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The latest news on Google Research.

  • Google Computer Science Capacity Awards
    By Maggie Johnson, Director of Education and University Relations and Chris Busselle, Google.org

    One of Google's goals is to surface successful strategies that support the expansion of high-quality Computer Science (CS) programs at the undergraduate level. Innovations in teaching and technologies, while additionally ensuring better engagement of women and underrepresented minority students, is necessary in creating inclusive, sustainable, and scalable educational programs.

    To address issues arising from the dramatic increase in undergraduate CS enrollments, we recently launched the Computer Science Capacity Awards program. For this three-year program, select educational institutions were invited to contribute proposals for innovative, inclusive, and sustainable approaches to address current scaling issues in university CS educational programs.

    Today, after an extensive proposal review process, we are pleased to announce the recipients of the Capacity Awards program:

    Carnegie Mellon University - Professor Jacobo Carrasquel
    Alternate Instructional Model for Introductory Computer Science Classes
    CMU will develop a new instructional model consisting of two optional mini lectures per week given by the instructor, and problem-solving sessions with flexible group meetings that are coordinated by undergraduate and graduate teaching assistants.

    Duke University - Professor Jeffrey Forbes
    North Carolina State University - Professor Kristy Boyer
    University of North Carolina - Professor Ketan Mayer-Patel
    RESEARCH TRIANGLE PEER TEACHING FELLOWS: Scalable Evidence-Based Peer Teaching for Improving CS Capacity and Diversity
    The project hopes to increase CS retention and diversity by developing a highly scalable, effective, evidence-based peer training program across three universities in the North Carolina Research Triangle.

    Mount Holyoke College - Professor Heather Pon-Barry
    MaGE (Megas and Gigas Educate): Growing Computer Science Capacity at Mount Holyoke College
    Mount Holyoke’s MaGE program includes a plan to grow enrollment in introductory CS courses, particularly for women and other underrepresented groups. The program also includes a plan of action for CS students to educate, mentor, and support others in inclusive ways.

    George Mason University - Professor Jeff Offutt
    SPARC: Self-PAced Learning increases Retention and Capacity
    George Mason University wants to replace the traditional course model for CS-1 and CS-2 with an innovative teaching model of self- paced introductory programming courses. Students will periodically demonstrate competency with practical skills demonstrations similar to those used in martial arts.

    Rutgers University - Professor Andrew Tjang
    Increasing the Scalability and Diversity in the Face of Large Growth in Computer Science Enrollment
    Rutger’s program addresses scalability issues with technology tools, as well as collaborative spaces. It also emphasizes outreach to Rutgers’ women’s college and includes original research on success in CS programs to create new courses that cater to the changing environment.

    University of California, Berkeley - Professor John DeNero
    Scaling Computer Science through Targeted Engagement
    Berkeley’s program plans to increase Software Engineering and UI Design enrollment by 500 total students/year, as well as increase the number of women and underrepresented minority CS majors by a factor of three.

    Each of the selected schools brings a unique and innovative approach to addressing current scaling issues, and we are excited to collaborate in developing concrete strategies to develop sustainable and inclusive educational programs. Stay tuned over the coming year, where we will report on program recipients' progress and share results with the broader CS education community.


  • Announcing the Google MOOC Focused Research Awards
    Posted by Maggie Johnson, Director of Education and University Relations, and Aimin Zhu, University Relations Manager, APAC

    Last year, Google and Tsinghua University hosted the 2014 APAC MOOC Focused Faculty Workshop, an event designed to share, brainstorm and generate ideas aimed at fostering MOOC innovation. As a result of the ideas generated at the workshop, we solicited proposals from the attendees for research collaborations that would advance important topics in MOOC development.

    After expert reviews and committee discussions, we are pleased to announce the following recipients of the MOOC Focused Research Awards. These awards cover research exploring new interactions to enhance learning experience, personalized learning, online community building, interoperability of online learning platforms and education accessibility:

    • “MOOC Visual Analytics” - Michael Ginda, Indiana University, United States
    • “Improvement of students’ interaction in MOOCs using participative networks” - Pedro A. Pernías Peco, Universidad de Alicante, Spain
    • “Automated Analysis of MOOC Discussion Content to Support Personalised Learning” - Katrina Falkner, The University of Adelaide, Australia
    • “Extending the Offline Capability of Spoken Tutorial Methodology” - Kannan Moudgalya, Indian Institute of Technology Bombay, India
    • “Launching the Pan Pacific ISTP (Information Science and Technology Program) through MOOCs” - Yasushi Kodama, Hosei University, Japan
    • “Fostering Engagement and Social Learning with Incentive Schemes and Gamification Elements in MOOCs” - Thomas Schildhauer, Alexander von Humboldt Institute for Internet and Society, Germany
    • “Reusability Measurement and Social Community Analysis from MOOC Content Users” - Timothy K. Shih, National Central University, Taiwan

    In order to further support these projects and foster collaboration, we have begun pairing the award recipients with Googlers pursuing online education research as well as product development teams.

    Google is committed to supporting innovation in online learning at scale, and we congratulate the recipients of the MOOC Focused Research Awards. It is our belief that these collaborations will further develop the potential of online education, and we are very pleased to work with these researchers to jointly push the frontier of MOOCs.


  • A step closer to quantum computation with Quantum Error Correction
    Posted by Julian Kelly, Rami Barends, and Austin Fowler, Quantum Electronics Engineers

    Computer scientists have dreamt of large-scale quantum computation since at least 1994 -- the hope is that quantum computers will be able to process certain calculations much more quickly than any classical computer, helping to solve problems ranging from complicated physics or chemistry simulations to solving optimization problems to accelerating machine learning tasks.

