Google AI Blog: December 2014

Little Box Challenge Academic Awards

Tuesday, December 16, 2014

Posted by Maggie Johnson, Director of Education and University RelationsIEEE PELSLittle Box Challengeaward programincreasing the power density for DC-to-AC power conversion

Primary Academic Institution	Principal Investigator
University of Colorado Boulder	Khurram K. Afridi
National Taiwan University of Science and Technology	Huang-Jen Chiu
Universidad Politécnica de Madrid	José A. Cobos
Texas A&M University	Prasad Enjeti
ETH Zürich	Johann W. Kolar
University of Bristol	Neville McNeill
Case Western Reserve University	Timothy Peshek
University of Illinois Urbana-Champaign	Robert Pilawa-Podgurski
University of Stuttgart	Jörg Roth-Stielow
Queensland University of Technology	Geoff Walker

request for proposalsLittle Box Challenge

Call for Research Proposals to participate in the Open Web of Things Expedition

Friday, December 12, 2014

Posted Vint Cerf, Chief Internet Evangelist, Roy Want and Max Senges, Google Researchdefies the constraintsconnected systems, services, devices and “things”the realization is complexopen call for research proposals

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.

Learning Digital Skills online with Google Activate

Thursday, December 11, 2014

Posted by Michel Benard, University Relations Manager and Cova Soto, Product Marketing Manager, Business Marketing MadridEurostat dataSpanish Ministry of IndustryEOIUniversidad Complutense de MadridInteractive Advertising BureauGoogle ActivateCourse BuilderGoogle Faculty SummitUnimooc Aemprende

MOOC Research and Innovation

Tuesday, December 9, 2014

Posted by Maggie Johnson, Director of Education and University RelationsTsinghua University2014 APAC MOOC Focused Faculty Workshop37 professors from 12 countries

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.

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.

online learning at scaleCourse Builder

High Quality Object Detection at Scale

Monday, December 8, 2014

Google Engineers Christian Szegedy, Scott Reed^*, Dumitru Erhan, and Dragomir AnguelovUpdate - 26/02/2015We recently discovered a bug in the evaluation methodology of our object detector. Consequently, the large numbers we initially reported below are not realistic, due to the fact that our separately trained context extractor was contaminated with half of the validation set images. Therefore, our initial results were overly optimistic and were not attainable by the methodology described in the paper. Re-evaluating our initial results, we have restricted ourselves to reporting only the single-model results on the other half of the dedicated validation set without retraining the models. With the updated evaluation, we are still able to report the best single-model result on the ILSVRC 2014 detection challenge data set, with 0.43 mAP when combining both Selective Search and MultiBox proposals with our post-classification model. The original draft of our paper "Scalable, High Quality Object Detection" has been updated to reflect this information. We are deeply sorry if our initial reported results caused any confusion in the community. Original post follows below. -C. Szegedy, S. Reed, D. Erhan, and D. AnguelovILSVRCreported top results[6]Scalable, High Quality Object Detection[7]

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]

[1][1,2,3,4][5][7]automatically generated image captionsReferences:Rich feature hierarchies for accurate object detection and semantic segmentationPrime Object Proposals with Randomized Prim’s AlgorithmEdge boxes: Locating object proposals from edgesBING: Binarized normed gradients for objectness estimation at 300fpsScalable Object Detection using Deep Neural NetworksGoing deeper with convolutionsScalable, high quality object detectionOverFeat: Integrated Recognition, Localization and Detection using Convolutional Network
* A PhD student at University of Michigan -- Ann Arbor and Software Engineering Intern at Google^↩

What we can learn about effective, meaningful and diverse organizations

Thursday, December 4, 2014

Posted by Beryl Nelson, Software Engineering Manager

A team that is heterogeneous in meaningful ways is good for innovation, and good for business.

There are many challenges to making such teams effective, such as unconscious decision making, stereotype threat, and other cognitive illusions.

There is repeatable quantitative research which shows ways to combat some of these effects.

The barriers to effectiveness may seem overwhelming, but there is hope! Meaningful change is possible, and some examples of successful change are cited below.

Diversity is good for innovation and business. There is a correlation between financial success and the diversity of leadership teams, as shown in research by Catalyst, McKinsey and Cedric Herring. Further, research shows a strong correlation between having women on teams and innovation; concluding that there is a strong correlation between the presence of women and the social skills required to get ideas percolating into the open.

We all make decisions unconsciously, influenced by our implicit associations. As an example of these effects, a large proportion of CEOs are taller than the average population and height is strongly correlated with financial and career success. It’s long been argued that women and underrepresented minorities are not represented in CEO leadership because there aren’t enough qualified individuals in the labor pool. This “pipeline issue” argument can’t be made for short and average-height people, however. Simple, repeatable tests measure, via response time and error rate, the implicit associations we have between concepts. These associations are created as an adaptive response, but we must understand our own implicit biases in order to make better decisions.

Stereotype threat plays a role in preventing people from being fully effective. The low representation of women and minorities in Science has long been the source of a troubling question: is this an indication of a difference in innate ability (see Ben Barre’s response to Lawrence Summers’ remarks), or the result of some other effect? Claude Steele and his colleagues elegantly showed that two groups of people can have similar or opposite reactions, depending on the way a situation is presented. These and other experiments show that stereotype threat can compromise the performance of the subject of a stereotype, if he or she knows about the stereotype and cares about it.

Change is possible. The above and other challenges may make it seem nearly impossible to create a diverse and highly functioning organization, but dramatic change can be made. Take, for example, the discovery of biased decision making and effective changes made via the use of data in the MIT Science Faculty Study, or the amazing changes at Harvey Mudd college, which not only increased participation of women as Computer Science majors from 12% to 40% in five years, but also increased the total number of CS majors from 25 to 30 per year to 70 CS graduates in the class of 2014.

researchNovember issue of Communications of the Association of Computing Machineryfull bibliography

Automatically making sense of data

Tuesday, December 2, 2014

Posted by Kevin Murphy, Research Scientist and David Harper, Head of University Relations, EMEAProfessor Zoubin GhahramaniGoogle Focused Research AwardThe Automatic Statistician

Schwabe cycleGaussian processeskernel functionalgorithm

Extract of the report for the solar irradiance data, automatically generated by the automatic statistician.

The Automatic Statistician

Advances in Variational Inference: Working Towards Large-scale Probabilistic Machine Learning at NIPS 2014

Monday, December 1, 2014

Posted by Shakir Mohamed and Charles Blundell, Google DeepMind, LondonVariational Inference1,2,3,4,5,6Advances in Variational InferenceNeural Information Processing Systemsstochastic gradient methodsstreamtime-series modelsdeep neural networksreinforcement learning

Contributed talks from 6 speakers who are leading the resurgence of variational inference, and shaping the debate on topics of stochastic optimisation, deep learning, Bayesian non-parametrics, and theory.

34 contributed papers covering significant advances in methodology, theory and applications including efficient optimisation, streaming data analysis, submodularity, non-parametric modelling and message passing.

A panel discussion with leading researchers in the field that will further interrogate these ideas. Our panelists are David Blei, Neil Lawrence, Shinichi Nakajima and Matthias Seeger.

www.variationalinference.orgReferences:1. , Stochastic Backpropagation and Approximate Inference in Deep Generative Models,
2. , Deep AutoRegressive Networks
3. Neural Variational Inference and Learning in Belief Networks
4. Auto-Encoding Variational Bayes
5. , Streaming Variational Bayes,
6. Stochastic Variational Inference

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