LessWrong 2.0 Reader

Are these PIBBSS fellows (MATS scholar analog) or PIBBSS affiliates (MATS mentor analog)?

Could be a thing where people can opt into getting the vibes or the vibes and the definitions.

This comment caused me to realize: even though generating LaTeX hoverovers involves more technical challenges, I might be able to tell it "if it's a term that gets defined in LaTeX, include an example equation in the hoverover" (or something like that), which might help for some of these.

No, I meant that the correlation between pay and how-competent-the-typical-participant-seems-to-me is, if anything, negative. Like, the hiring bar for Google interns is lower than any of the technical programs, and PIBBSS seems-to-me to have the most competent participants overall (though I'm not familiar with some of the programs).

I'd be pretty into having typography styling settings that auto-detect LM stuff (or, specifically track when users have used any LW-specific LM tools), and flag it with some kind of style difference so it's easy to track at a glance (esp if it could be pretty reliable).

First of all, even taking what Gwern says there at face value, how many of the posts here that are written “with AI involvement” would you say actually are checked, edited, etc., in the rigorous way which Gwern describes? Realistically?

My guess is very few people are using AI output directly (at least the present it's pretty obvious as their writing is kind of atrocious). I do think most posts probably involved people talking to an LLM through their thoughts, or ask for some editing help, or ask some factual questions. My guess is basically 100% of those went through the kind of process that Gwern was describing here. 

I currently wish I had a policy for knowing with confidence whether a user wrote part of their post with a language model. There's a (small) regular stream of new-user content that I look through, where I'm above 50% that AI wrote some of it (very formulaic, unoriginal writing, imitating academic style) but I am worried about being rude when saying "I rejected your first post because I reckon you didn't write this and it doesn't reflect your thoughts" if I end up being wrong like 1 in 3 times^[1].

Sometimes I use various online language-model checkers (1, 2, 3), but I don't know how accurate/reliable they are. If they are actually pretty good, I may well automatically run them on all submitted posts to LW so I can be more confident.

1. ^{^}

   Also one time I pushed back on this and the user explained they're not a native English speaker, so tried to use a model to improve their English, which I thought was more reasonable than many uses.

My main metric is "How smart do these people seem when I talk to them or watch their presentations?". I think they also tend to be older and have more research experience.

Also, my feedback is that some of the definitions seem kind of vague. Like, apparently an ultracontribution is "a mathematical object representing uncertainty over probability" - this tells me what it's supposed to be, but doesn't actually tell me what it is. The ones that actually show up in the text don't seem too vague, partially because they're not terms that are super precise.

How are you currently determining which words to highlight? You say "terms that readers might not know" but this varies a lot based on the reader (as you mention in the long-term vision section).