The circuits work in AI is impressive, showing that models can reason and understand complex relationships in data.

Recent developments in AI show models are increasingly capable of externalized reasoning, which can be monitored for control purposes.

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

The shift from merely scaling LLMs to incorporating reasoning techniques has opened new avenues for AI development, allowing models to perform better by thinking longer and more deeply.

Neural networks are now capable of reasoning through a process called chain of thought, allowing them to reflect on their outputs.

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.

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

As AI progresses, we might see models integrated into physical systems, which could lead to new forms of consciousness.

The complexity of AI systems is increasing, making it essential to understand how behaviors propagate through interconnected models.