Transformer Circuits, Induction Heads, In-Context Learning

Ashish Vaswani, Lucas Beyer & William Fedus · 59:34

Building on the deep learning revolution, this lecture introduces mechanistic interpretability by dissecting transformer circuits, specifically induction heads, to understand how models achieve in-context learning.

Objective-Driven AI: World Models that Learn, Remember, Reason, and Plan

Yann LeCun · 1:16:53

Expanding beyond token prediction, this step introduces objective-driven AI and world models, offering a framework for how AI systems learn, remember, reason, and plan, which is crucial for alignment.

Atomic Facts to Structured Knowledge: Rethinking Unlearning & Jailbreaking in Large Language Models

Rongzhe Wei · 56:22

With an understanding of world models, this lecture delves into structured internal knowledge modeling, which is essential for addressing unlearning and jailbreaking, directly impacting AI safety and alignment.

AI and Safety

Ron Rivest · 29:26

This lecture directly addresses critical aspects of AI safety, exploring methods and theoretical frameworks to ensure robust and reliable operation, building upon the technical insights from interpretability and world models.

Governance at the Technological Frontier: Translating AI Research into Oversight Policy

Inioluwa Deborah Raji · 39:55

The final step translates technical AI safety research into practical oversight policy, examining the institutional and regulatory mechanisms needed to govern advanced AI systems effectively.