NVIDIA GDC Vulkan Driver available now!

Vulkan, VRWorks, GameWorks Expert Developer, Maxwell, pascal, GDC17

Mathias Schott, posted Feb 27 2017

We are happy to announce the immediate availability of the NVIDIA GDC Vulkan developer driver which supports not only that extensions that Khronos just

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Create Realistic Synthetic Faces That Look Older With Deep Learning

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

Nadeem Mohammad, posted Feb 24 2017

Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger. A number of techniques already exist, but they are expensive and time consuming.

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Self-Taught AI Bot Beat Professional Players at Super Smash Bros

Features, News, CUDA, GeForce, Higher Education / Academia, Image Recognition, Machine Learning & Artificial Intelligence, Media & Entertainment, Tesla

Nadeem Mohammad, posted Feb 24 2017

Students from MIT and New York University developed an AI bot that ended up teaching itself in two weeks to beat professional gamers during the Genesis 4 Super Smash Bros tournament last month.

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Developer Spotlight: Computational Fluid Dynamics for Surgical Planning

Research, Computational Fluid Dynamics (CFD), Healthcare & Life Sciences, Higher Education / Academia, Machine Learning & Artificial Intelligence, Medical Imaging, OpenACC, Share Your Science, Tesla

Nadeem Mohammad, posted Feb 23 2017

Todd Raeker, Research Technology Consultant at the University of Michigan shares how a group of 50 researchers at University of Michigan are using GPUs and OpenACC to accelerate the codes for their data-driven physics simulations.

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Automatically Detect Nuclear Power Plant Cracks With Deep Learning

Research, Architecture/Engineering/Construction, CUDA, cuDNN, Energy/Oil & Gas, GeForce, Higher Education / Academia, Image Recognition, Machine Learning & Artificial Intelligence, Manufacturing

Nadeem Mohammad, posted Feb 22 2017

Researchers from Purdue University developed a deep learning-based system to automatically detect cracks in the steel components of nuclear power plants and has been shown to be more accurate than other automated systems.

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