Google AI Blog: October 2014

Google Flu Trends gets a brand new engine

Friday, October 31, 2014

Posted by Christian Stefansen, Senior Software EngineerGoogle Flu Trendsnicely complementDengue Trendsoverpredicted123since 2009"This system is not designed to be a replacement for traditional surveillance networks or supplant the need for laboratory-based diagnoses and surveillance."

Learning Statistics with Privacy, aided by the Flip of a Coin

Thursday, October 30, 2014

Posted by Úlfar Erlingsson, Tech Lead Manager, Security Research(Cross-posted on the Chromium Blog and the Google Online Security Blog)protect our users' security and privacyrandomized responsedifferential privacystrongest form of privacyintense research in academiaon the Internet, nobody should know you’re a dogunwanted softwarehijackingopen-source projectreportACM Conference on Computer and Communications Securityfeedbackrappor-discuss@googlegroups.com

HDR+: Low Light and High Dynamic Range photography in the Google Camera App

Monday, October 27, 2014

Posted by Marc Levoy, Google[x] Software Engineering Manager and Professor Emeritus, Stanford Universityimage noisehigh dynamic rangeGoogle Camera appCapturing low-light scenesoptical image stabilizationAveraging multiple shotslucky imaging

A low light example is captured at dusk. The picture at left was taken with HDR+ off and the picture at right with HDR+ on. The HDR+ image is brighter, cleaner, and sharper, with much more detail seen in the subject’s hair and eyelashes. Photos by Florian Kainz

Capturing high dynamic range scenesdynamic rangebracketing

A classic high dynamic range situation. With HDR+ off (left), the camera exposes for the subjects’ faces, causing the landscape and sky to blow out. With HDR+ on (right), the picture successfully captures the subjects, the landscape, and the sky. Photos by Ryan Geiss

Click here to see the full resolution image. Photo by Marc Levoy

albumTips on using HDR+

Google Camera app

Helping teachers teach computer science

Friday, October 24, 2014

(Cross-posted on the Google for Education Blog)CS4HS (Computer Science for High School)What went well:

89% of participants reported they would recommend their workshop to others

44% more participants reported a “high” or “very high knowledge” of CS after their workshop vs. before

More than half of participants said they would use “most” or “all” of the activities or resources presented during their workshop.

In 2014 the number of teachers who took part in a CS4HS professional development workshop increased by 50%, primarily due to the funding of multiple MOOCs.

Ways to make a bigger impact:

Just 53% of participants said they felt a sense of community among the other workshop participants. Research by Joyce & Showers (2002) and Wiske, Stone, & Levinson (1993) shows that peer-to-peer professional development, along with ongoing support, helps teachers implement new content, retain skills, and create lasting change. We’ll explore new ways to build community among participants as we plan future workshops.

83% of participants reported being Caucasian, which is consistent with the current demographics of CS educators. This indicates a need to increase efforts in diversifying the CS teacher population.

Outcome measures show us that the most knowledge gains were among teachers who had no prior experience teaching CS or participating in CS professional development -- a population that made up just 30% of participants. While we see that the workshops are meeting a need, there remains an opportunity to develop materials geared toward more experienced CS teachers while also encouraging more new teachers to participate.

At the University of Sydney CS4HS workshop teachers are learning how to teach

Computer Science without a computer during a CS Unplugged activity.

Smart Autofill - Harnessing the Predictive Power of Machine Learning in Google Sheets

Monday, October 13, 2014

Posted by Konstantin Davydov, Software Engineer and Afshin Rostamizadeh, Research Scientistmachine learningSmart Autofill Add-onPrediction API

Figure 1: Highlighting the dataset and selecting the target column.

Figure 2: After clicking "Start" a model is trained and applied to automatically fill in the missing values of the target column. Note, the estimated error of the model is reported in the sidebar.

All the News that's Fit to Read: A Study of Social Annotations for News Reading

Wednesday, October 8, 2014

Posted by Chinmay Kulkarni, Stanford University Ph.D candidate and former Google Intern, and Ed H. Chi, Google Research ScientistAll the News that’s Fit to Read: A Study of Social Annotations for News Reading2013 ACM SIGCHI Conference on Human Factors in Computing Systemsinfluential Google papers from 2013

News articles in different annotation conditions

withoutfriend

Announcing the Google CS Engagement Small Awards Program

Monday, October 6, 2014

Posted by Leslie Yeh Johnson, University RelationsGoogle for Education blogmost enrolled course on campusHarvey Mudd’s graduates2013 Taulbee SurveyRecent findingsResearch indicatesinstructordepartmentCS Engagement Small Grants ProgramNovember 15, 2014Call for Proposals

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