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Why the AI hype cycle won't end anytime soon

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Increasingly affordable AI maintenance and the increased speed of calculations thanks to GPU are significant factors in the unbridled growth of AI. The astonishing results that were achieved on training a neural network on GPU cards made Nvidia a key player, with 70 percent of the market share that Intel failed to gain. Compared with the results from the analog algorithms, and thanks to the combination of machine learning and big data, previously "unsolvable" problems are now being solved. Machine learning algorithms can directly analyze thousands of previous cases of different types of diseases and make their own conclusions as to what constitutes a sick individual versus a healthy individual, and consequently help diagnose dangerous conditions including cancer.


The New Intel: How Nvidia Went From Powering Video Games To Revolutionizing Artificial Intelligence

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It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. "We've been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia's platform," says Marc Andreessen of venture capital firm Andreessen Horowitz. Starting in 2006, Nvidia released a programming tool kit called CUDA that allowed coders to easily program each individual pixel on a screen. From his bedroom, Krizhevsky had plugged 1.2 million images into a deep learning neural network powered by two Nvidia GeForce gaming cards.


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Today, when Intel announced a new generation of Xeon Phi server chips, the emphasis was on their ability to handle A.I. Of all those servers, 7 percent were handling deep learning, while 95 percent were doing machine learning, she said. Of servers doing machine learning or deep learning, "the vast, vast majority of workloads are machine learning. They offer "advanced acceleration capabilities" for workloads like Google's TensorFlow deep learning framework, Google has said.


Google Details New TensorFlow Optimized ASIC

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Norm Jouppi, a distinguished hardware engineer at Google, detailed the company's public disclosure of the Tensor Processing Unit (TPU) last week after the CEO Sundar Pichai's earlier announcement at Google I/O. Several questions came up around how the TensorFlow-optimized chipset could compete with publicly available hardware like Nvidia's Tesla P100 and even PaaS providers like Nervana that provide machine learning services. Google's public disclosure of the TPU may have been related to Nvidia's release of the Tesla P100 in April. Jouppi noted that Google wants to lead the industry in machine learning and make the innovation available to its customers, but didn't disclose specific plans or offerings to do so at this time.