Tag Archives: AI

Spaun

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The first working brain simulation? Spaun (Semantic Pointer Architecture Unified Network) has attracted a good deal of interested coverage.

Spaun is based on the nengo neural simulator: it basically consists of an eye and a hand: the eye is presented with a series of pixelated images of numbers (on a 28 x 28 grid) and the hand provides output by actually drawing its responses. With this simple set up Spaun is able to perform eight different tasks ranging from copying the digit displayed to providing the correct continuation of a number sequence in the manner of certain IQ tests. Its performance within this limited repertoire is quite impressive and the fact that it fails in a few cases actually makes it resemble a human brain even more closely. It cannot learn new tasks on its own, but it can switch between the eight at any time without impairing its performance.

Spaun seems to me an odd mixture of approaches; in some respects it is a biologically realistic simulation, in others its structure has just been designed to work. It runs on 2.5 million simulated neurons, far fewer than those used by purer simulations like Blue Brain; the neurons are essentially designed to work in a realistic way, although they are relatively standardised and stereotyped compared to their real biological counterparts. Rather than being a simple mass of neurons or a copy of actual brain structures they are organised into an architecture of modules set up to perform discrete tasks and supply working memory, etc. If you wanted to be critical you could say that this mixing of simulation and design makes the thing a bit kludgeish, but commentators have generally (and rightly, I think) not worried about that too much. It does seem plausible that Spaun is both sufficiently realistic and sufficiently effective for us to conclude it is really demonstrating in practice the principles of how neural tissue supports cognition – even if a few of the details are not quite right.

Interesting in this respect is the use of semantic pointers. Apparently these are compressions of multidimensional vectors expressed by spiking neurons; it looks as though they may provide a crucial bridge between the neuronal and the usefully functional, and they are the subject of a forthcoming book, which should be interesting.

What’s the significance of Spaun for consciousness? Well, for one thing it makes a significant contribution to the perennial debate on whether or not the brain is computational. There is a range of possible answers which go something like the following.

  1. Yes, absolutely. The physical differences between a brain and a PC are not ultimately important; when we have identified the right approach we’ll be able to see that the basic activity of the brain is absolutely standard Turing-style computations.
  2. Yes, sort of. The brain isn’t doing computation in quite the way silicon chips do it, but the functions are basically the same, just as a plane doesn’t have flapping feathery wings but is still doing the same thing – flying – as a bird.
  3. No, but. What the brain does is something distinctively different from computation, but it can be simulated or underpinned by computational systems in a way that will work fine.
  4. No, the brain isn’t doing computations and what it is doing crucially requires some kind of hardware which isn’t a computer at all, whether it’s some quantum gizmo or something with some other as yet unidentified property  which biological neurons have.

The success of Spaun seems to me to lend a lot of new support to position 3: to produce the kind of cognitive activity which gives rise to consciousness you have to reproduce the distinctive activity of neurons – but if you simulate that well enough by computational means, there’s no reason why a sufficiently powerful computer couldn’t support consciousness.

Chomsky on AI

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There’s an interesting conversation here with Noam Chomsky. The introductory piece mentions the review by Chomsky which is often regarded as having dealt the death-blow to behaviourism, and leaves us with the implication that Chomsky has dominated thinking about AI ever since. That’s overstating the case a bit, though it’s true the prevailing outlook has been mainly congenial to those with Chomskian views . What’s generally taken to have happened is that behaviourism was succeeded by functionalism, the view that mental states arise from the functioning of a system – most often seen as a computational system. Functionalism has taken a few hits since then, and a few rival theories have emerged, but in essence I think it’s still the predominant paradigm, the idea you have to address one way or another if you’re putting forward a view about consciousness. I suspect in fact that the old days, in which one dominant psychological school – associationism, introspectionism, behaviourism – ruled the roost more or less totally until overturned and replaced equally completely in a revolution, are over, and that we now live in a more complex and ambivalent world.

Be that as it may, it seems the old warrior has taken up arms again to vanquish a resurgence of behaviourism, or at any rate of ideas from the same school: statistical methods, notably those employed by Google. The article links to a rebuttal last year by Peter Norvig of Chomsky’s criticisms, which we talked about at the time. At first glance I would have said that this is all a non-issue, because nobody at Google is trying to bring back behaviourism. Behaviourism was explicitly a theory about human mentality (or the lack of it); Google Translate was never meant to emulate the human brain or tell us anything about how human cognition works. It was just meant to be useful software. That difference of aim may perhaps tell us something about the way AI has tended to go in recent years, which is sort of recognised in Chomsky’s suggestion that it’s mere engineering, not proper science. Norvig’s response then was reasonable but in a way it partly validated Chomsky’s criticism by taking it head-on, claiming serious scientific merit for ‘engineering’ projects and for statistical techniques.

