OpenAI and the Quest for Superalignment
In a recent blog post, OpenAI announced an ambitious new research initiative called “superalignment”. This aims to ensure advanced AI systems reliably follow human instructions and values, even as the systems grow to superhuman intelligence levels.
The superalignment agenda represents OpenAI’s latest major safety push as AI capabilities advance rapidly. In this article, we’ll examine OpenAI’s motivations, their proposed techniques, and key unresolved challenges.
OpenAI and the Quest for Superalignment
The Alarm Bells of Superintelligence
OpenAI warns that superhuman artificial intelligence could arrive within this decade. While superintelligent AI might help solve global problems, uncontrolled superintelligence also poses catastrophic risks like human disempowerment or even extinction.
Current techniques for aligning AI won’t suffice, as they rely on human oversight. But humans won’t be able to reliably oversee superintelligent systems far beyond human-level cognitive abilities. We urgently need new techniques to align superintelligent AI.
OpenAI’s Strategy for Human-Aligned Superintelligence
OpenAI aims to iteratively build an “automated alignment researcher” – an AI system capable of assisting with and even exceeding human abilities at solving the alignment challenge.
The automated researcher would be trained to learn from human feedback, help humans evaluate other AI systems, and conduct cutting-edge alignment research. OpenAI believes that while human researchers will lead this effort initially, over time AI can take on more of the cognitive load in conceiving and testing new alignment strategies.
OpenAI proposes focusing on three pillars: 1) reinforcement learning from human feedback, 2) training AI to assist human evaluation of advanced systems, and 3) training AI researchers to pioneer better alignment techniques.
Key Challenges on the Road to Superalignment
Developing a superaligned automated researcher within four years, as OpenAI hopes, will require overcoming several deep technical obstacles:
– Training: Developing scalable training methods that provide a robust, human-aligned loss function. This must generalize safely beyond the training distribution.
– Validating: Ensuring rigorous standards so that the automated researcher’s outputs and internals promote human values. Formal verification may play a role.
– Stress Testing: Thorough adversarial testing against deliberately misaligned systems to detect bypass flaws early.
OpenAI also acknowledges issues around transparency and robustness in current systems. And there are open questions around whether human-level alignment abilities imply solving full superintelligent alignment.
Conclusion
Superalignment represents OpenAI’s latest major safety initiative as capabilities rapidly advance. Their empirical, iterative approach should reveal if dedicated alignment systems hit limitations.
However, many experts caution that solving alignment will require broader collaboration between technologists, governance experts, and civil society. OpenAI’s alignment agenda may buy time, but not replace the need for thoughtful oversight of superintelligent systems.
