ABOUT THE TASK FORCE
The National AI Initiative Act of 2020 calls for the National Science Foundation (NSF), in coordination with the White House Office of Science and Technology Policy (OSTP), to form a National AI Research Resource (NAIRR) Task Force to investigate the feasibility of establishing a NAIRR, and develop a roadmap detailing how such a resource could be established and sustained.
Composed of members from government, academia, and the private sector, the NAIRR Task Force is required to submit a report to Congress that addresses topics such as the appropriate ownership and administration of the NAIRR; a model for governance; required capabilities of the resource; opportunities to better disseminate high-quality government datasets; requirements for security; assessments of privacy, civil rights, and civil liberties requirements; and a plan for sustaining the resource, including through public-private partnerships.
Throughout its work, the Task Force will consult a range of experts and stakeholders from government agencies, private industry, academia, and civil and disabilities rights organizations; and be informed by ongoing interagency efforts around leveraging cloud computing resources in support of federally funded AI research and development.
A NATIONAL AI RESEARCH RESOURCE
The NAIRR is envisioned as a shared computing and data infrastructure that will provide AI researchers and students across scientific fields and disciplines with access to compute resources and high-quality data, along with appropriate educational tools and user support. The goal for such a national resource is to democratize access to the cyberinfrastructure that fuels AI research and development, enabling all of America’s diverse AI researchers to participate in exploring innovative ideas for advancing AI, including communities, institutions, and regions that have been traditionally underserved.
Congress directed that the Director of OSTP and the Director of the NSF, or their designees, serve as the co-chairpersons of the Task Force; and mandated that the Task Force be composed of 12 technical experts: four from government, four from institutions of higher education, and four from private organizations. The following experts serve on the Task Force:
Lynne Parker (co-chair), Director of the National AI Initiative Office, White House Office of Science and Technology Policy
Dr. Lynne Parker is the Founding Director of the National AI Initiative Office at the White House Office of Science and Technology Policy (OSTP). She also serves the Assistant Director of OSTP for artificial intelligence and is serving as co-chair of the National AI Research Resource Task Force. She has served in OSTP since 2018 and played an integral role in numerous landmark national AI policies. Dr. Parker is on assignment to OSTP from the University of Tennessee, Knoxville, where she is a professor of computer science and former interim dean of engineering. She has served as National Science Foundation’s Division Director for Information and Intelligent Systems, and as a Distinguished Research and Development Staff Member at Oak Ridge National Laboratory. She earned a Ph.D. from the Massachusetts Institute of Technology and is a Fellow of IEEE, a Fellow of AAAS, and a Distinguished Member of the Association for Computing Machinery.
Manish Parashar (co-chair), Office Director of the Office of Advanced Cyberinfrastructure, National Science Foundation
Dr. Manish Parashar is Office Director of the Office of Advanced Cyberinfrastructure at the National Science Foundation (NSF) where he oversees investments in national cyberinfrastructure. Dr. Parashar also serves as Co-Chair of the National Science and Technology Council’s Subcommittee on the Future Advanced Computing Ecosystem. Dr. Parashar served as Assistant Director for Strategic Computing at the White House Office of Science and Technology Policy in 2020 where he led strategic planning for the Nation’s Future Advanced Computing Ecosystem, and the development of the report “Pioneering the Future Advanced Computing Ecosystem: A Strategic Plan.” Dr. Parashar is on assignment to NSF from the University of Utah where he is the Director of the Scientific Computing and Imaging Institute, Chair in Computational Science and Engineering, and Professor in the School of Computing. Manish is Fellow of AAAS, ACM, and IEEE.
Daniela Braga, Founder & CEO of DefinedCrowd
Dr. Daniela Braga is the founder and CEO of DefinedCrowd, one of the fastest growing scale-ups in the AI space. With two decades of experience across research, industry and entrepreneurship, and a hybrid background that spans from linguistics to engineering to AI, Dr. Braga is a citizen of the world and one of the global leaders of crowdsourcing adoption in large enterprises. Previously at Microsoft, Dr. Braga worked across all stacks of speech technology, shipped 26 languages for Exchange 14, 10 TTS voices in Windows 8 and was involved in Cortana. At Voicebox Technologies, she created the data science team, introduced crowdsourcing for big data solutions and re-structured the engineering infrastructure. Dr. Braga is oftentimes a guest lecturer in the University of Washington and is the author of more than 90 scientific papers and several patents. As founder and CEO of DefinedCrowd, Dr. Braga has raised more than $63 million USD, including the largest ever Series B by a female-founded AI company in the US.
