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.

Data Science with Python Course: Hands-on Data Science 2020 Numpy, Pandas, Matplotlib, Scikit-Learn, WebScraping, Data Science, Machine Learning, Pyspark, statistics, Data Science What you'll learn Welcome to Complete Ultimate course guide on Data Science and Machine learning with Python. How Android speech Recognition or Apple siri understand your speech signal with such high accuracy. If you would like algorithm or technology running behind that, This is first course to get started in this direction. This course has more than 100 - 5 star rating. "This is a truly great course! It covers far more than it's written in its name: many data science libraries, frameworks, techniques, tips, starting from basics to advanced level topics. "This course has taught me many things I wanted to know about pandas.

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.

'Technology is the future,' the phrase no more represents the future. Technology has already invaded human's everyday life through various applications of Artificial Intelligence (AI). Henceforth, innovations are ruling the world today. Artificial Intelligence (AI) has spread its wings across various sectors. The technology is making all the pointers in the impossible bucket list possible.

Recent works on Multi-Armed Bandits (MAB) and Combinatorial Multi-Armed Bandits (COM-MAB) show good results on a global accuracy metric. This can be achieved, in the case of recommender systems, with personalization. However, with a combinatorial online learning approach, personalization implies a large amount of user feedbacks. Such feedbacks can be hard to acquire when users need to be directly and frequently solicited. For a number of fields of activities undergoing the digitization of their business, online learning is unavoidable. Thus, a number of approaches allowing implicit user feedback retrieval have been implemented. Nevertheless, this implicit feedback can be misleading or inefficient for the agent's learning. Herein, we propose a novel approach reducing the number of explicit feedbacks required by Combinatorial Multi Armed bandit (COM-MAB) algorithms while providing similar levels of global accuracy and learning efficiency to classical competitive methods. In this paper we present a novel approach for considering user feedback and evaluate it using three distinct strategies. Despite a limited number of feedbacks returned by users (as low as 20% of the total), our approach obtains similar results to those of state of the art approaches.

Large-scale planting of trees has been proposed as a low-cost natural solution for carbon mitigation, but is hampered by poor selection of plantation sites, especially in developing countries. To aid in site selection, we develop the ePSA (e-Plantation Site Assistant) recommendation system based on algorithm fusion that combines physics-based/traditional forestry science knowledge with machine learning. ePSA assists forest range officers by identifying blank patches inside forest areas and ranking each such patch based on their tree growth potential. Experiments, user studies, and deployment results characterize the utility of the recommender system in shaping the long-term success of tree plantations as a nature climate solution for carbon mitigation in northern India and beyond.