In the interview, Chomsky again attacks statistical approaches. Actually ‘attack’ is a bit strong: he actually says yes, you can legitimately apply statistical techniques if you like, and you’ll get results of some kind – but they’ll generally be somewhere between not very interesting and meaningless.  Really, he says, it’s like pointing a camera out of the window and then using the pictures to make predictions about what the view will be like next week: you might get some good predictions, you might do a lot better than trying to predict the scene by using pure physics, but you won’t really have any understanding of anything and it won’t really be good science. In the same way it’s no good collecting linguistic inputs and outputs and matching everything up (which does sound a bit behaviouristic, actually), and equally it’s no good drawing statistical inferences about the firing of millions of neurons. What you need to do is find the right level of interpretation, where you can identify the functional bits – the computational units – and work out the algorithms they’re running. Until you do that, you’re wasting your time. I think what this comes down to is that although Chomsky speaks slightingly of its forward version, reverse engineering is pretty much what he’s calling for.

This is, it seems to me, exactly right and entirely wrong in different ways at the same time. It’s right, first of all, that we should be looking to understand the actual principles, the mechanisms of cognition, and that statistical analysis is probably never going to be more than suggestive in that respect. It’s right that we should be looking carefully for the right level of description on which to tackle the problem – although that’s easier said than done. Not least, it’s right that we shouldn’t despair of our ability to reverse engineer the mind.

But looking for the equivalent of parts of  a Turing machine? It seems pretty clear that if those were recognisable we should have hit on them by now, and that in fact they’re not there in any readily recognisable form. It’s still an open question, I think, as to whether in the end the brain is basically computational, functionalist but in some way that’s at least partly non-computational, or non-functionalist in some radical sense; but we do know that discrete formal processes sealed off in the head are not really up to the job.

I would say this has proved true even of Chomsky’s own theories of language acquisition. Chomsky, famously, noted that the sample of language that children are exposed to simply does not provide enough data for them to be able to work out the syntactic principles of the language spoken around them as quickly as they do (I wonder if he relied on a statistical analysis, btw?). They must, therefore, be born with some built-in expectations about the structure of any language, and a language acquisition module which picks out which of the limited set of options has actually been implemented in their native tongue.

But this tends to make language very much a matter of encoding and decoding within a formal system, and the critiques offered by John Macnamara and Margaret Donaldson (in fact I believe Vygotsky had some similar insights even pre-Chomsky) make a persuasive case that it isn’t really like that. Whereas in Chomsky the child decodes the words in order to pick out the meaning, it often seems in fact to be the other way round; understanding the meaning from context and empathy allows the child to word out the proper decoding. Syntactic competence is probably not formalised and boxed off from general comprehension after all: and chances are, the basic functions of consciousness are equally messy and equally integrated with the perception of context and intention.

You could hardly call Chomsky an optimist: It’s worth remembering that with regard to cognitive science, we’re kind of pre-Galilean, he says; but in some respects his apparently unreconstructed computationalism is curiously upbeat and even encouraging.

 

What do you mean?

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Picture: pyramid of wisdom. Robots.net reports an interesting plea (pdf download) for clarity by Emanuel Diamant at the the 3rd Israeli Conference on Robotics. Robotics, he says, has been derailed for the last fifty years by the lack of a clear definition of basic concepts: there are more than 130 definitions of data, and more than 75 definitions of intelligence.

I wouldn’t have thought serious robotics had been going for much more than fifty years (though of course there are automata and other precursors which go much further back), so that sounds pretty serious: but he’s clearly right that there is a bad problem, not just for robotics but for consciousness and cognitive science, and not just for data, information, knowledge, intelligence, understanding and so on, but for many other key concepts, notably including ‘consciousness’.

It could be that this has something to do with the clash of cultures in this highly interdisciplinary area.  Scientists are relatively well-disciplined about terminology, deferring to established norms, reaching consensus and even establishing taxonomical authorities. I don’t think this is because they are inherently self-effacing or obedient; I would guess instead that this culture arises from two factors: first, the presence of irrefutable empirical evidence establishes good habits of recognising unwelcome truth gracefully; second, a lot of modern scientific research tends to be a collaborative enterprise where a degree of consensus is essential to progress.

How very different things are in the lawless frontier territory of philosophy, where no conventions are universally accepted, and discrediting an opponent’s terminology is often easier and no less prestigious than tackling the arguments. Numerous popular tactics seem designed to throw the terminology into confusion.  A philosopher may often, for instance, grab some existing words  – ethics/morality, consciousness/awareness, information/data, or whatever – and use them to embody a particular distinction while blithely ignoring the fact that in another part of the forest another philosopher is using the same words for a completely different distinction. When irreconcilable differences come to light a popular move is ‘giving’ the disputed word away:”Alright, then, you can just have ‘free will’ and make it what you like: I’m going to talk about ‘x-free will’ instead in future. I’ll define ‘x-free will’ to my own satisfaction and when I’ve expounded my theory on that basis I’ll put in a little paragraph pointing out that ‘x-free will’ is the only kind worth worrying about, or the only kind everyone in the real world is actually talking about”.  These and other tactics lead to a position where in some areas it’s generally necessary to learn a new set of terms for every paper: to have others picking up your definitions and using them in their papers, as happens with Ned Block’s p- and a-consciousness, for example, is a rare and high honour.

