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... (read more)
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&... (read more)
The data I gave is ultimately survey data, the table you post is based on marriage certificates issued. This has advantages but has one large disadvantage, namely ignoring marriages that take place overseas, while possibly counting marriages between two overseas residents that take place locally. It's mentioned on the 'Table 12 interpretation' tab:
These statistics are based on marriages registered in England and Wales. Because no adjustment has been made for marriages taking place abroad, the true proportion of men and women ever married cou
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... (read more)
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.