Posted by David Ha, Google Brain Resident Abstract visual communication is a key part of how people convey ideas to one another. From a young age, children develop the ability to depict objects, and arguably even emotions, with only a few pen strokes. These simple drawings may not resemble reality as captured by a photograph, but they do tell us something about how people represent and reconstruct

Posted by Julian Ibarz, Staff Software Engineer, Google Brain Team and Sujoy Banerjee, Product Manager, Ground Truth Team Every day, Google Maps provides useful directions, real-time traffic information and information on businesses to millions of people. In order to provide the best experience for our users, this information has to constantly mirror an ever-changing world. While Street View cars

Posted by Robert Obryk and Jyrki Alakuijala, Software Engineers, Google Research Europe (Cross-posted on the Google Open Source Blog) At Google, we care about giving users the best possible online experience, both through our own services and products and by contributing new tools and industry standards for use by the online community. That’s why we’re excited to announce Guetzli, a new open sourc

Posted by Moshe Looks, Marcello Herreshoff and DeLesley Hutchins, Software Engineers In much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches. This lets high-performance deep learning libraries like TensorFlow run the same computation graph across all the input

Posted by Esteban Real, Vincent Vanhoucke, Jonathon Shlens, Google Brain Team and Stefano Mazzocchi, Google Research One of the most challenging research areas in machine learning today is enabling computers to understand what a scene is about. For example, while humans know that a ball that disappears behind a wall only to reappear a moment later is very likely the same object, this is not at all

Posted by James Cook, Yechen Li, Software Engineers and Ravi Kumar, Research Scientist "When Solomon said there was a time and a place for everything he had not encountered the problem of parking his automobile." -Bob Edwards, Broadcast Journalist Much of driving is spent either stuck in traffic or looking for parking. With products like Google Maps and Waze, it is our long-standing goal to help p

Posted by Mike Schuster (Google Brain Team), Melvin Johnson (Google Translate) and Nikhil Thorat (Google Brain Team) In the last 10 years, Google Translate has grown from supporting just a few languages to 103, translating over 140 billion words every day. To make this possible, we needed to build and maintain many different systems in order to translate between any two languages, incurring signif

Posted by Sujith Ravi, Staff Research Scientist, Google Research Recently, there have been significant advances in Machine Learning that enable computer systems to solve complex real-world problems. One of those advances is Google’s large scale, graph-based machine learning platform, built by the Expander team in Google Research. A technology that is behind many of the Google products and features

Posted by Nick Johnston and David Minnen, Software Engineers Data compression is used nearly everywhere on the internet - the videos you watch online, the images you share, the music you listen to, even the blog you're reading right now. Compression techniques make sharing the content you want quick and efficient. Without data compression, the time and bandwidth costs for getting the information y

Posted by Slav Petrov, Senior Staff Research Scientist At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a f

Posted by Koray Kavukcuoglu, Research Scientist, Google DeepMind At DeepMind, we conduct state-of-the-art research on a wide range of algorithms, from deep learning and reinforcement learning to systems neuroscience, towards the goal of building Artificial General Intelligence. A key factor in facilitating rapid progress is the software environment used for research. For nearly four years, the ope

Posted by Derek Murray, Software Engineer Google uses machine learning across a wide range of its products. In order to continually improve our models, it's crucial that the training process be as fast as possible. One way to do this is to run TensorFlow across hundreds of machines, which shortens the training process for some models from weeks to hours, and allows us to experiment with models of

Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more

Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more

Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more