Machine Learning Alignment & Theory Scholars

MATS aims to find and train talented individuals for what we see as the world’s most urgent and talent-constrained problem: reducing risks from unaligned artificial intelligence (AI). We believe that ambitious researchers from a variety of backgrounds have the potential to meaningfully contribute to the field of alignment research. We aim to provide the training, logistics, and community necessary to aid this transition.

MATS is an independent research and educational seminar program that connects talented scholars with top mentors in the fields of AI alignment, interpretability, and governance. The main goal of MATS is to help scholars develop as AI alignment researchers.

During the Research phase, each scholar spends ~1-2 hours/week working with their mentor, with more frequent communication via Slack. Scholars' research directions will typically be chosen through a collaborative process with their mentors, and scholars are expected to develop their independent research direction as the program continues. Approximately one month into the program, scholars are expected to write a short Research Plan outlining their projects’ threat model, theory of change, and project deliverables. At the end of the program scholars will give a brief presentation at the Scholar Symposium on project work conducted over the course of MATS.

Educational seminars and workshops will be held 2-3 times per week. Previously, speakers have included Buck Shlegeris from Redwood Research, Adam Gleave from FAR AI, Neel Nanda from Google DeepMind, William Saunders from OpenAI, Andrew Critch from CHAI, Lennart Heim from GovAI, Ajeya Cotra from Open Philanthropy, and more.

The extent of mentor support will vary depending on the project and the mentor. Scholars will also receive support from MATS’s Research Management team, who help to scope out and structure research direction.