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Intro to AI Safety DeCal

Mondays 6–8 PM in Wheeler 120

The Intro to AI Safety curriculum provides a high-level understanding of the problems in AI safety and some of the key research directions which aim to solve it.

About

Course prerequisites

This course assumes fluency with linear algebra, multivariable calculus, and basic programming at a level equivalent to MATH 54, MATH 53, and CS 61A. Since the class will quickly move through the material and involve significant ML paper reading, familiarity with machine learning at the level of CS 189 is strongly recommended. Prior familiarity with the field of AI Safety is not required.

Weekly reading reflections

These reflections are based on the assigned readings and must be submitted before each class. Students will answer specific questions about the material and share their own insights, including points of interest, connections to other ideas, and areas of agreement or disagreement. Completing these reflections demonstrates engagement with the material and helps prepare students for in-class discussions.

Projects

Students will complete 2–3 coding projects focused on replicating results from technical AI safety papers. They are also encouraged to creatively extend the ideas presented in the papers. Projects may be completed in groups, and students should prepare a presentation for the final project.

Tentative schedule
01Issues in AI safety
02Machine Learning Review
03AGI Timelines, Takeoff, and Imagining the Future
04RLHF and Preference Training
05Adversarial Robustness
06LLM Agent Safety and Security
07Reward Hacking and Oversight Methods
08Inner Misalignment, Deception, and Alignment Faking
09Interpretability, Unlearning, and Representation Engineering
10Additional Research Agendas
11–12Guest Lectures and Final Project Presentation
What you'll learn
The theoretical and practical risks associated with using advanced AI systems
The difficulties inherent to addressing them
The current state of research regarding solutions

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