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Posted by Rob Knies
Research success can be characterized in any number of ways. It might be by the cleverness of an algorithm. It might be by paper citations, or product contributions, or helping to develop disruptive technologies.Sometimes, though, having a successful research career can be as simple as pursuing a path of lifelong learning. Just ask Andrew Fitzgibbon.On June 27, Fitzgibbon, a principal researcher at Microsoft Research Cambridge, was announced as one of four winners of the Royal Academy of Engineering’s Silver Medal for 2013. The award recognizes outstanding and demonstrated personal contributions to British engineering, resulting in successful market exploitation by an engineer with less than 22 years of full-time employment.
We live in a society obsessed with speed. Whether it’s download times on a mobile phone or Usain Bolt’s time in the 100 meters, the faster the better. We also live during an era when accuracy has become not just preferable but essential. The technological marvels of the 21st century demand it.Speed=good. Accuracy=good. Put them together, and you’ve got a leap forward, such as recent advancements in Bing Voice Search for Windows Phone that enable customers to get faster, more accurate results than ever before.Those improvements come, in part, from contributions delivered via Microsoft Research’s work on deep neural networks (DNNs). Such networks are a computational framework for automatic pattern recognition that is inspired by the basic circuits of the human brain. Refinements in mathematical formulas, coupled with greater computational power and large data sets, enable DNNs to learn and edge noticeably closer than traditional speech technologies to humans’ ability to recognize speech and images.
Computing today is generating and capturing a wealth of data previously unimaginable. Such information has great promise for unlocking some of society’s most elusive secrets, but how can those secrets be unearthed and identified?That pursuit provided the impetus behind Big Data Analytics 2013, a first-ever workshop held at Microsoft Research Cambridge on May 23-24. More than 130 participants from academia and industry—including a strong contingent from the hosting lab, Microsoft Research Redmond, Microsoft Research Silicon Valley, and Advanced Technology Labs Europe—gathered to discuss and identify the most important and challenging directions for the evolution of algorithms and systems for big data.“The organization of the workshop was prompted by a surge of interest and activity in the area of big-data analytics,” says Milan Vojnovic, co-organizer of the event and senior researcher in the Cambridge Systems and Networking group, “including platforms for various kinds of processing, such as batch processing and querying of massive data sets, real-time analytics, streaming computations, and analytics on special data structures such as graphical data.
What constitutes an annoying ad on the web? Is it the use of garish colors, as in a Halloween theme gone amok? Is it a page seemingly designed to cram in as many blinking, spinning, animated GIFs as possible? Is an ad annoying when clicking it generates a pop-up in response?Certainly the use of any of these tactics can act to irritate web users on occasion. Just as certainly, anybody with even a modest acquaintance with the web probably can cite at least one such ad-induced headache. The typical response might turn an old cliché on its head: “I don’t know much about annoying ads, but I know them when I see them.”That, though, isn’t sufficient for Dan Goldstein and Siddharth Suri of Microsoft Research New York City. They want to know exactly what people mean when complaining about ad annoyances—and what cost web publishers incur when displaying such ads.
Sometimes, it seems like we’re awash in video choices: broadcast, cable, satellite, Internet, PC, tablet, smartphone. It can seem overwhelming.Sometimes—and stop me if you’ve heard this one before—it seems like, with all these choices, none of them is offering anything particularly compelling.