The company made a series of AI-related announcements today at the Huawei Connect 2018 Conference in Shanghai, introducing two AI chips and a machine learning framework. Huawei’s AI push is expected to intensify its battle with domestic rivals Alibaba, Tencent and Baidu in the AI market.
Georgia Tech and Google Brain researchers have introduced the new interactive tool GAN Lab, which visually presents the training process of complex machine learning model Generative Adversarial Networks (GANs). Even machine learning newbs can now experiment with GAN models using only a common web browser.
Apple has unveiled the latest iteration of its smartphone chip: the A12 Bionic SoC (system-on-a-chip). The company made the announcement yesterday at its annual product showcase event in Cupertino, California, hailing the A12 as the industry’s first ever 7nm chip (the smallest current transistor scale). It will be embedded in Apple’s new XR, XS, and XS Max iPhones.
Google AI lead Jeff Dean recently posted a link to his 1990 senior thesis on Twitter, which set off a wave of nostalgia for the early days of machine learning in the AI community. Parallel Implementation of Neural Network Training: Two Back-Propagation Approaches may be almost 30 years old and only eight pages long, but the paper does a remarkable job of explaining the methods behind neural network training and the modern development of artificial intelligence.
Tencent AI Lab has announced that it will open source its multi-label image dataset ML-Images and deep residual network ResNet-101 by the end of September. ML-Images contains 18 million images and more than 11,000 common object categories; while ResNet-101 has reached the highest precision level in the industry.
Registration opened at 8:00 a.m. PDT today for December’s NIPS 2018 (Conference on Neural Information Processing Systems) in Montreal. The early birds were the fortunate ones this year — as tickets for the main conference were all snapped up less than a dozen minutes later.
Computers are now excellent at recognizing images of human faces, cats and dogs — but struggle when it comes to detecting continuous actions, for example determining if a character in a video might be “dancing the tango.” Computers also fall short in detecting nuanced expressions of human emotions.
Benjamin Sanchez-Lengeling from Harvard University and Alán Aspuru-Guzik from the University of Toronto have successfully applied machine learning models to speed up the materials discovery process. Their paper Inverse molecular design using machine learning: Generative models for matter engineering was published July 27 in Science Vol. 361.
Neural networks can be notoriously difficult to debug, but a Google Brain research team believes it may have come up with a novel solution. A paper by Augustus Odena and Ian Goodfellow introduces Coverage-Guided Fuzzing (CGF) methods for neural networks. The team also announced an open source software library for CGF, TensorFuzz 1.
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.
At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products.
On July 10th, with German Chancellor Angela Merkel and Chinese Premier Li Keqiang looking on, Siemens AG signed a partnership agreement with Alibaba Cloud — the cloud-computing arm of Internet conglomerate Alibaba — to bring an Industrial Internet of Things (IIoT) upgrade to China’s manufacturing industry.
Over the past three months, criticism and protests have been mounting over Google’s participation in Project Maven, a Pentagon pilot program to build machine learning models to detect and categorize objects in drone footage provided by the US Department of Defense.
NIPS’ peer reviewer selection process came under question in the AI community last week, when a Reddit user who identified as a predoctoral student posted that they had been selected as a NIPS reviewer, and needed advice on how to properly write paper reviews…
Google I/O 2018 kicked off today with an uptempo keynote from CEO Sundar Pichai. The tech giant’s annual developer conference is always a platform for big announcements, and this was no exception, with Google gearing up for what promises to be an especially busy year.
The McKinsey Global Institute this month released the report “Notes From the AI Frontier Insights From Hundreds of Use Cases”. The 36-page discussion paper surveys cutting-edge machine learning algorithms, and discusses how they can be integrated or transformed into practical applications across 19 selected industries.