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.