Conference: Pushing Towards Open-Source AI

This workshop will bring together researchers and practitioners to understand the key challenges in building a robust, open-source ecosystem for generative AI. The technological systems that underlie generative AI differ from other open-source systems and present a unique set of issues. This workshop serves a niche that is not being fulfilled in either the open-source or academic machine learning community alone, and will connect researchers and open-source developers to specifically target core shared challenges. The outcomes of the workshop will serve as a roadmap to foster open-source AI that is safe and equitable and can be deployed to increase American economic growth and worker productivity. Open-source software development contributes to enormous growth in diverse industries across the world. The goal of this workshop proposal is to study how to foster a robust open-source ecosystem for generative AI that is comparable to the general open-source software ecosystem. The technological systems underlying generative AI present novel and complex issues that make it non-trivial to adapt current open-source best practices. Successes of generative AI are also not primarily due to code; they are the product of several factors, including: carefully coordinated data curation, strategically coordinated training runs, tuning with large-amounts of human feedback, and rigorous evaluation on realistic use-cases. The workshop will focus on the following four themes to define and address the core challenges of open-source generative AI: model adaptation for a broader range of users; open ecosystems for human feedback; evaluation of ethical, safe, and accurate systems; and supporting decentralized AI development. The workshop will strive to identify the challenges and opportunities in open-source models for AI development that will serve as a roadmap for the coming years. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.