AI News, Do robots dream of Prada? How artificial intelligence is reprogramming fashion

Do robots dream of Prada? How artificial intelligence is reprogramming fashion

Instead, you will try on an outfit, turn to a wall-mounted, five megapixel camera with front lighting and dual-antennae wifi connectivity, ask, “Alexa, how do I look?” and within a few seconds the 1.6 watt speaker will deliver the data-driven, empirically-founded assessment.

No UK launch date is set, but the technology – which analyses your outfit through a combination of algorithms and (human) “fashion specialists” – is set to revolutionise what technology means to style.

Stitch Fix is a online personal styling service which sends its 2.7 million active American clients suggestion boxes of clothes chosen by cross-referencing a client’s stated preferences with the recent purchases of others of similar age and demographic.

In 2003, Kate Moss found a lemon-yellow 50s chiffon dress in Lily et Cie, a vintage store in Beverly Hills, and wore it to a dinner at New York fashion week, where the entire room fell in love with it and a million copycat versions were born.

The metric of a style algorithm based on likes, whether fed to you as feedback on your selfies or as a subscription box of suggested autumn clothes, will steer you towards a polished, palatable, mainstream look.

“It’s going to knock the edges off your preferences and guide you towards an aesthetic that is sort of ambient.” Early users of the Echo Look reported that it scored navy and muted colours higher than brighter shades, and gave the thumbs up to Instagram-approved styling accents such as popped collars and rolled-up shirtsleeves.

White or bright accent walls, raw wood, Nespresso machines, Eames chairs, patterned rugs on bare floors, open shelving, Scandi-chic, the industrial look, and a minimal version of mid-century were characteristics that Kyle Chayka identified as the Airbnb “look” two years ago.

And then renters taken by the bare Edison lightbulbs and gallery walls of black and white photography in homes they stayed in while on holiday began to bring the look into their own homes.

The power of fashion to make us spend is strongest not when we are presented with another version of the type of pencil skirt we already have and like, but the moment when we see, say, a kilt, and realise that, despite never having wanted one before, we simply have to have one right away.

For example, when a client writes in to her stylists that she needs something to wear to her ex-boyfriend’s wedding, only a human can understand the gravitas of that request,” Klingenberg says.) To futurists such as Sophie Hackford, we are wrong to romanticise the way fashion works now.

One day in the future you will be sitting on your sofa next to a virtual Diana Vreeland, or Alexa Chung, who will be talking you through the selection of virtual clothes you can see being modelled in front of you, and it will seem so funny that we once scrolled through two-dimensional skirts on the internet,” she says.

AI makes it possible to adjust manufacture in real time, responding to customer design as it happens, so that waste is minimised.” The opportunities for personalisation – from monograms to bespoke tailoring using 3D measurements taken online – hold the promise of clothes that we will value more, and wear for longer.

An artificial intelligence takeover of the power traditionally held by the population of magazine mastheads and the fashion week front row to anoint the “best-dressed” could bring about a democratic revolution in an elitist industry.

That the industry is still riven with snobbery and unconscious bias – or worse – about skin colour and body shape is evidenced by the way so-called “streetstyle” galleries on fashion websites, and the upper echelons of the “influencer” world, are dominated by thin white women.

“The majority of people have already developed an algorithm for style, even if they don’t think of it like that,” says Simon Lock, founder and CEO of Ordre, which offers fashion buyers a digital, streamlined alternative to physical showrooms.

Artificial intelligence is perfectly suited to perform this role for us.” Today’s teenagers have an ever more porous boundary between their IRL and online selves, with relationships – even romantic ones – sometimes conducted entirely via phones.

Long before this season’s Matrix trend, the widespread use of Photoshop had begun to shift our image of cover-girl perfection from that of a beautiful, real human being to that of a digitised version of human beauty, with impossibly even skin tone and unnaturally symmetrical features.

Style Is an Algorithm

A head-to-toe picture appears on my phone of a view I’m only used to seeing in large mirrors: me, standing awkwardly in my apartment, wearing a very average weekday outfit.

Each outfit in the comparison receives a percentage out of 100: black clothes score 73 percent against gray clothes at 27 percent, for example.

And yet it purports to show us our ideal style, just as algorithms like Netflix recommendations, Spotify Discover, and Facebook and YouTube feeds promise us an ideal version of cultural consumption tailored to our personal desires.

