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Suggested hiring practice tweak
There are typically two ways for organisations of running hiring rounds: deadlined, in which job applications are no longer processed after a publicised date, and rolling in which the organisation will keep allowing submissions until they've found someone they want.
The upside of a deadline is both to an applicant that they know they're not wasting their time on a job that's 99% assigned, and to the organisation, which doesn't have to delay giving an answer to an adequate candidate on the grounds that a potentially better one submits when you're most of the way through the hiring process, and incentivises people to apply slightly earlier than they would have.
The downsides are basically the complement. The individual doesn't get to go for a job that they've just missed and would be really suited to, and the org doesn't get to see as large a pool of applicants.
It occurred to me that an org might be able to get some of the best of both by explicitly giving a mostly-deadline, after which they will explicitly downweight new applications. So if you see the mostly-deadline in time, you're still incentivised to get your application in by the date given, and if it's passed you should rationally apply if and only if you think there's a good chance you're an exceptional fit..
One of the problems with AI benchmarks is that they can't effectively be backcast more than a couple of years. This prompted me to wonder if a more empirical benchmark might be something like 'Ability of a human in conjunction with the best technology available at time t'.
For now at least, humans are still necessary to have in the loop, so this should in principle be at least as good as coding benchmarks for gauging where we are now. When/if humans become irrelevant, it should still work - 'AI capability + basically nothing' = 'AI capability'. And looking back, it gives a much bigger reference class for forecasting future trends, allowing us to compare e.g.
- Human
- Human + paper & pen
- Human + log tables + paper & pen
- Human + calculator + log tables + paper & pen
- Human + computer with C + ...
- Human + computer with Python + ...
- Human + ML libraries + ...
- Human + GPT 1 + ...
etc.
Thoughts?
One problem is putting everything on a common scale when historical improvements are so sensitive to the distribution of tasks. A human with a computer with C, compared to a human with just log tables, is a billion times faster at multiplying numbers but less than twice as fast at writing a novel. So your distribution of tasks has to be broad enough that it captures the capabilities you care about, but it also must be possible to measure a baseline score at low tech level and have a wide range of possible scores. This would make the benchmark extremely difficult to construct in practice.
I think that's right, but modern AI benchmarks seem to have much the same issue. A human with a modern Claude instance might be able to write code 100x faster than without, but probably less than 2x as fast at choosing a birthday present for a friend.
Ideally you want to integrate over... something to do with the set of all tasks. But it's hard to say what that something would be, let alone how you're going to meaningfully integrate it.
Suppose we compare two nonprofit orgs doing related work. Let’s use some real examples: Rethink Priorities and Founders Pledge, both of who do global health and climate change research; CEA (who run EAGs) and any community groups who run EAGxes or otherwise movement build; perhaps CFAR and Khan Academy.
Ideally, in an effectiveness-minded movement, every donation to one of them rather than the other should express some view on the relative capability of that org to execute its priorities - it is essentially a bet that that org will make better use of its money.
We can use a simple combinatorial argument to show that the epistemic value of this view rapidly approaches 0, the more things either or both of those organisations are doing. If AlphaOrg does only project A1, and BetaOrg does only project B1 (and for the sake of simplicity, that both projects are have the same focus) then donating to Alphaorg clearly shows that you think Alphaorg will execute it better - that A1 > B1.
But if AlphaOrg adds a single (related or unrelated) project, A2, to their roster, the strength of the signal drops to 1/6th: now in donating to AlphaOrg, I might be expressing the view that A1 > B1 > A2, that A2 > B1 > A1, that A1 > A2 > B1, or that A2 > A1 > B1, or (if I think the lesser projects sum to more than the greater one), that B1 >A2 > A1 or B1 >A1 > A2.
In general, the number of possible preference orderings we can have between the projects of just two orgs respectively running m and n projects between them is (m + n)![^end] (meaning 3*2=6 for three, 4*6=24 for four, 5*24=120 for five, and so on). So if I give to AlphaOrg, my donation is consistent with (m + n)! - (m! * n!) possible preferences, where (m! * n!) is the number of preferences for all BetaOrg projects over all AlphaOrg projects, which my donation rules out.
