AI Technology & Industry Review
Researchers from Beijing-based AI unicorn SenseTime and Nanyang Technological University have trained ImageNet/AlexNet in a record-breaking 1.5 minutes, a significant 2.6 times speedup over the previous record of 4 minutes.
Since 2010, the annual ImageNet Large-Scale Visual Recognition Challenge has been the most widely recognized benchmark for testing image recognition algorithms. Tencent Machine Learning picks up the challenge with its new paper Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes.
Compute Scoreboard:
AlexNet 1
AlphaZero 300,000
To boost CNN performance, industry leaders and startups alike are now racing to develop specialized AI chips.
This is a comprehensive guide on how to calculate and visualize the receptive field information of a convolutional neural network.
“We want to be able to train a machine learning model, like Google Translate, in a few hours,” said Prof. Hwu. Best known for enabling the parallelism capability of the GPU to solve scientific problems like AI.
The article discusses about the limitation of using existing GPU to train neural network and how one can improve to train larger model.
On March 29, 2017, during the 2017 YunQi Computing Conference held in Shenzhen, Alibaba Cloud’s Chief Science Officer Dr Jingren Zhou officially launched the updated version of its machine learning platform “PAI 2.0”, intending to drastically reduce the technical threshold and development cost for AI.