The thing the “timelines are too short” was trying to get at was “it has to be competitive with mainstream AI in order to work” (pretty sure Paul has explicitly said this), with, what I thought was basically a followup assumption of “and timelines are too short to have time to get a competitive thing based off the kind of deconfusion work that MIRI does.”
I’d have thought the Paul-argument is less timeline-dependent than that—more like ‘even if timelines are long, there’s no reason to expect any totally new unexplored research direction to pay off so spectacularly that it can compete with the state of the art n years from now; and prosaic alignment seems like it may work, so we should focus more on that until we’re confident it’s a dead end’.
The base rate of new ideas paying off in a big way, even if they’re very promising-seeming at the outset, is super low. It may be useful for some people to pursue ideas like this, but (on my possibly-flawed Paul-model) the bulk of the field’s attention should be on AI techniques that already have a proven track record of competitiveness, until we know this is unworkable.
Whereas if you’re already confident that scaled-up deep learning in the vein of current ML is unalignable, then base rates are a bit of a moot point; we have to find new approaches one way or another, even if it’s hard-in-expectation. So “are scaled-up deep nets a complete dead end in terms of alignability?” seems like an especially key crux to me.
Caveat: I didn’t run the above comments by MIRI researchers, and MIRI researchers aren’t a monolith in any case. E.g., I could imagine people’s probabilities in “scaled-up deep nets are a complete dead end in terms of alignability” looking like “Eliezer ≈ Benya ≈ Nate >> Scott >> Abram > Evan >> Paul”, or something?
The thing the “timelines are too short” was trying to get at was “it has to be competitive with mainstream AI in order to work” (pretty sure Paul has explicitly said this), with, what I thought was basically a followup assumption of “and timelines are too short to have time to get a competitive thing based off the kind of deconfusion work that MIRI does.”
I’d have thought the Paul-argument is less timeline-dependent than that—more like ‘even if timelines are long, there’s no reason to expect any totally new unexplored research direction to pay off so spectacularly that it can compete with the state of the art n years from now; and prosaic alignment seems like it may work, so we should focus more on that until we’re confident it’s a dead end’.
The base rate of new ideas paying off in a big way, even if they’re very promising-seeming at the outset, is super low. It may be useful for some people to pursue ideas like this, but (on my possibly-flawed Paul-model) the bulk of the field’s attention should be on AI techniques that already have a proven track record of competitiveness, until we know this is unworkable.
Whereas if you’re already confident that scaled-up deep learning in the vein of current ML is unalignable, then base rates are a bit of a moot point; we have to find new approaches one way or another, even if it’s hard-in-expectation. So “are scaled-up deep nets a complete dead end in terms of alignability?” seems like an especially key crux to me.
Caveat: I didn’t run the above comments by MIRI researchers, and MIRI researchers aren’t a monolith in any case. E.g., I could imagine people’s probabilities in “scaled-up deep nets are a complete dead end in terms of alignability” looking like “Eliezer ≈ Benya ≈ Nate >> Scott >> Abram > Evan >> Paul”, or something?
Okay, that is compatible with the rest of my Paul model. Does still seem to fit into the ‘what’s least impossible’ frame.