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Showing posts with label google. Show all posts
Showing posts with label google. Show all posts
Tuesday, October 25, 2016
Largest collection of Google Logos on the web Set 7
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Sunday, October 23, 2016
Sign in to edx org with Google and Facebook and
Google is passionate about online education. In addition to our own Course Builder project, we’re also partners with edX, a not-for-profit that shares our desire for scalable, quality education for everyone. Their software, Open edX, lets people make educational content and deliver it online to anybody, anytime, anywhere. It powers their own site, edx.org, and is also used by companies and universities worldwide.
Today we’re very pleased to announce that you can now sign in to edx.org with your Google or Facebook account:
If you run Open edX, you can also use this feature now. The authentication module is extensible so you can add any third-party provider you want if your favorite is not yet supported. And the feature is completely configurable, so you can pick whatever third-party authentication systems are best for your users, including none at all. It’s totally up to you.
By simultaneously increasing user choice, convenience, and security, we hope to make open online education even easier and safer to use, whether people pick Course Builder or Open edX for authoring and delivering courses. We’re very grateful to our partners at edX for working with us in this exciting field.
Monday, October 17, 2016
2014 Google PhD Fellowships Supporting the Future of Computer Science
Nurturing and maintaining strong relations with the academic community is a top priority at Google. Today, we’re announcing the 2014 Google PhD Fellowship recipients. These students, recognized for their incredible creativity, knowledge and skills, represent some of the most outstanding graduate researchers in computer science across the globe. We’re excited to support them, and we extend our warmest congratulations.
The Google PhD Fellowship program supports PhD students in computer science or closely related fields and reflects our commitment to building strong relations with the global academic community. Now in its sixth year, the program covers North America, Europe, China, India and Australia. To date we’ve awarded 193 Fellowships in 72 universities across 17 countries.
As we welcome the 2014 PhD Fellows, we hear from two past recipients, Cynthia Liem and Ian Goodfellow. Cynthia studies at the Delft University of Technology, and was awarded a Fellowship in Multimedia. Ian is about to complete his PhD at the Université de Montréal in Québec, and was awarded a Fellowship in Deep Learning. Recently interviewed on the Google Student blog, they expressed their views on how the Fellowship affected their careers.
Cynthia has combined her dual passions of music and computing to pursue a PhD in music information retrieval. She speaks about the fellowship and her links with Google:
“Through the Google European Doctoral Fellowship, I was assigned a Google mentor who works on topics related to my PhD interests. In my case, this was Dr. Douglas Eck in Mountain View, who is part of Google Research and leads a team focusing on music recommendation. Doug has been encouraging me in several of my academic activities, most notably the initiation of the ACM MIRUM Workshop, which managed to successfully bring music retrieval into the spotlight of the prestigious ACM Multimedia conference.”
Ian is about to start as a research scientist on Jeff Dean’s deep learning infrastructure team. He was also an intern at Google, and contributed to the development of a neural network capable of transcribing the address numbers on houses from Google Street View photos. He describes the connection between this intern project and his PhD study supported by the Fellowship:
“The project I worked on during my internship was the basis for a publication at the International Conference on Learning Representations …. my advisor let me include this paper in my PhD thesis since there was a close connection to the subject area.… I can show that some of the work developed early in the thesis has had a real impact.“
We’re proud to have supported Cynthia, Ian, and all the other recipients of the Google PhD Fellowship. We continue to look forward to working with, and learning from, the academic community with great excitement and high expectations.
Sunday, October 9, 2016
Groundbreaking simulations by Google Exacycle Visiting Faculty
In April 2011, we announced the Google Exacycle for Visiting Faculty, a new academic research awards program donating one billion core-hours of computational capacity to researchers. The Exacycle project enables massive parallelism for doing science in the cloud, and inspired multiple proposals aiming to take advantage of cloud scale. Today, we would like to share some exciting results from a project built on Google’s infrastructure.
Google Research Scientist Kai Kohlhoff, in collaboration with Stanford University and Google engineers, investigated how an important signalling protein in the membrane of human cells can switch off and on by changing its three-dimensional structure following a sequence of local conformational changes. This research can help to better understand the effects of certain chemical compounds on the human body and assist future development of more potent drug molecules with fewer side effects.
The protein, known as the beta-2 adrenergic receptor, is a G protein-coupled receptor (GPCR), a primary drug target that plays a role in several debilitating health conditions. These include asthma, type-2 diabetes, obesity, and hypertension. The receptor and its close GPCR relatives bind to many familiar molecules, such as epinephrine, beta-blockers, and caffeine. Understanding their structure, function, and the underlying dynamics during binding and activation increases our chances to decode the causes and mechanisms of diseases.
