When cell towers fail, what happens to customers? In a resilient network where customers may simply move from a failed tower to another one nearby, the answer is not always clear. Nor can it be captured by manual assessments that count the number of failed towers and estimate population density, thus ignoring how an outage may be dispersed over a wide area.
What’s needed is an in-depth analysis of network data, and it's why AT&T researchers created the AT&T Tower Outage and Network Analyzer, which looks at the complex interactions among customers and towers to intelligently assess the impact to all customers in the immediate area. Read more.
Imagine arriving home and signaling your digital availability by moving pebbles from one bowl to another: one bowl for close friends only (or for text messages only), another for wider availability. It's a refreshingly simple use of physical objects to maintain control in a plugged-in age. And it is the idea behind Lana Yarosh's Availabowls project, which works by attaching programmable RFID tags to pebbles. For more information about Availabowls, see this article.
For the second year in a row, the ACM SIGMETRICS conference conferred its Test of Time Award on a paper co-authored by Nick Duffield. This year the award—given to a previous conference paper whose impact is still felt 10 years later—went to the 2003 Fast Accurate Computation of Large-Scale IP Traffic Matrices from Link Load for its "novel, remarkably fast, and accurate method for practical and rapid inference of traffic matrices in IP networks from link load measurements."
Machine speech translation is hard to do; it requires recognizing the language, transcribing it, doing the translation—all while the person is talking. Processing requirements are enormous.
In this Popular Mechanics interview, Mazin Gilbert describes how AT&T Research is incorporating machine learning and cloud technology to enhance its speech technologies—representing over 30 years of research—and make smooth, seamless translation a reality.