When we get to a point where literally just about everything can be done more cheaply and more efficiently by robots, the elite won't have any use for the rest of us at all. For most of human history, the wealthy have needed the poor to do the work that is necessary to run their businesses and make them even wealthier. In this day and age we like to call ourselves "employees", but in reality we are their servants. Some of us may be more well paid than others, but the vast majority of us are expending our best years serving their enterprises so that we can pay the bills. Unfortunately, that paradigm is rapidly changing, and many of the jobs that humans are doing today will be done by robots in the not too distant future.

AI is becoming one of the most popular investments in every business sector. And while it is already being used in certain parts of the retail sector, what else can we do to ensure that AI doesn't just improve the retail sector, but revolutionises it? While there is assistance in various factors such as robots improving in-store customer service, digital assistance in terms of AI means that we've got to ensure that the in-store workers are ready to cope with the workload associated with AI too. Robots work to improve processes in this respect but you've also got to remember that if you are looking to improve productivity that you need the foundation associated with the sheer workload of AI. This is where GPU optimised servers can help as they are specifically designed for AI workloads.

Artificial Intelligence (AI) in retail is changing this industry by playing a crucial role in the various key divisions. From design, to manufacturing, logistic supply chain and marketing, AI in retail is playing a big role in transforming this industry. Actually, in the age of digitalization, AI and machine learning based technologies in retail industry is providing an automated solution to manufacturers helping them to leverage the intelligence of AI into fashion and exhaust the best possibilities into their field. So, right here we brought a great discussion, how AI is changing fashion and retail with use cases and impact of AI on this industry. The design and patterns with right color combination is the key point to design a costume or other types of products to make it attractive for the customers.

It sounds like a tagline for a science fiction film, but we need only look as far as the retail industry to see the truth in this statement. Robots have been with us for a while in retail, and technology powered through AI and machine learning to incorporate the voice of the customer to transform how retailers make product and pricing decisions is something I've been advocating for years. Amazon and many retailers are operating robots behind the scenes to help with inventory management. Walmart is building its robot army as well, and according to this ABI Research release, the company deployed 350 systems for inventory management across its stores in 2019 alone. As retailers look to trim costs and streamline operations, particularly along the supply chain, robots are only going to become more entrenched in retail's day-to-day operations, taking on greater roles and interacting with employees and consumers alike as AI becomes smarter.

Retailers in Asia-Pacific are struggling to unite online and offline data and this will hinder their ability to recognise customers who engage with their brands across both channels. Furthermore, while most acknowledge the importance of artificial intelligence (AI) to their organisation's competitiveness, few have started to deploy such tools. While some retail organisations recognised the need to straddle both the online and offline channels, the biggest challenge these omnichannel retailers faced today was pulling data from both realms to establish a common view of their customers, said Raj Raguneethan, Microsoft's Asia regional business lead for retail and consumer goods. This gap hindered their ability, for instance, to recognise customers who had engaged the brand online when they walked into a physical store. To plug the gap, Raguneethan said retailers should establish a data management platform to pull together all customer information and stitch these together to provide unified profiles of their customers. With the launch of its national artificial intelligence (AI) strategy, alongside a slew of initiatives, the Singapore government aims to fuel AI adoption to generate economic value and provide a global platform on which to develop and testbed AI applications.

Sonos has finally made a portable wifi and Bluetooth speaker that sounds great – but it's not quite what most will have imagined. For years Sonos has made some of the very best wifi speakers, recently adding optional voice assistants from Google and Amazon. But they have never been truly wireless, needing to be plugged in and on your home wifi network. The Move changes that, essentially taking the excellent Sonos One and adding a battery to the bottom. But it comes at a hefty £399 price tag, although some retailers already have it at £329. The Move is slightly larger than the One.

With the rapid growth of e-Commerce, online product search has emerged as a popular and effective paradigm for customers to find desired products and engage in online shopping. However, there is still a big gap between the products that customers really desire to purchase and relevance of products that are suggested in response to a query from the customer. In this paper, we propose a robust way of predicting relevance scores given a search query and a product, using techniques involving machine learning, natural language processing and information retrieval. We compare conventional information retrieval models such as BM25 and Indri with deep learning models such as word2vec, sentence2vec and paragraph2vec. We share some of our insights and findings from our experiments.

Customer engagement and network connectivity are becoming more and more critical to the success of today's retailers. And with the soaring numbers of mobile and IoT devices, managing a distributed network of stores and engaging with customers is becoming more challenging. Join this session to hear how organizations such as top retailer, Petsmart, are investing in AI-driven technology to transform IT operations rather than being stuck in a technology refresh cycle, ultimately driving up profitability and in-store experience.

Walmart is embracing a robot takeover in order to compete with Amazon. The Arkansas-based firm is using robots to fulfill grocery orders in one of its Supercenters and is set to add shelf-scanning machines to 650 additional stores by the end of the summer. The shift is aimed at reducing costs, improving store performance and gaining credibility in its battle against to reign supreme as the king of retail. Walmart announced it would soon incorporate automated robotic carts, called Alphabots, in one of its superstores in Salem, New Hampshire in 2018. Walmart is using robots to fulfill grocery orders in one of its Supercenters and is set to add shelf-scanning machines (pictured) to 650 additional stores by the end of the summer.

Customers post online reviews at any time. With the timestamp of online reviews, they can be regarded as a flow of information. With this characteristic, designers can capture the changes in customer feedback to help set up product improvement strategies. Here we propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Then, inspired by the Kano model which classifies preferences of product attributes in five categories, conjoint analysis is used to quantitatively categorize the structured affordances. Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products. A case study based on the online reviews of Kindle e-readers downloaded from amazon.com shows that designers can use our proposed approach to evaluate their product improvement strategies for previous products and develop new product improvement strategies for future products.