"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
NEW DELHI: Bennett University"s computer science and engineering (CSE) department held its first international conference on machine learning and data science at its Greater Noida campus that saw researchers and academicians deliberating on the new wave of technologies and their impact on the world of big data, machine learning and artificial intelligence (AI). The two-day conference was organised by the department in association with the Institute of Electrical and Electronics Engineers (IEEE) Computer Society. Bennett University has been set up by the Times Group, which publishes ET. "We are pleased to be in India to establish a relationship with all the major universities including Bennett University, so that we can develop and nurture the relationship such that India becomes a very important player in the computer society," said Roger Fujii, 2016 president of the society. The event saw attendance from 50 organisations including IBM, Nvidia, Tata Consultancy Services, Wipro, Accenture, Dell and Infosys, among others. Also attending were about 400 participants representing premier institutions such as the Indian Institutes of Technology, National Institutes of Technology, Indian Institutes of Management, Jawaharlal Nehru University, Delhi Technological University, University of Delhi, Kalinga Institute of Industrial Technology and Malaviya National Institute of Technology, Jaipur.
GPU-accelerated analytics applications are now available in the NVIDIA DGX container registry and NVIDIA GPU Cloud (NGC). These applications provide customers the ability to abstract insights in milliseconds, build models with transparency and accuracy, and eliminate any integration complexity. They are tested and deployed on DGX systems and supported NGC platforms and are available for customers immediately. MapD is a GPU-accelerated platform with an open-source SQL engine called MapD Core and an integrated visualization system called MapD Immerse. Customers considering purchasing a license can access the software for a free trial on Amazon Web Services or as part of the NVIDIA registry on DGX and NGC.
It seems like Nvidia announces the fastest GPU in history multiple times a year, and that's exactly what's happened again today; the Titan V is "the most powerful PC GPU ever created," in Nvidia's words. It represents a more significant leap than most products that have made that claim, however, as it's the first consumer-grade GPU based around Nvidia's new Volta architecture. That said, a liberal definition of the word "consumer" is in order here -- the Titan V sells for $2,999 and is focused around AI and scientific simulation processing. Nvidia claims up to 110 teraflops of performance from its 21.1 billion transistors, with 12GB of HBM2 memory, 5120 CUDA cores, and 640 "tensor cores" that are said to offer up to 9 times the deep-learning performance of its predecessor. Also it comes in gold and black, which looks pretty cool.
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.
The obvious choice here isn't actually the "Price is no object" pick in our graphics cards buying guide. If you demand the pinnacle of PC gaming performance no matter the cost, you'll want to pick up Nvidia's Titan Xp ($1,200 on Nvidia's website). This second revision of the "Pascal" GPU generation's Titan uses Nvidia's full-blown GP102 graphics processor to power the most graphically demanding games of today without breaking a sweat, even at 4K resolution. The even more potent Titan V ($3,000 on Nvidia's website) pushes further with a next-gen "Volta" GPU and HBM2 memory, but it's specialized for machine learning tasks and data science. Realistically, most gamers should pick up the still-ridonkulously powerful GeForce GTX 1080 Ti ($800 for an EVGA GTX 1080 Ti FTW3 on Amazon).