In fact, this promise is inherent in the technology itself: Algorithms, as I’ll loosely define them, are sets of equations that work through machine learning to customize the delivery of content to individuals, prioritizing what they think we want, and evolving over time based on what we engage with.

Confronting the Echo Look’s opaque statements on my fashion sense, I realize that all of these algorithmic experiences are matters of taste: the question of what we like and why we like it, and what it means that taste is increasingly dictated by black-box robots like the camera on my shelf.

He quotes Montesquieu: “This effect is principally founded on surprise.” Algorithms are meant to provide surprise, showing us what we didn’t realize we’d always wanted, and yet we are never quite surprised because we know to expect it.

Every cultural object we aestheticize and consume — “the most everyday choices of everyday life, e.g., in cooking, clothing or decoration,” Pierre Bourdieu writes in his 1984 book Distinction: A Social Critique of the Judgement of Taste — is a significant part of our identities and reflects who we are.

Barthes scrutinizes a fragment of text from a fashion magazine — “blue is in fashion this year” — to see where its thesis, that a particular color is particularly tasteful right now, comes from.

His conclusion is that it doesn’t come from anywhere: “We are not talking about a rigorous production of meaning: the link is neither obligatory nor sufficiently motivated.” Blue is not in fashion because it is particularly functional, nor is it symbolically linked to some wider economic or political reality;

Further evidence of the artificial and hierarchical nature of style in the past can be found in that scene from the 2006 film The Devil Wears Prada, in which Meryl Streep (as magazine editor and Anna Wintour facsimile Miranda Priestly) tells her assistant played by Anne Hathaway that the chunky blue sweater she is wearing was, in essence, chosen for her.

“That blue represents millions of dollars and countless jobs, and it’s sort of comical how you think you made a choice that exempts you from the fashion industry when, in fact, you’re wearing a sweater that was selected for you by the people in this room from a pile of stuff,” Streep says.

Blue is in fashion this year because 83.7 percent of users purchased (or clicked like on) blue shirts, the Amazon Echo Look algorithm says, therefore it is in fashion, therefore businesses should manufacture more blue shirts, and you, the customer, will buy and wear them.

In the appended introduction to the 1997 edition, he uses the phrase “collapsing dominant” to describe a situation in which an older, established mode of cultural authority, or a taste regime, is fading and being replaced by a newer one.

Instead of the maximalist, celebrity-driven, intoxicant culture of ‘70s television — Nixon, Star Wars, shag rugs, cocaine, nuclear bombs — we now have the flattened, participatory, somehow salutary aesthetic of avocado toast, Outdoor Voices leggings, reclaimed wood, Sky Ting yoga classes, and succulents in ceramic planters.

It was a kind of online social network based on shopping, where invitation-only members could curate selections of products from elsewhere on the internet and users could follow their favorite tastemakers.

“When we lost the exclusivity, people didn’t really care anymore.” Svpply’s innate sense of uniqueness didn’t survive: “If everyone’s editing Vogue, it wouldn’t be Vogue.” Another question: How good of a tastemaker can a machine ultimately be?

Style is a superficial aesthetic code that is relatively simple to replicate, whereas taste is a kind of wider aesthetic intelligence, able to connect and integrate disparate experiences.

Algorithms can approximate the former — telling me I should wear a blue shirt — but can’t approximate the latter because the machine can’t tell me why it thinks I should wear a blue shirt or what the blue shirt might mean to me.

As one chair listing I encountered put it: “Goes with herman miller eames vintage mid century modern knoll Saarinen dwr design within reach danish denmark abc carpet and home arm chair desk dining slipper bedroom living room office.” Imagine the optimized average of all of these ideas.

The linguistic melange forms a taste vernacular built not on an individual brand identity or a human curator but a freeform mass of associations meant to draw the viewer in by any means necessary.

The brands that sell them are thin fictions whipped up in Squarespace and the actual products are the result of Alibaba manufacturing and Amazon drop-shipping, in which a product moves directly from manufacturer to consumer having never entered a store.

