Vonage has launched an artificial intelligence-powered assistant for its unified communications as a service (UCaaS) offering. The firm said its assistant is designed for businesses that use its UCaaS offering but do not have a fully-fledged contact centre. The assistant uses AI and machine learning to create conversational experiences for customers in natural language. Savinay Berry, EVP of Product and Engineering for Vonage, said: "In addition to reducing and optimizing IT costs and resources, enterprises are enhancing the customer experience with the use of AI as a part of their communications strategy. "In today's modern workplace, consumers expect to get the information they want, when they want it and they expect it to be easy to do business with a brand" "As one of the first providers to offer this kind of solution for unified communications, Vonage is enabling businesses to leverage AI to improve their business processes.

Matrix completion is a prevailing collaborative filtering method for recommendation systems that requires the data offered by users to provide personalized service. However, due to insidious attacks and unexpected inference, the release of user data often raises serious privacy concerns. Most of the existing solutions focus on improving the privacy guarantee for general matrix completion. As a special case, in recommendation systems where the observations are binary, one-bit matrix completion covers a broad range of real-life situations. In this paper, we propose a novel framework for one-bit matrix completion under the differential privacy constraint. In this framework, we develop several perturbation mechanisms and analyze the privacy-accuracy trade-off offered by each mechanism. The experiments conducted on both synthetic and real-world datasets demonstrate that our proposed approaches can maintain high-level privacy with little loss of completion accuracy.

News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news. Most of existing news recommender systems usually learn topic-level representations of users and news for recommendation, and neglect to learn more informative aspect-level features of users and news for more accurate recommendation. As a result, they achieve limited recommendation performance. Aiming at addressing this deficiency, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preference and news representation learning. Here, \textit{news aspect} is fine-grained semantic information expressed by a set of related words, which indicates specific aspects described by the news. In ANRS, \textit{news aspect-level encoder} and \textit{user aspect-level encoder} are devised to learn the fine-grained aspect-level representations of user's preferences and news characteristics respectively, which are fed into \textit{click predictor} to judge the probability of the user clicking the candidate news. Extensive experiments are done on the commonly used real-world dataset MIND, which demonstrate the superiority of our method compared with representative and state-of-the-art methods.

Recommendation systems play an important role in today's digital world. They have found applications in various applications such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs. In this paper, we develop a recommendation system for daily exercise activities to users based on their history, profile and similar users. The developed recommendation system uses a deep recurrent neural network with user-profile attention and temporal attention mechanisms. Moreover, exercise recommendation systems are significantly different from streaming recommendation systems in that we are not able to collect click feedback from the participants in exercise recommendation systems. Thus, we propose a real-time, expert-in-the-loop active learning procedure. The active learners calculate the uncertainty of the recommender at each time step for each user and ask an expert for a recommendation when the certainty is low. In this paper, we derive the probability distribution function of marginal distance, and use it to determine when to ask experts for feedback. Our experimental results on a mHealth dataset show improved accuracy after incorporating the real-time active learner with the recommendation system.

You might be heard about Machine Learning… Techies are well familiar with this, nowadays it is one of the emerging technology. Yes! Machine Learning is the beginning of a new era. Have you ever wondered about, how Amazon's Alexa works? How weather prediction is done? How those predictions, detection, and recognition will be done with the help of such models.

The decade isn't over yet, but we've seen some remarkable advancements in the field of artificial intelligence. We've marveled at the invention of the first self-driving car in 1995. We've witnessed Deep Blue beat Garry Kasparov in 1997. Lastly and more recently we've had the chance to enjoy the company of Apple's Siri, Google's Assistant, Microsoft's Cortana, and Amazon's Alexa. While much advancement in artificial intelligence came about relatively recently, the idea of a machine-based artificial intelligence actually existed even before the computer. Its theoretical basis came about in the 1950s, introduced by British mathematician Alan Turing.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Dave Chappelle's controversial new Netflix standup special "The Closer" has a very high audience rating on Rotten Tomatoes despite calls from activists to remove the piece of content from the streamer's library. The comedian, 48, is being criticized by many in the LGBTQ community for comments he made about transgender people. Despite many like Netflix's own "Dear White People'' showrunner Jaclyn Moore coming out strong against the special, Chappelle himself has been able to laugh off the backlash, even getting a standing ovation during an appearance at the Hollywood Bowl last week.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. National Geographic's new documentary about Dr. Anthony Fauci has been ignored by audiences on Rotten Tomatoes, despite overwhelmingly positive reviews from professional critics. Directed by John Hoffman and Janet Tobias, "Fauci" has been in select theaters since Sept. 10 and started streaming Oct. 6 on Disney . The film features interviews with Fauci as well as his wife Christine and daughter Jenny.

When I open any website offering services or goods, I always check how well a recommender system works. Big business also adores recommender engines as much as I do, so I am in good company. "Recommender engines or recommenders, as they are sometimes called, are the most useful applications of Machine Learning Algorithms." – Harvard Business Review. And they help me to choose another plant to the disappointment of my husband ( "One more plant? We have a dozen of them already!").

Amazon is adding a new accessibility feature to Alexa, that will tell the virtual assistant to wait just a bit longer to allow someone to complete their requests. The new feature is meant for people who speak slowly or may have speech impairment. Amazon added the optional setting after it received feedback from some customers, who needed a little more time to use the assistant effectively. Speaking with Forbes, Shehzad Mevawalla, Head of Speech Recognition at Amazon said, "Alexa is a voice-first experience, and we are always looking for ways to improve speech recognition for all speaking styles. Some customers have told us they just need a bit more time before Alexa responds to their requests."