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.

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.

Training AI models is an exciting field with much potential for progress, and it's essential to involve more people in shaping AI's future.

There's a significant amount of ongoing research and pilot projects in AI for healthcare, but many technologies have not yet been widely rolled out due to issues of interpretability and fairness that need to be addressed first.

We're still at the very beginning of how people understand how to use AI tools in their work, and there are many social opportunities that haven't been fully explored yet.

Concerns about AI include international competition, particularly with China's investments in AI, and the need for interpretability in AI systems to explain their decision-making processes.

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.

Transparency during the development of AI is crucial to prevent concentration of power and ensure alignment with human values.

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