Readout of the Fourth National Artificial Intelligence Research Resource (NAIRR) Task Force Meeting

The National Artificial Intelligence (AI) Research Resource (NAIRR) Task Force convened its fourth virtual, public meeting on December 13 to further develop a vision and implementation plan for a national cyberinfrastructure that would expand participation in AI innovation to researchers and communities currently lacking resources to pursue cutting-edge research. Through provision of computational, data, and training resources and a strong governance model, a NAIRR could fuel breakthroughs in areas ranging from climate change to healthcare, and facilitate groundbreaking research in foundational areas of AI such as auditing, testing and evaluation, bias mitigation, and security. The Task Force is working toward consensus recommendations on a NAIRR implementation plan and roadmap that will be provided to Congress in 2022.

Co-Chairs Dr. Lynne Parker, Director of the National AI Initiative Office within the White House Office of Science and Technology Policy (OSTP), and Dr. Manish Parashar, Office Director for the Office of Advanced Cyberinfrastructure at the National Science Foundation (NSF), opened the meeting by kicking off a discussion on the Task Force’s shared vision for the NAIRR. The discussion focused on issues central to the use and governance of a NAIRR, with particular emphasis on the appropriate user base and resource allocation model. The Task Force discussed aligning the user base with the strategic objectives of the NAIRR, which include strengthening AI innovation and lowering barriers to entry to AI research, while ensuring the envisioned uses of the NAIRR represent appropriate investments of public funds.

Building on ideas from external experts who spoke during the Task Force’s third public meeting, the Task Force discussed proposed recommendations on data resources, testbeds and other testing resources, and user tools and resources that could constitute key components of a NAIRR. The Task Force reached consensus that the NAIRR should institutionalize trust, curation, validation, and discoverability when facilitating user access to diverse and distributed data resources. Task Force members emphasized that the NAIRR’s user interface and training resources should be designed to support the full range of users and experience levels, from those seeking to engage in AI research for the first time to deep experts. In addition, the Task Force concluded that AI testbeds should be catalogued, made available, and nurtured through the NAIRR at a level that does not detract from investments in computational and data resources.

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