Dithering in Processing


To be honest, we’ve heard of dithering but that’s the extent of our knowledge on the topic. After looking through [Windell's] post about using Dithering in Processing we can now say we’ve got a base of knowledge on the topic.

Dithering is used to produce an image out of two colors that our eyes can put together into something meaningful. The history of the algorithms goes back to monochrome displays. But now the hobby electronics we work with for fun have comparable computing power and perhaps it’s time to rediscover these techniques. [Windell's] project implements the Atkinson dithering algorithm in real-time on your webcam. He’s doing this in Processing, which should make it pretty easy to port for your own purposes.

So why might you want to use dithering in your own projects? Because if it can be used to make very cool milled artwork there must be other undiscovered uses lurking around your workshop.


Calculating Pi to 10 Trillion Digits; the last number is 5

In August, 2010, [Alexander Yee] and [Shigeru Kondo] won a respectable amount of praise for calculating pi to more digits than anyone else. They’re back again, this time doubling the number of digits to 10 Trillion.

The previous calculation of 5 Trillion digits of Pi took 90 days to calculate on a beast of a workstation. The calculations were performed on 2x Xeon processors running at 3.33 GHz, 96 Gigabytes of RAM, and 32 Terabytes worth of hard drives. The 10 Trillion digit attempt used the same hardware, but needed 48 Terabytes of disk to store everything.

Unfortunately, the time needed to calculate 10 Trillion digits didn’t scale linearly. [Alex] and [Shigeru] waited three hundred and seventy-one days for the computer to finish the calculations. The guys used y-cruncher, a multithreaded pi benchmarking tool written by [Alex]. y-cruncher calculates hexadecimal digits of pi; conveniently, it’s fairly easy to find the nth hex digit of pi for verification.

If  you’re wondering if it would be faster to calculate pi on a top 500 supercomputer, you’d be right. Those boxes are a little busy predicting climate change, nuclear weapons yields, and curing cancer, though. Doing something nobody else has ever done is still an admirable goal, especially if it means building an awesome computer.

Microsoft Points algorithm cracked… they’re out $1M

Looks like someone figured out the algorithm that Microsoft uses to generate unique codes for their alternative currency: Microsoft Points. We were always a bit baffled by the need to do this sort of thing (Disney dollars, tokens at arcades, etc.) but figured it’s just a grift to get you to spend more dough. Looks like this time it may have come back to bite them in the ass with early loss projections somewhere in the $1-$1.2 million range.

But as most of you know, it’s not just an algorithm that can cause this kind of havoc. Whomever figured out how to crunch the numbers apparently packaged the method into a nice GUI and distributed it over the Internet. Check out the video after the break to see that not only will it give you a code, but you can verify that it works at the click of a button. Microsoft is taking steps to invalidate all of the ill-gotten redemption codes, but we wonder how effective they can be at it. Surely they already had contingency plans for this and we wonder if the company didn’t also carry insurance against this kind of loss.

Try as we might, we couldn’t turn up a press release on the subject. If you know of any, please be kind and leave a link in the comments.

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The chief cook robot

We feel the need to apologize immediately for the use of Yakkity Sax in the preceding video and recommend you watch the longer, yak free, video below. It shows researchers at the Learning Algorithms and Systems Laboratory teaching a robot how to make a ham and cheese omelet. Each working area and food item is labeled with a machine recognizable tag. The researcher demonstrates the task by guiding the robot’s hand. The robot combines multiple demonstrations to generalize the skill. It can then adapt the learned skill to the specific task. You can see this in the video when the robot adjusts to the location of the bowl and cutting board when they’re moved around. Teaching through demonstration would make the use of robotics much easier for the general population.

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