Artificial intelligence in fashion : how consumers and the fashion system are being impacted by AI-powered technologies

Artificial intelligence in fashion:

how consumers and the fashion system are being

impacted by AI-powered technologies

Politecnico di Milano

Master's Degree in Design for the Fashion System

Patricia Nicole Evangelista

Politecnico di Milano School of Design

Master's Degree in Design for the Fashion System

Artificial intelligence in fashion: how consumers and the fashion system are being impacted by AI-powered technologies

Supervisor: Prof. Elena Marinoni Patricia Nicole Evangelista ID Number 912579

Special thanks to

The Invest Your Talent in Italy scholarship, supported by the Ministry of Foreign Affairs and International Cooperation, ICE - Italian Trade Agency and Uni–Italia, that made possible my academic path in Politecnico di Milano.

Professor Elena Marinoni for the advising and insights.

Robson Evangelista and Dilceia Evangelista for the continuous love and support. Johannes Keiten for always believing in my research and pushing me to keep going.

And to my loved ones Rafael Evangelista, Maria Ligia Freire Guilherme, Ceci Freire Evangelista, Loraine Meister, Flavia Villatore, Luiza Medeiros, Luisa Wandscheer.

table of contents

1. Artificial Intelligence

1.1. definition and history 1.1.1. human intelligence 1.1.2. machine intelligence 1.2. how to make machines intelligent

1.2.1. understanding data 1.2.2. machine learning

1.2.2.1. supervised and unsupervised learning 1.2.2.2. deep learning

1.2.3. computer vision

1.2.4. natural language processing (NLP) 1.2.5. robotics

2. Fashion and AI

2.1. concept and design 2.1.1. design thinking 2.1.2. trend forecasting

2.1.2.1. AI-powered trend forecasting

2.1.2.1.1. case studies

2.1.3. generative design 2.1.3.1. case studies

2.2. materials

2.2.1. artificial intelligence in natural fiber production 2.2.1.1. case studies

2.2.2. artificial intelligence in textile production 2.2.2.1. case studies

2.3. production

2.3.1. production and human labour 2.3.2. manufacturing robotics 2.3.2.1. case studies

2.4. logistics and distribution 2.4.1. warehouse robotics 2.4.1.1. case studies 2.4.2. demand forecasting 2.4.2.1. case studies 2.5. retail 2.5.1. e-commerce 2.5.1.1. conversational commerce p. 14 p. 16 p. 17 p. 19 p. 19 p. 20 p. 20 p. 21 p. 22 p. 22 p. 23 p. 24 p. 25 p. 26 p. 29 p. 29 p. 30 p. 31 p. 33 p. 37 p. 38 p. 44 p. 46 p. 47 p. 50 p. 51 p. 55 p. 56 p. 57 p. 57 p. 60 p. 60 p. 61 p. 65 p. 67 p. 69 p. 70 p. 70

p. 76 p. 85 p. 86 p. 90 p. 90 p. 91 p. 94 p. 96 p. 98 p. 100 p. 100 p. 101 p. 102 p. 103 p. 104 p. 105 p. 109 p. 116 p. 117 p. 118 p. 120 p. 125 p. 126 p. 136 2.5.1.2.1. case studies 2.5.1.3. virtual try-on 2.5.1.3.1. case studies 2.5.2. brick-and-mortar 2.5.2.1. smart mirrors 2.5.2.1.1. case studies

2.6. artificial intelligence and ethics in the fashion industry 2.7. AI in fashion as a partner in the covid-19 crisis

3. Field Research 3.1. hypotheses

3.1.1. level of exposure to AI-powered solutions

3.1.2. privacy concerns regarding AI powered solutions 3.2. methodology

3.3. data analysis 3.4. findings

3.4.1. level of exposure to AI-powered solutions

3.4.2. privacy concerns regarding AI powered solutions 3.5. considerations

3.5.1. quality of AI tools for retail 3.5.2. notes on social media 4. Final Thoughts: Techlash and Implications Conclusion

References Annexes

list of figures

Figure 1.1. Artificial Intelligence and some of its fields.

Figure 1.2. Examples of different types of data and their nature: internal/external and structured/unstructured (Adapted from Wilson, E., 2019).

Figure 1.3. Machine learning and its approaches of supervised and unsupervised learning and deep learning.

Figure 1.4. Neural networks composition, with layers and data flow.

Figure 1.5. Machine learning and its approaches of image recognition and image processing.

Figure 1.6. Example of how computer vision can recognize specific features in an outfit in details, developed by the startup Glisten.

Figure 1.7. Natural language processing and its approach of conversational interface.

Figure 1.8. Robotics and its approach of embodied AI. 2. Fashion and AI

Figure 2.1. The fashion value chain and the current AI applications to each phase.

Figure 2.2. Actions of a design thinking approach to design projects (adapted from Interaction Design Foundation, 2020)

Figure 2.3. Example of how artificial intelligence can analyze visually and textually an Instagram post

Figure 2.4. DeepFashion's image labeling: "(a) Additional landmark locations improve clothes recognition. (b) Massive attributes lead to better partition of the clothing feature space." (Liu, Z. et al., 2016).

Figure 2.5. The NextAtlas methodology for AI-powered trend forecasting, where the last step circles back to the first, in a cyclical discovery process (NextAtlas, 2020).

Figure 2.6. The Heuritech methodology for AI trend forecasting for fashion and luxury (Heuritech, 2020). p. 20 p. 21 p. 21 p. 22 p. 23 p. 24 p. 25 p. 25 p. 28 p. 29 p. 31 p. 32 p. 33 p. 34

Figure 2.8. 8 by Yoox's first 2018 collection. The in-house brand used data-driven trend forecasting based on social media and online magazines to inspire the design team into creating the collection (Mau, D., 2018).

Figure 2.9. Example of how a generative adversarial network (GAN) model works. Adapted from Luce, L. (2019).

Figure 2.10. The output of the GAN used by The Fabricant was a generated image of an outfit without a clear shape or style. Amber Jae Slooten then created her own interpretation of the given inspiration, which was developed in 3D and became a digital garment (Cottrill, F., 2018)

Figure 2.11. The dressed avatars were then inserted in hyper-realistic landscapes, mixing the real and the digital in a way that sparks confusion and curiosity on the viewer. (Cottrill, F., 2018)

Figure 2.12. Example illustrating the method proposed by Yildirim, G. et al., that can alter three clothing attributes - color, texture and shape - generating new products (Zalando Research, 2018).

Figure 2.13. Results of Zalando's method for changing attributes of a generated garment using GANs (Yildirim, G., et al. 2018).

