I currently lead EA funds.
Before that, I worked on improving epistemics in the EA community at CEA (as a contractor), as a research assistant at the Global Priorities Institute, on community building, and Global Health Policy.
Unless explicitly stated otherwise, opinions are my own, not my employer's.
You can give me positive and negative feedback here.
A few things that come to mind that I appreciate in people’s applications:
To be clear you don’t need to do any of these things to get funding, but I often find that applications are improved after people consider some of these bullet points.
Idk, many of the people they are directing would just do something kinda random which an 80k rec easily beats. I'd guess the number of people for whom 80k makes their plans worse in an absolute sense is kind of low and those people are likely to course correct.
Otoh, I do think people/orgs in general should consider doing more strategy/cause prio research, and if 80k were like "we want to triple the size of our research team to work out the ideal marginal talent allocation across longtermist interventions" that seems extremely exciting to me. But I don't think 80k are currently being irresponsible (not that you explicitly said that, for some reason I got a bit of that vibe from your post).
I think it's worth noting that the two papers linked (which I agree are flawed and not that useful from an x-risk viewpoint)
I haven't read the papers but I am surprised that you don't think they are useful from an x-risk perspective. The second paper "A Model for Estimating the Economic Costs of Computer Vision Systems that use Deep Learning" seems highly relevant to forecasting AI progress which imo is one of the most useful AIS interventions.
The OP's claim
This paper has many limitations (as acknowledged by the author), and from an x-risks point of view, it seems irrelevant.
Seems overstated and I'd guess that many people working on AI safety would disagree with them.
Great post - I really enjoyed reading this.
I would have thought the standard way to resolve some of the questions above would be to use a large agent-based model, simulating disease transmission among millions of agents and then observing how successful some testing scheme is within the model (you might be able to backtest the model against well-documented outbreaks).
I'm not sure how much you'd trust these models over your intuitions, but I'd guess they'd have quite a lot of mileage.
I've only skimmed these papers, but these seem promising and illustrative of the direction to me:
Hi Markus,
For context I run EA Funds, which includes the EAIF (though the EAIF is chaired by Max Daniel not me). We are still paying out grants to our grantees — though we have been slower than usual (particularly for large grants). We are also still evaluating applications and giving decisions to applicants (though this is also slower than usual).
We have communicated this to the majority of our grantees, but if you or anyone else reading this urgently needs a funding decision (in the next two weeks), please email caleb [at] effectivealtruismfunds [dot] org with URGENT in the subject line, and I will see what I can do. Please also include:
You can also apply to one of Open Phil’s programs; in particular, Open Philanthropy’s program for grantees affected by the collapse of the FTX Future Fund may be particularly of note to people applying to EA Funds due to the FTX crash.