What to expect at the first AI Fashion Week

Launching later this month, AI Fashion Week promotes AI as a tool for fashion and supports emerging designers working with the technology. Three winners’ garments will be produced physically and sold by Revolve Group.
BY MADELEINE SCHULZ

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A new fashion week is coming to New York – and, as interest in the potential of AI reaches fever pitch, it couldn’t be better timed. The first AI Fashion Week (20-21 April), held at Soho’s Spring Studios, is showcasing collections from emerging AI designers.

Backed by Spring Studios and e-commerce retailer Revolve Group, the event is making the case for AI as a tool for fashion design, supporting new designers working with the still-nascent technology. An opening event on the evening of 20 April is for media, VIPs and participants, while the space is open all day on 21 April to the public.

Participants have until 15 April to submit a collection of 15 to 30 looks, which will be judged by the public via online and in-person voting, promoted on the social media channels of AI Fashion Week (AIFW), Spring Studios and Revolve, as well as through Revolve’s expansive influencer network. More than 350 submissions have been received to date. Ten finalists proceed to round two in May, with three winners selected by a panel including Tiffany Godoy, Vogue Japan’s head of editorial content; Natalie Hazzout, Celine head of men’s casting; Erika Wykes-Sneyd, VP of Adidas’s Three Stripes Studio; Matthew Drinkwater, head of London College of Fashion’s Fashion Innovation Agency; and Michael Mente, Revolve CEO and co-founder.

The winners will receive support from AIFW’s fashion-tech incubator in partnership with Revolve. A key requirement is that the garments must be possible to produce physically, and the winning collections will be made and sold online, either via Revolve or Fwrd, Revolve’s luxury site, depending on the garments. Designers will receive support throughout the launch process, including with pattern making, sample development and marketing and communications.

“The idea is to come back to the real world,” says Cyril Foiret, founder of digital publication Trendland and AI creative studio Maison Meta, which is producing AIFW. Maison Meta previously worked with Moncler on the AI-generated Genius campaign. The event is specifically designed to bridge the physical and the digital. “It’s about mixing both worlds together,” Foiret says.

This bridging is reflected in the creative process as well. Despite worries that AI could preclude human creativity and, in turn, designers’ jobs, the AIFW guidelines dictate that creatives must step in, revise and amend their input and creations to ensure the output is eligible. This is where expertise comes in. “Translating the design into a production process will be needed,” says McKinsey senior partner Holger Harreis. “Right now, human intelligence will still be needed in this step. Parts might be generative AI-supported, but it will stay human-centric for a while.”

“It’s a new type of designer,” Mente says. “It’s not like the computer is designing and replacing jobs. It’s a different type of creator that’s using different types of technologies to create different types of outputs that can be produced physically. It’s a fresh perspective.”

Nigerian-American creator and digital artist Walé Oyerinde, for instance, credits his physical fashion background with his ability to input “the right information to get the right results”. He adds, “It’s words that a normal person who is not a designer might not know — the more experience and knowledge you have in the field, the better the results.”

Consultancy McKinsey predicts that generative AI could add $150 to $275 billion to the operating profits of the fashion and luxury sectors within the next three to five years. “These are early days, but we do see a very high potential [for fashion],” Harreis says.

Luxury brands are testing the waters. At Metaverse Fashion Week in March, Tommy Hilfiger and DressX held an AI design competition. The industry is watching the first AIFW from the sidelines but Foiret notes plenty of expressions of interest. He hopes to bring on board brands including L’Oréal for season two.

Revolve’s Mente wants to empower people to leverage the toolset that AI provides. “I think this is a big opportunity for designers of the future,” he says. Fellow jury member Godoy, of Vogue Japan, has long been focused on innovation. “I like to jump on things early,” she says, noting her intrigue in AI’s potential to shape how people communicate — especially via fashion imagery.

The creative process

This first AI Fashion Week has drawn an interesting mix of participants, including many not involved in fashion full time. Around 60 per cent of creators have used the AI platform Midjourney to create their pieces. 

Godoy is keen to see the reference points, including photography, that designers look to. “Is it all [Steven] Meisel? Is it all Norbert Schoerner from 1990s/2000s Prada campaigns? What are they looking at?” 

Ukrainian designer Irina Perivy has been inspired by her home country. “In the neural network request, I wrote such words as: spikelet, Ukrainian traditions, field and embroidery. I created the clothes, mood and atmosphere as in a Ukrainian field so that the viewer could be transported for a second to my country.” 

