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This is: Continuing the takeoffs debate, published by Richard Ngo on the AI Alignment Forum.
Here’s an intuitively compelling argument: only a few million years after diverging from chimpanzees, humans became much more capable, at a rate that was very rapid compared with previous progress. This supports the idea that AIs will, at some point, also start becoming more capable at a very rapid rate. Paul Christiano has made an influential response; the goal of this post is to evaluate and critique it. Note that the arguments discussed in this post are quite speculative and uncertain, and also cover only a small proportion of the factors which should influence our views on takeoff speeds - so in the process of writing it I’ve made only a small update towards very fast takeoff. Also, given that Paul’s vision of a continuous takeoff occurs much faster than any mainstream view, I expect that even totally resolving this debate would have relatively few implications for AI safety work. So it's probably more useful to compare both Paul and Eliezer's scenarios against more mainstream views, than against each other. Nevertheless, it’s disappointing that such an influential argument has received so little engagement, so I wanted to use this post to explore some of the uncertainties around the issue.
I’ll call Paul’s argument the changing selection pressures argument, and quote it here at length:
Chimpanzees evolution is not primarily selecting for making and using technology, for doing science, or for facilitating cultural accumulation. The task faced by a chimp is largely independent of the abilities that give humans such a huge fitness advantage. It’s not completely independent - the overlap is the only reason that evolution eventually produces humans - but it’s different enough that we should not be surprised if there are simple changes to chimps that would make them much better at designing technology or doing science or accumulating culture.
Relatedly, evolution changes what it is optimizing for over evolutionary time: as a creature and its environment change, the returns to different skills can change, and they can potentially change very quickly. So it seems easy for evolution to shift from “not caring about X” to “caring about X,” but nothing analogous will happen for AI projects. (In fact a similar thing often does happen while optimizing something with SGD, but it doesn’t happen at the level of the ML community as a whole.)
If we step back from skills and instead look at outcomes we could say: “Evolution is always optimizing for fitness, and humans have now taken over the world.” On this perspective, I’m making a claim about the limits of evolution. First, evolution is theoretically optimizing for fitness, but it isn’t able to look ahead and identify which skills will be most important for your children’s children’s children’s fitness. Second, human intelligence is incredibly good for the fitness of groups of humans, but evolution acts on individual humans for whom the effect size is much smaller (who barely benefit at all from passing knowledge on to the next generation). Evolution really is optimizing something quite different than “humanity dominates the world.”
So I don’t think the example of evolution tells us much about whether the continuous change story applies to intelligence. This case is potentially missing the key element that drives the continuous change story: optimization for performance. Evolution changes continuously on the narrow metric it is optimizing, but can change extremely rapidly on other metrics. For human technology, features of the technology that aren’t being optimized change rapidly all the time. When humans build AI, they will be optimizing for usefulness, and so progress in usefulness is much more likely to be linear.
In other words, Paul argues fi...
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