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Research at Google
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Today, we’re publishing “Achievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm” in Scientific Reports. This paper describes the first results of Google’s collaboration with the physicists and engineers at +Tri Alpha Energy that led to unexpected plasma confinement results.

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Can we teach robots to understand semantic concepts, to get them to follow simple commands specified through categorical labels or user-provided examples? Below on the Google Research blog, we discuss some of our recent work on robotic learning that combines experience that is autonomously gathered by the robot, which is plentiful but lacks human-provided labels, with human-labeled data that allows a robot to understand semantics.

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If you are attending #CVPR17 this year, please stop by our booth and chat with our researchers who are actively pursuing the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception. Check out some of our publication on the Research blog, below.

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Today, we introduce an update to Open Images, which contains the addition of a total of ~2M bounding-boxes to the existing dataset, along with several million additional image-level labels. Learn all about the update on the Google Research blog, linked below.

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Last year, we launched Motion Stills, an iOS app that stabilizes your Live Photos and lets you view and share them as looping GIFs and videos. Today, we're excited to announce that we’re bringing this technology, and more, to devices running Android 5.1 and later.

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We’ve recently released Facets, an open source visualization tool to aid in understanding and analyzing ML datasets. Facets consists of two visualizations that allow users to see a holistic picture of their data at different granularities. Get a sense of the shape of each feature of the data using Facets Overview, or explore a set of individual observations using Facets Dive. Learn more, below!

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To explore how ML can learn subjective concepts, we introduce an experimental deep-learning system for artistic content creation that uses Street View panoramas to create professional-level photos. Check out some of the results, below!

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Today we are happy to announce a new Neural Machine Translation tutorial for #TensorFlow that gives readers a full understanding of seq2seq models and shows how to build a competitive translation model from scratch.

The tutorial is aimed at making the process as simple as possible, starting with some background knowledge on NMT and walking through code details to build a vanilla system. Learn more on the Research blog, linked below.

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As researchers, we have always wondered: if we scale up the amount of training data for computer vision deep learning models by 10x, will the accuracy double? How about 100x or maybe even 300x? Will the accuracy plateau or will we continue to see increasing gains with more and more data? We explore this on the Research blog as we Revisit the Unreasonable Effectiveness of Data.

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This month, as we "graduate" our first class of the Google Brain Residency Program ( g.co/brainresidency), we also welcome our next class. We look forward to another exciting year of research and innovation ahead of us! 
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