We propose an automatic music generation demo based on artificial neural networks, which integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our model can generalize to different musical styles and generate polyphonic music better than previous models.

This paper studies an interesting problem: is it possible to predict the crowd opinion about a movie before the movie is actually released? The crowd opinion is here expressed by the distribution of ratings given by a sufficient amount of people. Consequently, the pre-release crowd opinion prediction can be regarded as a Label Distribution Learning (LDL) problem. In order to solve this problem, a Label Distribution Support Vector Regressor (LDSVR) is proposed in this paper. The basic idea of LDSVR is to fit a sigmoid function to each component of the label distribution simultaneously by a multi-output support vector machine. Experimental results show that LDSVR can accurately predict peoples's rating distribution about a movie just based on the pre-release metadata of the movie.

It's been more than 50 years since HAL, the malevolent computer in the movie 2001: A Space Odyssey, first terrified audiences by turning against the astronauts he was supposed to protect. That cinematic moment captures what many of us still fear in AI: that it may gain superhuman powers and subjugate us. But instead of worrying about futuristic sci-fi nightmares, we should instead wake up to an equally alarming scenario that is unfolding before our eyes: We are increasingly, unsuspectingly yet willingly, abdicating our power to make decisions based on our own judgment, including our moral convictions. What we believe is "right" risks becoming no longer a question of ethics but simply what the "correct" result of a mathematical calculation is. Day to day, computers already make many decisions for us, and on the surface, they seem to be doing a good job. In business, AI systems execute financial transactions and help HR departments assess job applicants.

In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique.

We describe an unconventional line of attack in our quest to teach machines how to rap battle by improvising hip hop lyrics on the fly, in which a novel recursive bilingual neural network, TRAAM, implicitly learns soft, context-dependent generalizations over the structural relationships between associated parts of challenge and response raps, while avoiding the exponential complexity costs that symbolic models would require. TRAAM learns feature vectors simultaneously using context from both the challenge and the response, such that challenge-response association patterns with similar structure tend to have similar vectors. Improvisation is modeled as a quasi-translation learning problem, where TRAAM is trained to improvise fluent and rhyming responses to challenge lyrics. The soft structural relationships learned by our TRAAM model are used to improve the probabilistic responses generated by our improvisational response component.

Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficulty replicating realistic scenarios. Lack of effective offline evaluation methods restricts progress in music recommendation research. The challenge is finding suitable measures to score recommendation quality, and in particular avoiding popularity bias, whereby the quality is not recognised when the track is not well known. This paper presents a low cost method that leverages available social media data and shows it to be effective. Not only is it based on explicit feedback from many users, but it also overcomes the popularity bias that disadvantages new/niche music. Experiments show that its findings are consistent with those from an online study with real users. In comparisons with other offline measures, the social media score is shown to be a more reliable proxy for opinions of real users. Its impact on music recommendation is its ability to recognise recommenders that enable discovery, as well as suggest quality recommendations.

In this work, we introduce a context-aware hybrid artist recommender system (CAARS) that uses Twitter users' tweet-time patterns as context and users' bias about gender and types of musicians to recommend artists. Our model offers a novel approach to improve a personalized music recommender system (MRS) as it extracts implicit information from the users' past tweet-behavior and combines that with related content. The proposed model performs significantly better than collaborative and hybrid recommender systems and encourages further exploration.

Binge-watching TV shows on streaming services is becoming increasingly popular. However, there is a paucity of comprehensive metrics to effectively summarize such media watching behavior. We address this gap by presenting two new metrics--Bingeability and Ad Tolerance--to quantify key aspects of watching streaming TV interspersed with ads. These metrics are motivated by consumer psychology literature on hedonic adaptation and also reflect media consumption behavior. Using machine learning methods, including ensembles of classification trees, we identify the key predictors of these metrics, study non-linear effects, and rank the predictors in order of predictive power. The superiority and validity of these metrics is also discussed.

Gupshup, the US-based conversational messaging services company, has strengthened its Middle East presence with the acquisition of Singapore-based Knowlarity Communications, a global leader in cloud communications. With voice technology reforming the customer experience in the Middle East and North Africa (MENA), this acquisition by Gupshup will ensure that with Artificial Intelligence (AI) voice technology, customers can effortlessly connect with businesses and get support or post-sales assistance. This technology will reduce response times of businesses to customers and eventually help businesses retain customers. With the addition of Knowlarity's products, Gupshup will now be able to support businesses in building seamless conversational experiences across both messaging and voice channels. "As business-to-consumer (B2C) engagement becomes conversational, Gupshup is busy enabling more ways for businesses to deliver rich experiences. With the addition of Knowlarity's products, our customers in the Middle East and across the world will now be able to build seamless conversational experiences across both messaging and voice channels. A large number of large businesses and SMEs across sectors in the Middle East are starting to integrate AI technologies into their business and we see a huge potential for our business in the Middle East market."

Public discourse on environmental and health issues has risenon social media. Upon an environmental crisis, various chatterssuch as breaking news, misinformation, and rumor couldaggravate social confusion and proliferate negative publicsentiment. In an effort to study public sentiments on environmentalissues in South Korea, we analyzed 158,964 tweetsgenerated over a 4-year period following the Fukushima accidentin 2011, the largest release of radioactivity to environmentin recent history. This event led to a significant increasein public's interest on environmental and nuclear issues inKorea. We employed Bayesian network and recursive partitioningto observe the classification regression tree structureof major topics. Topics on health and environment were interlinkedclosely and represented both apprehension and concernabout health threats and pollution. Our methodologyhelps analyze large online discourse efficiently and offers insightto crisis response organizations.