Artificial intelligence is pushing into every area of our life with deep inroads to our mobile devices, our video games, and even our own brains. Video surveillance could the next step if NVIDIA's new video analytics platform, Metropolis, is successful. The initiative, announced just ahead of the annual GPU conference this week, will use learning AI to analyze the massive amount of data from surveillance video for "public safety, traffic management and resource optimization."
According to NVIDIA, there are already hundreds of millions of surveillance cameras around the globe, with the number expected to rise to the 1 billion mark by 2020. Human beings have a hard time sifting through the flood of moving images, storing the majority of it on hard drives for later viewing. NVIDA thinks that deep learning AI can help video analytics much more accurately than humans or even real-time computer monitoring. The company has partnered with more than 50 companies that make security cameras, including Hikvision. "The benefit of GPU deep learning is that data can be analyzed quickly and accurately to drive deeper insights," said Shiliang Pu, president at the Hikvision Research Institute in China.
NVIDIA's AI may keep watch over smart cities of the future
There were no updates on the financial outlook from Nvidia management, but the company did talk up what it believes are $70 billion worth of addressable markets for its products come 2020, and analysts seem encouraged by that today. As one analyst, Rosenblatt’s Hans Mosesmann, crowed today, "The level of disruption that the Nvidia GPU acceleration approach will do to the computing world will be epic in our opinion."
Among the intriguing topics that emerged amidst everything else was that of “inferencing.” As I discussed in a November interview with Jen-Hsun Huang, Nvidia’s CEO, inferencing is the corollary of what happens in today’s machine learning. In machine learning’s “training” phase, which is where Nvidia is getting most of its GPU contracts, computing facilities learn how to understand information by sifting through large data sets.
Inferencing is when the machine responds to an actual query from an internet user by applying the rules it has learned in training phase. Huang indicated to me in November that having his chips used for inferencing would spread their adoption to a much broader set of uses, where just about every Web page request could become a kind of inference.
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