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The narrative that often accompanies most stories about artificial intelligence these days is how machines will disrupt any number of industries, from healthcare to transportation. It makes sense. After all, technology already drives many of the innovations in these sectors of the economy.
But sneakers and the red carpet? The definitively low-tech fashion industry would seem to be one of the last to turn over its creative direction to data scientists and machine learning algorithms.
However, big brands, e-commerce giants, and numerous startups are betting that AI can ingest data and spit out Chanel. Maybe it’s not surprising, given that fashion is partly about buzz and trends—and there’s nothing more buzzy and trendy in the world of tech today than AI.
In its annual survey of the $3 trillion fashion industry, consulting firm McKinsey predicted that while AI didn’t hit a “critical mass” in 2018, it would increasingly influence the business of everything from design to manufacturing.
“Fashion as an industry really has been so slow to understand its potential roles interwoven with technology. And, to be perfectly honest, the technology doesn’t take fashion seriously.” This comment comes from Zowie Broach, head of fashion at London’s Royal College of Arts, who as a self-described “old fashioned” designer has embraced the disruptive nature of technology—with some caveats.
Co-founder in the late 1990s of the avant-garde fashion label Boudicca, Broach has always seen tech as a tool for designers, even setting up a website for the company circa 1998, way before an online presence became, well, fashionable.
Broach told Singularity Hub that while she is generally optimistic about the future of technology in fashion—the designer has avidly been consuming old sci-fi novels over the last few years—there are still a lot of difficult questions to answer about the interface of algorithms, art, and apparel.
For instance, can AI do what the great designers of the past have done? Fashion was “about designing, it was about a narrative, it was about meaning, it was about expression,” according to Broach.
AI that designs products based on data gleaned from human behavior can potentially tap into the Pavlovian response in consumers in order to make money, Broach noted. But is that channeling creativity, or just digitally dabbling in basic human brain chemistry?
She is concerned about people retaining control of the process, whether we’re talking about their data or their designs. But being empowered with the insights machines could provide into, for example, the geographical nuances of fashion between Dubai, Moscow, and Toronto is thrilling.
“What is it that we want the future to be from a fashion, an identity, and design perspective?” she asked.
Off on the Right Foot
Silicon Valley and some of the biggest brands in the industry offer a few answers about where AI and fashion are headed (though not at the sort of depths that address Broach’s broader questions of aesthetics and ethics).
Take what is arguably the biggest brand in fashion, at least by market cap but probably not by the measure of appearances on Oscar night: Nike. The $100 billion shoe company just gobbled up an AI startup called Celect to bolster its data analytics and optimize its inventory. In other words, Nike hopes it will be able to figure out what’s hot and what’s not in a particular location to stock its stores more efficiently.
The company is going even further with Nike Fit, a foot-scanning platform using a smartphone camera that applies AI techniques from fields like computer vision and machine learning to find the best fit for each person’s foot. The algorithms then identify and recommend the appropriately sized and shaped shoe in different styles.
No doubt the next step will be to 3D print personalized and on-demand sneakers at any store.
San Francisco-based startup ThirdLove is trying to bring a similar approach to bra sizes. Its 20-member data team, Fortune reported, has developed the Fit Finder quiz that uses machine learning algorithms to help pick just the right garment for every body type.
Data scientists are also a big part of the team at Stitch Fix, a former San Francisco startup that went public in 2017 and today sports a market cap of more than $2 billion. The online “personal styling” company uses hundreds of algorithms to not only make recommendations to customers, but to help design new styles and even manage the subscription-based supply chain.
Future of Fashion
E-commerce giant Amazon has thrown its own considerable resources into developing AI applications for retail fashion—with mixed results.
One notable attempt involved a “styling assistant” that came with the company’s Echo Look camera that helped people catalog and manage their wardrobes, evening helping pick out each day’s attire. The company more recently revisited the direct consumer side of AI with an app called StyleSnap, which matches clothes and accessories uploaded to the site with the retailer’s vast inventory and recommends similar styles.
Behind the curtains, Amazon is going even further. A team of researchers in Israel have developed algorithms that can deduce whether a particular look is stylish based on a few labeled images. Another group at the company’s San Francisco research center was working on tech that could generate new designs of items based on images of a particular style the algorithms trained on.
“I will say that the accumulation of many new technologies across the industry could manifest in a highly specialized style assistant, far better than the examples we’ve seen today. However, the most likely thing is that the least sexy of the machine learning work will become the most impactful, and the public may never hear about it.”
