Tests show that the popular AI still has a poor grasp of reality.| MIT Technology Review
A Chess-GPT Linear Emergent World Representation| Adam Karvonen
Mechanistic interpretability seeks to understand neural networks by breaking them into components that are more easily understood than the whole. By understanding the function of each component, and how they interact, we hope to be able to reason about the behavior of the entire network. The first step in that program is to identify the correct components to analyze. | transformer-circuits.pub