Tootfinder

Update - I think I managed to come to a compromise on timeline and firmly established that no AI will be used for this project.
They agreed, although they did make say that “it’s as if you decided to dig up a garden using a shovel instead of a tractor. As long as it’s done, it’s done”
I don’t wanna pick a fight so I left the comment alone but now that I think of it I have two responses:
- who digs a garden with a tractor?
- last I checked tractor didn’t have a ~30% chance of lying about the dirt they did or up to 70% odds of just giving up on working if the ground was too hard to dig

Multi-Project Contracts
Tal Alon, Matteo Castiglioni, Junjie Chen, Tomer Ezra, Yingkai Li, Inbal Talgam-Cohen
https://arxiv.org/abs/2506.05705 https://

Multi-Project Contracts
We study a new class of contract design problems where a principal delegates the execution of multiple projects to a set of agents. The principal's expected reward from each project is a combinatorial function of the agents working on it. Each agent has limited capacity and can work on at most one project, and the agents are heterogeneous, with different costs and contributions for participating in different projects. The main challenge of the principal is to decide how to allocate the agents t…

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI