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  • Little Box Challenge Academic Awards
    Posted by Maggie Johnson, Director of Education and University Relations

    Last July, Google and the Institute of Electrical and Electronics Engineers Power Electronics Society (IEEE PELS) announced the Little Box Challenge, a competition designed to push the forefront of new technologies in the research and development of small, high power density inverters.

    In parallel, we announced the Little Box Challenge award program designed to help support academics pursuing groundbreaking research in the area of increasing the power density for DC-­to­-AC power conversion. We received over 100 proposals and today we are proud to announce the following recipients of the academic awards:

    Primary Academic Institution
    Principal Investigator
    University of Colorado Boulder
    National Taiwan University of Science and Technology
    Universidad Politécnica de Madrid
    Texas A&M University
    ETH Zürich
    University of Bristol
    Case Western Reserve University
    University of Illinois Urbana-Champaign
    University of Stuttgart
    Queensland University of Technology

    The recipients hail from many different parts of the world and were chosen based on their very strong and thoughtful entries dealing with all the issues raised in the request for proposals. Each of these researchers will receive approximately $30,000 US to support their research into high power density inverters, and are encouraged to use this work to attempt to win the $1,000,000 US grand prize for the Little Box Challenge.

    There were many submissions beyond those chosen here that reviewers also considered to be very promising. We encourage all those who did not receive funding to still participate in the Little Box Challenge, and pursue improvements not only in power density, but also in the reliability, efficiency, safety, and cost of inverters (and of course, to attempt to win the grand prize!)

  • Call for Research Proposals to participate in the Open Web of Things Expedition
    Posted Vint Cerf, Chief Internet Evangelist, Roy Want and Max Senges, Google Research

    Imagine a world in which access to networked technology defies the constraints of desktops, laptops or smartphones. A future where we work seamlessly with connected systems, services, devices and “things” to support work practices, education, and daily interactions. While the Internet of Things (IoT) conjures a vision of “anytime, any place” connectivity for all things, the realization is complex given the need to work across interconnected and heterogeneous systems, and the special considerations needed for security, privacy, and safety.

    Google is excited about the opportunities the IoT presents for future products and services. To further the development of open standards, facilitate ease of use, and ensure that privacy and security are fundamental values throughout the evolution of the field, we are in the process of establishing an open innovation and research program around the IoT. We plan to bring together a community of academics, Google experts and potentially other parties to pursue an open and shared mission in this area.

    As a first step, we are announcing an open call for research proposals for the Open Web of Things:

    • Researchers interested in the Expedition Lead Grant should build a team of PIs and put forward a proposal outlining a draft research roadmap both for their team(s), as well as how they propose to integrate related research that is implemented outside their labs (e.g., Individual Project Grants).
    • For the Individual Project Grants we are seeking research proposals relating to the IoT in the following areas (1) user interface and application development, (2) privacy & security, and (3) systems & protocols research.

    Importantly, we are open to new and unorthodox solutions in all three of these areas, for example, novel interactions, usable security models, and new approaches for open standards and evolution of protocols.

    Additionally, to facilitate hands-on research supporting our mission driven research, we plan to provide participating faculty access to hardware, software and systems from Google. We look forward to your submission by January 21, 2015 and expect to select proposals early Spring. Selected PIs will be invited to participate in a kick-off workshop at Google shortly after.

  • Learning Digital Skills online with Google Activate
    Posted by Michel Benard, University Relations Manager and Cova Soto, Product Marketing Manager, Business Marketing Madrid

    According to Eurostat data, over 5 million people under age 25 are currently out of work in Europe, in contrast to an increasing demand for people with digital skills such as Digital Marketing, Big Data, Ecommerce, Mobile App Development and Cloud Computing. In particular, Spanish employers are finding it difficult to find individuals with the right skills, due to the lack of the digital education available.

    In an effort to make contributions towards solving Spain’s unemployment in this sector, Google Spain, the Spanish Ministry of Industry through their business school EOI, Universidad Complutense de Madrid and Interactive Advertising Bureau (IAB) are collaborating to build Google Activate, a series of massive open online courses (MOOCs) dedicated to teach digital skills to the young unemployed people in Spain. This is an example of how online education can be scaled to address educational and economic issues.

    The inspiration for Google Activate began with the summer 2012 launch of Course Builder, an experimental platform developed on Google technologies designed to provide the capability for anyone to create an online environment that can be used for a wide variety of education-related activities. In September of that same year, Course Builder was made available in Europe, as part of the Google Faculty Summit in London.

    Among the early adopters of Course Builder in Europe was a partnership that included the University of Alicante, who in October 2012 launched Unimooc Aemprende, a MOOC for entrepreneurs. This is just one example of the use of Course Builder to build a MOOC designed to solve a broad problem, in this case the acquisition of skills for launching a small business. More than 30,000 people have participated in Unimooc since its launch.

    As of today, more than 148,000 people have registered for Activate with 13% of participants earning a certificate, which is obtained after 13 exams certified by either the EOI, Universidad Complutense de Madrid or or the IAB (Interactive Advertising Bureau). Such certificates are being used by the awardees in their LinkedIn profile to position themselves for a job in the digital economy, where many jobs are being created. More than 19,000 students are already certified in one of the 5 digital areas.

    Google Activate has plans to increase the number of students with digital skills reaching 160,000 with plans to expand further to other countries in the world.

  • MOOC Research and Innovation
    Posted by Maggie Johnson, Director of Education and University Relations

    Recently, Tsinghua University and Google collaborated to host the 2014 APAC MOOC Focused Faculty Workshop in Shanghai, China. The workshop brought together 37 professors from 12 countries in APAC, NA and EMEA to share, brainstorm and generate important topics that are of mutual interests in the research behind MOOCs and how to foster MOOC innovation.

    During the 2-day workshop, faculty and Googlers shared lessons learned and best practices for the following focus areas:
    • Effectiveness of hybrid learning models.
    • Topics in adaptive learning and how they can tailor to individual students by Integrating MOOCs into a student's timetable / semester / curriculum.
    • Standards and practices for interoperability between online learning platforms.
    • Current focuses and important topics for future MOOC research.

    In addition to discussing these focus areas, here was ample time for participants to brainstorm and discuss innovative research ideas for the next-steps in potential research collaboration. Emerging from these discussions were the following themes identified as important future research topics:
    • Adding new interactions to MOOCs including social and gamification
    • Building a data & analytics Infrastructure that provides a foundation for personalized learning
    • Interoperability across platforms, and providing access to online content for audiences with limited access.

    Google is committed to supporting research and innovation in online learning at scale, through both grants and our open source Course Builder platform, and we are excited to pursue potential research collaborations with partner universities to move forward on the topics discussed. Stay tuned for future announcements on research and collaboration aimed at enabling further MOOC innovation.

  • High Quality Object Detection at Scale
    Google Engineers Christian Szegedy, Scott Reed*, Dumitru Erhan, and Dragomir Anguelov

    The ILSVRC detection challenge is an influential academic benchmark for measuring the quality of object detection. This summer, the GoogLeNet team reported top results in the 2014 edition of the challenge, with ~2X improvement over the previous year’s best results. However, the quality of our results came at a high computational cost: processing each image took about two minutes on a state-of-the-art workstation.

    Naturally, we began to think of how we could both improve the accuracy and reduce the computation time needed. Given the already high quality of previous results like those of GoogLeNet[6], we expected that further improvements to detection quality would be increasingly hard to achieve. In our recent paper Scalable, High Quality Object Detection[7], we detail advances that instead have resulted in an accelerated rate of progress in object detection:
    Evolution of detection quality over time. On the y axis is the mean average precision of the best published results at any given time. The blue line shows result using individual models, the red line is multi-model ensembles. Overfeat[8] was the state-of-the-art at end of last year, followed by R-CNN[1] published in May. The later measurement points are the results of our team.[6,7]
    As seen in the plot above, the mean average precision has been improved since August from 0.45 to 0.56: a 23% relative gain. The new approach can also match the quality of the former best solution with 140X reduced computational resources.

    Most current approaches for object detection employ two phases[1]: in the first phase, some hand-engineered algorithm proposes regions of interest in the image. In the second phase, each proposed region is run through a deep neural network, identifying which proposed patches correspond to an object (and what that object is).

    For the first phase, the common wisdom[1,2,3,4] was that it took skillfully crafted code to produce high quality region proposals. This has come with a drawback though: these methods don’t produce reliable scoring for the proposed regions. This forces the second phase to evaluate most of the proposed patches in order to achieve good results.

    So we revisited our prior “MultiBox” work[5], in which we let the computer learn to pick the proposals to see whether we could avoid relying on any of the hand-crafted methods above. Although the MultiBox method, using previous generation vision network architectures, could not compete with hand-engineered proposal approaches, there were several advantages of fully relying on machine learning only. First, the quality of proposals increases with each new improved network architecture or training methodology without additional programming effort. Second, the regions come with confidence scores which are used for trading off running time versus quality. Additionally, the implementation is simplified.

    Once we used new variants of the network architecture introduced in [6], MultiBox also started to perform much better; Now, we could match the coverage of alternative methods with half as many proposal patches. Also, we changed our networks to take the context of objects into account, fueling additional quality gains for the second phase. Furthermore, we came up with a new way to train deep networks to learn more robustly even when some objects are not annotated in the training set, which improved both phases.

    Besides the significant gains in mean average precision, we can now cut the number of evaluated patches dramatically at a modest loss of quality: the task that used to take 2 minutes of processing time for a single image on a workstation by the GoogLeNet ensemble (of 6 networks), is now performed under a second using a single network without using GPUs. If we constrain ourselves to a single category like “dog”, we can now process 50 images/second on the same machine by a more streamlined approach[7] that skips the proposal generation step altogether.

    As a core area of research in computer vision, object detection is used for providing strong signals for photo and video search, while high quality detection could prove useful for self-driving cars and automatically generated image captions. We look forward to the continuing research in this field.


    [1]  Rich feature hierarchies for accurate object detection and semantic segmentation
    by Ross Girshick and Jeff Donahue and Trevor Darrell and Jitendra Malik (CVPR, 2014)

    [2]  Prime Object Proposals with Randomized Prim’s Algorithm
    by Santiago Manen, Matthieu Guillaumin and Luc Van Gool

    [3]  Edge boxes: Locating object proposals from edges
    by Lawrence C Zitnick, and Piotr Dollàr (ECCV 2014)

    [4]  BING: Binarized normed gradients for objectness estimation at 300fps
    by Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin and Philip Torr (CVPR 2014)

    [5]  Scalable Object Detection using Deep Neural Networks
    by Dumitru Erhan, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov

    [6]  Going deeper with convolutions
    by Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich

    [7]  Scalable, high quality object detection
    by Christian Szegedy, Scott Reed, Dumitru Erhan and Dragomir Anguelov

    [8]  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Network by Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and Yann LeCun

    * A PhD student at University of Michigan -- Ann Arbor and Software Engineering Intern at Google

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