Global Companies Are Making AI Breakthroughs
Sony Breaks ResNet-50 Training Record on ImageNet
Researchers from Japanese electronics giant Sony have trained the ResNet-50 neural network model on ImageNet in a record-breaking 224 seconds — 43.4 percent better than the previous fastest time for the benchmark task.
Breakthrough Neural Network Paves The Way for Quantum AI
Italian researchers recently developed the first functioning quantum neural network by running a special algorithm on an actual quantum computer. The team, lead by Francesco Tacchino of the University of Pavia in Italy, pre-published their research on ArXiv earlier this month in a research paper titled “An Artificial Neuron Implemented on an Actual Quantum Processor.”
Samsung SDS Wins the Global StarCraft® AI Competition
Samsung SDS announced that its development team SAIDA (Samsung SDS AI & Data Analytics) won the AIIDE StarCraft® AI competition held in Canada. At the competition hosted by the AIIDE (Artificial Intelligence & Interactive Digital Entertainment) society, teams compete in leagues for three weeks using AI bots, with the winning team being the one with the highest win rate.
Chinese AI Teams Win Big in Global Facial Recognition Competition
AI algorithms developed by Chinese researchers have swept the top five places in a global competition in facial recognition technology, said the organizer. Two algorithms developed byYITU Technology have taken the top two places in the Face Recognition Vendor Test (FRVT), held by the National Institute of Standards and Technology (NIST) under the U.S. Department of Commerce.
Natural Environment Benchmarks for Reinforcement Learning
While current benchmark reinforcement learning (RL) tasks have been useful to drive progress in the field, they are in many ways poor substitutes for learning with real-world data. To combat this, researchers propose three new families of benchmark RL domains that contain some of the complexity of the natural world, while still supporting fast and extensive data acquisition.
How Agents See Things: On Visual Representations in An Emergent Language Game
Facebook conducted a study of language learning in which AI agents learn to communicate about images by exchanging symbols. The surprising finding is that the agents aren’t developing an understanding of the relationship between images and words.
Google Brain & Geoffrey Hinton Technique Thwarts Adversarial Attacks
A Google Brain team led by “Godfather of Deep Learning” Geoffrey Hinton has proposed a new way to accurately detect black box and white box FGSM and BIM attacks. This technique is named DARCCC (Detecting Adversaries by Reconstruction from Class Conditional Capsules).
You May Also Like
Mitsui and Preferred Networks Partner on Biomedical/Healthcare Solutions
Japanese global trading giant Mitsui & Co. and leading deep learning startup Preferred Networks (PFN) have announced a joint venture in the US to provide Biomedical/Healthcare Solutions, including Cancer Diagnostic Services, based on deep learning technology. Preferred Networks America COO Nobuyuki Ota has been named CEO of the joint venture.
Shanghai Tests Graph Recurrent Neural Networks for Traffic Prediction
A new study from Shanghai’s Transportation Information Center (STIC) and Shanghai Jiao Tong University uses Graph Recurrent Neural Networks (GRNN) for high-accuracy traffic prediction and city traffic control.
Global AI Events
|26–28 Nov||Intelligent Automation Week||London, UK|
|28–29 Nov||AI Expo North America||Santa Clara, USA|
|3–5 Dec||AI World||Boston, USA|
|3–8 Dec||NIPS||Montréal, Canada|