Figure 2.14. The generation of a styled image by having a shape mask and a style noise as inputs (Sbai, O. et al., 2018).

Figure 2.15. Best rated items by human evaluation on the following categories: row 1, overall score; row 2, shape novelty; row 3, shape complexity; row 4, texture novelty; row 5, texture complexity; row 6, realism (Sbai, O. et al., 2018). Figure 2.16. Fibers classification by origin: natural or manmade (Adapted from Sinclair, R., 2015).

Figure 2.17. Textiles classification by construction technique: knitted, woven and assembled.

Figure 2.18. A team using Wadhwani AI's algorithm on pest traps through a smartphone (Extracted from Nandgaonkar, S., 2019).

Figure 2.19. Green Cube's identification of plants through drone generated image and projection of data in vegetation map (Green Cube Solutions, 2019). Figure 2.20. Cognex Deep Learning identification of anomalies and defects on the processes of weaving, knitting, printing and finishing of textiles (Adapted from Cognex, 2020).

Figure 2.21. The result of an AI generated color tolerance (Datacolor, 2016). Figure 2.22. The traditional knitting production process of taking a pattern, introducing it to a machine to generate the textile versus the AI model that provides the pattern based on an inserted photo (Kaspar, A. et al., 2019).

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Figure 2.23. The process of garment manufacturing step by step (Adapted from Nayak, R., Padhye, R., 2018).

Figure 2.24. SoftWear's Sewbot working on a fabric (Hasan, S., 2018). The robot's arms are able to handle the fabric, position it properly and perform the sewing.

Figure 2.25. Sewts' robots are designed to perform advanced textile handling for different tasks. (Otto, A., 2019)

Figure 2.26. SoftWear's Sewbot working on a fabric (TRT World, 2018). The robot's arms are able to handle the fabric, position it properly and perform the sewing.

Figure 2.27. One of Covariant's robots picking and placing on the workline objects of different sizes and shapes (Adapted from Covariant, 2020).

Figure 2.28. Kindred's AutoGrasp technology in action: the robot identifies the objects through computer vision and then place them in the right bins, completing the sorting out process (Kindred, 2020).

Figure 2.29. The field of predictive analysis includes demand forecasting, a branch that aims to predict customer demand for a product or service. Artificial intelligence can be a technique to achieve unlimited data source demand forecasting through the use of machine learning.

Figure 2.30. Amazon Forecast uses historical and related data to, through machine learning, predict demand of a product or service (Amazon Web Services, 2020).

Figure 2.31. Different sales channels in the fashion industry (Adapted from Iannilli, V., 2019).

Figure 2.32. Interactions with Michael Kors' Facebook Messenger chatbot. The bot starts asking where the customer is from, and then offering different features accordingly. In these screenshots, the interaction led to a video of the Fall 2020 runway show backstage content.

Figure 2.33. Interactions with ASOS' Enki chatbot (Gilliland, N., 2018). Besides customer support, something more common in other chatbots, Enki is able to provide style recommendations through voice, text or image activation.

Figure 2.34. Amazon Echo Look technical details, including LED lighting, camera, microphone and speakers (Mallis, A. 2018).

Figure 2.35. Amazon Echo Look's Style Check tool that compares and rates different outfits based on fit, color, styling and current trends (Koifman, V., 2018). p. 55 p. 57 p. 58 p. 62 p. 62 p. 63 p. 66 p. 67 p. 69 p. 72 p. 73 p. 76 p. 77

Figure 2.37. SnapStyle trying to identify a denim jacket, mistakenly confusing with a fur coat (Halzack, S., 2019).

Figure 2.38. SnapStyle trying to identify a long sleeve, white blouse with puffy shoulders. The tool showed a lace blouse (even though that was not the fabric from the original photo) and even knitted blouses and sweatshirts.

Figure 2.39. In this case, SnapStyle analysis of the image was pretty accurate in identifying the floral printed midi skirt and recommending similar products. Figure 2.40. Stitch Fix's Shop Your Looks tool, in which the algorithm recommends products that go with items the client already has, creating a style card and personalizing the shopping experience (Alcedo, M., 2020).

Figure 2.41. An example of how a human - in this case, a stylist called Sophie - hand picks products of a particular style for a client with the help of AI (Cook, J., 2017).

Figure 2.42. The Nike Fit feature allows for customers to photograph and scan their feet in order to, through the use of artificial intelligence, get the most accurate size recommendation.

Figure 2.43. FaceCake released in 2020 a sneak peek video of their new feature Augmented Realism. In the video, it's possible to see sunglasses and a pair of earrings being worn, and as the models move their heads, the products also move and capture different reflections from the environment (FaceCake, 2020). Figure 2.44. Zeekit's algorithm divides the clothing and the user's images in 80,000 segments that are later combined to fit the item over the body (BBC, 2020).

Figure 2.45. MemoMi's Memory Mirror allows the user to try different colors of the same dress (MemoMi, 2020).

Figure 2.46. The voice interactive mirror placed in H&M flagship store allows customers to take selfies and share them on social media, get special discounts and get outfit inspiration that can be purchased on the spot (Ombori, 2018). Figure 2.47. The relationship between the growth of the discussions around coronavirus and the visibility of luxury products on Instagram can be a sign that consumers are more cautious and sensitive to the crisis, and over consumerism can be seen as morally questionable (Heuritech, 2020).

3. Field Research

Figure 3.1. The correlation matrix (Annex 3) with all correlation coefficients, differentiating positive and negative correlations by color (being green for positive and red for negative) and strengths by shades (darker being stronger and lighter being weaker).

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Figure 3.2. Composition of the respondents regarding age and region of origin. The regions of Africa, Asia and Oceania are not shown on the graph due to the low number of participants (0,7%, 1,7% and 0%, respectively).

Figure 3.3. Percentage of consumers that have used AI-powered solutions for retail by each specific product.

Figure 3.4. Percentage of consumers that have used AI-powered solutions for retail by region of origin. The regions of Africa, Asia and Oceania are not shown on the graph due to the low number of participants.

Figure 3.5. The matrix showing the coefficients of the correlation between age and occurence of use of AI-powered solutions. Although there is mostly a negative correlation, the coefficients are so low that there is no relevant relationship between the two variables.

Figure 3.6. The matrix showing the coefficients of the correlation between frequency of online shopping and occurence of use of AI-powered solutions. A low to almost moderate positive correlation was identified.

