Open source education software unveiled by Google

Online education startups such as the Khan Academy, along with new efforts by MIT, Stanford, and Harvard have helped spur interest in and add legitimacy to the notion of remote learning. Now Google is lending its brainpower to the rapidly growing area by releasing a tool called Course Builder, open source software designed to let anyone create online education courses.


The Course Builder project came by way of another program Google ran earlier this year called Power Searching With Google. The Massive Open Online Course (MOOC), which attracted approximately 155,000 students from 196 countries, allowed Google to marry some of the practices now common to online instruction with the company's robust array of collaboration and communication tools. A new Power Searching session begins in two weeks.

According to the introductory video (above), presented by Peter Norvig, director of Google Research, usage of the software won't require high-level programming skill, and should be accessible to anyone with the ability to build and maintain their own website.

"The Course Builder open source project is an experimental early step for us in the world of online education," Norvig said. "It is a snapshot of an approach we found useful and an indication of our future direction. We hope to continue development along these lines, but we wanted to make this limited code base available now, to see what early adopters will do with it, and to explore the future of learning technology."
In addition to offering a new platform for empowering educators, the effort is also a unique opportunity to connect with Google's research team. Over the course of the next two weeks, Google plans to directly interact with Course Builder users via Google Hangouts. The Course Builder support site is already live and the free software download has already received its first update. For those unsure about their level of skill in relation to use of the software, Google's Course Builder Checklist offers a reassuring primer on how to get started and exactly what to expect.

New Language For Image Processing is Halide

Halide is a new open source language designed specifically for image processing and computational photography. It not only makes it easy to implement photo algorithms, it also makes them run fast by semi-automatic parallelization.

Algorithms that work with images are ideal for parallel implementation because they usually work with small isolated blocks of data that means the task can be parallelized without worry about interactions. The only problem is that even converting something that is ripe for parallelization from serial code to something that runs on today's confusing architecture of CPU cores and GPUs is difficult.

Halide is a new functional programming language from MIT, (with help from Stanford and Adobe) that allows you to specify image processing algorithms, mostly block convolution methods, more easily and without having to worry about how the algorithm is implemented. A second section of the program then provides a general description of how the algorithm should be parallelized. It not only describes how the algorithm should be split up among computational elements but how to organize the data to keep the processing pipelines running at maximum efficiency by avoiding restarts.

The easiest way to understand the general idea is to see a simple example (taken from the paper):
Func halide_blur(Func in) f
 Func tmp, blurred;
  Var x, y, xi, yi;
  // The algorithm
  tmp(x, y) = (in(x-1, y) +
          in(x, y) + in(x+1, y))/3;
  blurred(x, y) = (tmp(x, y-1) +
          tmp(x, y) + tmp(x, y+1))/3;
  // The schedule
  blurred.tile(x, y, xi, yi, 256, 32)
        .vectorize(xi, 8).parallel(y);
  tmp.chunk(x).vectorize(x, 8);
 return blurred;
}

The first part of the program defines a simple 3x3 blur filter split into a blur horizontal followed by a blur vertical step. The last part of the program, the schedule specifies how the algorithm can be treated in a parallel implementation. The Schuyler is machine specific and has to be changed to get the best performance out of a particular processor pipeline.