In the first application for Hikrobot in the UK, global fashion brand, Superdry, is leveraging the flexibility of intelligent mini-robot carriers to transform order picking and put-away at its UK hub – just part of a phased roll-out of goods-to-person robotics that will boost productivity across its international network of multi-channel fulfilment centres. Superdry is an iconic, global fashion brand operating through 768 store locations in 65 countries. Since its foundation in Cheltenham in 1985, the business has experienced phenomenal growth and in 2019 reported revenue of £871 million – a success story built on a reputation for providing distinctive, high-quality products that fuse vintage Americana and Japanese inspired graphics with a British style. As an omni-channel retailer competing in the fast-moving fashion sector, maintaining high product availability, efficient fulfilment and the rapid processing of returns is essential for ensuring the best possible customer experience across multiple channels – retail, wholesale and ecommerce. Critically, all of these competitive differentiators depend upon the fast, accurate and efficient picking of products from across Superdry's extensive range of over 60,000 SKUs, held at the company's three regional distribution centres in the UK, Europe and USA.

Alana'CityStyleBot' is giving high street and independent fashion retailers an alternative virtual shop front to serve customers post COVID-19. Launched in February 2020, Cork Start Up Alana is an innovative Fashion and Beauty platform for consumers to purchase curated fashion looks and beauty products. Alana is powered by Artificial Intelligence meaning that it learns to recommend styles/brands that will suit each customer's unique taste. Alana suggests clothes from high street retailers and independent boutiques with a same day delivery service making the whole highstreet a virtual shopping center – one checkout – one delivery charge of €3.99. Alana helps retailers compete with major brands who have an established ecommerce foothold. According to ACI Worldwide there is a 74% growth in the average transaction volumes due to a dramatic rise in online retail this March in comparison to March 2019.

The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior. The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports. And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed. For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools. An online retailer found that its AI was ordering stock that no longer matched with what was selling.

Artificial intelligence (AI) software has always received most of the attention, however, as the computational resources required to process this software skyrocket, a new generation of hardware is being created endowed with artificial intelligence. Some experts have named this evolution "Cambrian explosion", referring to the current period of fervent innovation. Today, AI's range of innovative hardware accelerator architectures continues to expand. Although you tend to think that graphics processing units (GPUs) are the most advanced dominant AI hardware architecture, that's far from true. Over the past few years, both start-ups and established vendors have introduced an impressive generation of new hardware architectures optimized for machine learning, deep learning, natural language processing and other much more advanced Artificial Intelligence workloads.

The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior. The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports. And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed. For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools. An online retailer found that its AI was ordering stock that no longer matched with what was selling.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. In this tutorial, you create machine learning models automatically without writing a line of code! You use Amazon SageMaker Autopilot, an AutoML capability that automatically creates the best classification and regression machine learning models, while allowing full control and visibility. For this tutorial, you assume the role of a developer working at a bank. You have been asked to develop a machine learning model to predict whether a customer will enroll for a certificate of deposit (CD).

Artificial intelligence in the retail sector is being applied in new ways, from the whole product and service cycle to assembly-to-post customer service interactions, but the key questions for retail players. What AI applications play a role in the automation or growth of the retail process? How retail are companies today using this technology to stay ahead of their competitors, and what innovations are posed as potential retail game-changers over the next decade? Innovation is a double-edged sword, and like any innovation, the results are a mixed bag. Many AI applications have yielded increased ROI -- this case study of AI in the retail marketing department is an example -- while others have failed and failed to meet expectations, such innovations shed light on the obstacles that must be overcome before becoming industry drivers.

Data-driven methods -- such as machine learning and time series forecasting -- are widely used for sales forecasting in the food retail domain. However, for newly introduced products insufficient training data is available to train accurate models. In this case, human expert systems are implemented to improve prediction performance. Human experts rely on their implicit and explicit domain knowledge and transfer knowledge about historical sales of similar products to forecast new product sales. By applying the concept of Transfer Learning, we propose an analytical approach to transfer knowledge between listed stock products and new products. A network-based Transfer Learning approach for deep neural networks is designed to investigate the efficiency of Transfer Learning in the domain of food sales forecasting. Furthermore, we examine how knowledge can be shared across different products and how to identify the products most suitable for transfer. To test the proposed approach, we conduct a comprehensive case study for a newly introduced product, based on data of an Austrian food retailing company. The experimental results show, that the prediction accuracy of deep neural networks for food sales forecasting can be effectively increased using the proposed approach.

Fashion is an inherently visual concept and computer vision and artificial intelligence (AI) are playing an increasingly important role in shaping the future of this domain. Many research has been done on recommending fashion products based on the learned user preferences. However, in addition to recommending single items, AI can also help users create stylish outfits from items they already have, or purchase additional items that go well with their current wardrobe. Compatibility is the key factor in creating stylish outfits from single items. Previous studies have mostly focused on modeling pair-wise compatibility. There are a few approaches that consider an entire outfit, but these approaches have limitations such as requiring rich semantic information, category labels, and fixed order of items. Thus, they fail to effectively determine compatibility when such information is not available. In this work, we adopt a Relation Network (RN) to develop new compatibility learning models, Fashion RN and FashionRN-VSE, that addresses the limitations of existing approaches. FashionRN learns the compatibility of an entire outfit, with an arbitrary number of items, in an arbitrary order. We evaluated our model using a large dataset of 49,740 outfits that we collected from Polyvore website. Quantitatively, our experimental results demonstrate state of the art performance compared with alternative methods in the literature in both compatibility prediction and fill-in-the-blank test. Qualitatively, we also show that the item embedding learned by FashionRN indicate the compatibility among fashion items.