With Gavin Leech, I run Arb/arbresearch.com, an EA consultancy.
With Aaron Ho, I am working on Sage, a forecasting org.
With Nuño Sempere, Eli Lifland, and others, I forecast a lot as part of Samotsvety, a forecasting team.
Misha_Yagudin(Misha Yagudin)
Karma: 1,275
For sharing updates on Slack I would recommend https://geekbot.com/
Due to the rise of index funds (they “own” > 1⁄5 of American public companies), it seems that an alternative strategy might be trying to rise in the ranks of firms like BlackRock, Vanguard, or SSGA. It’s not unprecedented for them to take action (partly for selfish reasons); here are examples of BlackRock taking stances on environmental sustainability and coronavirus cure/vaccine.
Here is a paper exploring the potential implications of the rise of index funds and their stewardship: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3282794
A few considerations against, tied by generalism enables scale theme:
(1) There are a lot of domains where one can become an expert: it feels infeasible to train and select very capable forecasters in all of them. Being generally thoughtful person/forecaster allows to somewhat successfully go into areas outside your immediate expertise.
Training/selecting experts in a few especially important niches (e.g., AI, biosecurity, and certain topics in geopolitics) seems good and feasible.
(2) But at times of crisis, experts’ time is much more valuable than generalist’s time. Even now, it’s often the case that competent forecasters are quite busy with their main jobs — it’s not unlikely that competent forecaster-experts should be doing something different from forecasting.
To add to Eli’s comment, I think on such complex topics, it’s just common for even personal estimates to fluctuate quite a bit. For example, here is an excerpt from footnote 181 of Carlsmith report:
[...] And my central estimate varies between ~1-10% depending on my mood, what considerations are salient to me at the time, and so forth. This instability is yet another reason not to put too much weight on these numbers.
Good question. I think AI researchers views inform/can inform me. A few examples from the recent NLP Community Metasurvey. I would quote bits from this summary.
Few scaling maximalists: 17% agreed that Given resources (i.e., compute and data) that could come to exist this century, scaled-up implementations of established existing techniques will be sufficient to practically solve any important real-world problem or application in NLP.
This was surprsing and updated me somewhat against shorter timelines (and higher risk) as, for example, it clashes with the “+12 OOMS Enough” premise of the Kokotajlo’s argument for short timelines of the Carlsmith report (see also this and his review of Carlsmith report).
NLP is on a path to AGI: 58% agreed that Understanding the potential development of artificial general intelligence (AGI) and the benefits/risks associated with it should be a significant priority for NLP researchers. Related: 57% agreed that Recent developments in large-scale ML modeling (such as in language modeling and reinforcement learning) are significant steps toward the development of AGI.
If these numbers were significantly lower or higher, it would also probably update my views.
AGI could be catastrophic: 36% agreed that It is plausible that decisions made by AI or machine learning systems could cause a catastrophe this century that is at least as bad as an all-out nuclear war. 46% of women and 53% of URM respondents agreed. The comments suggested that people took a pretty wide range of interpretations to this, including things like OOD robustness failures leading to weapons launches.
This number is puzzling and hard to interpret. It seems appropriate in light of AI Impacts’ What do ML researchers think about AI in 2022? where “48% of respondents gave at least 10% chance of an extremely bad outcome”.
I don’t fully understand what this implies about the ML community’s views on AI alignment. But I can see myself updating positively if these concerns would lead to more safety culture, alignment research, etc.
I want to push back on the practical upshot (ii).
Getting rid of extreme suffering (and maybe eradicating suffering altogether) seems like a huge cultural achievement. I think it’d be hard to deny that, even for someone who treats any personal pursuit of trivial comforts as an ultimate distraction. For example, the eradication of smallpox is in the same reference class and is clearly on the list of the peak human achievements of the 20th century. It required worldwide cooperation and logistic, advances in medical science and technology, and probably much more.
Especially excited about “Immigration Specialist.”
I dunno “About the author” looks really annoying to me. It triggers ~disgust for “unwarranted status/authority projection” social games associated with some other outlets. It’s fine to say that you are experts and think X, Y, and Z (but your reasons should be good still); it’s also fine to have a friendly oneliner to remind a reader who the author is. But having all your bio recalled after each post is a bit “meh”.
Unfortunately, it seems there is no way to hide it in Account Settings > Site Customizations. If anyone is in my shoes, I killed it with the uBlock origin element picker.
Yeah, it would probably be good if people redirected this energy to climbing ladders in the government/civil service/military or important powerful corporate institutions. But I guess these ladders underpay you in terms of social credit/inner ringing within EA. Should we praise people aiming for 15y-to-high-impact careers more?
Thank you for you work!
A useful piece of context. When asked about recommendations on charitable giving Michael Nielsen writes:
Same answer as @TheZvi’s earlier, I’m afraid. I’m pretty Hayekian; I wish there were good price signals here! In some ways I view that as what EA is doing: it is trying to use community argument and institutions to price public goods (& the like) appropriately.
Yes, they did, see e.g. https://twitter.com/ApolloSurveys/status/1527362513200787469
Hm, is it just through calories or maybe through micronutrients as well: potatoes are high in potassium, vitamin C, B6, and K1 compared to other staple foods? Footnote 6 tends to suggest that it’s mostly about calories.
Thanks for clarifying! I think our misunderstanding comes from different framings: EA meta/infrastructure v. global health; within global health, MH v. disease/poverty alleviation. I agree with you and calebp on the former.
That’s fairly dismissive. You could have written:
If imposter syndrome and other easily preventable/treatable debilitating mental issues were common among EAs, I’d guess that should be a much higher priority to address than poor “physical” health among EAs.
Isn’t it in the end about what’s more cost-effective? I can interpret you as pointing to “if ~depression was more cost-effective to address than ~smallpox, it would have been addressed first in the developed nations,” this might be a good heuristic but a well-thought cost-effectiveness estimate is more convincing to me. Seems like criticizing MichaelPlant’s cost-effectiveness estimates could have been more productive here. (TBC: I myself haven’t engaged with HLI’s work/methal health charities much).
And mental health being comparatively cost-effective doesn’t sound ridiculous to me. StrongMinds attracted some donations from EAs in the past including GiveWell staff.
(I sorta feel that this comment and 23 upvotes / 7 votes support OP’s observation that people seem to be dismissive about mental health as global health and development intervention.)
Yes, thank you; that makes sense and is very helpful!
Impactful Forecasting Prize Results and Reflections
Is any forecaster or organizer willing to bet with me on “Will Russia place any nuclear weapons in Belarus before 2023?” at 3:7 odds (with me on “no”)?
re: 1 — agree, MM is nicer than Poly. And I view UX as a very important issue for adoption (think of Robinhood).
re: 2 — would be great if you’d work that out!
re: 3 — I think just using the proper scoring rules (like log or Brier) is good enough; what are the problems with these? Smart aggregation (based on track-record and some other info) would allow leveraging non-superforecasters (likely through putting weight on prospective superforecasters). I think another way to participate in prediction pools and markets is to bring new information and considerations, this can be rewarded through Reddit’s karma, r/changemyview deltas, or with StackOverflow upvotes (one interesting challenge is to figure that out).
A slightly edited section of my comment on the earlier draft: