In today's world of fast fashion, retailers sell only a fraction of their inventory, and consumers keep their clothes for about half as long as they did 15 years ago. As a result, the clothing industry has become associated with swelling greenhouse gas emissions and wasteful practices. The startup Armoire is addressing these issues with a clothing rental service designed to increase the utilization of clothes and save customers time. The service is based on machine-learning algorithms that use feedback from users to make better predictions about what they'll wear. Customers pay a flat monthly price to get access to a range of high-end styles.
My recent claim that fashion needs more imagination when it comes to using artificial intelligence has been unexpectedly answered by a project combining e-commerce data and artisanship. Not an obvious pairing, but the brainchild of passionate'dataphile' YOOX NET-A-PORTER GROUP Chairman and CEO, Federico Marchetti, and HRH The Prince of Wales, whose appreciation and support of artisanal craftsmanship (and dedication to safeguarding its future) is decades-long. Marchetti and the YOOX NET-A-PORTER team worked with The Prince's Foundation to create a unique year-long apprenticeship to cultivate the next generation of luxury fashion artisans, informed and guided by customer shopping data and AI analysis of millions of images of historically successful products. To breathe life into artisanship as a viable and attractive career option, underpinned by data that empowers it to deliver the right product, for the right customer on the right sales platform, crucially sustaining the artisans' craft methods and their livelihood. The Modern Artisan project brought together six designers from Milan's Politecnico Di Milano Fashion in Process (FiP) research laboratory and four apprentices undergoing certified training in small batch production and hand-craft skills at The Prince's Foundation, Dumfries House, Scotland.
Kendall Nicole Jenner is an American television personality, Instagram star, socialite, and modeling sensation who shot to fame with the reality television show, 'Keeping Up With The Kardashians' that airs on the'E!' cable network. The show follows personal and professional lives of the Kardashian-Jenner family, including her famous half-sisters Kim, Khloe, and Kourtney Kardashian and her younger sister Kylie Jenner among others. She stepped into the world of high fashion modeling after such popularity. Following an ad campaign for'Forever 21,' her career in modeling skyrocketed and she walked the ramp for almost each and every luxury brand available, including'Prada,' 'Givenchy,' 'Fendi,' and'Chanel.' She also walked the ramps for high-end fashion designers during various fashion events like'Paris Fashion Week,' 'New York Fashion Week,' and'Milan Fashion Week' and gradually emerged as one of the most sought after fashion models.
Online shopping has drastically grown in people's life during the pandemic. Earlier, when the work was mostly professional at an office space, people spared very less time on mobile phones. Things have turned upside down now. Employees who are on remote working spend their leisure time scrolling through applications in their smartphones. They are often drawn to shopping apps that serve both as a pass-time and fashion guide.
Wouldn't it be helpful if a food tracking or recipe app could determine what you're eating just by detecting what's on your table? You might get your wish. Researchers at Microsoft and multiple universities have developed Capacitivo, a smart fabric system that can detect food, drinks, and other objects based solely on touch. The cloth uses a combination of a capacitive electrode grid and machine learning to gauge both the material and shape of a given item. This works with some containers, too, including glasses and bowls. The researchers initially wove the technology into a tablecloth, and envisioned it as particularly helpful for cooking.
Now that the majority of New York Fashion Week's runway shows have gone digital, designers are seeking to replicate the aura and grandeur of the fashion show outside of the catwalk's limitations. From Dior's live-streamed presentations, to Louis Vuitton's short films, to Loewe's FedEx-shipped "Show in a Box", high-fashion has demonstrated how collections can be shared with consumers in new, socially-distant ways. However, one of the main limitations of runway shows was the necessity of models -- and a lot of them. Real-time, in-person runways saw models walking out one after the other. With digital showings-- such as the pre-photographed Resort 2021 collections -- the necessity for more-than-a-couple-of-models is much lower.
From the textiles used in garment manufacturing to creating a sustainable supply chain, technological advancements are set to innovate fashion in countless ways. For a long time, the processes used in the fashion sector have remained remarkably unchanged. In the coming years, however, we can expect big things! As of 2020, fashion generates an estimated $664 million in revenue, making it one of the biggest industries in the world. Because of this, technological innovations within this sector are set to be nothing less than world-changing and, if implemented correctly, technology in fashion could make an unfathomable change in creating a greener, cleaner world.
The fashion industry did $3 trillion in business, 2% of global GDP in 2018; e-commerce fashion amounted to $520 billion in 2019. AI is poised to revolutionize the fashion industry by providing insights into fashion trends, purchase patterns, and enabling better inventory management. The global brand H&M has been applying AI solutions to boost business operations. One example is a system to organize and allocate masses of unsold stock to retail stories with highest demand, reducing the need for discounted sales. This is achieved by optimizing the supply chain and inventory management, reducing the amount of wasted clothing.
Artificial intelligence (AI) in fashion is no longer a secret and has widely been used to mostly help businesses to streamline processes and increase sales. But the skillsets of fashion designers and computer scientists are miles apart, so it's not until recently that the creative applications of AI in this industry have been explored. "Initial uses of artificial intelligence have focused on quantifiable business needs, which has allowed for start-ups to offer a service to brands," Matthew Drinkwater, head of the fashion innovation agency (FIA) at London College of Fashion (LCF), told Forbes. "Creativity is much more difficult to quantify and therefore more likely to follow behind." Seeing the opportunity for AI to play a bigger role in the creative process, LFC has launched an AI course aiming to develop creative fashion solutions and experiences that challenge the current approaches to fashion design.
The fashion industry is looking forward to use artificial intelligence technologies to enhance their processes, services, and applications. Although the amount of fashion data currently in use is increasing, there is a large gap in data exchange between the fashion industry and the related AI companies, not to mention the different structure used for each fashion dataset. As a result, AI companies are relying on manually annotated fashion data to build different applications. Furthermore, as of this writing, the terminology, vocabulary and methods of data representation used to denote fashion items are still ambiguous and confusing. Hence, it is clear that the fashion industry and AI companies will benefit from a protocol that allows them to exchange and organise fashion information in a unified way. To achieve this goal we aim (1) to define a protocol called DDOIF that will allow interoperability of fashion data; (2) for DDOIF to contain diverse entities including extensive information on clothing and accessories attributes in the form of text and various media formats; and (3)To design and implement an API that includes, among other things, functions for importing and exporting a file built according to the DDOIF protocol that stores all information about a single item of clothing. To this end, we identified over 1000 class and subclass names used to name fashion items and use them to build the DDOIF dictionary. We make DDOIF publicly available to all interested users and developers and look forward to engaging more collaborators to improve and enrich it.