Congratulations to the EA Project For Awesome 2024 team, who managed to raise over $100k for AMF, GiveDirectly and ProVeg International by submitting promotional/informational videos to the project. There's been an effort to raise money for effective charities via Project For Awesome since 2017, and it seems like a really productive effort every time. Thanks to all involved! 

This could be a long slog but I think it could be valuable to identify the top ~100 OS libraries and identify their level of resourcing to avoid future attacks like the XZ attack. In general, I think work on hardening systems is an underrated aspect of defending against future highly capable autonomous AI agents.

FAQ: “Ways the world is getting better” banner The banner will only be visible on desktop. If you can't see it, try expanding your window. It'll be up for a week.  How do I use the banner? 1. Click on an empty space to add an emoji,  2. Choose your emoji,  3. Write a one-sentence description of the good news you want to share,  4. Link an article or forum post that gives more information.  If you’d like to delete your entry, click the cross that appears when you hover over it. It will be deleted for everyone. What kind of stuff should I write? Anything that qualifies as good news relevant to the world's most important problems.  For example, Ben West’s recent quick takes (1, 2, 3). Avoid posting partisan political news, but the passage of relevant bills and policies is on topic.  Will my entry be anonymous? All submissions are displayed without your Forum name, so they are ~anonymous to users, however, usual moderation norms still apply (additionally, we may remove duplicates or borderline trollish submissions. This is an experiment, so we reserve the right to moderate heavily if necessary). Ask any other questions you have in the comments below. Feel free to dm me with feedback or comments.  

Introducing Ulysses*, a new app for grantseekers.    We (Austin Chen, Caleb Parikh, and I) built an app! You can test the app out if you’re writing a grant application! You can put in sections of your grant application** and the app will try to give constructive feedback about your applicants. Right now we're focused on the "Track Record" and "Project Goals" section of the application. (The main hope is to save back-and-forth-time between applicants and grantmakers by asking you questions that grantmakers might want to ask. Austin, Caleb, and I hacked together a quick app as a fun experiment in coworking and LLM apps. We wanted a short project that we could complete in ~a day. Working on it was really fun! We mostly did it for our own edification, but we’d love it if the product is actually useful for at least a few people in the community! As grantmakers in AI Safety, we’re often thinking about how LLMs will shape the future; the idea for this app came out of brainstorming, “How might we apply LLMs to our own work?”. We reflected on common pitfalls we see in grant applications, and I wrote a very rough checklist/rubric and graded some Manifund/synthetic applications against the rubric.  Caleb then generated a small number of few shot prompts by hand and then used LLMs to generate further prompts for different criteria (e.g., concreteness, honesty, and information on past projects) using a “meta-prompting” scheme. Austin set up a simple interface in Streamlit to let grantees paste in parts of their grant proposals. All of our code is open source on Github (but not open weight 😛).*** This is very much a prototype, and everything is very rough, but please let us know what you think! If there’s sufficient interest, we’d be excited about improving it (e.g., by adding other sections or putting more effort into prompt engineering). To be clear, the actual LLM feedback isn’t necessarily good or endorsed by us, especially at this very early stage. As usual, use your own best judgment before incorporating the feedback. *Credit to Saul for the name, who originally got the Ulysses S. Grant pun from Scott Alexander. ** Note: Our app will not be locally saving your data. We are using the OpenAI API for our LLM feedback. OpenAI says that it won’t use your data to train models, but you may still wish to be cautious with highly sensitive data anyway.  *** Linch led a discussion on the potential capabilities insights of our work, but we ultimately decided that it was asymmetrically good for safety; if you work on a capabilities team at a lab, we ask that you pay $20 to LTFF before you look at the repo.  

Common prevalence estimates are often wrong. Example: snakebites and my experience reading Long Covid literature. Both institutions like the WHO and academic literature appear to be incentivized to exaggerate. I think the Global Burden of Disease might be a more reliable source, but have not looked into it. I advise everyone using prevalence estimates to treat them with some skepticism and look up the source.