The explosion of AI and machine learning is changing the very nature of computing, so says one of the biggest practitioners of AI, Google.
Google software engineer Cliff Young gave the opening keynote on Thursday morning at the Linley Group Fall Processor Conference, a popular computer-chip symposium put on by venerable semiconductor analysis firm The Linley Group, in Santa Clara, California.
Said Young, the use of AI has reached an "exponential phase" at the very same time that Moore's Law, the decades-old rule of thumb about semiconductor progress, has ground to a standstill.
"The times are slightly neurotic," he mused. "Digital CMOS is slowing down, we see that in Intel's woes in 10-nanometer [chip production], we see it in GlobalFoundries getting out of 7-nanometer, at the same time that there is this deep learning thing happening, there is economic demand." CMOS, or complementary metal-oxide semiconductor, is the most common material for computer chips.
As conventional chips struggle to achieve greater performance and efficiency, demand from AI researchers is surging, noted Young. He rattled off some stats: The number of academic papers about machine learning listed on the arXiv pre-print server maintained by Cornell University concerning is doubling every 18 months. And the number of internal projects focused on AI at Google, he said, is also doubling every 18 months. Even more intense, the number of floating-point arithmetic operations needed to carry out machine learning neural networks is doubling every three and a half months.
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