Wednesday 12 September 2018

Intel Execs Address the AI Talent Shortage, AI Education, and the “Cool” Factor

Intel’s VP of AI Architecture and head of AI talent acquisition discuss getting, retaining, and training engineers in an era of high and growing demand.

Intel's Gadi Singer in front of an illustrated panel of AI inspiration.

Last week, I sat down with Intel’s Gadi Singer, vice president and general manager of artificial intelligence architecture, and Chris Rice, head of AI talent acquisition, to talk about AI workforce issues. Here’s what they had to say.

On reports about huge and growing shortages of AI engineers forcing some companies to pay million-dollar salaries:

“What you see in the articles is relatively the truth,” said Rice. “One of the interesting things in AI is that it’s no longer just the technology companies that play in this space, you’ve got the finance industry, medical, retail, mobility, manufacturing—they are all starting to recruit AI engineers, whether they are developing a technology or applying a technology. Because of that, there is an increased global demand, and that is driving up the value of those engineers.”

“The state of the art in deep learning in 2016 is called ‘legacy’ by 2018.”—Gadi Singer

But, interjected Singer, remember that AI is not one skill, one job description. “It is a diverse set of skills. You’ve got hardware architect, you’ve got designers, software developers, data scientists, and researchers.”

Given that hottest area in AI deep learning, [encompassing] all the neural network-related techniques, Singer continued, “people who have expertise in knowing how to develop those new techniques, these topologies, or how to implement them in the most efficient manner in software and hardware obviously have high value.”
The other thing driving value, Singer said, “is that the frontier in this space is moving faster than any technology that I’ve seen. The state of the art in deep learning in 2016 is called ‘legacy’ by 2018. So, people who have the ability to continuously learn and be on or ahead of this fast-moving frontier of deep learning are obviously very valuable.”

The article was originally published in the IEEE Spectrum. To read the complete article follow this LINK.

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