This isn't a proper response to Paul Christiano or Katja Grace's recent writings about takeoff speed, but I wanted to cross-post Eliezer's first quick comments on Katja's piece someplace more linkable than Twitter:

There's a lot of steps in this argument that need to be spelled out in more detail. Hopefully I get a chance to write that up soon. But it already raises the level of debate by a lot, for which I am grateful.
E.g. it is not intuitive to me that "But evolution wasn't trying to optimize for STEM ability" is a rejoinder to "Gosh hominids sure got better at that quickly." I can imagine one detailed argument that this might be trying to gesture at, but I don't know if I'm imagining right.
Similarly it's hard to pin down which arguments say '"Average tech progress rates tell us something about an underlying step of inputs and returns with this type signature" and which say "I want to put the larger process in this reference class and demand big proof burdens."

I also wanted to caveat: Nate's experience is that the label "discontinuity" is usually assigned to misinterpretations of his position on AGI, so I don't want to endorse this particular framing of what the key question is. Quoting Nate from a conversation I recently had with him (not responding to these particular posts):

On my model, the key point is not "some AI systems will undergo discontinuous leaps in their intelligence as they learn," but rather, "different people will try to build AI systems in different ways, and each will have some path of construction and some path of learning that can be modeled relatively well by some curve, and some of those curves will be very, very steep early on (e.g., when the system is first coming online, in the same way that the curve 'how good is Google’s search engine' was super steep in the region between 'it doesn’t work' and 'it works at least a little'), and sometimes a new system will blow past the entire edifice of human knowledge in an afternoon shortly after it finishes coming online." Like, no one is saying that Alpha Zero had massive discontinuities in its learning curve, but it also wasn't just AlphaGo Lee Sedol but with marginally more training: the architecture was pulled apart, restructured, and put back together, and the reassembled system was on a qualitatively steeper learning curve.
My point here isn't to throw "AGI will undergo discontinuous leaps as they learn" under the bus. Self-rewriting systems likely will (on my models) gain intelligence in leaps and bounds. What I’m trying to say is that I don’t think this disagreement is the central disagreement. I think the key disagreement is instead about where the main force of improvement in early human-designed AGI systems comes from — is it from existing systems progressing up their improvement curves, or from new systems coming online on qualitatively steeper improvement curves?

Katja replied on Facebook: "FWIW, whenever I am talking about discontinuities, I am usually thinking of e.g. one system doing much better than a previous system, not discontinuities in the training of one particular system—if a discontinuity in training one system does not make the new system discontinuously better than the previous system, then I don't see why it would be important, and if it does, then it seems more relevant to talk about that."

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