All of Arb's Comments + Replies
Arb is a new research consultancy led by Misha Yagudin and Gavin Leech.
In our first 6 months we've worked on forecasting, vaccine strategy, AI risk, economics, cause prioritisation, grantmaking, and large-scale data collection. We're also working with Emergent Ventures and Schmidt Futures on their AI talent programme.
Consulting is reactive, but we have lots of ideas of our own which you can help shape.
We're looking for researchers with some background in ML, forecasting, technical writing, blogging, or some other hard thing. We only take work we think is i...
Language models for detecting bad scholarship
Epistemic institutions
Anyone who has done desk research carefully knows that many citations don't support the claim they're cited for - usually in a subtle way, but sometimes a total nonsequitur. Here's a fun list of 13 features we need to protect ourselves.
This seems to be a side effect of academia scaling so much in recent decades - it's not that scientists are more dishonest than other groups, it's that they don't have time to carefully read everything in their sub-sub-field (... while maintaining...
On malevolence: How exactly does power corrupt?
Artificial Intelligence / Values and Reflective Processes
How does it happen, if it happens? Some plausible stories:
- Backwards causation: People who are “corrupted” by power always had a lust for power but deluded others and maybe even themselves about their integrity;
- Being a good ruler (of any sort) is hard and at times very unpleasant, even the nicest people will try to cover up their faults, covering up causes more problems... and at some point it is very hard to admit that you were incompetent ruler al
Evaluating large foundations
Effective Altruism
Givewell looks at actors: object-level charities, people who do stuff. But logically, it's even more worth scrutinising megadonors (assuming that they care about impact or public opinion about their operations, and thus that our analysis could actually have some effect on them).
For instance, we've seen claims that the Global Fund, who spend $4B per year, meet a 2x GiveDirectly bar but not a Givewell Top Charity bar.
This matters because most charity - and even most good charity - is still not by EAs or run on EA...
More Insight Timelines
In 2018, the Median Group produced an impressive timeline of all of the insights required for current AI, stretching back to China's Han Dynasty(!)
The obvious extension is to alignment insights. Along with some judgment calls about relative importance, this would help any effort to estimate / forecast progress, and things like the importance of academia and non-EAs to AI alignment. (See our past work for an example of something in dire need of an exhaustive weighted insight list.)
Another set in need of collection are more genera...
Our World in Base Rates
Epistemic Institutions
Our World In Data are excellent; they provide world-class data and analysis on a bunch of subjects. Their COVID coverage made it obvious that this is a very great public good.
So far, they haven't included data on base rates; but from Tetlock we know that base rates are the king of judgmental forecasting (EAs generally agree). Making them easily available can thus help people think better about the future. Here's a cool corporate example.
e.g.
“85% of big data projects fail”;
“10% of people r...
I think this is neat.
Perhaps-minor note: if you'd do it at scale, I imagine you'd want something more sophisticated than coarse base rates. More like, "For a project that has these parameters, our model estimates that you have a 85% chance of failure."
I of course see this as basically a bunch of estimation functions, but you get the idea.
What we did in our first year