Mechanistic interpretability

Jul 10, 2026

Understanding the inner thoughts of AI

Neel Nanda, head of Google DeepMind's language model interpretability team, discusses the critical field of interpretability, likening it to the "neuroscience of AI." He explains why understanding the internal workings of "grown, not designed" neural networks is crucial for AI safety and scientific discovery. The episode explores cutting-edge techniques like Chain of Thought monitoring, mechanistic interpretability (steering and probing), and Sparse Autoencoders, highlighting their strengths and limitations in debugging, detecting deception, and uncovering hidden model objectives. Nanda emphasizes interpretability's role in building safe, aligned, and trustworthy AI as we approach AGI, acknowledging its pragmatic necessity despite inherent limits to full understanding.

Jul 08, 2025

Mapping the Mind of a Neural Net: Goodfire’s Eric Ho on the Future of Interpretability

Eric Ho, founder of Goodfire, discusses the critical challenge of AI interpretability. He shares how his team is developing techniques to understand, audit, and edit neural networks at the feature level, including breakthrough results in resolving superposition with sparse autoencoders, successful model editing demonstrations, and real-world applications in genomics with Arc Institute's DNA foundation models. Ho argues that these white-box approaches are essential for building safe, reliable, and intentionally designed AI systems.