Designer Oyerinde has come up with a collection titled Haute Futur that plays on concepts of high and low, appealing to the consumer with familiar shapes and lines while looking forward with futuristic fabrics. The creator says his experience working with physical textiles has helped him to input the words to generate what he calls “data fabrics”.

Fledgling fashion houses are also opting in. Ilona Song, founder of the eponymous phygital fashion house, has submitted a collection titled Futuristic Fauna, inspired by camellias. Nastaran Hashemi, founder of digital fashion platform Orbyline, is entering collections both personally and under the Orbyline name. For both, she and her team translated the mood board to define silhouettes, primary shapes, colours and materials. Using blending techniques and remix tools, they perfected seam lines, cuts and proportions, adding a “personal touch”, Hashemi says.

A new generation of creatives
While excitement about the potential of AI is rising fast, there’s also a degree of hesitancy. LCF’s Drinkwater argues for a more balanced perspective. “The discourse around AI has for too long been ‘utopia or dystopia’,” he says. “There are immediate use cases for the technology to improve efficiency across design, marketing, logistics, supply chain and store operations. Fashion must improve its knowledge, skill sets, understanding and application of these technologies to deliver more sustainable business models.”

Revolve’s Mente agrees. “As a company, it’s important for us to stay at the cutting edge of what’s going on. It’s going to be incredible to show the world that the technology — with a human element — is a big opportunity for a new generation of creatives.”

For Godoy, the potential extends beyond digital design. “It’s about image making and world building,” she says. Designer Oyerinde adds: “It’s not just the finished garment. It can also tell a brand story — what we strive for in editorials. Now, we have this tool to create that aspirational element through AI.”

AI tools also increase accessibility for younger people without resources. Godoy recalls a conversation with Marjorie Hernandez, co-founder of digital fashion platform The Dematerialised, about how AI tools will support creatives from just about anywhere. “It opens up doors to even more people to create images, to create fashion,” she says.

Walking the line
That at least some of the garments must be physically possible to produce might seem counter-intuitive. LCF’s Drinkwater is of two minds. “It’s important for the industry to see how these tools can be used to improve efficiency and to help in the design process. But, I’m more excited to see the designs that are freed from the limitations of the physical world [when] we let creativity — and computing — run wild.”

To fit within the guidelines, designers had to temper their more exuberant instincts. “There was some limitation, because the creative flow of AI cannot be stopped — it knows no boundaries. So, many moments had to be controlled,” says designer Perivy.

The physical guidelines were helpful for Orbyline’s Hashemi, who comes from what might be deemed a “traditional” fashion background. Producing more expressive, extravagant pieces and reworking them to be wearable was an enjoyable process, she says.

Designer Oyerinde agrees. “It helped me narrow down my process,” he says. “It’s such a powerful tool that makes you realise your imagination is just the beginning. So, you can easily get carried away.”

Looking forward, Maison Meta plans to continue to blend physical and digital. For season two, Foiret says, a physical runway show derived from AI-generated designs will be introduced. He would like to expand the concept to events in Paris, London and Milan.

Godoy believes the blended approach of physical and digital is likely to be the key to the success of the concept. “This fashion week is so interesting to me because it is very considered. There’s a real awareness of the two worlds not always connecting [but] in the end, we all have digital identities. There’s a TPO — time, place, occasion — for both sides.”

India takes on China, Vietnam in electronics manufacturing; eyes $300 billion in local production by FY26

NEW DELHI: India’s counter-attack on China and Vietnam in manufacturing of electronics is set to get additional fire-power.

On target is $300 billion in output over the next four years, including $120 billion reserved for exports; broadening of product-basket for incentives; specially crafted large industrial zones with modern facilities; and permissions for factories that may hold up to as many as 1 lakh workers with dormitories, kitchens, medical set-ups, and housing complexes.

The plan is to give scale to electronics manufacturing

 set up that would ultimately lead to creation of strong supplier eco-system, massive employment opportunities, and global servicing.

IT and electronics minister Ashwini Vaishnaw and junior minister Rajeev Chandrasekhar presented ‘Vision Document 2.0’, prepared by their ministry and presented by the India Cellular & Electronics Association through its chairman Pankaj Mohindroo.

Vaishnaw said that the government, which has already committed nearly $17 billion over the next six years across four production-linked incentive (PLI) schemes (semiconductor and design; smartphones; IT hardware, and components) will be coming out with more categories where benefits will be extended for local manufacturing

. These are likely to include hearables and wearables, industrial and auto electronics, and telecom equipment.

