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Apr 08, 2019 Simulated Policy Learning in Video Models
Posted by Łukasz Kaiser and Dumitru Erhan, Research Scientists, Google AI Deep reinforcement learning (RL) techniques can be used to learn policies for complex tasks from visual inputs, and have been applied with great success to classic Atari 2600 games . Recent work in this field has shown that it is possible to get super-human performance in many of them, even in challenging exploration regimes such as that exhibited by Montezuma's Revenge ...
Apr 04, 2019 Capturing Special Video Moments with Google Photos
Posted by Sudheendra Vijayanarasimhan and David Ross, Software Engineers Recording video of memorable moments to share with friends and loved ones has become commonplace. But as anyone with a sizable video library can tell you, it's a time consuming task to go through all that raw footage searching for the perfect clips to relive or share with family and friends.
Apr 02, 2019 Using Deep Learning to Improve Usability on Mobile Devices
Posted by Yang Li, Research Scientist, Google AI Tapping is the most commonly used gesture on mobile interfaces, and is used to trigger all kinds of actions ranging from launching an app to entering text. While the style of clickable elements (e.g., buttons) in traditional desktop graphical user interfaces is often conventionally defined, on mobile interfaces it can still be difficult for people to distinguish tappable versus non-tappable elements due to the ...
Mar 29, 2019 Unifying Physics and Deep Learning with TossingBot
Posted by Andy Zeng, Student Researcher, Robotics at Google Though considerable progress has been made in enabling robots to grasp objects efficiently , visually self adapt or even learn from real-world experiences , robotic operations still require careful consideration in how they pick up, handle, and place various objects -- especially in unstructured settings.
Mar 20, 2019 Reducing the Need for Labeled Data in Generative Adversarial Networks
Posted by Mario Lučić, Research Scientist and Marvin Ritter, Software Engineer, Google AI Zürich Generative adversarial networks (GANs) are a powerful class of deep generative models.The main idea behind GANs is to train two neural networks: the generator, which learns how to synthesise data (such as an image), and the discriminator, which learns how to distinguish real data from the ones synthesised by the generator.
Mar 19, 2019 Measuring the Limits of Data Parallel Training for Neural Networks
Posted by Chris Shallue, Senior Software Engineer and George Dahl, Senior Research Scientist, Google AI Over the past decade, neural networks have achieved state-of-the-art results in a wide variety of prediction tasks, including image classification , machine translation , and speech recognition . These successes have been driven, at least in part, by hardware and software improvements that have significantly accelerated neural network training.
Mar 18, 2019 A Summary of the Google Flood Forecasting Meets Machine Learning Workshop
Posted by Sella Nevo, Senior Software Engineer and Rainier Aliment, Program Manager Recently, we hosted the Google Flood Forecasting Meets Machine Learning workshop in our Tel Aviv office, which brought hydrology and machine learning experts from Google and the broader research community to discuss existing efforts in this space, build a common vocabulary between these groups, and catalyze promising collaborations.
Mar 15, 2019 Google Faculty Research Awards 2018
Posted by Maggie Johnson, VP, Education and Negar Saei, Program Manager, University Relations We just completed another round of the Google Faculty Research Awards , our annual open call for proposals on computer science and related topics, such as quantum computing, machine learning, algorithms and theory, natural language processing and more.
Mar 14, 2019 Harnessing Organizational Knowledge for Machine Learning
Posted by Alex Ratner, Stanford University and Cassandra Xia, Google AI One of the biggest bottlenecks in developing machine learning (ML) applications is the need for the large, labeled datasets used to train modern ML models. Creating these datasets involves the investment of significant time and expense, requiring annotators with the right expertise.
Mar 13, 2019 An All-Neural On-Device Speech Recognizer
Posted by Johan Schalkwyk, Google Fellow, Speech Team In 2012, speech recognition research showed significant accuracy improvements with deep learning , leading to early adoption in products such as Google's Voice Search . It was the beginning of a revolution in the field: each year, new architectures were developed that further increased quality, from deep neural networks (DNNs) to recurrent neural networks (RNNs), long short-term memory networks (LSTMs), convolutional networks (CNNs), and ...
