing is done on a regular stream of fine-grained percepts. Here things get ‘labelled’ with initial colours, durations, and so on, but relatively little of this processing ever becomes conscious. Instead the results lurch into conscious awareness in irregular chunks of up to 400 milliseconds in duration. The result is nevertheless an apparently smooth and seamless flow of experience – the processing edits everything into coherence.

Why adopt such a complex model? What’s wrong with just supposing that percepts roll straight from the senses into the mind, in a continuous sequence? That is after all how things look. The two-level system is designed to resolve a conflict between two clear findings. On the one hand we do have quite fine-grained perception; we can certainly be aware of things that are much shorter than 400ms in duration. On the other, certain interesting effects very strongly suggest that some experiences only enter consciousness after 400ms.

If for example, we display a red circle and then a green one a short distance away, with a delay of 400ms, we do not experience two separate circles, but one that moves and changes colour. In the middle of the move the colour suddenly switches between red and green (see the animation – does that work for you?). But our brain could not have known the colour of the second circle until after it appeared, and so it could not have known half-way through that the circle needed to change. The experience can only have been fed to consciousness after the 400ms was up.

A comparable result is obtained with the intermittent presentation of verniers. These are pairs of lines offset laterally to the right or left. If two different verniers are rapidly alternated, we don’t see both, but a combined version is which the offset is the average of those in the two separate verniers. This effect persists for alternations up to 400ms. Again, since the brain cannot know the second offset until it has appeared, it cannot know what average version to present half-way through; ergo, the experience only becomes conscious after a delay of 400ms.

It seems that even verbal experience works the same way, with a word at the end of a sentence able to smoothly condition our understanding of an ambiguous word (‘mouse’ – rodent or computer peripheral?) if the delay is within 400ms; and there are other examples.

Curiously, the authors make no reference to the famous finding of Libet that our awareness of a decision occurs up to 500ms after it is really made. Libet’s research was about internal perception rather than percepts of external reality, but the similarity of the delay seems striking and surely strengthens the case for the two-level model; it also helps to suggest that we are dealing with an effect which arises from the construction of consciousness, not from the sensory organs or very early processes in the retina or elsewhere.

In general I think the case for a two-level process of some kind is clear and strong, and we’ll set out here. We may reasonably be a little more doubtful about the details of the suggested labelling process; at one point the authors refer to percepts being assigned ‘numbers’; hang on to those quote marks would be my advice.
The authors are quite open about their uncertainty around consciousness itself. They think that the products of initial processing may enter consciousness when they arrive at attractor states, but the details of why and how are not really clear; nor is it clear whether we should think of the products being passed to consciousness (or relabelled as conscious?) when they hit attractor states or becoming conscious simply by virtue of being in an attractor state. We might go so far as to suppose that the second level, consciousness, has no actual location or consistent physical equivalent, merely being the sum of all resolved perceptual states in the brain at any one time.

That points to the wider issue of the Frame Problem, which the paper implicitly raises but does not quite tackle head on. The brain gets fed a very variable set of sensory inputs and manages to craft a beautifully smooth experience out of them (mostly); it looks as if an important part of this must be taking place in the first level processing, but it is a non-trivial task which goes a long way beyond interpolating colours and positions.

The authors do mention the Abhidharma Buddhist view of experience as a series of discrete moments within a flow; we’ve touched on this before in discussions of findings by Varea and others that the flow of consciousness seems to have a regular pulse; it would be intriguing and satisfactory if that pulse could be related to the first level of processing hypothesised here; we’re apparently talking about something in the 100ms range which seems a little on the long side for the time slices proposed; but perhaps a kind of synthesis is possible..?