Week Ending 07.28.19
RESEARCH WATCH: 07.28.19
Over the past week, 913 new papers were published in "Computer Science".
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), which was referenced 195 times, including in the article Chrome 76 blocks websites from detecting incognito mode in Sophos Anti Virus. The paper author, Elena Maris (Postdoctoral researcher at Microsoft), was quoted saying "The fact that the mechanism for adult site tracking is so similar to, say, online retail should be a huge red flag. This isn’t picking out a sweater and seeing it follow you across the web. This is so much more specific and deeply personal". The paper got social media traction with 95 shares. The researchers explore tracking and privacy risks on pornography websites. A user, @citadelo, tweeted "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".
Leading researcher Pieter Abbeel (University of California, Berkeley) came out with "BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks".
The paper shared the most on social media this week is by a team at Allen Institute for Artificial Intelligence: "Green AI" by Roy Schwartz et al (Jul 2019) with 401 shares. @ulliwaltinger (Ulli Waltinger) tweeted "#GreenAI needs to be an emerging trend as the computations required for #deeplearning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 #AISustainabilityQuest".
The most influential Twitter user discussing papers is Manu Romero who shared "OmniNet: A unified architecture for multi-modal multi-task learning" by Subhojeet Pramanik et al (Jul 2019) and said: "Awesome... Really awesome. #OmniNet".
Over the past week, 77 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 Massachusetts Institute of Technology: "Tackling Climate Change with Machine Learning" by David Rolnick et al (Jun 2019), which was referenced 27 times, including in the article On Artificial Intelligence, Ethics, And Video Games: Eye on A.I. in Fortune. The paper author, David Rolnick (Massachusetts Institute of Technology), was quoted saying "Climate change does not present one problem, it presents multiple problems. AI is only one of the tools that can have an impact in the fight to mitigate the effects of climate change". The paper also got the most social media traction with 1485 shares. The investigators describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. A Twitter user, @ikdeepl, posted "Yikes “In 2006, at least two Scottish seafood firms flew hundreds of metric tons of shrimp from Scotland to China and Thailand for peeling, then back to Scotland for sale – because they could save on labor costs”".
Leading researcher Samy Bengio (Google) published "Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy" The investigators propose a method for learning a trajectory - conditioned policy to imitate diverse demonstrations from the agents own past experiences.
The paper shared the most on social media this week is by a team at University of the Basque Country (UPV/EHU): "Bilingual Lexicon Induction through Unsupervised Machine Translation" by Mikel Artetxe et al (Jul 2019) with 57 shares. @IxaGroup (Ixa Group) tweeted "Our group members and nominated for the best paper award Congratulations!!!".
The most influential Twitter user discussing papers is Alan Woodward who shared "Map-Z: Exposing the Zcash Network in Times of Transition" by Erik Daniel et al (Jul 2019) and said: "Exposing the Zcash network. Very interesting research into how you can infer the Zcash peer to peer network".
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 246 new papers.
The paper discussed most in the news over the past week was by a team at UC Berkeley: "Natural Adversarial Examples" by Dan Hendrycks et al (Jul 2019), which was referenced 17 times, including in the article This Week In AI Stats: $7.4 Billion Invested In AI Startups In Q2 in Forbes.com. The paper author, Steven Basart, was quoted saying "Anyone willing to test their models against our data set is free to do so". The paper got social media traction with 530 shares. On Twitter, @DanHendrycks posted "Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months. Paper: Dataset and code".
Leading researcher Abhinav Gupta (Carnegie Mellon University) published "Canonical Surface Mapping via Geometric Cycle Consistency".
The paper shared the most on social media this week is by a team at Allen Institute for Artificial Intelligence: "Green AI" by Roy Schwartz et al (Jul 2019) with 393 shares. @ulliwaltinger (Ulli Waltinger) tweeted "#GreenAI needs to be an emerging trend as the computations required for #deeplearning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 #AISustainabilityQuest".