Mark E. Dean, Ph.D.
Dr. Mark E Dean is professor emeritus at the University of Tennessee, Knoxville (UTK). His research focus was in advanced computer architecture (neuromorphic computing). Prior to joining UTK, Dr. Dean had a 34-year career in the computer industry working in various executive, research, and development positions at IBM. He served as an IBM Fellow, Chief Technology Officer of the Middle East and Africa, and VP World Wide Strategy and Operations for IBM Research. Dr. Dean holds three of the nine patents for the original IBM PC and created the Industry Standard Architecture (ISA), which permitted add-on devices like the keyboard, disk drives and printers to be connected to the motherboard, earning him election to the National Inventors Hall of Fame. Dr. Dean has a Ph.D. in Electrical Engineering from Stanford University and is a member of the American Academy of Arts and Sciences and National Academy of Engineering.
Oren Etzioni, CEO, Allen Institute for AI
Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He is Professor Emeritus, University of Washington and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards, including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder/co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Julia Lane, Professor, New York University; CEO, the Coleridge Initiative
Dr. Julia Lane is a professor at New York University’s Robert F. Wagner Graduate School of Public Service and co-founder of the Coleridge Initiative. Dr. Lane is an economist and statistician who has been actively involved in the management and linkage of federal, state, and local data as well as producing new data products. She has been involved in founding many successful and accessible data analysis platforms, including the U.S. Census Bureau’s Longitudinal Employer–Household Dynamics program, the U.S. Patent and Trademark Office’s PatentView, the integrated data infrastructure for Statistics New Zealand, the Institute for Research on Innovation and Science, and the Coleridge Initiative’s Administrative Data Research Facility (ADRF). She is a fellow of the American Association for the Advancement of Science, the American Statistical Association and the International Statistical Institute. Her most recent book is Democratizing Our Data: A Manifesto, published by MIT Press.
Fei-Fei Li, Sequoia Professor of Computer Science at Stanford University and Denning Co-Director of the Stanford Institute for Human-Centered AI (HAI)
Dr. Fei-Fei Li is the Sequoia Professor of Computer Science at Stanford University and Denning Co-Director of the Stanford Institute for Human-Centered AI (HAI). Her research includes cognitively inspired AI, machine learning, deep learning, computer vision and AI+healthcare. Before co-founding HAI, she served as Director of Stanford’s AI Lab. During her Stanford sabbatical from 2017 – 2018, Dr. Li was a Vice President at Google and Chief Scientist of AI/ML at Google Cloud. Prior to joining Stanford, she was on faculty at Princeton University and University of Illinois Urbana-Champaign. Dr. Li is co-founder and chairperson of the national non-profit AI4ALL, which is increasing inclusion and diversity in AI education. She is an elected member of the National Academy of Engineering, among other distinctions. She holds a B.A. degree in physics from Princeton with High Honors, and a Ph.D. degree in electrical engineering from California Institute of Technology.
Andrew Moore, VP & General Manager, Google Cloud AI & Industry Solutions
Dr. Andrew W. Moore is a distinguished computer scientist with expertise in machine learning and robotics. He became the Head of Google Cloud Artificial Intelligence division in January 2019. Dr. Moore previously worked at Google from 2006 to 2014 and was the founding director of Google’s Pittsburgh engineering office in 2006. He then spent a four-year hiatus at Carnegie Mellon University as the dean of the School of Computer Science. Dr. Moore’s research interests encompass the field of “big data” — applying statistical methods and mathematical formulas to massive quantities of information, ranging from web searches to astronomy to medical records, in order to identify patterns and extract meaning from that information. His past research has included improving the ability of robots and other automated systems to sense the world around them and respond appropriately.
Michael L. Norman, Distinguished Professor, University of California, San Diego
Dr. Michael L. Norman is a distinguished professor of physics at University of California, San Diego and director of the San Diego Supercomputer Center (SDSC). Dr. Norman is a globally recognized astrophysicist who pioneered the use of high performance computing (HPC) to explore the universe and its beginnings. At SDSC Dr. Norman is Principal Investigator of the NSF-funded CloudBank project, which facilitates the use of public clouds for academic research and education, as well as Expanse, a 5 petaflop HPC resource. Dr. Norman is the author of over 300 research articles in diverse areas of astrophysics, including star and galaxy formation, the evolution of intergalactic medium, as well as numerical methods. Dr. Norman’s work has earned him numerous honors, including Germany’s prestigious Alexander von Humboldt Research Prize and the IEEE Sidney Fernbach Award. He is a Fellow of the American Academy of Arts and Sciences, and the American Physical Society. He holds an M.S. and Ph.D. in engineering and applied sciences from University of California, Davis.
