Don’t buy the ‘big data’ hype, says cofounder of Google Brain Ng’s short response to that argument is, “Don’t buy into that hype.” The machine-learning expert shared that “there are lots of opportunities in lots of sectors for startups, entrepreneurs, and for big companies to go and create new innovations,” even without the massive data sets needed for current A.I. development. Instead, the next frontier for A.I. will be how to build algorithms around much smaller data sets. Ng cited the example of a system trained to recognize scratched smartphones on an assembly line: There’s no data set of “a million pictures of scratched smartphones” that could be used to train an A.I. system. Yet a big challenge to embracing a more “data-centric” perspective of A.I.—where quality of data is more important than quantity—is standardization. Ng noted that different people, even two inspectors in the same factory, will transcribe data completely differently, making it difficult to use that smaller data set. Progress on this front will help smooth A.I. adoption in sectors that are still in the “relatively early stage of development,” such as health care, manufacturing, and agriculture, and help achieve the $13 trillion in additional value from A.I. adoption that research from the McKinsey Global Institute has predicted. “The value of A.I. in these traditional industries will turn out to be even greater than the value unlocked potentially in [the consumer Internet].” https://fortune.com/2021/07/30/ai-adoption-big-data-andrew-ng-consumer-internet/