AI for Scientific Discovery

Workshop

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Over the past century, artificial intelligence (AI) has evolved from computational theory to everyday conversational technologies, capturing the attention and interest of the public and the media. It has also caught the attention of the scientific community, where it has provided a new tool to support inquiry and exploration. While AI in the context of scientific investigation has existed for decades, advances in computational technology and sensing in the physical world have created opportunities to integrate AI into science in unexpected ways, with capabilities that are rapidly accelerating. As a result, AI has been leveraged by an expanding collection of disciplines in the physical and biological sciences, as well as engineering domains. While the opportunities for AI in scientific discovery seem endless, there are numerous questions about what makes for trustworthy and reliable discovery, whether such investigation should be performed without human oversight or intervention, and how best to prioritize the research agenda and allocation of resources without magnifying disparities for individuals and nations alike.

In recognition of the timeliness and sizable implications of AI in our world, the National Academies of Sciences, Engineering, and Medicine hosted AI for Scientific Discovery - A Workshop on October 12-23, 2023. Leaders from across the globe in the field of AI, preeminent researchers in various science and engineering disciplines, and experts in ethics, law, and social sciences met to appraise the state of the field and provide guidance on the opportunities, as well as the challenges, that lay ahead. Presentations and discussion explored the future of AI in terms of its role as an autonomous researcher carrying out discovery and considered ethical aspects of AI used for independent scientific discovery.

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68 pages · 6 x 9 · paperback
ISBN Paperback: 0-309-71497-4
ISBN Ebook: 0-309-71498-2
DOI: https://doi.org/10.17226/27457

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National Academies of Sciences, Engineering, and Medicine. 2024. AI for Scientific Discovery: Proceedings of a Workshop. Washington, DC: The National Academies Press.

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Bradley A. Malin

BRADLEY A. MALIN (NAM) is the vice chair for research and professor of biomedical informatics at Vanderbilt University. He is also a professor of biostatistics, a professor of computer science, and is affiliated faculty in the Center for Biomedical Ethics and Society. He co-directs the Health Data Science (HEADS) Center, the Center for Genetic Privacy and Identity in Community Settings (GetPreCiSe)—a National Institutes of Health (NIH) Center of Excellence in Ethical, Legal, and Social Implications Research (CEER), and the Big Biomedical Data Science Ph.D. program. He is also the director of the Health Information Privacy Laboratory (HIPLab), which was established to address the growing need for data privacy research and development for the health information technology sector. Dr. Malin’s research is in big health data analytics and the infrastructure necessary to support such investigations. He has made specific contributions to a number of health-related areas, including distributed data processing methods for medical record linkage and predictive modeling, intelligent auditing technologies to protect electronic medical records from misuse in the context of primary care, and algorithms to formally anonymize patient information disseminated for secondary research purposes. He is an elected fellow of the National Academy of Medicine and American College of Medical Informatics and was honored as a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). Dr. Malin completed his education at Carnegie Mellon University, where he received a bachelor's in biological sciences, a master's in machine learning, a master's in public policy and management, and a doctorate in computer science (with a focus on databases and software systems).

Keith Andrew Brown

Keith A. Brown is an Associate Professor of Mechanical Engineering, Physics, and Materials Science & Engineering, at Boston University. Keith runs an experimental research group that studies polymers with a major focus on accelerating materials research using self-driving labs, transformative formats for experiments, and machine learning. Keith has been a leader in the AI-accelerated materials research community and currently chairs the AI for materials development staging group of the Materials Research Society in addition to service to the Acceleration Consortium. Keith has co-authored over 90 peer-reviewed publications, six issued patents, and his work has been recognized through awards including the Frontiers of Materials Award from The Minerals, Metals, & Materials Society (TMS), being recognized as a “Future Star of the AVS,” the Omar Farha Award for Research Leadership from Northwestern University, and the AVS Nanometer-Scale Science and Technology Division Postdoctoral Award. He earned an S.B. in Physics from MIT, a Ph.D. in Applied Physics at Harvard University with Robert M. Westervelt, and was an International Institute for Nanotechnology postdoctoral fellow with Chad A. Mirkin at Northwestern University.

Missy Cummings

Professor Mary (Missy) Cummings received her B.S. in Mathematics from the US Naval Academy in 1988, her M.S. in Space Systems Engineering from the Naval Postgraduate School in 1994, and her Ph.D. in Systems Engineering from the University of Virginia in 2004. A naval officer and military pilot from 1988-1999, she was one of the U.S. Navy's first female fighter pilots. She is a Professor in the George Mason University College of Engineering and Computing and is the director of the Mason Autonomy and Robotics Center. She is an American Institute of Aeronautics and Astronautics Fellow, and recently served as the senior safety advisor to the National Highway Traffic Safety Administration. Her research interests include the application of artificial intelligence in safety-critical systems, assured autonomy, human-systems engineering, and the ethical and social impact of technology.

