Five Types of “Corrigibility Projects” I Don’t Want to Fund

I'm currently evaluating grant applications for the Corrigibility Research Fund . For the most part, my methodology is the same as it was in September . But the applications I get keep falling into the same familiar patterns, so I thought I'd write a little about those patterns, and why I don't want to fund them. The most superficial pattern of note is SLOP. So much slop. You don't even know . Slop is more than just whether an application is AI generated. We're now in an era where people across the world, including those who don't really speak English, can contribute to work that would otherwise be restricted to people of a single language or nationality or place, and I like that. When I see an application that discloses its use of AI and gives a reasonable justification for it, I try to look past the stylistic tics and focus purely on whether the underlying application is promising. [1] Still, we're not yet in the domain where AIs can write good grant applications without some human assistance, so a high Pangram score is definitely evidence that an application is junk. Another remarkably common sign of slop was messed up formatting or other indications that people just copy-pasted