We're officially a month into 2020 and the new decade is well underway. So much so, it is worth reflecting back as it jolted our eyes open and set the stage for what's to come. To sum it up in a word, data. Data, data everywhere – how to get it, how to use it, how to see it. Everywhere you looked there were analytics dashboards.

At a Stop & Shop supermarket near Hartford, Connecticut, one of the nation's first micro-fulfillment centers (MFCs for short) opened at the end of last year. Ahold Delhaize, Stop & Shop's Dutch-Belgian parent, carved out 12,000 square feet from the store during a recent remodel to make room for the MFC, which is operated by the retailer and with support from Takeoff Technologies. Through a glass window in a corner of the store, curious shoppers can get a glimpse at the automated mini-warehouse, where robots whoosh around grabbing cereal and soup. The system can handle up to 3,500 orders a week, although it's nowhere near that level yet. Stop & Shop's not alone: Walmart, Albertsons and others are also testing MFCs.

In recent times, organizations have been competing with one another to implement chatbots for various reasons, including enhancing customer experience, streamlining processes, and fueling the demand for digital and innovative technologies. Cognitive technologies such as chatbots have become an apt candidate for end-use application as they have high automation feasibility, high potential of accuracy, low complexity and low execution time. Raising the bar through intelligence, virtual assistants have been propelled by advancements of mobile technology. Technology giants are putting their weight on a platform designed to answer ad-hoc queries in real-time and fuel sales as chatbots can remember customer preference and use order history to learn from customer responses to the product advertisements, suggest products, and cross-sell aptly. For instance, if a customer asks for a pizza recommendation with a chatbot, it can remember which pizza the customer ordered and follow up with it when offering a recommendation for another pizza or a restaurant.

Consider the grocery clerks at two Safeway stores in the San Francisco Bay Area. A few weeks ago, over 200 workers who are members of the United Food and Commercial Workers Local 5 (UFCW5) union picketed a Safeway store in San Jose, Calif. to voice concerns about a push by parent company Albertsons to add more A.I to its operations. Albertsons recently partnered with the startup Takeoff Technologies to create mini warehouses where computer vision technology automatically sorts items that shoppers order online. Using A.I. reduces the need for Safeway staff to manually locate and grab items for delivery--workers now just retrieve the finalized orders from a conveyor belt and sign off on them for eventual delivery. Several grocery store chains are investing heavily in micro-fulfillment centers after Amazon helped to popularize as-fast-as-you-can deliveries, said Andrew Lipsman, a principal analyst at research firm eMarketer.

This special issue of Media-N gathers perspectives on artistic labor in an increasingly automated global economy, focusing on the impact of artificial intelligence and the role of Amazon.com, Inc. in reshaping how work is defined, valued, and performed. Our contributors are artists, curators, scholars, and critics interested in labor value, emerging forms of exploitation and alienation, as well as new possibilities for collective resistance, solidarity, and critique.

Over the last decade, technology has changed the way businesses operate and consumers buy. The field of fashion has been plugged into this changing scenario from the very start. Physical retail stores are now being replaced by e-commerce websites. Online and offline retailers today are using artificial intelligence (AI) to understand their customers tastes and preferences better. Using data science, fashion stylists and designers are able to identify trends and match the expectations of their end consumers. Simply put, data science is technology's ability to aggregate a large set of data, analyse it, and accordingly produce insights that determine future business decisions.

Over the last decade, technology has changed the way businesses operate and consumers buy. The field of fashion has been plugged into this changing scenario from the very start. Physical retail stores are now being replaced by e-commerce websites. Online and offline retailers today are using artificial intelligence (AI) to understand their customers tastes and preferences better. Using data science, fashion stylists and designers are able to identify trends and match the expectations of their end consumers. Simply put, data science is technology's ability to aggregate a large set of data, analyse it, and accordingly produce insights that determine future business decisions.

An unmanned convenience store began operations Monday at a recently opened station on Tokyo's Yamanote loop line, using artificial intelligence not just to allow speedy self-checkouts but to also prevent shoplifting. The store is a key feature at the Takanawa Gateway Station, which opened on March 14 as the first new stop on the line in nearly 50 years. About 50 cameras installed inside the roughly 60-square-meter store identify every item customers pick up. The store's exit gates open once the customer makes a payment. The AI used at the shop has been trained to recognize customer behavior, including how items are carried, and it almost fully prevents shoplifting by accurately recognizing when merchandise is taken from shelves, according to its developer Touch To Go Co. Attempts in a demonstration to hide merchandise under clothes or avoid being seen on the cameras while stashing it in a bag were all detected.

First, it was the advent of the World Wide Web that changed how the world worked. Then with digitalization came eCommerce as businesses around the world established their presence online to reach people beyond borders. With innovations in technology, digital commerce is rising to the top with an expected grossing of $700 billion by the year 2022. The integration of AI will take it a step further with access to online portals for safe and profitable transactions. Artificial intelligence will influence the digital market, not just for entrepreneurs but for consumers as well.

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.