Share Your Science: Accelerating Cognitive Workloads with Machine Learning

Research, Big Data & Data Mining, Machine Learning & Artificial Intelligence, Share Your Science, Simulation, SuperComputing

Nadeem Mohammad, posted Jan 18 2016

Ruchir Puri, an IBM Fellow at IBM’s Thomas J. Watson Research Center shares how they are building large-scale big data systems and delivering real-time solutions, such as using machine learning and GPUs to predict drug reactions. Learn more about the work IBM Thomas J. Watson Research Center is doing with GPUs at research.ibm.com/labs/watson Share your

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Weekly Social Roundup

News, CUDA, GeForce, Machine Learning & Artificial Intelligence, Tesla

Nadeem Mohammad, posted Jan 15 2016

We love seeing all of the social media posts from developers using NVIDIA GPUs – here are a few highlights from the week: a good improvement on training CNN on #DeepLearning install cuDNN, for me in GTX-970 give me 3 times better performance per layer — angelo337 (@angelo337) January 11, 2016 Titan X ready to

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Can Machine Learning Help Write a Hit Musical?

News, Big Data & Data Mining, Cloud, Higher Education / Academia, Machine Learning & Artificial Intelligence

Nadeem Mohammad, posted Jan 14 2016

Computers can drive, create recipes, even compose rap songs. London audiences will soon find out whether they can write a hit musical, too. The new show, “Beyond the Fence,” is the world’s first musical conceived and substantially crafted by computer. Opening next month in the West End, it’s an experiment to determine how technology affects

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Engaging the Voyage to Vulkan

Vulkan, GameWorks Expert Developer, GameWorks

Christoph Kubisch, posted Jan 14 2016

Vulkan is currently in its final stage of development and we want to share with you what makes it a great graphics API and what some of the differences to OpenGL are. NVIDIA believes strongly that Vulkan supplements OpenGL, and that both APIs have their own strengths.

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Pedestrian-Following Service Robots Made Possible with CUDA Acceleration

Research, Academic, Autonomous, C/C++, CUDA, GeForce, Robotics, Social Impact

Nadeem Mohammad, posted Jan 13 2016

A team of researchers from Seoul National University built a pedestrian-following service robot to drive smart shopping carts and other autonomous helpers. The performance of their initial CPU-only implementation was “not acceptable for a real-time system” so they now use a GPU-accelerated CUDA implementation for a 13x performance boost. Mobile robots that track a person

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