Global Workspace theories have been popular ever since Bernard Baars put forward the idea back in the eighties; in ‘Applying global workspace theory to the frame problem’*, Murray Shanahan and Baars suggest that among its other virtues, the global workspace provides a convenient solution to that old bugbear, the frame problem.
What is the frame problem, anyway? Initially, it was a problem that arose when early AI programs were attempting simple tasks like moving blocks around. It became clear that when they moved a block, they not only had to update their database to correct the position of the block, they had to update every other piece of information to say it had not been changed. This led to unexpected demands on memory and processing. In the AI world, this problem never seemed too overwhelming, but philosophers got hold of it and gave it a new twist. Fodor, and in a memorable exposition, Dennett, suggested that there was a fundamental problem here. Humans had the ability to pick out what was relevant and ignore everything else, but there didn’t seem to be any way of giving computers the same capacity. Dennett’s version featured three robots: the first happily pulled a trolley out of a room to save it from a bomb, without noticing that the bomb was on the trolley, and came too; the second attempted to work out all the implications of pulling the trolley out of the room; but there were so many logical implications that it was stuck working through them when the bomb went off. The third was designed to ignore irrelevant implications, but it was still working on the task of identifying all the many irrelevant implications when again the bomb exploded.
Shanahan and Baars explain this background and rightly point out that the original frame problem arose in systems which used formal logic as their only means of drawing conclusions about things, no longer an approach that many people would expect to succeed. They don’t really believe that the case for the insolubility of the problem has been convincingly made. What exactly is the nature of the problem, they ask: is it combinatorial explosion? Or is it just that the number of propositions the AI has to sort through to find the relevant one is very large (and by the way, aren’t there better ways of finding it than searching every item in order?). Neither of those is really all that frightening; we have techniques to deal with them.
I think Shanahan and Baars, understandably enough, under-rate the task a bit here. The set of sentences we’re asking the AI to sort through is not just very large; it’s infinite. One of the absurd deductions Dennett assigns to his robots is that the number of revolutions the wheels of trolley will perform in being pulled out of the room is less than the number of walls in the room. This is clearly just one member of a set of valid deductions which goes on forever; the number of revolutions is also less than the number of walls plus one; it’s less than the number of walls plus two… It may be obvious that these deductions are uninteresting; but what is the algorithm that tells us so? More fundamentally, the superficial problems are proxies for a deeper concern; that the real world isn’t reducible to a set of propositions at all, that, as Borges put it
“it is clear that there is no classification of the Universe that is not arbitrary and full of conjectures. The reason for this is very simple: we do not know what thing the universe is.”
There’s no encyclopaedia which can contain all possible facts about any situation. You may have good heuristics and terrific search algorithms, but when you’re up against an uncategorisable domain of infinite extent, you’re surely still going to have problems.
However, the solution proposed by Shanahan and Baars is interesting. Instead of the mind having to search through a large set of sentences, it has a global workspace where things are decided and a series of specialised modules which compete to feed in information (there’s an issue here about how radically different inputs from different modules manage to talk to each other: Shanahan and Baars mention a couple of options and then say rather loftily that the details don’t matter for their current purposes. It’s true that in context we don’t need to know exactly what the solution is – but we do need to be left believing that there is one).
Anyway, the idea is that while the global workspace is going about its business each module is looking out for just one thing. When eventually the bomb-is-coming-too module gets stimulated, it begins sending very vigorously and that information gets into the workspace. Instead of having to identify relevant developments, the workspace is automatically fed with them.
That looks good on the face of it; instead of spending time endlessly sorting through propositions, we’ll just be alerted when it’s necessary. Notice, however, that instead of requiring an indefinitely large amount of time, we now need an indefinitely large number of specialised modules. Moreover, if we really cover all the bases, many of those modules are going to be firing off all the time. So when the bomb-is-coming-too module begins to signal frantically, it will be competing with the number-of-rotations-is-less-than-the-number-of-walls module and all the others, and will be drowned out. If we only want to have relevant modules, or only listen to relevant signals, we’re back with the original problem of determining just what is relevant.
