ML Safety Scholars Program
20 June - 19 August 2022
Applications to this course have closed
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 contribute. Our hope is that the Machine Learning Safety Scholars (MLSS) program can help produce knowledgeable and motivated undergraduates who can then apply their skills to the most pressing research problems within AI safety.
MLSS 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. The course will have three main parts:
We expect each week of the program to cover the equivalent of about 3 weeks of the university lectures we are drawing our curriculum from (you can find the preliminary syllabus here). As a result, the program will likely take roughly 30-40 hours per week, depending on speed and prior knowledge.
MLSS is designed for motivated undergraduates who are interested in doing empirical AI safety research. We will accept ‘Scholars’ who will be enrolled undergraduate students after the conclusion of the program (this includes graduated/soon graduating high school students about to enroll in their first year of undergrad). Here are crucial prerequisites for joining the program:
We don’t assume any ML knowledge, though we expect that the course could be helpful even for people who have some knowledge of ML already (e.g. fast.ai or Andrew Ng’s Coursera course).
Here is some other important information you should know about MLSS: