Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.


Archive for the ‘robotics’ category

 

Odest Chadwicke Jenkins – NSF Distinguished Lecture Series: Semantic Robot Programming and the Irresistible Tastiness of Seed Corn

February 14th, 2022 / in CCC, NSF, robotics / by Maddy Hunter

Chad Jenkins, CCC Council Member, Professor of Computer Science and Engineering and Associate Director of the Robotics Institute at the University of Michigan will be featured as a part of the National Science Foundation’s Distinguished Lecture Series on March 3rd from 11am-12:30pm EDT.  Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). He previously served on the faculty of Brown University in Computer Science (2004-15). His research addresses problems in interactive robotics and human-robot interaction, primarily focused on mobile manipulation, robot perception, and robot learning from […]

Listen to the Catalyzing Computing Podcast, Episode 39 – Medical Applications for AI and Robotics with Gregory D. Hager (Part 2)

January 7th, 2022 / in AI, Healthcare, podcast, robotics / by Khari Douglas

A new episode of the Computing Community Consortium‘s (CCC) official podcast, Catalyzing Computing, is now available. Khari Douglas interviews Gregory D. Hager, a professor of computer science at Johns Hopkins University and the founding director of the Johns Hopkins Malone Center for Engineering in Healthcare. In this episode, Hager discusses medical applications for AI and robotics, tactile perception, the founding of the Malone Center, and data privacy. This will be the​ last episode of Catalyzing Computing hosted and produced by Khari, because he will be joining the editorial team at Overheard at National Geographic, “a podcast which follows explorers, photographers, and scientists to the edges of our big, weird, beautiful world.” Thanks for listening […]

Pharma Giant, Bayer, partners with AI-based Assessment Platform

December 1st, 2021 / in AI, CCC-led white papers, Healthcare, robotics / by Maddy Hunter

Bayer, the pharmaceutical company that owns big name brands such as Aspirin, Aleve, Midol, Cenesten and Iberogast, recently partnered with Ada Health, an AI-based assessment platform. This free app uses an AI chat robot to collect information on symptoms, patient history and other user targeted questions to generate data-driven suggestions for next steps and proper care. “Ada’s technology is based on a custom-built reasoning engine and a highly comprehensive medical knowledge base, covering thousands of conditions. In fact, in a recent vignettes study testing the eight most popular online symptom assessment apps, Ada was proven to have the most comprehensive condition coverage, providing a condition suggestion 99% of the time, […]

Great Innovative Idea: Robot Language Learning

November 23rd, 2021 / in Great Innovative Idea, research horizons, robotics / by Maddy Hunter

The following Great Innovative Idea is from Yonatan Bisk, Assistant Professor in the School of Computer Science and member of the Language Technologies Institute at Carnegie Mellon University. The Idea Language learning requires physically existing in the world (not being just a brain in a jar). A child doesn’t learn language from reading Wikipedia.  They build rich models of the world by seeing and doing.  They understand other humans as socially intelligent and cooperative agents with whom to speak and from whom to learn.  Yet, natural language “understanding” systems have focused on training in an impoverished disembodied text-only setting — trying to download as much text off the internet and […]

Third National Artificial Intelligence Research Resource (NAIRR) Task Force Meeting and CCC Response to the NAIRR Implementation Plan

November 1st, 2021 / in Announcements, big science, Blue Sky, CCC, NSF, research horizons, Research News, robotics / by Helen Wright

The National Artificial Intelligence (AI) Research Resource (NAIRR) Task Force convened its third virtual public meeting to further develop a vision and implementation plan for the NAIRR. Computing Community Consortium (CCC) Council member Ian Foster (Argonne National Laboratory and University of Chicago), was invited to speak about the recently published A National Discovery Cloud: Preparing the US for Global Competitiveness in the New Era of 21st Century Digital Transformation white paper.  In addition, 84 responses from industry, academia, and government stakeholders were recently released regarding the White House Office of Science and Technology Policy and the National Science Foundation request for information to develop an implementation roadmap for the NAIRR. […]

Active Learning of Transferable Priors, Kernels and Latent Representations for Robotics

May 26th, 2021 / in CCC, CIFellows, CIFellows Spotlight, research horizons, robotics / by Maddy Hunter

Rika Antonova began her CIFellowship in January 2021 after receiving her PhD from KTH Royal Institute of Technology in Stockholm in December 2020. Antonova is at Stanford University working with Jeannette Bohg, Assistant Professor of Robotics at Stanford.  Current Project Machine learning is transforming robotics: we can now solve high-dimensional problems that have been intractable before, if given large amounts of data and ample training time. However, to go beyond structured factory settings, it is important for robots to adapt to changes in the environment/task without lengthy re-training and data collection. A related problem is closing the simulation-to-reality gap: adapting to the real world after training in simulation. My goal […]