Figure 3.7. The matrix showing the coefficients of the correlation between the confidence that brands keep and process personal data safely and occurence of use of AI-powered solutions. Most of the coefficients are so low that no correlation is found.

Figure 3.8. The matrix showing the coefficients of the correlation between the importance of personalized shopping assistance and the openness to share personal data in order to get that type of assistance. The correlation coefficient is a moderate positive, meaning that when one variable grows, the other does as well.

Figure 3.9. Table with the results of the survey concerning the willingness to share image-based data by age group. The total numbers show a resistance to hand out data of this nature, with a high number of strong disagreements to the questions asked.

Figure 3.10. Table with the results of the survey concerning the willingness to share non image-based data by age group. The total numbers show a lower resistance to hand out data of this nature, with a lower number of strong disagreements to the questions asked in comparison to image-based data. The number of strong agreements is also significantly higher.

Figure 3.11. Table with the results of the survey concerning the willingness to use virtual try-on by giving webcam access by age group. The total numbers show a high reluctance to adopting this kind of feature, with more than half of the participants strongly disagreeing with the statement.

Figure 3.12. Table with the results of the survey concerning the willingness to

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Annex 1. Questionnaire “Consumer Behavior in Apparel Retail” Annex 2. Correlation matrix

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list of annexes

abstract

Artificial intelligence and its many subfields are powerful tools that can be applied to every step of the fashion value chain - from concept and design to the source of materials, production, logistics and retail. It can bring improvements such as speeding and scaling up processes, handling amounts of data that humans can't and offering to consumers new ways of experiencing retail. This document aims to explore how artificial intelligence is changing the fashion industry, by first defining basic concepts of computer science in order to understand how they relate to fashion, such as the difference between human intelligence and machine intelligence and a walk through the history of AI development. Then, specifics of AI were described, including machine learning, computer vision, robotics and natural language processing. Every part of the fashion’s value chain was analyzed in order to find the current applications and possibilities, as well as the startups and innovators that are shaping the field with examples of case studies. In addition, a field research was conducted through the application of a survey, designed in order to evaluate the level of exposure of the end consumer to AI-powered solutions for retail, as well as how they perceive and trust them. The results were analyzed in order to uncover all the layers and correlations of data that can help identify hurdles and barriers that can present challenges for these technologies to be adopted in a wider way, such as privacy concerns and quality of the solutions. At last, the findings of the field research are contextualized in the framework of the so-called techlash, the feeling of animosity towards digitalism in a world that relies heavily on technology.

estratto

L'intelligenza artificiale e i suoi numerosi sottocampi sono strumenti potenti che possono essere applicati ad ogni fase della catena del valore della moda - dal concetto e dal design alla fonte dei materiali, alla produzione, alla logistica e alla vendita al dettaglio. Possono apportare miglioramenti quali la velocizzazione e l'aumento di scala dei processi, la gestione di quantità di dati che gli esseri umani non possono gestire e l'offerta ai consumatori di nuovi modi di vivere l'esperienza della vendita al dettaglio. Questo documento mira ad esplorare come l'intelligenza artificiale stia cambiando l'industria della moda, definendo prima di tutto i concetti di base dell'informatica per capire come si relazionano con la moda, come la differenza tra l'intelligenza umana e l'intelligenza delle macchine e una passeggiata nella storia dello sviluppo dell'intelligenza artificiale. Poi sono state descritte le specifiche dell'IA, tra cui l’apprendimento automatico, la visione artificiale, la robotica e l'elaborazione del linguaggio naturale. Ogni parte della catena del valore della moda è stata analizzata per trovare le applicazioni e le possibilità attuali, così come le startup e gli innovatori che stanno cambiando il settore con esempi di casi di studio. Inoltre, è stata condotta una ricerca sul campo attraverso l'applicazione di un'indagine, progettata al fine di valutare il livello di esposizione del consumatore finale alle soluzioni basate sull'IA per la vendita al dettaglio così come il modo in cui le percepisce e si fida di loro. I risultati sono stati analizzati al fine di scoprire tutti i livelli e le correlazioni di dati che possono aiutare a identificare gli ostacoli e le barriere che possono presentare sfide per l'adozione di queste tecnologie in modo più ampio, come le preoccupazioni sulla privacy e la qualità delle soluzioni. Infine, i risultati della ricerca sul campo sono contestualizzati nel quadro del cosiddetto techlash, il sentimento di animosità verso il digitalismo in un mondo che si affida fortemente alla tecnologia.

introduction

Some may think that artificial intelligence is the future of fashion - but actually, it is already the present. AI entered in almost every part of the fashion value chain (Luce, L., 2019), but it’s still not being applied on a bigger scale. Innovators in the market already understood the power of it, but there are a lot of opportunities available to be explored.

In concept and design, AI is being used for data-driven trend forecasting and generative design, helping designers and companies in the creative process. In materials, AI is seen improving the farming of natural fibers as well as serving as a tool for quality control and helping production of knitted and woven textiles. In the production step, the groundbreaking manufacturing robots are being developed to speed up processes and bring garment production back to western countries, while in logistics and distribution robots can aid the process of warehousing. Another important part in which artificial intelligence is being used is for demand forecasting, predicting consumer demand for a product or service, allowing companies to plan the amount of goods to be manufactured and avoid overproduction. In the final step of the fashion value chain, AI is being applied in retail through conversational commerce - or chatbots -, virtual personal stylists that give fashion advice based on data, and virtual try-on and smart mirrors, that rely on a combination of AI and augmented reality.

The adoption of these AI products fuels a lot of discussion around ethics in the fashion field. According to Renda (2019), artificial

intelligence can be disruptive and empowering, but also unpredictable and can pose challenges such as exacerbating biases, inequalities and discrimination.

The research tackles ethical issues such as the replacement of garment factory workers by sewing robots and how this could negatively impact countries that have their economies greatly based in this industry. It also discusses a concern that has been emerging in the last years: the protection of privacy. Especially in retail, personal data is being collected and processed by companies to offer a personalized shopping experience, but the lack of knowledge on how this data is being treated is concerning for many consumers.

After the desk research and the analysis of the current scenario, this research aims to investigate the consumer's perspective and understand the level of exposure to the current AI-powered solutions in retail and also concerns and points of reluctance that can impede someone from using such solutions. Understanding these barriers is fundamental to analyze how to deal with the techlash trend that brings a more pessimistic approach to new technologies and the Big Tech - which are the giant companies that dominate the tech world - as well as to how AI can be a partner to fashion in humanitarian crises such as the COVID-19 pandemic in 2020.

