About (6), I think we’re more likely to get AGI /ASI by composing pre-trained ML models and other elements than by a fresh training run. Think adding iterated reasoning and api calling to a LLM.
About the race dynamics. I’m interested in founding / joining a guild / professional network for people committed to advancing alignment without advancing capabilities. Ideally we would share research internally, but it would not be available to those not in the network. How likely does this seem to create a worthwhile cooling of the ASI race? Especially if the network were somehow successful enough to reach across relevant countries?
Re 6 -- I dearly hope you are right but I don’t think you are. That scaffolding will exist of course but the past two years have convinced me that it isn’t the bottleneck to capabilities progress (tens of thousands of developers have been building language model programs / scaffolds / etc. with little to show for it) (tbc even in 2021 I thought this, but I feel like the evidence has accumulated now)
Re race dynamics: I think people focused on advancing alignment should do that, and not worry about capabilities side-effects. Unless and until we can coordinate an actual pause or slowdown. There are exceptions of course on a case-by-base basis.
re 6 -- Interesting. It was my impression that “chain of thought” and other techniques notably improved LLM performance. Regardless, I don’t see compositional improvements as a good thing. They are hard to understand as they are being created, and the improvements seem harder to predict. I am worried about RSI in a misaligned system created/improved via composition.
Re race dynamics: It seems to me there are multiple approaches to coordinating a pause. It doesn’t seem likely that we could get governments or companies to head a pause. Movements from the general population might help, but a movement lead by AI scientists seems much more plausible to me. People working on these systems ought to be more aware of the issues and more sympathetic to avoiding the risks, and since they are the ones doing the development work, they are more in a position to refuse to do work that hasn’t been shown to be safe.
Based on your comment and other thoughts, my current plan is to publish research as normal in order to move forward with my mechanistic interpretability career goals, but to also seek out and/or create a guild or network of AI scientists / workers with the goal of agglomerating with other such organizations into a global network to promote alignment work & reject unsafe capabilities work.
About (6), I think we’re more likely to get AGI /ASI by composing pre-trained ML models and other elements than by a fresh training run. Think adding iterated reasoning and api calling to a LLM.
About the race dynamics. I’m interested in founding / joining a guild / professional network for people committed to advancing alignment without advancing capabilities. Ideally we would share research internally, but it would not be available to those not in the network. How likely does this seem to create a worthwhile cooling of the ASI race? Especially if the network were somehow successful enough to reach across relevant countries?
Re 6 -- I dearly hope you are right but I don’t think you are. That scaffolding will exist of course but the past two years have convinced me that it isn’t the bottleneck to capabilities progress (tens of thousands of developers have been building language model programs / scaffolds / etc. with little to show for it) (tbc even in 2021 I thought this, but I feel like the evidence has accumulated now)
Re race dynamics: I think people focused on advancing alignment should do that, and not worry about capabilities side-effects. Unless and until we can coordinate an actual pause or slowdown. There are exceptions of course on a case-by-base basis.
re 6 -- Interesting. It was my impression that “chain of thought” and other techniques notably improved LLM performance. Regardless, I don’t see compositional improvements as a good thing. They are hard to understand as they are being created, and the improvements seem harder to predict. I am worried about RSI in a misaligned system created/improved via composition.
Re race dynamics: It seems to me there are multiple approaches to coordinating a pause. It doesn’t seem likely that we could get governments or companies to head a pause. Movements from the general population might help, but a movement lead by AI scientists seems much more plausible to me. People working on these systems ought to be more aware of the issues and more sympathetic to avoiding the risks, and since they are the ones doing the development work, they are more in a position to refuse to do work that hasn’t been shown to be safe.
Based on your comment and other thoughts, my current plan is to publish research as normal in order to move forward with my mechanistic interpretability career goals, but to also seek out and/or create a guild or network of AI scientists / workers with the goal of agglomerating with other such organizations into a global network to promote alignment work & reject unsafe capabilities work.
Sounds good to me!