Thanks! I appreciated these distinctions. The worst-case argument for modularity came up in a past argument I had with Eliezer, where I argued that this was a reason for randomization (even though Bayesian decision theory implies you should never randomize). See section 2 here: The Power of Noise.
Re: 50% vs. 10% vs. 90%. I liked this illustration, although I don’t think your argument actually implies 50% specifically. For instance if it turns out that everyone else is working on the 50% worlds and no one is working on the 90% worlds, you should probably work on the 90% worlds. In addition:
* It seems pretty plausible that the problem is overall more tractable in 10% worlds than 50% worlds, so given equal neglectedness you would prefer the 10% world.
* Many ideas will generalize across worlds, and recruitment / skill-building / organization-building also generalizes across worlds. This is an argument towards working on problems that seem tractable and relevant to any world, as long as they are neglected enough that you are building out distinct ideas and organizational capacity (vs. just picking from the same tree as ML generally). I don’t think that this argument dominates considerations, but it likely explains some of our differences in approach.
In the terms laid out in your post, I think my biggest functional disagreement (in terms of how it affects what problems we work on) is that I expect most worst-case assumptions make the problem entirely impossible, and I am more optimistic that many empirically-grounded assumptions will generalize quite far, all the way to AGI. To be clear, I am not against all worst-case assumptions (for instance my entire PhD thesis is about this) but I do think they are usually a source of significant added difficulty and one has to be fairly careful where they are making them.
For instance, as regards Redwood’s project, I expect making language models fully adversarially robust is impossible with currently accessible techniques, and that even a fairly restricted adversary will be impossible to defend against while maintaining good test accuracy. On the other hand I am still pretty excited about Redwood’s project because I think you will learn interesting things by trying. (I spent some time trying to solve the unrestricted adversarial example competition, totally failed, but still felt it was a good use of time for similar reasons, and the difficulties for language models seem interestingly distinct in a way that should generate additional insight.) I’m actually not sure if this differs that much from your beliefs, though.
Thanks! I appreciated these distinctions. The worst-case argument for modularity came up in a past argument I had with Eliezer, where I argued that this was a reason for randomization (even though Bayesian decision theory implies you should never randomize). See section 2 here: The Power of Noise.
Re: 50% vs. 10% vs. 90%. I liked this illustration, although I don’t think your argument actually implies 50% specifically. For instance if it turns out that everyone else is working on the 50% worlds and no one is working on the 90% worlds, you should probably work on the 90% worlds. In addition:
* It seems pretty plausible that the problem is overall more tractable in 10% worlds than 50% worlds, so given equal neglectedness you would prefer the 10% world.
* Many ideas will generalize across worlds, and recruitment / skill-building / organization-building also generalizes across worlds. This is an argument towards working on problems that seem tractable and relevant to any world, as long as they are neglected enough that you are building out distinct ideas and organizational capacity (vs. just picking from the same tree as ML generally). I don’t think that this argument dominates considerations, but it likely explains some of our differences in approach.
In the terms laid out in your post, I think my biggest functional disagreement (in terms of how it affects what problems we work on) is that I expect most worst-case assumptions make the problem entirely impossible, and I am more optimistic that many empirically-grounded assumptions will generalize quite far, all the way to AGI. To be clear, I am not against all worst-case assumptions (for instance my entire PhD thesis is about this) but I do think they are usually a source of significant added difficulty and one has to be fairly careful where they are making them.
For instance, as regards Redwood’s project, I expect making language models fully adversarially robust is impossible with currently accessible techniques, and that even a fairly restricted adversary will be impossible to defend against while maintaining good test accuracy. On the other hand I am still pretty excited about Redwood’s project because I think you will learn interesting things by trying. (I spent some time trying to solve the unrestricted adversarial example competition, totally failed, but still felt it was a good use of time for similar reasons, and the difficulties for language models seem interestingly distinct in a way that should generate additional insight.) I’m actually not sure if this differs that much from your beliefs, though.