Ultimately, the goal is to understand the all the problematics of the current scenario in order to have a glimpse of how artificial intelligence, the fashion industry and its consumers can co-exist in the near future.

ar

definition and history

In order to begin to understand the applications of AI in

fashion, basic concepts of computer science have to be

explained.

The first thing that has to be taken in consideration to begin to understand what artificial intelligence is, is knowing that it is an umbrella term - meaning, it encompasses a set of processes, methods, tools and techniques to reach the goal of making computers behave intelligently, like humans do, including abilities like “reasoning, problem solving, memory recall,

planning, learning, processing natural language, perception, manipulation, social intelligence and creativity” (Luce, L., 2019).

The field of artificial intelligence was created in 1956, in the Dartmouth Summer Research Project On Artificial Intelligence - held in the Dartmouth College in Hanover, New Hampshire - where a group of scientists gathered for two months to brainstorm around the new subject. On the workshop’s proposal, released a year before, the researchers stated that “An attempt will be made to find how to

make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

(McCarthy, J., Minsky, M., Rochester, N., Shannon, C.E., 1955).

After that summer, artificial intelligence was officially a field of study, and from that point up until the 70’s, AI research thrived. There was a common sentiment of fascination over the wonders of this new powerful discipline, with a lot of hopeful and promising projections. Marvin Minsky, an American scientist known for his contributions to AI studies, said in 1970 in a Life magazine interview that “in from three

to eight years we will have a machine with the general intelligence of an average human being”.

Expectations were high, and with that came the disappointment when researchers realized things were more complex than what

they have predicted. One of the main obstacles was the fact that computers simply couldn’t store or fast process information as desired (Anyoha, R., 2017). Shifting from an optimistic perspective to a sceptical one made the research fundings and interest shrink and AI research entered a phase called AI winter, lasting from mid 70’s to early 1980.

In the 80’s, interests in AI began to rise again, with the development of initiatives like the expert systems by Edward Feigenbaum, that allowed computers to mimic the decision making and problem solving of experts of a given field (Anyoha, R., 2017). With the return of interests comes also the return on fundings, and AI was then being developed and researched again. Big investors included the Japanese government with the Fifth Generation computer project, that aimed to give computers reasoning capabilities to eventually perform tasks like “diagnose diseases, analyze lawsuits

and understand language” (Pollack, A., 1992).

But once again the prospects were too high and, after a 400 million dollars investment, the initiative failed to achieve many of its goals and commercial interests decreased, leading Japan to offer the software to anyone who was interested, even foreigners (Pollack, A., 1992) - which is a relevant fact since Japan and the USA were on competing grounds at that time to lead the computer technology market.

After another set of unmet expectations, once again an AI winter period arrived in the late 80’s and early 90’s. Investments were cut again and commercial interests in artificial intelligence were low. But even without government fundings and much less hype, the field continued being studied and is known that even during AI winter periods there were

developments and achievements. By the late 90’s to mid 2000’s, AI was back and big milestones were being reached. The approach began to be more on specific areas, like machine learning and natural language processing, and AI began to be a constant in society.

In the new century, artificial intelligence - backed up by all evolutions in computer science - is not just a trend, but part of everyday life, being found

in products and processes. Companies are investing in research and development, with the global players being the American companies Amazon, Google and Microsoft, accompanied also by the Chinese company Alibaba. Predictions estimate that AI can

potentially add to global economic activity in 13 trillions of dollars by 2030 and that 70 percent of companies may have adopted at least one type of AI technology by that same year (McKinsey Global Institute, 2018).

As discussed, the history of AI is far from linear when it comes to development and investments. In a lot of periods, there has been a lot of disbelief in the field and its potentialities. However, at this moment artificial intelligence has reached a point of no return as it is not confined anymore at research centers in Universities or small projects. Its uses are spread across industries and a world without the applications of AI would be hard to imagine at this point.

I believe that in about fifty years’ time it will be possible to

programme computers, with a storage capacity of about 10 ,

to make them play the imitation game so well that an average

interrogator will not have more than 70 percent chance of

making the right identification after five minutes of questioning.

… I believe that at the end of the century the use of words and

general educated opinion will have altered so much that one

will be able to speak of machines thinking without expecting to

be contradicted.

(Turing, A., 1950, extracted from the Stanford Encyclopedia of Philosophy)

the computer mislead the interrogator into not being able to differentiate the two, and if the machine was able to trick them, it would pass the test and prove its intelligence.

This ability of a computer to respond like a person is possible thanks to natural

language processing (NLP), a form of artificial

intelligence that understands human language - which is completely different from machines' code-based languages - and that will be further explained in the chapter 1.2.4. of this document.

human intelligence

machine intelligence

providing machines with human intelligence, it is important to define what the last stands for. In general words, intelligence “involves language

and the capacity to develop and transmit a culture, to think, reason, test hypotheses, and

understand rules.” (Mackintosh, N. J., 2011).

It is not only about receiving and storing information, it is being able to make sense of it, put into context and make connections, with the capacity of learning - another key concept for the field of AI.

In order to understand if a machine can think like a human - or, in better words, can mimic human behaviors and reactions -, the British computer scientist Alan Turing created in 1950 the Turing Test, which consisted of a game with two humans (one interrogator and one responder) and one computer. The interrogator would ask questions to both the person and the computer, and later was asked to identify who was the human and who was the machine based on the answers. The point of this so-called Imitation Game was to make

Figure 1.1. Artificial Intelligence and some of its fields.

how to make machines

intelligent

understanding data

To start talking about artificial intelligence, first it is essential to understand the concept of data, since AI relies heavily in its collection and analysis.

According to Luce, L. (2019), data is generally a "raw set of information that requires

some processing in order to have meaning" and

it can be structured - when it is organized,

data, like blog posts or emails.

A company can have a large set of data that can be either internal, originated within the organization like the company's website or purchase orders, or external, which are those originated outside the firm, such as social media or macroeconomic indicators.

The four main areas of AI that are specially relevant for the fashion industry include machine learning, computer vision, natural language processing (NLP) and robotics. Machine Learning Natural Language Processing (NLP) Computer Vision Robotics

Figure 1.2. Examples of different types of data and their nature: internal/external and structured/ unstructured (Adapted from Wilson, E., 2019).

Figure 1.3. Machine learning and its approaches of supervised and unsupervised learning and deep learning.

machine learning

computers could learn from experience and thus automatically improve the efficiency of their own programs during execution".

