Holden also mentions something a bit like Eliezer’s criticism in his own write-up,
In particular, I think it’s hard to rule out the possibility of ingenuity leading to transformative AI in some far more efficient way than the “brute-force” method contemplated here.
When Holden talks about ‘ingenuity’ methods that seems consistent with Eliezer’s
They’re not going to be taking your default-imagined approach algorithmically faster, they’re going to be taking an algorithmically different approach that eats computing power in a different way than you imagine it being consumed.
I.e. if you wanted to fold this consideration into OpenAI’s estimate you’d have to do it by having a giant incredibly uncertain free-floating variable for ‘speedup factor’ because you’d be nonsensically trying to estimate the ‘speed-up’ to brain processing applied from using some completely non-Deep Learning or non-brainlike algorithm for intelligence. All your uncertainty just gets moved into that one factor, and you’re back where you started.
It’s possible that Eliezer is confident in this objection partly because of his ‘core of generality’ model of intelligence—i.e. he’s implicitly imagining enormous numbers of varied paths to improvement that end up practically in the same place, while ‘stack more layers in a brainlike DL model’ is just one of those paths (and one that probably won’t even work), so he naturally thinks estimating the difficulty of this one path we definitely won’t take (and which probably wouldn’t work even if we did try it) out of the huge numbers of varied paths to generality is useless.
However, if you don’t have this model, then perhaps you can be more confident that what we’re likely to build will look at least somewhat like a compute-limited DL system and that these other paths will have to share some properties of this path. Relatedly, it’s an implication of the model that there’s some imaginable (and not e.g. galaxy sized) model we could build right now that would be an AGI, which I think Eliezer disputes?
Holden also mentions something a bit like Eliezer’s criticism in his own write-up,
When Holden talks about ‘ingenuity’ methods that seems consistent with Eliezer’s
I.e. if you wanted to fold this consideration into OpenAI’s estimate you’d have to do it by having a giant incredibly uncertain free-floating variable for ‘speedup factor’ because you’d be nonsensically trying to estimate the ‘speed-up’ to brain processing applied from using some completely non-Deep Learning or non-brainlike algorithm for intelligence. All your uncertainty just gets moved into that one factor, and you’re back where you started.
It’s possible that Eliezer is confident in this objection partly because of his ‘core of generality’ model of intelligence—i.e. he’s implicitly imagining enormous numbers of varied paths to improvement that end up practically in the same place, while ‘stack more layers in a brainlike DL model’ is just one of those paths (and one that probably won’t even work), so he naturally thinks estimating the difficulty of this one path we definitely won’t take (and which probably wouldn’t work even if we did try it) out of the huge numbers of varied paths to generality is useless.
However, if you don’t have this model, then perhaps you can be more confident that what we’re likely to build will look at least somewhat like a compute-limited DL system and that these other paths will have to share some properties of this path. Relatedly, it’s an implication of the model that there’s some imaginable (and not e.g. galaxy sized) model we could build right now that would be an AGI, which I think Eliezer disputes?