AI Technology & Industry Review
Tencent Graph Computing (TGraph) officially announced the release of Plato, an open source high-performance graph computing framework that meets the ultra-large-scale graph computing requirement of billion-level nodes.
A team of researchers from Texas A&M University and Canada’s Simon Fraser University have open-sourced a toolkit called “RLCard” for applying RL research to card games.
Alibaba’s chip subsidiary Pingtouge (平头哥) has become the first Chinese company to open-source its Microcontroller (MCU) design platform.
Machine learning research resource Papers with Code last week introduced Sotabench, a free and open website created to benchmark and rate the performance of state-of-the-art open source models from GitHub.
Alibaba’s chip subsidiary Pingtouge (平头哥) yesterday introduced its first product, an RISC-V (Reduced Instruction Set Computer) processor.
Synced Global AI Weekly July 21st
Synced Global AI Weekly July 7th
Facebook AI research team show how they trained a large convolutional network to predict hashtags on some 3.5 billion social media images. The research returned a state-of-the-art top-1 accuracy result of 85.4 percent on ImageNet.
A team of researchers from Carnegie Mellon University and Google Brain have now proposed XLNet, a new language model which outperforms BERT on 20 language tasks including SQuAD, GLUE, and RACE; and has achieved SOTA results on 18 of these tasks.
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.
A favourite is the collaborative card game Hanabi, which won the 2013 Spiel des Jahres and 2013 Fairplay À la Carte Award. The game has now drawn the attention of AI researchers.
Facebook announced today that it is open-sourcing Pythia, a deep learning framework for vision and language multimodal research framework that enables researchers to “more easily build, reproduce, and benchmark AI models.”
Google has released its updated open-source image dataset Open Image V5 and announced the second Open Images Challenge for this autumn’s 2019 International Conference on Computer Vision (ICCV 2019).
Synced Global AI Weekly May 19th
UK-based national research organization The Alan Turing Institute has released a new machine learning toolbox, Machine Learning in Julia (MLJ), which provides a uniform interface enabling users to easily train, evaluate, and tune machine learning models.
Facebook AI Research has announced it is open-sourcing PyTorch-BigGraph (PBG), a tool that can easily process and produce graph embeddings for extremely large graphs. PBG can also process multi-relation graph embeddings where a model is too large to fit in memory.
SyncedLeg is a tool designed to help with that by mining influential keywords from the corpus with traffic data. A team of Synced interns developed the tool over an internal two-day Hackathon, naming it after their team “机器之腿” (“Machine’s leg” in Chinese).
Google yesterday announced a new program, Seasons of Docs, that aims to make a substantive contribution to open source software development. The eight-month project will assemble a team of technical writers to work on improving documentation development for various open source projects.
Synced Global AI Weekly March 10th
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.
oogle this week introduced GPipe, an open-source library that dramatically improves training efficacy for large-scale neural network models.
Synced Global AI Weekly February 24th
Facebook AI Research (FAIR) introduced their own Go bot last year, aiming to reproduce AlphaGo Zero results using their Extensible, Lightweight Framework (ELF) for reinforcement learning research. FAIR recently added new features to ELF OpenGo and has open-sourced the project.
There are some things that some people just don’t want showing up on their websites, and this has spawned a wide range of activities and technologies that fall under “content review.”
The San Francisco-based AI non-profit however has raised eyebrows in the research community with its unusual decision to not release the language model’s code and training dataset. In a statement sent to Synced, OpenAI explained the choice was made to prevent malicious use: “it’s clear that the ability to generate synthetic text that is conditioned on specific subjects has the potential for significant abuse.”
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.
Last December some 9,000 attendees packed a single venue in Montreal for a week-long academic conference. NeurIPS was completely sold out, the latest indication of just how hot AI is nowadays. As AI and machine learning continue to ignite discussion across a wide variety of disciplines, novel approaches to the tech are also garnering interest.
To help keep our readers abreast of the trend, Synced has identified five high-quality open-source datasets that were released this month (January 2019) and that AI researchers and engineers might find useful in their work.
Subscribe to Synced Global AI Weekly Natural Questions: a New Corpus and Challenge for Question Answering Research To help spur researchContinue Reading
Alibaba Cloud recently announced that it has open sourced Mars — its tensor-based framework for large-scale data computation — on Github. Mars can be regarded as “a parallel and distributed NumPy.” Mars can tile a large tensor into small chunks and describe the inner computation with a directed graph, enabling the running of parallel computation on a wide range of distributed environments, from a single machine to a cluster comprising thousands of machines.
In the conclusion to our year-end series, Synced spotlights ten datasets that were open sourced in 2018 and takes a peek into the papers behind them. We hope this list can provide the AI community with insight into what 2019 might hold in store for big data.
Synced Global AI Weekly December 23rd
Alibaba has announced it will open-source X-Deep Learning (XDL), the algorithm framework behind its marketing technology and big data platform Alimama. The source code and support documents’ release is slated for December.
Tencent AI Lab has announced an open-source NLP dataset comprising vector representations for eight million Chinese words and phrases. The dataset aims to provide large-scale and high-quality support for deep learning-based Chinese language NLP research in both academic and industrial applications.
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.
The DeepMimic paper’s first author, Berkeley PhD student Xue Bin Peng, has now open-sourced the project’s codes, data, and frameworks. Moreover, Peng’s new research demonstrates that DeepMimic’s simulated characters can also learn to perform highly dynamic movements by using regular video clips of human examples as input data.
DeepMind announced today that it is open-sourcing its TRFL (pronounced ‘truffle’) library, which contains a variety of building blocks useful for developing reinforcement learning (RL) agents in TensorFlow.
In conjunction with yesterday’s release of open source AI software framework PyTorch 1.0, leading deep learning course developer Fast.ai has announced its first open source library for deep learning — fastai v1.
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.
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.