Thanks for the link. I’ll have to do a thorough read through your post in the future. From scanning it, I do disagree with much of it, many of those points of disagreement were laid out by previous commenters. One point I didn’t see brought up: IIRC the biological anchors paper suggests we will have enough compute to do evolution-type optimization before the end of the century. So even if we grant your claim that learning to learn is much harder to directly optimize for, I think it’s still a feasible path to AGI. Or perhaps you think evolution like optimization takes more compute than the biological anchors paper claims?
Nah, I’m pretty sure the difference there is “Steve thinks that Jacob is way overestimating the difficulty of humans building AGI-capable learning algorithms by writing source code”, rather than “Steve thinks that Jacob is way underestimating the difficulty of computationally recapitulating the process of human brain evolution”.
For example, for the situation that you’re talking about (I called it “Case 2” in my post) I wrote “It seems highly implausible that the programmers would just sit around for months and years and decades on end, waiting patiently for the outer algorithm to edit the inner algorithm, one excruciatingly-slow step at a time. I think the programmers would inspect the results of each episode, generate hypotheses for how to improve the algorithm, run small tests, etc.” If the programmers did just sit around for years not looking at the intermediate training results, yes I expect the project would still succeed sooner or later. I just very strongly expect that they wouldn’t sit around doing nothing.
Ok, interesting. I suspect the programmers will not be able to easily inspect the inner algorithm, because the inner/outer distinction will not be as clear cut as in the human case. The programmers may avoid sitting around by fiddling with more observable inefficiencies e.g. coming up with batch-norm v10.
Oh, you said “evolution-type optimization”, so I figured you were thinking of the case where the inner/outer distinction is clear cut. If you don’t think the inner/outer distinction will be clear cut, then I’d question whether you actually disagree with the post :) See the section defining what I’m arguing against, in particular the “inner as AGI” discussion.
Ok, seems like this might have been more a terminological misunderstanding on my end. I think I agree with what you say here, ‘What if the “Inner As AGI” criterion does not apply? Then the outer algorithm is an essential part of the AGI’s operating algorithm’.
Thanks for the link. I’ll have to do a thorough read through your post in the future. From scanning it, I do disagree with much of it, many of those points of disagreement were laid out by previous commenters. One point I didn’t see brought up: IIRC the biological anchors paper suggests we will have enough compute to do evolution-type optimization before the end of the century. So even if we grant your claim that learning to learn is much harder to directly optimize for, I think it’s still a feasible path to AGI. Or perhaps you think evolution like optimization takes more compute than the biological anchors paper claims?
Nah, I’m pretty sure the difference there is “Steve thinks that Jacob is way overestimating the difficulty of humans building AGI-capable learning algorithms by writing source code”, rather than “Steve thinks that Jacob is way underestimating the difficulty of computationally recapitulating the process of human brain evolution”.
For example, for the situation that you’re talking about (I called it “Case 2” in my post) I wrote “It seems highly implausible that the programmers would just sit around for months and years and decades on end, waiting patiently for the outer algorithm to edit the inner algorithm, one excruciatingly-slow step at a time. I think the programmers would inspect the results of each episode, generate hypotheses for how to improve the algorithm, run small tests, etc.” If the programmers did just sit around for years not looking at the intermediate training results, yes I expect the project would still succeed sooner or later. I just very strongly expect that they wouldn’t sit around doing nothing.
Ok, interesting. I suspect the programmers will not be able to easily inspect the inner algorithm, because the inner/outer distinction will not be as clear cut as in the human case. The programmers may avoid sitting around by fiddling with more observable inefficiencies e.g. coming up with batch-norm v10.
Oh, you said “evolution-type optimization”, so I figured you were thinking of the case where the inner/outer distinction is clear cut. If you don’t think the inner/outer distinction will be clear cut, then I’d question whether you actually disagree with the post :) See the section defining what I’m arguing against, in particular the “inner as AGI” discussion.
Ok, seems like this might have been more a terminological misunderstanding on my end. I think I agree with what you say here, ‘What if the “Inner As AGI” criterion does not apply? Then the outer algorithm is an essential part of the AGI’s operating algorithm’.