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crossposting from LessWrong since I think this is more common on EA Forum
Errors are my own
At first blush, I find this common caveat amusing.
1. If there are errors, we can infer that those providing feedback were unable to identify them.
2. If the author was fallible enough to have made errors, perhaps they are are fallible enough to miss errors in input sourced from others.
What purpose does it serve? Given its often paired with "credit goes to..<list of names> it seems like an attempt that people providing feedback/input to a post are only exposed to upside from doing so, and the author takes all the downside reputation risk if the post is received poorly or exposed as flawed.
Maybe this works? It seems that as a capable reviewer/feedback haver, I might agree to offer feedback on a poor post written by a poor author, perhaps pointing out flaws, and my having given feedback on it might reflect poorly on my time allocation, but the bad output shouldn't be assigned to me. Whereas if my name is attached to something quite good, it's plausible that I contributed to that. I think because it's easier to help a good post be great than to save a bad post.
But these inferences seem like they're there to be made and aren't changed by what an author might caveat at the start. I suppose the author might want to remind the reader of them rather than make them true through an utterance.
Upon reflection, I think (1) doesn't hold. The reviewers/input makers might be aware of the errors but be unable to save the author from them. (2) That the reviewers made mistakes that have flowed into the piece seems all the more likely the worse the piece is overall, since we can update that the author wasn't likely to catch them.
On the whole, I think I buy the premise that we can't update too much negatively on reviewers and feedback givers from them having deigned to give feedback on something bad, though their time allocation is suspect. Maybe they're bad at saying no, maybe they're bad at dismissing people's ideas aren't that good, maybe they have hope for this person. Unclear. Upside I'm more willing to attribute.
Perhaps I would replace the "errors are my my own[, credit goes to]" with a reminder or pointer that these are the correct inferences to make. The words themselves don't change them? Not sure, haven't thought about this much.
Edited To Add: I do think "errors are my own" is a very weird kind of social move that's being performed in an epistemic contexts and I don't like.
Just a thought: there's the common advice that fighting all out with the utmost desperation makes sense for very brief periods, a few weeks or months, but doing so for longer leads to burnout. So you get sayings like "it's a marathon, not a sprint." But I wonder if length of the "fight"/"war" isn't the only variable in sustainable effort. Other key ones might be the degree of ongoing feedback and certainty about the cause.
Though I expect a multiyear war which is an existential threat to your home and family to be extremely taxing, I imagine soldiers experiencing less burnout than people investing similar effort for a far-mode cause, let's say global warming which might be happening, but is slow and your contributions to preventing it unclear. (Actual soldiers may correct me on this, and I can believe war is very traumatizing, though I will still ask how much they believed in the war they were fighting.)
(Perhaps the relevant variables here are something like Hanson's Near vs Far mode thinking, where hard effort for far-mode thinking more readily leads to burnout than near-mode thinking even when sustained for long periods.)
Then of course there's generally EA and X-risk where burnout is common. Is this just because of the time scales involved, or is it because trying to work on x-risk is subject to so much uncertainty and paucity of feedback? Who knows if you're making a positive difference? Contrast with a Mario character toiling for years to rescue the princess he is certain is locked in a castle waiting. Fighting enemy after enemy, sleeping on cold stone night after night, eating scraps. I suspect Mario, with his certainty and much more concrete sense of progress, might be able expend much more effort and endure much more hardship for much longer than is sustainable in the EA/X-risk space.
Related: On Doing the Improbable
It seems to me that many jobs that involve more immediate concerns have high rates of burnouts. I would guess that's the case for, e.g. nurses (though I haven't found statistics comparing different occupations). Thus I'm not sure that jobs whose impact is more distant and uncertain generally have higher rates of burnout. That means that if the EA and X-risk community have higher rates of burnout, that may not be due to that factor, or at least not to that alone.
Curated and popular this week
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Cross-posted to Lesswrong
Introduction
Several developments over the past few months should cause you to re-evaluate what you are doing. These include:
1. Updates toward short timelines
2. The Trump presidency
3. The o1 (inference-time compute scaling) paradigm
4. Deepseek
5. Stargate/AI datacenter spending
6. Increased internal deployment
7. Absence of AI x-risk/safety considerations in mainstream AI discourse
Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on.
In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!
Implications of recent developments
Updates toward short timelines
There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months).
We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achi
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Hey everyone, I’ve been going through the EA Introductory Program, and I have to admit some of these ideas make sense, but others leave me with more questions than answers. I’m trying to wrap my head around certain core EA principles, and the more I think about them, the more I wonder: Am I misunderstanding, or are there blind spots in EA’s approach?
I’d really love to hear what others think. Maybe you can help me clarify some of my doubts. Or maybe you share the same reservations? Let’s talk.
Cause Prioritization. Does It Ignore Political and Social Reality?
EA focuses on doing the most good per dollar, which makes sense in theory. But does it hold up when you apply it to real world contexts especially in countries like Uganda?
