AI Technology & Industry Review
Technical review of the newest machine intelligence research.
Mask R-CNN (Regional Convolutional Neural Network) has been the state-of-the-art model for object instance segmentation since it was proposed by Facebook Research Scientist Kaiming He in 2017 and won Best Paper at ICCV the same year.
Synced Global AI Weekly March 10th
Last November Synced ran an interview with Yoshua Bengio, in which the deep learning maverick, Université de Montréal Professor and MILA Scientific Director discussed his research and commented on the current state of deep learning and AI.
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 paper recently accepted for ICLR 2019 challenges this with a novel optimizer — AdaBound — that authors say can train machine learning models “as fast as Adam and as good as SGD.” Basically, AdaBound is an Adam variant that employs dynamic bounds on learning rates to achieve a gradual and smooth transition to SGD.
oogle this week introduced GPipe, an open-source library that dramatically improves training efficacy for large-scale neural network models.
The Conference on Computer Vision and Pattern Recognition (CVPR) is one of the world’s top computer vision (CV) conferences. CVPR 2019 runs June 15 through June 21 in Long Beach, California, and the list of accepted papers for the prestigious gathering has now been released.
Microsoft Research Asia and University of Science and Technology of China have jointly released a new human pose estimation model which has set records on three COCO benchmarks.
As Synced previously reported, these hyperrealistic images now flooding the Internet come from US chip giant NVIDIA’s StyleGAN, a generative adversarial network based face generator that performs so well that most people can’t distinguish its creations from photos of real people.
Beijing-based AI chip startup Horizon Robotics today announced a staggering US$600 million in Series B funding led by South Korea conglomerate SK Group. The investment brings Horizon’s value to an estimated US$3 billion, making it the world’s highest-valued AI chip startup.
Having notched impressive victories over human professionals in Go, Atari Games, and most recently StarCraft 2 — Google’s DeepMind team has now turned its formidable research efforts to soccer. In a paper released last week, the UK AI company demonstrates a novel machine learning method that trains a team of AI agents to play a simulated version of “the beautiful game.”
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.
Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).”
In its new paper Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search, Xiaomi’s research team introduces a deep convolution neural network (CNN) model using a neural architecture search (NAS) approach. Performance is comparable to cutting-edge models such as CARN and CARN-M.
Github developer Hugging Face has updated its repository with a PyTorch reimplementation of the GPT-2 language model small version that OpenAI open-sourced last week, along with pretrained models and fine-tuning examples.
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.”
Microsoft researchers have released technical details of an AI system that combines both approaches. The new Multi-Task Deep Neural Network (MT-DNN) is a natural language processing (NLP) model that outperforms Google BERT in nine of eleven benchmark NLP tasks.
In 2017 Google introduced Federated Learning (FL), “a specific category of distributed machine learning approaches which trains machine learning models using decentralized data residing on end devices such as mobile phones.” A new Google paper has now proposed a scalable production system for federated learning to enable increasing workload and output through the addition of resources such as compute, storage, bandwidth, etc.
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.”
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.
CUHK researchers recently teamed up with Chinese AI giant SenseTime to develop a greatly improved iteration in DeepFashion2, a large-scale benchmark with comprehensive tasks and annotations of fashion image understanding.
Synced Global AI Weekly February 10th
In December Synced reported on a hyperrealistic face generator developed by US chip giant NVIDIA. The GAN-based model performs so well that most people can’t distinguish the faces it generates from real photos. This week NVIDIA announced that it is open-sourcing the nifty tool, which it has dubbed “StyleGAN”.
Facebook researchers have introduced two new methods for pretraining cross-lingual language models (XLMs). The unsupervised method uses monolingual data, while the supervised version leverages parallel data with a new cross-lingual language model.
Researchers from NLP startup Shannon.AI have published a study proposing Glyce — a set of Chinese glyph-vectors for Chinese character representations. Glyce has already mastered 13 core Chinese natural language processing tasks.
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.
A cooperative research group from Facebook and the University of Southern California has introduced an alternative evaluation task for visual-grounding systems: BISON (Binary Image Selection).
Many scientists believe that a quantum computing device will one day perform highly complex computational tasks which are far beyond the ability of today’s classical computers.
A research team from the University of Rochester, University of Illinois at Urbana-Champaign and Adobe Research has proposed a foreground-aware image inpainting system to achieve superior results on challenging cases with complex compositions.
DeepMind and Blizzard Entertainment will present a StarCraft II live-stream demonstration on Thursday, Jan 24. The London-based Alphabet company known for taking down the world’s top Go master in 2017 tweeted the announcement yesterday.
The internet loves those little looping action images we call GIFs. They can tell a short visual story in a small file size that has high portability. The visual quality of GIFs is however usually low compared to the videos they were sourced from. If you are sick of fuzzy, low resolution GIFs, then researchers from Stony Brook University, UCLA, and Megvii Research have just the thing for you: “the first learning-based method for enhancing the visual quality of GIFs in the wild.”
To make ML-based solutions available for a wider variety of deployment scenarios, Waymo’s autonomous driving team has collaborated with Google AI Brain Team researchers on a system that automates the creation of high quality and low latency neural networks on existing AutoML architectures.
A cooperative research group from Google, Stanford, and Johns Hopkins has proposed “Auto-DeepLab,” a new method which utilizes hierarchical Neural Architecture Search (NAS) for semantic image segmentation. The project team includes top AI researchers Director of the Stanford Vision Lab Fei-Fei Li; and UCLA Center for Cognition, Vision, and Learning Director Alan Yuille.
The proliferation of social media in our daily lives has profoundly changed the way we work and play with others. It has also created an entirely new job: thousands of people worldwide now work for Google, Facebook and Twitter “Community Operations Teams.” Whenever a user flags content as offensive, it’s sent to these guys for review.
Researchers using enhanced super-resolution technology are giving classic video games of the past incredible, texture-rich visual makeovers. The team has released ‘remastered’ versions of Return to Castle Wolfenstein, Doom, The Elder Scrolls III: Morrowind, and most recently — a visually enhanced version of 2001 third-person shooter game Max Payne.
Boston-based biotech and digital health tech company FDNA has developed a deep learning application, Face2Gene, which can help doctors spot rare genetic disorders.
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.
The world’s largest and most exciting technology show, CES officially kicks off this morning in Las Vegas, USA. With an abundance of products to present, tech giants such as Nvidia, Qualcomm, Samsung, Intel started revving up their engines two days ago, hosting press conferences to showcase their “New Year’s Resolutions.”
Researchers at the Korea Advanced Institute of Science and Technology and Pohang University of Science and Technology have introduced a machine learning algorithm system, InstaGAN, which can perform multiple instance-aware image-to-image translation tasks — such as replacing sheep in photos with giraffes — on multiple image datasets.