Redwood (I think Buck?) sometimes talks about how labs should have the A-team on control and the B-team on alignment, and I have the same complaint about that claim. It doesn’t make much sense for research, most of which helps with both. It does make sense as a distinction for “what plan will you implement in practice”—but labs have said very little publicly about that.
You’re potentially thinking about footnote 4 in our post on control:
As one operationalization, suppose that you had two teams at your AI lab, one of which was much more competent, and you had to assign one to be in charge of minimizing the probability that the AIs you’re about to train and deploy were scheming, and the other to be in charge of ensuring control. To address risk due to powerful AI in the next 8 years, we think it would be better to assign the more competent team to ensuring control, because we think that the quality of control interventions is more influential on this risk.
This footnote is a bit confusingly worded, but I think the situation we were trying to say is “Suppose you expect dangerously powerful AI in the next year, and the current year is prior to 2032. We’re guessing you should put the better team on control.”
This is different than research in the run up.
I also think that to the extent people are trying to do backchained research focused on specific applications, it makes sense to put the better team on control over reducing the chance that scheming arises. (But these aren’t the only classes of interventions and some interventions don’t nicely fit into these buckets, e.g., you can do work on differentially making AIs more useful for alignment work which isn’t well classified as either and you can work on high level interpretability which aims to roughly understand how AIs make decisions in some cases (this high-level interp doesn’t clearly help with reducing the chance that scheming arises very directly, but could help with a bunch of stuff).)
You’re potentially thinking about footnote 4 in our post on control:
This footnote is a bit confusingly worded, but I think the situation we were trying to say is “Suppose you expect dangerously powerful AI in the next year, and the current year is prior to 2032. We’re guessing you should put the better team on control.”
This is different than research in the run up.
I also think that to the extent people are trying to do backchained research focused on specific applications, it makes sense to put the better team on control over reducing the chance that scheming arises. (But these aren’t the only classes of interventions and some interventions don’t nicely fit into these buckets, e.g., you can do work on differentially making AIs more useful for alignment work which isn’t well classified as either and you can work on high level interpretability which aims to roughly understand how AIs make decisions in some cases (this high-level interp doesn’t clearly help with reducing the chance that scheming arises very directly, but could help with a bunch of stuff).)