CAMPBELL, Calif., May 24, 2018 – Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

CAMPBELL, CA, USA, May 24, 2018 – Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

As part of our guest blog series, Accenture Nordic Banking Practice Lead Satu Pulkkinen explores how banks can take the next step in evolving customer relations. First came online and mobile banks, with Nordic banks leading the way. The conversational bank that operates within messaging applications. Technology that can see and hear us, continuously growing ecosystems around increasingly popular messaging applications, as well as the amazing progress of artificial intelligence (AI), are enabling personalized, fully digital banking assistants that you can talk to anytime, anyplace. Technology is an integral part of our daily lives. Increasingly, devices that used to simply respond to our commands and actions can now also hear and see us.

Tinder is testing a new location-sharing feature to connect lonely hearts. Dubbed Places, the tool aims to match users who are in the same specific location, like the cinema, or local coffee shop. Places is completely separate from the usual swipe-based feed Tinder users are accustomed to. Tinder users need to tap the newly-added pin icon, which lives at the top of the main app window, to access a map which shows all the locations you have visited within the last month. Tinder Places lets users view potential matches who have visited the same locations.

Ricardo Baeza-Yates ([email protected]) is Chief Technology Officer of NTENT, a search technology company based in Carlsbad, CA, USA, and Director of Computer Science Programs at Northeastern University, Silicon Valley campus, San Jose, CA, USA.

Planning, designing and executing marketing campaigns to get users to download your app take a heavy toll on your resources. On top of that, 24% of users abandon an app after just one use. The reason is quite simple, if your app is not indispensable, they'll abandon/uninstall it and move on. Considering the fact that your existing customers are much more profitable than the new ones, retention should be on the top of your mind. If you manage to lower your churn rate by 5%, you can increase your profitability by 25-125%!

"To remain competitive, these large banks will have to adapt their traditional services by incorporating more robotics in banking that will attract more tech-savvy customers." The unprecedented popularity of messaging platforms, such as Facebook Messenger, WhatsApp, and WeChat, can be seen across all geographies, demographics, and psychographics. And, messaging-based first-line engagements that occur on these platforms, as conducted with chatbots, has become the premier choice for consumers engaging with brands. And, with the ongoing advancements in artificial intelligence – enabling these virtual agents to better understand and address customer requests – chatbot adoption is quickly growing across multiple industries. According to Gartner, by 2020, 85% of customer interactions will be managed without any human intervention.

The Church of England is offering worshippers the chance to use voice-activated virtual assistants to pray. People can ask Amazon's Alexa device to read a prayer of the day, the Ten Commandments or the Lord's Prayer or to recite grace before a meal. But the smart speakers will also have a "church near you" function to encourage people to visit their local church. The move is part of an online campaign by the Church after figures showed fewer people were attending services. The Church of England also hopes to also offer the service through Google Play in the future.

Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. The billion-scale data in Taobao creates three major challenges to Taobao's RS: scalability, sparsity and cold start. In this paper, we present our technical solutions to address these three challenges. The methods are based on the graph embedding framework. We first construct an item graph from users' behavior history. Each item is then represented as a vector using graph embedding. The item embeddings are employed to compute pairwise similarities between all items, which are then used in the recommendation process. To alleviate the sparsity and cold start problems, side information is incorporated into the embedding framework. We propose two aggregation methods to integrate the embeddings of items and the corresponding side information. Experimental results from offline experiments show that methods incorporating side information are superior to those that do not. Further, we describe the platform upon which the embedding methods are deployed and the workflow to process the billion-scale data in Taobao. Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.

Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.