AI Technology & Industry Review
Technical review of the newest machine intelligence research.
Google’s latest contribution came last week with “Tiny Video Networks (TVN),” a new method for reducing the runtime of neural networks when analyzing videos that is significantly faster than existing models.
The MIT researchers’ novel method can speed up the grasp-planning process from more than 10 minutes to less than a second.
With deep learning emerging as something of a panacea in the world of science, AI researchers and seismologists alike are leveraging the tech in pursuit of better aftershock forecast solutions.
The recent rapid development of pretrained language models has produced significant performance improvements on downstream NLP tasks.
Synced Global AI Weekly October 20th
Purging your favourite photos or videos of an unsightly trash pile, a parked car or even an ex-partner has never been easier, thanks to the rapid progress of AI models designed for such tasks.
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.
A team of researchers has proposed a Generative Adversarial Network based model tasked with creating playable and aesthetically appealing game levels for popular action-adventure video game series The Legend of Zelda.
We’ve lost the brain race, but humans still have unmatched dexterity, right? Wrong. OpenAI’s humanlike five-fingered gripper Dactyl just single-handedly solved a Rubik’s cube.
Do you have two left feet? Do you avoid the dance floor out of fear of embarrassment? If you’ve ever secretly wished you could move your body like Joaquín Cortés — well, at least in a video — a new AI-powered 3D body mesh recovery module called Liquid Warping GAN can give you a leg up.
Synced Global AI Weekly October 13rd
Inspired by OpenCV, Kornia is based on PyTorch and designed to solve generic computer vision problems.
For better or worse, AI can now figure out what you’re doing even without “seeing” you. The MIT Computer Science & AI Lab (CSAIL) has unveiled a neural network model that can detect human actions through walls or in extremely dark places.
In the paper ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer, a team of researchers from Peking University and Tsinghua University propose an end-to-end GAN-based architecture that can transfer input photos into the style of Chinese ink wash paintings.
Researchers from the Huazhong University of Science and Technology and Huawei Noah’s Ark Lab have introduced TinyBERT, a smaller and faster version of Google’s popular large-scale pre-trained language processing model BERT.
Tech giant Google’s recent claim regarding quantum supremacy created a buzz in the computer science community and got global mainstream media talking about quantum computing breakthroughs.
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.
A research team from NVIDIA, Oak Ridge National Laboratory (ORNL), and Uber has introduced new techniques that enabled them to train a fully convolutional neural network on the world’s fastest supercomputer, Summit, with up to 27,600 NVIDIA GPUs.
DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis.
In a bid to help software developers and foster innovative code search research, GitHub last week announced the CodeSearchNet Challenge in a joint effort with California-based machine learning development tools startup Weights & Biases.
An R&D team from Japanese multinational conglomerate Hitachi has proposed a similar, sound-based technique for identifying malfunctioning industrial robots on a factory floor.
Google today announced the final release of TensorFlow 2.0. This follows the platform’s alpha release at the TensorFlow Dev Summit in June.
A multi-institute research group recently released the paper PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer, which proposes a novel method for transferring makeup styles from a reference picture to a user’s source image.
In an attempt to unlock the science behind hugs, a team of researchers from Arizona State University slapped wearable sensors on 33 humans and collected data on their more than 350 huggings with the humanoid remote-controlled robot “Baxter.”
Google’s new “ALBERT” language model has achieved state-of-the-art results on three popular benchmark tests for natural language understanding (NLU): GLUE, RACE, and SQuAD 2.0.
Hugging Face, a startup specializing in natural language processing, today released a landmark update to their popular Transformers library, offering unprecedented compatibility between two major deep learning frameworks, PyTorch and TensorFlow 2.0.
A team of researchers have proposed a number of “recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward” in their new paper Reproducibility in Machine Learning for Health.
This summer Google Research extended BERT to video and introduced VideoBERT, a self-supervised learning model that can learn the relationship between videos and texts.
Researchers from Intel AI and University of California at Santa Barbara have introduced a new generative hate speech intervention model, along with two large-scale fully-labeled hate speech datasets collected from Reddit and Gab.
Researchers from Element AI, MILA (Montréal Institute for Learning Algorithms), and Université de Montréal have introduced a powerful transfer language model that can summarize long scientific articles effectively, outperforming traditional seq2seq approaches.
DeepMind researchers have brought quantum Monte Carlo (QMC) to a higher level with the Fermionic Neural Network — or Fermi Net — a neural network with more flexibility and higher accuracy.
A new Adobe-developed AI tool significantly lowers the threshold for producing dynamic images with a framework that synthesizes a “3D Ken Burns effect” from a single image.
A new paper from San Francisco-based OpenAI proposes training models in the children’s game of hide-and-seek and pitting them against each other.
A team of researchers from the Norwegian University of Science and Technology recently proposed a new architecture that can anonymize faces in images automatically while the original data distribution remains uninterrupted.
Synced Global AI Weekly September 15th
Now a group of researchers from the Seattle-based Allen Institute for Artificial Intelligence (AI2) have shown how trigger words and phrases can “inflict targeted errors” on natural language processing (NLP) model outputs, prompting them to generate racist and hostile content.
For an AI system to acquire knowledge the way humans generally do it would need to interact with its surroundings and extract information through its own attention and analysis choices. That’s the idea behind a new paper from Microsoft Research, Polytechnique Montreal, MILA and and the University of Montreal.
A team of researchers from the Chinese gaming giant NetEase have developed a method to automatically create players’ in-game characters from a standard portrait photo.
In an official statement released today on python.org, the volunteer team that manages the programming language announced they will sunset Python 2 on January 1, 2020.
In collaboration with Partnership on AI, Microsoft, and academics from top universities, Facebook today announced the Deepfake Detection Challenge (DFDC) with the aim of finding innovative deepfake detection solutions to help the media industry spot videos that have been morphed by AI models.