Development by Davis: “We the People: Seceding from open government?” plus 5 more

We the People: Seceding from open government?
MOOCs trend towards open enrollment, not licensing
The Future of Knowledge Work
Mining Big Data on Big Clusters (Intel Labs@SC12)
Introducing Android 4.2, A New and Improved Jelly Bean
AMD and the National Academy of Engineering Recognize Engineering Programs with Real-World Relevance

We the People: Seceding from open government?

Posted: 14 Nov 2012 02:00 AM PST

The petitions for Texas and other states to secede from the U.S. on the White House's "We the People" site have drawn a great deal of attention in the past couple of days. I find it highly ironic that people are protesting following the re-election of the Administration by using the very platform set up by said Administration for grassroots advocacy. That being said, it's also a testament to the idea that open government works—even for those who believe it doesn't.

Let's recap.

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MOOCs trend towards open enrollment, not licensing

Posted: 14 Nov 2012 01:00 AM PST

MOOCs—or Massive Open Online Courses—have been getting a lot of attention lately. Just in the last year or so there's been immense interest in the potential for large scale online learning, with significant investments being made in companies (Coursera, Udacity, Udemy), similar non-profit initiatives (edX), and learning management systems (Canvas, Blackboard). The renewed interest in MOOCs was ignited after last year's Introduction to Artificial Intelligence course offered via Stanford University, when over 160,000 people signed up to take the free online course.

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The Future of Knowledge Work

Posted: 13 Nov 2012 05:07 PM PST

Intel Labs White Paper: The Future of Knowledge Work

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Mining Big Data on Big Clusters (Intel Labs@SC12)

Posted: 13 Nov 2012 12:17 PM PST

Many of the most promising applications of Big Data, the vast and growing data repositories accumulating across the world, work by scouring millions or billions of interrelated things to discover interesting new relationships. The result could be a new scientific theory, a business insight that reveals a new market opportunity, or connections to people who share a common interest with you across the planet.

One can envision these relationships as web of interconnected points. Computers see these webs in the form of a data structure called a 'graph.' Real world examples include the network of roads connecting cities in a country, the neural connections in your brain, or the Internet itself. A graph can be created from almost any collection of data, with the connections specifying relationships. On social networks, you see graphs in the form of your networks of friends, families, and colleagues. Mining relationships within big graphs is the subject of an Intel Labs paper presented today at SC12.

The more data you add to a graph, the more potential you have to discover insights that are personally, culturally, or financially valuable. The challenge is that today the datasets are growing faster than compute systems can handle.

Imagine you are mapping out a highway system of an unfamiliar country starting from scratch. Until you get to the next town, you have no idea where the next road will take you. Likewise as a computer searches a graph, going from node to node in the web, it doesn't know where in memory it will need to look for the next set of nodes. The best way to do this search efficiently is to keep all of the data together in main memory so anything can be readily retrieved.

However, datasets have grown well beyond what can be stored in one computer's memory. They may require tens, hundreds, or even thousands of computers for applications like cosmology, where the relationships between billions of galaxies are being explored to better understand the evolution and fate of the universe. As such, the next challenge for graph computing is to run across large clusters of computers. Since they span the memory of many systems, searching them means constantly looking for data that resides on another machine. Today, these calculations are severely limited by the amount of information that can flow between the many processors in a cluster at one time.

To help address this, Intel Labs has developed new methods to accelerate the computations of large graphs by reducing the amount of data communication required across the cluster's networks. As described in the SC12 paper, this is accomplished though a collection of techniques to efficiently compress data, eliminate unnecessary transfers, and pipeline computations to make the most of time spent waiting for data to return.

The results were demonstrated using the Graph500 benchmark, an emerging benchmark used to rate high performance computing systems on their ability to compute graphs. Through these improvements, Intel Labs showed that these graph searches can be done more than six times faster, and in a way that is more than eight times more energy-efficient. For both Big Data and HPC this efficiency is critical, as energy and cooling have become major concerns for future datacenters and supercomputers alike.

These results represent one of the most efficient implementations of Graph500 benchmark, based on the most recent list. More efficient computation will make these graph insights more accessible to a wider array of people, business, and scientific institutions.

Click here for more information on all six of Intel's papers at SC12.

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Introducing Android 4.2, A New and Improved Jelly Bean

Posted: 13 Nov 2012 10:41 AM PST

Posted by Angana Ghosh, Product Manager in Android, and Dirk Dougherty, Android Developer Relations Team

Today we are making Android 4.2 (Jelly Bean) SDK platform available for download. Below are some of the highlights of Android 4.2, API level 17.

Performance

We've worked with our partners to run Renderscript computation directly in the GPU on the Nexus 10, a first for any mobile computation platform.

New ways to engage users

Users can now place interactive lock screen widgets directly on their device lock screens, for instant access to favorite apps and content. With just a small update, you can adapt any app widget to run on the lock screen. Daydream is an interactive screensaver mode that users can encounter when their devices are charging or docked in a desk dock. You can create interactive daydreams that users display in this mode, and they can include any type of content.

New interaction and entertainment experiences

Android 4.2 introduces platform support for external displays that goes beyond mirroring. Your apps can now target unique content to any number of displays attached to an Android device.

Enhancements for international users

To help you create better apps for users in languages such as Arabic, Hebrew, and Persian, Android 4.2 includes native RTL support, including layout mirroring. With native RTL support, you can deliver the same great app experience to all of your users with minimal extra work. Android 4.2 also includes a variety of font and character optimizations for Korean, Japanese, Indic, Thai, Arabic and Hebrew writing systems.

To get started developing and testing, download the Android 4.2 Platform from the Android SDK Manager. For a complete overview of what's new, take a look at the Android 4.2 platform highlights or read more of the details in the API overview.

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