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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%.

Any ideas?

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

EA: 18%

US: 45%

UK: 22%

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.

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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.