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Research at Google

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Interested in genomics research? Sign up for a free workshop at the +Institute for Systems Biology in Seattle to learn how to leverage the resources of the ISB-CGC and +Google Cloud Platform to analyze publicly available TCGA data. 
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“...nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical...”
Richard Feynman
Simulating Physics with Computers

One of the most promising applications of quantum computing is the ability to efficiently model quantum systems in nature that are considered intractable for classical computers. In collaboration with Harvard, Lawrence Berkeley National Labs, UC Santa Barbara, Tufts University and University College London, we have performed the first completely scalable quantum simulation of a molecule. Learn more in the Google Research blog, linked below.
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Hey folks, What was the software you used?
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Check out an interview with Google Senior Research Scientist Greg Corrado, where he discusses machine learning, and the benefit of open-sourcing software such as #TensorFlow .
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Where is Victory Rojas angel announcer

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Announcing Wide & Deep Learning in #TensorFlow  - useful for generic large-scale regression and classification problems with sparse inputs, such as recommender systems, search, and ranking problems. Our Wide & Deep Learning implementation is open-sourced as part of the TF.Learn API so that you can easily train a model yourself. Please check out the Google Research blog, linked below, to learn more!
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Thanks a lot +Research at Google 
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Congratulations to Google Research Scientist Ce Liu and Google Faculty Advisor Abhinav Gupta, who were selected as this year’s recipients of the PAMI Young Researcher Award for outstanding research contributions within computer vision.

We also congratulate Googler Henrik Stewenius for receiving the Longuet-Higgins Prize, a retrospective award that recognizes up to two CVPR papers from ten years ago that have made a significant impact on computer vision research, for his 2006 CVPR paper “Scalable Recognition with a Vocabulary Tree”, co-authored with David Nister.

Learn about the research being presented at #cvpr2016  on the Google Research blog, linked below
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Seen in +Google Photos, nice results.
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Introducing Project Bloks: making code physical for kids

When we were kids, physical things like toys and blocks helped us learn—inspiring curiosity and imagination in a fun, playful way. Today, we’re announcing Project Bloks. Its goal is to create an open hardware platform for tangible programming experiences to help kids develop computational thinking through physical play. Learn more on the Research at Google blog, linked below, and at g.co/projectbloks
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Waiting for news about this!! 
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Check out PRUDAQ, a open source BeagleBone cape built around the Analog Devices AD9201 ADC, which samples two inputs simultaneously at up to 20 megasamples per second per channel, making it useful for scientific applications where a built-in ADC isn't quite up to the task. Learn more on the Google Research blog, linked below.
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Not bad +Kevin Clift​! But the 20MS/s is a bit slow for our applications. I have presently a crush on the Redpitaya which tops at 125MS/s. But I wonder whether there are open source boards with ADCs beyond that. 
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Congratulations to Research Scientist David Silver (Google DeepMind) and Software Engineer Sylvain Gelly (Google Research, Europe), who received the Artificial Intelligence Journal’s 2016 Prominent Paper Award for their 2011 paper “Monte-Carlo tree search and rapid action value estimation in computer Go” (http://goo.gl/3Qq5L4).

Given at the IJCAI Conference in New York this week (http://goo.gl/GF8R7d, #IJCAI16 ), this award was given to recognize the impact of the research into augmented Monte-Carlo Tree Search algorithms (part of Sylvain's PhD thesis while at Université Paris Sud) that eventually led to the recent defeat of Go player Lee Se-dol by Google DeepMind’s AlphaGo.#
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Deep Learning rocks
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A #motionstill  of the #motionstills  demo at the Google booth at #CVPR2016 , with Research Scientist +Matthias Grundmann.
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Great work +Research at Google 
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Curious about some of the Google papers presented at #CVPR2016 ? The +TechCrunch article below summarizes three of them. To see the full list, visit the Research blog at goo.gl/i59kQS
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Bellissime +Research at Google 
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The Google NLU Team develops innovative techniques & algorithms in Natural Language Understanding, focusing on improving Google services and user experiences. Learn more about the people on the team, their projects and publications on the new webpage, linked below
About our work. Our team comprises multiple research groups working on a range of Natural Language Understanding (NLU) projects. We collaborate closely with teams across Google, leveraging efficient algorithms, neural networks, and graphical and probabilistic models to help guide product ...
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Book me a ticket to Google. Too.
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"For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by “neural nets” that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans. Google is committed to expanding that elite within its walls, with the hope of making it the norm." -Steven Levy
If you want to build artificial intelligence into every product, you better retrain your army of coders. Check.
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Introduction
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.