Have you ever shopped online for a mattress? Then you know the drill. For weeks after you made a decision, and perhaps even after you took delivery, you still see ads following you around the internet, promoting this mattress manufacturer or that. You think, "if this is a sample of artificial intelligence, then where's the intelligence?" AI and efforts to make digital services more personalized remain "a fairly blunt instrument," says Alan McIntyre, senior managing director, Accenture Global Banking, in an interview with Banking Exchange.

Streaming music revenues surpassed income from the sale of traditional formats for the first time last year, as the booming popularity the service put an end to the era of the CD. Revenue from music fans paying for services such as Spotify, Apple Music and Amazon Music surged more than 41% to $6.6bn (£4.7bn), accounting for more than 38% of the total global market for recorded music. The sale of physical formats, primarily CDs, fell 5.4% to $5.2bn to account for 30%. It marks a tipping point for the music industry, which has depended on income from CDs to fill record labels' coffers and artists' pockets since the 1980s. CD sales have been in decline for years as the industry was hammered by illegal piracy and failed to make much money as user numbers and margins fell in the early days of streaming.

AI-Driven Data Could Be the Music Industry's Best Marketing Instrument The music industry is using artificial intelligence (AI) to transform its marketing model by applying it to consumer data sorted via machine learning, offering improved insights to harmonize artists with the industry and fans while keeping profits to a maximum. One area of AI development is audience engagement metrics that gauge how audiences respond to new music genres, trends, artists, and songs. Industry professionals can use this data to increase visibility for their signed artists and reach more fans. Music labels also can target audiences and track patterns to inform business decisions and stimulate revenue. Furthermore, data filtering can be a marketing advantage that spurs subscriber growth and better design of outreach initiatives and content to fuel competition.

Four research teams at the University of Michigan will explore the intersection of music and data science thanks to the support of the Michigan Institute for Data Science's (MIDAS) Data Science for Music Challenge Initiative. The challenge asked participants to propose research projects that applied data science tools such as data mining or machine learning to the study of areas such as music theory, the connection between music and words, performance and more. Possible areas of research suggested by the challenge's coordinators include algorithms and computer composition, big data-based instrument deign, music education method analysis, collaborative music making and music recommendation systems, among others. "MIDAS is excited to catalyze innovative, interdisciplinary research at the intersection of data science and music," said Alfred Hero, co-director of MIDAS and the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science, in a prepared statement. "The four proposals selected will apply and demonstrate some of the most powerful state-of-the-art machine learning and data mining methods to empirical music theory, automated musical accompaniment of text and data-driven analysis of music performance."

The EU has launched a formal investigation into Apple's proposed acquisition of UK music-recognition app Shazam. The European commission announced its in-depth investigation into the deal over concerns that it would harm consumer choice and give Apple an unfair advantage through access to user data, which could aid in poaching customers from rivals. Shazam has been downloaded 1bn times and is used 20m times a day. It is the world's leading music recognition system, able to listen to and identify tracks via a smartphone and then link those tracks to multiple music subscription services, which means it could therefore hold commercially sensitive data on Apple's competitors and their consumers. Noting that Apple Music has become the second-largest music streaming service in Europe, the EC said: "Access to such data could allow Apple to directly target its competitors' customers and encourage them to switch to Apple Music. As a result, competing music streaming services could be put at a competitive disadvantage."

Japanese engineer Masaaki Nagumo had always dreamed of operating a robot from "Mobile Suit Gundam." That is the name of a popular Japanese animation television series. It was Nagumo's favorite show when he was a child. Now Nagumo has made the show come to life. He has built a large robot inspired by the machines.

Fake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles. In contrast, we focus on content-based detection of fake news articles, while assuming that we have a small amount of labels, made available by manual fact-checkers or automated sources. We argue this is a more realistic setting in the presence of massive amounts of content, most of which cannot be easily factchecked. To that end, we represent collections of news articles as multi-dimensional tensors, leverage tensor decomposition to derive concise article embeddings that capture spatial/contextual information about each news article, and use those embeddings to create an article-by-article graph on which we propagate limited labels. Results on three real-world datasets show that our method performs on par or better than existing models that are fully supervised, in that we achieve better detection accuracy using fewer labels. In particular, our proposed method achieves 75.43% of accuracy using only 30% of labels of a public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our method achieves 70.92% of accuracy in a large dataset using only 2% of labels.

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

Daniel Ek is a Swedish entrepreneur and a technologist who started the music streaming service Spotify. Just three weeks after it went public, Spotify plans to host an event for the press in New York Tuesday morning, where we'll likely get the first clues to where the music streaming service goes from here. The Swedish company didn't reveal much in its press invitation, other to say that we'd hear what's next for the company's mobile app. That doesn't necessarily rule out a new hardware announcement from Spotify, though it is hard to predict what that product or products could be. One logical guess might be a smart, voice-driven speaker, a market currently dominated by Amazon's Echo speakers with Alexa, and that also includes various Google Homes and the Apple HomePod.

This paper explores the use of convolutional (PixelCNN) and recurrent units (PixelRNN) for modeling the distribution of images, in the framework of autoregression distribution estimation. In this framework, the input distribution $p(x)$ is factorized into a product of conditionals $\Pi p(x_i x_i-1)$. Previous work has shown that very good models can be obtained by using a neural network parametrization of the conditionals (e.g.