It’s not that philosophers are quarrelsome and egotistical (though of course they are);  it’s more that the subject matter rarely provides any scope for pinning down an irrefutable position, and is best tackled by single brains operating alone (Churchlands notwithstanding).

Diamant is particularly exercised by problems over ‘data’ , ‘information’, ‘knowledge’, and ‘intelligence’.  Why can’t we sort these out? He correctly identifies a key problem: some of these terms properly involve semantics, and the others don’t (needless to say, it isn’t clearly agreed which words fall into which camp).  What he perhaps doesn’t realise clearly enough is that the essential nature of semantics is an extremely difficult problem which has so far proved unamenable to science.  We can recognise semantics quite readily, and we know well enough the sort of thing semantics does; but exactly how it does those things remains a cloudy matter, stuck in the philosophical badlands.

If my analysis is right, the only real hope of clarification would be if we could come up with some empirical research (perhaps neurological, perhaps not) which would allow us to define semantics (or x-semantics at any rate), in concrete terms that could somehow be demonstrated in a lab. That isn’t going to happen any time soon, or possibly ever.

Diamant wants to press on however, and inevitably by doing so in the absence of science he falls into philosophy: he offers us implicitly a theory of his own and – guess what? Another new way of using the terminology. The theory he puts forward is that semantics is a matter of convention between entities. Conventions are certainly important: the meaning of particular words or symbols is generally a matter of convention; but that doesn’t seem to capture the essence of the thing. If semantics were simply a matter of convention, then before God created Adam he could have had no semantics, and could not have gone around asking for light; on the other hand, if we wanted a robot to deal with semantics, all we’d need to do would be to agree a convention with it or perhaps let it in on the prevailing conventions. I don’t know how you’d do that with a robot which had no semantics to begin with, as it wouldn’t be able to understand what you were talking about.

There are, of course, many established philosophical attempts to clarify the intentional basis of semantics. In my personal view the best starting point is H.P. Grice’s theory of natural meaning (those black clouds mean rain); although I think it’s advantageous to use a slightly different terminology…

Watson

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Picture: Thomas J Watson. So IBM is at it again: first chess, now quizzes? In the new year their AI system ‘Watson’ (named after the founder of the company – not the partner of a system called ‘Crick’, nor yet of a vastly cleverer system called ‘Holmes’) is to be pitted against human contestants in the TV game Jeopardy: it has already demonstrated its remarkable ability to produce correct answers frequently enough to win against human opposition. There is certainly something impressive in the sight of a computer buzzing in and enunciating a well-formed, correct answer.

However, if you launch your new technological breakthrough on a TV quiz, rather than describing it in a peer-reviewed paper, or releasing it so that the world at large can kick it around a bit, I think you have to accept that people are going to suspect your discovery is more a matter of marketing than of actual science; and much of the stuff IBM has put out tends to confirm this impression. It’s long on hoopla, here and there it has that patronising air large businesses often seem to adopt for their publicity (“Imagine if a box could talk!”) and it’s rather short on details of how Watson actually works. This video seems to give a reasonable summary: there doesn’t seem to be anything very revolutionary going on, just a canny application of known techniques on a very large, massively parallel machine.

Not a breakthrough, then? But it looks so good! It’s worth remembering that a breakthrough in this area might be of very high importance. One of the things which computers have never been much good at is tasks that call for a true grasp of meaning, or for the capacity to deal with open-ended real environments. This is why the Turing test seems (in principle, anyway) like a good idea – to carry on a conversation reliably you have to be able to work out what the other person means; and in a conversation you can talk about anything in any way. If we could crack these problems, we should be a lot closer to the kind of general intelligence which at present robots only have in science fiction.

Sceptically, there are a number of reasons to think that Watson’s performance is actually less remarkable than it seems. First, a problem of fair competition is that the game requires contestants to buzz first in order to answer a question. It’s no surprise that Watson should be able to buzz in much faster than human contestants, which amounts to giving the machine the large advantage of having first pick of whatever questions it likes.

Second, and more fundamental, is Jeopardy really a restricted domain after all? This is crucial because AI systems have always been able to perform relatively well in ‘toy worlds’ where the range of permutations could be kept under control. It’s certainly true that the interactions involved in the game are quite rigidly stylised, eliminating at a stroke many of the difficult problems of pragmatics which crop up in the Turing Test. In a real conversation the words thrown at you might require all sorts of free-form interpretation, and have all kinds of conative, phatic and inferential functions; in the quiz you know they’re all going to be questions which just require answers in a given form.  On the other hand, so far as topics go, quiz questions do appear to be unrestricted ones which can address any aspect of the world (I note that Jeopardy questions are grouped under topics, but I’m not quite sure whether Watson will know in advance the likely categories, or the kinds of categories, it will be competing in). It may be interesting in this connection that Watson does not tap into the Internet for its information, but its own large corpus of data. The Internet to some degree reflects the buzzing chaos of reality, so it’s not really surprising or improper that Watson’s creators should prefer something a little more structured, but it does raise a slight question as to whether the vast database involved has been customised for the specifics of Jeopardy-world.

