Contributions to this post were provided by CCC Council member Odest Chadwicke Jenkins.  Computing Community Consortium (CCC) Council member Odest Chadwicke Jenkins (University of Michigan) was recently interviewed by the New York Times about his thoughts on the AI field’s apparent failure to make systems that are accurate for everyone. Many of today’s AI systems have biases against people of color and the broader diversity beyond the white, male, affluent and able-bodied developers  of most computer and robot systems. We need to be sure that when autonomous robots make their decisions, the designer’s flaws and judgements are not “baked in.”   Robotics researchers in our community are committed to ending the […]

The Computing Community Consortium (CCC) is pleased to announce the release of the Assured Autonomy report, titled Assured Autonomy: Path Toward Living With Autonomous Systems We Can Trust.   The report is the result of a year-long effort by the CCC and over 100 members of the research community, led by Ufuk Topcu (The University of Texas at Austin). Workshop organizers included Nadya Bliss (Arizona State University and CCC), Nancy Cooke (Arizona State University), Missy Cummings (Duke University), Ashley Llorens (Johns Hopkins University, Applied Physics Laboratory), Howard Shrobe (Massachusetts Institute of Technology), and Lenore Zuck (University of Illinois at Chicago).  Given the immense interest and investment in autonomy, a series of […]

In an effort to prepare for the next pandemic and perhaps aid in the current one, the Computing Community Consortium (CCC), along with the National Academy of Engineering, hosted a virtual workshop entitled Role of Robotics in Infectious Disease Crises on July 9-10, 2020. Organized by Gregory Hager (The Johns Hopkins University), Vijay Kumar (The University of Pennsylvania), Robin Murphy (Texas A&M University), Daniela Rus (Massachusetts Institute of Technology), and Russell Taylor (The Johns Hopkins University), the workshop consisted of over forty participants including representatives from the engineering/robotics community, clinicians, critical care workers, public health and safety experts, and emergency responders. Today we are pleased to release the resulting report […]

In 2009, the Computing Community Consortium (CCC) published A Roadmap for US Robotics, From Internet to Robotics (a.k.a. the Robotics Roadmap), which explored the capacity of robotics to act as a key economic enabler, specifically in the areas of manufacturing, healthcare, and the service industry, 5, 10, and 15 years into the future. An updated version of the Robotics Roadmap was released in March 2013, November 2016, and now most recently in September 2020.  With the support of the CCC (and others on the cover), three community workshops took place 11-12 September 2019 in Chicago, IL, 17-18 October 2019 in Los Angeles, CA, and 15-16 November 2019 in Lowell, MA. […]

The Computing Community Consortium (CCC) is pleased to announce the release of the Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery Workshop Report!   Chaired by Lana Yarosh from the University of Minnesota, the Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery November 2019 workshop brought together an interdisciplinary group of leading researchers and practitioners to identify opportunities and challenges for enabling innovative computational support for prevention, detection, treatment, and long-term recovery from SUDs. The steering committee members were Suzanne Bakken (Columbia University), Alan Borning (University of Washington), Munmun De Choudhury (Georgia Institute of Technology), Cliff Lampe (University of Michigan), Elizabeth Mynatt (Georgia Tech), Stephen […]

The Computing Community Consortium (CCC) recently released the Wide-Area Data Analytics workshop report. The workshop was organized by Rachit Agarwal (Cornell University) and CCC Council Member Jen Rexford (Princeton University) to identify challenges and opportunities in data analytics and related research given that modern datasets are often distributed across many locations. In some cases, datasets are naturally distributed because they are collected from multiple locations, such as sensors spread throughout a geographic region. In other cases, datasets are distributed across different data centers to improve scalability or reliability, or to reduce cost; these distributed locations could be a mix of public clouds, private data centers, and edge computing sites.  “The […]