Amazon isn’t synonymous with high fashion yet,
by Will Knight MIT Technology Review
The company may be poised to lead the way when it comes to replacing stylists and designers with ever-so-chic AI algorithms. Researchers at the e-commerce juggernaut are currently working on several machine-learning systems that could help provide an edge for spotting, reacting to, and perhaps even shaping the latest fashion trends. The effort points to ways in which Amazon and other companies could improve the tracking of trends in other retail areas—making recommendations based on products popping up in social-media posts, for instance. And it could help the company expand its clothing business or even dominate the area.
“There’s been a whole move from companies like Amazon trying to understand how fashion develops in the world,” says Kavita Bala, a professor at Cornell University who took part in a workshop on machine learning and fashion organized by Amazon last week. “This is completely changing the industry.” Some forward-thinking retailers are already using social networks like Instagram and Pinterest to track the latest fashion trends and react quickly. And startups like the subscription service Stitch Fix already make personalized recommendations based on user preferences and social-media activity.
Meanwhile, Amazon is making moves to bolster its apparel business, developing its own clothing brands, investing in high-quality photography for products, and launching Prime Wardrobe, which lets customers try on clothes before buying them. Its Echo Look app will even give you feedback on your outfits. But Amazon appears to be pushing that algorithmic approach even further. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. The software could conceivably provide fashion feedback or recommendations for adjustments. The work is innovative because computers usually require extensive labeling to learn from visual information. But in many real-world situations, such as an image posted to Instagram, there might be just one label.