This is a special post for quick takes by yanni. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.
Sorted by Click to highlight new quick takes since:

Question: I've noticed CE is investing in tobacco regulation. This has made me wonder if alcohol regulation been considered as a cause area? In some ways its externalities are worse (e.g. domestic violence). I'm very uncertain about its tractability and neglectedness compared to tobacco though.

GiveWell has funded Vital Strategy's alcohol work, OP has their global health policy focus area (inclusive of alcohol) and CE has incubated the Centre for Alcohol Policy Solutions (though I have limited visibility on their success since incubation a few years ago).

Check out CE's report on alcohol and tobacco for a short primer; you can also compare their assessment of success rates and neglectedness.

https://www.charityentrepreneurship.com/health-reports

I'm not sure of an org that deals with ultra-high net worth individuals (longview?), but someone should reach out to Bryan Johnson. I think he could be persuaded to invest in AI Safety (skip to 1:07:15)

I think it would be great to have the option to listen to comments on the forum (i.e. audio comments).

The ea forum has some very long comments. Sometimes longer than the original post. This is a good thing, but for reasons I think are obvious (LMK if they aren't) I think it would be good to be able to listen to them.

I subscribe to naturalreaders.com (best $90 I ever spent FYI), and it plugs into the desktop version of chatGPT like the below. I am suggesting something similar for the forum.

Would it be interesting to gather a representative sample of EA's personalities?

The ClearerThinking team has released a new tool: "The Ultimate Personality Test".

We believe it is important to understand diversity in EA across a variety of dimensions, why not this one?

https://programs.clearerthinking.org/personality.html?_gl=1%2Afx9cfe%2A_ga%2ANDIwMjkxNjY3LjE2ODI0NjI3NjM.%2A_ga_58RPQ2D860%2AMTcwMTMxMTc4NS40MS4xLjE3MDEzMTMzMTguMjcuMC4w

Would people eat factory farmed animals if they knew what they were screaming saying? 

Interesting goal, but the initial plan being recording and playing back animal audio doesn't inspire confidence they'll make progress soon

Should We Push For An AI Pause Might Be The Wrong Question

A quick thought on the recent discussion on whether pushing for a pause on frontier AI models is a good idea or not.

It seems obvious to me that within the next 3 years the top AI labs will be producing AI that causes large swaths of the public to push for a pause. 

Is it therefore more prudent to ask the following question: when much of the public wants a pause, what should our (the EA community) response be?

Interesting framing.

It's unclear to me how to integrate that theory with our decisions today given how much the strategic situation is likely to have shifted in that time.

Which public. Each country in this AI race has a different view on this, and some do not consult their public as much as others. The EA community ideally should take this into account. If the other countries aren't going to pause, and they will not, what should the USA do?

(The historical action would be AI progress stops being publicly discussed and all the current experts get drafted into secret labs with the goal of AGI first)

What are the animal welfare interventions that (1) have potential for high impact and (2) are very short term [i.e. if they work, they work within 10 years]? Basically, my AGI timelines are something like 40% ≤ 10 years and 40% ≤ 15 years. And I believe there isn't much point worrying about much after these timelines.

I think there is an argument that animal welfare intervention prioritisation should consider an AGI timeline of ~ 5 years, but not put too much stock in it.

yanni
-10
0
10
yanni
-12
0
18
1
Curated and popular this week
LintzA
 ·  · 15m read
 · 
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
Dr Kassim
 ·  · 4m read
 · 
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
Paul Present
 ·  · 28m read
 · 
Note: I am not a malaria expert. This is my best-faith attempt at answering a question that was bothering me, but this field is a large and complex field, and I’ve almost certainly misunderstood something somewhere along the way. Summary While the world made incredible progress in reducing malaria cases from 2000 to 2015, the past 10 years have seen malaria cases stop declining and start rising. I investigated potential reasons behind this increase through reading the existing literature and looking at publicly available data, and I identified three key factors explaining the rise: 1. Population Growth: Africa's population has increased by approximately 75% since 2000. This alone explains most of the increase in absolute case numbers, while cases per capita have remained relatively flat since 2015. 2. Stagnant Funding: After rapid growth starting in 2000, funding for malaria prevention plateaued around 2010. 3. Insecticide Resistance: Mosquitoes have become increasingly resistant to the insecticides used in bednets over the past 20 years. This has made older models of bednets less effective, although they still have some effect. Newer models of bednets developed in response to insecticide resistance are more effective but still not widely deployed.  I very crudely estimate that without any of these factors, there would be 55% fewer malaria cases in the world than what we see today. I think all three of these factors are roughly equally important in explaining the difference.  Alternative explanations like removal of PFAS, climate change, or invasive mosquito species don't appear to be major contributors.  Overall this investigation made me more convinced that bednets are an effective global health intervention.  Introduction In 2015, malaria rates were down, and EAs were celebrating. Giving What We Can posted this incredible gif showing the decrease in malaria cases across Africa since 2000: Giving What We Can said that > The reduction in malaria has be