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Feb 21, 2020 Setting Fairness Goals with the TensorFlow Constrained Optimization Library
Posted by Andrew Zaldivar, Responsible AI Advocate, Google Research, on behalf of the TFCO Team Many technologies that use supervised machine learning are having an increasingly positive impact on peoples’ day-to-day lives, from catching early signs of illnesses to filtering inappropriate content. There is, however, a growing concern that learned models, which generally satisfy the narrow requirement of minimizing a single loss function , may have difficulty addressing broader societal issues such ...
Feb 19, 2020 Generating Diverse Synthetic Medical Image Data for Training Machine Learning Models
Posted by Timo Kohlberger and Yuan Liu, Software Engineers, Google Health The progress in machine learning (ML) for medical imaging that helps doctors provide better diagnoses has partially been driven by the use of large, meticulously labeled datasets. However, dataset size can be limited in real life due to privacy concerns, low patient volume at partner institutions, or by virtue of studying rare diseases.
Feb 13, 2020 AutoFlip: An Open Source Framework for Intelligent Video Reframing
Posted by Nathan Frey, Senior Software Engineer, Google Research, Los Angeles and Zheng Sun, Senior Software Engineer, Google Research, Mountain View Videos filmed and edited for television and desktop are typically created and viewed in landscape aspect ratios (16:9 or 4:3). However, with an increasing number of users creating and consuming content on mobile devices, historical aspect ratios don’t always fit the display being used for viewing.
Feb 12, 2020 Learning to See Transparent Objects
Posted by Shreeyak Sajjan, Research Engineer, Synthesis AI and Andy Zeng, Research Scientist, Robotics at Google Optical 3D range sensors, like RGB-D cameras and LIDAR , have found widespread use in robotics to generate rich and accurate 3D maps of the environment, from self-driving cars to autonomous manipulators .
Feb 06, 2020 TyDi QA: A Multilingual Question Answering Benchmark
Posted by Jonathan Clark, Research Scientist, Google Research Question answering technologies help people on a daily basis — when faced with a question, such as “Is squid ink safe to eat?”, users can ask a voice assistant or type a search and expect to receive an answer .
Feb 05, 2020 ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems
Posted by Hansa Srinivasan, Software Engineer, Google Research Machine learning systems have been increasingly deployed to aid in high-impact decision-making, such as determining criminal sentencing , child welfare assessments , who receives medical attention and many other settings. Understanding whether such systems are fair is crucial, and requires an understanding of models’ short- and long-term effects.
Jan 31, 2020 Encode, Tag and Realize: A Controllable and Efficient Approach for Text Generation
Posted by Eric Malmi and Sebastian Krause, Software Engineers, Google Research Sequence-to-sequence (seq2seq) models have revolutionized the field of machine translation and have become the tool of choice for various text-generation tasks, such as summarization , sentence fusion and grammatical error correction. Improvements in model architecture (e.g., Transformer ) and the ability to leverage large corpora of unannotated text via unsupervised pre-training have enabled the quality gains in neural network approaches we ...
Jan 30, 2020 Announcing the Third Workshop and Challenge on Learned Image Compression
Posted by Nick Johnston, Software Engineer, Google Research With the large amount of media content being downloaded and streamed across the internet, minimizing bandwidth while maintaining quality remains a constant challenge. In 2015, researchers demonstrated that neural network-based image compression could yield significant improvements to image resolution while retaining good quality and high compression speed .
Jan 29, 2020 Towards a Conversational Agent that Can Chat About…Anything
Posted by Daniel Adiwardana, Senior Research Engineer, and Thang Luong, Senior Research Scientist, Google Research, Brain Team Modern conversational agents (chatbots) tend to be highly specialized — they perform well as long as users don’t stray too far from their expected usage. To better handle a wide variety of conversational topics, open-domain dialog research explores a complementary approach attempting to develop a chatbot that is not specialized but can still chat about ...