Yolanda Gil

Dr. Yolanda Gil is Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute of the University of Southern California, Director of AI and Data Science Initiatives in the Viterbi School of Engineering, and Research Professor in Computer Science and in Spatial Sciences. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Dr. Gil collaborates with scientists in many domains on semantic workflows and metadata capture, provenance and trust, social knowledge collection, computer-mediated collaboration, and automated discovery. In 2019 she co-chaired the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US with key strategic recommendations based on extensive community engagement. She is a Fellow of the Association for Computing Machinery (ACM), the Association for the Advancement of Science (AAAS), and the Institute of Electrical and Electronics Engineers (IEEE). She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and served as its 24th President. In 2022, she received the M. Lee Allison Award for Outstanding Contributions to Geoinformatics and Data Science from the Geological Society of America (GSA).

Patrick Francis Riley

Patrick Riley leads the artificial intelligence group at Relay Therapeutics, applying learning methods to the discovery process. He has over 15 years of data science and machine learning experience. He came to Relay Therapeutics from Google, where he was a principal software engineer and a lead of the Accelerated Science team. His work spanned areas as diverse as cell imaging, nuclear fusion and materials science. His most important work was on the application of machine learning to small molecules, including foundational work on graph neural networks and their application to DNA encoded small molecule library screening.

Patrick holds a Ph.D., M.S. and B.S. in Computer Science from Carnegie Mellon University.

Aarti Singh

Dr. Singh is a tenured associate professor in the Machine Learning Department at Carnegie Mellon University. Her research lies at the intersection of machine learning, statistics, and signal processing, and focus on designing statistically and computationally efficient algorithms that can leverage inherent structure of the data using direct, compressive and interactive queries. She received a B.E. in electronics and communication engineering from the University of Delhi, and a M.S. and Ph.D. in electrical engineering from the University of Wisconsin-Madison. Prior to her position at CMU, Dr. Singh was a postdoctoral research associate at the Program in Applied and Computational Mathematics at Princeton University. Her work is recognized by an NSF Career Award, a United States Air Force Young Investigator Award, A. Nico Habermann Junior Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and four best student paper awards. Her service honors include serving as program chair for the International Conference on Machine Learning (ICML) 2020, program chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference, associate editor for IEEE Transactions of Information Theory and IEEE Transactions on Signal and Information Processing over Networks, steering committee for an NSF innovation lab, and the National Academy of Sciences (NAS) committee on Applied and Theoretical Statistics.

Alex Temple

Alex Temple is a Program Officer at the National Academy of Sciences, Engineering, and Medicine (NASEM). Alex works to bring scientists, government leaders, and industry experts together to discuss pressing scientific issues. Prior to NASEM, Alex worked at the U.S. Department of State in the Office of Science and Technology Cooperation where he covered Africa, Science and technology Agreements, and innovation programming activities. Alex specializes in science diplomacy, capacity building, and convening of scientists. Alex earned his bachelors at The Ohio State University, a Masters from Carnegie Mellon University, and it currently pursuing a PhD at George Washington University in Science and Technology Policy.

AI for Scientific Discovery: Proceedings Release

May 13, 2024

10:00 AM - 12:00 PM (ET)

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Join the National Academies of Sciences, Engineering, and Medicine for the launch of the AI for Scientific Discovery workshop proceedings!

The AI for Scientific Discovery workshop, which took place in October 2023, aimed to explore the future of artificial intelligence (AI) in terms of its role as an autonomous researcher carrying out discovery, build international collaboration amongst AI experts, and reveal funding opportunities for future research endeavors.

The proceedings release event will highlight key topics of discussion from the workshop complemented by AI activities around the National Academies and international perspectives. The first panel will consist of experts from across the National Academies to discuss key points in relation to the workshop. The last conversation will bring together global leaders to share international activities dealing with AI for Scientific Discovery. These panels will point to possible next steps in designing a roadmap for global collaboration in enhancing AI for Scientific Discovery.

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Alex Temple

atemple@nas.edu

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National Academy of Sciences Building

2101 Constitution Ave NW

Washington DC 20418, USA

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It is essential to the National Academies mission of providing evidence-based advice that participants in any of our meetings or events avoid political or partisan statements or commentary and maintain a culture of mutual respect. The statements and presentations during our meetings or events are solely those of the individual participants and do not necessarily represent the views of other participants or the National Academy of Sciences, which is a non-partisan, tax exempt organization that includes under its Charter the National Academy of Engineering and National Academy of Medicine, and that operates the National Research Council.

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