AI engineer
EDIT: This comment described a bunch of emails between me and Leverage that I think would be relevant here, but I misremembered something about the thread (it was from 2017) and I'm not sure if I should post the full text so people can get the most accurate info (see below discussion), so I've deleted it for now. My apologies for the confusion
Note: I have deleted a long comment that I didn't feel like arguing with. I reserve the right to do this for future comments. Thank you.
This is just a guess, but I think CFAR and the CFAR-sphere would be more effective if they focused more on hypothesis generation (or "imagination", although that term is very broad). Eg., a year or so ago, a friend of mine in the Thiel-sphere proposed starting a new country by hauling nuclear power plants to Antarctica, and then just putting heaters on the ground to melt all the ice. As it happens, I think this is a stupid idea (hot air rises, so the newly heated air would just blow away, pulling in more cold air from the surroundings). But it is an idea, and the same person came up with (and implemented) a profitable business plan six months or so later. I can imagine HPJEV coming up with that idea, or Elon Musk, or von Neumann, or Google X; I don't think most people in the CFAR-sphere would, it's just not the kind of thing I think they've focused on practicing.
Seconding Anna and Satvik
Was including tech support under "admin/moderation" - obviously, ability to eg. IP ban people is important (along with access to the code and the database generally). Sorry for any confusion.
If the money is there, why not just pay a freelancer via Gigster or Toptal?
I appreciate the effort, and I agree with most of the points made, but I think resurrect-LW projects are probably doomed unless we can get a proactive, responsive admin/moderation team. Nick Tarleton talked about this a bit last year:
"A tangential note on third-party technical contributions to LW (if that's a thing you care about): the uncertainty about whether changes will be accepted, uncertainty about and lack of visibility into how that decision is made or even who makes it, and lack of a known process for making pull requests or getting feedback on ideas are incredibly anti-motivating." (http://lesswrong.com/lw/n0l/lesswrong_20/cy8e)
That's obviously problematic, but I think it goes way beyond just contributing code. As far as I know, right now, there's no one person with both the technical and moral authority to:
Pretty much any successful subreddit, even smallish ones, will have a team of admins who handle this stuff, and who can be trusted to look at things that pop up within a day or so (at least collectively). The highest intellectual-quality subreddit I know of, /r/AskHistorians, has extremely active and rigorous moderation, to the extent that a majority of comments are often deleted. Since we aren't on Reddit itself, I don't think we need to go quite that far, but there has to be something in place.
I mostly agree with the post, but I think it'd be very helpful to add specific examples of epistemic problems that CFAR students have solved, both "practice" problems and "real" problems. Eg., we know that math skills are trainable. If Bob learns to do math, along the way he'll solve lots of specific math problems, like "x^2 + 3x - 2 = 0, solve for x". When he's built up some skill, he'll start helping professors solve real math problems, ones where the answers aren't known yet. Eventually, if he's dedicated enough, Bob might solve really important problems and become a math professor himself.
Training epistemic skills (or "world-modeling skills", "reaching true beliefs skills", "sanity skills", etc.) should go the same way. At the beginning, a student solves practice epistemic problems, like the ones Tetlock uses in the Good Judgement Project. When they get skilled enough, they can start trying to solve real epistemic problems. Eventually, after enough practice, they might have big new insights about the global economy, and make billions at a global macro fund (or some such, lots of possibilities of course).
To use another analogy, suppose Carol teaches people how to build bridges. Carol knows a lot about why bridges are important, what the parts of a bridge are, why iron bridges are stronger than wood bridges, and so on. But we'd also expect that Carol's students have built models of bridges with sticks and stuff, and (ideally) that some students became civil engineers and built real bridges. Similarly, if one teaches how to model the world and find truth, it's very good to have examples of specific models built and truths found - both "practice" ones (that are already known, or not that important) and ideally "real" ones (important and haven't been discovered before).
Hey! Thanks for writing all of this up. A few questions, in no particular order:
The CFAR fundraiser page says that CFAR "search[es] through hundreds of hours of potential curricula, and test[s] them on smart, caring, motivated individuals to find the techniques that people actually end up finding useful in the weeks, months and years after our workshops." Could you give a few examples of curricula that worked well, and curricula that worked less well? What kind of testing methodology was used to evaluate the results, and in what ways is that methodology better (or worse) than methods used by academic psychologists?
One can imagine a scale for the effectiveness of training programs. Say, 0 points is a program where you play Minesweeper all day; and 100 points is a program that could take randomly chosen people, and make them as skilled as Einstein, Bismarck, or von Neumann. Where would CFAR rank its workshops on this scale, and how much improvement does CFAR feel like there has been from year to year? Where on this scale would CFAR place other training programs, such as MIT grad school, Landmark Forum, or popular self-help/productivity books like Getting Things Done or How to Win Friends and Influence People? (One could also choose different scale endpoints, if mine are too suboptimal.)
While discussing goals for 2015, you note that "We created a metric for strategic usefulness, solidly hitting the first goal; we started tracking that metric, solidly hitting the second goal." What does the metric for strategic usefulness look like, and how has CFAR's score on the metric changed from 2012 through now? What would a failure scenario (ie. where CFAR did not achieve this goal) have looked like, and how likely do you think that failure scenario was?
CFAR places a lot of emphasis on "epistemic rationality", or the process of discovering truth. What important truths have been discovered by CFAR staff or alumni, which would probably not have been discovered without CFAR, and which were not previously known by any of the staff/alumni (or by popular media outlets)? (If the truths discovered are sensitive, I can post a GPG public key, although I think it would be better to openly publish them if that's practical.)
You say that "As our understanding of the art grew, it became clear to us that “figure out true things”, “be effective”, and “do-gooding” weren’t separate things per se, but aspects of a core thing." Could you be more specific about what this caches out to in concrete terms; ie. what the world would look like if this were true, and what the world would look like if this were false? How strong is the empirical evidence that we live in the first world, and not the second? Historically, adjusted for things we probably can't change (like eg. IQ and genetics), how strong have the correlations been between truth-seeking people like Einstein, effective people like Deng Xiaoping, and do-gooding people like Norman Borlaug?
How many CFAR alumni have been accepted into Y Combinator, either as part of a for-profit or a non-profit team, after attending a CFAR workshop?
I have them, but I'm generally hesitant to share emails as they normally aren't considered public. I'd appreciate any arguments on this, pro or con