Your financial health has improved since last month. Would you like to know by how much?" Blue, BBVA's new virtual assistant (VA), is the one doing the talking. Blue is integrated in the bank's mobile banking app in Spain (for Android and iOS) and can respond to over 100 user requests from the more than 800 features available in the application. Blue is pleasant, patient, and able to put itself in the shoes of others. It loves talking to humans and is always willing and ready to help customers. And it wants to learn lots of things in order to become even more insightful. These are just some of the personality traits that characterize BBVA's new voice assistant, which came to life out of a complex process that factored even the most minor details. The IT consulting firm, Gartner, defines virtual assistants as tools that help users perform a series of tasks that previously required human assistance. VAs use predictive models, natural language processing tools, recommendation engines, personalization systems based on artificial intelligence and advanced data analytics to do their job: assisting users and automating tasks. "VAs listen to and observe behaviors, build and maintain data models, and predict and recommend actions," the consulting firm explains. Blue's artificial intelligence capabilities are the result of a hybrid development: made up mostly of parts created by BBVA and others based on technologies that were readily available on the market and benefit from an advanced level of maturity, such as natural language processing (NLP) techniques. Specifically, the core of the system's artificial intelligence functionality is a 100 percent BBVA in-house development called Lenny. It is based on a set of cloud-based microservices and is responsible for orchestrating all the pieces that go into making Blue work. Thanks to this BBVA-developed'artificial brain', Blue is connected to the application functionalities that are powered by advanced data analytics. Examples include predicted banking transactions and financial health features, which the BBVA virtual assistant makes readily available to the customers by using natural, human-like dialog. "During the development of Blue, one of the most significant challenges was ensuring that we could cover the full range of functionality that BBVA, as a major player, offers in its app -- recognized as the most complete on the market -- in a voice and text-based virtual assistant," Eliseo Catalán, Head of BBVA Spain's Smart Assistants explains. Achieving this required that all the features in the app -- offered thanks to BBVA's digital capabilities -- were correctly set up and that each use case was thoroughly trained so that the user is given the appropriate response at every juncture of the customer journey. "We do all of this for the various platforms that might have different capabilities.

Alexa Conversations, a new deep learning-based approach that developers can use for creating natural voice experiences on Alexa with less effort, fewer lines of code, and less training data than before. Alexa Conversations let's you create skills with more configuration and less coding. Come along to this workshop to learn how to build an Alexa Conversations skill and take skills to the next level with less effort. Attendees will receive an introduction to Alexa Conversations and create a simple skill to get started on your journey. You will also need to create an Amazon Alexa Developer account and an AWS account if you want to follow along.

Pets need companions too, and now there's a website to help them. Pinder, a pet website styled after human dating app Tinder, allows owners to find pals for their pets, the New York Post reported. "We're just taking the effective format of Tinder and applying it to the pet community," Kevin Botero, the founder of Pinder, told the Post. The website shows only pet profiles – the profile setup page says "no humans allowed" – and currently all pets have to be in costume for the website's Halloween costume contest. According to the Post, the contest is a way to kick off the website's launch.

Over 111.8 million people in the U.S. talk to voice assistants like Siri, Alexa, and Google Assistant every month, eMarketer estimates. Tens of millions of those people use assistants as data-finding tools, with the Global Web Index reporting that 25% of adults regularly perform voice searches on smartphones. But while voice assistants can answer questions about pop culture and world events like a pro, preliminary evidence suggests they struggle to supply information about elections. In a test of popular assistants' abilities to provide accurate, localized context concerning the upcoming U.S. presidential election, VentureBeat asked Alexa, Siri, and Google Assistant a set of standardized questions about procedures, deadlines, and misconceptions about voting. In general, the assistants fared relatively poorly, often answering questions with information about voting in other states or punting questions to the web instead of answering them directly. In light of historic misinformation efforts around the election, the shortcomings have the potential to sow confusion or hamper get-out-the-vote efforts -- especially among those with accessibility challenges who rely heavily on voice assistants.

The Echo Show 5 fits into small spaces while still bringing all Alexa has to offer. The Amazon Echo Show 5 offers Alexa's visual cues and video functionality without taking up much room, which makes it perfect for apartments where space is at a premium. The combination of the small but super useful screen with a sleek, modern design makes this little smart speaker look just as at home on a bedside table as it does on a home office desk, a living room shelf, or a kitchen counter. The screen displays a customizable clock but also cycles through useful information to give you insights at a glance. It also gives you the ability to make video calls, which are surprisingly perfect even on the 5-inch screen.

YouTube viewers are being asked to become "watchdogs" and record their use of the site to help uncover the ways in which its recommendation algorithm can lead to online radicalisation. Mozilla, the non-profit behind the Firefox web browser, has produced a new browser extension, called RegretsReporter, which will allow YouTube users to record and upload information about harmful videos recommended by the site, as well as the route they took to get there. "For years, people have raised the alarm about YouTube recommending conspiracy theories, misinformation, and other harmful content," said Ashley Boyd, Mozilla's head of engagement and advocacy. "One of YouTube's most consistent responses is to say that they are making progress on this and have reduced harmful recommendations by 70%. But there is no way to verify those claims or understand where YouTube still has work to do. "That's why we're recruiting YouTube users to become YouTube watchdogs.

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Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. OkCupid is encouraging its users to vote, and it's doing so with a provocative saying. The online dating platform is launching a VILF badge, which is a play on popular suggestive terms like MILF or DILF. However, in this case, OkCupid users can tack VILF badges onto their profiles to let all their potential dates know that they are voters and have a risqué sense of humor.

COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data utilization. The presented framework utilizes advanced web technologies to ensure reusability and reliability, and an inference engine for natural language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework's usage and benefits.

Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial node representations (i.e., the node representations before the first-time graph convolution) largely affect the final model performance. However, when learning the initial representation for a node, most existing work linearly combines the embeddings of node features, without considering the interactions among the features (or feature embeddings). We argue that when the node features are categorical, e.g., in many real-world applications like user profiling and recommender system, feature interactions usually carry important signals for predictive analytics. Ignoring them will result in suboptimal initial node representation and thus weaken the effectiveness of the follow-up graph convolution. In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical. Specifically, we integrate two ways of explicit interaction modeling into the learning of initial node representation, i.e., local interaction modeling on each pair of node features and global interaction modeling on an artificial feature graph. We then refine the enhanced initial node representations with the neighborhood aggregation-based graph convolution. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification. Extensive experiments on three tasks of user profiling (the prediction of user age, city, and purchase level) from Tencent and Alibaba datasets validate the effectiveness of CatGCN, especially the positive effect of performing feature interaction modeling before graph convolution.