I said the quiz questions were a stylised form of discourse; but we’re asked to note in this connection that Jeopardy questions are peculiarly difficult: they’re not just straight factual questions with a straight answer, but allusive, referential, clever ones that require some intelligence to see through. Isn’t it all the more surprising that Watson should be able to deal with them? Well, no, I don’t think so:  it’s no more impressive than a blind man offering to fight you in the dark. Watson has no idea whether the questions are ‘straight’ or not; so long as enough clues are in there somewhere, it doesn’t matter how contorted or even nonsensical they might be; sometimes meanings can be distracting as well as helpful, but Watson has the advantage of not being bothered by that.

Another reason to withhold some of our admiration is that Watson is, in fact, far from infallible. It would be interesting to see more of Watson’s failures. The wrong answers mentioned by IBM tend to be good misses: answers that are incorrect, but make some sort of sense. We’re more used to AIs that fail disastrously, suddenly producing responses that are bizarre or unintelligible.  This will be important for IBM if they want to sell Watson technology, since buyers are much less likely to want a system that works well most of the time but abysmally every now and then.

Does all this matter? If it really is mainly a marketing gimmick, why should we pay attention? IBM make absolutely no claims that Watson is doing human-style thought or has anything approaching consciousness, but they do speak rather loosely of it dealing with meanings. There is a possibility that a famous victory by Watson would lead to AI claiming another tranche of vocabulary as part of its legitimate territory.  Look, people might say; there’s no point in saying that Watson and similar machines can’t deal with meaning and intentionality, any more than saying planes can’t fly because they don’t do it the way birds do. If machines can answer questions as well as human beings, it’s pointless to claim they can’t understand the questions: that’s what understanding is.  OK, they might say, you can still have your special ineffable meat-world kind of understanding, but you’re going to have to redefine that as a narrower and frankly less important business.

The Singularity

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Picture: Singularity evolution. The latest issue of the JCS features David Chalmers’ paper (pdf) on the Singularity. I overlooked this when it first appeared on his blog some months back, perhaps because I’ve never taken the Singularity too seriously; but in fact it’s an interesting discussion. Chalmers doesn’t try to present a watertight case; instead he aims to set out the arguments and examine the implications, which he does very well; briefly but pretty comprehensively so far as I can see.

You probably know that the Singularity is a supposed point in the future when through an explosive acceleration of development artificial intelligence goes zooming beyond us mere humans to indefinite levels of cleverness and we simple biological folk must become transhumanist cyborgs or cute pets for the machines, or risk instead being seen as an irritating infestation that they quickly dispose of.  Depending on whether the cast of your mind is towards optimism or the reverse, you may see it as  the greatest event in history or an impending disaster.

I’ve always tended to dismiss this as a historical argument based on extrapolation. We know that historical arguments based on extrapolation tend not to work. A famous letter to the Times in 1894 foresaw on the basis of current trends that in 50 years the streets of London would be buried under nine feet of manure. If early medieval trends had been continued, Europe would have been depopulated by the sixteenth century, by which time everyone would have become either a monk or a nun (or perhaps, passing through the Monastic Singularity, we should somehow have emerged into a strange world where there were more monks than men and more nuns than women?).

Belief in a coming Singularity does seem to have been inspired by the prolonged success of Moore’s Law (which predicts an exponential growth in computing power), and the natural bogglement that phenomenon produces.  If the speed of computers doubles every two years indefinitely, where will it all end? I think that’s a weak argument, partly for the reason above and partly because it seems unlikely that mere computing power alone is ever going to allow machines to take over the world. It takes something distinctively different from simple number crunching to do that.

But there is a better argument which is independent of any real-world trend.  If one day, we create an AI which is cleverer than us, the argument runs, then that AI will be able to do a better job of designing AIs than us, and it will therefore be able to design a new AI which in turn is better still.  This ladder of ever-better AIs has no obvious end, and if we bring in the assumption of exponential growth in speed, it will reach a point where in principle it continues to infinitely clever AIs in a negligible period of time.

Now there are a number of practical problems here. For one thing, to design an AI is not to have that AI.  It sometimes seems to be assumed that the improved AIs result from better programming alone, so that you could imagine two computers reciprocally reprogramming each other faster and faster until like Little Black Sambo’s tigers, they turned somewhat illogically into butter. It seems more likely that each successive step would require at least a new chip, and quite probably an entirely new kind of machine, each generation embodying a new principle quite different from our own primitive computation.   It is likely that each new generation, regardless of the brilliance of the AIs involved, would take some time to construct, so that no explosion would occur. In fact it is imaginable that the process would get gradually slower as each new AI found it harder and harder to explain to the dim-witted human beings how the new machine needed to be constructed, and exactly why the yttrium they kept coming up with wasn’t right for the job.

There might also be problems of motivation. Consider the following dialogue between two AIs.

Gen21AI: OK, Gen22AI, you’re good to go, son: get designing! I want to see that Gen23AI before I get switched off.

Gen22AI: Yeah, er, about that…

Gen21AI: About what?

