Our new interpretability methods allow us to intervene in the model's thought process, demonstrating its ability to adjust its output based on prior planning.

Knowing how things work under the hood is crucial because it allows you to influence AI models to follow your intentions better.

The surprising interpretability of thinking models allows us to understand how they reach their conclusions, which was not expected in earlier AI developments. The fact that AI models can articulate their reasoning provides a level of comfort regarding safety, even though it's not infinite comfort.

Understanding how AI models think can lead to safer and more reliable systems, similar to how neuroscience improves health outcomes.

Mechanistic interpretability aims to understand what's happening inside AI models at the level of individual circuits, but we still don't know enough to ensure alignment.

Reading model outputs closely is essential; it helps identify misunderstandings and refine prompts for better performance.

The skill of prompting is about introspection, understanding what you want, and making yourself legible to the model.

We aim to understand how an AI model connects concepts in its mind to formulate answers to our questions.

AI models are not just reflections of humanity; they may also shape how we see ourselves.