Other ways in which our experiences are warped by algorithmic platforms include Spotify possibly commissioning original music from “fake” artists to match the latent content desires of its audience, as Noisey noticed;

(This lack of discernibility also contributes to the problems of fake news, which algorithmic feeds promote like any other content, however inaccurate.) Spotify’s fake artists aren’t fake, per se;

That the simple possibility of non-genuine music fed to us by an algorithmic platform without our knowledge created a media frenzy speaks to our fundamental fear — a possibly irrational or at least abstruse 21st-century anxiety — of an algorithmic culture.

The newfound reproducibility of the individual work of art through these technologies meant that art was deprived of its “aura”: “the here and now of the original” or “the abstract idea of its genuineness,” as Benjamin writes.

Algorithmic machine learning, however, can mimic an entire stylistic mode, generating new examples at will or overlaying a pre-existing object with a new style unrelated to its origins.

Another cultural crisis is looming as we realize that “new” or popular styles will be increasingly optimized for their algorithmic reproducibility (in other words, designed to spread meme-like over digital platforms) instead of their originality.

It’s possible to consciously resist the algorithm, like someone might buck the current fashion trend — wearing bell-bottoms and tie-dye, say, instead of trim, blank basics.

I might only read books I stumble across in used bookstores, only watch TV shows on local channels, only buy vinyl, only write letters, forsake social media for print newspapers, wear only found vintage.

The primary ways I discovered new things were through forums, where members suggested which shoes to buy or bands to listen to, and through digital piracy, which gave me a relatively unfiltered list of possible cultural artifacts to consume on Kazaa or BitTorrent, which did not come with “You May Also Like This” recommendations.

(I did not live in a city and the local comprehensive bookstore was a Borders 45 minutes away.) These services were the digital equivalent of used vinyl shops: You take what you find, either you like it or not, and then you try again, constantly refining an image of what you want and (thus) who you are.

If Gap is a mainstream platform for fashion basics, then Everlane, with its transparent manufacturing and minimalist branding, and now Scott Sternberg’s Entireworld, which purports to offer a utopian clothing system, are its more niche, though no less generic, hipster equivalents.

FilmStruck, for example, streams “critically acclaimed classic movies, hard-to-find gems, and cult favorites” like those in the Criterion Collection, while MUBI selects “cult, classic, independent and award-winning films from around the world.” The full-bleed, black-and-white stills on their websites differentiate them as far hipper than Netflix or cable — you might feel safer about identifying your taste with them (“I don’t watch TV;

The start-up Feather will rent you a “hip bedroom” bundle of faux-mid century side tables and bed frame for $109 a month in a kind of minimally stylish pre-packaged taste kit, a thinly reproduced aesthetic lacking any aura.

And, like the drop-shipped generic watches, they are extremely boring, releasing wave after wave of artisanal fabrics turned into rustic, vaguely outdoorsy gear.

What these businesses suggest is that you can have the benefits of a digital platform and an algorithmic feed while still feeling self-satisfied, pretentious, and exclusive in the knowledge that your content has been carefully curated by humans.

Platformization is something the fashion industry is already familiar with, of course: Each major brand is its own platform, expanding in a profusion of seasonal lines and accessories meant to cater to your every need within a single taste-system.

the only customizations are a few stylistic choices — short socks or long, crew-neck or V-neck — and that the items come with your name emblazoned on them, like a black duffle bag I recently received that says KYLE CHAYKA in raised black thread.

“Eventually we may opt to shift our definition of art in order to make accommodation for the creativity of artificial intelligence,” says Marian Mazzone, an art history professor at the College of Charleston who worked on a project in which AI created original styles of painting (they mostly look like mash-ups of Impressionism, Fauvism, and Cubism).

“It’s like you’re working on a big TV show with a very powerful showrunner who has written the episode, and the showrunner got drunk last night, passed out, and you couldn’t not make the episode,” Sharp says.

The automated clothing service Stitch Fix, kind of a preppy version of LOT, uses algorithmic help to optimize their new original designs to increase sales and address gaps in the market, what they call “Hybrid Design”: customers like ruffles and plaid, so why not plaid ruffles?

It doesn’t know me at all — it can’t tell what kind of clothes I’m comfortable in nor how the clothes I wear will function as symbols outside, in the place I live, in the contexts of class or gender.

When I asked the machine about my plaid shirt, an ad popped up on the app’s feed showing me a few other, similarly colored plaid shirts — none particularly stylish or different enough from the one I own, bereft of brand name — that I could buy on Amazon.

