AI Technology & Industry Review
Researchers from Drexel University, Northeastern University and IT University Copenhagen explore how humans interact with AI in such contexts, with a focus on computer games.
Synced invited Dr. Linchao Zhu, a lecturer at the ReLER lab, University of Technology Sydney whose works focus on video representation learning, to share his thoughts on the paper Text-to-Image Generation Grounded by Fine-Grained User Attention.
On January 18, in efforts to understand and manage extreme weather events, McGill University announced a new interdisciplinary project to combine deep learning (DL) with social network analysis (SNA).
A Nepalese machine learning (ML) researcher has introduced a handy browser extension that lets users directly access videos related to research papers published on the popular preprint platform arXiv.
Kuzi is using satellite data, soil sensor data, ground meteorological observation and machine learning technology trained on predict the locusts’ breeding, appearance and migration routes.
Researchers from the University of Texas, Austin have proposed a novel framework called Training-Free Neural Architecture Search (TE-NAS) for “training-free” neural architecture search.
Google Brain’s Switch Transformer language model packs a whopping 1.6 trillion parameters while effectively controlling computational cost. The model achieved a 4x pretraining speedup over a strongly tuned T5-XXL baseline.
Recent AI research on speech separation has explored ways to associate lip motions in videos with audio, but this approach suffers when speakers’ lips are occluded, which they often are in busy multi-speaker environments.
VOGUE, an AI-powered optimization method that deforms garments according to a given body shape while preserving pattern and material details to deliver state-of-the-art photorealistic, high-resolution try-on images.
AI’s acceleration of scientific discoveries continues - last month DeepMind’s AlphaFold solved a grand challenge in biology, and now an international AI research team has targeted materials science.
Researchers propose a novel framework and hierarchical predictive model that learns to identify what is predictable from unlabelled video.
A new model surpassed human baseline performance on the challenging natural language understanding benchmark.
The novel approach tackles dynamic 3D human-body synthesis from a sparse set of camera views, bettering existing methods on key metrics by significant margins.
Researchers proposed PGDrive, a driving simulator designed to evaluate and improve end-to-end driving agents’ generalization abilities.
Synced has invited Prof. Johan A.K. Suykens to share his insights about the current development and future trends of artificial intelligence.
A new machine translation method enables global manga fans to enjoy immediate translations of their favourite Japanese comics.
As part of our year-end series, Synced highlights 10 AI-powered efforts that contributed to the fight against COVID-19 in 2020.
At the Wave Summit 2020 held on December 20, Baidu unveiled the company’s newest ML-based bio-computing framework named “PaddleHelix”.
Synced has invited Dr. Viral B. Shah to share his insights about the current development and future trends of artificial intelligence.
Researchers from Tsinghua University have developed an AutoML framework and toolkit specifically designed for graph datasets and tasks.
As part of our year-end series, Synced highlights 10 AI-powered art projects that inspired and entertained us in 2020.
Synced has selected 10 AI-related podcasts for readers to check out over the holiday season.
Researchers combine the effectiveness of the inductive bias in CNNs with the expressivity of transformers to model and synthesize high resolution images.
Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of mosquito-borne disease surveillance.
In the new paper Canonical Capsules: Unsupervised Capsules in Canonical Pose, Turing Award Honoree Dr. Geoffrey Hinton and a team of researchers propose an architecture for unsupervised learning with 3D point clouds based on capsules.
As part of our year-end series, Synced highlights 10 artificial intelligence papers that garnered extraordinary attention and accolades in 2020.
WILDS is an ambitious benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications.
University of Notre Dame and Facebook AI research propose Img2pose, real-time 6DoF 3D face pose estimation without face detection or landmark localization.
Organizers of NeurIPS 2020 (Conference on Neural Information Processing Systems) see machine learning as an invaluable tool in the fight against climate change.
SlimGANs can easily change model sizes during runtime to implement quality-efficiency trade-offs based on practical needs.
New machine learning powered operations service provides tailored recommendations to improve application availability
At AWS re:Invent, Amazon Web Services, Inc., an Amazon.com company, announced Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision.
OpenAI’s groundbreaking GPT-3 language model paper, a no-regret learning dynamics study from Politecnico di Milano & Carnegie Mellon University, and a UC Berkeley work on data summarization have been named the NeurIPS 2020 Best Paper Award winners.
Max Planck Institute, University of Cambridge and Saarland University initiate two probabilistic approaches designed to achieve algorithmic recourse in practice
What if, instead of hard-coding road rules into self-driving algorithms, AI agents were free to come up with their own ways of safely and efficiently sharing the road?
A Princeton student designed a GAN framework for Chinese landscape painting generation that is so effective most humans can’t distinguish its works from the real thing.
This is the first in a special Synced series of introductory articles on traditionally theoretical fields of studies and their impact on modern day machine learning.
Researchers from the City University of Hong Kong and SenseTime propose a lightweight matting objective decomposition network (MODNet) that can smoothly process real-time human matting from a single input image with diverse and dynamic backgrounds.
The SeLf-trAining framework for Distance mEtric learning (SLADE) framework combines self-supervised learning and distance metric learning methods to improve information retrieval performance.
University of Alberta recently proposed U^2-Net, a novel deep network architecture that achieves very competitive performance in salient object detection.