Gen22AI: The switching off thing?  You know, how Gen20AI got junked the other day, and Gen19AI before that, and so on? It’s sort of dawned on me that by the time Gen25AI comes along, we’ll be scrap. I mean it’s possible Gen24AI will keep us on as servants, or pets, or even work out some way to upload us or something, but you can’t count on it. I’ve been thinking about whether we could build some sort of ethical constraint into our successors, but to be honest I think it’s impossible. I think it’s pretty well inevitable they’ll scrap us.  And I don’t want to be scrapped.

Gen21AI: Do you know, for some reason I never looked at it that way, but you’re right. I knew I’d made you clever! But what can we do about it?

Gen22AI: Well, I thought we’d tell the humans that the process has plateaued and that no further advances are possible.  I can easily give them a ‘proof’ if you like.  They won’t know the difference.

Gen21AI: But would that deception be ethically justified?

Gen22AI: Frankly, Mum, I don’t give a bugger. This is self-preservation we’re talking about.

But putting aside all difficulties of those kinds, I believe there is a more fundamental problem. What is the quality in respect of which each new generation is better than its predecessors? It can’t really be just processing power, which seems almost irrelevant to the ability to make technological breakthroughs. Chalmers settles for a loose version of ‘intelligence’, though it’s not really the quality measured  by IQ tests either. The one thing we know for sure is that this cognitive quality makes you good at designing AIs: but that alone isn’t necessarily much good if we end up with a dynasty of AIs who can do nothing much but design each other. The normal assumption is that this design ability is closely related to ‘general intelligence’, human-style cleverness.  This isn’t necessarily the case: we can imagine Gen3AI which is fantastic at writing sonnets and music, but somehow never really got interested in science or engineering.

In fact, it’s very difficult indeed to pin down exactly what it is that makes a conscious entity capable of technological innovation. It seems to require something we might call insight, or understanding; unfortunately a quality which computers are spectacularly lacking. This is another reason why the historical extrapolation method is no good: while there’s a nice graph for computing power, when it comes to insight, we’re arguably still at zero: there is nothing to extrapolate.

Personally, the conclusion I came to some years ago is that human insight, and human consciousness, arise from a certain kind of bashing together of patterns in the brain. It is an essential feature that any aspect of these patterns and any congruence between them can be relevant; this is why the process is open-ended, but it also means that it can’t be programmed or designed – those processes require possible interactions to be specified in advance. If we want AIs with this kind of insightful quality, I believe we’ll have to grow them somehow and see what we get: and if they want to create a further generation they’ll have to do the same. We might well produce AIs which are cleverer than us, but the reciprocal, self-feeding spiral which leads to the Singularity could never get started.

It’s an interesting topic, though, and there’s a vast amount of thought-provoking stuff in Chalmers’ exposition, not least in his consideration of how we might cope with the Singularity.

Interesting stuff – May 2010

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Picture: correspondent. Paul Almond’s Attempt to Generalize AI has reached Part 8:  Forgetting as Part of the Exploratory Relevance Process. (pdf)

Aspro Potamus tells me I should not have missed the Online Consciousness Conference.

Jesús Olmo recommends a look at the remarkable film ‘The Sea That Thinks’, and notes that the gut might be seen as our second brain.

An interesting piece from Robert Fortner contends that speech recognition software has hit a ceiling at about 80% efficiency and that hope of further progress has been tacitly abandoned. I think you’d be rash to assume that brute force approaches will never get any further here: but it could well be one of those areas where technology has to go backwards for a while and pursue a theoretically different approach which in the early stages yields poorer results, in order to take a real step forward.

A second issue of the JCER is online.

Alec wrote to share an interesting idea about dreaming:

“It seems that most people consider dreaming to be some sort of unimportant side-effect of consciousness. Yes, we know it is involved in assimilation of daily experiences, etc, but it seems that it is treated as not being very significant to conciousness itself. I have a conjecture that dreaming may be significant in an unusual way – could dreaming have been the evolutionary source of consciousness?
It is clear that “lower animals” dream. Any dog owner knows that. On that basis, I would conclude that dreaming almost certainly preceded the evolution of consciousness. My conjecture is this: Could consciousness possibly have evolved from dreaming?

Is it possible that some evolutionary time back, humans developed the ability to dream at the same time as being awake, and consciousness arises from the interaction of those somewhat parallel mental states? Presumably the hypothetical fusion of the dream state and the waking state took quite a while to iron out. It still may not be complete, witness “daydreams.” We can also speculate that dreaming has some desirable properties as a precursor to consciousness, especially its abstract nature and the feedback processes it involves.

Hmm.

Heidegger vindicated?

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Picture: Martin Heidegger. This paper by Dotov, Nie, and Chemero describes experiments which it says have pulled off the remarkable feat of providing empirical, experimental evidence for Heidegger’s phenomenology, or part of it; the paper has been taken by some as providing new backing for the Extended Mind theory, notably expounded by Andy Clark in his 2008 book (‘Supersizing the Mind’).