Dan Stanzione, Executive Director, Texas Advanced Computing Center/Associate Vice President for Research, The University of Texas at Austin
Dr. Dan Stanzione is the Executive Director of the Texas Advanced Computing Center (TACC) and the Associate Vice President for Research at the University of Texas at Austin. Dr. Stanzione is a nationally recognized leader in high performance computing. He is the principal investigator (PI) for the National Science Foundation (NSF) grant which deployed Frontera, the fastest supercomputer at a U.S. university. He has been involved in a number of other large cyberinfrastructure and software efforts, including serving for six years as the co-director of CyVerse, a large-scale NSF life sciences environment, and DesignSafe, a platform for Natural Hazards Engineering Research data and computing. Prior to coming to TACC, Dr. Stanzione led the High Performance Computing Initiative at Arizona State University, and was an AAAS Science and Technology Policy Fellow in Washington, D.C. Dr. Stanzione received his bachelor’s degree in electrical engineering and his master’s degree and doctorate in computer engineering from Clemson University.
Frederick H. Streitz, Chief Science Advisor, Artificial Intelligence and Technology Office, U.S. Department of Energy
Dr. Fred Streitz serves as the chief science advisor for the Artificial Intelligence and Technology Office (AITO) in the Department of Energy, where he provides technical assistance on the planning, development, and execution of strategies for advancing and adopting AI and related technologies agency-wide. Prior to this appointment, Dr. Streitz held a number of technical leadership roles at Lawrence Livermore National Laboratory, most recently as the founding Director of the High Performance Computing Innovation Center (HPCIC) and as the Lab’s Chief Computational Scientist. Dr. Streitz is a Fellow of the American Physical and a two-time winner of the IEEE Gordon Bell Prize for outstanding achievement in high performance computing. He earned a B.S. in Physics from Harvey Mudd College in Claremont, California and his M.A. and Ph.D. in Physics from the Johns Hopkins University in Baltimore, Maryland.
Elham Tabassi, Chief of Staff, Information Technology Laboratory, National Institute of Standards and Technology
Elham Tabassi is the Chief of Staff in the Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST). She leads the NIST Trustworthy AI program that aims to cultivate trust in the design, development, and use of AI technologies by improving measurement science, standards, and related tools in ways that enhance economic security and improve quality of life. She has been working on various machine learning and computer vision research projects with applications in biometrics evaluation and standards since she joined NIST in 1999. She is the principal architect of NIST Fingerprint Image Quality (NFIQ) which is now an international standard for measuring fingerprint image quality and has been deployed in many large-scale biometric applications worldwide. She is a senior member of IEEE, and a fellow of Washington Academy of Sciences.
TASK FORCE MEETINGS
- Meeting #1: July 28, 2021, from 1:00-5:00PM EDT. For more information, refer to Federal Register Notice 86 FR 33380.
- Meeting #2: August 30, 2021, from 11:00AM-5:00PM EDT. For more information, refer to Federal Register Notice 86 FR 41997.
- Meeting #3: October 25, 2021, from 11:00AM-5:00PM EDT. For more information, refer to Federal Register Notice 86 FR 43684.
- Meeting #4: December 13, 2021, from 11:00AM-6:00PM EST. For more information, refer to Federal Register Notice 86 FR 43684.
- Meeting #5: February 16, 2022, from 11:00AM-5:00PM EDT. For more information, refer to Federal Register Notice 86 FR 67500.
Further information about future Task Force meetings will be posted as they are scheduled.
TASK FORCE CHARTER
The National Artificial Intelligence Research Resource Task Force has been established under the authority of Section 5106 of the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 (Public Law 116-283; § 5106) and the Federal Advisory Committee Act of 1972 (5 U.S.C, Appendix 2, as amended). For more information, please see the National AI Research Resource Charter.
PUBLIC COMMENT TO THE TASK FORCE
The White House Office of Science and Technology Policy and the National Science Foundation requested input from the public to inform the Task Force’s work and development of an implementation roadmap for the NAIRR through a Request for Information (RFI) published to the Federal Register. The comment period, open from July 23, 2021, through October 1, 2021 (extended from an original deadline of September 1, 2021), generated 84 responses from industry, academia, and government stakeholders.
The responses received through the RFI are posted at the link below. Please note that these responses do not represent the views and/or opinions of the U.S. Government nor those of the National AI Research Resource Task Force.