Week Ending 9.5.2021
RESEARCH WATCH: 9.5.2021
Over the past week, 1,042 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was by a team at OpenAI: "Evaluating Large Language Models Trained on Code" by Mark Chen et al (Jul 2021), which was referenced 31 times, including in the article OpenAI Announces 12 Billion Parameter Code-Generation AI Codex in InfoQ. The paper author, Greg Brockman (OpenAI), was quoted saying "It could basically do any language task you would ask it. So the thing that was funny for us was to see that the applications that most captured people's imaginations, ones that most inspired people, were the programming applications, because we didn't make that model to be good at coding at all. And so we knew if we put in some effort we could probably make something happen."
Leading researcher Aaron Courville (Université de Montréal) published "Deep Reinforcement Learning at the Edge of the Statistical Precipice".
This week was very active for "Computer Science - Artificial Intelligence", with 179 new papers.
The paper discussed most in the news over the past week was by a team at Stanford University: "On the Opportunities and Risks of Foundation Models" by Rishi Bommasani et al (Aug 2021), which was referenced 10 times, including in the article The Coming Identity Crisis for AI in Discover Magazine. The paper author, Rishi Bommasani, was quoted saying "The commercial incentive can lead companies to ignore social externalities such as the technological displacement of labor, the health of an informational ecosystem required for democracy, the environmental cost of computing resources, and the profit-driven sale of technologies to non-democratic regimes".
Leading researcher Aaron Courville (Université de Montréal) came out with "Deep Reinforcement Learning at the Edge of the Statistical Precipice".
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 228 new papers.
The paper discussed most in the news over the past week was by a team at The University of Adelaide: "Reading Race: AI Recognises Patients Racial Identity In Medical Images" by Imon Banerjee et al (Jul 2021), which was referenced 23 times, including in the article Radar trends to watch: September 2021 in O'Reilly Network. The paper author, Judy Gichoya, was quoted saying "That means that we would not be able to mitigate the bias".
Leading researcher Abhinav Gupta (Carnegie Mellon University) published "The Functional Correspondence Problem".
Over the past week, 19 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was by a team at The University of Adelaide: "Reading Race: AI Recognises Patients Racial Identity In Medical Images" by Imon Banerjee et al (Jul 2021)
Over the past week, 21 new papers were published in "Computer Science - Human-Computer Interaction".
The paper discussed most in the news over the past week was by a team at Georgia Institute of Technology: "The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations" by Upol Ehsan et al (Jul 2021), which was referenced 4 times, including in the article How Does Understanding Of AI Shape Perceptions Of XAI? in Analytics India Magazine.
This week was very active for "Computer Science - Learning", with 308 new papers.
The paper discussed most in the news over the past week was by a team at OpenAI: "Evaluating Large Language Models Trained on Code" by Mark Chen et al (Jul 2021)
Leading researcher Aaron Courville (Université de Montréal) published "Deep Reinforcement Learning at the Edge of the Statistical Precipice".
Over the past week, 15 new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 14 new papers were published in "Computer Science - Neural and Evolutionary Computing".
Leading researcher Max Welling (University of Amsterdam) published "Topographic VAEs learn Equivariant Capsules".
This week was very active for "Computer Science - Robotics", with 67 new papers.
The paper discussed most in the news over the past week was "Efficient and Reactive Planning for High Speed Robot Air Hockey" by Puze Liu et al (Jul 2021), which was referenced 3 times, including in the article A policy to enable the use of general-purpose manipulators in high-speed robot air hockey in Tech Xplore. The paper author, Puze Liu (Researchers), was quoted saying "In simple words, one could see the manipulator as a human arm and our method optimizes the elbow position to perform fast-hitting movements".
Leading researcher Abhinav Gupta (Carnegie Mellon University) published "The Functional Correspondence Problem".