"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
The latest proprietary Power servers from IBM, armed by the long-awaited IBM Power9 processors, look for relevance among next-generation enterprise workloads, but the company will need some help from its friends to take on its biggest market challenger. IBM emphasizes increased speed and bandwidth with its AC922 Power Systems to better take on high-performance computing tasks, such as building models for AI and machine learning training. The company said it plans to pursue mainstream commercial applications, such as building supply chains and medical diagnostics, but those broader-based opportunities may take longer to materialize. "Most big enterprises are doing research and development on machine learning, with some even deploying such projects in niche areas," said Patrick Moorhead, president and principal analyst at Moor Insights & Strategy. "But it will be 12 to 18 months before enterprises can even start driving serious volume in that space."
Whether they drive themselves or improve the safety of their driver, tomorrow's vehicles will be defined by software. However, it won't be written by developers but by processing data. To prepare for that future, the transportation industry is integrating AI car computers into cars, trucks and shuttles and training them using deep learning in the data center. A benefit of such a software-defined system is that it's capable of handling a wide range of automated driving -- from Level 2 to Level 5. Speaking in Tokyo at the last stop on NVIDIA's seven-city GPU Technology Conference world tour, NVIDIA founder and CEO Jensen Huang demonstrated how the NVIDIA DRIVE platform provides this scalable architecture for autonomous driving. "The future is surely a software defined car," said Huang.
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.
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.
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.
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.
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.
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.
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.
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.