Showing posts with label next. Show all posts
Showing posts with label next. Show all posts

Moore’s Law Part 3 Possible extrapolations over the next 15 years and impact



This is the third entry of a series focused on Moore’s Law and its implications moving forward, edited from a White paper on Moore’s Law, written by Google University Relations Manager Michel Benard. This series quotes major sources about Moore’s Law and explores how they believe Moore’s Law will likely continue over the course of the next several years. We will also explore if there are fields other than digital electronics that either have an emerging Moores Law situation, or promises for such a Law that would drive their future performance.

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More Moore
We examine data from the ITRS 2012 Overall Roadmap Technology Characteristics (ORTC 2012), and select notable interpolations; The chart below shows chip size trends up to the year 2026 along with the “Average Moore’s Law” line. Additionally, in the ORTC 2011 tables we find data on 3D chip layer increases (up to 128 layers), including costs. Finally, the ORTC 2011 index sheet estimates that the DRAM cost per bit at production will be ~0.002 microcents per bit by ~2025. From these sources we draw three More Moore (MM) extrapolations, that by the year 2025:

  • 4Tb Flash multi-level cell (MLC) memory will be in production
  • There will be ~100 billion transistors per microprocessing unit (MPU)
  • 1TB RAM Memory will cost less than $100


More than Moore
It should be emphasized that “More than Moore” (MtM) technologies do not constitute an alternative or even a competitor to the digital trend as described by Moore’s Law. In fact, it is the heterogeneous integration of digital and non-digital functionalities into compact systems that will be the key driver for a wide variety of application fields. Whereas MM may be viewed as the brain of an intelligent compact system, MtM refers to its capabilities to interact with the outside world and the users.

As such, functional diversification may be regarded as a complement of digital signal and data processing in a product. This includes the interaction with the outside world through sensors and actuators and the subsystem for powering the product, implying analog and mixed signal processing, the incorporation of passive and/or high-voltage components, micro-mechanical devices enabling biological functionalities, and more. While MtM looks very promising for a variety of diversification topics, the ITRS study does not give figures from which “solid” extrapolations can be made. However, we can make safe/not so safe bets going towards 2025, and examine what these extrapolations mean in terms of the user.

Today we have a 1TB hard disk drives (HDD) for $100, but the access speed to data on the disk does not allow to take full advantage of this data in a fully interactive, or even practical, way. More importantly, the size and construction of HDD does not allow for their incorporation into mobile devices, Solid state drives (SSD), in comparison, have similar data transfer rates (~1Gb/s), latencies typically 100 times less than HDD, and have a significantly smaller form factor with no moving parts. The promise of offering several TB of flash memory, cost effectively by 2025, in a device carried along during the day (e.g. smartphone, watch, clothing, etc.) represents a paradigm shift with regard of today’s situation; it will empower the user by moving him/her from an environment where local data needs to be refreshed frequently (as with augmented reality applications) to a new environment where full contextual data will be available locally and refreshed only when critically needed.

If data is pre-loaded in the order of magnitude of TBs, one will be able to get a complete contextual data set loaded before an action or a movement, and the device will dispatch its local intelligence to the user during the progress of the action, regardless of network availability or performance. This opens up the possibility of combining local 3D models and remote inputs, allowing applications like 3D conferencing to become available. The development and use of 3D avatars could even facilitate many social interaction models. To benefit from such applications the use of personal devices such as Google Glass may become pervasive, allowing users to navigate 3D scenes and environments naturally, as well as facilitating 3D conferencing and their “social” interactions.

The opportunities for more discourse on the impact and future of Moore’s Law on CS and other disciplines are abundant, and can be continued with your comments on the Research at Google Google+ page. Please join, and share your thoughts.
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The Next Chapter for Flu Trends



When a small team of software engineers first started working on Flu Trends in 2008, we wanted to explore how real-world phenomena could be modeled using patterns in search queries. Since its launch, Google Flu Trends has provided useful insights and served as one of the early examples for “nowcasting” based on search trends, which is increasingly used in health, economics, and other fields. Over time, we’ve used search signals to create prediction models, updating and improving those models over time as we compared our prediction to real-world cases of flu.

Instead of maintaining our own website going forward, we’re now going to empower institutions who specialize in infectious disease research to use the data to build their own models. Starting this season, we’ll provide Flu and Dengue signal data directly to partners including Columbia University’s Mailman School of Public Health (to update their dashboard), Boston Children’s Hospital/Harvard, and Centers for Disease Control and Prevention (CDC) Influenza Division. We will also continue to make historical Flu and Dengue estimate data available for anyone to see and analyze.

Flu continues to affect millions of people every year, and while it’s still early days for nowcasting and similar tools for understanding the spread of diseases like flu and dengue fever—we’re excited to see what comes next. To download the historical data or learn more about becoming a research partner, please visit the Flu Trends web page.
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Google’s Course Builder 1 9 improves instructor experience and takes Skill Maps to the next level



(Cross-posted on the Google for Education Blog)

When we last updated Course Builder in April, we said that its skill mapping capabilities were just the beginning. Today’s 1.9 release greatly expands the applicability of these skill maps for you and your students. We’ve also significantly revamped the instructor’s user interface, making it easier for you to get the job done while staying out of your way while you create your online courses.

First, a quick update on project hosting. Course Builder has joined many other Google open source projects on GitHub (download it here). Later this year, we’ll consolidate all of the Course Builder documentation, but for now, get started at Google Open Online Education.

Now, about those features:
  • Measuring competence with skill maps
    In addition to defining skills and prerequisites for each lesson, you can now apply skills to each question in your courses’ assessments. By completing the assessments and activities, learners will be able to measure their level of competence for each skill. For instance, here’s what a student taking Power Searching with Google might see:
This information can help guide them on which sections of the course to revisit. Or, if a pre-test is given, students can focus on the lessons addressing their skill gaps.

To determine how successful the content is at teaching the desired skills across all students, an instructor can review students’ competencies on a new page in the analytics section of the dashboard.

  • Improving usability when creating a course Course Builder has a rich set of capabilities, giving you control over every aspect of your course -- but that doesn’t mean it has to be hard to use. Our goal is to help you spend less time setting up your course and more time educating your students. We’ve completely reorganized the dashboard, reducing the number of tabs and making the settings you need clearer and easier to find.
We also added in-place previewing, so you can quickly edit your content and immediately see how it will look without needing to reload any pages.
For a full list of the other features added in this release (including the ability for students to delete their data upon unenrollment and removal of the old Files API), see the release notes. As always, please let us know how you use these new features and what you’d like to see in Course Builder next to help make your online course even better.

In the meantime, take a look at a couple recent online courses that we’re pretty excited about: Sesame Street’s Make Believe with Math and our very own Computational Thinking for Educators.
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how to Put Previous and next link in blogger

Many bloggers try to edit each and everything on the blog to customize the blog appearance. Weather it be images, widgets or anything. Sometime they fall into confusion that how to change the Newer and older post link at the bottom of the post. Here is a simple trick to change this or replace it with whatever you want in seconds. You can see a sample at the bottom of this post. You can even place an image in place of that or image and text both. Below steps will guide you to achieve this.
  • Login to your Blogger Dashbord.
  • Click on Layout.
  • Click on Edit html
  • Search for <data:newerPageTitle/> -This is Newer post link.
  • Replace it with Previous or to place an image, replace it with <img src="Your-Image-Link" border="0" alt="Previous" /> or you can put both.
  • Now, to change the older posts link, Search for <data:olderPageTitle/> and repeat the above step with appropriate changes.
  • Click on Save Template.
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