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Research at Google
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Research at Google

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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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At Google, we believe in rigorous, open, cross-institution work on how to build machine learning systems that work as intended. In collaboration with UC Berkeley, Stanford and OpenAI, we have published Concrete Problems in AI Safety (https://goo.gl/8D9KIz), a technical paper that discusses potential challenges and how best to address associated risks of new technologies.
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Today, we’re excited to announce a dedicated Machine Learning research group in Europe, based in our Zurich office. Google Research, Europe, will foster an environment where software engineers and researchers specializing in ML will have the opportunity to develop products and conduct research right here in Europe, as part of the wider efforts at Google.
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How about converting law texts into a rule based machine format that can be understood and analyzed by an AI expert system?
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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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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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Announcing Google Cloud Platform Education Grants for computer science faculty and students!
 
If you teach or study higher ed computer science in the U.S., we’ve got good news. We’re introducing Google Cloud Platform Education Grants. Faculty can apply for free credits for students to use across all of our platform’s tools—from BigQuery to TensorFlow. We can’t wait to see what your students build. https://goo.gl/3PEItD
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Congratulations to the authors of Dueling Network Architectures for Deep Reinforcement Learning (Wang et al.) and Pixel Recurrent Neural Networks (Van den Oord et al.), recipients of #ICML2016  Best Paper Awards! Learn more about all the research Google is presenting this year (including links to the papers) on the Research blog.
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Congratulations to Wang and Van den Oord et al.
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A step closer to quantum computing with Digitized adiabatic quantum computing with a superconducting circuit, published in Nature today. Check out a nice summary of the paper by Google Quantum Electronics Engineers Rami Barends and Alireza Shabani on the Research blog, linked below
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Sounds like brainiac heaven
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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.