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Nov 15, 2019 New Solutions for Quantum Gravity with TensorFlow
Posted by Thomas Fischbacher, Researcher in Compression, Google Research, Zürich Recent strides in machine learning (ML) research have led to the development of tools useful for research problems well beyond the realm for which they were designed. The value of these tools when applied to topics ranging from teaching robots how to throw to predicting the olfactory properties of molecules is now beginning to be realized.
Nov 14, 2019 Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU
Posted by Andrew Howard, Software Engineer and Suyog Gupta, Silicon Engineer, Google Research On-device machine learning (ML) is an essential component in enabling privacy-preserving, always-available and responsive intelligence. This need to bring on-device machine learning to compute and power-limited devices has spurred the development of algorithmically-efficient neural network models and hardware capable of performing billions of math operations per second, while consuming only a few milliwatts of power.
Nov 14, 2019 SPICE: Self-Supervised Pitch Estimation
Posted by Marco Tagliasacchi, Research Scientist, Google Research A sound’s pitch is a qualitative measure of its frequency, where a sound with a high pitch is higher in frequency than one of low pitch. Through tracking relative differences in pitch, our auditory system is able to recognize audio features, such as a song’s melody.
Nov 12, 2019 New Insights into Human Mobility with Privacy Preserving Aggregation
Posted by Adam Sadilek, Software Engineer and Xerxes Dotiwalla, Product Manager, Google Research Understanding human mobility is crucial for predicting epidemics , urban and transit infrastructure planning , understanding people’s responses to conflict and natural disasters and other important domains . Formerly, the state-of-the-art in mobility data was based on cell carrier logs or location "check-ins" , and was therefore available only in limited areas — where the telecom provider is operating.
Nov 12, 2019 Highlights from the 3rd Cohort of the Google AI Residency Program
Posted by Katie Meckley, Program Manager, Google AI Residency This fall marks the successful conclusion for the third cohort of the Google AI Residency Program . Started in 2016 with 27 individuals in Mountain View, CA, the 12-month program has grown to nearly 100 residents from nine locations across the globe.
Nov 12, 2019 Quantum Supremacy Using a Programmable Superconducting Processor
Posted by John Martinis, Chief Scientist Quantum Hardware and Sergio Boixo, Chief Scientist Quantum Computing Theory, Google AI Quantum Physicists have been talking about the power of quantum computing for over 30 years, but the questions have always been: will it ever do something useful and is it worth investing in? For such large-scale endeavors it is good engineering practice to formulate decisive short-term goals that demonstrate whether the designs are going ...
Nov 08, 2019 Google at Interspeech 2019
Andrew Helton, Editor, Google Research Communications This week, Graz, Austria hosts the 20th Annual Conference of the International Speech Communication Association (Interspeech 2019), one of the world‘s most extensive conferences on the research and engineering for spoken language processing. Over 2,000 experts in speech-related research fields gather to take part in oral presentations and poster sessions and to collaborate with streamed events across the globe.
Nov 08, 2019 Google at ICCV 2019
Andrew Helton, Editor, Google Research Communications This week, Seoul, South Korea hosts the International Conference on Computer Vision 2019 (ICCV 2019), one of the world's premier conferences on computer vision. As a leader in computer vision research and a Gold Sponsor, Google will have a strong presence at ICCV 2019 with over 200 Googlers in attendance, more than 40 research presentations, and involvement in the organization of a number of workshops and ...
Nov 06, 2019 The Visual Task Adaptation Benchmark
Posted by Neil Houlsby, Research Scientist and Xiaohua Zhai, Research Engineer, Google Research, Zürich Deep learning has revolutionized computer vision , with state-of-the-art deep networks learning useful representations directly from raw pixels, leading to unprecedented performance on many vision tasks. However, learning these representations from scratch typically requires hundreds of thousands of training examples.
Oct 31, 2019 Audio and Visual Quality Measurement using Fréchet Distance
Posted by Kevin Kilgour, Software Engineer and Thomas Unterthiner, Research Software Engineer, Google Research, Zürich "I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind.” — William Thomson (Lord Kelvin), Lecture on "Electrical Units of Measurement" (3 ...
