The rise of e-commerce over the last 10 years or so has forced retailers to adapt to the changes demanded by consumers. E-commerce growth continues to accelerate and outpace growth in the brick-and-mortar channel, and online sales accounted for almost 20% of total US sales this holiday season, based on preliminary estimates. In addition, department stores have offered discounts and promotions as a key tool to drive demand and bring consumers into stores. Over time, this strategy can dilute a store's brand and leave stores looking picked through. Also, it trains consumers to wait for discounts instead of buying products at full price. There has been a significant number of store closures in the last few years, and we expect that to accelerate in 2017 and in the following few years. As the department store channel shrinks, and more brands fight for less space, we think brands will need to be more creative, flexible and diversified in their approaches. One way brands can disrupt the more traditional wholesale channel without taking on the significant real estate risk that comes with opening their own stores is to open pop-up stores. With pop-ups, brands have complete creative control of the brand experience and how their messaging is communicated to consumers. They can tell the story they want to tell and explain in their own voice what the brand stands for. In some cases, brands use pop-ups more as an advertising tool than as a place to transact commerce. These kinds of pop-ups usually offer some kind of special experience to draw consumers into the store. Pop-ups can also be set up in locations other than malls, allowing brands to reach their target customers where they are. Retailers and brands can also use pop-ups to test the waters in the most expensive shopping areas, often at discounted rents, while landlords can use the temporary stores to show off the space to prospective long-term tenants. Mall operators are receptive to pop-ups, as they bring something new and unique to consumers. Real estate firm Related Companies has used pop-up shops at the Time Warner Center in New York City to provide a fresh feel and add variety for consumers.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
China's second-biggest e-commerce company, JD.com (Alibaba is first), is testing mobile robots to make deliveries to its customers, and imagining a future with fully unmanned logistics systems. On the last day of a two-week-long shopping bonanza that recorded sales of around $13 billion, some deliveries were made using mobile robots designed by JD. It's the first time that the company has used delivery robots in the field. The bots delivered packages to multiple Beijing university campuses such as Tsinghua University and Renmin University. JD has been testing delivery robots since November last year.
In this paper, we study the problem of spectrum scarcity in a network of unmanned aerial vehicles (UAVs) during mission-critical applications such as disaster monitoring and public safety missions, where the pre-allocated spectrum is not sufficient to offer a high data transmission rate for real-time video-streaming. In such scenarios, the UAV network can lease part of the spectrum of a terrestrial licensed network in exchange for providing relaying service. In order to optimize the performance of the UAV network and prolong its lifetime, some of the UAVs will function as a relay for the primary network while the rest of the UAVs carry out their sensing tasks. Here, we propose a team reinforcement learning algorithm performed by the UAV's controller unit to determine the optimum allocation of sensing and relaying tasks among the UAVs as well as their relocation strategy at each time. We analyze the convergence of our algorithm and present simulation results to evaluate the system throughput in different scenarios.