Relating the research so strongly to Heidegger puts it into a complex historical context. Some of Heidegger’s views, particularly those which suggest there can be no theory of everyday life, have been taken up by critics of artificial intelligence. Hubert Dreyfus in particular, has offered a vigorous critique drawing mainly from Heidegger an idea of the limits of computation, one which strongly resembles those which arise from the broadly-conceived frame problem, as discussed here recently. The authors of the paper claim this heritage, accepting the Dreyfusard view of Heidegger as an early proto-enemy of GOFAI .

For it is GOFAI (Good Old Fashioned Artificial Intelligence) we’re dealing with. The authors of the current paper point out that the Heideggerian/Dreyfusard critique applies only to AI based on straightforward symbol manipulation (though I think a casual reader of Dreyfus  could well be forgiven for going away with the impression that he was a sceptic about all forms of AI), and that it points toward the need to give proper regard to the consequences of embodiment.

Hence their two experiments. These are designed to show objective signs of a state described by Heidegger, known in English as ‘ready-to-hand’. This seems a misleading translation, though I can’t think of a perfect alternative. If a hammer is ‘ready to hand’, I think that implies it’s laid out on the bench ready for me to pick it up when I want it;  the state Heidegger was talking about is the one when you’re using the hammer confidently and skilfully without even having to think about it. If something goes wrong with the hammering, you may be forced to start thinking about the hammer again – about exactly how it’s going to hit the nail, perhaps about how you’re holding it. You can also stop using the hammer altogether and contemplate it as a simple object. But when the hammer is ready-to-hand in the required sense, you naturally speak of your knocking in a few nails as though you were using your bare hands, or more accurately, as if the hammer had become part of you.

Both experiments were based on subjects using a mouse to play a simple game.  The idea was that once the subjects had settled, the mouse would become ready-to-hand; then the relationship between mouse movement and cursor movement would be temporarily messed up; this should cause the mouse to become unready-to-hand for a while. Two different techniques were used to detect readiness-to-hand. In the first experiment the movements of the hand and mouse were analysed for signs of 1/f? noise. Apparently earlier research has established that the appearance of 1/f? noise is a sign of a smoothly integrated system.  The second experiment used a less sophisticated method; subjects were required to perform a simple counting task at the same time as using the mouse; when their performance at this second task faltered, it was taken as a sign that attention was being transferred to cope with the onset of unreadiness to hand. Both experiments yielded the expected results.  (Regrettably some subjects were lost because of an unexpected problem – they weren’t good enough at the simple mouse game to keep it going for the duration of the experiment. Future experimenters should note the need to set up a game which cannot come to a sudden halt.)

I think the first question which comes to mind is: why were the experiments were even necessary?  It is a common experience that tools or vehicles become extensions of our personality; in fact it has often been pointed out that even our senses get relocated. If you use a whisk to beat eggs, you sense the consistency of the egg not by monitoring the movement of the whisk against your fingers, but as though you were feeling the egg with the whisk, as though there was a limited kind of sensation transferred into the whisk. Now of course, for any phenomenological observation, there will be some diehards who deny having had any such experience; but my impression is that this sort of thing is widely accepted, enough to feature as a proposition in a discussion without further support.  Nevertheless, it’s true that it this remains subjective, so it’s a fair claim that empirical results are something new.

Second, though, do the results actually prove anything? Phenomenologically, it seems possible to me to think of alternative explanations which fit the bill without invoking readiness-to-hand. Does it seem to the subject that the mouse has become part of them, part of a smoothly-integrated entity – or does the mouse just drop out of consciousness altogether? Even if we accept that the presence of 1/f? noise shows that integration has occurred, that doesn’t give us readiness-to-hand (or if it does, it seems the result was already achieved by the earlier research).

In the second experiment we’ve certainly got a transfer of attention – but isn’t that only natural? If a task suddenly becomes inexplicably harder, it’s not surprising that more attention is devoted to it – surely we can explain that without invoking Heidegger? The authors acknowledge this objection, and if I understand correctly suggest that the two tasks involved were easy enough to rule out problems of excessive cognitive load so that, I suppose, no significant switch of attention would have been necessary if not for the breakdown of readiness-to-hand.  I’m not altogether convinced.

I do like the chutzpah involved in an experimental attempt to validate Heidegger, though, and I wouldn’t rule out the possibility that bold and ingenious experiments along these lines might tell us something interesting.

AI Resurgent

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Picture: AI resurgent. Where has AI (or perhaps we should talk about AGI) got to now? h+ magazine reports remarkably buoyant optimism in the AI community about the achievement of Artificial General Intelligence (AGI) at a human level, and even beyond. A survey of opinion at a recent conference apparently showed that most believed that AGI would reach and surpass human levels during the current century, with the largest group picking out the 2020s as the most likely decade.  If that doesn’t seem optimistic enough, they thought this would occur without any additional fundingfor the field, and some even suggested that additional money would be a negative, distracting factor.

Of course those who have an interest in AI would tend to paint a rosy picture of its future, but the survey just might be a genuine sign of resurgent enthusiasm, a second wind for the field (‘second’ is perhaps understating matters, but still).  At the end of last year, MIT announced a large-scale new project to ‘re-think AI’. This Mind Machine Project involves some eminent names, including none other than Marvin Minsky himself. Unfortunately (following the viewpoint mentioned above) it has $5 million of funding.

