AI Safety Fundamentals Introduction to AI Control
Sep 18, 2025
Explore the fascinating world of AI control and its crucial distinction from alignment. Discover why controlling AI might be more practical than aligning it, especially when navigating deception risks. Hear about innovative strategies like using trusted models for monitoring and delegating tasks to minimize risks. The conversation touches on the limitations of control and the need for widespread adoption and regulatory measures. With a focus on the potential failure modes and the urgency for long-term solutions, this is a deep dive into AI safety essentials.
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Control Versus Alignment
- AI control focuses on preventing misaligned systems from causing harm rather than changing their intentions.
- Controlled AIs can't harm humans even if they want to, while aligned AIs don't want to harm humans.
Evaluating Capability Over Intent
- Control may be easier short-term because we only need to assess capabilities, not intentions.
- Detecting deception in neural networks is hard, so evaluating whether a model can cause harm may be simpler.
Use Trusted Models To Monitor Outputs
- Use a weaker, trusted model to monitor outputs from a stronger, untrusted model and flag risky items.
- Apply trusted editing to automatically remove suspicious code before human review.