Program Overview

The Machine Learning Safety Scholars program is a paid, 9-week summer program designed to help undergraduate students gain skills in machine learning with the aim of using those skills for empirical AI safety research in the future. Apply for the program here by May 31st.

The course will have three main parts:

Machine learning, with lectures and assignments from MIT
Deep learning, with lectures and assignments from the University of Michigan, NYU, and Hugging Face
ML safety, with lectures and assignments produced by Dan Hendrycks at UC Berkeley

The first two sections are based on public materials, and we plan to make the ML safety course publicly available soon as well. The purpose of this program is not to provide proprietary lessons but to better facilitate learning:

The program will have a Slack, regular office hours, and active support available for all Scholars. We hope that this will provide useful feedback over and above what’s possible with self-studying.
The program will have designated “work hours” where students will cowork and meet each other. We hope this will provide motivation and accountability, which can be hard to get while self-studying.
We will pay Scholars a $4,500 stipend upon completion of the program. This is comparable to undergraduate research roles and will hopefully provide more people with the opportunity to study ML.

MLSS will be fully remote, so participants will be able to do it from wherever they’re located.

Why have this program?

Much of AI safety research currently focuses on existing machine learning systems, so it’s necessary to understand the fundamentals of machine learning to be able to make contributions. While many students learn these fundamentals in their university courses, some might be interested in learning them on their own, perhaps because they have time over the summer or their university courses are badly timed. In addition, we don’t think that any university currently devotes multiple weeks to AI Safety.

There are already sources of funding for upskilling within EA, such as the Long Term Future Fund. Our program focuses specifically on ML and therefore we are able to provide a curriculum and support to Scholars in addition to funding, so they can focus on learning the content.

Our hope is that this program can contribute to producing knowledgeable and motivated undergraduates who can then use thei...

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