Fall 2025, Thursdays 3:45pm-6:30pm (First lecture September 4)
Course: CS 2881R - AI Safety
| YouTube Lecture Playlist | Course Lecture Notes and Experiments |
Time and Place: Thursdays 3:45pm-6:30pm Eastern Time, SEC LL2.229 (SEC is in 150 Western Ave, Allston, MA)
Instructor: Boaz Barak
Teaching Fellows: Natalie Abreu (natalieabreu@g.harvard.edu), Roy Rinberg (royrinberg@g.harvard.edu), Hanlin Zhang (hanlinzhang@g.harvard.edu), Sunny Qin (Harvard)
Course Description: This will be a graduate level course on challenges in alignment and safety of artificial intelligence. We will consider both technical aspects as well as questions on societal and other impacts of the field.
Prerequisites: We require mathematical maturity, and proficiency with proofs, probability, and information theory, as well as the basics of machine learning, at the level of an undergraduate ML course such as Harvard CS 181 or MIT 6.036. You should be familiar with topics such as empirical and population loss, gradient descent, neural networks, linear regression, principal component analysis, etc. On the applied side, you should be comfortable with Python programming, and be able to train a basic neural network.
Important: Read the Course Introduction!
Course Introduction Blog Post - This contains Homework Zero and important course information. Students who filled in the form will receive more instructions by email.
Homework Zero: Homework Zero (github repository)
Questions? If you have any questions about the course, please email harvardcs2881@gmail.com
Related reading by Boaz:
| Previous versions: Spring 2023 ML Theory Seminar | Spring 2021 ML Theory Seminar |
The course will have 13 in person lectures - each lecture will involve also discussion and presentation of an experiment by a group of students.
The assignments, project, and other requirements for the course will be determined later.
Attendance: Attendance is mandatory. Students are expected to attend all lectures and do the reading in advance as well discuss these in electronic forum.
Generative AI: Students are allowed and encouraged to use generative AI as much as they can for studying, exploring concept, and their assignments and projects. Given the availability of AI tools, expectations for projects and assignments will have more ambitious than in past years.
Electronic device policy students can use laptops in class but we will ask those using them to sit in the back so they don’t distract other students.
Lecture recordings To the extent technically possible we intend to record and publish the lectures online, though we might have some time lag in doing that. However note that recording is done automatically by a static in-room camera, and some parts of the lecture (e.g. whiteboard, or discussions) may not be captured as well. Also we will honor requests by external speakers not to record their talks.
POTENTIAL CONFLICT OF INTEREST NOTE: In addition to his position at Harvard, Boaz is also a member of the technical staff at OpenAI. The course will include discussions of models from multiple providers, including OpenAI, and students are also encouraged to use AIs from multiple providers while doing their work. If students in the course feel any issue with this conflict, please do not hesitate to contact Boaz, the other staff, or the Harvard SEAS administration. For what it’s worth, I (Boaz) will see it as a great success of the course if its graduates work in AI safety in any capacity, including at academia, non-profit, governments, and any of OpenAI’s competitors.
Classes begin September 2, 2025. Reading period December 4-9, 2025.
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