It’s about better data, not bigdata, DeepLearning

Andrew Ng is among the pioneers of deep learning—the use of large neural networks in A.I. He’s also one of the most thoughtful A.I. experts on how real businesses are using the technology. His company, Landing AI, where Ng is founder and CEO, is building software that makes it easy for people, even without coding skills, to build and maintain A.I. systems. This should allow almost any business adopt A.I. —especially computer vision applications. Landing AI’s customers include major manufacturing firms such as toolmaker StanleyBlack & Decker, electronics manufacturer Foxconn, and automotive parts maker Denso.

Ng has become an evangelist for what he calls “data-centric A.I.” The basic premise is that state-of-the-art A.I. algorithms are increasingly ubiquitous thanks to open-source repositories and the publication of cutting edge A.I. research. Companies that would struggle to hire PhDs from top computer science schools can nonetheless access the same software code that Google or NASA might use. The real differentiator between businesses that are successful at A.I. and those that aren’t, Ng argues, is down to data: What data is used to train the algorithm, how it is gathered and processed, and how it is governed? Data-centric A.I., Ng tells me, is the practice of “smartsizing” data so that a successful A.I. system can be built using the least amount of data possible. And he says that “the shift to data-centric A.I.” is the most important shift businesses need to make today to take full advantage of A.I.—calling it as important as the shift to deep learning that has occurred in the past decade.

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