Achieving five nines reliability in AI control is extremely challenging.

We aim to minimize errors over time while acknowledging that mistakes will happen, which is crucial for AI safety.

The consequences of success in AI are so significant that even a low probability of success makes it worth pursuing.

To ensure reliability in AI systems deployed in complex fields, we need continuous evaluation methods like Arena.

To ensure reliability in AI systems deployed in complex fields, we need continuous evaluation methods like Arena.

To improve the reliability of AI agents, it is crucial to ensure that their actions are correct a high percentage of the time, which requires significant computational resources.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

We need to keep AI on a leash, as over-reliance on AI agents can lead to issues like introducing bugs or security vulnerabilities.

It's crucial to understand the complexities of working with AI systems, including the need to manage issues like hallucinations and model evaluations effectively.