I’m just going to name random examples of fields, I think it’s true essentially all the time but I only have personal experience in a small number of domains where I’ve actually worked:
It’s easier to recognize a good paper in computer science or ML than to write one. I’m most familiar with theoretical computer science, where this is equally true in domains that are not yet formalized, e.g. a mediocre person in the field is still able to recognize important new conceptual ideas without being able to generate them. In ML it requires more data than is typically present in a paper (but e.g. can be obtained by independent replications or by being able to inspect code).
Verifying that someone has done a good job writing software is easier than writing it yourself, if you are able to e.g. interact with the software, get clear explanations of what they did and why, and have them also write good tests.
Verifying a theory in physics is easier than generating it. Both in the sense that it’s much easier to verify that QM or the standard model or general relativity is a good explanation of existing phenomena than it is to come up with those models from scratch, and in the sense that e.g. verifying claims about how the LHC supports a given claim is easier than designing and building the LHC.
Verifying that someone has built a good GPU or a quantum computer is much easier than building one. This is completely clear if you are able to perform experiments on the computer. I also think it’s almost certainly true if you are trying to evaluate a design and manufacturing process though I have less firsthand experience
There are a ton of fuzzy domains where we have less objective evidence but the claim seems obviously true to me. Evaluating papers in philosophy, useful exercises in futurism, alignment ideas, etc. all seem meaningfully easier than generating them (particularly if we require them to come with convincing justification). I think other people have different intuitions here but I’m not sure how to engage and if there are disagreements about more established fields that’s obviously nicer to use as an example.
This feels like stepping on a rubber duck while tip-toeing around sleeping giants but:
Don’t these analogies break if/when the complexity of the thing to generate/verify gets high enough? That is, unless you think the difficulty of verification of arbitrarily complex plans/ideas is asymptotic to some human-or-lower level of verification capability (which I doubt you do) then at some point humans can’t even verify the complex plan.
So, the deeper question just seems to be takeoff speeds again: If takeoff is too fast, we don’t have enough time to use “weak” AGI to help produce actually verifiable plans which solve alignment. If takeoff is slow enough, we might. (And if takeoff is too fast, we might not notice that we’ve passed the point of human verifiability until it’s too late.)
(I am consciously not bringing up ideas about HCH / other oversight-amplification ideas because I’m new to the scene and don’t feel familiar enough with them.)
I’m just going to name random examples of fields, I think it’s true essentially all the time but I only have personal experience in a small number of domains where I’ve actually worked:
It’s easier to recognize a good paper in computer science or ML than to write one. I’m most familiar with theoretical computer science, where this is equally true in domains that are not yet formalized, e.g. a mediocre person in the field is still able to recognize important new conceptual ideas without being able to generate them. In ML it requires more data than is typically present in a paper (but e.g. can be obtained by independent replications or by being able to inspect code).
Verifying that someone has done a good job writing software is easier than writing it yourself, if you are able to e.g. interact with the software, get clear explanations of what they did and why, and have them also write good tests.
Verifying a theory in physics is easier than generating it. Both in the sense that it’s much easier to verify that QM or the standard model or general relativity is a good explanation of existing phenomena than it is to come up with those models from scratch, and in the sense that e.g. verifying claims about how the LHC supports a given claim is easier than designing and building the LHC.
Verifying that someone has built a good GPU or a quantum computer is much easier than building one. This is completely clear if you are able to perform experiments on the computer. I also think it’s almost certainly true if you are trying to evaluate a design and manufacturing process though I have less firsthand experience
There are a ton of fuzzy domains where we have less objective evidence but the claim seems obviously true to me. Evaluating papers in philosophy, useful exercises in futurism, alignment ideas, etc. all seem meaningfully easier than generating them (particularly if we require them to come with convincing justification). I think other people have different intuitions here but I’m not sure how to engage and if there are disagreements about more established fields that’s obviously nicer to use as an example.
This feels like stepping on a rubber duck while tip-toeing around sleeping giants but:
Don’t these analogies break if/when the complexity of the thing to generate/verify gets high enough? That is, unless you think the difficulty of verification of arbitrarily complex plans/ideas is asymptotic to some human-or-lower level of verification capability (which I doubt you do) then at some point humans can’t even verify the complex plan.
So, the deeper question just seems to be takeoff speeds again: If takeoff is too fast, we don’t have enough time to use “weak” AGI to help produce actually verifiable plans which solve alignment. If takeoff is slow enough, we might. (And if takeoff is too fast, we might not notice that we’ve passed the point of human verifiability until it’s too late.)
(I am consciously not bringing up ideas about HCH / other oversight-amplification ideas because I’m new to the scene and don’t feel familiar enough with them.)