Editor's Pick
Learning to Run approach
The trained skeleton has a cheerful gait: an entry to the “Learning to Run” competition, by modeling muscle excitations for hamstrings, biceps femoris, gluteus maximus, & others.
Competition: https://www.crowdai.org/challenges/nips-2017-learning-to-run
Featured
Future of Employment, Jobs, and Automation Survey
What jobs and tasks can AI actually automate? A new initiative from the University of Oxford is inviting you to answer this question – creating a new database of automatability predictions. You might recognize the researchers as Frey & Osborne, whose 2013 paper ignited the recent discussion on automation.
News & Articles
City of Toronto begins sharing data with popular traffic app Waze
The city of Toronto has begun data-sharing with traffic app Waze in order to reduce congestion. Traffic congestion authorities and apps throughout are beginning to team up.
Adobe's Scribbler AI Automatically Colorizes Any Portrait
Papers & Tutorials
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Computer Vision paper from Apple: 3D bounding boxes in 3D point clouds. Similar to our FusionNet paper, but with bounding boxes. Operates on the raw point cloud. Looking forward to more Machine Learning papers from Apple.
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Imposing custom emotions and facial characteristics on faces using Generative Adversarial Networks. Can make input faces angry, contemptuous, disgusted, fearful, happy, sad, surprised, or even change gender.
Code: https://t.co/DPMcUySKex
Video
How does the brain learn so much so quickly?
Yann LeCun keynote at the first Cognitive and Computational Neuroscience conference at Columbia University on September 6, 2017.
CCN 2017 Videos
Videos from Cognitive Computational Neuroscience (CCN) 2017
Podcast
The Pace of Change and The Public View of ML
Talk with Peter Donnelly in his capacity with the Royal Society’s Machine Learning Working Group about the work they’ve done on the public’s views on AI and ML.
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