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This article by Jen-Hsun Hunag, Founder, President and CEO at NVIDIA, was originally entitled "The Intelligent Industrial Revolution".
Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. In my three decades in the computer industry, none has held more potential, or been more fun. The era of AI has begun.
Our industry drives large-scale industrial and societal change. As computing evolves, new companies form, new products are built, our lives change. Looking back at the past couple of waves of computing, each was underpinned by a revolutionary computing model, a new architecture that expanded both the capabilities and reach of computing.
In 1995, the PC-Internet era was sparked by the convergence of low-cost microprocessors (CPUs), a standard operating system (Windows 95), and a new portal to a world of information (Yahoo!). The PC-Internet era brought the power of computing to about a billion people and realized Microsoft’s vision to put “a computer on every desk and in every home.” A decade later, the iPhone put “an Internet communications” device in our pockets. Coupled with the launch of Amazon’s AWS, the Mobile-Cloud era was born. A world of apps entered our daily lives and some 3 billion people enjoyed the freedom that mobile computing afforded.
Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. This new model — where deep neural networks are trained to recognize patterns from massive amounts of data — has proven to be “unreasonably” effective at solving some of the most complex problems in computer science. In this era, software writes itself and machines learn. Soon, hundreds of billions of devices will be infused with intelligence. AI will revolutionize every industry.
Why now? As I wrote in an earlier post, 2012 was a landmark year for AI. Alex Krizhevsky of the University of Toronto created a deep neural network that automatically learned to recognize images from 1 million examples. With just several days of training on two NVIDIA GTX 580 GPUs, “AlexNet” won that year’s ImageNet competition, beating all the human expert algorithms that had been honed for decades. That same year, recognizing that the larger the network, or the bigger the brain, the more it can learn, Stanford’s Andrew Ng and NVIDIA Research teamed up to develop a method for training networks using large-scale GPU-computing systems.