Researchers use moss to overcome machine-learning limitations
Deep-learning algorithms can identify objects and faces better than humans in some cases but are limited in their ability to recognize amorphous forms, like grasses and trees, that can take different shapes and sizes and are continually changing. But a team, led by Takeshi Ise at the Kyoto University in Japan, has developed a new technique that will help machines overcome these limitations, per the MIT Technology Review. The method centers on teaching machines to recognize different types of moss, a plant that doesn't take a well-defined, distinctive shape.
Why it matters: The method could be used to better recognize trees and crops in aerial photos, which would be valuable for monitoring agriculture and for conservation and land management efforts.