PIBBSS had 12 fellows last cohort and MATS had 90 scholars. The mean/median MATS Summer 2024 scholar was 27; I’m not sure what this was for PIBBSS. The median age of the 12 oldest MATS scholars was 35 (mean 36). If we were selecting for age (which is silly/illegal, of course) and had a smaller program, I would bet that MATS would be older than PIBBSS on average. MATS also had 12 scholars with completed PhDs and 11 in-progress.
Several PIBBSS fellows/affiliates have done MATS (e.g., Ann-Kathrin Dombrowski, Magdalena Wache, Brady Pelkey, Martín Soto).
I suspect that your estimation of “how smart do these people seem” might be somewhat contingent on research taste. Most MATS research projects are in prosaic AI safety fields like oversight & control, evals, and non-”science of DL” interpretability, while most PIBBSS research has been in “biology/physics-inspired” interpretability, agent foundations, and (recently) novel policy approaches (all of which MATS has supported historically).
Also, MATS is generally trying to further a different research porfolio than PIBBSS, as I discuss here, and has substantial success in accelerating hires to AI scaling lab safety teams and research nonprofits, helping scholars found impactful AI safety organizations, and (I suspect) accelerating AISI hires.
I suspect that your estimation of “how smart do these people seem” might be somewhat contingent on research taste. Most MATS research projects are in prosaic AI safety fields like oversight & control, evals, and non-”science of DL” interpretability, while most PIBBSS research has been in “biology/physics-inspired” interpretability, agent foundations, and (recently) novel policy approaches (all of which MATS has supported historically).
I think this is less a matter of my particular taste, and more a matter of selection pressures producing genuinely different skill levels between different research areas. People notoriously focus on oversight/control/evals/specific interp over foundations/generalizable interp because the former are easier. So when one talks to people in those different areas, there’s a very noticeable tendency for the foundations/generalizable interp people to be noticeably smarter, more experienced, and/or more competent. And in the other direction, stronger people tend to be more often drawn to the more challenging problems of foundations or generalizable interp.
So possibly a MATS apologist reply would be: yeah, the MATS portfolio is more loaded on the sort of work that’s accessible to relatively-mid researchers, so naturally MATS ends up with more relatively-mid researchers. Which is not necessarily a bad thing.
I don’t agree with the following claims (which might misrepresent you):
“Skill levels” are domain agnostic.
Frontier oversight, control, evals, and non-”science of DL” interp research is strictly easier in practice than frontier agent foundations and “science of DL” interp research.
The main reason there is more funding/interest in the former category than the latter is due to skill issues, rather than worldview differences and clarity of scope.
MATS has mid researchers relative to other programs.
Y’know, you probably have the data to do a quick-and-dirty check here. Take a look at the GRE/SAT scores on the applications (both for applicant pool and for accepted scholars). If most scholars have much-less-than-perfect scores, then you’re probably not hiring the top tier (standardized tests have a notoriously low ceiling). And assuming most scholars aren’t hitting the test ceiling, you can also test the hypothesis about different domains by looking at the test score distributions for scholars in the different areas.
We don’t collect GRE/SAT scores, but we do have CodeSignal scores and (for the first time) a general aptitude test developed in collaboration with SparkWave. Many MATS applicants have maxed out scores for the CodeSignal and general aptitude tests. We might share these stats later.
I think there some confounders here:
PIBBSS had 12 fellows last cohort and MATS had 90 scholars. The mean/median MATS Summer 2024 scholar was 27; I’m not sure what this was for PIBBSS. The median age of the 12 oldest MATS scholars was 35 (mean 36). If we were selecting for age (which is silly/illegal, of course) and had a smaller program, I would bet that MATS would be older than PIBBSS on average. MATS also had 12 scholars with completed PhDs and 11 in-progress.
Several PIBBSS fellows/affiliates have done MATS (e.g., Ann-Kathrin Dombrowski, Magdalena Wache, Brady Pelkey, Martín Soto).
I suspect that your estimation of “how smart do these people seem” might be somewhat contingent on research taste. Most MATS research projects are in prosaic AI safety fields like oversight & control, evals, and non-”science of DL” interpretability, while most PIBBSS research has been in “biology/physics-inspired” interpretability, agent foundations, and (recently) novel policy approaches (all of which MATS has supported historically).
Also, MATS is generally trying to further a different research porfolio than PIBBSS, as I discuss here, and has substantial success in accelerating hires to AI scaling lab safety teams and research nonprofits, helping scholars found impactful AI safety organizations, and (I suspect) accelerating AISI hires.
I think this is less a matter of my particular taste, and more a matter of selection pressures producing genuinely different skill levels between different research areas. People notoriously focus on oversight/control/evals/specific interp over foundations/generalizable interp because the former are easier. So when one talks to people in those different areas, there’s a very noticeable tendency for the foundations/generalizable interp people to be noticeably smarter, more experienced, and/or more competent. And in the other direction, stronger people tend to be more often drawn to the more challenging problems of foundations or generalizable interp.
So possibly a MATS apologist reply would be: yeah, the MATS portfolio is more loaded on the sort of work that’s accessible to relatively-mid researchers, so naturally MATS ends up with more relatively-mid researchers. Which is not necessarily a bad thing.
I don’t agree with the following claims (which might misrepresent you):
“Skill levels” are domain agnostic.
Frontier oversight, control, evals, and non-”science of DL” interp research is strictly easier in practice than frontier agent foundations and “science of DL” interp research.
The main reason there is more funding/interest in the former category than the latter is due to skill issues, rather than worldview differences and clarity of scope.
MATS has mid researchers relative to other programs.
Y’know, you probably have the data to do a quick-and-dirty check here. Take a look at the GRE/SAT scores on the applications (both for applicant pool and for accepted scholars). If most scholars have much-less-than-perfect scores, then you’re probably not hiring the top tier (standardized tests have a notoriously low ceiling). And assuming most scholars aren’t hitting the test ceiling, you can also test the hypothesis about different domains by looking at the test score distributions for scholars in the different areas.
We don’t collect GRE/SAT scores, but we do have CodeSignal scores and (for the first time) a general aptitude test developed in collaboration with SparkWave. Many MATS applicants have maxed out scores for the CodeSignal and general aptitude tests. We might share these stats later.