Still, let’s not dismiss the whole thing too glibly. It reminded me to some degree of Edelman’s analogy with the immune system, which in a way really does work like that. The immune system cannot know in advance what antibodies it will need to produce, so instead it produces lots of random variations; then when one gets triggered it is quickly reproduced in large numbers. Perhaps we can imagine that if the global workspace were served by modules which were not pre-defined, but arose randomly out of chance neural linkages, it might work something like that. However, the immune system has the advantage of knowing that it has to react against anything foreign, whereas we need relevant responses for relevant stimuli. I don’t think we have the answer yet.
*Thanks to Lloyd for the reference.
Aspro Potamos has drawn my attention to the emerging series of YouTube videos, somewhat polemical in tone, on the
Where has AI (or perhaps we should talk about
Introspection, the direct examination of the contents of our own minds, seems itself to be in many minds at the moment. The latest issue of the Journal of Consciousness Studies was devoted to papers on introspection, marking the tenth anniversary of the publication of The View from Within, by Francisco Varela and Jonathan Shear (which was itself a special edition of the JCS); and now
I don’t know about that, but aren’t they right to emphasise the potential value of introspection? Isn’t it the case that introspection is our only source of infallible information? Most of the things we perceive are subject to error and delusion, but we can’t, for example, be wrong about the fact that we are feeling pain, can we? That seems interesting to me. Our impressions of the outside world come to us through a chain of cause and effect, and at any stage errors or misinterpretations can creep in; but because introspection is direct, there’s no space for error to occur. You could well say it’s our only source of certain knowledge – isn’t that worth pursuing a little more systematically?
Infallible? That is the exact reverse of the truth: in fact all introspections are false. Think about it. Introspection can only address the contents of consciousness, right? You can’t introspect the unconscious mental processes that keep you balanced, or regulate your heartbeat. But all of the contents of consciousness have intentionality – they’re all about things, yes? So to have direct experience of mental content is to be thinking about something else – not about the mental state itself, but about the thing it’s about! Now when we attempt to think directly about our own mental states, it follows that we’re not experiencing them in themselves – we’re experiencing a different mental state which is about them. In short, we’re necessarily imagining our mental states. Far from having direct contact, we are inevitably thinking about something we’ve just made up.
I was wondering recently what we could do with all the new computing power which is becoming available. One answer might be calculating phi, effectively a measure of consciousness, which was very kindly drawn to my attention by Christof Koch. Phi is actually a time- and state-dependent measure of integrated information developed by Giulio Tononi in support of the Integrated Information Theory (IIT) of consciousness which he and Koch have championed. Some readable expositions of the theory are
Kenneth Rogoff is
.
Edge has an interesting
Among a number of interesting features, The Ego Tunnel includes a substantial account of out-of-body experiences (OBEs) and similar phenomena. Experiments where the subjects are tricked into mistaking a plastic dummy for their real hand (all done with mirrors), or into feeling themselves to be situated somewhere behind their own head (you need a camera for this) show that our perception of our own body and our own location are generated within our brain and are susceptible to error and distortion; and according to Metzinger this shows that they are really no more than illusions (Is that right, by the way – or are they only illusions when they’re wrong or misleading? The fact that a camera can be made to generate false or misleading pictures doesn’t mean that all photographs are delusions, does it?).
The denial of one’s own existence might seem a desperate philosophical strategy, but denying the reality of the self is a line which a number of people have taken, and Thomas Metzinger is prominent among them. The thesis of his massive 2003 work is summed up in the title: Being No One: The Self-Model Theory of Subjectivity. In that book, Metzinger made a commendable effort to balance philosophy and science; but the sheer size of the resulting text may have deterred some readers – I confess to being somewhat daunted myself. Now he has come back with a slimmer volume The Ego Tunnel which is aimed at a wider public and raises wider issues which Metzinger suggests need public attention.




