AI Technology & Industry Review
Google introduces Gemma, a suite of lightweight, cutting-edge open models derived from the same research and technology underpinning the powerful Gemini models, which mark a significant leap forward in performance relative to existing open models across academic benchmarks for language comprehension, reasoning, and safety.
A research team from DeepMind and University College London have released Alchemy, a novel open-source benchmark for meta-RL research.
The new benchmark for wide-baseline image matching includes a 30k image dataset with depth maps and accurate pose information.
Researchers recently developed and open-sourced COVID-Net, a convolutional neural network for detecting COVID-19 through chest radiography.
In a bid to simplify 3D deep learning and improve processing performance and efficiency, Facebook recently introduced an open-source framework for 3D computer vision.
Researchers have proposed DiffTaichi, a new differentiable programming language based on Taichi and specially tailored for building high-performance differentiable physical simulators.
FAIR as now open-sourced PySlowFast, along with a pretrained model library and a pledge to continue adding cutting-edge resources to the project.
In the conclusion to our year-end series, Synced spotlights ten datasets that were open-sourced in 2019.
Although the term “open source” conveys sentiments such as research sharing and community building, leading semiconductor IP provider Arm, which supports 95 percent of smartphone embedded processors, is not a fan.
Google today announced the release of a new and improved landmark recognition dataset. Google-Landmarks-v2 includes over 5 million images, doubling the number in the landmark recognition dataset the tech giant released last year. The dataset now covers more than 200 thousand different landmarks, a seven times increase over the first version.
Chinese technology giant Tencent has open-sourced its face detection algorithm DSFD (Dual Shot Face Detector). The related paper DSFD: Dual Shot Face Detector achieves state-of-the-art performance on WIDER FACE and FDDB dataset benchmarks, and has been accepted by top computer vision conference CVPR 2019.
A collaboration between researchers from China’s Beihang University and Microsoft Research Asia has produced TableBank, a new image-based dataset for table detection and recognition built with novel weak supervision from Word and Latex documents on the Internet.
Synced Global AI Weekly April 7th
Synced Global AI Weekly March 31st
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.
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.
A Stanford Intelligent Systems Laboratory (SISL) research group has announced it is open-sourcing its NeuralVerification.jl project, which helps verify deep neural networks’ training, robustness and safety results.
Facebook announced today that it is open-sourcing QNNPACK, a high-performance kernel library optimized for mobile AI. The computing power of mobile devices is but a tiny fraction of that of data center servers. As such it is essential to find ways to optimize mobile devices’ hardware performance in order to run today’s compute-hungry AI applications.
Hikvision AI Cloud is a distributed structure incorporating cloud computing and edge computing. Launched last year, it can extend an AI algorithm from the cloud centre to an edge network of on-premises video recorders and servers, and further to edge devices, such as security cameras.