Walmart Inc. is expanding its use of robots in stores to help monitor inventory, clean floors and unload trucks, part of the retail giant's efforts to control labor costs as it spends more to raise wages and offer new services like online grocery delivery. The country's largest private employer said at least 300 stores this year will add machines that scan shelves for out-of-stock products. Autonomous floor scrubbers will be deployed in 1,500 stores to help speed up cleaning, after a test in hundreds of stores last year....

Multidimensional data have become ubiquitous and are frequently involved in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location or group affiliation. We often have access to multiple aggregated views of the same data, each aggregated in one or more dimensions, especially when data are collected or measured by different agencies. However, data mining and machine learning models require detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions. The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform disaggregation on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.

Essentially, it has invested in a new artificial intelligence (AI) platform. The Schwarz Group has acquired shares in DFKI, and as a result becomes the first trading company to become one of its 29 shareholders. The Group confirmed that it will closely work with DFKI to develop applications that evolve around new AI based technologies in the retail sector. It also believes that the partnership will help "relevant research knowledge convert faster into innovations that improve operational processes and support the stationary retail business." It's important to note that, over the last few years, the Schwarz Group and DFKI have already worked together on successful projects in the field of language assistance systems and robotics, amongst other areas.

In this paper, we consider same-day delivery with a heterogeneous fleet of vehicles and drones. Customers make delivery requests over the course of the day and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but travel relatively slowly due to the urban traffic. Drones travel faster, but they have limited capacity and require charging or battery swaps. To exploit the different strengths of the fleets, we propose a deep Q-learning approach. Our method learns the value of assigning a new customer to either drones or vehicles as well as the option to not offer service at all. To aid feature selection, we present an analytical analysis that demonstrates the role that different types of information have on the value function and decision making. In a systematic computational analysis, we show the superiority of our policy compared to benchmark policies and the effectiveness of our deep Q-learning approach.

With less than 40 days to re:Invent 2019, the excitement is building up and we are looking forward to seeing you all soon! Continuing our journey on artificial intelligence and machine learning, we are bringing a lot of technical content this year, with over 200 breakout sessions, deep-dive chalk talks, hands-on exercises with workshops featuring Amazon SageMaker, AWS DeepRacer, and deep learning frameworks such as TensorFlow, PyTorch, and more. You'll hear from many customers including Vanguard, BBC, Autodesk, British Airways, Fannie Mae, Thermo Fisher, Intuit, and many more. We are also hosting the Machine Learning Summit again this year, where you will hear from researchers and entrepreneurs about the latest breakthroughs today and the future possibilities tomorrow. To get you started on planning, here are a few highlights for the AI and ML sessions from the re:Invent 2019 session catalog.

ABSTRACT With growing consumer adoption of online grocery shopping through platforms such as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressing business need to provide relevant recommendations throughout the customer journey. In this paper, we introduce a production within-basket grocery recommendation system, RTT2V ec, which generates real-time personalized product recommendations to supplement the user's current grocery basket. We conduct extensive offline evaluation of our system and demonstrate a 9.4% uplift in prediction metrics over baseline state-of-the-art within-basket recommendation models. We also propose an approximate inference technique 11.6x times faster than exact inference approaches. In production, our system has resulted in an increase in average basket size, improved product discovery, and enabled faster user checkout.

There is no bigger industry on our planet than food and agriculture, with a consistent, loyal customer base of 7 billion. In fact, the World Bank estimates that food and agriculture comprise about 10% of the global GDP, meaning that, food and agriculture would be valued at about $8 trillion globally based on the projected global GDP of $88 trillion for 2019. On the food front, a record $1.71 trillion was spent on food and beverages in 2018 at grocery stores and other retailers and away-from-home meals and snacks in the United States alone. During the same year, 9.7% of Americans' disposable personal income was spent on food -- 5% at home and 4.7% away from home -- a percentage that has remained steady amidst economic changes over the past 20 years. However, despite a stalwart customer base, the food industry is facing unprecedented challenges in production, demand and regulations stemming from consumer trends.

Video is the format that receives the most engagement, often 10 times more than static content, and the power of using video assets to drive sales is well-proven. The issue is that premium video is really hard to generate, making it the privilege of brands with the luxury of big budgets and long lead times. Capturing professional-quality video is a painful process, requiring a camera and talent to be present, synchronously, on location, with all the associated paraphernalia that comes with it: crew, hair and makeup, agents and so on. A single video can barely be produced at a $10,000 budget. As such, when it comes to marketing tailored to many individual products, target audiences, or regional messages, all refreshed on a frequent basis, formats have been largely restricted to just an image and text.

More money is being poured into Artificial Intelligence than ever before, and the market is anticipated to grow tremendously in the near future. AI has brought about a new era of personalisation and consumer experience The modern customers are looking for business organizations that provide them with personalised services alongside recommendations. This has been made possible with Artificial Intelligence, also known as AI. Recently, Pointsource carried out a survey and it revealed that most clients would be happy to shop time and again if AI is available. The study further revealed that at least 34% of consumers are willing to spend money, while 38% are more likely to share their experience with relatives and friends if they had an incredible shopping experience.

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