Oct 31, 2019 Learning to Assemble and to Generalize from Self-Supervised Disassembly
Posted by Kevin Zakka, Research Intern and Andy Zeng, Research Scientist, Robotics at Google Our physical world is full of different shapes, and learning how they are all interconnected is a natural part of interacting with our surroundings — for example, we understand that coat hangers hook onto clothing racks, power plugs insert into wall outlets, and USB cables fit into USB sockets.
Oct 31, 2019 On-Device Captioning with Live Caption
Posted by Michelle Tadmor-Ramanovich and Nadav Bar, Senior Software Engineers, Google Research, Tel-Aviv Captions for audio content are essential for the deaf and hard of hearing, but they benefit everyone. Watching video without audio is common — whether on the train, in meetings, in bed or when the kids are asleep — and studies have shown that subtitles can increase the duration of time that users spend watching a video by almost ...
Oct 30, 2019 A New Workflow for Collaborative Machine Learning Research in Biodiversity
Posted by Serge Belongie, Visiting Faculty and Hartwig Adam, Engineering Director, Google Research The promise of machine learning (ML) for species identification is coming to fruition, revealing its transformative potential in biodiversity research. International workshops such as FGVC and LifeCLEF feature competitions to develop top performing classification algorithms for everything from wildlife camera trap images to pressed flower specimens on herbarium sheets .
Oct 30, 2019 Introducing the Schema-Guided Dialogue Dataset for Conversational Assistants
Posted by Abhinav Rastogi, Software Engineer and Pranav Khaitan, Engineering Lead, Google Research Today's virtual assistants help users to accomplish a wide variety of tasks, including finding flights, searching for nearby events and movies, making reservations, sourcing information from the web and more. They provide this functionality by offering a unified natural language interface to a wide variety of services across the web.
Oct 24, 2019 Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules
Posted by Alexander B Wiltschko, Senior Research Scientist, Google Research Smell is a sense shared by an incredible range of living organisms, and plays a critical role in how they analyze and react to the world. For humans, our sense of smell is tied to our ability to enjoy food and can also trigger vivid memories .
Oct 17, 2019 Video Architecture Search
Posted by Michael S. Ryoo, Research Scientist and AJ Piergiovanni, Student Researcher, Robotics at Google Video understanding is a challenging problem. Because a video contains spatio-temporal data, its feature representation is required to abstract both appearance and motion information. This is not only essential for automated understanding of the semantic content of videos, such as web-video classification or sport activity recognition, but is also crucial for robot perception and learning.
Oct 11, 2019 Exploring Massively Multilingual, Massive Neural Machine Translation
Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is to descend, from each language, down to the common base of human communication — the real but as yet undiscovered universal language — and then re-emerge by whatever particular route is convenient.” — Warren Weaver , 1949 Over the last few years there has been enormous progress in the quality of machine ...
Oct 10, 2019 ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
Posted by Michael Ahn, Software Engineer and Vikash Kumar, Research Scientist, Robotics at Google Learning-based methods for solving robotic control problems have recently seen significant momentum, driven by the widening availability of simulated benchmarks (like dm_control or OpenAI-Gym ) and advancements in flexible and scalable reinforcement learning techniques ( DDPG , QT-Opt , or Soft Actor-Critic ).
Oct 03, 2019 Improving Quantum Computation with Classical Machine Learning
Posted by Murphy Yuezhen Niu and Sergio Boixo, Research Scientists One of the primary challenges for the realization of near-term quantum computers has to do with their most basic constituent: the qubit . Qubits can interact with anything in close proximity that carries energy close to their own—stray photons (i.e., unwanted electromagnetic fields), phonons (mechanical oscillations of the quantum device), or quantum defects (irregularities in the substrate of the chip formed during ...
Oct 02, 2019 Releasing PAWS and PAWS-X: Two New Datasets to Improve Natural Language Understanding Models
Posted by Yuan Zhang, Research Scientist and Yinfei Yang, Software Engineer, Google Research Word order and syntactic structure have a large impact on sentence meaning — even small perturbations in word order can completely change interpretation. For example, consider the following related sentences: Flights from New York to Florida.