Interpretability in AI is crucial for understanding how models operate at various levels, and I am optimistic about the ongoing work in this area, which is essential for mainstream AI adoption.

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.

Even when we open the black box of AI, interpreting the internal workings can be challenging without proper tools.

We don't know why a model can suddenly perform addition correctly after previously struggling with it. This is a question we're trying to answer through mechanistic interpretability.

Interpretability in AI is a crucial debate, questioning whether it truly matters to end-users, such as doctors, and if the field is overemphasizing its importance.

To enhance the usefulness, reliability, and security of AI, we need to understand the reasoning behind their actions.

The next step for AI models like 'o1' is to enhance interpretability, allowing users to edit and direct the reasoning process more effectively.

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.

AI is often described as a black box, where inputs lead to outputs, but the reasoning behind its responses remains unclear.