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Valentin Bazarevsky and Andrei Tkachenka, Software Engineers, Google Research Video segmentation is a widely used technique that enables movie directors and video content creators to separate the foreground of a scene from the background, and treat them as two different visual layers. By modifying or replacing the background, creators can convey a particular mood, transport themselves to a fun loc
Posted by Sam Hasinoff, Software Engineer, Machine Perception Burst photography is the key idea underlying the HDR+ software on Google's recent smartphones, and a fundamental computational photography technique for improving image quality. Every photo taken with HDR+ is actually a composite, generated by capturing and merging a short burst of full-resolution photos. HDR+ has helped the Pixel and t
Posted by Jianing Wei and Tyler Mullen, Software Engineers, Google Research Last summer, we launched Motion Stills on Android, which delivered a great video capture and viewing experience on a wide range of Android phones. Then, we refined our Motion Stills technology further to enable the new motion photos feature in Pixel 2. Today, we are excited to announce the new Augmented Reality (AR) mode i
Posted by Jeff Dean, Google Senior Fellow, on behalf of the entire Google Brain Team The Google Brain team works to advance the state of the art in artificial intelligence by research and systems engineering, as one part of the overall Google AI effort. In Part 1 of this blog post, we shared some of our work in 2017 related to our broader research, from designing new machine learning algorithms an
Posted by Jeff Dean, Google Senior Fellow, on behalf of the entire Google Brain Team The Google Brain team works to advance the state of the art in artificial intelligence by research and systems engineering, as one part of the overall Google AI effort. Last year we shared a summary of our work in 2016. Since then, we’ve continued to make progress on our long-term research agenda of making machine
Posted by Enrique Alfonseca, Staff Research Scientist, Google Assistant Voice interactions with technology are becoming a key part of our lives — from asking your phone for traffic conditions to work to using a smart device at home to turn on the lights or play music. The Google Assistant is designed to provide help and information across a variety of platforms, and is built to bring together a nu
Posted by Chia-Kai Liang, Senior Staff Software Engineer and Fuhao Shi, Android Camera Team One of the most important aspects of current smartphones is easily capturing and sharing videos. With the Pixel 2 and Pixel 2 XL smartphones, the videos you capture are smoother and clearer than ever before, thanks to our Fused Video Stabilization technique based on both optical image stabilization (OIS) an
Posted by Mike Krainin, Software Engineer and Ce Liu, Research Scientist, Machine Perception In 2007, we introduced Google Street View, enabling you to explore the world through panoramas of neighborhoods, landmarks, museums and more, right from your browser or mobile device. The creation of these panoramas is a complicated process, involving capturing images from a multi-camera rig called a roset
Posted by Alex Wiltschko, Research Scientist, Google Brain Team (Crossposted on the Google Open Source Blog) Tangent is a new, free, and open-source Python library for automatic differentiation. In contrast to existing machine learning libraries, Tangent is a source-to-source system, consuming a Python function f and emitting a new Python function that computes the gradient of f. This allows much
Posted by Chi Zeng and Justine Tunney, Software Engineers, Google Brain Team When we open-sourced TensorFlow in 2015, it included TensorBoard, a suite of visualizations for inspecting and understanding your TensorFlow models and runs. Tensorboard included a small, predetermined set of visualizations that are generic and applicable to nearly all deep learning applications such as observing how loss
Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are now at the core of the leading approaches to language understanding tasks such as language modeling, machine translation and question answering. In “Attention Is All You Need”, we introduce the Transformer, a novel neural network architecture based on a
Posted by Christian Howard, Editor-in-Chief, Research Communications Machine learning (ML) is a key strategic focus at Google, with highly active groups pursuing research in virtually all aspects of the field, including deep learning and more classical algorithms, exploring theory as well as application. We utilize scalable tools and architectures to build machine learning systems that enable us t
Using Deep Learning to Create Professional-Level Photographs Posted by Hui Fang, Software Engineer, Machine Perception Machine learning (ML) excels in many areas with well defined goals. Tasks where there exists a right or wrong answer help with the training process and allow the algorithm to achieve its desired goal, whether it be correctly identifying objects in images or providing a suitable tr
Posted by Abhinav Gupta, Faculty Advisor, Machine Perception There has been remarkable success in the field of computer vision over the past decade, much of which can be directly attributed to the application of deep learning models to this machine perception task. Furthermore, since 2012 there have been significant advances in representation capabilities of these systems due to (a) deeper models
Posted by Luke Metz, Research Associate and Yun Liu, Software Engineer, 2016 Google Brain Resident Alumni “Coming from a background in statistics, physics, and chemistry, the Google Brain Residency was my first exposure to both deep learning and serious programming. I enjoyed the autonomy that I was given to research diverse topics of my choosing: deep learning for computer vision and language, re
Posted by Łukasz Kaiser, Senior Research Scientist, Google Brain Team and Aidan N. Gomez, Researcher, Department of Computer Science Machine Learning Group, University of Toronto Over the last decade, the application and performance of Deep Learning has progressed at an astonishing rate. However, the current state of the field is that the neural network architectures are highly specialized to spec
As an example of the kind of improvements T2T can offer, we applied the library to machine translation. As you can see in the table above, two different T2T models, SliceNet and Transformer, outperform the previous state-of-the-art, GNMT+MoE. Our best T2T model, Transformer, is 3.8 points better than the standard GNMT model, which itself was 4 points above the baseline phrase-based translation sys
Posted by Zak Stone, Product Manager for TensorFlow Researchers require enormous computational resources to train the machine learning (ML) models that have delivered recent breakthroughs in medical imaging, neural machine translation, game playing, and many other domains. We believe that significantly larger amounts of computation will make it possible for researchers to invent new types of ML mo
Posted by Quoc Le & Barret Zoph, Research Scientists, Google Brain team At Google, we have successfully applied deep learning models to many applications, from image recognition to speech recognition to machine translation. Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult
Posted by Jennifer Daniel, Expressions Creative Director, Allo Taking, sharing, and viewing selfies has become a daily habit for many — the car selfie, the cute-outfit selfie, the travel selfie, the I-woke-up-like-this selfie. Apart from a social capacity, self-portraiture has long served as a means for self and identity exploration. For some, it’s about figuring out who they are. For others it’s
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 Ian Goodfellow, Staff Research Scientist, Google Brain Team This week, Toulon, France hosts the 5th International Conference on Learning Representations (ICLR 2017), a conference focused on how one can learn meaningful and useful representations of data for Machine Learning. ICLR includes conference and workshop tracks, with invited talks along with oral and poster presentations of some
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 Brendan McMahan and Daniel Ramage, Research Scientists Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we're introducing an additi
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 Amy McDonald Sandjideh, Technical Program Manager, TensorFlow In just its first year, TensorFlow has helped researchers, engineers, artists, students, and many others make progress with everything from language translation to early detection of skin cancer and preventing blindness in diabetics. We’re excited to see people using TensorFlow in over 6000 open-source repositories online. Tod
Posted by Sujith Ravi, Staff Research Scientist, Google Research To build the cutting-edge technologies that enable conversational understanding and image recognition, we often apply combinations of machine learning technologies such as deep neural networks and graph-based machine learning. However, the machine learning systems that power most of these applications run in the cloud and are computa
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
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