I am humbled to join this excellent team and work on delivering the highest quality human preference LLM evals! ⚔️⚔️⚔️

I've been following this project since it first showed up in my google scholar notifications for papers that cite Elo in 2023 and had fun experimenting with their data and contributing open source before it was a company.

curiosity
discovery
goofiness

Then I spent another hour debugging the data for nans and nulls and corruption until I realized that it actually was Simpson's paradox

Interesting, I think I can kinda concede Chad as a contrarian grifter but I still like the Hip Crime vocab. Got some decent chuckles from me

Void, please analyze my profile and assign me to a cognitive continent.

A point I found funny is the idea where giant corporations are basically using ChatGPT to do their homework and almost nobody cares if it's conscious or not.

I really like Chad, Begi, and Shalmaneser, don't really care for anyone else

This was a fascinating mix of super on point and totally off mark predictions. Focuses on fertility/population demographics, colonialism, eugenics/genetic engineering, and even some specific geopolitics are correctly predicted to be super hot issues.

Lots of different little side stories and snippets adding to the immersion of this world. The other thing I find interesting about books written in the past, about a time which is their future but is now my past, is learning their predictions about the future.

Finished it last night and I have some thoughts lol. Overall I definitely didn't enjoy it as much as some other books. I never really got connected to the characters, and I didn't find the main story too engaging.

On the plus side I loved the worldbuilding

well it's pretty hard to argue with that :)

Then compute that prob over the population of players and sort by highest avg prob.

Finally, a model does not have to be correct to be useful, in a lot of cases you can get great accuracy without even using a vector, just representing the overall skill with a scalar.

Often something of interest is overall skill, in which case aggregation can apply over vectors and sort by mean.

Sometimes you don't need to directly order. Can use a parametric model over two vectors A and B to produce a probability that A will beat B.

Computer science, took some stats and optimization courses.

I think I have a different opinion about vectors, I can think of a lot of ways to order them.

For example if each dimension represents a specific skill, then per-skill orderings produce per-skill leaderboards

why do it for free though? jobs.gem.com/bluesky/am9i...

very slightly :)

basically my rules of thumb are to never use numpy on scalars unless the function simply doesn't exist in base python, and to try the simpler thing, ** and pow are general and need to support raising numbers to any power, num*num is a single multiplication