For the 2019 Review, I think it would’ve helped if you/Rob/others had posted something like this as reviews of the post. Then voters would at least see that you had this take, and maybe people who disagree would’ve replied there which could’ve led to some of this getting hashed out in the comments.
I agree. In retrospect, I think I avoided doing this for a couple reasons:
I didn’t have much time to spend on the review process, and it was hard for me to know how to prioritize my time without knowing vote totals, number of voters, etc. E.g., I also would have been sad if “Risks from Learned Optimization” hadn’t made the cut, and I could imagine worlds where this had happened too (say, if everyone reasoned “this post will definitely make the cut, so there’s no need for me personally to put a bunch of votes into it”).
I felt like there was something uncouth about leaving a review that basically just says “I think this post deserves n votes!”, without giving detailed reasoning like many other reviewers were doing.
I’m co-workers with Scott (one of the authors of Thoughts on Human Models), and I’m not an alignment researcher myself. So I felt like absent detailed supporting arguments, my merely saying “I think this is one of the most valuable alignment posts to date” ought to carry little weight.
Point 2 seems wrong to me now. Points 1 and 3 seem right, but I think they deserved less weight in my deliberation than “just saying something quick and pithy might spark a larger conversation about the post, resulting in others writing better reviews; and it might also encourage more people to read it who initially skipped over it”.
Encouraging more discussion of ‘is human modeling a good idea in the first AGI systems? what might alignment and AI research look like if it didn’t rely on human models?’ is the thing I care about anyway (hence my bringing it up now). This would have felt much less like a missed opportunity if the review process had involved a lot of argument about human models followed by the community going ‘we reviewed the arguments back and forth and currently find them uncompelling/unimportant enough that this doesn’t seem to warrant inclusion in the review’.
That would make me more confident that the ideas had gotten a good hearing, and it would likely make it clearer where the conversation should go next.
For the 2019 Review, I think it would’ve helped if you/Rob/others had posted something like this as reviews of the post. Then voters would at least see that you had this take, and maybe people who disagree would’ve replied there which could’ve led to some of this getting hashed out in the comments.
I agree. In retrospect, I think I avoided doing this for a couple reasons:
I didn’t have much time to spend on the review process, and it was hard for me to know how to prioritize my time without knowing vote totals, number of voters, etc. E.g., I also would have been sad if “Risks from Learned Optimization” hadn’t made the cut, and I could imagine worlds where this had happened too (say, if everyone reasoned “this post will definitely make the cut, so there’s no need for me personally to put a bunch of votes into it”).
I felt like there was something uncouth about leaving a review that basically just says “I think this post deserves n votes!”, without giving detailed reasoning like many other reviewers were doing.
I’m co-workers with Scott (one of the authors of Thoughts on Human Models), and I’m not an alignment researcher myself. So I felt like absent detailed supporting arguments, my merely saying “I think this is one of the most valuable alignment posts to date” ought to carry little weight.
Point 2 seems wrong to me now. Points 1 and 3 seem right, but I think they deserved less weight in my deliberation than “just saying something quick and pithy might spark a larger conversation about the post, resulting in others writing better reviews; and it might also encourage more people to read it who initially skipped over it”.
Encouraging more discussion of ‘is human modeling a good idea in the first AGI systems? what might alignment and AI research look like if it didn’t rely on human models?’ is the thing I care about anyway (hence my bringing it up now). This would have felt much less like a missed opportunity if the review process had involved a lot of argument about human models followed by the community going ‘we reviewed the arguments back and forth and currently find them uncompelling/unimportant enough that this doesn’t seem to warrant inclusion in the review’.
That would make me more confident that the ideas had gotten a good hearing, and it would likely make it clearer where the conversation should go next.