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Ariel Spivakovsky
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Global internet and smartphone user growth are slowing dramatically, but at least things are looking up in India.
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A cache of internal documents shows that despite growing revenue, Palantir has lost top-tier clients, is struggling to stem staff departures, and isn't collecting most of the money it touts in high...
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In this episode of the Waking Up podcast, Sam Harris answers questions from listeners about the recent attacks in Brussels, dealing with anxiety, the science of immortality, fame, liberalism, the Golden Age of Islam, and other topics.
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Every week, over 10 million users encounter harmful websites that deliver malware and scams. And while Safe Browsing and Google Search protect visitors from dangerous content by displaying browser warnings and labeling search results with ‘this site may harm your computer’, the compromised site remains a problem that needs to be fixed.

In order to find the best way to help webmasters clean-up from compromise, we recently teamed up with the University of California, Berkeley to explore how to quickly contact webmasters and expedite recovery while minimizing the distress involved.

Head over to the Google Research blog to read a summary of the key lessons learned from the study, which was presented at the International World Wide Web Conference (http://www2016.ca/) last week. You can also read the full paper at http://goo.gl/GYijVD
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Academics at Google: The Power of Scale

Last December, the Google Research Blog highlighted a collaboration between Google Research Scientist Kai Kohlhoff (http://goo.gl/Tqxpgj), Stanford University, and Google engineers, in which detailed molecular simulations using hundreds of millions of core hours were carried out on Google’s infrastructure (http://goo.gl/0AIUbB).

In The Power of Scale presentation from our Academics at Google series, Kai and Google Software Engineer +David Konerding discuss the development of experimental architecture that enabled the computation of hundreds of thousands of simulations of molecular dynamics across Google's data centers. 

Watch the short introduction video, linked below, to learn more about the scientific benefits of massive parallel computing  in the cloud. To see the full presentation by Kai, where he outlines the computationally expensive application of Newton’s laws of motion to molecular simulations, visit http://goo.gl/kKQgyK.
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cross posted from the Google Research Blog

Launching the Quantum Artificial Intelligence Lab
Posted by +Hartmut Neven, Director of Engineering

We believe quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what’s happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.

So today we’re launching the Quantum Artificial Intelligence Lab. NASA’s Ames Research Center will host the lab, which will house a quantum computer from D-Wave Systems (http://goo.gl/fIHvk), and the Universities Space Research Association (USRA, http://goo.gl/M6GmK) will invite researchers from around the world to share time on it. Our goal: to study how quantum computing might advance machine learning.

Machine learning is highly difficult. It’s what mathematicians call an “NP-hard” problem. That’s because building a good model is really a creative act. As an analogy, consider what it takes to architect a house. You’re balancing lots of constraints -- budget, usage requirements, space limitations, etc. -- but still trying to create the most beautiful house you can. A creative architect will find a great solution. Mathematically speaking the architect is solving an optimization problem and creativity can be thought of as the ability to come up with a good solution given an objective and constraints. 

Classical computers aren’t well suited to these types of creative problems. Solving such problems can be imagined as trying to find the lowest point on a surface covered in hills and valleys. Classical computing might use what’s called “gradient descent”: start at a random spot on the surface, look around for a lower spot to walk down to, and repeat until you can’t walk downhill anymore. But all too often that gets you stuck in a “local minimum” -- a valley that isn’t the very lowest point on the surface.

That’s where quantum computing comes in. It lets you cheat a little, giving you some chance to “tunnel” through a ridge to see if there’s a lower valley hidden beyond it. This gives you a much better shot at finding the true lowest point -- the optimal solution.

We’ve already developed some quantum machine learning algorithms. One produces very compact, efficient recognizers -- very useful when you’re short on power, as on a mobile device. Another can handle highly polluted training data, where a high percentage of the examples are mislabeled, as they often are in the real world. And we’ve learned some useful principles: e.g., you get the best results not with pure quantum computing, but by mixing quantum and classical computing.

Can we move these ideas from theory to practice, building real solutions on quantum hardware? Answering this question is what the Quantum Artificial Intelligence Lab is for. We hope it helps researchers construct more efficient and more accurate models for everything from speech recognition, to web search, to protein folding. We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics. We’re excited to get started with NASA Ames, D-Wave, the USRA, and scientists from around the world.
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Ariel Spivakovsky

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The rise of Donald Trump should inject a dose of humility into those of us who practice political science or political journalism (I plead guilty on both counts). In this post, William Galston explores how the politics in In...
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Google is committed to supporting innovation in online learning at scale. We are pleased to announce the recipients of the Google MOOC Focused Research Awards to support research that explores new interactions to enhance learning experience, personalized learning, online community building, interoperability of online learning platforms and education accessibility. Head over to the Google Research blog to learn more.
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SageMathCloud: A Collaborative Mathematics Research and Teaching Tool

University of Washington Professor +William Stein, recipient of a 2013 Google Research Award, has created SageMathCloud (SMC, http://goo.gl/B30gya), a robust, low-latency web application for collaboratively editing mathematical documents and code. Built on top of standard open-source tools and using Google Compute Engine (http://goo.gl/6PZT6D) as a hosting platform, SMC a viable platform for mathematics research, as well as a powerful tool for teaching any mathematically-oriented course.  

Learn more on the Google Research Blog, linked below, and follow +The SageMathCloud here on Google+.
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Check out the Google Research blog, linked below, where Software Engineer +Pete Warden explains how you can use a TensorFlow pre-trained deep network to identify objects in an image. And if spotting objects like “bow tie”, “military uniform” and “nematode” do not suit your application, you can use TensorFlow to train your very own network. 
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