    One of the primary challenges is that quantum memory elements (“qubits”) have always been too prone to errors. They’re fragile and easily disturbed -- any fluctuation or noise from their environment can introduce memory errors, rendering the computations useless. As it turns out, getting even just a small number of qubits together to repeatedly perform the required quantum logic operations and still be nearly error-free is just plain hard. But our team has been developing the quantum logic operations and qubit architectures to do just that.

    In our paper “State preservation by repetitive error detection in a superconducting quantum circuit”, published in the journal Nature, we describe a superconducting quantum circuit with nine qubits where, for the first time, the qubits are able to detect and effectively protect each other from bit errors. This quantum error correction (QEC) can overcome memory errors by applying a carefully choreographed series of logic operations on the qubits to detect where errors have occurred.
    Photograph of the device containing nine quantum bits (qubits). Each qubit interacts with its neighbors to protect them from error.

    So how does QEC work? In a classical computer, we can monitor bits directly to detect errors. However, qubits are much more fickle -- measuring a qubit directly will collapse entanglement and superposition states, removing the quantum elements that make it useful for computation.

    To get around this, we introduce additional ‘measurement’ qubits, and perform a series of quantum logic operations that look at the 'measurement' and 'data' qubits in combination. By looking at the state of these pairwise combinations (using quantum XOR gates), and performing some careful cross-checking, we can pull out just enough information to detect errors without altering the information in any individual qubit.
    The basics of error correction. ‘Measurement’ qubits can detect errors on ‘data’ qubits through the use of quantum XOR gates.

    We’ve also shown that storing information in five qubits works better than just storing it in one, and that with nine qubits the error correction works even better. That’s a key result -- it shows that the quantum logic operations are trustworthy enough that by adding more qubits, we can detect more complex errors that otherwise may cause algorithmic failure.

    While the basic physical processes behind quantum error correction are feasible, many challenges remain, such as improving the logic operations behind error correction and testing protection from phase-flip errors. We’re excited to tackle these challenges on the way towards making real computations possible.


  • Large-Scale Machine Learning for Drug Discovery
    Posted by Patrick Riley and Dale Webster, Google Research and Bharath Ramsundar, Google Research Intern and Stanford Ph.D. candidate

    Discovering new treatments for human diseases is an immensely complicated challenge; Even after extensive research to develop a biological understanding of a disease, an effective therapeutic that can improve the quality of life must still be found. This process often takes years of research, requiring the creation and testing of millions of drug-like compounds in an effort to find a just a few viable drug treatment candidates. These high-throughput screens are often automated in sophisticated labs and are expensive to perform.

    Recently, deep learning with neural networks has been applied in virtual drug screening1,2,3, which attempts to replace or augment the high-throughput screening process with the use of computational methods in order to improve its speed and success rate.4 Traditionally, virtual drug screening has used only the experimental data from the particular disease being studied. However, as the volume of experimental drug screening data across many diseases continues to grow, several research groups have demonstrated that data from multiple diseases can be leveraged with multitask neural networks to improve the virtual screening effectiveness.

    In collaboration with the Pande Lab at Stanford University, we’ve released a paper titled "Massively Multitask Networks for Drug Discovery", investigating how data from a variety of sources can be used to improve the accuracy of determining which chemical compounds would be effective drug treatments for a variety of diseases. In particular, we carefully quantified how the amount and diversity of screening data from a variety of diseases with very different biological processes can be used to improve the virtual drug screening predictions.

    Using our large-scale neural network training system, we trained at a scale 18x larger than previous work with a total of 37.8M data points across more than 200 distinct biological processes. Because of our large scale, we were able to carefully probe the sensitivity of these models to a variety of changes in model structure and input data. In the paper, we examine not just the performance of the model but why it performs well and what we can expect for similar models in the future. The data in the paper represents more than 50M total CPU hours.
    This graph shows a measure of prediction accuracy (ROC AUC is the area under the receiver operating characteristic curve) for virtual screening on a fixed set of 10 biological processes as more datasets are added.

    One encouraging conclusion from this work is that our models are able to utilize data from many different experiments to increase prediction accuracy across many diseases. To our knowledge, this is the first time the effect of adding additional data has been quantified in this domain, and our results suggest that even more data could improve performance even further.

    Machine learning at scale has significant potential to accelerate drug discovery and improve human health. We look forward to continued improvement in virtual drug screening and its increasing impact in the discovery process for future drugs.

    Thank you to our other collaborators David Konerding (Google), Steven Kearnes (Stanford), and Vijay Pande (Stanford).

    References:

    1. Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Marvin Steijaert, Jörg Kurt Wegner, Hugo Ceulemans, Sepp Hochreiter. Deep Learning as an Opportunity in Virtual Screening. Deep Learning and Representation Learning Workshop: NIPS 2014

    2. Dahl, George E, Jaitly, Navdeep, and Salakhutdinov, Ruslan. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231, 2014.

    3. Ma, Junshui, Sheridan, Robert P, Liaw, Andy, Dahl, George, and Svetnik, Vladimir. Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 2015.

    4. Peter Ripphausen, Britta Nisius, Lisa Peltason, and Jürgen Bajorath. Quo Vadis, Virtual Screening? A Comprehensive Survey of Prospective Applications. Journal of Medicinal Chemistry 2010 53 (24), 8461-8467


  • From Pixels to Actions: Human-level control through Deep Reinforcement Learning
    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.”


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