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A preliminary estimate, and a request for better ones.
Summary
I believe the standard literature estimates for the number of DALYs attributable to a case of stunting are too low, largely because they don’t account for the long term effects. This means that childhood nutritional interventions that reduce the prevalence of stunting may be substantially more cost-effective than previously believed.
Epistemic status
Exploratory and back-o...
Note: This post was crossposted from Planned Obsolescence by the Forum team, with the author's permission. The author may not see or respond to comments on this post.
Subtitle: It’s a major warning shot, and might be the last one we get
All opinions are my personal view, and don’t represent my employer or fellow investigators.
This week, METR and Redwood Research published...
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Overview
This post summarizes a new preprint on alternative proteins from the Humane and Sustainable Food Lab: New alternatives, same orders. We investigated 19...
Recent opportunities to take action
Do I need a technical background to work on AI Governance? I think no, not really. Quick take because I don't justify many of my claims.
Context: I haver been a technical ML engineer and (briefly) a researcher, and I'm now trying to work on AI governance (and spending a lot of time speaking to people who do work on AI governance).
Examples of things that are useful to understand to do AI governance:
1. Knowing about the train, test, deploy cycle at industrial AI companies.
2. 1. Knowing the psyche of ML engineers at those orgs.
3. Knowing which media channels machine learning engineers & researchers use to stay on top of news, including twitter & ML companies.
You don't get any of those insights by doing an ML coursera course. It might be fun / gratifying to do that course for other reasons, but I think it won't make you better at governance. It's better to have a few friends who are ML engineers and to get them to sketch out what it's like at a lab, some day (or - more costly but more thorough - to take a role at a lab, technical or nontechnical).
What I do think you need to engage with technically is not to be afraid to read below the surface of techincal memes - but I think not much below the surface.
Concrete example: watermarking.
It's enough for policymakers to be able to read a few watermarking papers and understand:
a) watermarking is a way of tagging your model's outputs to prove it was produced by AI
b) There are no tried & tested, reliable watermarking methods at the moment.
Where I see nontechnincal folk fall down (less so in this community) is when they throw out the term 'watermarking' but couldn't tell you about what methods can be used or what the reliability of those methods is. I think that can be read about, and you don't need to have direct experience having tried to watermarking something (I certainly haven't).