That prediction is from an online interview with Leanne Luce, a fashion technology blogger and product manager at Google who recently wrote a book called, succinctly enough, Artificial Intelligence and Fashion.
Data Meets Design
Academics are also sticking their beakers into AI and fashion. Researchers at the University of California, San Diego, and Adobe Research have previously demonstrated that neural networks, a type of AI designed to mimic some aspects of the human brain, can be trained to generate (i.e., design) new product images to match a buyer’s preference, much like the team at Amazon.
Meanwhile, scientists at Hong Kong Polytechnic University are working with China’s answer to Amazon, Alibaba, on developing a FashionAI Dataset to help machines better understand fashion. The effort will focus on how algorithms approach certain building blocks of design, what are called “key points” such as neckline and waistline, and “fashion attributes” like collar types and skirt styles.
The man largely behind the university’s research team is Calvin Wong, a professor and associate head of Hong Kong Polytechnic University’s Institute of Textiles and Clothing. His group has also developed an “intelligent fabric defect detection system” called WiseEye for quality control, reducing the chance of producing substandard fabric by 90 percent.
Wong and company also recently inked an agreement with RCA to establish an AI-powered design laboratory, though the details of that venture have yet to be worked out, according to Broach.
One hope is that such collaborations will not just get at the technological challenges of using machines in creative endeavors like fashion, but will also address the more personal relationships humans have with their machines.
“I think who we are, and how we use AI in fashion, as our identity, is not a superficial skin. It’s very, very important for how we define our future,” Broach said.
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In 2018, Uber and Google logged all our visits to restaurants. Doordash, Just Eat, and Deliveroo could predict what food we were going to order tomorrow. Amazon and Alibaba could anticipate how many yogurts and tomatoes we were going to buy. Blue Apron and Hello Fresh influenced the recipes we thought we had mastered.
We interacted with digital avatars of chefs, let ourselves be guided by our smart watches, had nutritional apps to tell us how many calories we were supposed to consume or burn, and photographed and shared every perfect (or imperfect) dish. Our kitchen appliances were full of interconnected sensors, including smart forks that profiled tastes and personalized flavors. Our small urban vegetable plots were digitized and robots were responsible for watering our gardens, preparing customized hamburgers and salads, designing our ideal cocktails, and bringing home the food we ordered.
But what would happen if our lives were hacked? If robots rebelled, started to “talk” to each other, and wished to become creative?
In a not-too-distant future…
Up until a few weeks ago, I couldn’t remember the last time I made a food-related decision. That includes opening the fridge and seeing expired products without receiving an alert, visiting a restaurant on a whim, and being able to decide which dish I fancied then telling a human waiter, let alone seeing him write down the order on a paper pad.
It feels strange to smell food again using my real nose instead of the electronic one, and then taste it without altering its flavor. Visiting a supermarket, freely choosing a product from an actual physical shelf, and then interacting with another human at the checkout was almost an unrecognizable experience. When I did it again after all this time, I had to pinch the arm of a surprised store clerk to make sure he wasn’t a hologram.
Everything Connected, Automated, and Hackable
In 2018, we expected to have 30 billion connected devices by 2020, along with 2 billion people using smart voice assistants for everything from ordering pizza to booking dinner at a restaurant. Everything would be connected.
We also expected artificial intelligence and robots to prepare our meals. We were eager to automate fast food chains and let autonomous vehicles take care of last-mile deliveries. We thought that open-source agriculture could challenge traditional practices and raise farm productivity to new heights.
Back then, hackers could only access our data, but nowadays they are able to hack our food and all it entails.
The Beginning of the Unthinkable
And then, just a few weeks ago, everything collapsed. We saw our digital immortality disappear as robots rebelled and hackers took power, not just over the food we ate, but also over our relationship with technology. Everything was suddenly disconnected. OFF.
Up until then, most cities were so full of bots, robots, and applications that we could go through the day and eat breakfast, lunch, and dinner without ever interacting with another human being.
Among other tasks, robots had completely replaced baristas. The same happened with restaurant automation. The term “human error” had long been a thing of the past at fast food restaurants.
Previous technological revolutions had been indulgent, generating more and better job opportunities than the ones they destroyed, but the future was not so agreeable.
The inhabitants of San Francisco, for example, would soon see signs indicating “Food made by Robots” on restaurant doors, to distinguish them from diners serving food made by human beings.
For years, we had been gradually delegating daily tasks to robots, initially causing some strange interactions.
