The time for experiments is over: Artificial Intelligence (AI) solutions are ready for the enterprise. Already, the early adopters are reaping the rewards and moving from trials to large-scale adoption. The chief executives of these companies are confident that - within just two years - AI deployments will deliver around 30% of both their new revenues and cost savings. Other businesses are now following suit, eager not to miss out on the AI advantage. I'm certain that companies which delay drafting and delivering their own AI strategies are bound for obsolescence.

Have you ever wondered how self-driving cars are running on roads or how Netflix recommends the movies which you may like or how Amazon recommends you products or how Google search gives you such an accurate results or how speech recognition in your smartphone works or how the world champion was beaten at the game of Go? Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning. Most of the domains that were considered specializations are now being merged into Machine Learning. Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning.

Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial intelligence research. While still nascent, several different approaches have been employed to tackle what may broadly be referred to as "musical intelligence." This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit, with a particular emphasis on machine learning methods.

Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep learningbased method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems. The core of the proposed method is the gradient-based localization used in the image processing literature to highlight the representative areas of each classification class. Validation experiments make use of two public datasets and current baselines. Results show the superiority of DeepUnHide to make feature selection and demographic classification, compared to the state of art of feature selection methods. Relevant and direct applications include recommendations explanation, fairness in collaborative filtering and recommendation to groups of users. Keywords: Feature selection, collaborative filtering, demographic information, matrix factorization, gradient based localization, deep learning.

Apple says that most of the money being "facilitated" by its App Store does not go through the company, as it unveiled new research into the scale of the digital economy. The App Store helped facilitate some $519 billion in billings and sales last year, new analysis showed. Most of that money did not go through App Store purchases or in-app payments, it said, but rather simply relied on software for Apple's platforms to make goods and services available. In its announcements about the App Store in the past, Apple has focused primarily on the amount of money it has paid to developers directly, through sales of apps and in-app purchases on its platforms. But the new report attempts to highlight the broader impact on the economy that its digital store has, by giving an account of how many sales are "facilitated" through those apps more broadly.

Complex black-box machine learning models are regularly used in critical decision-making domains. This has given rise to several calls for algorithmic explainability. Many explanation algorithms proposed in literature assign importance to each feature individually. However, such explanations fail to capture the joint effects of sets of features. Indeed, few works so far formally analyze \coloremph{high dimensional model explanations}. In this paper, we propose a novel high dimension model explanation method that captures the joint effect of feature subsets. We propose a new axiomatization for a generalization of the Banzhaf index; our method can also be thought of as an approximation of a black-box model by a higher-order polynomial. In other words, this work justifies the use of the generalized Banzhaf index as a model explanation by showing that it uniquely satisfies a set of natural desiderata and that it is the optimal local approximation of a black-box model. Our empirical evaluation of our measure highlights how it manages to capture desirable behavior, whereas other measures that do not satisfy our axioms behave in an unpredictable manner.

We have recently launched an ongoing online seminar series about Bayesian machine learning as scale. The intended audience includes machine learning practitioners and statisticians from academia and industry. Registration is now open for Jake Hofman's 17 June talk: "How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results". Jake is a Senior Principal Researcher at Microsoft Research, New York. The talk is at 15.00 UTC this Wednesday, June 17; to see it in your local time zone please go to the registration page.

A new Star Wars video game will put you in the cockpit of an X-Wing starfighter or TIE fighter. "Star Wars: Squadrons," due Oct. 2, for PlayStation 4, Xbox One and PCs, will let players take flight in five-on-five multiplayer dogfights and progress in a story as a pilot for the New Republic or the Empire. The first-person flight game, being developed by Electronic Arts, Lucasfilm and Motive Studios ("Star Wars Battlefront II") and unveiled with a cinematic trailer Monday, can be played across the various game platforms and will also be playable in VR on PS4 and PCs. The "Star Wars: Squadrons" story is set after the events of final installment of the original movie trilogy, "Star Wars: Return of the Jedi." In addition to dogfights, squadrons will engage in fleet-sized battles and attempt to destroy the opposing side's flagship.

This week we speak to Justin McLeod, founder and chief executive of dating app Hinge. When recovering alcoholic Justin McLeod set up his dating app, it was to help him get over his heartbreak. Five years earlier, his college sweetheart, the woman he thought was the love of his life, had split up with him because of his drink problem. He had subsequently gone to rehab and successfully sobered up, but he had not been able to move on romantically. Not comfortable going into bars because of his addiction issue, he started work on Hinge in 2011 to help him find a new partner.

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.