Much of the conversation we hear today about Artificial Intelligence (AI) focuses on fears concerning loss of privacy, lack of transparency and accountability, increase in inequality, and other social and economic issues. The widespread availability of generative AI is fueling much of this debate. However, AI is more than just large language models, and in fact versions of AI have been fueling scientific discovery and exploration for several decades now. This session features speakers at the forefront of developing AI to advance research by automating workflows, finding patterns in large and complex data sets, mitigating human bias, improving models, speeding up tedious tasks, and exploring domains inhospitable to humans. The session explores both the promise of and various possible futures for AI-assisted research.
Organizers: Marcia McNutt, President, National Academy of Sciences; William H. Press, Warren J. and Viola M. Raymer Professor, The University of Texas at Austin; Michael S. Witherell, Laboratory Director, Lawrence Berkeley National Laboratory
Speakers
Jeannette M. Wing
Columbia University
Pushmeet Kohli
Google DeepMind
Daphne Koller
insitro
Jennifer Listgarten
University of California, Berkeley
Michael Pritchard
University of California, Irvine and NVIDIA
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