A new paper argues that the computing demands of deep learning are so great that progress on tasks like translation and self-driving is likely to slow.
EARLY LAST YEAR, a large European supermarket chain deployed artificial intelligence to predict what customers would buy each day at different stores, to help keep shelves stocked while reducing costly spoilage of goods.
The company already used purchasing data and a simple statistical method to predict sales. With deep learning, a technique that has helped produce spectacular AI advances in recent years—as well as additional data, including local weather, traffic conditions, and competitors’ actions—the company cut the number of errors by three-quarters…
