United States government agency. Provides research funding in the area of artificial intelligence.
- NSF supports fundamental research, education and workforce development, and advanced, scalable computing resources that collectively enhance fundamental research in AI.
- NSF’s ability to bring together numerous fields of scientific inquiry — including computer and information science and engineering, along with cognitive science and psychology, economics and game theory, engineering and control theory, ethics, linguistics, mathematics, philosophy — uniquely positions the agency to lead the nation in expanding the frontiers of AI.
- NSF-funding discoverers and discoveries will help the U.S. capitalize on the full potential of AI to strengthen our economy, advance job growth, and better our society for decades to come.
Funding Opportunities with Special Emphasis on AI
AI and Society, supported jointly with the Partnership on AI — NSF’s Directorates for Computer and Information Science and Engineering (CISE) and Social, Behavioral and Economic Sciences (SBE) together with the Partnership on AI (PAI) are jointly supporting EArly-concept Grants for Exploratory Research (EAGERs) to understand the social challenges arising from AI technology and enable scientific contributions to overcome them. With increases in the scale and diversity of deployments of AI systems comes the need to better understand AI in the open world, including unforeseen circumstances and social impacts, and to craft approaches to AI that consider these from the start.
Fairness, Ethics, Accountability, and Transparency (FEAT) — NSF’s CISE directorate invites researchers to submit proposals to its core programs that contribute to discovery in research and practice related to fairness, ethics, accountability, and transparency in computer and information science and engineering, including AI.
NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon — NSF’s CISE and SBE directorates and Amazon are partnering to jointly support research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to, transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity.
Real-Time Machine Learning (RTML) — NSF and the Defense Advanced Research Projects Agency (DARPA) have teamed up to explore high-performance, energy-efficient hardware and machine learning architectures that can learn from a continuous stream of new data in real time. Both agencies have issued calls for proposals focused on RTML, and will offer collaboration opportunities to awardees from both programs throughout the duration of their projects. Overall, this partnership will contribute significantly to the foundation for next-generation co-design of RTML algorithms and hardware.
NSF’s investments in AI research and infrastructure are accompanied by investments in education and workforce development. NSF is funding research and development that is building the necessary foundations for implementing rigorous and engaging computer science education at all levels: preK-12, colleges/universities, and continuing education programs.
Computer Science for All: Researcher Practitioner Partnerships (CSforAll: RPP) — This program aims to provide all U.S. students the opportunity to participate in computer science (CS) and computational thinking (CT) education in their schools at the preK-12 levels. NSF focuses on researcher-practitioner partnerships (RPPs) that foster the research and development needed to bring CS and CT to all schools.
Improving Undergraduate STEM Education: Computing in Undergraduate Education (IUSE: CUE) — Increasingly, undergraduate CS programs are being called upon to prepare larger and more diverse student populations for careers in both CS and non-CS fields, including careers in scientific and non-scientific disciplines. Many of these students aim to acquire the understandings and competencies needed to learn how to use computation collaboratively across different contexts and challenging problems. However, standard CS course sequences do not always serve these students well. With this solicitation, NSF will support teams of Institutions of Higher Education (IHEs) in re-envisioning the role of computing in interdisciplinary collaboration within their institutions. In addition, NSF will encourage partnering IHEs to use this opportunity to integrate the study of ethics into their curricula, both within core CS courses and across the relevant interdisciplinary application areas.
Graduate Research Fellowships (GRF) — The GRF program recognizes and supports outstanding graduate students in NSF-supported STEM disciplines, including AI and data science, who are pursuing research-based Master’s and doctoral degrees at accredited U.S. institutions.
NSF Research Traineeship (NRT) — The NRT program is designed to encourage the development and implementation of bold, new, and potentially transformative models for STEM graduate education training. NRT is dedicated to effective training of STEM graduate students in high-priority interdisciplinary or convergent research areas, and includes HDR and FW-HTF among these.