deep learning

“AI is the new electricity,” said Andrew Ng, co-founder of Coursera and an adjunct Stanford professor who founded the Google Brain Deep Learning Project, in a keynote speech at the AI Frontiers conference that was held this past weekend in Silicon Valley. “About 100 years ago, electricity transformed every major industry. AI has advanced to the point where it has the power to transform” every major sector in coming years. And even though there’s a perception that AI was a fairly new development, it has actually been around for decades, he said. But it is taking off now because of the ability to scale data and computation.

Ng said most of the value created through AI today has been through supervised learning, in which an input of X leads to Y. But there have been two major waves of progress: One wave leverages deep learning to enable such things as predicting whether a consumer will click on an online ad after the algorithm gets some information about him. The second wave came when the output no longer has to be a number or integer but things like speech recognition, a sentence structure in another language or audio. For example, in self-driving cars, the input of an image can lead to an output of the positions of other cars on the road.

Indeed, deep learning — where a computer learns from datasets to perform functions, instead of just executing specific tasks it was programmed to do — was instrumental in achieving human parity in speech recognition, said Xuedong Huang, who led the team at Microsoft on the historic achievement in 2016 when their system booked a 5.9% error rate, the same as a human transcriptionist. “Thanks to deep learning, we were able to reach human parity after 20 years,” he said at the conference. The team has since lowered the error rate even more, to 5.1%.