Artificial Intelligence

The phrasing “application areas” refers to the specific areas where machine learning tools and techniques can be applied. Natural language processing, computer vision, predictive analytics, and robotics might use some of the same techniques like neural networks to solve different types of problems. Those application areas can be further extended to industry applications.

Natural Language Processing

Machine language and human language meet in natural language processing (NLP). NLP is a way for computers to comprehend human languages. Every day, our interactions on the Web—things we post on social media, text messages we write, and so forth—contribute to an ever-expanding mass of data. Of this, it is estimated that 80% of the 2.5 quintillion bytes of data created every day is unstructured data. This is written in free form, unorganized and historically hard to parse. We can use NLP to understand the content and context of this unstructured data, unlocking a rich treasure trove of information about ourselves.

Natural language processing is applied in multiple product categories including conversational shopping, AI customer service chatbots, and virtual assistants and stylists.

Computer Vision

Computer vision (CV) is used to process and analyze images and videos. CV automates tasks we might associate with the human visual system and more. Although computer vision is a field of its own, artificial intelligence has played a major role in recent progress. Computer vision is frequently used in fashion applications because the fashion industry is so visual.

In the fashion industry, computer vision is being used in technologies such as visual search, smart mirrors, social shopping, trend forecasting, virtual reality, and augmented reality.

Predictive Analytics

AI can identify upcoming trends faster than industry insiders to enhance the design process —Avery Baker, chief brand officer, Tommy Hilfiger.

Predictive analytics uses a variety of methods that use historical information to predict events that will happen in the future. These methods range in complexity and include data mining, basic statistics, and machine learning.

In this website, predictive analytics show up in two other areas:

recommender systems and demand forecasting.

Recommender systems are part of predictive analytics. They seek to understand user or customer behavior and recommend products or services that the user is likely to like or purchase. Recommender systems have played a critical role for discovering products in e-commerce. You’ll find them everywhere, from fashion retail web sites to behind the scenes in subscription box services. You’ll also notice them in other areas including music and video streaming on sites like Netflix or YouTube.

Demand forecasting is used to optimize supply-chain planning. By predicting demand for products, the fashion industry can reduce overproduction, thereby cutting costs and reducing waste.


Robotics, especially in apparel manufacturing, is a unique area of study that requires domain expertise across fashion, mechanical engineering, and machine learning. Robots have been used in industrial settings for many years in manufacturing for automotive, aerospace, and other industries that deal with mostly rigid parts.

Robotic manufacturing in the fashion industry is still a nascent field because of the complexities involved with handling fabrics. Nonetheless, with improvements in computer vision and in the planning algorithms needed to perform complex tasks, robotics is being adopted in fashion.