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Research at Google

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Google is full of smart people working on some of the most difficult problems in computer science today. Most people know about the research activities that back our major products, such as search algorithms, systems infrastructure, machine learning, and programming languages. Those are just the tip of the iceberg; Google has a tremendous number of exciting challenges that only arise through the vast amount of data and sheer scale of systems we build.

What we discover affects the world both through better Google products and services, and through dissemination of our findings by the broader academic research community.  We value each kind of impact, and often the most successful projects achieve both.



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Date Following Followers Gained
Recent Popular Posts
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About 7 weeks ago Project Tango: a human-scale understanding of space and motion

Over the past year, Google’s Advanced Technologies and Products group, in collaboration with universities and research labs worldwide, has been working on Project Tango, an effort to incorporate the latest research in robotics and computer vision into a unique mobile phone. 

The prototype device incorporates a tracking camera and a depth sensor that takes a quarter of a million measurements per second, constructing a 3D map of the local environment, and allows the exploration of the many ways we interact with our environment and each other.

To learn more, watch the short video, linked below. To apply for one of the 200 prototype development kits available, visit the Project Tango page at http://goo.gl/T1IUBU. 





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About 19 hours ago Lens Blur in the new Google Camera app
Posted by Carlos Hernández, Software Engineer

One of the biggest advantages of SLR cameras (http://goo.gl/XeAhti) over camera phones is the ability to achieve shallow depth of field effects (also known as bokeh, http://goo.gl/xHR774), which makes the object of interest "pop" by bringing the foreground into focus and de-emphasizing the background. 

Achieving this optical effect has traditionally required a big lens and aperture, and therefore hasn’t been possible using the camera on your mobile phone or tablet. That all changes with Lens Blur, a new mode in the Google Camera app (http://goo.gl/UpMubk). 

Lens Blur  replaces the need for a large optical system with computer vision algorithms and optimization techniques that are run entirely on the mobile device, simulating a larger lens and aperture in order to creating a 3D model of the world. 

To learn more about the algorithms and optimization that makes Lens Blu
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About 7 weeks ago Making Sense of Data with Google
posted by +John Atwood, Program Manager

The world is filled with lots of information; learning to make sense of it all helps us to gain perspective and make decisions. We’re pleased to share tools and techniques to structure, visualize, and analyze information in our latest self-paced, online course: Making Sense of Data.

Making Sense of Data is intended for anybody who works with data on a daily basis, such as students, teachers, journalists, and small business owners, and who wants to learn more about how to apply that information to practical problems. Knowledge of statistics or experience with programming is not required.

Making Sense of Data runs from March 18 - April 4, 2014. Visit g.co/datasense to learn more and register today. We look forward to seeing you make sense of all the information out there!






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About 4 weeks ago 3D Nearest-Neighbor Geometry Matching: Detailed 3D models of a scene from a single image

People are remarkably good at inferring the geometry of a physical space from single photograph, or recognizing that two dissimilar photographs are merely images of the same room taken from different physical perspectives. While these may be simple and natural tasks to us, the creation of systems that can understand images as accurately as humans is one of the main goals in the field of Computer Vision. 

Although many current techniques for scene understanding, such as 2D Nearest Neighbor Search (http://goo.gl/Z4hZb), show impressive results, they are limited in that they are unable to generalize a scene to the arbitrary geometric perspective from which a photo was taken, Furthermore, they require large amount of manually annotated data and precise geometry estimates to match a single image to an accurate model.  

In 3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding, G
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About 1 week ago It was the social aspect of coding competitions, developing friendships and reading other’s solutions to problems, that gave me the opportunity and confidence to see what I could do to possibly develop a better solution.
-+Ishani Parekh, Software Engineer

Several weeks ago, Research at Google featured an interview with two of the four founding members of Google’s Code Jam team, Software Engineers +Igor Naverniouk and +Bartholomew Furrow(http://goo.gl/GcyNPM).  With the Qualification Round beginning this Friday, April 11th, we wanted to share another perspective on coding competitions from a newer member to the Code Jam team.