If the final goal of AI is to make machines think, behave and respond like a human, they should have the ability to

learn from past experiences and apply this knowledge in future processes, just like humans do. That is possible through machine learning, an extremely important field of artificial intelligence that aims to identify patterns in data that was previously fed to the machine, predicting the value of non existent data (Luce, L., 2019) thus making it possible for machines to learn without being manually programmed. Machine Learning Deep Learning Supervised Learning Unsupervised Learning Internal Structured Unstructured External

E-commerce sales data Sales transactions Purchase orders Inventory Company’s point of sale info Loyalty card Customer service Weather Customer store ship/receipts Third-party syndicated data Macroeconomic indicators Government census Customer point of sale info Household panel data

Websites Reviews Marketing campaigns Apps In-store devices Texts

Customer relationship data Social media

Click streams

Internet of things (IoT) Geolocation devices Digital personal assistants Videos

Figure 1.4. Neural networks composition, with layers and data flow.

supervised and unsupervised

learning

deep learning

Deep learning is a category inside the macro area of machine learning and consists of

"learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text" (GitHub, 2011),

usually relying on neural networks.

Neural networks are a subcategory of machine learning that provide a higher level

of complexity because, instead of taking input data and generating an output in a linear way, it is composed by intermediate nodes - in the so-called hidden layers with its own synapses - that are interconnected with other nodes, that then transform data of the input layers in output layer results (Luce, L., 2019).

When there is only input data and no previous responses, the machine learns through unsupervised learning, relying just on underlying patterns and inferences from a set of data. The applications of each learning mode will depend on the nature of the available data.

Input Data Output data

Neural Network

Although the definition may seem too abstract for someone not familiar with the field of computer science, the applications of deep learning to fashion include one very known tool: the recommendation of products for

"If we want machines to think, we need to teach them to see" (Li, F., 2015). One of the

most fascinating senses of the human being is sight, that is used to understand the world that surrounds us, from survival to socialization.

The power of sight has also been given to machines through computer vision, allowing them to recognize, interpret and process digital images and videos. Through image recognition, the machine can analyze the pixels and their numeric values on the given image, being able to recognize it as containing an object, person, animal, etc. (Towards Data Science, 2018).

Image processing, in the other hand, is able to manipulate the image, with the goal of making it more readable by a machine (Luce, L., 2019), including "smoothing, sharpening,

contrasting and stretching on the image" (Sagar,

R., 2018).

customers that are shopping on e-commerce platforms based on previously searched products. The effectiveness of this engine can be a combination of deep learning and computer vision, another AI field.

Computer vision then comes with the ability to take action over that collected information, as we can see in smart mirrors, for example. Through machine learning, it can recognize patterns and interpret the image (Sagar, R. 2018).

Applications to computer vision are various, from face recognition to autonomous cars. In the fashion field, one example is the ability to analyze images on fashion blogs and social media platforms, understand which kind of products are being shown and its features and, together with other artificial intelligence tools, predict market demand and fashion trends. It can also be used for recommendation systems in e-commerce, by sorting products out in a lookbook picture.

computer vision

Computer Vision

Image Recognition

Image Processing

made to reach it (Liddy, E. D., 2001). NLU still remains one of NLP's challenges and it's considered an AI-complete, which is the term used in computer science to designate AI problems which the solution require a solution to the strong AI problem, meaning, the "synthesis

of a human-level intelligence" (Raymond, E. S.,

1991).

Through NLP it was possible to create softwares and devices like Amazon Alexa, Apple Siri and Google Home. These devices are called conversational interfaces, and allow humans and computers to interact using natural language - in this case, oral. For textual communication, NLP enabled the creation of It was previously explored how

computers can learn and see, and they are also capable of communicating through natural language processing. This area studies and analyzes texts, both oral and written, that occur naturally without being done for the purpose of the analysis and with the restriction of being a text used by humans to communicate with other humans, having as final goal “to

accomplish human-like language processing”

(Liddy, E. D., 2001).

NLP was also referred as natural language understanding (NLU), but the term was left behind because in order to achieve full understanding, the computer should be able

natural language processing (NLP)

Figure 1.6. Example of how computer vision can recognize specific features in an outfit in details, developed by the startup Glisten.

Natural Language Processing (NLP)

Conversational Interfaces

Figure 1.7. Natural language processing and its approach of conversational interface.

Figure 1.8. Robotics and its approach of embodied AI.

robotics

Robots have been present in the popular imagination and culture for a long time now. When science fiction imagined what the future would look like, it was often filled with robots living among humans, performing all kinds of tasks, socializing and even falling in love like they were humans themselves. But the reality is that robotics and how it is applied nowadays is way less scenographic than that.

Robots are used from the manufacturing industry, performing automated tasks helping or replacing field workers, to medicine, executing surgeries or aiding in the rehabilitation of patients. In the fashion industry, robotics can help in the supply chain - with warehousing tasks of picking and packing, for example (Luce, L., 2019), or in the manufacturing processes - like automated sewing and knitting.

Inside robotics, there is the approach of embodied AI, which unites the fields of computer science and engineering. It can be defined as "embodied systems in the real physical

and social world" (Pfeifer, R., Iida, F., 2003),

meaning that AI capabilities are integrated in physical devices.

One example of embodied AI are Internet of things (IoT) devices. IoT is a set of physical objects such as devices, vehicles and appliances that are equipped with sensors, software, internet and network connectivity and computing capability to process and act on data autonomously (PwC, 2017). Examples range from smart home devices like Amazon Echo to wearables like Apple Watch (Meola, A., 2019). Robotics

The clarification of the basic concepts of artificial intelligence mentioned above is necessary to begin to understand how AI is being applied to the fashion industry's value chain.

Value chain is a term theorized by

Porter (1985) which divides a company into the "discrete activities it performs in designing,

producing, marketing, and distributing its product" or, in other words, is the set of steps

necessary to deliver a product or service to the market.

The fashion value chain - particularly in the apparel industry - is in general composed by the following parts (Figure 2.1.):

Concept and design, including

research, briefing, moordboarding and sketching;

Materials, including natural and

manmade fibers as well as the textiles production, which can be knitted, woven or assembled;

Production, which is the manufacturing of the garments;

Logistics and distribution, including

warehousing and inventory, domestic and export distribution;

Retail, including both e-commerce

and brick-and-mortar stores

The end consumer

Artificial intelligence is present in all these areas - in some, this presence is more spread while in others it's still in experimental phases.

In the phase of concept and design, applications include the use of AI for trend forecasting and generative design; in materials, AI is used to aid farming; in the textiles field, it can help both the production and the quality control phases, making processes more efficient and decreasing errors; in the production, manufacturing robotics is used, while in logistics there are supply chain robots as well as the area of demand forecasting. Retail has a lot of examples of AI applications, both in e-commerce - with virtual style assistants, virtual try-on and conversational commerce - as in brick and mortar stores, with smart mirrors.