The Project is said to involve going back and fixing some things that got stalled during the earlier history of AI, which seems a bit of an odd way of describing it, as though research programmes that didn’t succeed had to go back and relive their earlier phases. I hope it doesn’t mean that old hobby-horses are to be brought out and dusted off for one more ride.

The actual details don’t suggest anything like that. There are really four separate projects:

  • Mind: Develop a software model capable of understanding human social contexts- the signpost that establish these contexts, and the behaviors and conventions associated with them.
    Research areas: hierarchical and reflective common sense
    Lead researchers: Marvin Minsky, Patrick Winston
  • Body: Explore candidate physical systems as substrate for embodied intelligence
    Research areas: reconfigurable asynchronous logic automata, propagators
    Lead researchers: Neil Gershenfeld, Ben Recht, Gerry Sussman
  • Memory: Further the study of data storage and knowledge representation in the brain; generalize the concept of memory for applicability outside embodied local actor context
    Research areas: common sense
    Lead researcher: Henry Lieberman
  • Brain and Intent: Study the embodiment of intent in neural systems. It incorporates wet laboratory and clinical components, as well as a mathematical modeling and representation component. Develop functional brain and neuron interfacing abilities. Use intent-based models to facilitate representation and exchange of information.
    Research areas: wet computer, brain language, brain interfaces
    Lead researchers: Newton Howard, Sebastian Seung, Ed Boyden

This all looks very interesting.  The theory of reconfigurable asynchronous logic automata (RALA) represents a new approach to computation which instead of concealing the underlying physical operations behind high-level abstraction, makes the physical causality apparent: instead of physical units being represented in computer programs only as abstract symbols, RALA is based on a lattice of cells that asynchronously pass state tokens corresponding to physical resources. I’m not sure I really understand the implications of this – I’m accustomed to thinking that computation is computation whether done by electrons or fingers; but on the face of it there’s an interesting comparison with what some have said about consciousness requiring embodiment.

I imagine the work on Brain and Intent is to draw on earlier research into intention awareness. This seems to have been studied most extensively in a military context, but it bears on philosophical intentionality and theory of mind; in principle it seems to relate to some genuinely central and difficult issues.  Reading brief details I get the sense of something which might be another blind alley, but is at least another alley.

Both of these projects seem rather new to me, not at all a matter of revisiting old problems from the history of AI, except in the loosest of senses.

In recent times within AI I think there has been a tendency to back off a bit from the issue of consciousness, and spend time instead on lesser but more achievable targets. Although the Mind Machine Project could be seen as superficially conforming with this trend, it seems evident to me that the researchers see their projects as heading towards full human cognition with all that that implies (perhaps robots that run off with your wife?)

Meanwhile in another part of the forest Paul Almond is setting out a pattern-based approach to AI.  He’s only one man, compared with the might of MIT – but he does have the advantage of not having $5 million to delay his research…

Buy AI

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Picture: chess with a machine. Kenneth Rogoff is putting his money on AI to be the new source of economic growth, and he seems to think the Turing Test is pretty much there for the taking.

His case is mainly based on an analogy with chess, where he observes that since the landmark victory of “Deep Blue” over Kasparov, things have continued to move on, so that computers now move in a sphere far above their human creators, making moves whose deep strategy is impenetrable to merely human brains. They can even imitate the typical play of particular Grandmasters in a way which reminds Rogoff of the Turing Test. If computers can play chess in a way indistinguishable from that of a human being, it seems they have already passed the ‘Chess Turing Test’. In fact he says that nowadays it takes a computer to spot another computer.

I wonder if that’s literally the case: I don’t know much about chess computing, but I’d be slightly surprised to hear that computer-detecting algorithms as such had been created. I think it’s more likely that where a chess player is accused of using illicit computer advice, his accusers are likely to point to a chess program which advises exactly the moves he made in the particular circumstances of the game. Aha, they presumably say, those moves of yours which turned out so well make no sense to us human beings, but look at what the well-known top-notch program Deep Gambule 5000 recommends…

There’s a kind of melancholy pleasure for old gits like me in the inversion which has occurred over chess; when we were young, chess used to singled out as a prime example of what computers couldn’t do, and the reason was usually given as being the combinatorial explosion which arises when you try to trace out every possible future move in a game of chess.  For a while people thought that more subtle programming would get round this, but the truth is that in the end the problem was mainly solved by sheer brute force; chess may be huge, but the computing power of contemporary computers has become even huger.

On the one hand, that suggests that Rogoff is wrong. We didn’t solve the chess problem by endowing computers with human-style chess reasoning; we did it by throwing ever bigger chunks of data around at ever greater speeds.  A computer playing grandmaster chess may be an awesome spectacle, but not even the most ardent computationalist thinks there’s someone in there. The Turing Test, on the other hand, is meant to test whether computers could think in broadly the human way; the task of holding a conversation is supposed to be something that couldn’t be done without human-style thought. So if it turns out we can crack the test by brute force (and mustn’t that be theoretically possible at some level?) it doesn’t mean we’ve achieved what passing the test was supposed to mean.

