Artificial Intelligence has unleashed the power for e-commerce businesses to explore countless opportunities to dramatically improve customer experiences, generate new leads and better understand their customers. Businesses are continuing to evolve and are steadily incorporating Artificial Intelligence into their strategies – a prediction from Business Insider has suggested as much as 85% of customer interactions will be managed without a human by as soon as 2020. There are many innovative ways businesses are exploring the potential of AI – I've spent a little time researching the best uses so far. With the advancement in Natural Language Processing, and a huge improvement in a machines ability to understand human language including words and text, the technology is there for retailers to explore Virtual Agents/Assistants. The two most well-known and obvious examples would be the Amazon Alexa Echo – they have already begun to integrate and partner with SkyScanner for flights, Dominos for pizza delivery, JustEat for takeaway delivery and Uber to ask Alexa to request a taxi ride.
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system modelling such problem as a graph constrained coalition formation (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the applicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to -36.22%). Moreover, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).
Did you ever stop to wonder: What is Amazon not doing with technology? These days, you'd be hard-pressed to answer that question, given the company's incessant schedule for announcing updates and new products. The Seattle-based e-commerce giant is seemingly everywhere--whether it's the latest cloud offerings in AWS, new entertainment shows on Prime, automated retail stores, leased fleets of Boeing jets, smart speakers, payment systems, autonomous cars and trucks, freight forwarding companies, or airborne warehouses. Amazon also happens to have warehouses within 20 miles of 44% of the population of the United States, according to Piper Jaffray analyst Gene Munster. In many of the company's recent announcements, Amazon's voice assistant Alexa plays a central role.
Belloni, Aline (Ardans SA) | Berger, Alain (Ardans SA) | Boissier, Olivier (ENS Mines Saint-Etienne) | Bonnet, Grégory (Normandie Université) | Bourgne, Gauvain (Pierre and Marie Curie University) | Chardel, Pierre-Antoine (Telecom Management School) | Cotton, Jean-Pierre (Ardans SA) | Evreux, Nicolas (Ardans SA) | Ganascia, Jean-Gabriel (Pierre and Marie Curie University) | Jaillon, Philippe (ENS Mines Saint-Etienne) | Mermet, Bruno (Normandie University) | Picard, Gauthier (ENS Mines Saint-Etienne) | Rever, Bernard (Paris Descartes University) | Simon, Gaële (Normandie University) | Swarte, Thibault de (Telecom Management School) | Tessier, Catherine (Onera) | Vexler, François (Ardans SA) | Voyer, Robert (Telecom Management School) | Zimmermann, Antoine (ENS Mines Saint-Etienne)
Autonomy and agency are a central property in robotic systems, human-machine interfaces, e-business, ambient intelligence and assisted living applications. As the complexity of the situations the autonomous agents may encounter in such contexts is increasing, the decisions those agents make must integrate new issues, e.g. decisions involving contextual ethical considerations. Consequently contributions have proposed recommendations, advice or hard-wired ethical principles for systems of autonomous agents. However, socio-technical systems are more and more open and decentralized, and involve autonomous artificial agents interacting with other agents, human operators or users. For such systems, novel and original methods are needed to address contextual ethical decision-making, as decisions are likely to interfere with one another. This paper aims at presenting the ETHICAA project (Ethics and Autonomous Agents) whose objective is to define what should be an autonomous entity that could manage ethical conflicts. As a first proposal, we present various practical case studies of ethical conflicts and highlight what their main system and decision features are.
The uses of artificial intelligence (AI) that get the most press are usually the big, splashy ones. Whether it's IBM's Watson beating Ken Jennings at Jeopardy, DeepMind besting Lee Sedol at Go, the massive influx of news about self-driving cars, the growing personal marketplace, or Elon Musk's increasingly public trepidation, these kinds of AI stories have a way of capturing public attention. But quietly, AI powers search and recommendation engines at places like Google and Netflix, filters out obscene images on your favorite social networks, and proves complex mathematical theorems. You probably hear far less about AI applications in retail. However, AI in retail is something that will affect everyone who shops online in the coming years.