We have a preparedness framework that outlines how we check for safety before releasing AI models.

It's essential to define rigorous testing for AI models to ensure society understands the risks associated with their deployment.

AI systems must undergo rigorous internal testing while also engaging with external experts to ensure safety and reliability before public deployment.

Red teaming tests AI models under extreme scenarios to identify potential societal harms and improve their robustness.

As AI systems become more capable, we need to ensure that we have safety processes in place throughout their development, not just when they become powerful.

Monitoring for external influences on AI research is crucial, as models might assist unauthorized entities in conducting AI research.

Robust internal security is crucial for AI labs to prevent exploitation and vulnerabilities, especially as AI models begin to write more of their own code.

Testing frameworks are essential for understanding and adapting to the behaviors of AI systems as they evolve and change.

We need to find a way to combine extended training for alignment with extended testing to ensure AI models are aligned in various situations, and to ensure the training process works effectively, we need models that are at or beyond the current frontier on certain topics.