I know you’re pointing out the easier case still not working, but I just want to caution against the “drive it to zero” mentality, since I worry strongly that it’s the exact mentality researchers often have.
When that’s your mental model, reducing rates will seem like progress.
The part I find unlikely is that we would not be able to see multiple failures along the way that are growing in magnitude
IMO the default failure mode here is:
We do observe them (or early versions of them)
The lab underinvests in the problem
It becomes enough of a problem that its painful to product or internal capabilities usage
We didn’t invest enough to actually solve the underlying problem, and we can’t afford to not use the model while we wait for alignment research to catch up
The lab patches over the problem with some “reduces but does not eliminate” technique
The model is then usable, but with harder to detect misalignment
Scale capabilities and repeat
This is the exact loop we’re in now, and the dynamics only intensify with time and capabilities.
The situation you’re describing definitely concerns me, and is about mid-way up the hierarchy of nested problems as I see it (I don’t mean ‘hierarchy of importance’ I mean ’spectrum from object-level-empirical-work to realm-of-pure-abstraction).
I tried to capture this at the end of my comment, by saying that even success as I outlined it probably wouldn’t change my all-things-considered view (because there’s a whole suite of nested problems at other levels of abstraction, including the one you named), but it would at least update me toward the plausibility of the case they’re making.
As is, their own tests say they’re doing poorly, and they’ll probably want to fix that in good faith before they try tackling the kind of dynamic group epistemic failures that you’re pointing at.
I know you’re pointing out the easier case still not working, but I just want to caution against the “drive it to zero” mentality, since I worry strongly that it’s the exact mentality researchers often have.
When that’s your mental model, reducing rates will seem like progress.
IMO the default failure mode here is:
We do observe them (or early versions of them)
The lab underinvests in the problem
It becomes enough of a problem that its painful to product or internal capabilities usage
We didn’t invest enough to actually solve the underlying problem, and we can’t afford to not use the model while we wait for alignment research to catch up
The lab patches over the problem with some “reduces but does not eliminate” technique
The model is then usable, but with harder to detect misalignment
Scale capabilities and repeat
This is the exact loop we’re in now, and the dynamics only intensify with time and capabilities.
The situation you’re describing definitely concerns me, and is about mid-way up the hierarchy of nested problems as I see it (I don’t mean ‘hierarchy of importance’ I mean ’spectrum from object-level-empirical-work to realm-of-pure-abstraction).
I tried to capture this at the end of my comment, by saying that even success as I outlined it probably wouldn’t change my all-things-considered view (because there’s a whole suite of nested problems at other levels of abstraction, including the one you named), but it would at least update me toward the plausibility of the case they’re making.
As is, their own tests say they’re doing poorly, and they’ll probably want to fix that in good faith before they try tackling the kind of dynamic group epistemic failures that you’re pointing at.