Microsoft’s Voice Recognition Technology Almost as Accurate as Humans

News, Research, cuDNN, Machine Learning & Artificial Intelligence, Media & Entertainment, Signal / Audio Processing, Tesla

Nadeem Mohammad, posted Sep 15 2016

Microsoft reached a new milestone in the development of more accurate speech recognition. Using a cluster of Tesla M40 GPUs and the cuDNN version of Computational Network Toolkit (CNTK), their latest version of the technology achieved the lowest word error rate (WER) in the industry. “Our best single system achieves an error rate of 6.9%

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Artificial Intelligence Software Easily Generates Digital Art

Research, CUDA, cuDNN, GeForce, Higher Education / Academia, Image Recognition, Machine Learning & Artificial Intelligence, Media & Entertainment

Nadeem Mohammad, posted Sep 15 2016

Researchers from Adobe and University of California, Berkeley developed software that automatically generates images inspired by the color and shape of the digital brushstroke. The software uses deep neural networks to learn the features of landscapes and architecture, like the appearance of grass or blue skies. Drawing a dark-colored, upside-down V triggers the AI to

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Rover Trained on GPUs Wins $750k at NASA’s Autonomous Robotics Challenge

News, Research, CUDA, cuDNN, Higher Education / Academia, Image Recognition, Machine Learning & Artificial Intelligence, Robotics, Tesla

Nadeem Mohammad, posted Sep 14 2016

The team from West Virginia University took home the largest prize awarded in the five-year long NASA Sample Return Robot Challenge. This challenge began in 2012 with more than 50 teams and to qualify for the final level, the team’s autonomous robot had to return a single sample in 30 minutes. Using CUDA, and a

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Using Virtual Reality at the IBM Watson Image Recognition Hackathon

News, Research, Gaming, GeForce, Image Recognition, Machine Learning & Artificial Intelligence, Media & Entertainment, Signal / Audio Processing, Virtual Reality

Nadeem Mohammad, posted Sep 13 2016

Five teams of developers gathered at the Silicon Valley Virtual Reality (SVVR) headquarters in California last month to learn about the new features of IBM Watson’s Visual Recognition service, like the ability to train and retrain custom classes on top of the stock API, that allow the service to have new and interesting use cases

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