MATS Winter 2023-24 Retrospective
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Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: LessWrong
A program retrospective from MATS, a Berkeley-based AI safety research mentorship initiative; useful for understanding field-building efforts, researcher pipeline development, and the landscape of early-career AI safety researchers as of early 2024.
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Summary
Detailed retrospective on the fifth iteration of the ML Alignment & Theory Scholars (MATS) program, covering 63 scholars and 20 mentors. Reports high scholar satisfaction (9.2/10 NPS), strong mentor assessments of scholar capabilities, and measurable skill development in technical depth, research taste, and theory of change. Documents operational changes and reduced career obstacles for participants post-program.
Key Points
- •63 scholars received mentorship from 20 research mentors; 77% assessed capable of first-author papers at top conferences by mentors.
- •Scholars reported significant self-assessed improvements: +1.93/10 breadth, +1.53/10 technical depth, +1.35/10 research taste.
- •Program introduced Research Management model replacing Scholar Support; median scholar valued it at $1000 and gained ~10 productive hours.
- •52% of scholar presentations featured interpretability research, reflecting dominant research interest in the cohort.
- •Post-program obstacles to alignment careers decreased most in mentorship access, collaborators, agenda experience, and grant writing.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| MATS ML Alignment Theory Scholars program | Organization | 60.0 |
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x This website requires javascript to properly function. Consider activating javascript to get access to all site functionality. MATS Winter 2023-24 Retrospective — LessWrong AI Alignment Fieldbuilding MATS Program Postmortems & Retrospectives Personal Blog 90
MATS Winter 2023-24 Retrospective
by utilistrutil , LauraVaughan , deus_ex_maki , Christian Smith , Juan Gil , Henry Sleight , Matthew Wearden , Ryan Kidd 11th May 2024 59 min read 28 90
Co-Authors: @Rocket , @LauraVaughan , @McKennaFitzgerald , @Christian Smith , @Juan Gil , @Henry Sleight , @Matthew Wearden , @Ryan Kidd
The ML Alignment & Theory Scholars program (MATS) is an education and research mentorship program for researchers entering the field of AI safety. This winter, we held the fifth iteration of the MATS program, in which 63 scholars received mentorship from 20 research mentors. In this post, we motivate and explain the elements of the program, evaluate our impact, and identify areas for improving future programs.
Summary
Key details about the Winter Program:
The four main changes we made after our Summer program were: Reducing our scholar stipend from $40/h to $30/h based on alumni feedback;
Transitioning Scholar Support to Research Management ;
Using the full Lighthaven campus for office space as well as housing;
Replacing Alignment 201 with AI Strategy Discussions .
Educational attainment of MATS scholars: 48% of scholars were pursuing a bachelor’s degree, master’s degree, or PhD;
17% of scholars had a master’s degree as their highest level of education;
10% of scholars had a PhD.
If not for MATS, scholars might have spent their counterfactual winters on the following pursuits (multiple responses allowed): Conducting independent alignment research without mentor (24%);
Working at a non-alignment tech company (21%);
Conducting independent alignment research with a mentor (13%);
Taking classes (13%).
Key takeaways from scholar impact evaluation:
Scholars are highly likely to recommend MATS to a friend or colleague (average likelihood is 9.2/10 and NPS is +74).
Scholars rated the mentorship they received highly (average rating is 8.1/10). For 38% of scholars, mentorship was the most valuable element of MATS.
Scholars are likely to recommend Research Management to future scholars (average likelihood is 7.9/10 and NPS is +23). The median scholar valued Research Management at $1000.
The median scholar reported accomplishing 10% more at MATS because of Research Management and gaining 10 productive hours.
The median scholar made 5 professional connections and found 5 potential future collaborators during MATS.
The average scholar self-assessed their improvement on the depth of their technical skills by +1.53/10, their breadth of knowledge by +1.93/10, their research taste by +1.35/10, and their theory of change construction by +1.25/10.
According to mentors, of the 56 scholars evaluated, 77% could achieve a “First-author paper at t
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