It is being rolled out now, and will be available to all users by August. In addition to this new function, Microsoft is introducing a smattering of other features. This includes Dynamic view – which will let users frame work content and call participants side-by-side – as well as video filters to adjust lighting levels or soften the focus on your camera. It has also extended the number of people you can have in a meeting up to 1000, while presentations can now have 20,000 participants watching. Suggested replies, similar to the automated replies in email apps including Gmail, is coming to Microsoft Teams too. Finally, Cortana integration is being brought in to the mobile version of the app, giving the software usability via voice commands. Users can ask Cortana to make a call, join meetings, share files, or send messages.

Creativity Support Tools (CST) aim to enhance human creativity, but the deeply personal and subjective nature of creativity makes the design of universal support tools challenging. Individuals develop personal approaches to creativity, particularly in the context of commercial design where signature styles and techniques are valuable commodities. Artificial Intelligence (AI) and Machine Learning (ML) techniques could provide a means of creating 'intelligent' CST which learn and adapt to personal styles of creativity. Identifying what kind of role such tools could play in the design process requires a better understanding of designers' attitudes towards working with AI, and their willingness to include it in their personal creative process. This paper details the results of a survey of professional designers which indicates a positive and pragmatic attitude towards collaborating with AI tools, and a particular opportunity for incorporating them in the research stages of a design project.

"Can artificial intelligence actually help connect with customers, let alone engage them?" Nod, if you too have wondered about this at one point or another. The answer is deceptively simple: AI has direct and indirect applications, both of which ultimately benefit the end-customer. AI is no longer a nice-to-have business advantage. It has emerged as a must-have and how.

The global artificial intelligence (AI) software market is expected to undergo a major growth in the coming years, with revenues increasing from around ten billion U.S. dollars in 2018 to about 126 billion by 2025. As artificial intelligence continues to power cloud technology, cloud computing is the fuel that has been increasing the scope of and the impact AI can have on the software market. The increasing adoption of digital assistants like Alexa, Siri, Google Home, and others points towards the ways in which the combination of AI and cloud computing is improving everyone's daily lives. Whether it is playing a song or making an online purchase, the fusion of these two technologies enables connected and intuitive experiences. On a macro level, AI capabilities are being merged with enterprise cloud computing infrastructure so organizations can be more strategic, agile, efficient, and insight-driven.

A customer checks an iPhone 7 inside the Apple store Saint-Germain during the first opening day on December 03, 2016 in Paris, France - file photo. At this point, digital privacy is long gone. There's always another device, feature or service tracking what we say, what we look at online and the places we go. Some devices are more intrusive than others, and you may be feeding digital assistants more information than you realize. Tap or click here to stop all the smart tech in your home from listening.

What comes to mind when you think of Artificial Intelligence (AI)? Maybe you think of robots taking over the world, like in the movies, or self-driving cars. Merriam-Webster defines artificial intelligence as 1) a branch of computer science dealing with the simulation of intelligent behavior in computers and 2) the capability of a machine to imitate intelligent human behavior. Did you know that AI is gradually changing the way consumers shop at various stages of the buyer's journey? In subtle ways, artificial intelligence affects the way a potential buyer searches for product or brand information during the awareness stage.

Alexa allows hands-free control of all manner of devices, but there's been one glaring exception: its own smartphone app. Amazon has finally brought that feature directly to Android and iOS devices via a new update. All you need to do is open the Alexa app, either manually or (ironically) via Google Assistant or Siri to stay hands-free. From there, you can control Alexa with your voice as you normally would on an Echo or other device and ask it to play music, control your smart home or anything else Alexa can do. The Alexa assistant has always worked on the app, but until now, you needed to touch the Alexa button on the bottom control bar for voice control.

Evolution in business concepts and implementation of the latest technology trends are driving the thriving growth of businesses. Among all, artificial intelligence is best known to transform a business by automating the processes and making the tasks seamless. And the inventions like virtual assistant and chatbots are practically implementing these concepts to showcase results that promise skyrocketing growth and boost in business. A virtual assistant is a bot that assists the users to complete tasks throughout the time. By implementing the practical concepts of AI and machine learning, these assistants are created to provide a personalized feel to users and offer excellent user experience.

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KG-enhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks.

Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive them as spam, which eventually leads to a decrease in user satisfaction. Targeted advertising, which serves ads to individuals who may exhibit interest in a particular advertising message, is strongly required. The key to the success of precise user targeting lies in learning the accurate user and ad representation in the embedding space. Most of the previous studies have limited the representation learning in the Euclidean space, but recent studies have suggested hyperbolic manifold learning for the distinct projection of complex network properties emerging from real-world datasets such as social networks, recommender systems, and advertising. We propose a framework that can effectively learn the hierarchical structure in users and ads on the hyperbolic space, and extend to the Multi-Manifold Learning. Our method constructs multiple hyperbolic manifolds with learnable curvatures and maps the representation of user and ad to each manifold. The origin of each manifold is set as the centroid of each user cluster. The user preference for each ad is estimated using the distance between two entities in the hyperbolic space, and the final prediction is determined by aggregating the values calculated from the learned multiple manifolds. We evaluate our method on public benchmark datasets and a large-scale commercial messenger system LINE, and demonstrate its effectiveness through improved performance.