Week Ending 4.26.2020

 

RESEARCH WATCH: 4.26.2020

 
ai-research.png

Over the past week, 75 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 Google: "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch" by Esteban Real et al (Mar 2020), which was referenced 21 times, including in the article This Is How Algorithms Will Evolve Themselves in Popular Mechanics. The paper also got the most social media traction with 1550 shares. A user, @tomvarsavsky, tweeted "One of the most interesting results I've seen in ML in the last 5 years. Evolving programs using a generic search space and generic mutations leads to the discovery of not only SGD and two layer NNs but also rand init, ReLU, Grad Norm. Can someone find a hidden inductive bias?".

  • Leading researcher Yoshua Bengio (Université de Montréal) published "Experience Grounds Language", which had 36 shares over the past 4 days. @pyoudeyer tweeted "On the topic of grounding language in action and social context, the work and roadmap made 10 years ago in development robotics (Italk EU project) is still fully relevant for #AI, #NPL and #DeepRL. Turn off the radio and go explore the world :)".

  • The paper shared the most on social media this week is by a team at Google: "Chip Placement with Deep Reinforcement Learning" by Azalia Mirhoseini et al (Apr 2020) with 523 shares. The authors present a learning - based approach to chip placement, one of the most complex and time - consuming stages of the chip design process. @ilmarihei (Ilmari Heikkinen) tweeted "Chip layouter makes 20% better layouts than team of experts, 100x faster. Interesting that human experts can get to within 20% of the layouter solution".

Over the past week, 192 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".

This week was active for "Computer Science - Computers and Society", with 32 new papers.

  • The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic" by Ramesh Raskar et al (Mar 2020), which was referenced 15 times, including in the article MIT, Apple, Google Build Apps to Trace COVID-19 Contact in Government Technology US. The paper author, Ramesh Raskar (Massachusetts Institute of Technology), was quoted saying "We are dedicated to privacy-first solutions — user location and contact history should never leave a user’s phone without direct consent". The paper got social media traction with 96 shares. A user, @dhmackenzie, tweeted "Hi James, it's probably worth you taking a look first at this paper from the Safe Paths team, which explains some of the thinking. Safe Paths is a Privacy-First contact tracing app. Privacy has absolutely been a key consideration from day 1".

  • The paper shared the most on social media this week is "How Reliable are University Rankings?" by Ali Dasdan et al (Apr 2020) with 98 shares. @bianhaan (Dr. Bianca de Haan 🇬🇧🇪🇺🇳🇱#StayHomeSaveLives) tweeted "University rankings „we both formally and experimentally show in multiple ways that this ranking scheme is not reliable and cannot be trusted as authoritative because it is too sensitive to weight changes and can easily be gamed.“".

Over the past week, 15 new papers were published in "Computer Science - Human-Computer Interaction".

This week was very active for "Computer Science - Learning", with 287 new papers.

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

Over the past week, 30 new papers were published in "Computer Science - Neural and Evolutionary Computing".

Over the past week, 34 new papers were published in "Computer Science - Robotics".


EYE ON A.I. GETS READERS UP TO DATE ON THE LATEST FUNDING NEWS AND RELATED ISSUES. SUBSCRIBE FOR THE WEEKLY NEWSLETTER.