As Large Language Models (LLMs) transition into core enterprise infrastructure, treating them as "black boxes" is no longer an acceptable engineering risk. Can we turn LLM agents into debuggable, predictable software systems?
The answer lies in an accelerating field of research made famous by an LLM obsessed with the Golden Gate Bridge: mechanistic interpretability (mechinterp). This discipline aims to reverse-engineer neural networks into human-understandable algorithms.
Aimed at data scientists and AI engineers familiar with transformer basics, this talk showcases what the current state of the art can actually achieve. Moving past theoretical toy examples, we will explore recent breakthroughs in extracting high-level semantic features, such as isolating abstract concepts like emotional subtext within model weights.
You will learn the intuitions behind how we reconstruct model features from LLM layers, how they can be actively manipulated via activation steering to change model behaviour without fine-tuning, and a realistic assessment of where current tooling hits a hard wall.
