If you run a web site, you may already be familiar with Google Webmaster Tools and how it lets you know if Safe Browsing finds something problematic on your site. For example, we’ll notify you if your site is delivering malware, which is usually a sign that it’s been hacked. We’re extending our Safe Browsing protections to automatically display notifications to all Google Analytics users via familiar Google Analytics Notifications.
Google Safe Browsing has been protecting people across the Internet for over eight years and we're always looking for ways to extend that protection even further. Notifications like these help webmasters like you act quickly to respond to any issues. Fast response helps keep your site—and your visitors—safe.
Posted by: Stephan Somogyi, Product Manager, Security and Privacy
Posted by Dharshan Kumaran and Demis Hassabis, Google DeepMind, London
Remember the classic videogame Breakout on the Atari 2600? When you first sat down to try it, you probably learned to play well pretty quickly, because you already knew how to bounce a ball off a wall in real life. You may have even worked up a strategy to maximise your overall score at the expense of more immediate rewards. But what if you didn't possess that real-world knowledge — and only had the pixels on the screen, the control paddle in your hand, and the score to go on? How would you, or equally any intelligent agent faced with this situation, learn this task totally from scratch?
This is exactly the question that we set out to answer in our paper “Human-level control through deep reinforcement learning”, published in Nature this week. We demonstrate that a novel algorithm called a deep Q-network (DQN) is up to this challenge, excelling not only at Breakout but also a wide variety of classic videogames: everything from side-scrolling shooters (River Raid) to boxing (Boxing) and 3D car racing (Enduro). Strikingly, DQN was able to work straight “out of the box” across all these games – using the same network architecture and tuning parameters throughout and provided only with the raw screen pixels, set of available actions and game score as input.
The results: DQN outperformed previous machine learning methods in 43 of the 49 games. In fact, in more than half the games, it performed at more than 75% of the level of a professional human player. In certain games, DQN even came up with surprisingly far-sighted strategies that allowed it to achieve the maximum attainable score—for example, in Breakout, it learned to first dig a tunnel at one end of the brick wall so the ball could bounce around the back and knock out bricks from behind.
So how does it work? DQN incorporated several key features that for the first time enabled the power of Deep Neural Networks (DNN) to be combined in a scalable fashion with Reinforcement Learning (RL)—a machine learning framework that prescribes how agents should act in an environment in order to maximize future cumulative reward (e.g., a game score). Foremost among these was a neurobiologically inspired mechanism, termed “experience replay,” whereby during the learning phase DQN was trained on samples drawn from a pool of stored episodes—a process physically realized in a brain structure called the hippocampus through the ultra-fast reactivation of recent experiences during rest periods (e.g., sleep). Indeed, the incorporation of experience replay was critical to the success of DQN: disabling this function caused a severe deterioration in performance.
Comparison of the DQN agent with the best reinforcement learning methods in the literature. The performance of DQN is normalized with respect to a professional human games tester (100% level) and random play (0% level). Note that the normalized performance of DQN, expressed as a percentage, is calculated as: 100 X (DQN score - random play score)/(human score - random play score). Error bars indicate s.d. across the 30 evaluation episodes, starting with different initial conditions. Figure courtesy of Mnih et al. “Human-level control through deep reinforcement learning”, Nature 26 Feb. 2015.
This work offers the first demonstration of a general purpose learning agent that can be trained end-to-end to handle a wide variety of challenging tasks, taking in only raw pixels as inputs and transforming these into actions that can be executed in real-time. This kind of technology should help us build more useful products—imagine if you could ask the Google app to complete any kind of complex task (“Okay Google, plan me a great backpacking trip through Europe!”).
We also hope this kind of domain general learning algorithm will give researchers new ways to make sense of complex large-scale data creating the potential for exciting discoveries in fields such as climate science, physics, medicine and genomics. And it may even help scientists better understand the process by which humans learn. After all, as the great physicist Richard Feynman famously said: “What I cannot create, I do not understand.”
Posted by Maggie Johnson, Director of Education and University Relations
We have just completed another round of the Google Faculty Research Awards, our biannual open call for research proposals on Computer Science and related topics, including systems, machine perception, structured data, robotics, and mobile. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.
This round we received 808 proposals, an increase of 12% over last round, covering 55 countries on 6 continents. After expert reviews and committee discussions, we decided to fund 122 projects, with 20% of the funding awarded to universities outside the U.S. The subject areas that received the highest level of support were systems, human-computer interaction, and machine perception.
