Laboratory researchers contribute to book on artificial intelligence

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The new textbook features contributions from Laboratory researchers on the methods and applications of machine learning in science.

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A new textbook, “Artificial Intelligence for Science: A Deep Learning Revolution” explores the use of machine learning, especially deep learning, in science. Geared toward researchers, academics, students and others, the book “introduces AI, Machine Learning, and deep neural network technologies leading to scientific discovery from […] datasets generated both by supercomputer simulation and by modern experimental facilities.”

Several Los Alamos National Laboratory researchers have made key contributions to the book. Rajan Gupta is the lead author of a chapter on “AI and Theoretical Particle Physics” — joined as a Laboratory author by colleague Tanmoy Bhattacharya and Boram Yoon, formerly with the Lab — on a chapter that discusses how theoretical particle physics makes use of machine learning. The authors explore the issue of guarantees of correctness in machine learning by use of algorithms that accept, reject or refine educated guesses to solve difficult problems. Recently it has been shown that deep learning can provide the good guesses to improve the efficiency of algorithms, based on maintenance of the physics-based symmetries of a given problem. The efforts represent a useful collaboration between machine learning and theoretical physics communities.

Bhattacharya led the authorship of a chapter on “Uncertainty Quantification in AI for Science,” with contributions from Cristina Garcia Cardona and Jamaludin Mohd-Yusof. Along with a survey of the field, the authors describe methods developed at the Laboratory to carry out rigorous uncertainty quantification in machine learning models for the Pilot 3 component of the Joint Design of Advanced Computing for Cancer, as well as the CANcer Distributed Learning Environment project. Through these activities, it was realized that in using machine learning, knowing the average uncertainties on a project was not sufficient; rather, the specific uncertainty on a given response was necessary. For instance, knowing that a classifier is correct 97% of the time is not as useful as separating the confident — and correct — answers from those with a larger uncertainty.

Reference

“Artificial Intelligence for Science: A Deep Learning Revolution,” World Scientific (2023). Los Alamos contributors: Rajan Gupta, Tanmoy Bhattacharya, Cristina Garcia Cardona and Jamaludin Mohd-Yusof (Los Alamos National Laboratory).

Mission

This work supports the Global Security mission and the Information, Science and Technology capability pillar.

Technical contact: Tanmoy Bhattacharya (T-2)