This straightforward order to display pictures of delicious fried confections, spoken into a Google Pixel 2 smartphone with the Google Assistant, is the type of command that users have been executing in Alphabet Inc.'s GOOGL, 1.71% GOOG, 1.64% search engine for years. Behind the scenes, however, the response to this type of query now leverages an enormous amount of machine-learning technology that Google has spent years and billions of dollars developing, in hopes of being a leader in artificial intelligence. For that command to function, software produced by Alphabet-owned Google needed to deploy image content analysis systems, voice recognition and a host of other technologies that revolve around machine learning and AI, mostly pumped through high-tech data centers the company has built. It also decided to make the hardware that runs it, with an eye on pushing the abilities of its services to new places in 2018 and beyond. Since 2013, Alphabet has ramped up its infrastructure spending, pouring $57.36 billion into capital expenditures--roughly $10 billion a year.

We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, and citizen science. The 2014 conference continued the tradition with a selection of 7 deployed applications describing systems in use by their intended end users, and 14 emerging applications describing works in progress. This year's special issue on innovative applications features articles describing four deployed and two emerging applications. The articles include three different types of recommender systems, which may be as much of a critique of the role of technology in society as it is an indication of recent research trends.

With so much music readily available, tools that help a user find new, interesting music that matches his or her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening tastes, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.

This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk's existing customer involvement program (CIP) data to deliver in-product contextual recommendations to end users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.

In the not so distant future, we will have machines capable not only of storing data, but of thinking, feeling, and being as intelligent as the human being. Following the new trends in the information technology market, such as tracking digital transformation, is a key factor for organizations seeking to remain competitive with the great competition in the market. With increasingly advanced and sophisticated resources, technological innovations have computer machines that promise to facilitate the routine of companies, where intelligent machines can perform their activities in an optimized way. We are talking about the trend of the future, a revolutionary technology – Artificial Intelligence (AI). Artificial Intelligence is already part of our everyday life, but many times we do not even notice it.

This article is part of a series about season four of Black Mirror, in which Futurism considers the technology pivotal to each episode and evaluates how close we are to having it. Please note that this article contains mild spoilers. Season four of Black Mirror is now available on Netflix. A 20-something man walks into a restaurant and takes a seat at an empty table. A few minutes later, a young woman enters.

This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Such information is commonly exchanged between a sales assistant and a customer during in-store recommendation processes and is usually termed an explanation (Brewer, Chinn, and Samarapungavan 1998). We define explanations in recommender systems by two properties. First, they are information about recommendations, where a recommendation is typically a ranked list of items.

This article explores how contextual information can be used to create intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in the recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems. As additional observations are made about users' preferences, the user models are extended, and the full collection of user preferences is used to generate recommendations or make predictions. This approach, therefore, ignores the notion of "situated actions" (Suchman 1987), the fact that users interact with the system within a particular "context" and that preferences for items within one context may be different from those in another context.

The much awaited Apple's Siri voice assistance based AI speaker HomePod is all set to launch soon. In order to ensure takers, existing smart speakers were being sold at heavy discounts on Amazon. Google and Amazon heavily discounted their smart speakers during the Christmas break. The Amazon Echo Dot and Google Home Mini were available to the customers for around $29 from $50. And even though this move might have costed them a few dollars per unit, this may point out towards a whole new strategy.

From the digital assistants like Siri in our phones to the algorithms that recommend us things to buy on Amazon, artificial intelligence is truly ingrained in the function of our technological society. Some of the most innovative forms of media exist because machine learning has exponentially become more prominent, and it is no surprise researchers at Stanford University use it to learn more about humans and their political patterns. These researchers identified a unique association among U.S. voters and the type of cars they own. If a city has a higher percent of sedans than pickup trucks, then there is an 88% chance the city will vote Democrat in the 2020 presidential election. And vice versa, if a city has a higher percent of pickup trucks than sedans, then there is an 82% it will vote Republican.