Week Ending 09.01.19
RESEARCH WATCH: 09.01.19
Over the past week, 838 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was "Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Survey" by P. Murali Doraiswamy et al (Jul 2019), which was referenced 41 times, including in the article Are psychiatrists really ready for the AI revolution? in Technology Review. The paper author, Murali Doraiswamy, was quoted saying "It is time for us to stop thinking about AI as a battle of machines versus humans. We need to instead focus on how AI can optimize and improve clinicians’ abilities to deliver better care".
Leading researcher Yoshua Bengio (Université de Montréal) published "Interactive Language Learning by Question Answering", which has 0 shares on Twitter so far.
Over the past week, 58 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was by a team at NYU: "Why Build an Assistant in Minecraft?" by Arthur Szlam et al (Jul 2019), which was referenced 17 times, including in the article Facebook Is Building An AI Assistant Inside 'Minecraft' in Forbes.com. The paper author, Arthur Szlam (New York University), was quoted saying "The set of things a player could possibly do in the game is enormous; in the most naive sense, it is all possible ways of placing all the possible blocks into as big a world as fits in RAM". The paper got social media traction with 21 shares. The investigators describe a rationale for a research program aimed at building an open `` assistant in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue. On Twitter, @MichaelFNunez commented "“In this work, we have argued for building a virtual assistant situated in the game of Minecraft, in order to study learning from interaction, and especially learning from language interaction,” the researchers say".
Leading researcher Kyunghyun Cho (New York University) published "Dynamics-aware Embeddings" The authors consider self - supervised representation learning to improve sample efficiency in reinforcement learning (RL).
The paper shared the most on social media this week is by a team at DeepMind: "OpenSpiel: A Framework for Reinforcement Learning in Games" by Marc Lanctot et al (Aug 2019) with 843 shares. @kawamuramasahar (川村正春@五城目人工知能超研究所 所長) tweeted "OpenSpiel: A Framework for Reinforcement Learning in Games supports n-player zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games".
The most influential Twitter user discussing papers is Aly-Khan Satchu who shared "Distinct properties of the radio burst emission from the magnetar XTE J1810-197" by Yogesh Maan et al (Aug 2019) and said: "Distinct properties of the radio burst emission from the magnetar XTE J1810−197 first ever magnetar which was found to emit transient radio emission. The specialist is monitoring data on his mission console when a voice breaks in".
Over the past week, 169 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".
The paper discussed most in the news over the past week was "Progressive Face Super-Resolution via Attention to Facial Landmark" by Deokyun Kim et al (Aug 2019), which was referenced 10 times, including in the article AI Turns Harmless Emojis Into Horrible Nightmares in Yahoo! News. The paper author, Deokyun Kim et al, was quoted saying "the main challenge of face Super-Resolution (SR) is to restore essential facial features without distortion." The paper got social media traction with 55 shares. A user, @pkedrosky, tweeted "tl;dr: Don't go around pixellating those private images of yours anymore and thinking you've fooled anyone. /v Progressive Face Super-Resolution via Attention to Facial Landmark", while @NighswanderArt posted "What if I just look better this way?".
Leading researcher Luc Van Gool (Computer Vision Laboratory) published "Texture Underfitting for Domain Adaptation".
Over the past week, 23 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was "Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Survey" by P. Murali Doraiswamy et al (Jul 2019). The paper got social media traction with 11 shares. A user, @SERMO, tweeted "75% of psychiatrists think that & #ML will likely replace medical documentation & the synthesis of patient info, according to our study w/ & researchers. Read more on the risks & benefits of #futuretech via".
Over the past week, 18 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 Yonsei University: "Context-Aware Emotion Recognition Networks" by Jiyoung Lee et al (Aug 2019), which was referenced 1 time, including in the article A deep learning technique for context-aware emotion recognition in PhysOrg.com. The paper got social media traction with 9 shares.
This week was active for "Computer Science - Learning", with 247 new papers.
The paper discussed most in the news over the past week was "SqueezeNAS: Fast neural architecture search for faster semantic segmentation" by Albert Shaw et al (Aug 2019), which was referenced 3 times, including in the article Does Your AI Chip Have Its Own DNN? in EE Times. The paper got social media traction with 19 shares. The researchers present what they believe to be the first proxyless hardware - aware search targeted for dense semantic segmentation.
Leading researcher Yoshua Bengio (Université de Montréal)
The paper shared the most on social media this week is by a team at DeepMind: "OpenSpiel: A Framework for Reinforcement Learning in Games" by Marc Lanctot et al (Aug 2019)
The most influential Twitter user discussing papers is Aly-Khan Satchu who shared "Distinct properties of the radio burst emission from the magnetar XTE J1810-197" by Yogesh Maan et al (Aug 2019)
Over the past week, 12 new papers were published in "Computer Science - Multiagent Systems".
The paper discussed most in the news over the past week was by a team at DeepMind: "OpenSpiel: A Framework for Reinforcement Learning in Games" by Marc Lanctot et al (Aug 2019), which was referenced 2 times, including in the article DeepMind details OpenSpiel, a collection of AI training tools for video games in Venturebeat. The paper also got the most social media traction with 843 shares. On Twitter, @finbarrtimbers posted "I've spent a substantial chunk of the last year working on this. Check it out if you're working on AI research", while @kawamuramasahar observed "OpenSpiel: A Framework for Reinforcement Learning in Games supports n-player zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games".
Over the past week, six new papers were published in "Computer Science - Neural and Evolutionary Computing".
Over the past week, 22 new papers were published in "Computer Science - Robotics".
The paper discussed most in the news over the past week was "AGDC: Automatic Garbage Detection and Collection" by Siddhant Bansal et al (Aug 2019), which was referenced 1 time, including in the article A system to automatically detect and collect garbage in PhysOrg.com. The paper was shared 1 time in social media. The authors propose a system which is very hygienic and cheap that uses Artificial Intelligence algorithms for detection of the garbage.