The most influential Twitter user discussing papers is Manu Romero who shared "OmniNet: A unified architecture for multi-modal multi-task learning" by Subhojeet Pramanik et al (Jul 2019)
Over the past week, 21 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 shared the most on social media this week is by a team at Allen Institute for Artificial Intelligence: "Green AI" by Roy Schwartz et al (Jul 2019)
The most influential Twitter user discussing papers is Alan Woodward who shared "Map-Z: Exposing the Zcash Network in Times of Transition" by Erik Daniel et al (Jul 2019)
This week was very active for "Computer Science - Human-Computer Interaction", with 36 new papers.
The paper discussed most in the news over the past week was "GLTR: Statistical Detection and Visualization of Generated Text" by Sebastian Gehrmann et al (Jun 2019), which was referenced 6 times, including in the article A new tool uses AI to spot text written by AI in Technology Review. The paper author, Sebastian Gehrmann, was quoted saying "Our goal is to create human and AI collaboration systems". The paper got social media traction with 26 shares. A Twitter user, @GrahamIMac, observed "College students in an NLP course could only correctly identify AI generated text (GPT-2, Heliograf) 54% of the time, barely better than flipping a coin. Highlighting predictable words improves that to 72%. Great, but... still kind of scary. #DataScience".
The paper shared the most on social media this week is by a team at University of California, Los Angeles: "Understanding the Political Ideology of Legislators from Social Media Images" by Nan Xi et al (Jul 2019) with 67 shares. The researchers seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U.S. House and Senate. @JohnJost1 (John T. Jost) tweeted "Legislator ideology can be predicted automatically w. 60% accuracy based on a single Facebook photo; multiple photos gets you 82% accuracy".
The most influential Twitter user discussing papers is Manu Romero who shared "OmniNet: A unified architecture for multi-modal multi-task learning" by Subhojeet Pramanik et al (Jul 2019)
This week was very active for "Computer Science - Learning", with 288 new papers.
The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "Tackling Climate Change with Machine Learning" by David Rolnick et al (Jun 2019)
Leading researcher Pieter Abbeel (University of California, Berkeley) came out with "BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks".
The paper shared the most on social media this week is by a team at Allen Institute for Artificial Intelligence: "Green AI" by Roy Schwartz et al (Jul 2019)
The most influential Twitter user discussing papers is Alan Woodward who shared "Map-Z: Exposing the Zcash Network in Times of Transition" by Erik Daniel et al (Jul 2019)
Over the past week, 19 new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 25 new papers were published in "Computer Science - Neural and Evolutionary Computing".
The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "A Power Efficient Artificial Neuron Using Superconducting Nanowires" by Emily Toomey et al (Jun 2019), which was referenced 3 times, including in the article Scientists Aim to Mimic the Power Efficiency of Human Brain with Superconducting Neurons in Interesting Engineering. The paper got social media traction with 13 shares. On Twitter, @mehrdad_hp_ posted "Make a worm first! o_O", while @chicago_quantum posted "Spiking Neural Networks (SNNs) research by Toomey, Segall, Berrggren. Pretty advanced engineering (e.g., superconducting nanowires), with the goal of more processing power/watt, just like for #QuantumComputing".
Leading researcher Pieter Abbeel (University of California, Berkeley) published "BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks".
This week was active for "Computer Science - Robotics", with 47 new papers.
The paper discussed most in the news over the past week was by a team at University of North Carolina: "Identifying Emotions from Walking using Affective and Deep Features" by Tanmay Randhavane et al (Jun 2019), which was referenced 7 times, including in the article Indian Researcher & His Team Build AI That Can Tell How We Feel Just By Seeing Us Walk in Indiatimes. The paper author, Aniket Bera (University of North Carolina), was quoted saying "Though we do not make any claims about the actual emotions a person is experiencing, our approach can provide an estimate of the perceived emotion of that walking style". The paper got social media traction with 196 shares. A user, @joemmac, tweeted "Gait is also sufficiently individualized that it can be used to ID targets with far less resolution and perfect angles than facial detection. And yes I used "targets" on purpose... welcome to the dystopia!".