This is a final reminder to submit feedback on the joint CCC/GAC response and the CRA-Industry (CRA-I) response to the Request for Information (RFI) to inform the development of a roadmap for the recently established Technology, Innovations, and Partnerships (TIP) Directorate. We are very interested in hearing your feedback on our RFI responses and your thoughts on the TIP Directorate in general.    If you have any comments or feedback on these responses, please share them here. All of the feedback we receive will be treated anonymously. We will summarize the feedback we receive and post an anonymized addendum to our RFI responses to the CRA website. The deadline to […]

In April 2023, the National Science Foundation (NSF) published a Request for Information (RFI) to inform the development of a roadmap for the recently established Technology, Innovations, and Partnerships (TIP) Directorate, and the Computing Research Association (CRA) submitted two responses. The Computing Community Consortium (CCC) and CRA’s Government Affairs Committee (GAC) submitted a joint response and CRA-Industry (CRA-I) submitted a separate response. The joint CCC/GAC  response can be viewed here and the CRA-I response can be viewed here.   We are interested in hearing your feedback on our RFI responses and your thoughts on TIP, in general. If you have any comments or feedback on these responses, please share them […]

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 […]

In April 2023, the National Science Foundation (NSF) published a Request for Information (RFI) to inform the development of a roadmap for the recently established Technology, Innovations, and Partnerships (TIP) Directorate. This roadmap will help guide TIP’s investments in translational use-inspired research to maintain US competitiveness in scientific research.   The Computing Research Association (CRA) submitted two responses: a joint response  from the Computing Community Consortium (CCC) and CRA’s Government Affairs Committee (GAC) and another from CRA-Industry (CRA-I). The joint CCC/GAC response can be viewed here and the CRA-I response can be viewed here.    In the joint CCC and GAC response, the authors comment on workforce development, addressing societal […]

This blog post is a continuation of yesterday’s summary of the Maintaining a Rich Breadth for Artificial Intelligence panel at the 2023 AAAS meeting. This panel was moderated by Maria Gini (University of Minnesota) and the panel comprised David Danks (University of California – San Diego), Bo Li (University of Illinois – Urbana-Champaign), and Melanie Mitchell (Santa Fe Institute)   Following the panel, Dr. Gini opened the discussion up to the audience for Q&A. The first question came from a researcher in the audience:   To what extent do you think homogeneity is an effect of cost in terms of the available hardware? Neural networks are cheap to create and […]

The final CCC panel of AAAS 2023, “Maintaining a Rich Breadth for Artificial Intelligence”, was held on Sunday, March 5th, the last day of the conference. This panel was composed of David Danks (University of California – San Diego), Bo Li (University of Illinois – Urbana-Champaign), and Melanie Mitchell (Santa Fe Institute) and was moderated by Maria Gini (University of Minnesota).    Dr. Bo Li began the panel by discussing the importance of conducting trustworthy machine learning (ML), and the ways in which we can ensure ML is safe, equitable, and inclusive. Machine learning is ubiquitous, Li said, and today is used in a significant number of everyday activities, such […]