The latest Voice AI trend has been evolving day by day by adding value to third-party platforms such as Alexa or Google for implementing omnichannel voice AI experiences with the help of voice assistants to enhance customer experiences. According to the latest survey, 46% of the businesses across various industries are not sure about the change voice technology could bring into their organizations. Though this is the same case with the consumers, over the adoption of voice user interfaces emerges to increase at an expanding rate. According to Statista, the number of digital voice assistants is likely to reach 8.4billion units by 2024, which is more than the world's population. This is due to the increasing demand for Voice AI assistants for better convenience and accessibility.

The artificial intelligence (AI) and machine learning is getting stronger than ever. Many applications and projects have been developed based on AI already. Take the example of Apple Siri or the advertising algorithms that pushes products and services based on our Google search. The question though is, can AI take the place of a human and replace him or her?! Some believe we will be able to teach a robot or artificial material to perform tasks quickly and efficiently than a human. The idea falls on the line of a screwdriver where we use it because we cannot unscrew just by using our bare hands.

With the increasing importance of e-commerce and the immense variety of products, users need help to decide which ones are the most interesting to them. This is one of the main goals of recommender systems. However, users' trust may be compromised if they do not understand how or why the recommendation was achieved. Here, explanations are essential to improve user confidence in recommender systems and to make the recommendation useful. Providing explanation capabilities into recommender systems is not an easy task as their success depends on several aspects such as the explanation's goal, the user's expectation, the knowledge available, or the presentation method. Therefore, this work proposes a conceptual model to alleviate this problem by defining the requirements of explanations for recommender systems. Our goal is to provide a model that guides the development of effective explanations for recommender systems as they are correctly designed and suited to the user's needs. Although earlier explanation taxonomies sustain this work, our model includes new concepts not considered in previous works. Moreover, we make a novel contribution regarding the formalization of this model as an ontology that can be integrated into the development of proper explanations for recommender systems.

With the rapid development of mobile devices, speech-related technology is booming like never before. Many service providers like Google offer the ability to search through the voice on the android platform. For android mobile phones, 'Ok Google' uses this functionality to search a particular keyword to initiate the voice-based commands. Keyword recognition refers to speech technology that recognizes the existence of a word or short phrase within a given stream of audio. It is synonymously referred to as keyword spotting.

When it comes to the mobile app industry, businesses of all sizes and specialisations confront strong competition. This position compels them to keep up with all developing digital developments in order to maintain their worth. Recognizing the huge influence of artificial intelligence on business, top firms such as Amazon, eBay, and Tinder make extensive use of AI in their applications to generate tailored mobile user experiences and improve profitability. Start-ups also raise more investment for AI integrations, propelling them to high marketability and competitiveness. Annually, more AI apps go viral, bringing greater exposure and revenues to their owners.

In 1984, Heathkit presented HERO Jr. as the first robot that could be used in households to perform a variety of tasks, such as guarding people's homes, setting reminders, and even playing games. Following this development, many companies launched affordable "smart robots" that could be used within the household. Some of these technologies, like Google Home, Amazon Echo and Roomba, have become household staples; meanwhile, other products such as Jibo, Aniki, and Kuri failed to successfully launch despite having all the necessary resources. Why were these robots shut down? The simple answer is that most of these personal robots do not work well--but this is not necessarily because we do not have the technological capacity to build highly functional robots.

The moment that sports fans around the world have been waiting for is almost here, with the Tokyo 2020 Olympics finally kicking off today. With over 200 countries and regions competing across 33 sports and 46 disciplines, it can be hard to keep up with all the latest news. Thankfully, Amazon has your back, and has equipped its smart assistant, Alexa, with facts to give users quick and easy access to the latest news and successes throughout the games. 'Alexa, what's Team GB's Olympics update?' 'Alexa, what's Team Ireland's Olympics update?' 'Alexa, which country has the most gold meals?' 'Alexa, who is the athlete of the day?' Users of Alexa-enabled devices can ask the smart assistant a range of questions, including'Alexa, which country has the most gold medals?', and receive answers almost immediately. Amazon explained: 'Following the Olympics Opening Ceremony on Friday 23rd July, Alexa will give customers the low down on Team GB, Team Ireland, ParalympicsGB and Paralympics Ireland athletes competing at the Tokyo 2020 Olympic and Paralympic Games this year.

The maker of our favorite budget-priced color A19 smart bulb is back with a slew of new smart lights, including this Edison-style ST19 filament bulb. Priced at a reasonable $13 (or $75 for a six-pack), the tunable white Cree Lighting Connected Max ST19 boasts easy setup; connects directly to Wi-Fi networks; supports Alexa, Google Assistant, and SmartThings; and packs an impressive arsenal of automation tools. But the quirky Cree app can be confusing to use (why must you group lights that are already in a room?), and the ST19's lowest brightness setting is still a bit bright for our taste. Cree Lighting offers a wide range of bulbs in its Connected Max line, including a color A19 bulb that's our current Editors' Choice for budget-priced smart bulbs. Back in May, Cree unveiled a series of new Connected Max products, including this ST19 filament bulb.

Indoor navigation systems leverage shortest path algorithms to calculate routes. In order to define the "shortest path", a cost function has to be specified based on theories and heuristics in the application domain. For the domain of indoor routing, we survey theories and criteria identified in the literature as essential for human path planning. We drive quantitative definitions and integrate them into a cost function that weights each of the criteria separately. We then apply an exhaustive grid search to find weights that lead to an ideal cost function. "Ideal" here is defined as guiding the algorithm to plan routes that are most similar to those chosen by humans. To explore which criteria should be taken into account in an improved pathfinding algorithm, eleven different factors whose favorable impact on route selection has been established in past research were considered. Each factor was included separately in the Dijkstra algorithm and the similarity of thus calculated routes to the actual routes chosen by students at the University of Regensburg was determined. This allows for a quantitative assessment of the factors' impact and further constitutes a way to directly compare them. A reduction of the number of turns, streets, revolving doors, entryways, elevators as well as the combination of the aforementioned factors was found to have a positive effect and generate paths that were favored over the shortest path. Turns and the combination of criteria turned out to be most impactful.

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are often trained on large datasets from the internet, and as a result, may learn undesirable behaviors from this data, such as toxic or otherwise harmful language. Researchers must thus wrestle with the issue of how and when to release these models. In this paper, we survey the problem landscape for safety for end-to-end conversational AI and discuss recent and related work. We highlight tensions between values, potential positive impact and potential harms, and provide a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. We additionally provide a suite of tools to enable researchers to make better-informed decisions about training and releasing end-to-end conversational AI models.