Just to make this a little more accessible to people who aren't familiar with SERI-MATS, MATS is Machine Learning Alignment Theory Scholars Program, a training program for young researchers who want to contribute to AI alignment research.
MATS alumni have gone on to publishsafetyresearch (LW posts here), join alignment research teams (including at Anthropic and MIRI), and found alignment research organizations (including a MIRI team, Leap Labs, and Apollo Research). Our alumni spotlight is here.
We broadened our advertising approach for the Summer 2023 Cohort, including a Twitter post and a shout-out on Rob Miles' YouTube and TikTok channels. We expected some lowering of average applicant quality as a result but have yet to see a massive influx of applicants from these sources. We additionally focused more on targeted advertising to AI safety student groups, given their recent growth. We will publish updated applicant statistics after our applications close.
In addition to applicant selection and curriculum elements, our Scholar Support staff, introduced in the Winter 2022-23 Cohort, supplement the mentorship experience by providing 1-1 research strategy and unblocking support for scholars. This program feature aims to:
Supplement and augment mentorship with 1-1 debugging, planning, and unblocking;
Solve scholars’ problems, giving more time for research.
Defining "good alignment research" is very complicated and merits a post of its own (or two, if you also include the theories of change that MATS endorses). We are currently developing scholar research ability through curriculum elements focused on breadth, depth, and epistemology (the "T-model of research"):
Our Alumni Spotlight includes an incomplete list of projects we highlight. Many more past scholar projects seem promising to us but have yet to meet our criteria for inclusion here. Watch this space.
Since Summer 2022, MATS has explicitly been trying to parallelize the field of AI safety as much as is prudent, given the available mentorship and scholarly talent. In longer-timeline worlds, more careful serial research seems prudent, as growing the field rapidly is a risk for the reasons outlined in the above article. We believe that MATS' goals have grown more important from the perspective of timelines shortening (though MATS management has not updated on timelines much as they were already fairly short in our estimation).
MATS would love to support senior research talent interested in transitioning into AI safety! Our scholars generally comprise 10% Postdocs, and we would like this number to rise. Currently, our advertising strategy is contingent on the AI safety community adequately targeting these populations (which seems false) and might change for future cohorts.
Just to make this a little more accessible to people who aren't familiar with SERI-MATS, MATS is Machine Learning Alignment Theory Scholars Program, a training program for young researchers who want to contribute to AI alignment research.
Thanks Joseph! Adding to this, our ideal applicant has:
MATS alumni have gone on to publish safety research (LW posts here), join alignment research teams (including at Anthropic and MIRI), and found alignment research organizations (including a MIRI team, Leap Labs, and Apollo Research). Our alumni spotlight is here.