Global AI Hackathon in Toronto
On June 23-24, the first Global Artificial Intelligence Hackathon took place in Toronto at University of Toronto, alongside 14 otherContinue Reading
Author: Synced Review
Machine Intelligence | Technology & Industry | Information & Analysis
A Survey of Question Answering for Math and Science Problem
Overview of the ways to make a machine smart enough to pass the standardized math and science test.
Building AI Superclusters in Canada
Canada is determined to build AI superclusters in Toronto-Waterloo, Montréal, and Edmonton.
L2-constrained Softmax Loss for Discriminative Face Verification
Softmax loss has become a standard build-in loss function for a lot of mathematical tools like TensorFlow, Torch and Caffe. It is mainly used for classification and has its advantages and disadvantages, the latter of which is the focus of this paper.
Deep Learning Paper Sparks Online Feud!
A tweet from Google DeepMind’s Nenad Tomasev, concerning a paper from the prestigious Montreal Institute for Learning Algorithms has sparked a well-known natural language processing expert Yoav Goldberg to tweet back: “…I really dislike this work.”
Failures of Deep Learning
In 2015, Anh Nguyen published a paper in CVPR that identified a limit in computer vision, where you can fool a deep neural network (DNN) by changing an image in a way that’s imperceptible to humans, but can cause the DNN to label the image as something else entirely.
Scientists and Stakeholders in Geneva for Good Artificial Intelligence
“AI for Good Global Summit,” held June 7-9 in Geneva, Switzerland.
Ready, Set, Go! Meet Yuandong Tian: AI Maverick
“Scientists need passion to do research, and that is my passion.” – Yuandong Tian, Facebook Artificial Intelligence Researcher
Epic’s Tim Sweeney: Deep Learning A.I. Will Open New Frontiers in Game Design
Tim Sweeney predicts that video game companies will ultimately utilize advanced AI techniques when the VR/AR games (or so-called “The Metaverse” games) become popular, due to advancements of cameras and displays
How to Teach Machines to Think Like Humans
Google’s DeepMind introduced its Relation Network (RN) module and a Visual Interaction Network (VIN) that achieved superhuman performance in specific tests by employing a different type of reasoning concerning objects and physical interactions.
