Artificial Intelligence enables marketers to personalise and create more effective customer experiences, and improve ROI. The second, the Zeitgeist Tool, again using Visual Recognition APIs, analysed trends of colours and styles from Instagram images, to help predict colors, styles, necklines, cuts and fabrics. Building APIs to measure social sentiment analysis is possible using natural language and tone analysis to understand what content is relevant to your brand, what the sentiment is and helps you to make decisions on which posts to act on. How: Our wonderful partner Servian has created a compelling solution which, leverages tone analysis, social sentiment analysis – and more – to help your organisation profile the persona of the best possible fit for your organisation, and then find that person – leveraging their CV, social profiles, references and more.
For such problems, an AI-powered image search is a really helpful technology which can assist the user to search an item just by clicking the pic. In general, when we search for any particular apparel on any of the ecommerce websites, the results shown are based on current fashion trends in the market. For the sake of better understanding, let's take an example of a user searching for a "Floral Anarkali Kurti." After carefully analysing the popular trends and studying the images a user has typically searched for, artificial intelligence makes room for real-time and most precise recommendations.
Project Muze, a machine learning-based endeavor that utilizes Google's Tensor Flow system, is essentially a virtual fashion designer. Drawing from a neural network trained on various design preferences like color, style and texture of over 600 fashion trendsetters and Google Fashion Trend Report information, Project Muze can create various fashion ensembles simply by asking users a few questions about their interests. Tapping into users' music interests, moods, favorite art style and gender allows Project Muze to draft some rather, shall we say, unique fashions that are generated in real-time. But hey, maybe it's some wild new fashion trend!
Indian artificial intelligence startups are disrupting the fashion industry by helping brands identify trends on how consumers buy clothes. Brands are crunching time to bring new designs to market using the underlying technology that help predict consumer trends, while increasing sales of their new selections. Mad Street Den, based in Chennai, has developed Vue.ai, a artificial intelligence visual recommendation platform that it offers to fashion e-commerce portals, both in India and overseas. With our product, the e-commerce companies get an artificial intelligence enabled end to end stylist assistance," says Ashwini Asokan, co-founder and CEO of Mad Street Den.
Today, on a separate note, we'll look at some of the initiatives where machine learning is disrupting the consumer fashion market in some really novel and unique ways. The model uses Orbeus ReKognition API to score facial elements, metadata scan of the outfit post, background analysis using trained scene classifiers, data from Flickr80k image styles and location data into the model feed to give a recommendation on "fashionability" of the image. Researchers at the Indiana University at Bloomington are focusing on building machine learning algorithms to identify future supermodels. Machine as the "Fashion Designer" Machine learning intervention isn't limited to merely predicting in the fashion industry.
This transformation will have a few important qualities: 1) AI will be a platform play, not something brands build and launch individually; 2) the effects will mostly be invisible to the consumer; and 3) AI is as much a mindset as it is pure technology. This has a crucial implication for the fashion industry: with a few exceptions, infusing AI into the industry will be a platform play. This means that AI will be the next big platform play for the industry, possibly mapping to the growth in ecommerce platforms. The platform play means brands will have much less control because of their lack of technology expertise and resources.