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"I Am AI" docuseries spotlights innovators' groundbreaking achievements NVIDIA Blog

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Medical devices that monitor and respond to changes in our health. Robotic assistants that know what we want before we do. Kitchens that help us with our shopping and plan our meals. Every day, we hear about how artificial intelligence is going to change the world. Amid all this focus on the future, it's easy to ignore an unavoidable truth: AI is already changing the world in significant ways.


Updated AWS Deep Learning AMIs: New Versions of TensorFlow, Apache MXNet, Keras, and PyTorch

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The AMIs also come with improved framework support for NVIDIA Volta. They include PyTorch v0.3.0, and support NVIDIA CUDA 9 and cuDNN 7, with significant performance improvements for training models on NVIDIA Volta GPUs. As well, they include a version of TensorFlow built from the master and merged with NVIDIA processors for Volta support. We've also added Keras 2.0 support on the CUDA 9 version of the AWS Deep Learning AMIs to work with TensorFlow as the default backend.


Medical Imaging Drives GPU Accelerated Deep Learning Developments

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Although most recognize GE as a leading name in energy, the company has steadily built a healthcare empire over the course of decades, beginning in the 1950s in particular with its leadership in medical X-ray machines and later CT systems in the 1970s and today, with devices that touch a broad range of uses. Much of GE Healthcare's current medical device business is rooted in imaging hardware and software systems, including CT imaging machines and other diagnostic equipment. The company has also invested significantly in the drug discovery and production arena in recent years--something the new CEO of GE, John Flannery (who previously led the healthcare division at GE), identified as one of three main focal points for GE's financial future. According to Flannery, the company's healthcare unit has one million scanners in service globally, which generate 50,000 scans every few moments. As one might imagine, this kind of volume will increasingly require more processing and analysis capabilities cooked in--something the company is seeking to get ahead with in today's partnership with Nvidia.


IBM Designs a "Performance Beast" for AI

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Companies running AI applications often need as much computing muscle as researchers who use supercomputers do. IBM's latest system is aimed at both audiences. The company last week introduced its first server powered by the new Power9 processor designed for AI and high-performance computing. The powerful technologies inside have already attracted the likes of Google and the US Department of Energy as customers. The new IBM Power System AC922 is equipped with two Power9 CPUs and from two to six NVIDIA Tesla V100 GPUs.


NVIDIA Researchers Showcase Major Advances in Deep Learning at NIPS NVIDIA Blog

@machinelearnbot

AI has become part of the public consciousness. Researchers and data scientists have been sharing their groundbreaking work -- at what is officially known as the Conference and Workshop on Neural Information Processing Systems -- for three decades. But it's only with the recent explosion of interest in deep learning that NIPS has really taken off. We had two papers accepted to the conference this year, and contributed to two others. The researchers involved are among the 120 people on the NVIDIA Research team focused on pushing the boundaries of technology in machine learning, computer vision, self-driving cars, robotics, graphics, computer architecture, programming system, and other areas.


TITAN V: Now NVIDIA is talking deep-learning horsepower

@machinelearnbot

This is a graphics card created for the PC. VentureBeat's Blair Frank said "The new Titan V card will provide customers with a Nvidia Volta chip that they can plug into a desktop computer." Thursday marked its debut, positioned as "the world's most powerful GPU for the PC." CEO Jensen Huang did the introduction. The announcement took place at the annual AI gathering, the NIPS (Neural Information Processing Systems) conference. It can carry massive amounts of power and speed AI computation.


Titan V and Nvidia's bleeding-edge Volta GPU: 5 things PC gamers need to know

PCWorld

Seven long months after the next-generation "Volta" graphics architecture debuted in the Tesla V100 for data centers, the Nvidia Titan V finally brings the bleeding-edge tech to PCs in traditional graphics card form. But make no mistake: This golden-clad monster targets data scientists, with a tensor core-laden hardware configuration designed to optimize deep learning tasks. You won't want to buy this $3,000 GPU to play Destiny 2. But that doesn't mean we humble PC gamers can't glean information from Volta's current AI-centric incarnations. Here are five key things you need to know about the Titan V and Nvidia's Volta GPU. Editor's note: This article was originally published on May 11, 2017 but was updated on December 8 to include information from the Titan V.


OpenAI uses cunning code to speed up GPU machine learning

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Researchers at OpenAI have launched a library of tools that can help researchers build faster, more efficient neural networks that take up less memory on GPUs. Neural networks are made up of layers of connected nodes. The architecture for these networks are highly variable depending on the data and application, but all models are limited by the way they run on GPUs. One way to train larger models for less computation is to introduce sparse matrices. A matrix is considered sparse if it is filled with mostly zeroes.


How To Keep Your Job Regardless Of AI

International Business Times

Nvidia deep learning consultant Michelle Gill never imagined herself working in California's robot-crazed tech industry. When she left Nebraska and got a PhD in biochemistry and biophysics at Yale University, she saw herself as more of a scientist who studied life than a technologist prepared to build new creations. It wasn't until she started working at the National Cancer Institute that she first became interested in machine learning. Analyzing medical images with data science opened the door to a whole new world. "A lot of the concepts I had learned in science applied in some way to machine learning," Gill told Newsweek at the Artificial Intelligence & Data Science conference in New York City.


Where AI Is Headed: 13 Artificial Intelligence Predictions for 2018 NVIDIA Blog

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Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.