The Faculty Research Award program enables us to build strong relationships with faculty around the world who are pursuing innovative research, and plays an important role for Google’s Research organization by fostering an exchange of ideas that advances the state of the art. Each round, we receive proposals from faculty who may be just starting their careers, or who might be experimenting in new areas that help us look forward and innovate on what's emerging in the CS community.
Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is April 15), please visit our website for more information.
Science is about observing and experimenting. It’s about exploring unanswered questions, solving problems through curiosity, learning as you go and always trying again.
That’s the spirit behind the fifth annual Google Science Fair, kicking off today. Together with LEGO Education, National Geographic, Scientific American and Virgin Galactic, we’re calling on all young researchers, explorers, builders, technologists and inventors to try something ambitious. Something imaginative, or maybe even unimaginable. Something that might just change the world around us.
From now through May 18, students around the world ages 13-18 can submit projects online across all scientific fields, from biology to computer science to anthropology and everything in between. Prizes include $100,000 in scholarships and classroom grants from Scientific American and Google, a National Geographic Expedition to the Galapagos, an opportunity to visit LEGO designers at their Denmark headquarters, and the chance to tour Virgin Galactic’s new spaceship at their Mojave Air and Spaceport. This year we’re also introducing an award to recognize an Inspiring Educator, as well as a Community Impact Award honoring a project that addresses an environmental or health challenge.
It’s only through trying something that we can get somewhere. Flashlights required batteries, then Ann Makosinski tried the heat of her hand. His grandfather would wander out of bed at night, until Kenneth Shinozuka tried a wearable sensor. The power supply was constantly unstable in her Indian village, so Harine Ravichandran tried to build a different kind of regulator. Previous Science Fair winners have blown us away with their ideas. Now it’s your turn.
Reflecting our continuing commitment to building strong relations with the global academic community, we are excited to announce the latest North American Google PhD Fellows. The following 15 fellowship recipients were chosen from a highly competitive group, and represent the outstanding quality of nominees provided by our university partners:
Justin Meza, Google US/Canada Fellowship in Systems Reliability (Carnegie Mellon University)
Waleed Ammar, Google US/Canada Fellowship in Natural Language Processing (Carnegie Mellon University)
Aaron Parks, Google US/Canada Fellowship in Mobile Networking (University of Washington)
Kyle Rector, Google US/Canada Fellowship in Human Computer Interaction (University of Washington)
Nick Arnosti, Google US/Canada Fellowship in Market Algorithms (Stanford University)
Osbert Bastani, Google US/Canada Fellowship in Programming Languages (Stanford University)
Carl Vondrick, Google US/Canada Fellowship in Machine Perception, (Massachusetts Institute of Technology)
Wojciech Zaremba, Google US/Canada Fellowship in Machine Learning (New York University)
Xiaolan Wang, Google US/Canada Fellowship in Structured Data (University of Massachusetts Amherst)
Muhammad Naveed, Google US/Canada Fellowship in Security (University of Illinois at Urbana-Champaign)
Masoud Moshref Javadi, Google US/Canada Fellowship in Computer Networking (University of Southern California)
Riley Spahn, Google US/CanadaFellowship in Privacy (Columbia University)
Saurabh Gupta, Google US/Canada Fellowship in Computer Vision (University of California, Berkeley)
Yun Teng, Google US/Canada Fellowship in Computer Graphics (University of California, Santa Barbara)
Tan Zhang, Google US/Canada Fellowship in Mobile Systems (University of Wisconsin-Madison)
This group of students represent the next generation of researchers who endeavor to solve some of the most interesting challenges in Computer Science. We offer our congratulations, and look forward to their future contributions to the research community with high expectations.
Like sunshine and the beach, or dogs and tennis balls, Google AdWords and Google Analytics are great by themselves but even better together. You'll get high-performance insights into your ads and your website when you link your AdWords and Analytics accounts. Google Analytics does a vital job in this pairing: it shows you what happened after users clicked on your AdWords ads.
We’ve put together a new Best Practices guide, Better Together: AdWords and Google Analytics, to help you get deep insight into your performance. When you analyze performance with the combination of GA and AdWords you can find all sorts of actionable info:
Which parts of your account drive actual on-site engagement
Which keywords attract new users to your site
What messaging and landing pages connect with the different users on your site
How your business compares across your entire industry
To whet your appetite, here’s a rundown of ten useful GA reports included in the guide (with links that lead you directly to these reports in your own GA account). Like what you see here? Download the full version and the condensed one-page checklist to view our complete coverage of GA + AW goodness.
Love Analytics and AdWords being paired together? Please take our survey about your past success and what else we can do to improve the experience.
Posted by Matt Lawson, Director, Performance Ads Marketing
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