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I don't have a strong opinion about this in the context of fellowships, but I can refer to setting a high entry bar in recruiting community members and volunteers in general, and specifically, by asking them to invest time in reading content. I hope this helps and not completely off-topic.

Though EA is a complex set of ideas and we want people to have a good understanding of what it's all about, demanding a lot from new people can be fairly offputting and counterproductive.

From my experience, people who are of high potential to be both highly-engaged and of high value to the community are often both quite busy people and relatively normal human beings.
   
As for the first point, if you sent someone a long list of content, he/she might just say "this is too demanding, can't handle this right now". As for the second point, we have to accept that people have much shorter attention spans than we would like to imagine, especially if they are not really familiar with the content.   

Me and Gidon Kadosh from EA Israel have thought long and hard about how to lower the perceived effort of people who come to our website by creating this "Learn More" page on our website. Though it's in Hebrew, you might be able to understand what we tried to do here on a structural level. We plan to make it even more attractive for readers, possibly by splitting this page into individual pages focusing on a specific subject, and allowing the user to conveniently move on to the next/previous subject - This way we both lower perceived effort of reading this content and create a feeling of progress for the user.    

I'm really not sure there is a correlation between the willingness of someone to invest a lot of time in reading lots of content or filling a long application before they have a clear view of the value in doing this. Going back to recruiting new community members and volunteers, there are brilliant people who are value-aligned, but just don't have the time (or are not willing) to invest the time needed to fill in a highly-demanding application, or read 20 articles about something they are not sure they care about yet.

Hello, Juan here. Here's the final version of the paper I mentioned we were working on during my talk for those who would like to know more: https://doi.org/10.1016/j.spc.2020.08.011

Thanks for taking the time to transcribe the talks

Thanks for publishing negative results. I think that it is important to do so in general, and especially given that many other group may have relied on your previous recommendations.

If possible, I think you should edit the previous post to reflect your new findings and link to this post.

It's an interesting idea, but even if this ends up producing very engaged participants you have to be careful.

If you (deliberately and successfully) only select for people who are super keen, you end up with a super keen cohort but potentially only minimal counterfactual impact as all those you selected would have ended up really involved anyway. This was briefly mentioned in the post and I think is worth exploring further.

Two good examples mentioned by Ajeya on the 80,000 Hours podcast: eyes vs cameras, and leaves vs solar panels.

As someone in their late 30s with kids often identified as one of the "older" EAs, I strongly agree with this.

And to quote Monty Python: "I'm thirty seven - I'm not old!"

Thanks. I agree that we should have non-infinitesimal credence that physics is wrong, but to change the conclusion, we would need to "insist that modern physics is incorrect in very specific ways." Given the strength of evidence about the existence of many of the limits, regardless of their actual form or value, that is a higher bar. I also advise looking closely at the discussion of the "Pessimistic Meta-induction," and why we think that it's reasonable to be at least incredibly confident that these limits exist.

That doesn't guarantee their existence. But after accepting a non-zero credence in those specific types of incorrect theory, we need to pin our hopes for infinite value on those specific occurrences; we would need to maximize expected value conditional on that very small probability in order to find infinite value, and neglect the very large but finite value we are nearly certain exists in the physical universe. That seems difficult to me.

Thanks, and thanks for posting this both places. I've responded on the lesswrong post [LW(p) · GW(p)], and I'm going to try to keep only one thread going, given my finite capacity to track things :)

Well said. I agree that that is a path to impact for the sort of work QRI is doing, it just seems lower-priority to me than other things like working on AI alignment or AI governance.  Not to mention the tractability / neglectedness concerns (philosophy is famously intractable, and there's an entire academic discipline for it already)

Hi Daniel,

Thanks for the reply! I am a bit surprised at this:

Getting more clarity on emotional valence does not seem particularly high-leverage to me. What's the argument that it is?

The quippy version is that, if we’re EAs trying to maximize utility, and we don’t have a good understanding of what utility is, more clarity on such concepts seems obviously insanely high-leverage. I’ve written about specific relevant to FAI here: https://opentheory.net/2015/09/fai_and_valence/ Relevance to building a better QALY here: https://opentheory.net/2015/06/effective-altruism-and-building-a-better-qaly/ And I discuss object-level considerations on how better understanding of emotional valence could lead to novel therapies for well-being here: https://opentheory.net/2018/08/a-future-for-neuroscience/ https://opentheory.net/2019/11/neural-annealing-toward-a-neural-theory-of-everything/ On mobile, pardon the formatting.

Your points about sufficiently advanced AIs obsoleting human philosophers are well-taken, though I would touch back on my concern that we won’t have particular clarity on philosophical path-dependencies in AI development without doing some of the initial work ourselves, and these questions could end up being incredibly significant for our long-term trajectory — I gave a talk about this for MCS that I’ll try to get transcribed (in the meantime I can share my slides if you’re interested). I’d also be curious to flip your criticism and ping your models for a positive model for directing EA donations — is the implication that there are no good places to donate to, or that narrow-sense AI safety is the only useful place for donations? What do you think the highest-leverage questions to work on are? And how big are your ‘metaphysical uncertainty error bars’? What sorts of work would shrink these bars?