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In its early years, information theory was dominated by research on error-correcting codes: How do you encode information so as to guarantee its faithful transmission, even in the presence of the corrupting influences engineers call "noise"? Recently, one of the most intriguing developments in information theory has been a different kind of coding, called network coding, in which the question is how to encode information in order to maximize the capacity of a network as a whole. For information theorists, it was natural to ask how these two types of coding might be combined: If you want to both minimize error and maximize capacity, which kind of coding do you apply where, and when do you do the decoding?

Scientists have taken Alan Turing's work to its next logical step, translating her 1993 discovery into an adaptable computational system that learns and evolves way much more like our brains do.

Facebook's IPO filing with the SEC warns potential investors of the chance that the value of their investment may go down as well as up. This post looks at growth and why you might think twice...

Hosted Virtual Desktops are a bit like driving in the snow. Every link in the chain between the data on a hard drive in the datacenter and the pixels on the user's screen introduces a delay that...

The tech firm putting the smart into smartphone

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Programming can be fun, so can cryptography; however they should not be combined. - Kreitzberg and Shneiderman

Computers must be male. As soon as you commit to one you realize that if you had waited a little longer, you could have obtained a better model. In order to get their attention, you have to turn them on. Big power surges knock them out for the rest of the day. - Author Unknown