Superalignment Fast Grants: A $10 Million Initiative by OpenAI
Introduction to Superalignment Fast Grants
OpenAI announces the launch of the “Superalignment Fast Grants,” a substantial $10 million grant program aimed at fostering technical research on the alignment and safety of superhuman AI systems. This program is a response to the imperative need for new breakthroughs in controlling AI systems that surpass human intelligence, a challenge acknowledged as one of the most critical unsolved technical problems of our era.
The Urgency of Superhuman AI Alignment
In the near future, possibly within a decade, AI systems could evolve to be significantly more intelligent than humans. These systems, while potentially beneficial, might pose considerable risks. The current method of aligning AI systems through reinforcement learning from human feedback (RLHF) may be inadequate for future superhuman AI systems, which will likely exhibit complex and creative behaviors beyond human comprehension. The ability to steer and trust AI systems smarter than humans is a pivotal challenge that demands innovative solutions.
Grant Details and Application Process
OpenAI, in partnership with Eric Schmidt, is offering grants ranging from $100,000 to $2 million to academic labs, nonprofits, and individual researchers. Additionally, a one-year $150,000 OpenAI Superalignment Fellowship is available for graduate students, comprising a $75,000 stipend and $75,000 in compute and research funding. The program is open to researchers new to the field of AI alignment, and the application process is streamlined for prompt responses. The deadline for applications is February 18, 2024.
Research Focus Areas
The grant program prioritizes several key research areas:
- Weak-to-Strong Generalization: Exploring how AI models can generalize from weak human supervision to tackle complex tasks.
- Interpretability: Investigating how to understand AI model internals and utilize this understanding to detect misalignments like dishonesty or deception.
- Scalable Oversight: Developing methods for AI systems to assist humans in evaluating the outputs of other AI systems, especially in complex tasks.
Additional research directions include honesty, chain-of-thought faithfulness, adversarial robustness, evaluation methodologies, and testbeds for AI systems.
Weak-to-Strong Generalization
This area focuses on supervising more capable AI models with less capable ones, aiming to elicit latent capabilities and improve generalization in challenging scenarios. Research includes scalable methods, scientific understanding, and validation or improvement of research setups. OpenAI encourages collaboration from various machine learning domains to contribute to this area.
Interpretability
Interpretability is vital for verifying the success of alignment methods and detecting potential alignment failures. Two approaches are emphasized:
- Mechanistic Interpretability: Reverse-engineering AI models to understand their fundamental operations.
- Top-Down Interpretability: Locating and interpreting information in models without full comprehension of their processing mechanisms.
Scalable Oversight
This research direction explores using AI systems to assist humans in providing feedback on complex tasks, leveraging the principle that evaluation is often easier than generation. OpenAI is interested in open-source evaluation datasets, empirical work with humans, and analogies with model-graded oversight.
Other Research Directions
Additional areas of interest include:
- Honesty: Ensuring AI models’ honesty and reliability.
- Chain-of-Thought Faithfulness: Investigating the transparency and accuracy of AI models’ reasoning processes.
- Adversarial Robustness: Enhancing AI models’ resilience to adversarial attacks and ensuring reliability in diverse settings.
- Evaluations and Testbeds: Developing methods to measure and predict the dangers of AI models and evaluate their alignment.
The Superalignment Team and OpenAI’s Approach
OpenAI is assembling a team of leading machine learning researchers and engineers to tackle the challenge of aligning superintelligent AI systems with human intent. This team will focus on developing scalable training methods, validating models, and stress-testing alignment techniques through adversarial testing. OpenAI dedicates significant computational resources and emphasizes a collaborative approach, sharing findings with the broader AI and safety communities.
The Importance of Superintelligence Alignment
Superintelligence alignment is not just a technical challenge but a critical issue for the future of humanity. OpenAI is committed to addressing this through interdisciplinary collaboration and innovation. The organization invites talented researchers, especially those new to the field of AI alignment, to join this groundbreaking effort.
Conclusion and Call to Action
The Superalignment Fast Grants program represents a concerted effort by OpenAI to address the imminent challenges posed by superhuman AI systems. By funding research in key areas and assembling a dedicated team, OpenAI aims to pave the way for safe and aligned AI development. The program invites participation from the global research community, recognizing the importance of diverse contributions to solving one of the most crucial technical problems of our time.
Additional information regarding application and support during the program
OpenAI provides advice for grant applicants. They emphasize the need for clear and concise proposals detailing research activities related to AGI alignment. Grant recipients are expected to conduct outstanding technical research, provide quarterly progress reports, and publish their results. The $150,000 OpenAI Superalignment Fellowship supports graduate students with $75,000 stipends and $75,000 for research and computing resources. Mentoring and collaboration opportunities are offered through a Slack channel. Machine learning researchers with no alignment experience, as well as non-machine learning researchers with relevant skills, are encouraged to apply. Uncertain applicants can consult the research directions page. Applications for research projects outside proposed directions are welcome but must justify their alignment and safety contributions. Governance and non-technical work fall outside the program’s scope. Evaluation is conducted by OpenAI’s Superalignment team and external experts. Eric Schmidt generously donated $5 million to support the program, with grants typically ranging from $100,000 to $2 million. International applications are welcome, and additional support for the program can be offered via email contact with OpenAI.