Making Arii ial I tellige e S arter ith Deep Learning GPU-accelerated DL is cha gi g ho sot are is developed and how it runs Machines powered by Artiicial Intelligence (AI) are no longer science iction. Computers can learn, reason and interact with people. I believe, in the history of the computer industry, no technology or concept has held more potential, or been more fun. The era of AI has begun. While the concept of AI has been around for decades, it is the combination of research breakthroughs, the wider availability of big data and advances in graphics processing unit (GPU) technology that has ignited the AI explosion taking place today. This AI computing era is driven by a new computing model, GPU-accelerated Deep Learning. This new model — where artiicial neural networks modelled on the human brain are trained to recognize patterns from massive amounts of data — has proven to be ‘unreasonably efective’ at solving some of the most complex problems in computer science. In this era, software writes itself and machines learn. Very quickly, hundreds of billions of devices will be infused with intelligence. Thanks to this approach, AI won’t be an industry – it will be part of every industry, not to mention enabling exciting new experiences for consumers via their apps and devices.
AI for India
As India moves towards creating sustainable, tech-enabled Smart Cities, there will be a strong focus on security, intelligence and investigative capabilities. We believe India has a signiicant advantage over other countries in terms of talent, a vibrant start-up ecosystem, strong IT services and an ofshoring industry to harness the power of DL. The country has reached an inlexion point where large industries, start-ups and researchers understand the power of parallel programming to transform their work. These advances are happening primarily because of the current government’s push for digital initiatives and its subsequent investments in projects such as Digital India and National Supercomputing Mission. Next, we are very much on track to achieve one of the world’s grandest goals for supercomputing - exascale computing. The next generation of supercomputers will tackle the most complex computational science challenges, such as predicting physical and biological behaviour. We won’t just have intelligent computers, but intelligent supercomputers helping solve our biggest problems. And these computing resources will be more accessible than ever before, thanks to cloud-based usage models. For example, start-up incubator T-Hub will house the irst AI supercomputer-in-a-box, NVIDIA’s DG-X1, for start-ups in India. Such democratization of supercomputing will enable entrepreneurs to harness accelerated computing and AI to create a new class of intelligent applications that can learn, see and perceive the world as humans do.
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NVIDIA’s GPU-Accelerated Deep Learning
NVIDIA has been attracting a lot of attention in AI space recently. Baidu, Google, Facebook, Microsoft were the irst adopters of NVIDIA GPUs for DL. Start-ups and established companies are now racing to use AI to create new products and services, or improve their operations. Several years ago, NVIDIA anticipated potential of DL and invested heavily in ensuring that our compute platform includes features speciically designed for this application. At that time, a lot of people thought we were crazy! But we see this decision to pivot towards computing’s future, rather than focusing only on its present, as crucial. By collaborating closely with AI developers, we are continuing to improve our GPU designs to help data scientists get the most from their DL applications. We’ve been successful in speeding up training deep neural networks by 50x in just three years. Faster training and iteration ultimately means faster innovation and faster time to a solution or market. In December 2016, we also brought our lagship GPU Technology Conference to India for the irst time, bringing together the growing ecosystem around DL in India, from self-driving cars to robotics and AI.
AI for Everyone
GPU-accelerated DL is being applied to solve challenges in every industry around the world; it has now grown into a mainstream movement. Industries such as healthcare, life sciences, government, energy, inancial services, automotive, manufacturing, and entertainment are realizing the power of GPUs to accelerate computational workloads. While CPU-based computing faces the death of Moore’s Law, the capabilities of the GPU continue to advance faster than ever. The progress is exponential. Adoption is exponential. And we believe the impact on industry and society will also be exponential. Self-driving cars will transform the $10 trillion transportation industry. In healthcare, doctors will use AI to detect disease at the earliest possible moment, to understand the human genome and tackle cancer, or to learn from the massive volume of medical data to recommend the best treatments. And AI will usher in the 4th industrial revolution — intelligent robotics will drive a new wave of productivity improvements and enable mass consumer customization. Startups and established companies are now racing to use AI to create new products and services, or improve their operations. NVIDIA plays in a very diverse range of markets, from consumer gaming and professional graphics to the enterprise datacenter and supercomputing.
The country has reached an inflexion point where large industries, start-ups and researchers understand the power of parallel programming to transform their work. These advances are happening primarily because of the current government’s push for digital initiatives and its subsequent investments in projects such as Digital India and National Supercomputing Mission Vishal Dhupar Managing Director, NVIDIA – South Asia