Artificial intelligence analyzed design elements in Airbnb photos of 50,000 living rooms around the world.
Because the task of going through a million images to note multiple decorative elements would be too time-consuming for the researchers, the team used deep learning, a type of artificial intelligence, to detect decorative items, such as wall art, plants, books, and paint colors, in the pictures. Human trainers initially picked out decorative elements in pictures to program the computer to recognize the decorations, then the computer could pick out and classify these features on its own.
Furniture shopping has traditionally had two major limitations: inventory and imagination.
The typical retail customer’s crap shoot: walk into a showroom, examine the limited number of offerings on the floor and pick out something, even if it’s not exactly what they’re looking for. The more ambitious might up the game by looking in a catalog or comparing color swatches, and then try to imagine what the new loveseat might look like in their living room.
Thanks to advances in artificial intelligence and machine learning, that’s changing. A handful of companies and university researchers are testing GPU-powered deep learning techniques in online and offline retail settings that will make shopping easier and more precise than ever.