I agree it would be good to go more fine grained in the categories though.
824 karma · joined September 29, 2013
I agree it would be good to go more fine grained in the categories though.
However, if you did really believe the hype, and think that ML will lead to major breakthroughs in the next decade or so, then that would make it even more urgent.
At 80,000 Hours, our focus is also not on giving money. We're trying to help people find jobs in these areas, often in academia or policy and many other sectors.
http://www.copenhagenconsensus.com/publication/post-2015-con...
http://blog.givewell.org/2016/05/03/reservations-water-quali...
However, they focus on what will most help the global poor in the near term. We're interested in considering the full range of issues, especially those that are important from the perspective of the long-term future (for reasons explained in the article).
There also more in-depth discussion here: https://80000hours.org/2017/04/how-accurately-does-anyone-kn...
You're right that you also need to account for the chance of dropping out of the role all together, which is hard. (Though drop out rates are high in both sports and finance.)
I totally agree you can't easily use these figures as your expected earnings, since you need to adjust for the chance of drop out. (And also future industry prospects and many other factors.) Also agree about quant funds being different.
We don't mean portfolio management in the sense of advising individuals on what to do with their portfolio, which is what some investment banks use. That's a totally different job.
Like I say, this article is only based on a couple of interviews, and we still have a lot to clear. I'd be keen to get more data.
It's really hard to do a voting ring on HN. The algorithm is very sensitive. We've triggered it accidentally before, just because friends of ours, who we didn't ask, upvoted it.
One of former staff members got out of this rut by using commitment techniques. https://80000hours.org/2013/04/increase-your-productivity-to... (And click through to the next post)
You might also find The Motivation Hacker by Nick Winter a good, quick summary.
However, you don't need to have strong evidence behind a technique for it to be worth trying – if the upside is high enough or the costs are low enough, it could still be worth it.
> In many cases, the evidence isn’t as strong as we’d like. Rather, it’s the best we’re aware of. We’ve tried to come to an all-considered view of what makes sense to try, given (i) the strength of the empirical evidence, (ii) whether it seems reasonable to us, (iii) the size of the potential upside, (iv) how widely applicable the advice is, and (v) the costs of trying. The details are given in the further reading we link to and the footnotes.
> We’ve put the advice roughly in order. The first items are easier and more widely applicable, so start with them, then move on to the more difficult areas later. The order is also partly based on an upcoming analysis of which skills are most valuable.
I agree some of the advice overlaps, but it's a little hard to avoid. Most of the areas are inter-related. I'd be interested in ideas on how to reorg.
But we'd like to find more.