I expect that MIRI would mostly disagree with claim 6.
Can you suggest something specific that MIRI should change about their agenda?
When I try to imagine problems for which imperfect value loading suggests different plans from perfectionist value loading, I come up with things like “don’t worry about whether we use the right set of beings when creating a CEV”. But MIRI gives that kind of problem low enough priority that they’re acting as if they agreed with imperfect value loading.
I’m pretty sure I also mostly disagree with claim 6. (See my other reply below.)
The only specific concrete change that comes to mind is that it may be easier to take one person’s CEV than aggregate everyone’s CEV. However, this is likely to be trivially true, if the aggregation method is something like averaging.
If that’s 1 or 2 more lines of code, then obviously it doesn’t really make sense to try and put those lines in last to get FAI 10 seconds sooner, except in a sort of spherical cow in a vacuum sort of sense. However, if “solving the aggregation problem” is a couple years worth of work, maybe it does make sense to prioritize other things first in order to get FAI a little sooner. This is especially true in the event of an AI arms race.
I’m especially curious whether anyone else can come up with scenarios where a maxipok strategy might actually be useful. For instance, is there any work being done on CEV which is purely on the extrapolation procedure or procedures for determining coherence? It seems like if only half our values can easily be made coherent, and we can load them into an AI, that might generate an okay outcome.
I expect that MIRI would mostly disagree with claim 6.
Can you suggest something specific that MIRI should change about their agenda?
When I try to imagine problems for which imperfect value loading suggests different plans from perfectionist value loading, I come up with things like “don’t worry about whether we use the right set of beings when creating a CEV”. But MIRI gives that kind of problem low enough priority that they’re acting as if they agreed with imperfect value loading.
I’m pretty sure I also mostly disagree with claim 6. (See my other reply below.)
The only specific concrete change that comes to mind is that it may be easier to take one person’s CEV than aggregate everyone’s CEV. However, this is likely to be trivially true, if the aggregation method is something like averaging.
If that’s 1 or 2 more lines of code, then obviously it doesn’t really make sense to try and put those lines in last to get FAI 10 seconds sooner, except in a sort of spherical cow in a vacuum sort of sense. However, if “solving the aggregation problem” is a couple years worth of work, maybe it does make sense to prioritize other things first in order to get FAI a little sooner. This is especially true in the event of an AI arms race.
I’m especially curious whether anyone else can come up with scenarios where a maxipok strategy might actually be useful. For instance, is there any work being done on CEV which is purely on the extrapolation procedure or procedures for determining coherence? It seems like if only half our values can easily be made coherent, and we can load them into an AI, that might generate an okay outcome.