AI Technology & Industry Review
Stanford researchers’ DERL (Deep Evolutionary Reinforcement Learning) is a novel computational framework that enables AI agents to evolve morphologies and learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information.
The research develop machine learning methods that enable virtual agents (such as avatars from a computer game) to communicate non-verbally.
Researchers from the University of Sheffield, Beihang University, and Open University’s Knowledge Media Institute have proposed a transfer learning approach that can automatically process historical texts at a semantic level to generate modern language summaries.
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
On January 22, Beijing-based AI firm 4Paradigm announced USD 700 million series D investments from Boyu Capital, Primavera Capital and Hopu Investments.
Is it possible for machines to understand and integrate human emotions in visual arts? Meet ArtEmis, a new large-scale dataset of emotional reactions and explanations for visual artworks.
Analyzing papers and patent data collected from top AI journals and conferences, 44 in total from 2011 to 2020, Tsinghua University’s new AI development report revealed new statistical insights.
This is the second in a special Synced series of introductory articles on traditionally theoretical fields of studies and their impact on modern-day machine learning.
This paper is the first to systematically study the security of Multi-Sensor Fusion (MSF) based localization in high-autonomy Autonomous Vehicles (AVs). Researchers design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities.
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.
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.
DeepMind and Universidade de Lisboa introduce graph-based toolkit for analyzing and comparing multiplayer games