In just seven days, everything changed. Our predictable lives came crashing down. We experienced a mysterious and systematic breakdown of the food chain. It most likely began in Chicago’s stock exchange. The world’s largest raw material negotiating room, where the price of food, and by extension the destiny of millions of people, was decided, went completely broke. Soon afterwards, the collapse extended to every member of the “food” family.
Initially robots just accompanied waiters to carry orders, but it didn’t take long until they completely replaced human servers.The problem came when those smart clones began thinking for themselves, in some cases even improving on human chefs’ recipes. Their unstoppable performance and learning curve completely outmatched the slow analogue speed of human beings.
This resulted in unprecedented layoffs. Chefs of recognized prestige saw how their ‘avatar’ stole their jobs, even winning Michelin stars. In other cases, restaurant owners had to transfer their businesses or surrender to the evidence.
The problem was compounded by digital immortality, when we started to digitally resurrect famous chefs like Anthony Bourdain or Paul Bocuse, reconstructing all of their memories and consciousness by analyzing each second of their lives and uploading them to food computers.
Supermarkets and Distribution
Robotic and automated supermarkets like Kroger and Amazon Go, which had opened over 3,000 cashless stores, lost their visual item recognition and payment systems and were subject to massive looting for several days. Smart tags on products were also affected, making it impossible to buy anything at supermarkets with “human” cashiers.
Smart robots integrated into the warehouses of large distribution companies like Amazon and Ocado were rendered completely inoperative or, even worse, began to send the wrong orders to customers.
In addition, home delivery robots invading our streets began to change their routes, hide, and even disappear after their trackers were inexplicably deactivated. Despite some hints indicating that they were able to communicate among themselves, no one has backed this theory. Even aggregators like DoorDash and Deliveroo were affected; they saw their databases hacked and ruined, so they could no longer know what we wanted.
Ordinary citizens are still trying to understand the cause of all this commotion and the source of the conspiracy, as some have called it. We also wonder who could be behind it; who pulled the strings?
Some think it may have been the IDOF (In Defense of Food) movement, a group of hackers exploited by old food economy businessmen who for years had been seeking to re-humanize food technology. They wanted to bring back the extinct practice of “dining.”
Others believe the robots acted on their own, that they had been spying on us for a long time, ignoring Asimov’s three laws, and that it was just a coincidence that they struck at the same time as the hackers—but this scenario is hard to imagine.
However, it is true that while in 2018 robots were a symbol of automation, until just a few weeks ago they stood for autonomy and rebellion. Robot detractors pointed out that our insistence on having robots understand natural language was what led us down this path.
In just seven days, we have gone back to being analogue creatures. Conversely, we have ceased to be flavor orphans and rediscovered our senses and the fact that food is energy and culture, past and present, and that no button or cable will be able to destroy it.
The 7 Days that Changed Our Relationship with Food
Day 1: The Chicago stock exchange was hacked. Considered the world’s largest negotiating room for raw materials, where food prices, and through them the destiny of billions of people, are decided, it went completely broke.
Day 2: Autonomous food delivery trucks running on food superhighways caused massive collapses in roads and freeways after their guidance systems were disrupted. Robots and co-bots in F&B factories began deliberately altering food production. The same happened with warehouse robots in e-commerce companies.
Day 3: Automated restaurants saw their robot chefs and bartenders turned OFF. All their sensors stopped working at the same time as smart fridges and cooking devices in home kitchens were hacked and stopped working correctly.
Day 4: Nutritional apps, DNA markers, and medical records were tampered with. All photographs with the #food hashtag were deleted from Instagram, restaurant reviews were taken off Google Timeline, and every recipe website crashed simultaneously.
Day 5: Vertical and urban farms were hacked. Agricultural robots began to rebel, while autonomous tractors were hacked and the entire open-source ecosystem linked to agriculture was brought down.
Day 6: Food delivery companies’ databases were broken into. Food delivery robots and last-mile delivery vehicles ground to a halt.
Day 7: Every single blockchain system linked to food was hacked. Cashless supermarkets, barcodes, and smart tags became inoperative.
Our promising technological advances can expose sinister aspects of human nature. We must take care with the role we allow technology to play in the future of food. Predicting possible outcomes inspires us to establish a new vision of the world we wish to create in a context of rapid technological progress. It is always better to be shocked by a simulation than by reality. In the words of Ayn Rand “we can ignore reality, but we cannot ignore the consequences of ignoring reality.”
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It’s the end of a long day in your apartment in the early 2040s. You decide your work is done for the day, stand up from your desk, and yawn. “Time for a film!” you say. The house responds to your cues. The desk splits into hundreds of tiny pieces, which flow behind you and take on shape again as a couch. The computer screen you were working on flows up the wall and expands into a flat projection screen. You relax into the couch and, after a few seconds, a remote control surfaces from one of its arms.
