Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 12·10 min read
Order Is Not Control
TL;DR
AI alignment and interpretability studies often confuse order with control, which requires a specific response mechanism. The authors propose a framework for understanding control through a receiver-gated response law, demonstrated across biological systems and large language models (LLMs).
✦ Why It Matters
Engineers can leverage this framework to better design AI systems with predictable control mechanisms.
Key Takeaways
- Understand the distinction between order and control in AI systems.
- Utilize the receiver-gated response law for better AI design.
- Measure control through local response mechanisms.
- Apply findings from biological systems to improve AI interpretability.
Full Summary
AI alignment and interpretability research has identified various order-inducing objects, but the authors argue that order does not equate to control. They introduce a receiver-gated response law, which maps various states and actions to measurable outcomes, demonstrating this concept across biological systems like mice and zebrafish, as well as large language models (LLMs).
The methodology involved analyzing response vectors under different conditions, achieving a prediction accuracy of 72.8-73.7% for component signs and up to 93.6% for system effects. Their results suggest that control is local and can be quantified, with implications for understanding how drives operate through different media.
This framework separates measurable responses from deployable action policies, providing a clearer picture of control mechanisms in AI and biological systems. The findings challenge existing notions of control in AI, emphasizing the need for precise definitions and measurements.