Leveraging Large Language Models to Build and Execute Computational Workflows

no code implementations • 12 Dec 2023 • Alejandro Duque, Abdullah Syed, Kastan V. Day, Matthew J. Berry, Daniel S. Katz, Volodymyr V. Kindratenko

The recent development of large language models (LLMs) with multi-billion parameters, coupled with the creation of user-friendly application programming interfaces (APIs), has paved the way for automatically generating and executing code in response to straightforward human queries.

Management

FAIR AI Models in High Energy Physics

no code implementations • 9 Dec 2022 • Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.

Vocal Bursts Intensity Prediction

A FAIR and AI-ready Higgs boson decay dataset

no code implementations • 4 Aug 2021 • Yifan Chen, E. A. Huerta, Javier Duarte, Philip Harris, Daniel S. Katz, Mark S. Neubauer, Daniel Diaz, Farouk Mokhtar, Raghav Kansal, Sang Eon Park, Volodymyr V. Kindratenko, Zhizhen Zhao, Roger Rusack

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models.

Fairness