In general, for k orgs in a comparable space, a donation to AlphaOrg with x1 projects is (based on some hasty interpretation) consistent with is (x1 + x2 + ... xk)! - x1! * (x2 + x3 + ... + xk)! preference orderings.
Assuming a typical EA org receives money from a couple of hundred donors a year, each of which we might consider a ‘vote’ or endorsement, that means on naive accounting (where we divide votes by preference orderings), as few as 6 projects between two relevant orgs give us less than a single endorsement’s worth of info on which of their projects effectiveness-minded donors actually support.
Obviously there are other considerations. Reduced administrative burden from combining is perhaps the foremost; also major donations can be restricted, somewhat mimicking the effect of donating to a more focused org (though if the org also receives unrestricted donations, it can undo this effect by just redirecting the unrestricted money) ; also one might want a very strong team to expand their focus on priors - though doing so would strongly reduce our confidence that they remain a strong team for the expanded purpose.
Nonetheless, with the central EA orgs typically having at least 3 or 4 focus areas each (giving ~40320 possible preference orderings between two of them), and more if you count in-house support work - software development, marketing etc - as separate projects, I think the magnitude of this epistemic cost is something a purportedly effectiveness-minded and data-driven movement should consider very seriously.
Some of the combinatoric effect here is picking up on the number of projects, I think? If you have three projects in three separate orgs, your vote to fund one conveys information on which you rank first but not the rank order between the other two. If you have ten orgs with one project each, there are I think 36 pairwise comparisons and a first-place donation vote addresses only nine of them.
More generally: it may be worthwhile to distinguish more between donation/vote as an information mechanism and as an influence mechanism. It's plausible to me that other features of the ecosystem could significantly impair both the potential informational and influential power of "votes" before we even got to considering the issue you describe here.
Donation/vote as a donor influence mechanism has some significant limitations in an ecosystem where the bulk of the funding comes from a few megadonors. To the extent that smaller donors think their donations funge with those of the megadonors, and that megadonors are more capable of adjusting to enact their global preferred funding allocations, the small donors may not believe that their votes have any meaningful influence on overall funding allocations. To the extent that smaller donors believe that, I expect the belief would have an significant effect on small-donor willingness to invest in casting informed "votes." So it may seriously affect the epistemic / informational value of the votes too.
You could get some of the informational effect by merely asking donors to identify the specific program they'd like to donate to in a non-binding fashion. Of course, the advisory nature of the project-specific vote would likely make donors less willing to spend time on casting informed votes.
Curated and popular this week
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Epistemic status: This post — the result of a loosely timeboxed ~2-day sprint[1] — is more like “research notes with rough takes” than “report with solid answers.” You should interpret the things we say as best guesses, and not give them much more weight than that.
Summary
There’s been some discussion of what “transformative AI may arrive soon” might mean for animal advocates.
After a very shallow review, we’ve tentatively concluded that radical changes to the animal welfare (AW) field are not yet warranted. In particular:
* Some ideas in this space seem fairly promising, but in the “maybe a researcher should look into this” stage, rather than “shovel-ready”
* We’re skeptical of the case for most speculative “TAI<>AW” projects
* We think the most common version of this argument underrates how radically weird post-“transformative”-AI worlds would be, and how much this harms our ability to predict the longer-run effects of interventions available to us today. Without specific reasons to believe that an intervention is especially robust,[2] we think it’s best to discount its expected value to ~zero.
