Mark Bissell

This talk explores mechanistic interpretability, a field focused on reverse-engineering neural networks to understand their internal workings. It argues that interpretability is moving from research labs into practical applications, offering AI engineers new tools for debugging, enhancing user experiences, and advancing scientific discovery. The core thesis is that understanding how AI models function internally is becoming crucial for building more reliable, controllable, and insightful AI systems.