+Ishani Parekh  is a Software Engineer with the Ads Review team sitting in our Mountain View, CA office, who joined Google in 2012 after obtaining her degree in Computer Science at Dhirubhai Ambani Institute of Information and Communication Technology in India. Code Jam 2014 marks the first time that Ishani has been involved with the Code Jam Team, so we invited
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About 10 weeks ago Improving battery life by harvesting ambient energy 

Yesterday, the New York Times featured an article discussing the ongoing research towards alternate methods of powering mobile devices (http://goo.gl/78N9tN). Among the methods mentioned was the design of a system that enables devices to communicate using ambient radio frequency (RF) energy as the only source of power.

In the paper Ambient Backscatter: Wireless Communication Out of Thin Air, recipient of Best Paper at SIGCOMM 2013 (http://goo.gl/FsOmpa), University of Washington PhD student Vincent Liu and co-authors demonstrate a new communication system that backscatters ambient RF signals, allowing data transmission without drawing on the device’s battery.

This work builds upon the Wireless Ambient Radio Power (WARP)
research by two of the paper’s co-authors, PhD student +Aaron Parks and Associate Professor +Joshua R Smith, on development of remote sensor units powered by the ambient RF signals. Read more about WARP,
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About 8 weeks ago Yesterday, the Ivanpah Solar Electric Generating System, located in California’s Mojave Desert, began delivering electricity to customers. The world’s largest solar thermal energy plant, Ivanpah will generate 392 megawatts of clean, renewable power by using an array of 300,000 computer controlled mirrors to focus sunlight on 3 water towers, generating steam to power turbines.

The project, co-owned by Google, will generate ~⅓ of solar thermal energy produced in the United States. Google Energy & Sustainability Director +Rick Needham believes that Ivanpah “is an exciting first of a kind project, at this scale, for proving the viability of solar thermal power, which can play an important role in the future of clean energy generation from solar resources.”

To learn more, view the article, linked below. To watch a short video of the Ivanpah construction footage, visit http://goo.gl/fmLAjW.



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About 11 weeks ago AI and Deep Learning

The development of neural networks, systems of hardware and software that operate at multiple interconnected levels, has been an active area of research for many years. Taking inspiration from how biological systems learn, neural networks process information in parallel, dynamically solving tasks and recognizing patterns in a fashion that mimics the human brain.

Recently, +WIRED featured a profile of University of Toronto Distinguished Professor and Google Distinguished Scholar +Geoffrey Hinton, and his ongoing work in developing neural networks. These networks form an artificial intelligence called “deep learning” that is able to recognize speech and objects without the need for human labeling input.

Read the article (linked below) to learn more, and visit the Google Research Blog to see how neural networks are applied to the complex tasks of speech recognition (http://goo.gl/FZYhSK) and computer vision (http://goo.gl/9wOUWj).




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About 9 weeks ago Live and Learn: How Big Data and Machine Learning Power the Internet

This week, Stanford University hosted the NASA Innovative Advanced Concepts (NAIC) 2014 Symposium. As part of NASA's Space Technology Mission Directorate (http://goo.gl/JXYoFK), the NAIC supports early studies of visionary concepts that may enable new missions or advances for use in NASA's future missions.

+Peter Norvig, a Director of Research at Google and former Head of NASA Ames's Computational Sciences Division, was on hand to present Live and Learn: How Big Data and Machine Learning Power the Internet. 

In the talk, Peter provides a qualitative overview of the different ways in which data enables machine algorithms to classify, learn, and discover information, allowing advances in fields such as Natural Language Understanding (NLU), machine translation, computer vision, and more.

After the ~30 minute talk, Peter stayed for a Q&A session with the audience, fielding questions cov
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About 4 weeks ago The Creation of a Code Jam Problem

Since its inception in 2003, Google’s Code Jam has drawn the top amateur and professional coders in the world together in a contest to determine who will stand alone as Code Jam Champion. Now in its 11th year, Code Jam once again will be throwing intense algorithmic puzzles at programmers from around the world starting April 11, 2014.

To provide a behind-the-scenes glimpse of what is involved in the problem development for Code Jam, we recently sat down with two members of the Code Jam development team, Software Engineers +Bartholomew Furrow and +Igor Naverniouk, two of the four people who founded Google's Code Jam team in June 2007. 

Bartholomew learned to love computer science while earning a B.Sc. in Physics from Queen's University, when he discovered programming contests. In 2006, shortly after obtaining a M.Sc. in Physics from the University of British Columbia (UBC) with his thesis "A Panoply of Quantum Algorithms", Ba