AI Faces Speed Bumps and Potholes on Its Road From the Research Lab to Everyday Use

Rigid IT departments and job-hopping data scientists are just two of the challenges that make implementing machine learning harder than you might think

3 min read
Image of a data scientist sitting in front of multiple screens of code.
Photo: iStock

Implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many.

That was the conclusion of panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, held at Cisco’s San Jose, Calif., campus last week.

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Andrew Ng: Unbiggen AI

The AI pioneer says it’s time for smart-sized, “data-centric” solutions to big issues

10 min read
​Andrew Ng listens during the Power of Data: Sooner Than You Think global technology conference in Brooklyn, New York, on Wednesday, October 30, 2019.

Andrew Ng was involved in the rise of massive deep learning models trained on vast amounts of data, but now he’s preaching small-data solutions.

Cate Dingley/Bloomberg/Getty Images

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.

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