Posted by Alfred Spector, Vice President, Engineering
The Association for Computing Machinery (ACM) recently announced a new option for publication rights management, wherein researchers can choose to pay for the public to have perpetual open access to the publication. Google applauds this new option, and today we are announcing that we will pay the open access fees for all articles by Google researchers that are published in ACM journals. IEEE also has an open access option for some of its publications, and we also pay the open access fee for them and for publications in like organizations.
Google has always believed that by improving access to the world’s knowledge, we can help improve everyone’s lives. When it comes to scientific research, we have consistently said that open access to publications speeds up research, accelerates innovation, and helps grow the global economy.
Policies like ACM’s continue to demonstrate the sustainability of open access publishing. It will also provide better access to the papers that we write at Google. We encourage researchers everywhere to pursue open access options whenever publishing articles, and to continue to make publications available as widely as possible, within your rights.
A year ago, we released Course Builder, an experimental platform for online education at scale. Since then, individuals have created courses on everything from game theory to philanthropy, offered to curious people around the world. Universities and non-profit organizations have used the platform to experiment with MOOCs, while maintaining direct relationships with their participants. Google has published a number of courses including Introduction to Web Accessibility which opens for registration today. This platform is helping to deliver on our goal of making education more accessible through technology, and enabling educators to easily teach at scale on top of cloud platform services.
Today, Google will begin working with edX as a contributor to the open source platform, Open edX. We are taking our learnings from Course Builder and applying them to Open edX to further innovate on an open source MOOC platform. We look forward to contributing to edX’s new site, MOOC.org, a new service for online learning which will allow any academic institution, business and individual to create and host online courses.
Google and edX have a shared mission to broaden access to education, and by working together, we can advance towards our goals much faster. In addition, Google, with its breadth of applicable infrastructure and research capabilities, will continue to make contributions to the online education space, the findings of which will be shared directly to the online education community and the Open edX platform.
We support the development of a diverse education ecosystem, as learning expands in the online world. Part of that means that educational institutions should easily be able to bring their content online and manage their relationships with their students. Our industry is in the early stages of MOOCs, and lots of experimentation is still needed to find the best way to meet the educational needs of the world. An open ecosystem with multiple players encourages rapid experimentation and innovation, and we applaud the work going on in this space today.
We appreciate the community that has grown around the Course Builder open source project. We will continue to maintain Course Builder, but are focusing our development efforts on Open edX, and look forward to seeing edX’s MOOC.org platform develop. In the future, we will provide an upgrade path to Open edX and MOOC.org from Course Builder. We hope that our continued contributions to open source education projects will enable anyone who builds online education products to benefit from our technology, services and scale. For learners, we believe that a more open online education ecosystem will make it easier for anyone to pick up new skills and concepts at any time, anywhere.
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.
Posted by Sean Lip, Software Engineer, Open Online Education
Google has offered a number of open online courses in the past two years, and some of our recent research highlights the importance of having effective and relevant activities in these courses. Over the past decade, the Open Learning Initiative (OLI) at Carnegie Mellon, and now at Stanford, has successfully offered free open online courses that are centered around goal-directed activities that provide students with targeted feedback on their work. In order to improve understanding about how to design online courses based around effective activities, Google and OLI recently collaborated on a white paper that outlines the skill-based approach that OLI uses to create its courses.
OLI courses are focused around a set of learning objectives which identify what students should be able to do by the time they have completed a course module. These learning objectives are broken down into skills, and individual activities in the course are aimed towards developing students’ mastery with these skills. A typical activity from the Engineering Statics course is shown below:
During the course, students’ attempts at questions related to a particular skill are then fed as inputs into a probabilistic model which treats the degrees of mastery for each skill as mathematically independent variables. This model estimates how likely a student is to have mastered individual skills, and its output can help instructors determine which students are struggling and take appropriate interventions, as well as inform the design of future versions of the same course. The paper also outlines the advantages and limitations of the existing system, which could be useful starting points for further research.
We hope that this white paper provides useful insight for creators of online courses and course platforms, and that it stimulates further discussion about how to help people learn online more effectively.
Posted by Jeff Dean, Senior Google Fellow, and Rajat Monga, Technical Lead
Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.
While DistBelief was very successful, it had some limitations. It was narrowly targeted to neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure - making it nearly impossible to share research code externally.
Today we’re proud to announce the open source release of TensorFlow -- our second-generation machine learning system, specifically designed to correct these shortcomings. TensorFlow is general, flexible, portable, easy-to-use, and completely open source. We added all this while improving upon DistBelief’s speed, scalability, and production readiness -- in fact, on some benchmarks, TensorFlow is twice as fast as DistBelief (see the whitepaper for details of TensorFlow’s programming model and implementation). TensorFlow has extensive built-in support for deep learning, but is far more general than that -- any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some examples). Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface.
Inspecting a model with TensorBoard, the visualization tool
TensorFlow is great for research, but it’s ready for use in real products too. TensorFlow was built from the ground up to be fast, portable, and ready for production service. You can move your idea seamlessly from training on your desktop GPU to running on your mobile phone. And you can get started quickly with powerful machine learning tech by using our state-of-the-art example model architectures. For example, we plan to release our complete, top shelf ImageNet computer vision model on TensorFlow soon.
But the most important thing about TensorFlow is that it’s yours. We’ve open-sourced TensorFlow as a standalone library and associated tools, tutorials, and examples with the Apache 2.0 license so you’re free to use TensorFlow at your institution (no matter where you work).
Our deep learning researchers all use TensorFlow in their experiments. Our engineers use it to infuse Google Search with signals derived from deep neural networks, and to power the magic features of tomorrow. We’ll continue to use TensorFlow to serve machine learning in products, and our research team is committed to sharing TensorFlow implementations of our published ideas. We hope you’ll join us at www.tensorflow.org.
We have a new computer museum! The project to create the Techvana computer museum in Auckland is making good progress. The museum has occupied its premises at 105 Cook Street, Auckland and for the near future is open from 12 to 5 on weekends. Visitors are most welcome. The museum is built around the collection of Mark and Katie Barlow – much of their collection is on display. Many of the computers and game-machines are in working order and may be tried-out.
from The Universal Machine http://universal-machine.blogspot.com/
