Fwiw the thing that scares me is the combination of three things:
Producing an accurate estimate requires “understanding” the input
Our standard for the quality of estimates will be very high
The approach is purely algorithmic (rather than e.g. using the flexible intelligence of LLMs / humans)
You presumably agree with (1) and (3). For (2), it seems like there’s a lot of correlations you need to take into account for this to produce estimates that aren’t wildly off:
Underestimates of risk: These are cases where the presumption of independence neglects correlations between things that happen for catastrophe-causing events. For example, maybe a model is planning to defect when RSA-2048 is factored, and it has N different consistency checks it runs to make sure we aren’t fooling it about whether it has been factored. The method has to see that there’s a common cause for all the consistency checks to return 1, otherwise it will underestimate risk by a factor exponential in N. (And tbc I think even separately from more exotic examples like RSA-2048, it will be relatively common for this pattern to arise with networks, e.g. it happens any time that in reality there is a common cause that produces many pieces of evidence, and then the network aggregates those pieces of evidence to infer the common cause.)
Overestimates of risk: These are cases where the presumption of independence neglects correlations that rule out catastrophe, and so lead to overestimates. For example, the model might be willing to make up fake instructions for creating bioweapons to help with scifi writing, but never provide real instructions. So the method needs to capture the “understanding” of which instructions are fake vs real.
I agree this isn’t a proof of impossibility, since a purely algorithmic approach (SGD) produced the “understanding” in the first place, so in theory a purely algorithmic approach could still capture all that understanding to produce accurate estimates. But it does seem heuristically like you should assign a fairly low probability that this pans out.
Yes I agree with what you have written, and do think it’s overall not that likely that everything pans out as hoped. We do also have other hopes for how this general picture can still cohere if the specific path doesn’t work out, eg we’re open to learning some stuff empirically and adding an “algorithmic cherry on top” to produce the estimate.
Fwiw the thing that scares me is the combination of three things:
Producing an accurate estimate requires “understanding” the input
Our standard for the quality of estimates will be very high
The approach is purely algorithmic (rather than e.g. using the flexible intelligence of LLMs / humans)
You presumably agree with (1) and (3). For (2), it seems like there’s a lot of correlations you need to take into account for this to produce estimates that aren’t wildly off:
Underestimates of risk: These are cases where the presumption of independence neglects correlations between things that happen for catastrophe-causing events. For example, maybe a model is planning to defect when RSA-2048 is factored, and it has N different consistency checks it runs to make sure we aren’t fooling it about whether it has been factored. The method has to see that there’s a common cause for all the consistency checks to return 1, otherwise it will underestimate risk by a factor exponential in N. (And tbc I think even separately from more exotic examples like RSA-2048, it will be relatively common for this pattern to arise with networks, e.g. it happens any time that in reality there is a common cause that produces many pieces of evidence, and then the network aggregates those pieces of evidence to infer the common cause.)
Overestimates of risk: These are cases where the presumption of independence neglects correlations that rule out catastrophe, and so lead to overestimates. For example, the model might be willing to make up fake instructions for creating bioweapons to help with scifi writing, but never provide real instructions. So the method needs to capture the “understanding” of which instructions are fake vs real.
I agree this isn’t a proof of impossibility, since a purely algorithmic approach (SGD) produced the “understanding” in the first place, so in theory a purely algorithmic approach could still capture all that understanding to produce accurate estimates. But it does seem heuristically like you should assign a fairly low probability that this pans out.
Yes I agree with what you have written, and do think it’s overall not that likely that everything pans out as hoped. We do also have other hopes for how this general picture can still cohere if the specific path doesn’t work out, eg we’re open to learning some stuff empirically and adding an “algorithmic cherry on top” to produce the estimate.