AI Technology & Industry Review
A research team from DeepMind explores how neural networks can be fused with algorithmic computation and demonstrates an elegant neural end-to-end pipeline that goes straight from raw inputs to general outputs while emulating an algorithm internally.
A research team from DeepMind and Alberta University proposes Policy-guided Heuristic Search (PHS), a novel search algorithm that uses both a heuristic function and a policy while offering guarantees on the search loss that relate to both the quality of the heuristic and the policy.
The Beijing Academy of Artificial Intelligence (BAAI) releases Wu Dao 1.0, China’s first large-scale pretraining model.
A research team from DeepMind proposes manipulation-independent representations (MIR) that can support successful imitation of behaviours demonstrated by previously unseen manipulator morphologies using only visual observations.
A team from Huawei, ISCAS and UCAS propose the novel Transformer-iN-Transformer (TNT) for modelling both patch-level and pixel-level representations.
A team from Google Research explores why most transformer modifications have not transferred across implementation and applications, and surprisingly discovers that most modifications do not meaningfully improve performance.
A team from Max-Planck Institute for Intelligent Systems, ETH Zurich, Google Research Amsterdam, Mila and the University of Montreal make an effort to bring together causality and machine learning research programs, delineate implications of causality for machine learning and propose critical areas for future research.
Google AI created Lyra, a high-quality, very low-bitrate speech codec that makes voice communication available even on the slowest networks.
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.
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.
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.
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.
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.
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.
A new study proposes using human feedback and interaction logs to boost offline reinforcement learning (RL) in natural language processing (NLP).
In a new paper, researchers from Google, OpenAI, and DeepMind introduce “behaviour priors,” a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts.
A team of researchers from Shanghai Jiao Tong University (SJTU) has built a lightweight benchmark suite designed to help make life-saving models of medical image analysis even stronger.
Researchers analyzed different subareas of CADD process with a focus on anticancer drugs.
To adapt conventional deep models to real scenarios, a research work carried by a team (XPixel) from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences investigated the use of additional branches to tune imagery effects.
Researchers from academia and industry offer input on the design, deployment, and operation of trustworthy AI inference systems.
Zurich-based student and aspiring full-stack software engineer Vincent Dörig has taken LaTeX to the website level with his GitHub project LaTeX.css.
The team introduces photon sources fabricated in silicon that meet a variety of requirements for scalable quantum photonics: high purity, high heralding efficiency, and high indistinguishability.
Organizers of the 58th annual meeting of the Association for Computational Linguistics (ACL) on Sunday announced the list of accepted papers for the world-leading natural language processing (NLP) conference.
SIGKDD is announcing a funding opportunity through its Community Impact Program.
In this article, we take a look at Edwin Catmull’s doctoral dissertation published in 1974, which laid the groundwork for 3D computer graphics.
The PyTorch Team yesterday announced the release of PyTorch 1.5, along with new and updated libraries.
Wolfram announced this week that he may have found a path that leads to a fundamental theory of physics, and that it is “beautiful.”
The Association for Computing Machinery (ACM) this morning announced Patrick M. (Pat) Hanrahan and Edwin E. (Ed) Catmull as its 2019 Turing Award winners.
Carnegie Mellon University computer science major Lingdong Huang has developed ‘Wenyan-Lang,’ a programming language based on Chinese hanzi characters and the wenyan classical Chinese grammar system.
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.
A new math tool called “Snip” is creating a buzz on Twitter. Thousands of netizens are sharing the tool, which is being heralded as a “life changer” for scientific writing.
The Massachusetts Institute of Technology (MIT) today announced they will invest US$1 billion into a new college for artificial intelligence. The MIT Stephen A. Schwarzman College of Computing will “constitute both a global center for computing research and education, and an intellectual foundry for powerful new AI tools.”
New York-based investment corporate D. E. Shaw Group yesterday announced that esteemed University of Washington Professor of Computer Science Pedro Domingos will join the firm as a Managing Director and lead its new Machine Learning Research Group.
Google is looking to expand its AI research activities in the Japanese capital. The company’s deep learning and AI research team Google Brain yesterday posted a “Tokyo job listing seeking talented experts to participate in cutting edge research on machine learning”.