#higher

Higher salaries in AI safety

The salaries are too damn low AI safety has a salary overhang. A bunch of people are paid way below their market rate. (Also true of EA, but I’ll lump them under “AI safety” for this piece). This has been getting a bit better, very recently — I hear about eg Redwood Research technical talent making ~$800k/y in salary. But when you compare this to the $10-100m/y comp packages for talent in labs, it still seems kind of low. And CG’s money cannon has fired very few shots to date — maybe ~15 orgs hit, see Trace — so the raises are still very unequally distributed. It’s not just rank-and-file emplo

LessWrong · EN

GitLab Orbit is GA: Higher agent accuracy, lower token cost

An agent’s true cost isn’t only the tokens it burns. It’s the review that calls your code “safe to merge” after seeing the diff, but without any context on the pipeline. We call that artificial confidence, not artificial intelligence. Agents guess because code is all they can see; they can’t see the merge request that last changed it, the pipeline that ran it, the vulnerability it carries, or the people who wrote and reviewed it. Guessing is expensive. If you’ve tried running agents in a large monorepo, one with tens of thousands of files, you know how it goes: the agent iterates, pinging the

GitLab · EN

Will the Real Higher-Order Logic Please Stand Up?

Abstract: In this work-in-progress talk, I will attempt to clarify some of the issues surrounding second- and higher-order logics. How should we understand the relationship between the proof-theoretic inferentialist presentations of such logics and their model-theoretic truth conditional semantics? I will argue that in an important sense, the so-called full semantics for second- and higher-order logics is too strong, and that a wider class of models is not only necessary, but is mathematically and philosophically desirable. The good news: such a class of models already exists and is well under

Kagi Small Web · EN