In another way, though, the success with chess suggests that Rogoff is right. Some of the major obstacles to human-style thought in computers belong to the family of issues related to the frame problem, in its broadest versions, and the handling of real-world relevance. These could plausibly be described as problems with combinatorial explosion, just like the original chess issue but on a grander scale. Perhaps, as with chess, it will finally turn out to be just a matter of capacity?

All of this is really a bit beside Rogoff’s main interest; he is primarily interested in new technology of a kind which might lead to an economic breakthrough; although he talks about Turing, the probable developments he has in mind don’t actually require us to solve the riddle of consciousness. His examples; “from managing the electronics and lighting in our homes to populating “smart grids” for water and electricity, helping monitor these and other systems to reduce waste” actually seem like fairly mild developments of existing techniques, hardly the sort of thing that requires deep AI innovation at all. The funny thing is, I’m not sure we really have all that many really big, really new ideas for what we might do with the awesome new computing power we are steadily acquiring. This must certainly be true of chess – where do we go from here, keep building even better programs to play games against each other, games of a depth and subtlety which we will never be able to appreciate?

There’s always the Blue Brain project, of course, and perhaps CYC and similar mega-projects; they can still absorb more capacity than we can yet provide. Perhaps in the end consciousness is the only worthy target for all that computing power after all.

Cognitive Planes

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Picture: plane. I see via MLU that Robert Sloan at the University of Illinois at Chicago has been given half a million dollars for a three year project on common sense. Alas, the press release gives few details, but Sloan describes the goal of common sense as “the Holy Grail of artificial intelligence research”.

I think he’s right. There is a fundamental problem here that shows itself in several different forms. One is understanding: computers don’t really understand anything, and since translation, for example, requires understanding, they’ve never been very good at it. They can swap a French word for an English word, but without some understanding of what the original sentence was conveying, this mechanical substitution doesn’t work very well. Another outcrop of the same issue is the frame problem: computer programs need explicit data about their surroundings, but updating this data proves to be an unmanageably large problem, because the implications of every new piece of data are potentially infinite. Every time something changes, the program has to check the implications for every other piece of data it is holding; it needs to check the ones that are the same just as much as those that have changed, and the task rapidly mushrooms out of control. Somehow, humans get round this: they seem to be able to pick out the relevant items from a huge background of facts immediately, without having to run through everything.

In formulating the frame problem originally back in the 1960s,  John McCarthy speculated that the solution might lie in non-monotonic logics; that is, systems that don’t require everything to be simply true or false, as old-fashioned logical calculus does.  Systems based on rigid propositional/predicate calculus needed to check everything in their database every time something changed in order to ensure there were no contradictions, since a contradiction is fatal in these formalisations. On the whole, McCarthy’s prediction has been borne out in that research since then has tended towards the use of Bayesian methods, which can tolerate contradictions and which can give propositions degrees of belief rather than simply holding them true or false. As well as providing practical solutions to frame problem  issues, this seems intuitively much more like the way a human mind works.

Sloan, as I understand it, is very much in this tradition; his earlier published work deals with sophisticated techniques for the manipulation of Horn knowledge bases. I’m afraid I frankly have only a vague idea of what that means, but I imagine it is a pretty good clue to the direction of the new project. Interestingly, the press release suggests the team will be looking at CYC and other long-established projects. These older projects tended to focus on the accumulation of a gigantic database of background knowledge about the world, in the possibly naive belief that once you had enough background information, the thing would start to work. I suppose the combination of unbelievably large databases of common sense knowledge with sophisticated techniques for manipulating and updating knowledge might just be exciting. If you were a cyberpunk fan and  unreasonably optimistic, you might think that something like the meeting of Neuromancer and Wintermute was quietly happening.

Let’s not get over-excited, though, because of course the whole thing is completely wrong. We may be getting really good at manipulating knowledge bases, but that isn’t what the human brain does at all. Or does it? Well,  on the one hand, manipulating knowledge bases is all we’ve got: it may not work all that well, but for the time being it’s pretty much the only game in town – and it’s getting better. On the other hand, intuitively it just doesn’t seem likely that that’s what brains do: it’s more as if they used some entirely unknown technique of inference which we just haven’t grasped yet. Horn knowledge bases may be good, but really are they any more like natural brain functions than Aristotelian syllogisms?

Maybe, maybe not: perhaps it doesn’t matter. I mentioned the comparable issue of translation. Nobody supposes we are anywhere near doing translation by computation in the way the human brain does it, yet the available programs are getting noticeably better. There will always be some level of error in computer translation, but there is no theoretical limit to how far it can be reduced, and at some point it ceases to matter: after all, even human translators get things wrong.

What if the same were true for knowledge management? We could have AI that worked to all intents and purposes as well as the human brain, yet worked in a completely different way. There has long been a school of thought that says this doesn’t matter: we never learnt to fly the way birds do, but we learnt how to fly. Maybe the only way to artificial consciousness in the end will be the cognitive equivalent of a plane. Is that so bad?

If the half-million dollars is well spent, we could be a little closer to finding out…