To be clear: I’m not sure that my “supporting argument” above addressed an objection to Ryan that you had. It’s plausible that your objections were elsewhere.
But I’ll respond with my view.
If your argument is “brain-like AGI will work worse before it works better”, then sure, but my claim is that you only get “impressive and proto-AGI-ish” when you’re almost done, and “before” can be “before by 0–30 person-years of R&D” like I said.
Ok, so this describes a story where there’s a lot of work to get proto-AGI and then not very much work to get superintelligence from there. But I don’t understand what’s the argument for thinking this is the case vs. thinking that there’s a lot of work to get proto-AGI and then also a lot of work to get superintelligence from there.
Going through your arguments in section 1.7:
“I think the main reason is what I wrote about the “simple(ish) core of intelligence” in §1.3 above.”
But I think what you wrote about the simple(ish) core of intelligence in 1.3 is compatible with there being like (making up a number) 20 different innovations involved in how the brain operates, each of which gets you a somewhat smarter AI, each of which could be individually difficult to figure out. So maybe you get a few, you have proto-AGI, and then it takes a lot of work to get the rest.
Certainly the genome is large enough to fit 20 things.
I’m not sure if the “6-ish characteristic layers with correspondingly different neuron types and connection patterns, and so on” is complex enough to encompass 20 different innovations. Certainly seems like it should be complex enough to encompass 6.
(My argument above was that we shouldn’t expect the brain to run an algorithm that only is useful once you have 20 hypothetical components in place, and does nothing beforehand. Because it was found via local search, so each of the 20 things should be useful on their own.)
“Plenty of room at the top” — I agree.
“What’s the rate limiter?” — The rate limiter would be to come up with the thinking and experimenting needed to find the hypothesized 20 different innovations mentioned above. (What would you get if you only had some of the innovations? Maybe AGI that’s incredibly expensive. Or AGIs similarly capable as unskilled humans.)
“For a non-imitation-learning paradigm, getting to “relevant at all” is only slightly easier than getting to superintelligence”
I agree that there are reasons to expect imitation learning to plateau around human-level that don’t apply to fully non-imitation learning.
That said...
For some of the same reasons that “imitation learning” plateaus around human level, you might also expect “the thing that humans do when they learn from other humans” (whether you want to call that “imitation learning” or “predictive learning” or something else) to slow down skill-acquisition around human level.
There could also be another reason for why non-imitation-learning approaches could spend a long while in the human range. Namely: Perhaps the human range is just pretty large, and so it takes a lot of gas to traverse. I think this is somewhat supported by the empirical evidence, see this AI impacts page (discussed in this SSC).
To be clear: I’m not sure that my “supporting argument” above addressed an objection to Ryan that you had. It’s plausible that your objections were elsewhere.
But I’ll respond with my view.
Ok, so this describes a story where there’s a lot of work to get proto-AGI and then not very much work to get superintelligence from there. But I don’t understand what’s the argument for thinking this is the case vs. thinking that there’s a lot of work to get proto-AGI and then also a lot of work to get superintelligence from there.
Going through your arguments in section 1.7:
“I think the main reason is what I wrote about the “simple(ish) core of intelligence” in §1.3 above.”
But I think what you wrote about the simple(ish) core of intelligence in 1.3 is compatible with there being like (making up a number) 20 different innovations involved in how the brain operates, each of which gets you a somewhat smarter AI, each of which could be individually difficult to figure out. So maybe you get a few, you have proto-AGI, and then it takes a lot of work to get the rest.
Certainly the genome is large enough to fit 20 things.
I’m not sure if the “6-ish characteristic layers with correspondingly different neuron types and connection patterns, and so on” is complex enough to encompass 20 different innovations. Certainly seems like it should be complex enough to encompass 6.
(My argument above was that we shouldn’t expect the brain to run an algorithm that only is useful once you have 20 hypothetical components in place, and does nothing beforehand. Because it was found via local search, so each of the 20 things should be useful on their own.)
“Plenty of room at the top” — I agree.
“What’s the rate limiter?” — The rate limiter would be to come up with the thinking and experimenting needed to find the hypothesized 20 different innovations mentioned above. (What would you get if you only had some of the innovations? Maybe AGI that’s incredibly expensive. Or AGIs similarly capable as unskilled humans.)
“For a non-imitation-learning paradigm, getting to “relevant at all” is only slightly easier than getting to superintelligence”
I agree that there are reasons to expect imitation learning to plateau around human-level that don’t apply to fully non-imitation learning.
That said...
For some of the same reasons that “imitation learning” plateaus around human level, you might also expect “the thing that humans do when they learn from other humans” (whether you want to call that “imitation learning” or “predictive learning” or something else) to slow down skill-acquisition around human level.
There could also be another reason for why non-imitation-learning approaches could spend a long while in the human range. Namely: Perhaps the human range is just pretty large, and so it takes a lot of gas to traverse. I think this is somewhat supported by the empirical evidence, see this AI impacts page (discussed in this SSC).