ML Safety Scholars Program

Course title

ML Safety Scholars Program

Host organization

MLSS Team

Time Zone

Global

Dates

20 June - 19 August 2022

Contact email

thomas.woodside@gmail.com

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:

  • 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

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:

  • Differential calculus
  • At least one of linear algebra or introductory statistics (e.g., AP Statistics). Note that if you only have one of these, you may need to make a conscious effort to pick up material from the other during the program.
  • Programming. You will be using Python in this course, so ideally you should be able to code in that language (or at least be able to pick it up quickly). The courses will not teach Python or programming.

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:

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