AI Technology & Industry Review
PracticalAI recently released “practicalAI 2.0,” a platform that includes illustrative machine learning lessons in TensorFlow 2.0 + Keras and has garnered over 23k stars on GitHub.
Synced Global AI Weekly October 6th
NLP-focused startup Hugging Face recently released a major update to their popular “PyTorch Transformers” library which establishes compatibility between PyTorch and TensorFlow 2.0.
Google today announced the final release of TensorFlow 2.0. This follows the platform’s alpha release at the TensorFlow Dev Summit in June.
In a joint effort with the Perimeter Institute for Theoretical Physics and Alphabet (Google) X, Google AI researchers recently announced a new open source library, TensorNetwork, which can greatly improve the efficiency of tensor calculations for tensor network algorithms.
Google today announced the release of the Beta version of TensorFlow 2.0. The new version of the world’s most popular open source machine learning library is being welcomed by developers.
Might there be a more efficient approach to scaling up CNNs to improve accuracy? Researchers from Google AI say “yes” and have proposed a new model scaling method in their ICML 2019 paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
Google’s deep learning TensorFlow platform has added Differentiable Graphics Layers with TensorFlow Graphics, a combination of computer graphics and computer vision. Google says TensorFlow Graphics can solve data labeling challenges for complex 3D vision tasks by leveraging a self-supervised training approach.
Thanks to the CUDA architecture [1] developed by NVIDIA, developers can exploit GPUs’ parallel computing power to perform general computation without extra efforts. Our objective is to evaluate the performance achieved by TensorFlow, PyTorch, and MXNet on Titan RTX.
DeepMind’s Research Platform Team has open-sourced TF-Replicator, a framework that enables researchers without previous experience with the distributed system to deploy their TensorFlow models on GPUs and Cloud TPUs. The move aims to strengthen AI research and development.
Synced Global AI Weekly March 10th
TensorFlow is the world’s most popular open source machine learning library. Since its initial release in 2015, the Google Brain product has been downloaded over 41 million times. At this week’s 2019 TensorFlow Dev Summit, Google announced a major upgrade on the framework, the TensorFlow 2.0 Alpha version.
Natural language processing has made significant progress in the past year, but few frameworks focus directly on NLP or sequence modeling. Google Brain recently released Lingvo, a deep learning framework based on TensorFlow. Synced invited Ni Lao, Chief Science Officer at Mosaix, to share his thoughts on Lingvo.
Uber has unveiled Ludwig, a new TensorFlow-based toolkit that enables users to train and test deep learning models without writing any code. The toolkit will help non-experts understand models and accelerate their iterative development by simplifying the prototyping process and data processing.
Google rang in the Lunar New Year with a couple of AI-powered treats: a new Live Transcribe service to help the deaf and hard of hearing, and a Google Doodle showcasing the ancient Chinese art of Shadow Puppetry.
Subscribe to Synced Global AI Weekly Microsoft Awarded Five-Year $1.76 Billion IDIQ Contract by DoD Microsoft was awarded a $1,760,000,000 indefinite-delivery/indefinite-quantity (IDIQ)Continue Reading
Photos of an artificial intelligence textbook for Chinese preschoolers have gone viral. Artificial Intelligence Experiment Materials is a 33-volume textbook series aimed at Chinese students from kindergarten to high school that was published this July by Henan People’s Publishing House.
A Google engineer has used AI to enable humans to throw virtual punches and kicks at the tough guys in popular fighting video game Mortal Kombat3. Instead of jabbing at a controller, the user fights via a web camera, with the AI rendering their air strikes to an onscreen clone in real time. You punch and kick, and Kano goes down.
DeepMind announced today that it has opened its Graph Nets (GN) library to the public, enabling the use of graph networks in TensorFlow and Sonnet. Graph Nets is a machine learning framework that was published by DeepMind, Google Brain, MIT and University of Edinburgh on Jun 15.
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.
The annual NVIDIA GTA Conference opened in Beijing on September 26th.
Before TensorFlow, PyTorch and Caffe; Theano was the major library for deep learning development. However, the library’s development and support will end after the upcoming Theano 1.0 release.
China’s biggest domestic mobile phone maker, Huawei, is now powering its phones with artificial intelligence (AI) engines. The Chinese techContinue Reading
This repository did some toy experiments based on Tensorflow in order to introduce some deep learning concepts which are used for image recognition and language modeling.
Google introduced a new algorithm: ‘Dynamic Batching’, and developed a Tensorflow-based library called ‘TensorFlow Fold’, which solved the DCGs problem in both theoretical and empirical fields.
The reason why TensorFlow is so widely used is due to its automatic derivation of functions and distributed computing capability. This increase in interest can also be found in commercial applications.
At the 10th Google I/O, held May 17-19 at the Shoreline Amphitheatre in Mountain View, California, Google took a different approach from unveiling exciting new products, by putting its focus on the convergence of existing products, aimed at providing a better user experience.
TensorFlow is one of the most popular open source AI libraries. Its high in computing efficiency, and the rich development resources make it widely adopted by companies and individual developers.
This review will introduce and compare the three commonly used machine learning frameworks: Matlab Neural Network Toolbox, Torch and Tensorflow.