In this chapter, each part of the fashion value chain and its current relationships with artificial intelligence will be analyzed and exemplified with case studies.

concept and design

design thinking

The concept and design phase of the fashion value chain is a critical one, because it's when the decisions that will impact all the development of the project are made. There are many methodologies and approaches to the design process with different goals - for example,

While the practices of design thinking have been explored for decades now, the term gained popularity with the giant design and consulting firm IDEO, that's been applying a human-centered methodology for 40 years (IDEO, 2020). Tim Brown, the Executive Chair of the company, defines design thinking as: "(...) a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success."

a more technical and engineering approach, a conceptual one or an innovation-driven perspective. In terms of innovation, one of the most popular and discussed methodologies in the design world is design thinking.

Another definition by Tjendra (2014) describes it as a "creative-problem solving

approach designers use to create new values that are different and create positive impact".

The Hasso-Plattner Institute of Design at Stanford University - also known as d.school - synthesized the design thinking process in five actions: empathise, define, ideate, prototype and test (Interaction Design Foundation, 2020)

(Figure 2.2.). Those actions don't have to be in

order and the processes can overlap.

Empathise

UsersU sers needs

Problems

Ideate Prototype Test

Challenging

assumptions Creating solutions Solutions

Creating ideas Insights

Figure 2.2. Actions of a design thinking approach to design projects (adapted from Interaction Design Foundation, 2020)

trend forecasting

Trend is a common jargon in the fashion

industry. The color of the year, the jacket of the season, the bag of the moment: all those are called trends, or fashion trends. But the term is way deeper and broader than that - a trend is a pattern of change, it's the direction in which something is developing and changing (Marinoni, E., 2019). These changes can be in every field and sector of the society and have an impact on its belonging areas.

Trend research is about identifying these patterns, understanding and acting on them, and forecasting ultimately predicts future scenarios. This understanding is fundamental for companies to study their field, the cultural, economic and social changes surrounding it and plan ahead, especially if they are seeking innovation. In the design thinking methodology, trend forecasting can be a powerful tool in the empathize and define phases.

For fashion, more than knowing which is the next season's hyped print, it is about studying the impact of trends on their market, their consumer behaviors, their generational target and aspirations.

The industry knew the power of trend research since the 60's, when trend books were used to inspire designers and manufacturers

trend forecasting online platforms started to emerge. The shift was big: from the physical and seasonal trend book that predicted trends that would be applied on products months later to a fast, digital, cross-industry and international point of view (Marinoni, E., 2019). This qualitative approach of trend forecasting is based on the opinion of experts - industry specialists, intellectuals and innovators. Examples of companies that adopt this method are WGSN, Trend Watching and Trend Hunter.

The late 2000's saw another big shift in the trend research industry with the emerging of platforms with a data-driven approach, all favoured by the exponential development of technology and artificial intelligence. Contemporary world is fast paced and with the popularization of social media dictating the trends, they can appear with the same speed they disappear (Worth, M., 2015).

Data collected from Instagram, Pinterest, Facebook and other social media can give insights about emerging trends and where they place in the trend diffusion curve - meaning, if they are popular only among trendsetters and innovators, if they've already hit majority or if they are in decline. With this information, it is possible to predict the future of that given trend with a fair level of certainty and make informed decisions on a project. In the empathize phase, designers need

to put themselves in the users' shoes and try to understand their needs and problems, leading to possible insights in the define phase. Ideation comes with challenging previously conceived assumptions and creating new ideas, while prototyping in an important step where new solutions can emerge. Testing also can generate new insights and ideas as the designer can put in practice the decisions made prior to that. As already mentioned, the whole design thinking process does not have to be linear, with step

after step - it's rather a continuous approach. Design thinking comes as an example of design methodology to understand the steps a designer can make in the concept and design phase. It's important to understand these steps to locate where artificial intelligence can come as a tool to aid designers and create new opportunities.

As seen in the previous chapter, AI is currently being applied in concept and design through trend forecasting research and generative design methods.

shift in methodologies throughout time - while in the past, the predictions and analysis came from top to bottom (experts to brands and then consumers), now trends emerge from bottom

to up (consumer demands impacting brands). The analysis of big amounts of data collected from social media and other online platforms is made possible through artificial intelligence.

(Chakrabarti, S. et al., 2006), allowing to filter and obtain only the desired information.

The data then can be scanned through computer vision to analyze visual content and natural language process for textual content. For example, a picture posted on Instagram by an influencer can generate a lot of insights

(Figure 2.3.), both visually like the colors of the

outfit, prints, lengths, textures and fabrics, as textually, with analysis of captions, hashtags and emojis.

As seen previously, the power of social media for brands and consulting companies is massive nowadays, because they provide valuable information about consumers by what they spontaneously post. But gathering millions of pictures, videos and texts generate a huge amount of data that, if not organized, clustered and analyzed, is simply not useful.

In order to sort this out, data mining can be used, a process that uncovers patterns in a large dataset (Luce, L., 2019) and extracts useful knowledge from those big repositories

AI-powered trend forecasting

It includes over 800,000 images, annotated with "massive attributes, clothing

landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer" (Liu, Z. et al., 2016).

The dataset is available to the public for research purposes.

Through deep learning, it is possible to train the machine to apply these attribute classifiers to all future images without human supervision (Matzen, K., Bala, K., Snavely, N., 2017).

The combination of deep learning, computer vision and NLP allows to cluster images with similar attributes, understanding characteristics like relevance and geographical occurrence. Moreover, it allows trend forecasters to monitor those clusters and their

evolution with time, making it possible to make assumptions about the future of the trend from which the cluster is a part of.

This AI method can be applied not only on social media posts, but also in runway photos from fashion weeks, street style pictures and e-commerce images. A cross platform analysis can be particularly interesting to understand how a trend can impact consumers and brands in different ways.

The computer knows that this is a jacket, a pair of boots, a bag and so on because it had learned from a previously introduced dataset of photos annotated with clothing attributes. One example of a dataset of this kind is the DeepFashion initiative, developed in The Chinese University of Hong Kong.

