Developing a Series of AI Challenges for the United States Department of the Air Force

1 code implementation • 14 Jul 2022 • Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L Weiss, Allan Wollaber, Sophia Yuditskaya, Jeremy Kepner

Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.

Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting

1 code implementation • 23 Nov 2018 • Clement Fung, Jamie Koerner, Stewart Grant, Ivan Beschastnikh

Distributed machine learning (ML) systems today use an unsophisticated threat model: data sources must trust a central ML process.

BIG-bench Machine Learning Federated Learning