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Intel: New chip materials will enable massive AI research gains

[2019.07.12, Fri 01:06] In a recent survey conducted by Lopez Research, 86% of companies said that they thought AI would be strategic to their business, while only 36% believed they'd actually made meaningful progress with AI. Why the disparity? Intel VP and CTO of AI products Amir Khosrowshahi and general manager of IoT Jonathan Ballon shared their thoughts onstage at VentureBeat's 2019 Transform conference in San Francisco. It's undoubtedly true that the barriers to AI adoption are much lower than they once were, according to Khosrowshahi. "If you're doing something that's cloud-based, you've got access to vast computing resources, power, and cooling, and all of these things with which you can perform certain tasks. But what we're finding is that almost half of all of the deployments and half of all the world's data sits outside of the data center, and so customers are looking for the ability to access that data at the point of origination," he said. Training state-of-the-art AI models is infinitely more time-consuming without the aid of cutting-edge cloud chips like Google's Tensor Processing Units and Intel's forthcoming Nervana Neural Network Processor for training, a purpose-built high-speed AI accelerator card. "Processor cooling infrastructure, software frameworks, and so forth have really enabled , and it's kind of an enormous amount of compute," said Khosrowshahi. There's no magic bullet, but both Ballon and Khosrowshahi believe that hardware innovations have the potential to further democratize powerful AI. Khosrowshahi is bullish on new types of transistors that rely on multiferroics and topological materials to run machine learning algorithms. "There are novel materials that we can exploit for the future of data center computing, and I think this is actually the future," said Khosrowshahi. Google the news >>

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