Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

“Recent Developments in Deep Learning”

November 29th, 2012 / in Uncategorized / by Shar Steed

Scientists have seen significant progress in developing software that can perform human activities like seeing, listening and thinking. A New York Times article recently highlighted advances in this type of cutting edge technology, called “deep learning.”

Deep learning uses artificial intelligence to create things like speech recognition technology, and machines that can drive cars and work in factories. It is available today in programs like Apple’s Siri virtual personal assistant, which uses voice recognition software and Google’s Street View, which uses machine vision to identify specific addresses.

But what is new in recent months is the growing speed and accuracy of deep-learning programs, often called artificial neural networks or just “neural nets” for their resemblance to the neural connections in the brain.


“There has been a number of stunning new results with deep-learning methods,” said Yann LeCun, a computer scientist at New York University who did pioneering research in handwriting recognition at Bell Laboratories. “The kind of jump we are seeing in the accuracy of these systems is very rare indeed.”

The accuracy of the deep learning systems are coming to the point of outperforming humans in certain recognition tests.

Last year, for example, a program created by scientists at the Swiss A. I. Lab at the University of Lugano won a pattern recognition contest by outperforming both competing software systems and a human expert in identifying images in a database of German traffic signs.


The winning program accurately identified 99.46 percent of the images in a set of 50,000; the top score in a group of 32 human participants was 99.22 percent, and the average for the humans was 98.84 percent.

Click here to read the full article.

“Recent Developments in Deep Learning”

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