The Deep Learning Revolution (Turing Award Lecture)

Geoffrey Hinton · 32:28

This foundational lecture provides the historical context and conceptual genesis of deep learning, setting the stage for understanding the complex systems we aim to interpret and align.

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.