Figure 2.4. DeepFashion's image labeling: "(a) Additional landmark locations improve clothes recognition. (b) Massive attributes lead to better partition of the clothing feature space." (Liu, Z. et al., 2016).

case studies

Companies using AI for trend forecasting are on the rise. One example is the platform NextAtlas, that "meshes together data

from a wide variety of sources into actionable insights that inform and inspire the creative process" (NextAtlas, 2019). Through the use

of algorithms and artificial intelligence, the company detects cross-industry emerging trends on social media following the steps of:

case study 1

NextAtlas

Data sourcing, tracking down who

are the innovators and trendsetters of a specific industry;

Data analysis, through machine

learning, content and visual analysis, generating insights;

Foresight, predicting patterns,

behavioral shifts and trends.

According to the company, the AI tools used for predicting trends are computer vision, text analysis and deep learning.

Figure 2.5. The NextAtlas methodology for AI-powered trend forecasting, where the last step circles back to the first, in a cyclical discovery process (NextAtlas, 2020).

case study 2

Heuritech

Figure 2.6. The Heuritech methodology for AI trend forecasting for fashion and luxury (Heuritech, 2020).

Another example of trend forecasting using artificial intelligence is Heuritech. Focused specifically in the fashion and luxury sectors, the company applies visual recognition to analyze 3 million images per day and claims that their model can "recognize more than

2000 fashion details in any given image from a

specific model to general trends, including colors, patterns, materials, environments, textures, products, shapes" (Heuritech, 2020).

Their methodology is also focused on social media insights and the AI tools used include computer vision and machine learning.

case study 3

Stylumia

Stylumia is a tech company that relies completely on artificial intelligence to provide an array of services to fashion companies. It also has a data-driven approach to fashion trend forecasting, providing different services:

Market intelligence tool, collecting

data through computer vision from brands, retail and runway shows in order to provide consumer-driven forecast and validation of trends and products in the market;

Fashion intelligence tool, analyzing

through computer vision product performance, allowing companies to make informed decisions on product stocks, orders and promotion, thus generating recommendations and predicting future moves.

The company also offers other services using AI applied to fashion, through generative design and demand forecasting, topics that will be further discussed in this document.

Figure 2.7. Stylumia's approach to fashion forecasting (Stylumia, 2020)

case study 4

8 by Yoox

In-house labels are really interesting for multi-brand retailers, since they can offer higher profit margins, fill gaps in the shop's catalogue as well as being able to rely on the huge amount of customer data that these companies can gather throughout time (Mau, D., 2018).

The multi-brand e-commerce Yoox understood this potential and launched in 2018 its first private-label, called 8 by Yoox. The first collection was composed by garment essentials

and the design team had an important partner to develop it: artificial intelligence. AI allowed the company to analyze social media and online magazines in their key markets, with algorithms filtering "predictive indicators into

emerging lifestyle and style trends, analysis of own data from products sold on its site, customer feedback, industry purchasing trends as well as text search and image recognition" (Yoox

Net-A-Porter Group, 2018). The result of this AI-powered research was a series of mood

Figure 2.8. 8 by Yoox's first 2018 collection. The in-house brand used data-driven trend forecasting based on social media and online magazines to inspire the design team into creating the collection (Mau, D., 2018).

boards that guided the designers into creating a collection that appealed to their target customer. Trend research was used as a starting point for the collection, and the garments were based on the forecasts.

The brand is still active and new collections were developed since the launch, all sold in Yoox online store.

Companies that offer data-driven trend forecasting services or brands that use the technology inside their own teams to base their design decisions are on the rise. Trend forecasting evolved from being an expert-opinion industry into relying more on data, understanding the signals that the digital world gives, especially on social media. Before the development of AI, analyzing such amounts of data was not possible, and now the insights extracted directly from the users are allowing brands to understand better their targets as well as their wishes, needs and anxieties, being able to plan their collections accordingly.

considerations

generative design

The idea of computers replacing humans was once a science fiction idea, but it's been a reality for quite some time now. In different fields, machines can effectively and accurately perform tasks that used to be done by people, often reducing or completely eliminating the need of a human in the loop - for example, for industry production and manufacturing, or solving complex mathematical equations.

However, when it comes to creative tasks, it is still a present idea that a computer can't replace a person such as an artist, a designer or a writer, because machines cannot think in a subjective, sensitive, political or contextual way. That could be partly true, but generative models prove that computers can reproduce creativity and actually create a completely new image, based on existing visual information.

Generative models are an AI branch related to unsupervised learning, a type of machine learning. Its basis consists of gathering a large dataset and training a model to generate

new data similar to it (Karpathy, A, 2016). For example, if a model is fed with an input of thousands of street style pictures from Milan's last Fashion Week season, the output can be a series of new and similar street style pictures - but these ones are not going to be real.

Even higher levels of complexity and efficiency can be achieved through generative adversarial networks (GANs), that use two neural networks - one to generate results and another one to evaluate the accuracy of the results (Luce, L., 2019). In the example of the Fashion Week street style pictures, what the second neural network would do is take a new image generated by the model and use it as input, estimating the probability of that image being from the original input - meaning, of it being real or fake. The ultimate goal is to train the model enough so the second neural network will not be able to distinguish if the output is from the real world or was generated by the first neural network (Luce, L., 2019), producing convincing images.

Neural Network Generative Neural Network Adversarial Input INPUT OUTPUT OUTPUT Labeled dataset with probability of image being from the initial input

or from the generated

output

Initial set of street style images

Figure 2.9. Example of how a generative adversarial network (GAN) model works. Adapted from Luce, L. (2019).

Through generative models it is possible to develop generative design, a very promising tool within this field. Generative design is applying the same concept - of the computer producing non-existent images based on

existent ones - into new products, making the computer some sort of AI fashion designer. Not only can new garments be generated, but also their colors, textures and shapes can be modified through the model.

a workshop for academic researchers where it discussed its new algorithm for generative design, that will be applied to sets of fashion images and generate new ones aligned with the current trends (Knight, W., 2017). There is no further information about the research though, as Amazon didn't comment on the topic.

The brand launched in 2018 a project called DEEP - Faster Fashion, designed by Amber Jae Slooten, with a series of digital outfits that were created taking as inspiration AI-generated pictures of runway garments. The process started with Paris Fashion Week pictures as input, and through a general adversarial network the algorithm generated new images as output.

The designer then used those images as the starting point of her designs. The interpretations were based in a post-fashion western aesthetic and the clothing was created in the software CLO 3D. She then rendered them in silver avatars and the final result is an extremely interesting collection, merging the hyper-realistic images with imaginary settings. The Fabricant is a digital clothing brand

- that means that they do not produce or sell real garments, but digital 3D ones that can be applied to an avatar or to the picture of a real person in a hyper-realistic way, giving the impression that the person is actually wearing it. This is especially interesting nowadays with the rising importance and power of fashion influencers on social media, in which a picture is often more important than the reality. Those influencers need to produce large quantities of content, including outfit pictures to inspire the followers. Digital fashion allows them to wear exclusive items without having to have them physically - a more sustainable solution since the garment production is non existent.

