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AI Safety Fundamentals
Summer 2026 Curriculum
An 8-week introductory reading group covering the current trajectory of AI, evidence for misalignment, threat models, technical safety approaches, and the AI policy landscape. Participants meet weekly in small sections facilitated by experienced TAs. No work is assigned outside of weekly meetings.
Introduction to Machine Learning
Self-paced ML fundamentals: neural networks, transformers, and backpropagation.
Trends & Timelines
Scaling drivers, capability trends, and time-horizon forecasting toward AGI.
Outer Alignment
Reward misspecification, specification gaming, RLHF, and the gap between intended and operationalized goals.
Inner Alignment
Deception, reward tampering, alignment faking, and goal misgeneralization.
Threat Models
Instrumental convergence, power-seeking, bioterrorism, cyberwarfare, and gradual disempowerment.
Control & Scalable Oversight
AI control framework, resampling, monitoring, weak-to-strong generalization, and debate.
Interpretability & Evals
Attribution graphs, linear probes, natural language autoencoders, evaluation awareness, and detecting deceptive AI.
AI Governance & Liability
Tort law, compute governance, US export controls on China, and the regulator's toolbox.
Research & Careers in Safety
Empirical research workflow, active alignment agendas, and career paths in AI safety.
Looking for a previous cohort? View the Spring 2026 curriculum archive.