UNIVERSITY of NOTRE DAME
The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud
Daniel E. Ho; Jennifer King; Russell C. Wald; Christopher Wan
Introduction
Artificial intelligence (AI) appears poised to transform the economy across sectors ranging from healthcare and finance to retail and education. What some have coined the “Fourth Industrial Revolution” is driven by three key trends: greater availability of data, increases in computing power, and improvements to algorithm design. First, increasingly large amounts of data have fueled the ability for computers to learn, such as by training an algorithmic language model on all of Wikipedia. Second, better computational capacity (often termed “compute”) and compute capability have enabled researchers to build models that were unimaginable merely ten years ago, spanning billions of parameters (an exponential increase in scope from previous models). Third, basic innovations in algorithms are helping scientists to drive forward AI, such as the reinforcement learning techniques that enabled a computer to defeat the world champion in the board game Go.
Historically, partnerships between the government, universities, and industries have anchored the U.S. innovation ecosystem. The federal government played a critical role in subsidizing basic research, enabling universities to undertake high-risk research that can take decades to commercialize. This approach catalyzed radar technology, the internet, and GPS devices. As the economists Ben Jones and Larry Summers put it, “[e]ven under very conservative assumptions, it is difficult to find an average return below $4 per $1 spent” on innovation, and the social returns might be closer to $20 for every dollar spent. Industry, in turn, scales and commercializes applications.
References
Daniel E. Ho, J.D., Ph.D., is the William Benjamin Scott and Luna M. Scott Professor of Law, Professor of Political Science, and Senior Fellow at the Stanford Institute for Economic Policy Research at Stanford University. He directs the Regulation, Evaluation, and Governance Lab (RegLab) at Stanford, and is a Faculty Fellow at the Center for Advanced Study in the Behavioral Sciences and Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). He received his J.D. from Yale Law School and Ph.D. from Harvard University and clerked for Judge Stephen F. Williams on the U.S. Court of Appeals for the District of Columbia Circuit.
Jennifer King, Ph.D., is the Privacy and Data Policy Fellow at the Stanford HAI. She completed her doctorate in information management and systems (information science) at the University of California, Berkeley School of Information. Prior to joining HAI, she was the Director of Consumer Privacy at the Center for Internet and Society at Stanford Law School from 2018 to 2020.
Russell C. Wald is the Director of Policy for the Stanford HAI, leading the team that advances HAI’s engagement with governments and civil society organizations. Since 2013, he has held various government affair roles representing Stanford University. He is a Term Member with the Council on Foreign Relations, Visiting Fellow with the National Security Institute at George Mason University, and a Partner with the Truman National Security Project. He is a graduate of UCLA.
Christopher Wan, J.D., is a graduate of Stanford Law School and an investor at Bessemer Venture Partners. He was the teaching assistant for the Stanford Policy Practicum: Creating a National Research Cloud and a research assistant for the Stanford HAI. He received his B.S. in computer science from Yale University and worked as a software engineer at Facebook and as a venture investor at In-Q-Tel and Tusk Ventures.
- artificial intelligence
Article by Major D. Nicholas Allen
Notre Dame Journal on Emerging Technologies ©2020