AI Technology & Industry Review
A new GitHub project, PyTorch Geometric (PyG), is attracting attention across the machine learning community. PyG is a geometric deep learning extension library for PyTorch dedicated to processing irregularly structured input data such as graphs, point clouds, and manifolds.
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.
oogle this week introduced GPipe, an open-source library that dramatically improves training efficacy for large-scale neural network models.
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.
Machine learning models based on deep neural networks have achieved unprecedented performance on many tasks. These models are generally considered to be complex systems and difficult to analyze theoretically. Also, since it’s usually a high-dimensional non-convex loss surface which governs the optimization process, it is very challenging to describe the gradient-based dynamics of these models during training.
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.
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.
In his 1988 IEEE paper Cellular Neural Networks: Theory, UC Berkeley PhD student Lin Yang proposed Cellular Neural Network theory, a predecessor of the Convolutional Neural Networks (CNN) that would later revolutionize machine learning. Based on this theory, Yang blueprinted a 20*20 parallel simulated circuit chip in the university lab.
The announcement seemed too early for April Fools’, but a Yelp spokesperson confirmed with Synced that it was in fact a gag: “we typically use a joking tone when we write our release notes for the app store. Our latest note was meant in jest.”
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.”
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.
NVIDIA researchers have developed a deep learning-based system which can produce high-quality slow-motion video from a standard (30 fps) video clip. In comparison with manual slow motion results, the NVIDIA demonstration video shows far superior smoothness.
Synced Global AI Weekly November 25th
A new study from Shanghai’s Transportation Information Center (STIC) and Shanghai Jiao Tong University uses Graph Recurrent Neural Networks (GRNN) for high-accuracy traffic prediction and city traffic control.
The computational power of smartphones and tablets has skyrocketed to the point where they approach the level of desktop computers on the market not long ago. Although it’s easy for mobile devices to run all the standard smartphone apps, today’s artificial intelligence algorithms can be too compute-heavy for even high-end devices to handle.
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.
Researchers from Stanford University and University of California Berkeley have introduced Gibson Environment, a real-world-based virtual environment for training and testing active perception agents.
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.
DeepMind’s 2018 AlphaGo Zero requires 300,000 times more computing power than AlexNet did in 2013. With larger-than-ever datasets and demandingContinue Reading
The dearth of AI talents capable of manually designing neural architecture such as AlexNet and ResNet has spurred research in automatic architecture design. Google’s Cloud AutoML is an example of a system that enables developers with limited machine learning expertise to train high quality models. The trade-off, however, is AutoML’s high computational costs.
Last week, Singh posted a YouTube video, Making Amazon Alexa respond to Sign Language using AI, in which he smoothly communicates with Alexa using sign language. Alexa, which is installed in a laptop with a built-in camera, interprets Singh’s signed queries in real-time, converting them into text and delivering appropriate responses.
Facebook AI chief Yann LeCun gave the keynote speech “Learning World Models: the Next Step towards AI” today at the 2018 International Joint Conference on Artificial Intelligence (IJCAI) in Stockholm, Sweden.
In Beijing today Qualcomm announced the release of its Snapdragon 710 Mobile Platform, part of its recently introduced 700 series portfolio. Snapdragon 710 is aimed at smartphone enhancement and features a multi-core AI Engine and neural network processing capabilities.
Intel today opened its first-ever AI Developer Conference, AIDevCon, a two-day gathering at San Francisco’s prestigious Palace of Fine Arts. While day one was short on hot announcements, the company did detail some aggressive plans for AI.
According to an AWS blog post, Apache MXNet now supports Keras 2 and the Keras-MXNet deep learning backend is now available to developers.
Farming is becoming a data-centric business powered by artificial intelligence. China’s big tech firms are using neural network-backed computer vision, wearable devices, and predictive analytics algorithms to reimagine pig, chicken, cow, goose, and cockroach farming.
When Bayesian Cognitive Scientist Josh Tenenbaum recently told a packed University of Toronto lecture hall that “intelligence is not just about pattern recognition,” the significance of the statement was not lost on the audience.
At last month’s RE•WORK Deep Learning in Finance Summit in London, leading AI industry practitioners and academics from prestigious universities discussed their research, provided insights on business trends and real-life AI applications, and addressed current challenges facing the AI industry as a whole.
When the NBA’s Golden State Warriors decided to favour three-pointers over two-point shots in 2016, the winning strategy sent a shockwave through professional basketball. This was a “data-driven decision” based on higher score probability, explains Alex Martynov.
As Facebook struggles with fallout from the Cambridge Analytica scandal, its research arm today delivered a welcome bit of good news in deep learning. Research Engineer Dr. Yuxin Wu and Research Scientist Dr. Kaiming He proposed a new Group Normalization (GN) technique they say can accelerate deep neural network training with small batch sizes.
I purchased a Tmall Genie X1 — Alibaba’s flagship smart speaker — at the discounted price of US$15 during China’s November 11 “Singles Day” shopping festival. I was given order number 560,000-ish, and received the product a month later. The speaker is regularly priced at US$79, about the same as its American counterpart Google Mini.
Paige.AI is a New York-based startup that fights cancer with AI. Launched last month as a spinoff from the Memorial Sloan Kettering Cancer Center (MSK) — the largest cancer research institute in the US — Paige.AI has exclusive access to MSK’s IP in the field of computational pathology as well as its dataset of 25 million pathology cancer images (“slides”).
The “Godfather of AI” Professor Geoffrey Hinton told a packed room of professionals and students today that machine learning is reinventing our relationship with our own thoughts.
MIT scientists teach AI to have emotions; AI company Stem works with Mitsui to build the first energy storage in Japan; New York City implements expert team to correct algorithm biases
STN-OCR, a single semi-supervised Deep Neural Network(DNN), consist of a spatial transformer network – which is used to detected text regions in images, and a text recognition network – which recognizes the textual content of the identified text regions.
The Conference on Neural Information Processing (NIPS) kicks off today in Long Beach, California and runs to December 9.
China’s biggest domestic mobile phone maker, Huawei, is now powering its phones with artificial intelligence (AI) engines. The Chinese techContinue Reading
In the article “Memory, attention, sequences”, the author predicts that future work on neural networks will emphasize understanding complex spatio-temporal data from the real world, which is highly contextual and noisy.
Researchers from Microsoft Research Asia and Shanghai Jiaotong University have recently published a paper on transferring visual attributes of images using a new technique called Deep Image Analogy.
From May 14 to 18, the 30th International Joint Conference on Neural Networks (IJCNN 2017) was held in Anchorage, AK, USA. Continuing the long tradition, the conference is organized by the International Neural Network Society (INNS), in cooperation with the IEEE Computational Intelligence Society (IEEE-CIS).