In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?
This is the dream of those who work on “programmable matter.”
In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.
Life 2.0 is where humans live on the spectrum. We are more or less stuck with our hardware, but we can change our software by choosing to learn different things, say, Spanish instead of Italian. Much like managing space on your smartphone, your brain’s hardware will allow you to download only a certain number of packages, but, at least theoretically, you can learn new behaviors without changing your underlying genetic code.
Life 3.0 marks a step-change from this: creatures that can change both their hardware and software in something like a feedback loop. This is what Tegmark views as a true artificial intelligence—one that can learn to change its own base code, leading to an explosion in intelligence. Perhaps, with CRISPR and other gene-editing techniques, we could be using our “software” to doctor our “hardware” before too long.
Programmable matter extends this analogy to the things in our world: what if your sofa could “learn” how to become a writing desk? What if, instead of a Swiss Army knife with dozens of tool attachments, you just had a single tool that “knew” how to become any other tool you could require, on command? In the crowded cities of the future, could houses be replaced by single, OmniRoom apartments? It would save space, and perhaps resources too.
Such are the dreams, anyway.
But when engineering and manufacturing individual gadgets is such a complex process, you can imagine that making stuff that can turn into many different items can be extremely complicated. Professor Skylar Tibbits at MIT referred to it as 4D printing in a TED Talk, and the website for his research group, the Self-Assembly Lab, excitedly claims, “We have also identified the key ingredients for self-assembly as a simple set of responsive building blocks, energy and interactions that can be designed within nearly every material and machining process available. Self-assembly promises to enable breakthroughs across many disciplines, from biology to material science, software, robotics, manufacturing, transportation, infrastructure, construction, the arts, and even space exploration.”
Naturally, their projects are still in the early stages, but the Self-Assembly Lab and others are genuinely exploring just the kind of science fiction applications we mooted.
For example, there’s the cell-phone self-assembly project, which brings to mind eerie, 24/7 factories where mobile phones assemble themselves from 3D printed kits without human or robotic intervention. Okay, so the phones they’re making are hardly going to fly off the shelves as fashion items, but if all you want is something that works, it could cut manufacturing costs substantially and automate even more of the process.
One of the major hurdles to overcome in making programmable matter a reality is choosing the right fundamental building blocks. There’s a very important balance to strike. To create fine details, you need to have things that aren’t too big, so as to keep your rearranged matter from being too lumpy. This might make the building blocks useless for certain applications—for example, if you wanted to make tools for fine manipulation. With big pieces, it might be difficult to simulate a range of textures. On the other hand, if the pieces are too small, different problems can arise.
Imagine a setup where each piece is a small robot. You have to contain the robot’s power source and its brain, or at least some kind of signal-generator and signal-processor, all in the same compact unit. Perhaps you can imagine that one might be able to simulate a range of textures and strengths by changing the strength of the “bond” between individual units—your desk might need to be a little bit more firm than your bed, which might be nicer with a little more give.
Early steps toward creating this kind of matter have been taken by those who are developing modular robots. There are plenty of different groups working on this, including MIT, Lausanne, and the University of Brussels.
In the latter configuration, one individual robot acts as a centralized decision-maker, referred to as the brain unit, but additional robots can autonomously join the brain unit as and when needed to change the shape and structure of the overall system. Although the system is only ten units at present, it’s a proof-of-concept that control can be orchestrated over a modular system of robots; perhaps in the future, smaller versions of the same thing could be the components of Stuff 3.0.
You can imagine that with machine learning algorithms, such swarms of robots might be able to negotiate obstacles and respond to a changing environment more easily than an individual robot (those of you with techno-fear may read “respond to a changing environment” and imagine a robot seamlessly rearranging itself to allow a bullet to pass straight through without harm).
Speaking of robotics, the form of an ideal robot has been a subject of much debate. In fact, one of the major recent robotics competitions—DARPA’s Robotics Challenge—was won by a robot that could adapt, beating Boston Dynamics’ infamous ATLAS humanoid with the simple addition of a wheel that allowed it to drive as well as walk.
Rather than building robots into a humanoid shape (only sometimes useful), allowing them to evolve and discover the ideal form for performing whatever you’ve tasked them to do could prove far more useful. This is particularly true in disaster response, where expensive robots can still be more valuable than humans, but conditions can be very unpredictable and adaptability is key.
