I’m noticing there are still many interp mentors for the current round of MATS—was the “fewer mech interp mentors” change implemented for this cohort, or will that start in Winter or later?
Last program, 44% of scholar research was on interpretability, 18% on evals/demos, 17% on oversight/control, etc. In summer, we intend for 35% of scholar research to be on interpretability, 17% on evals/demos, 27% on oversight/control, etc., based on our available mentor pool and research priorities. Interpretability will still be the largest research track and still has the greatest interest from potential mentors and applicants. The plot below shows the research interests of 1331 MATS applicants and 54 potential mentors who have applied for our Summer 2024 or Winter 2024-25 Programs.
Note that number of scholars is a much more important metric than number of mentors when it comes to evaluating MATS resources, as scholar per mentors varies a bunch (eg over winter I had 10 scholars, which is much more than most mentors). Harder to evaluate from the outside though!
I don’t know the answer to your actual question, but I’ll note there are slightly fewer mech interp mentors than mentors listed in the “AI interpretability” area (though all of them are at least doing “model internals”). I’d say Stephen Casper and I aren’t focused on interpretability in any narrow sense, and Nandi Schoots’ projects also sound closer to science of deep learning than mech interp. Assuming we count everyone else, that leaves 11 out of 39 mentors, which is slightly less than ~8 out of 23 from the previous cohort (though maybe not by much).
I’m noticing there are still many interp mentors for the current round of MATS—was the “fewer mech interp mentors” change implemented for this cohort, or will that start in Winter or later?
Last program, 44% of scholar research was on interpretability, 18% on evals/demos, 17% on oversight/control, etc. In summer, we intend for 35% of scholar research to be on interpretability, 17% on evals/demos, 27% on oversight/control, etc., based on our available mentor pool and research priorities. Interpretability will still be the largest research track and still has the greatest interest from potential mentors and applicants. The plot below shows the research interests of 1331 MATS applicants and 54 potential mentors who have applied for our Summer 2024 or Winter 2024-25 Programs.
Note that number of scholars is a much more important metric than number of mentors when it comes to evaluating MATS resources, as scholar per mentors varies a bunch (eg over winter I had 10 scholars, which is much more than most mentors). Harder to evaluate from the outside though!
I don’t know the answer to your actual question, but I’ll note there are slightly fewer mech interp mentors than mentors listed in the “AI interpretability” area (though all of them are at least doing “model internals”). I’d say Stephen Casper and I aren’t focused on interpretability in any narrow sense, and Nandi Schoots’ projects also sound closer to science of deep learning than mech interp. Assuming we count everyone else, that leaves 11 out of 39 mentors, which is slightly less than ~8 out of 23 from the previous cohort (though maybe not by much).