I'm not a huge fan of this option because it doesn't feel like my comparative advantage and I don't like (and am not good at) splitting my focus like this.If EA is vetting-constrained, then plausibly even though it doesn't feel like my comparative advantage, it's useful to the community if I spend time vetting things. But my impression from my brief stint in EA funds was that the funds felt more good-opportunity-constrained than vetting-constrained, and I don't feel as well-positioned to address that bottleneck.
I'm in a similar position and I largely agree with this. One loophole is that I have a large set of friends who I might trust instinctively but that EA Funds'd need substantial convincing. With a little advertising I might be able to find projects that are below even EAF's very-low barrier-to-entry.
I haven't tried this out yet, though I've a colleague who seems to have had a good experience with it.
What'd you feel your comparative advantage is versus other organisations in this space? In particular, the Long-Term Future Fund and Survival & Flourishing?
Is that tax advice published anywhere? I'd assumed any grants I received in the UK would be treated as regular income, and if that's not the case it's a pleasant surprise!
Sample size of 1:
After suffering mild RSI in my early 20s, it was completely and permanently resolved by switching to DVORAK for a year, and then back to QWERTY. It wasn't an intentional solution, but forcing myself to re-learn to type from scratch, twice, massively improved my typing habits.
Googling around on the benefits of DVORAK for RSI offers more anecdotes in either direction, so take this with a whole pile of salt.
Thanks very much for figuring that out! I've retracted my original comment; my estimate of the background rate is now 30-40%ish - I think the various perturbations'll near-enough cancel - and with that, the diff against the American rate is no longer the majority of the anomaly.
I had trouble finding the ever-married stat, so I went to the 2017 marriages dataset. Table 12 is 'Proportions of men and women who had ever married by certain ages', and for the 1984 men's cohort - the last one with complete data - the rates are 6% for 25yos through 39% for 34yos. The average over that age range is 22%.
So upfront, I didn't notice it was the mens' cohort. My bad, and I'll fix my post once I've figured out the other problem: these figures are decidedly inconsistent with the figures you've found. It's not obvious to me what the difference is.
First, momentary thought was I'd been totally daft and averaged married-this-year data. Second thought was that I'd averaged over birth-cohort rather than reporting year. But that gives 20%, an even lower figure. Third thought was that 'married by 34' is different from 'married at 34', but shifting the age range up a notch only gets the figure up to 26%.
I think a big chunk of the discrepancy here comes from comparing against US expectations rather than a weighted average over countries.
Case in point, the 'ever married 25-34' stat (which is the one with biggest sample size) is
I can't find directly comparable stats for Australia, Canada or Germany - which make up the other major countries of the 2018 survey. What is available is age-at-first-marriage:
US: 29, UK: 33, DE: 33, AU: 32, CA: 31
Doing linear interpolation on the UK/US values, estimated ever-married 25-34 stats are
US: 45%, UK: 22%, DE: 22%, AU: 28%, CA: 34%
which, weighted by the survey fractions of .36/.16/.07/.06/.04 gives an expected ever-married 25-34 rate of 35%.
Some other things worth noting:
All in all I'd expect a properly-calibrated expected rate to be 25-30%-ish.
I'm also curious if the higher-education-higher-marriage-rate thing holds in the UK/Europe, but damned if I can find solid stats. Anecdotally it doesn't, but anecdotes are awful for this kind of thing. Sample bias'll kill you dead.
One of them is called “How far can deep learning take us? A look at performance and economic limits.”
In case anyone else is looking for this, it seems to have been published as The Computational Limits of Deep Learning.