In fact, Amazon is already using the data it collects to manufacture its own clothing lines, and the results are about what you’d expect from a robot: wan imitations of whatever is currently popular, from the “globally inspired” Ella Moon to the cool-French-girl knockoff Paris Sunday.

Amazon made a freaky algorithm that designs clothes based on popular styles

Instead of human designers creating your favorite looks, Amazon's algorithm would become the creative genius.  The tool that accomplishes this task is called a generative adversarial network (GAN).

Once it has learned the style, it can transform an existing piece of clothing to fit in that style.  The system will be able to identify rising fashion trends and start creating its own unique pieces for Amazon customers to purchase.

One way Amazon might be able to spot trends ahead of the game is by identifying new looks that start to appear in a social media posts.  This isn't Amazon's first glimpse into the fashion world.

Thread's fashion algorithm has 3.7 trillion style tips

This article was first published in the January 2016 issue of WIRED magazine.

Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online.

Thread employs eight human personal stylists, who perform an initial consultation with each new client on sign-up.

Its machine-learning algorithm then trawls through more than 31 million customer-submitted ratings, along with 3.7 trillion possible item combinations (each piece is tagged, to identify its characteristics) to recommend outfits.

it now has 200,000, and in August secured an $8 million (£5.2m) funding round led by Balderton Capital and including DeepMind co-founders Demis Hassabis and Mustafa Suleyman.

Algorithms Tour

To be sure, we had a difficult time limiting the scope to the ten stories featured above - there are vastly more already in production and even more still being framed.

Perhaps the only thing special we’ve done is endow ourselves with rich data via a unique business model and then foster an environment where a data scientists can be successful.

What Not To Wear: How Algorithms Are Taking Uncertainty Out Of Fashion

D on’t you hate those moments when you are standing in front of a mirror in a clothing store’s fitting room, trying on a new outfit, with multiple thoughts rushing through your head: Is this still in fashion?

Stitch Fix, a popular personal shopping service, promises to spare its customers from the drama of shopping by matching each person with a personal stylist who selects clothing and accessories based on the individual’s size, style and budget.

SEE MORE FROM PART ONE OF A SPECIAL SERIES ON AI The secret sauce is the algorithms, which are at the core of the company’s business model and do everything from drive the clothing selections to assign human stylists to optimize production and logistics.

Eric Colson, chief algorithms officer, Stitch FixStitch Fix In this article, Eric Colson, Stitch Fix’s chief algorithms officer, takes us on an algorithms tour, explaining how the fickle business of fashion runs like a smooth machine when driven by algorithms.

The company also uses mixed-effects modeling algorithms, which incorporate longitudinal data into complex statistical models, allowing Stitch Fix to get a sense of how fashion tastes are evolving over time, both for individual customers and for its customer base as a whole.

Questions range from fundamentals (height and weight), to taste and preferences (do you like your shirts tucked or untucked?), to personal traits (are you a risk taker?) and lifestyle (are you a new mom?).

These algorithms figure out how much each characteristic matters to a particular person, and how to trade off one for the other (e.g., a new mom probably does not want to buy casual clothes made from fabrics that need to be dry-cleaned).

This is possible thanks to the feedback data collected from customers, which is transmitted back to the algorithms so that they can see how their decisions worked in real life—and use this information to constantly improve their decision-making formulas (machine learning).

Matching the right stylist with the right customer Ultimately, at Stitch Fix, it is a human stylist who finalizes the clothing selections and even writes a personal notedescribing how the client might accessorize the items for a particular occasion and how to pair them with other clothing in his or her closet.

Lower transportation costs At Stitch Fix, smart algorithms and an ocean of feedback data are behind each delivery on a customer's doorstep.Stitch Fix The costs of transportation can also eat deeply into many retailers’ profits, as items travel to different pickup and delivery locations across the country.

At Stitch Fix, algorithms calculate a cost function for each warehouse based on a combination of its location relative to the client and how well the inventories in the various warehouses match the client’s needs, thus cutting down on unnecessary transportation costs.

Designer algorithms use the same mechanisms to develop new styles as mother nature does in evolution by natural selection: recombining attributes from existing styles and possibly mutating them slightly, then testing for “fitness” (customer feedback on similar styles).


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