AI Technology & Industry Review
Google recently has launched an adorable new feature for its Arts and Culture app named Pet Portraits that can compare your pet photo to artworks from museums worldwide and find their art doubles.
A research team from Technical University of Munich, Google, Nvidia and LMU München proposes CodeTrans, an encoder-decoder transformer model which achieves state-of-the-art performance on six tasks in the software engineering domain, including Code Documentation Generation, Source Code Summarization, Code Comment Generation, etc.
Researchers from Google, Georgia Tech and Emory University examine the semantic changes in abolitionist newspapers and explore whether the papers exhibited leadership or were followers, providing an overall picture of their network of semantic influence.
A research team from Google and EPFL proposes a novel approach that sheds light on the operation and inductive biases of self-attention networks, and finds that pure attention decays in rank doubly exponentially with respect to depth.
A team from Facebook and Google has proposed LazyTensor — a technique for targeting domain-specific compilers without sacrificing define-by-run ergonomics.
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.
Google AI created Lyra, a high-quality, very low-bitrate speech codec that makes voice communication available even on the slowest networks.
A research team from Google and Johns Hopkins University identifies variance-limited and resolution-limited scaling behaviours for dataset and model size in four scaling regimes.
Researchers from UC Berkeley and Google Research have introduced BoTNet, a “conceptually simple yet powerful” backbone architecture that boosts performance on computer vision (CV) tasks such as image classification, object detection and instance segmentation.
A recent study by the Google Brain Team proposes a new way of programming automated machine learning (AutoML) based on symbolic programming.
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.
Google Research and DeepMind debut Long-Range Arena (LRA) benchmark for Transformer research on tasks with long sequence lengths.
Google AI yesterday released its Objectron dataset — a collection of short, object-centric video clips capturing a large set of common objects from different angles.
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.
Google recently introduced mT5, a multilingual variant of its “Text-to-Text Transfer Transformer” (T5), pretrained on a new Common Crawl-based dataset covering 101 languages.
“Trust in AI systems is becoming, if not already, the biggest barrier for enterprises — as they start to move from exploring AI or potentially piloting or doing some proof of concept works into deploying AI into a production system”
A team from Google, University of Cambridge, DeepMind, and Alan Turing Institute have proposed a new type of Transformer dubbed Performer, based on a Fast Attention Via positive Orthogonal Random features (FAVOR+) backbone mechanism.
It was announced yesterday in a PyTorch blog post that the PyTorch / XLA library, a package that enables PyTorch to connect to Google TPUs and use TPU cores as devices, is now generally available on Google Cloud, with support for a broad set of entry points for developers.
The trimmed-down pQRNN extension to Google AI’s projection attention neural network PRADO compares to BERT on text classification tasks for on-device use.
Google Research released a paper tackling this issue with a new open-source analytic platform: the Language Interpretability Tool (LIT).
Google researchers have introduced a series of extensions to the SOTA view-synthesis method Neural Radiance Fields (NeRF) that enable it to produce high-quality 3D representations of complex scenes with only unstructured image collections as input.
Researchers conducted a thorough empirical study of hyperparameter selection for offline RL, aiming to identify and develop more reliable and effective approaches.
BigBird is shown to dramatically improve performance across long-context NLP tasks, producing SOTA results in question answering and summarization.
Researchers proposed a new transformer architecture called “Performer” — based on what they call fast attention via orthogonal random features (FAVOR).
The industry-standard MLPerf benchmark today released the results of the third round of its ongoing ML Training Systems competition.
Google researchers propose an “AutoML-Zero” approach designed to automatically search for machine learning (ML) algorithms from scratch, requiring minimal human expertise or input.
Google CEO Sundar Pichai announced the set up of a 10 billion dollar “Google for India Digitization Fund” to help accelerate India’s digital economy.
Google is investing USD 4 billion in India’s Jio platforms, a subsidiary of conglomerate Reliance Group, to create affordable smartphones for the Indian market.
A team of Google researchers recently proposed a novel “complete and label” domain adaptation approach.
Google Research team proposes the automatic metric BLEURT which is based on the highly successful Google language model BERT.
Google AI researchers introduce Meta-Dataset, a large-scale and diverse benchmark for measuring the ability of few-shot classification models.
A “data echoing” technique that enables these time-consuming upstream training stages to also benefit from accelerators.
Google affiliated company Sidewalk Labs announced today that it is no longer pursuing the development of Toronto’s Quayside smart city project.
Researchers unify DNN normalization layers and activation functions into a single computation graph.
XTREME, a multi-task benchmark that evaluates cross-lingual generalization capabilities of multilingual representations across 40 languages and nine tasks.
Researchers have proposed a new image generative model that leverages the hierarchical space of deep features learned by pretrained classification networks and provides a unified and versatile framework for image generation and manipulation tasks.
A trio of AI detecting breast cancer papers from Google, NYU, and DeepHealth have triggered huge discussions. What are the breakthroughs? Is AI truly beating radiologists? Where exactly are we right now?
A recent paper accepted by ICLR 2020 proposes a new transformer model called “Reformer” which achieves impressive performance even when running on only a single GPU.
Synced Global AI Weekly December 8th