Although still often used as an experimental tool, generative design is subject to many research papers and big companies are getting interested in developing all its potentialities in the future.

The giant Amazon has been investing heavily in fashion for the last few years. In 2017, news revealed that the company organized

case studies

case study 1

Figure 2.10. The output of the GAN used by The Fabricant was a generated image of an outfit without a clear shape or style. Amber Jae Slooten then created her own interpretation of the given inspiration, which was developed in 3D and became a digital garment (Cottrill, F., 2018)

Figure 2.11. The dressed avatars were then inserted in hyper-realistic landscapes, mixing the real and the digital in a way that sparks confusion and curiosity on the viewer. (Cottrill, F., 2018)

Figure 2.12. Example illustrating the method proposed by Yildirim, G. et al., that can alter three clothing attributes - color, texture and shape - generating new products (Zalando Research, 2018).

case study 2

Disentangling Multiple Conditional Inputs in GANs

have their own tech research departments to investigate new and innovative technologies. One example is the fashion e-commerce platform Zalando, that published a paper in 2018 called "Disentangling Multiple Conditional

Inputs in GANs", explaining its method of

adjusting color, texture and shape of a garment created by a generative model. Through a Generative Adversarial Network (GAN), the company developed a sort of style assistant that provides computer aided fashion design (Yildirim, G., et al., 2018). The ultimate goal is

to make the fashion design process faster and more well-structured, by helping designers to visualize a garment in different variations, saving time and aiding style choices.

Their method take into consideration three attributes as input - color, defined by a 3-dimensional vector of RGB values; shape, defined by a segmentation mask; and texture, defined by a vector that represents the texture

and the local structure of an article - giving as

output a new piece of garment. The training dataset consisted of 120,000 images of dresses from Zalando's website.

In Figure 2.13, it is possible to see the

model working on changing the color of a dress, maintaining the shape outlined by the mask in

the right. Secondly, the texture was redefined and lastly, the shape was altered maintaining the colors and textures.

The results still have limitations of image quality but, if better developed, this tool can be an even more valuable asset for

big fashion companies that have to create and visualize a huge portfolio of products regularly.

Figure 2.13. Results of Zalando's method for changing attributes of a generated garment using GANs (Yildirim, G., et al. 2018).

Similar research was conducted by Facebook AI Research department with École des Ponts and New York University, in a paper named "DesIGN: Design Inspiration

from Generative Networks". It explores the

generation of products using random or shape masks instead of images as input. Starting from the fact that the traditional process of fashion production requires different professionals

with varied skills for the steps of pattern making (handling the materials and textures) and sample making (handling the shape models), the algorithm proposed by the researchers also works with the two processes separately by placing textures (called style noises) onto the shape masks, in order to generate a product through the use of GANs (Sbai, O. et al., 2018).

results in the given categories: shape novelty, shape complexity, texture novelty, texture complexity and realism.

The results of the paper are rather interesting but, as the previous example, still lack a full image quality.

As mentioned previously, GANs consist of having another layer of neural network - the adversarial one - in order to classify the outputs that come closest to real life products, improving the model. The research also used human evaluation at the end of the output set to identify, in the human point of view, the best

Figure 2.14. The generation of a styled image by having a shape mask and a style noise as inputs (Sbai, O. et al., 2018).

case study 3

Figure 2.15. Best rated items by human evaluation on the following categories: row 1, overall score; row 2, shape novelty; row 3, shape complexity; row 4, texture novelty; row 5, texture complexity; row 6, realism (Sbai, O. et al., 2018).

Perhaps the biggest challenge of generative design is creating not only garment images that are convincing as being real, but also make them high quality. As of now, generative models can be used as style assistants, giving inspiration to designers to play with different possibilities of shapes, textures and colors and also making those processes faster.

The role of a designer is much more complex than just generating images, it is about concept, references, subjectivity and also the technical skills. As seen on DEEP by The Fabricant, AI offered an inspiration for the designer with pixelated, blurry generated images, but it was the designer's interpretation and creativity that made the digital garments come into life.

considerations

Figure 2.16. Fibers classification by origin: natural or manmade (Adapted from Sinclair, R., 2015).

materials

An enormously important part of the fashion value chain is the material sourcing. Deciding which material to use in each project is fundamental, and the textile industry is an ever evolving field.

Materials in the fashion field - with a particular focus on clothing production - include two main areas: fibers and textiles. In general terms, fibers are the foundation for textiles and textiles are the foundation for garments.

Fiber sources can be natural or manmade. Natural fibers are the ones derived from natural elements, and as classificated by Sinclair, R. (2015) can be distinguished by the resource of origin:

Vegetable/cellulosic, for example:

cotton, hemp, jute and sisal

Animal/protein, for example: wool,

cashmere and silk

Mineral, with one example: asbestos

(no longer in use because of its toxicity)

Manmade fibers, on the other hand, are the ones not found in nature and are produced by humans, like the following:

Regenerated, for example: viscose,

modal, acetate

Synthetic polymer, for example:

polyesters, polyamides, elastomers and acrylics

Inorganic, for example: carbon,

glass, ceramic and metallic fibers

Natural

Vegetable/ Cellulosic

Animal/ Protein

Mineral Regenerated Synthetic Inorganic polymer

Manmade

Fibers of great length are called filaments while the ones with shorter length are called staple fibers. Both are the base to form yarns, just with different techniques - filaments are twisted while staple fibers are spun (Sinclair, R., 2015). Yarns can then be knitted, woven or assembled to form textiles.

Textiles can be constructed by three different techniques:

Knitting, consisting of

interconnected series of loops made of yarns, in which a row of loops catches the preceding row. Lengthwise loops are called wales and running crosswise loops are named courses (Whewell, C., Abrahart, E., 2020). Knitting can be handmade or done through a machine.

Weaving, in which lengthwise

yarns (warps) are combined with widthwise yarns (wefts), being interlaced in a regular order, through the use of looms - being them handloom or industrial machines.

Assembling, that are also called

non-woven, consisting of an entanglement of fibers or filaments that are bonded together mechanically, thermally or chemically (INDA - Association of the Nonwoven Fabrics Industry, 2019).

Defining the basics of materials in the fashion industry is necessary in order to understand the contribution of artificial intelligence in this specific field.