To gain insights into the receptor’s dynamics, Kai performed detailed molecular simulations using hundreds of millions of core hours on Google’s infrastructure, generating hundreds of terabytes of valuable molecular dynamics data. The Exacycle program enabled the realization of simulations with longer sampling and higher accuracy than previous experiments, exposing the complex processes taking place on the nanoscale during activation of this biological switch.
The paper summarizing the results of Kai’s and his collaborators’ work is featured on the January cover of Nature Chemistry, with artwork by Google R&D UX Creative Lead Thor Lewis, to be published on December 17, 2013. The online version of his paper was published on their website today.
We are extremely pleased with the results of this program. We look forward to seeing this research continue to develop.
Tuesday, September 20, 2016
How Google Translate squeezes deep learning onto a phone
Today we announced that the Google Translate app now does real-time visual translation of 20 more languages. So the next time you’re in Prague and can’t read a menu, we’ve got your back. But how are we able to recognize these new languages?
In short: deep neural nets. When the Word Lens team joined Google, we were excited for the opportunity to work with some of the leading researchers in deep learning. Neural nets have gotten a lot of attention in the last few years because they’ve set all kinds of records in image recognition. Five years ago, if you gave a computer an image of a cat or a dog, it had trouble telling which was which. Thanks to convolutional neural networks, not only can computers tell the difference between cats and dogs, they can even recognize different breeds of dogs. Yes, they’re good for more than just trippy art—if youre translating a foreign menu or sign with the latest version of Googles Translate app, youre now using a deep neural net. And the amazing part is it can all work on your phone, without an Internet connection. Here’s how.
Step by step
First, when a camera image comes in, the Google Translate app has to find the letters in the picture. It needs to weed out background objects like trees or cars, and pick up on the words we want translated. It looks at blobs of pixels that have similar color to each other that are also near other similar blobs of pixels. Those are possibly letters, and if they’re near each other, that makes a continuous line we should read.
But interestingly, if we train just on very “clean”-looking letters, we risk not understanding what real-life letters look like. Letters out in the real world are marred by reflections, dirt, smudges, and all kinds of weirdness. So we built our letter generator to create all kinds of fake “dirt” to convincingly mimic the noisiness of the real world—fake reflections, fake smudges, fake weirdness all around.
Why not just train on real-life photos of letters? Well, it’s tough to find enough examples in all the languages we need, and it’s harder to maintain the fine control over what examples we use when we’re aiming to train a really efficient, compact neural network. So it’s more effective to simulate the dirt.
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| Some of the “dirty” letters we use for training. Dirt, highlights, and rotation, but not too much because we don’t want to confuse our neural net. |
Finally, we render the translation on top of the original words in the same style as the original. We can do this because we’ve already found and read the letters in the image, so we know exactly where they are. We can look at the colors surrounding the letters and use that to erase the original letters. And then we can draw the translation on top using the original foreground color.
Crunching it down for mobile
Now, if we could do this visual translation in our data centers, it wouldn’t be too hard. But a lot of our users, especially those getting online for the very first time, have slow or intermittent network connections and smartphones starved for computing power. These low-end phones can be about 50 times slower than a good laptop—and a good laptop is already much slower than the data centers that typically run our image recognition systems. So how do we get visual translation on these phones, with no connection to the cloud, translating in real-time as the camera moves around?
We needed to develop a very small neural net, and put severe limits on how much we tried to teach it—in essence, put an upper bound on the density of information it handles. The challenge here was in creating the most effective training data. Since we’re generating our own training data, we put a lot of effort into including just the right data and nothing more. For instance, we want to be able to recognize a letter with a small amount of rotation, but not too much. If we overdo the rotation, the neural network will use too much of its information density on unimportant things. So we put effort into making tools that would give us a fast iteration time and good visualizations. Inside of a few minutes, we can change the algorithms for generating training data, generate it, retrain, and visualize. From there we can look at what kind of letters are failing and why. At one point, we were warping our training data too much, and ‘$’ started to be recognized as ‘S’. We were able to quickly identify that and adjust the warping parameters to fix the problem. It was like trying to paint a picture of letters that you’d see in real life with all their imperfections painted just perfectly.
To achieve real-time, we also heavily optimized and hand-tuned the math operations. That meant using the mobile processor’s SIMD instructions and tuning things like matrix multiplies to fit processing into all levels of cache memory.
In the end, we were able to get our networks to give us significantly better results while running about as fast as our old system—great for translating what you see around you on the fly. Sometimes new technology can seem very abstract, and its not always obvious what the applications for things like convolutional neural nets could be. We think breaking down language barriers is one great use.
Monday, September 19, 2016
Google Computer Science Capacity Awards
One of Googles 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.
Saturday, September 17, 2016
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