Deep Learning Fashion
How is data making designers more efficient?
In collaboration with Gigi Hadid, Tommy Hilfiger’s new collection goes from design to market within six months, three times faster than traditional lines. “I want the fastest delivery and the most incredible experience. If there’s no risk, there’s no reward, and our risk was changing our entire design and production process.” said Hilfiger.
He was referring to the brand’s “see now, buy now” strategy enabled by artificial intelligence (AI) in the creative design process. Hilfiger partners with IBM and the Fashion Institute of Technology to decipher real-time trends, ongoing customer sentiment around product and runway images, and recurring themes in patterns, silhouettes, colors, and styles. AI draws informed inspiration from all-time trends immediately — a task that would take a design team weeks to achieve.
AI is catching on among legacy brands, technology providers, student designers and retail startups alike. Utilizing massive amount of personal customer data and AI, online styling service Stitch Fix launched its first in-house line made from a series of algorithms. The purpose isn’t to replace the creative process — that gut feeling, human eye, or instinct — with a series of 0s and 1s. The purpose is to reduce “brain clutter,” as Chris Palmer, the global cognitive offerings lead at IBM called it: the laborious tasks that delay the creative process.
Sources: Glossy (January 22. 2018) | Medium - Inside EDITED (Deep Learning Our Way Through Fashion Week) | Medium (@IBM Industrious) | Image: Laura Pittaccio