AI Roundup - Monday, January 1st 2024
Superalignment Fast Grants
The authors of this podcast summary believe that superintelligence, or AI systems with capabilities surpassing human intelligence, could emerge within the next decade. While such systems have the potential to be highly beneficial, they also pose significant risks. Currently, AI systems are aligned to ensure safety using reinforcement learning from human feedback (RLHF). However, aligning future superhuman AI systems will require addressing fundamentally new and different challenges. These systems will exhibit complex and creative behaviors that humans may not fully understand. Consequently, traditional alignment techniques that rely on human supervision may no longer be sufficient. The main challenge is how to steer and trust AI systems that are much smarter than humans. The authors argue that this problem is solvable and there are many promising approaches and directions to explore. They believe there is a significant opportunity for the machine learning research community and individual researchers to make significant progress on this issue. Through their Superalignment project, they aim to bring together the best researchers and engineers to tackle this challenge and encourage new people to contribute to the field.
Practices for Governing Agentic AI Systems
This white paper discusses the potential benefits and risks of agentic AI systems and emphasizes the importance of integrating them responsibly into society. Agentic AI systems are capable of pursuing complex goals with limited supervision, making them valuable in helping people achieve their own objectives more efficiently. However, these systems also pose risks of harm. The paper proposes a definition of agentic AI systems and identifies the parties involved in their life-cycle. It stresses the need for establishing a baseline of responsibilities and safety best practices for each of these parties. As a contribution, the paper presents an initial set of practices to ensure the safety and accountability of agents' operations. These practices aim to serve as the foundation for future agreed-upon best practices. The paper acknowledges the uncertainties and questions surrounding the implementation of these practices, which must be addressed before they can be standardized. Additionally, it highlights the potential indirect impacts associated with the widespread adoption of agentic AI systems, emphasizing the necessity for additional governance frameworks. Overall, the paper calls for responsible integration of agentic AI systems and proposes measures to ensure their safe and accountable operation.
Weak-to-strong generalization
The Superalignment team has released their first paper proposing a new research direction for aligning superhuman artificial intelligence (AI) systems. The team believes that superintelligence, which refers to AI systems that are vastly smarter than humans, could be developed within the next decade. However, controlling and steering these advanced AI systems remain a challenge. Current methods, such as reinforcement learning from human feedback, rely on human supervision. But with future AI systems capable of complex and creative behaviors, humans would struggle to supervise and understand them effectively. For instance, superhuman models might generate millions of lines of intricate and potentially dangerous code that are difficult for even expert humans to comprehend. The challenge is how to enable "weak supervisors" (humans) to trust and control significantly stronger AI models. Addressing this problem is crucial to ensuring that advanced AI systems in the future are safe and beneficial for humanity.