Here’s a brief overview of our (tentative!) actionable takes on this question[3]:
✅ Some things we recommend❌ Some things we don’t recommend
* Dedicating some amount of (ongoing) attention to the possibility of “AW lock ins”[4]
* Pursuing other exploratory research on what transformative AI might mean for animals & how to help (we’re unconvinced by most existing proposals, but many of these ideas have received <1 month of research effort from everyone in the space combined — it would be unsurprising if even just a few months of effort turned up better ideas)
* Investing in highly “flexible” capacity for advancing animal interests in AI-transformed worlds
* Trying to use AI for near-term animal welfare work, and fundraising from donors who have invested in AI
* Heavily discounting “normal” interventions that take 10+ years to help animals
* “Rowing” on na
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About the program
Hi! We’re Chana and Aric, from the new 80,000 Hours video program.
For over a decade, 80,000 Hours has been talking about the world’s most pressing problems in newsletters, articles and many extremely lengthy podcasts.
But today’s world calls for video, so we’ve started a video program[1], and we’re so excited to tell you about it!
80,000 Hours is launching AI in Context, a new YouTube channel hosted by Aric Floyd. Together with associated Instagram and TikTok accounts, the channel will aim to inform, entertain, and energize with a mix of long and shortform videos about the risks of transformative AI, and what people can do about them.
[Chana has also been experimenting with making shortform videos, which you can check out here; we’re still deciding on what form her content creation will take]
We hope to bring our own personalities and perspectives on these issues, alongside humor, earnestness, and nuance. We want to help people make sense of the world we're in and think about what role they might play in the upcoming years of potentially rapid change.
Our first long-form video
For our first long-form video, we decided to explore AI Futures Project’s AI 2027 scenario (which has been widely discussed on the Forum). It combines quantitative forecasting and storytelling to depict a possible future that might include human extinction, or in a better outcome, “merely” an unprecedented concentration of power.
Why?
We wanted to start our new channel with a compelling story that viewers can sink their teeth into, and that a wide audience would have reason to watch, even if they don’t yet know who we are or trust our viewpoints yet. (We think a video about “Why AI might pose an existential risk”, for example, might depend more on pre-existing trust to succeed.)
We also saw this as an opportunity to tell the world about the ideas and people that have for years been anticipating the progress and dangers of AI (that’s many of you!), and invite the br
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I donated my left kidney to a stranger on April 9, 2024, inspired by my dear friend @Quinn Dougherty (who was inspired by @Scott Alexander, who was inspired by @Dylan Matthews). By the time I woke up after surgery, it was on its way to San Francisco. When my recipient woke up later that same day, they felt better than when they went under. I'm going to talk about one complication and one consequence of my donation, but I want to be clear from the get: I would do it again in a heartbeat.
Correction: Quinn actually donated in April 2023, before Scott’s donation. He wasn’t aware that Scott was planning to donate at the time. The original seed came from Dylan's Vox article, then conversations in the EA Corner Discord, and it's Josh Morrison who gets credit for ultimately helping him decide to donate. Thanks Quinn!
I met Quinn at an EA picnic in Brooklyn and he was wearing a shirt that I remembered as saying "I donated my kidney to a stranger and I didn't even get this t-shirt." It actually said "and all I got was this t-shirt," which isn't as funny. I went home and immediately submitted a form on the National Kidney Registry website. The worst that could happen is I'd get some blood tests and find out I have elevated risk of kidney disease, for free.[1]
I got through the blood tests and started actually thinking about whether to do this. I read a lot of arguments, against as well as for. The biggest risk factor for me seemed like the heightened risk of pre-eclampsia[2], but since I live in a developed country, this is not a huge deal. I am planning to have children. We'll just keep an eye on my blood pressure and medicate if necessary.
The arguments against kidney donation seemed to center around this idea of preserving the sanctity or integrity of the human body: If you're going to pierce the sacred periderm of the skin, you should only do it to fix something in you. (That's a pretty good heuristic most of the time, but we make exceptions to give blood and get pier
I think that's right, but modern AI benchmarks seem to have much the same issue. A human with a modern Claude instance might be able to write code 100x faster than without, but probably less than 2x as fast at choosing a birthday present for a friend.
Ideally you want to integrate over... something to do with the set of all tasks. But it's hard to say what that something would be, let alone how you're going to meaningfully integrate it.