I was not aware. Are these outreach strategies towards the general public with the aim of getting uninvolved people to support AI alignment efforts, or are they toward DeepMind employees to get them to stop working so hard on AGI? I know there are lots of people raising awareness in general, but that’s not really the goal of the strategy that I’ve outlined.
Much of the outreach efforts are towards governments, and some to AI labs, not to the general public.
I think that because of the way crisis governance often works, if you’re the designated expert in a position to provide options to a government when something’s clearly going wrong, you can get buy in for very drastic actions (see e.g. COVID lockdowns). So the plan is partly to become the designated experts.
I can imagine (not sure if this is true) that even though an ‘all of the above’ strategy like you suggest seems like on paper it would be the most likely to produce success, you’d get less buy in from government decision-makers and be less trusted by them in a real emergency if you’d previously being causing trouble with grassroots advocacy. So maybe that’s why it’s not been explored much.
This post by David Manheim does a good job of explaining how to think about governance interventions, depending on different possibilities for how hard alignment turns out to be: https://www.lesswrong.com/posts/xxMYFKLqiBJZRNoPj/
In case of interest, I’ve been conducting AI strategy research with CSER’s AI-FAR group, amongst others a project to survey historical cases of (unilaterally decided; coordinated; or externally imposed) technological restraint/delay, and their lessons for AGI strategy (in terms of differential technological development, or ‘containment’).
(see longlist of candidate case studies, including a [subjective] assessment of the strength of restraint, and the transferability to the AGI case) https://airtable.com/shrVHVYqGnmAyEGsz This is still in-progress work, but will be developed into a paper / post within the next month or so. --- One avenue that I’ve recently gotten interested in, though I’ve only just gotten to read about it and have large uncertainties about it, is the phenomenon of ‘hardware lotteries’ in the historical development of machine learning—see https://arxiv.org/abs/2009.06489 -- to describe cases were the development of particular types of domain specialized compute hardware make it more costly [especially for e.g. academic researchers, probably less so for private labs] to pursue particular new research directions.
I was not aware. Are these outreach strategies towards the general public with the aim of getting uninvolved people to support AI alignment efforts, or are they toward DeepMind employees to get them to stop working so hard on AGI? I know there are lots of people raising awareness in general, but that’s not really the goal of the strategy that I’ve outlined.
Much of the outreach efforts are towards governments, and some to AI labs, not to the general public.
I think that because of the way crisis governance often works, if you’re the designated expert in a position to provide options to a government when something’s clearly going wrong, you can get buy in for very drastic actions (see e.g. COVID lockdowns). So the plan is partly to become the designated experts.
I can imagine (not sure if this is true) that even though an ‘all of the above’ strategy like you suggest seems like on paper it would be the most likely to produce success, you’d get less buy in from government decision-makers and be less trusted by them in a real emergency if you’d previously being causing trouble with grassroots advocacy. So maybe that’s why it’s not been explored much.
This post by David Manheim does a good job of explaining how to think about governance interventions, depending on different possibilities for how hard alignment turns out to be: https://www.lesswrong.com/posts/xxMYFKLqiBJZRNoPj/
In case of interest, I’ve been conducting AI strategy research with CSER’s AI-FAR group, amongst others a project to survey historical cases of (unilaterally decided; coordinated; or externally imposed) technological restraint/delay, and their lessons for AGI strategy (in terms of differential technological development, or ‘containment’).
(see longlist of candidate case studies, including a [subjective] assessment of the strength of restraint, and the transferability to the AGI case)
https://airtable.com/shrVHVYqGnmAyEGsz
This is still in-progress work, but will be developed into a paper / post within the next month or so.
---
One avenue that I’ve recently gotten interested in, though I’ve only just gotten to read about it and have large uncertainties about it, is the phenomenon of ‘hardware lotteries’ in the historical development of machine learning—see https://arxiv.org/abs/2009.06489 -- to describe cases were the development of particular types of domain specialized compute hardware make it more costly [especially for e.g. academic researchers, probably less so for private labs] to pursue particular new research directions.
These are really interesting, thanks for sharing!