Jan 28, 2020 Google Research: Looking Back at 2019, and Forward to 2020 and Beyond
Posted by Jeff Dean, Senior Fellow and SVP of Google Research and Health, on behalf of the entire Google Research community The goal of Google Research is to work on long-term, ambitious problems, with an emphasis on solving ones that will dramatically help people throughout their daily lives.
Jan 22, 2020 Releasing the Drosophila Hemibrain Connectome — The Largest Synapse-Resolution Map of Brain Connectivity
Posted by Michal Januszewski, Software Engineer and Viren Jain, Research Scientist and Technical Lead, Connectomics at Google A fundamental way to describe a complex system is to measure its “network” — the way individual parts connect and communicate with each other. For example, biologists study gene networks , social scientists study social networks and even search engines rely, in part, on analyzing the way web pages form a network by linking to ...
Jan 16, 2020 Reformer: The Efficient Transformer
Posted by Nikita Kitaev, Student Researcher, UC Berkeley and Łukasz Kaiser, Research Scientist, Google Research Understanding sequential data — such as language, music or videos — is a challenging task, especially when there is dependence on extensive surrounding context. For example, if a person or an object disappears from view in a video only to re-appear much later, many models will forget how it looked.
Jan 16, 2020 Can You Trust Your Model’s Uncertainty?
Posted by Jasper Snoek, Research Scientist and Zachary Nado, Research Engineer, Google Research In an ideal world, machine learning (ML) methods like deep learning are deployed to make predictions on data from the same distribution as that on which they were trained. But the practical reality can be quite different: camera lenses becoming blurry, sensors degrading, and changes to popular online topics can result in differences between the distribution of data on ...
Jan 14, 2020 Using Machine Learning to “Nowcast” Precipitation in High Resolution
Posted by Jason Hickey, Senior Software Engineer, Google Research The weather can affect a person’s daily routine in both mundane and serious ways, and the precision of forecasting can strongly influence how they deal with it. Weather predictions can inform people about whether they should take a different route to work, if they should reschedule the picnic planned for the weekend, or even if they need to evacuate their homes due to ...
Dec 20, 2019 ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
Posted by Radu Soricut and Zhenzhong Lan, Research Scientists, Google Research Ever since the advent of BERT a year ago, natural language research has embraced a new paradigm, leveraging large amounts of existing text to pretrain a model’s parameters using self-supervision, with no data annotation required.
Dec 19, 2019 The On-Device Machine Learning Behind Recorder
Posted by Itay Inbar and Nir Shemy, Software Engineers, Google Research Over the past two decades, Google has made information widely accessible through search — from textual information, photos and videos, to maps and jobs. But much of the world’s information is conveyed through speech. Yet even though many people use audio recording devices to capture important information in conversations, interviews, lectures and more, it can be very difficult to later parse ...
Dec 17, 2019 New Solutions for Quantum Gravity with TensorFlow
Posted by Thomas Fischbacher, Software Engineer, 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.
Dec 17, 2019 Improving Out-of-Distribution Detection in Machine Learning Models
Posted by Jie Ren, Research Scientist, Google Research and Balaji Lakshminarayanan, Research Scientist, DeepMind Successful deployment of machine learning systems requires that the system be able to distinguish between data that is anomalous or significantly different from that used in training. This is particularly important for deep neural network classifiers, which might classify such out-of-distribution (OOD) inputs into in-distribution classes with high confidence.
Dec 16, 2019 Improvements to Portrait Mode on the Google Pixel 4 and Pixel 4 XL
Posted by Neal Wadhwa, Software Engineer and Yinda Zhang, Research Scientist, Google Research Portrait Mode on Pixel phones is a camera feature that allows anyone to take professional-looking shallow depth of field images. Launched on the Pixel 2 and then improved on the Pixel 3 by using machine learning to estimate depth from the camera’s dual-pixel auto-focus system, Portrait Mode draws the viewer’s attention to the subject by blurring out the background.
Dec 11, 2019 Google at NeurIPS 2019
Posted by Andrew Helton, Editor, Google Research Communications This week, Vancouver hosts the 33rd annual Conference on Neural Information Processing Systems (NeurIPS 2019), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research.