Mar 08, 2019 Real-Time AR Self-Expression with Machine Learning
Posted by Artsiom Ablavatski and Ivan Grishchenko, Research Engineers, Google AI Augmented reality (AR) helps you do more with what you see by overlaying digital content and information on top of the physical world. For example, AR features coming to Google Maps will let you find your way with directions overlaid on top of your real world.
Mar 07, 2019 RNN-Based Handwriting Recognition in Gboard
Posted by Sandro Feuz and Pedro Gonnet, Senior Software Engineers, Handwriting Team In 2015 we launched Google Handwriting Input , which enabled users to handwrite text on their Android mobile device as an additional input method for any Android app. In our initial launch , we managed to support 82 languages from French to Gaelic, Chinese to Malayalam.
Mar 06, 2019 Exploring Neural Networks with Activation Atlases
Posted by Shan Carter, Software Engineer, Google AI Neural networks have become the de facto standard for image-related tasks in computing, currently being deployed in a multitude of scenarios, ranging from automatically tagging photos in your image library to autonomous driving systems. These machine-learned systems have become ubiquitous because they perform more accurately than any system humans were able to directly design without machine learning.
Mar 04, 2019 Introducing GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models
Posted by Yanping Huang, Software Engineer, Google AI Deep neural networks (DNNs) have advanced many machine learning tasks, including speech recognition, visual recognition, and language processing. Recent advances by BigGan , Bert , and GPT2.0 have shown that ever-larger DNN models lead to better task performance and past progress in visual recognition tasks has also shown a strong correlation between the model size and classification accuracy.
Mar 01, 2019 On the Path to Cryogenic Control of Quantum Processors
Posted by Joseph Bardin, Visiting Faculty Researcher and Erik Lucero, Staff Research Scientist and Hardware Lead, Google AI Quantum Team Building a quantum computer that can solve practical problems that would otherwise be classically intractable due to the computation complexity, cost, energy consumption or time to solution, is the longstanding goal of the Google AI Quantum team .
Feb 28, 2019 Long-Range Robotic Navigation via Automated Reinforcement Learning
Aleksandra Faust, Senior Research Scientist and Anthony Francis, Senior Software Engineer, Robotics at Google In the United States alone, there are 3 million people with a mobility impairment that prevents them from ever leaving their homes. Service robots that can autonomously navigate long distances can improve the independence of people with limited mobility, for example, by bringing them groceries, medicine, and packages.
Feb 25, 2019 Transformer-XL: Unleashing the Potential of Attention Models
Posted by Zhilin Yang and Quoc Le, Google AI To correctly understand an article, sometimes one will need to refer to a word or a sentence that occurs a few thousand words back. This is an example of long-range dependence — a common phenomenon found in sequential data — that must be understood in order to handle many real-world tasks.
Feb 22, 2019 Learning to Generalize from Sparse and Underspecified Rewards
Posted by Rishabh Agarwal, Google AI Resident and Mohammad Norouzi, Research Scientist Reinforcement learning (RL) presents a unified and flexible framework for optimizing goal-oriented behavior, and has enabled remarkable success in addressing challenging tasks such as playing video games , continuous control , and robotic learning .
Feb 19, 2019 Introducing PlaNet: A Deep Planning Network for Reinforcement Learning
Posted by Danijar Hafner, Student Researcher, Google AI Research into how artificial agents can improve their decisions over time is progressing rapidly via reinforcement learning (RL). For this technique, an agent observes a stream of sensory inputs (e.g. camera images) while choosing actions (e.g. motor commands), and sometimes receives a reward for achieving a specified goal.
Feb 11, 2019 Using Global Localization to Improve Navigation
Posted by Tilman Reinhardt‎, Software Engineer, Google Maps One of the consistent challenges when navigating with Google Maps is figuring out the right direction to go: sure, the app tells you to go north - but many times you're left wondering, "Where exactly am I, and which way is north?" Over the years, we've attempted to improve the accuracy of the blue dot with tools like GPS and compass, but found that ...