Eye On AI

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Week Ending 09.08.19

RESEARCH WATCH: 09.08.19

Over the past week, 878 new papers were published in "Computer Science".

Over the past week, 87 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 19 times, including in the article Facebook taps Minecraft as training ground for next stage of A.I. in Digital Trends. 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. A user, @MichaelFNunez, tweeted "“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 Aaron Courville (Université de Montréal) published "No Press Diplomacy: Modeling Multi-Agent Gameplay" The researchers focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players.

  • The paper shared the most on social media this week is by a team at DeepMind: "Making Efficient Use of Demonstrations to Solve Hard Exploration Problems" by Tom Le Paine et al (Sep 2019) with 248 shares. The authors introduce R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. @odbmsorg (odbms.org) tweeted "They also introduce a suite of 8 tasks that combine these three properties, and show that #R2D3 can solve several of the tasks where other state of the art methods fail to see even a single successful trajectory after tens of billions of steps of exploration".

Over the past week, 198 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".

Over the past week, 26 new papers were published in "Computer Science - Computers and Society".

  • The paper discussed most in the news over the past week was "Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites" by Elena Maris et al (Jul 2019), the paper got social media traction with 179 shares. The investigators explore tracking and privacy risks on pornography websites. On Twitter, @citadelo commented "This research focuses on porn-sites users' tracking: "analysis of 22,484 pornography websites indicated that 93% leak user data to a third party." Unfortunately even incognito window does not solve the entire problem. Another reason to use".

This week was active for "Computer Science - Human-Computer Interaction", with 34 new papers.

This week was very active for "Computer Science - Learning", with 294 new papers.

Over the past week, 16 new papers were published in "Computer Science - Multiagent Systems".

  • Leading researcher Aaron Courville (Université de Montréal) came out with "No Press Diplomacy: Modeling Multi-Agent Gameplay" The authors focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players.

Over the past week, 20 new papers were published in "Computer Science - Neural and Evolutionary Computing".

This week was active for "Computer Science - Robotics", with 46 new papers.

  • 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 3 times, including in the article Indian robot automatically finds and collects litter in GlobalSpec. The paper got social media traction with 25 shares. The investigators propose a system which is very hygienic and cheap that uses Artificial Intelligence algorithms for detection of the garbage. A user, @drmillsrit, tweeted "It's too bad that none of my #GSCI101 students follow me yet. This would be perfect for them", while @arduino said "This AI-powered system automatically detects and collects garbage using a robotic arm".


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