The CCC, in collaboration with INFORMS and ACM SIGAI, will be hosting the final workshop of a three-part series titled, Artificial Intelligence (AI)/Operations Research (OR), in late February or early March 2024, to set a course for fundamental research that needs the partnership of both disciplines. Organized by Yu Ding (Georgia Institute of Technology), Pascal Van Hentenryck (Georgia Institute of Technology), Sven Koenig (University of Southern California), Ramayya Krishnan (Carnegie Mellon University), Radhika Kulkarni (INFORMS), and Phebe Vayanos (University of Southern California), the workshop will focus on drafting a strategic plan for increasing the AI/OR partnership and on outlining real-world opportunities for collaboration, based on discussions from the previous two […]

A number of institutions are coming together to discuss how researchers are using generative AI as a tool for research in The Generative AI Coast to Coast webinar series. Co-hosted by Johns Hopkins University; the Ohio State University; Rice University; the International Computer Science Institute, an Affiliated Institute of the University of California, Berkeley; the University of Michigan; and the University of Washington the webinars will bring feature speakers from peer institutions to discuss Generative AI in research with the larger goal of fostering a broad community of cross-institutional and interdisciplinary researchers, and building research ideas and collaboration. Each webinar will include extensive audience discussion time. Included in the line-up of speakers is CCC […]

In May 2023, the President’s Council of Advisors on Science and Technology (PCAST) published a Request for Public Input to help assess key opportunities and risks and provide input on how best to ensure that these GenAI technologies are developed and deployed as equitably, responsibly, and safely as possible. The Computing Community Consortium (CCC) responded emphasizing the role of academic research in addressing risks associated with generative AI. Solutions will require multidisciplinary, multi-pronged approaches that simultaneously implement solutions in the form of policies and regulations, technological advances, and education. These will necessitate transparent communication among the various stakeholders, the use of basic computing research, major contributions and collaborations from social sciences, and a determined, […]

In May 2023, the Office of Science and Technology (OSTP) published a request for information on to inform the Biden-Harris Administration development of a National Artificial Intelligence (AI) Strategy. The strategy will chart a path for the United States to harness the benefits and mitigate the risks of AI and build on the actions that the Federal Government have already taken to responsibly advance the development and use of AI. Public comments will go towards helping to update U.S. national priorities and future actions on AI. You can read the Computing Community Consortium’s (CCC) response here. The CCC’s response focused on promoting academic research’s role in the research ecosystem, as […]

On July 10, CCC responded to a Request for Feedback released by the Food and Drug Administration (FDA) on Using AI and ML in the Development of Drug and Biological Products. The response was written by two CCC Council Members of CCC’s Computational Challenges in Health Task Force, David Danks (University of California, San Diego), and Mona Singh (Princeton University), and former Council Member Kevin Fu (Northeastern University). The FDA’s Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), including the Digital Health Center of Excellence (DHCoE), released a discussion paper and […]

Last month, the National Telecommunications and Information Administration (NTIA) released a request for comment on Artificial Intelligence (AI) system accountability measures and policies. The request sought comments pertaining to both potential and existing self-regulatory, regulatory, and other measures designed to provide reliable evidence to external stakeholders that AI systems are legal, effective, ethical, safe, and otherwise trustworthy. Written by Nadya Bliss (Arizona State University), David Danks (University of California, San Diego), Maria Gini (University of Minnesota), Jamie Gorman (Arizona State University), William Gropp (University of Illinois), Madeline Hunter (Computing Community Consortium), Odest Chadwick Jenkins (University of Michigan), David Jensen (University of Massachusetts Amherst), Daniel Lopresti (Lehigh University), Bart Selman (Cornell […]