Researchers at Valuates Reports say the global use of artificial intelligence (AI) in retail markets is set to reach a market share of $14.7 Billion by 2026 from $2.7 Billion in 2020, at a CAGR of 32.7%. The versatility of the technology makes it valuable for optimizing the supply chain, using existing data to improve conversion, and customizing shopping experiences through predictive modeling and micro-targeting. The company's report, titled "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026", points out several trends and factors influencing the use of AI in retail markets. Some major companies in the AI arena include IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, Intel, Google, Sentient Technologies, Salesforce, and Visenze. For more details, ask for a sample of the "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026" report.

Fashion retailers require accurate demand forecasts for the next season, almost a year in advance, for demand management and supply chain planning purposes. Accurate forecasts are important to ensure retailers' profitability and to reduce environmental damage caused by disposal of unsold inventory. It is challenging because most products are new in a season and have short life cycles, huge sales variations and long lead-times. In this paper, we present a novel product age based forecast model, where product age refers to the number of weeks since its launch, and show that it outperforms existing models. We demonstrate the robust performance of the approach through real world use case of a multinational fashion retailer having over 300 stores, 35k items and around 40 categories. The main contributions of this work include unique and significant feature engineering for product attribute values, accurate demand forecast 6-12 months in advance and extending our approach to recommend product launch time for the next season. We use our fashion assortment optimization model to produce list and quantity of items to be listed in a store for the next season that maximizes total revenue and satisfies business constraints. We found a revenue uplift of 41% from our framework in comparison to the retailer's plan. We also compare our forecast results with the current methods and show that it outperforms existing models. Our framework leads to better ordering, inventory planning, assortment planning and overall increase in profit for the retailer's supply chain.

Users can create a room in app, invite friends or family and users can sing along in karaoke-style without the vocals. Music students, teachers, or anyone who wants to improve their singing skills can also use it for solo practice. If you're shy about singing karaoke, you can enter a private or solo room and listen to the isolated song vocals. To use MusicBucket for a karaoke social, invite your friends to a karaoke room. They can select a song or upload their own song and start singing, as shown in the following screenshot. Users take turns singing songs with the background music.

Launched at AWS re:Invent 2018, Amazon Sagemaker Ground Truth is a capability of Amazon SageMaker that makes it easy to annotate machine learning datasets. Customers can efficiently and accurately label image, text and 3D point cloud data with built-in workflows, or any other type of data with custom workflows. Data samples are automatically distributed to a workforce (private, 3rd party or MTurk), and annotations are stored in Amazon Simple Storage Service (S3). Optionally, automated data labeling may also be enabled, reducing both the amount of time required to label the dataset, and the associated costs. As models become more sophisticated, AWS customers are increasingly applying machine learning prediction to video content.

GPUs can significantly speed up deep learning training, and have the potential to reduce training time from weeks to just hours. Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In this post, we focus on general techniques for improving I/O to optimize GPU performance when training on Amazon SageMaker, regardless of the underlying infrastructure or deep learning framework. You can typically see performance improvements up to 10-fold in overall GPU training by just optimizing I/O processing routines. A single GPU can perform tera floating point operations per second (TFLOPS), which allows them to perform operations 10–1,000 times faster than CPUs.

Online retailers have long lured customers with the ability to browse vast selections of merchandise from home, quickly compare prices and offers, and have goods conveniently delivered to their doorstep. But much of the in-person shopping experience has been lost, not the least of which is trying on clothes to see how they fit, how the colors work with your complexion, and so on. Companies like Stitch Fix, Wantable, and Trunk Club have attempted to address this problem by hiring professionals to choose clothes based on your custom parameters and ship them out to you. You can try things on, keep what you like, and send back what you don't. Stitch Fix's version of this service is called Fixes.

Microsoft has acquired Snoqualmie, Wash.-based Orions Systems for an undisclosed amount. Orions Systems makes AI-powered vision systems, and its founder, Nils Lahr, has worked on imaging, content distribution network (CDN), and streaming media services. "Orions Systems has developed a strong reputation and leading technology for organizations seeking to gather and analyze high-value data specifically in the areas of video and image content. The acquisition will bring additional technologies that will allow solutions like Dynamics 365 Connected Store and the Microsoft Power Platform to offer retailers and other organizations a way to build and train their own AI models to customize and optimize how they can learn from their physical space. This extra set of tools will deliver on scenarios beyond what is offered out-of-the-box today and can adapt to the truly unique dimensions and needs of their physical spaces," said Microsoft Dynamics 365 Corporate Vice President Muhammad Alam in a July 7 blog post announcing the deal.

In this paper, we report an industry-academia collaborative study on the distribution method of fashion products using an artificial intelligence (AI) technique combined with an optimization method. To meet the current fashion trend of short product lifetimes and an increasing variety of styles, the company produces limited volumes of a large variety of styles. However, due to the limited volume of each style, some styles may not be distributed to some off-line stores. As a result, this high-variety, low-volume strategy presents another challenge to distribution managers. We collaborated with KOLON F/C, one of the largest fashion business units in South Korea, to develop models and an algorithm to optimally distribute the products to the stores based on the visual images of the products. The team developed a deep learning model that effectively represents the styles of clothes based on their visual image. Moreover, the team created an optimization model that effectively determines the product mix for each store based on the image representation of clothes. In the past, computers were only considered to be useful for conducting logical calculations, and visual perception and cognition were considered to be difficult computational tasks. The proposed approach is significant in that it uses both AI (perception and cognition) and mathematical optimization (logical calculation) to address a practical supply chain problem, which is why the study was called "Breaking Moravec's Paradox."

In this paper, we describe a novel solution to compute optimal warehouse allocations for fashion inventory. Procured inventory must be optimally allocated to warehouses in proportion to the regional demand around the warehouse. This will ensure that demand is fulfilled by the nearest warehouse thereby minimizing the delivery logistics cost and delivery times. These are key metrics to drive profitability and customer experience respectively. Warehouses have capacity constraints and allocations must minimize inter warehouse redistribution cost of the inventory. This leads to maximum Regional Utilization (RU). We use machine learning and optimization methods to build an efficient solution to this warehouse allocation problem. We use machine learning models to estimate the geographical split of the demand for every product. We use Integer Programming methods to compute the optimal feasible warehouse allocations considering the capacity constraints. We conduct a back-testing by using this solution and validate the efficiency of this model by demonstrating a significant uptick in two key metrics Regional Utilization (RU) and Percentage Two-day-delivery (2DD). We use this process to intelligently create purchase orders with warehouse assignments for Myntra, a leading online fashion retailer.

Amazon's Summer Sale has just passed the halfway mark, and there are still thousands of discounts to be had on tech, beauty and home products until 11.59 pm on July 12, with new items dropping each day. As we expected, some of the most popular deals have included up to 50 per cent off Amazon's Echo smart speaker and reduced prices on other top tech from the likes of Garmin and Shark. However, we recommend signing up to Amazon's Prime subscription service now with a 30-day free trial to receive free next-day delivery and access to some exclusive offers. There are still many offers worth checking out, and if you're curious to know what everyone else has snapped up during Amazon's Summer Sale, you can check out the top-selling and best deals so far below. Here's what MailOnline readers are buying: Amazon has slashed the price of this all-singing-all-dancing Garmin Vivoactive 3 GPS Smartwatch, now 53 per cent off in the Summer Sale.