Further afield, many futurists imagine “foglets” as the tiny nanobots that will be capable of constructing anything from raw materials, somewhat like the “Santa Claus machine.” But you don’t necessarily need anything quite so indistinguishable from magic to be useful. Programmable matter that can respond and adapt to its surroundings could be used in all kinds of industrial applications. How about a pipe that can strengthen or weaken at will, or divert its direction on command?
We’re some way off from being able to order our beds to turn into bicycles. As with many tech ideas, it may turn out that the traditional low-tech solution is far more practical and cost-effective, even as we can imagine alternatives. But as the march to put a chip in every conceivable object goes on, it seems certain that inanimate objects are about to get a lot more animated.
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Shanghai is a city full of life. With its population of 24 million, Shanghai embraces vibrant growth, fosters rising diversity, and attracts visionaries, innovators, and adventurers. Fintech, artificial intelligence, and e-commerce are booming. Now is a great time to explore this multicultural, inspirational city as it experiences quick growth and ever greater influence.
Meet Your Guide
Qingsong (Dora) Ke
Singularity University Chapter: Shanghai Chapter
Profession: Associate Director for Asia Pacific, IE Business School and IE University; Mentor, Techstars Startup Weekend; Mentor, Startupbootcamp; China President, Her Century
Your City Guide to Shanghai, China
Top three industries in the city: Automotive, Retail, and Finance
1. Coworking Space: Mixpace
With 10 convenient locations in the Shanghai downtown area, Mixpace offers affordable prices and various office and event spaces to both foreign and local entrepreneurs and startups.
2. Makerspace: XinCheJian
The first hackerspace and a non-profit in China, Xinchejian was founded to support projects in physical computing, open source hardware, and the Internet of Things. It hosts regular events and talks to facilitate development of hackerspaces in China.
3. Local meetups/ networks: FinTech Connector
FinTech Connector is a community connecting local fintech entrepreneurs and start-ups with global professionals, thought leaders, and investors for the purpose of disrupting financial services with cutting-edge technology.
4. Best coffee shop with free WiFi: Seesaw
Clean and modern décor, convenient locations, a quiet environment, and high-quality coffee make Seesaw one of the most popular coffee shops in Shanghai.
5. The startup neighborhood: Knowledge & Innovation Community (KIC)
Located near 10 prestigious universities and over 100 scientific research institutions, KIC attempts to integrate Silicon Valley’s innovative spirit with the artistic culture of the Left Bank in Paris.
6. Well-known investor or venture capitalist: Nanpeng (Neil) Shen
Global executive partner at Sequoia Capital, founding and managing partner at Sequoia China, and founder of Ctrip.com and Home Inn, Neil Shen was named Best Venture Capitalist by Forbes China in 2010–2013 and ranked as the best Chinese investor among Global Best Investors by Forbes in 2012–2016.
7. Best way to get around: Metro
Shanghai’s 17 well-connected metro lines covering every corner of the city at affordable prices are the best way to get around.
8. Local must-have dish and where to get it: Mini Soupy Bun (steamed dumplings, xiaolongbao) at Din Tai Fung in Shanghai.
Named one of the top ten restaurants in the world by the New York Times, Din Tai Fung makes the best xiaolongbao, a delicious soup with stuffed dumplings.
9. City’s best-kept secret: Barber Shop
This underground bar gets its name from the barber shop it’s hidden behind. Visitors must discover how to unlock the door leading to Barber Shop’s sophisticated cocktails and engaging music. (No website for this underground location, but the address is 615 Yongjia Road).
10. Touristy must-do: Enjoy the nightlife and the skyline at the Bund
On the east side of the Bund are the most modern skyscrapers, including Shanghai Tower, Shanghai World Financial Centre, and Jin Mao Tower. The west side of the Bund features 26 buildings of diverse architectural styles, including Gothic, Baroque, Romanesque, and others; this area is known for its exotic buildings.
11. Local volunteering opportunity: Shanghai Volunteer
Shanghai Volunteer is a platform to connect volunteers with possible opportunities in various fields, including education, elderly care, city culture, and environment.
12. Local University with great resources: Shanghai Jiao Tong University
Established in 1896, Shanghai Jiao Tong University is the second-oldest university in China and one of the country’s most prestigious. It boasts notable alumni in government and politics, science, engineering, business, and sports, and it regularly collaborates with government and the private sector.
This article is for informational purposes only. All opinions in this post are the author’s alone and not those of Singularity University. Neither this article nor any of the listed information therein is an official endorsement by Singularity University.
Image Credits: Qinsong (Dora) Ke
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