The government wants the charge to come not only from global players such as Taiwanese Foxconn and Wistron (both Apple’s contract manufacturers) and Korean Samsung, but also from ‘domestic champions’ such as Optiemus, Dixon, and Lava.

Vaishnaw said he has spoken to the labour ministry regarding issues in setting up large factories that can have as many as one lakh employees, and have housing complexes for the workers.

Also, he said that his ministry is identifying land for building vast integrated manufacturing zones (going up to as much as 1,000 acres) with all requisite facilities such as land, power, roads, and connectivity built-in, much in line with what’s allotted in China and Vietnam.

The industry welcomed the proposal. “The plug-and-play model is indeed welcome. It takes away concerns around land acquisition, road and infra, power, and connectivity,” Sunil Vachhani, chairman of Dixon said.

Lava’s chairman Hari Om Rai also said that local companies need to go global while expanding manufacturing within India.

Minister Chandrasekhar said growth of digital consumption and diversification of global value chains will help achieve the targets.

Chinese conspicuous by absence

The mega meet of local and global electronics makers did not have Chinese players as participants or speakers. Asked what role does India sees for the Chinese in Indian electronics manufacturing, junior IT & electronics minister Chandrasekhar said, “We don’t have any particular view… the concept of trust in value chain is an important attribute in post-Covid world and any investment and any investment partner that meets the trust criteria can manufacture.”

TOP 10 DEEP LEARNING PROJECTS FOR ENGINEERING STUDENTS IN 2022

If you are one of them wanting to start a career in deep learning, then you must read these top deep 10 learning projects.

Deep learning is a domain with diverse technologies such as tablets and computers that can learn based on programming and other data. Deep learning is emerging as a futuristic concept that can meet the requirements of people. When we take a look at the speech recognition technology and virtual assistants, they are run using machine learning and deep learning technologies. If you are one of them wanting to start a career in deep learning, then you must read this article as this article features current ideas for your upcoming deep learning project. Here is the list of the top 10 deep learning projects to know about in 2022.

Chatbots

Due to their skillful handling of a profusion of customer queries and messages without any issue, Chatbots play a significant role for industries. They are designed to lessen the customer service workload by automating the hefty part of the process. Nonetheless, chatbots execute this by utilizing their promising methods supported by technologies like machine learning, artificial intelligence, and deep learning. Therefore, creating a chatbot for your final deep learning project will be a great idea.

Forest Fire Prediction

Creating a forest fire prediction system is one of the best deep learning projects and it will be another considerable utilization of the abilities provided by deep learning. Forest fire is an uncontrolled fire in a forest causing a hefty amount of damage to not only nature but the animal habitat, and human property as well. To control the chaotic nature of forest fires and even predict them, you can create a deep learning project utilizing k-means massing to comprehend major fire hotspots and their intensity.

Digit Recognition System

This project involves developing a digit recognition system that can classify digits based on the set tenets. The project aims to create a recognition system that can classify digits ranging from 0 to 9 using a combination of shallow network and deep neural network and by implementing logistic regression. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive.

Image Caption Generator Project in Python

This is one of the most interesting deep learning projects. It is easy for humans to describe what is in an image but for computers, an image is just a bunch of numbers that represent the color value of each pixel. This project utilizes deep learning methods where you implement a convolutional neural network (CNN) with a Recurrent Neural Network (LSTM) to build the image caption generator.

Traffic Signs Recognition

Traffic signs and rules are crucial that every driver must obey to prevent accidents. To follow the rule, one must first understand what the traffic sign looks like. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. For a final-year engineering student, it is one of the best deep learning projects to try.

Credit Card Fraud Detection

With the increase in online transactions, credit card frauds have also increased. Banks are trying to handle this issue using deep learning techniques. In this deep learning project, you can use python to create a classification problem to detect credit card fraud by analyzing the previously available data. 

Customer Segmentation

This is one of the most popular deep learning projects every student should try. Before running any campaign companies create different groups of customers. Customer segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base.

Movie Recommendation System

In this deep learning project, you have to utilize R to perform a movie recommendation through technologies like Machine Learning and Artificial Intelligence. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Visual Tracking System

A visual tracking system is designed to track and locate moving object(s) in a given time frame via a camera. It is a handy tool that has numerous applications such as security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction.

Drowsiness Detection System

The drowsiness of drivers is one of the main reasons behind road accidents. It is natural for drivers who drive long routes to doze off when behind the steering wheel. Even stress and lack of sleep can cause drivers to feel drowsy while driving. This project aims to prevent and reduce such accidents by creating a drowsiness detection agent.  

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