"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.

Someone recently asked my opinion on how artificial intelligence is going to transform banks, capital markets and insurers. The implication is that both established firms -- as well as disruptive FinTech players -- have an AI strategy sure to change the status quo. For example, much has been written about the rise of robo-advisors and the slacking demand for "smartest person in the room" human portfolio managers. To a degree, that may be true. However, the data uncovered by AI won't be truly beneficial unless employees are able to access it anytime, anywhere.

One of the hardest parts of dating has always been getting up the nerve to hit on someone you see often, but don't know--like the guy on the other side of the bar. Tinder says it might soon have a fix for that specific issue: The dating app is testing a new feature aimed at connecting people who like to hang out in the same bars, restaurants, and other public places. The new product, aptly named "Places," will begin testing in cities in Australia and Chile today. The announcement comes a month after Facebook announced it too was testing a new dating product, which will allow users to connect with people who attend the same events. The move feels like a marked change for Tinder, which has been best-known for facilitating hookups since it launched in 2012.

So many streaming services and so little time to find the one that suits you best. What's a music lover to do? Talking Tech has you covered. Which of the monthly streaming music services makes the best recommendations, is easiest to use and has the best prices? After the newest kid on the block, YouTube Music Premium, debuted in a soft launch this week, we set to find out, comparing YouTube to the Big 3: No. 1 Spotify (75 million subscribers,) Apple Music (50 million) and Amazon, which won't be more specific other than to say it has "tens of millions" of users. For several days, we have been searching for our favorites, looking for clues to discover stuff we didn't know about, creating playlists, looking for song recommendations and playing the music on the computer, phone and through Echo, Google Home, Apple HomePod and Sonos speakers.

Independent Women's Forum's Nan Hayworth and Democratic strategist Wendy Osefo discuss the report that researchers discovered a way to hack Amazon's Alexa.

For a company as big and sprawling as Google, an annual developer conference can feel overwhelming. Google offers a dizzying number of services and dabbles in almost every consumer tech industry under the sun, including seven core products with more than a billion users each. But the company has a measured tempo when it comes to Google I/O, and we've come to understand how it prioritizes certain products over others come May every year. At this year's I/O, which will be held again at the Shoreline Amphitheater in Mountain View, California starting Tuesday, May 8th, we know we'll be hearing about the future of Android and Google's artificial intelligence efforts. But there will also be news on everything from its new wearable platform, Wear OS, and Google Assistant to Android TV, Google Home, Google Play, and Search. This is the time of the year when Google pulls out all the stops to showcase how its software is smarter and more forward-looking than the products from its rivals Amazon, Apple, Facebook, and Microsoft.

Microsoft is acquiring conversational AI startup Semantic Machines in an effort to make bots and intelligent assistants like Cortana sound and respond more like humans. Founded in 2014, Semantic Machines uses machine learning to make bots respond in a more natural way to queries. Semantic Machines is led by UC Berkeley professor Dan Klein and former Apple chief speech scientist Larry Gillick. Both are considered pioneers in conversational AI. Microsoft's acquisition will boost the company's Cortana digital assistant, as well as the company's Azure Bot Service that's used by 300,000 developers.