Take malaria prevention. It’s a top EA cause because it’s highly cost effective $5,000 can save a life through bed nets (GiveWell, 2023). But what happens when government corruption or instability disrupts these programs? The Global Fund scandal in Uganda saw $1.6 million in malaria aid mismanaged (Global Fund Audit Report, 2016). If money isn’t reaching the people it’s meant to help, is it really the best use of resources?
And what about leadership changes? Policies shift unpredictably here. A national animal welfare initiative I supported lost momentum when political priorities changed. How does EA factor in these uncertainties when prioritizing causes? It feels like EA assumes a stable world where money always achieves the intended impact. But what if that’s not the world we live in?
Long termism. A Luxury When the Present Is in Crisis?
I get why long termists argue that future people matter. But should we really prioritize them over people suffering today?
Long termism tells us that existential risks like AI could wipe out trillions of future lives. But in Uganda, we’re losing lives now—1,500+ die from rabies annually (WHO, 2021), and 41% of children suffer from stunting due to malnutrition (UNICEF, 2022). These are preventable d
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Cross-posted from my blog.
Contrary to my carefully crafted brand as a weak nerd, I go to a local CrossFit gym a few times a week. Every year, the gym raises funds for a scholarship for teens from lower-income families to attend their summer camp program. I don’t know how many Crossfit-interested low-income teens there are in my small town, but I’ll guess there are perhaps 2 of them who would benefit from the scholarship. After all, CrossFit is pretty niche, and the town is small.
Helping youngsters get swole in the Pacific Northwest is not exactly as cost-effective as preventing malaria in Malawi. But I notice I feel drawn to supporting the scholarship anyway. Every time it pops in my head I think, “My money could fully solve this problem”. The camp only costs a few hundred dollars per kid and if there are just 2 kids who need support, I could give $500 and there would no longer be teenagers in my town who want to go to a CrossFit summer camp but can’t. Thanks to me, the hero, this problem would be entirely solved. 100%.
That is not how most nonprofit work feels to me.
You are only ever making small dents in important problems
I want to work on big problems. Global poverty. Malaria. Everyone not suddenly dying. But if I’m honest, what I really want is to solve those problems. Me, personally, solve them. This is a continued source of frustration and sadness because I absolutely cannot solve those problems.
Consider what else my $500 CrossFit scholarship might do:
* I want to save lives, and USAID suddenly stops giving $7 billion a year to PEPFAR. So I give $500 to the Rapid Response Fund. My donation solves 0.000001% of the problem and I feel like I have failed.
* I want to solve climate change, and getting to net zero will require stopping or removing emissions of 1,500 billion tons of carbon dioxide. I give $500 to a policy nonprofit that reduces emissions, in expectation, by 50 tons. My donation solves 0.000000003% of the problem and I feel like I have f
crossposting from LessWrong since I think this is more common on EA Forum
At first blush, I find this common caveat amusing.
1. If there are errors, we can infer that those providing feedback were unable to identify them.
2. If the author was fallible enough to have made errors, perhaps they are are fallible enough to miss errors in input sourced from others.
What purpose does it serve? Given its often paired with "credit goes to..<list of names> it seems like an attempt that people providing feedback/input to a post are only exposed to upside from doing so, and the author takes all the downside reputation risk if the post is received poorly or exposed as flawed.
Maybe this works? It seems that as a capable reviewer/feedback haver, I might agree to offer feedback on a poor post written by a poor author, perhaps pointing out flaws, and my having given feedback on it might reflect poorly on my time allocation, but the bad output shouldn't be assigned to me. Whereas if my name is attached to something quite good, it's plausible that I contributed to that. I think because it's easier to help a good post be great than to save a bad post.
But these inferences seem like they're there to be made and aren't changed by what an author might caveat at the start. I suppose the author might want to remind the reader of them rather than make them true through an utterance.
Upon reflection, I think (1) doesn't hold. The reviewers/input makers might be aware of the errors but be unable to save the author from them. (2) That the reviewers made mistakes that have flowed into the piece seems all the more likely the worse the piece is overall, since we can update that the author wasn't likely to catch them.
On the whole, I think I buy the premise that we can't update too much negatively on reviewers and feedback givers from them having deigned to give feedback on something bad, though their time allocation is suspect. Maybe they're bad at saying no, maybe they're bad at dismissing people's ideas aren't that good, maybe they have hope for this person. Unclear. Upside I'm more willing to attribute.
Perhaps I would replace the "errors are my my own[, credit goes to]" with a reminder or pointer that these are the correct inferences to make. The words themselves don't change them? Not sure, haven't thought about this much.
Edited To Add: I do think "errors are my own" is a very weird kind of social move that's being performed in an epistemic contexts and I don't like.
Yeah I have long thought this.
Though sometimes it is true, eg if someone makes an insigntful comment and then I write it up, it really is reasonable to say thanks to them for making but that errors are my own.