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Nvidia AI research points to an evolution of the chip business 7wData

#artificialintelligence

What happens as more of the world's computer tasks get handed over to neural networks? That's an intriguing prospect, of course, for Nvidia, a company selling a whole heck of a lot of chips to train neural networks. The prospect cheers Bryan Catanzaro, who is the head of applied deep learning research at Nvidia. "We would love for model-based to be more of the workload," Catanzaro told ZDNetthis week during an interview at Nvidia's booth at the NeurIPS machine learning conference in Montreal. Catanzaro was the first person doing neural network work at Nvidia when he took a job there in 2011 after receiving his PhD from the University of California at Berkeley in electrical engineering and computer science.


Nvidia AI research points to an evolution of the chip business 7wData

#artificialintelligence

What happens as more of the world's computer tasks get handed over to neural networks? That's an intriguing prospect, of course, for Nvidia, a company selling a whole heck of a lot of chips to train neural networks. The prospect cheers Bryan Catanzaro, who is the head of applied deep learning research at Nvidia. "We would love for model-based to be more of the workload," Catanzaro told ZDNetthis week during an interview at Nvidia's booth at the NeurIPS machine learning conference in Montreal. Catanzaro was the first person doing neural network work at Nvidia when he took a job there in 2011 after receiving his PhD from the University of California at Berkeley in electrical engineering and computer science.


GauGAN Turns Doodles into Stunning, Realistic Landscapes NVIDIA Blog

#artificialintelligence

A novice painter might set brush to canvas aiming to create a stunning sunset landscape -- craggy, snow-covered peaks reflected in a glassy lake -- only to end up with something that looks more like a multi-colored inkblot. But a deep learning model developed by NVIDIA Research can do just the opposite: it turns rough doodles into photorealistic masterpieces with breathtaking ease. The tool leverages generative adversarial networks, or GANs, to convert segmentation maps into lifelike images. The interactive app using the model, in a lighthearted nod to the post-Impressionist painter, has been christened GauGAN. GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers.


GauGAN Turns Doodles into Stunning, Realistic Landscapes NVIDIA Blog

#artificialintelligence

A novice painter might set brush to canvas aiming to create a stunning sunset landscape -- craggy, snow-covered peaks reflected in a glassy lake -- only to end up with something that looks more like a multi-colored inkblot. But a deep learning model developed by NVIDIA Research can do just the opposite: it turns rough doodles into photorealistic masterpieces with breathtaking ease. The tool leverages generative adversarial networks, or GANs, to convert segmentation maps into lifelike images. The interactive app using the model, in a lighthearted nod to the post-Impressionist painter, has been christened GauGAN. GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers.


Nvidia AI research points to an evolution of the chip business

ZDNet

What happens as more of the world's computer tasks get handed over to neural networks? That's an intriguing prospect, of course, for Nvidia, a company selling a whole heck of a lot of chips to train neural networks. The prospect cheers Bryan Catanzaro, who is the head of applied deep learning research at Nvidia. "We would love for model-based to be more of the workload," Catanzaro told ZDNet this week during an interview at Nvidia's booth at the NeurIPS machine learning conference in Montreal. Catanzaro was the first person doing neural network work at Nvidia when he took a job there in 2011 after receiving his PhD from the University of California at Berkeley in electrical engineering and computer science.