Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many cases, such models are expected to learn abstract concepts such as harmony, meter, and rhythm from raw musical data without any additional information. In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information. Specifically, we study the effects of four different conditioning inputs on the performance of a recurrent monophonic melody generation model. Several combinations of these conditioning inputs are used to train different model variants which are then evaluated using three objective evaluation paradigms across two genres of music. The results indicate musically relevant conditioning significantly improves learning and performance, and reveal how this information affects learning of musical features related to pitch and rhythm. An informal subjective evaluation suggests a corresponding improvement in the aesthetic quality of generations.

John Lennon and Paul McCartney were one of the Twentieth Century's most celebrated musical writing partnerships. Exactly what elements each of the pair contributed to their joint-credited hits has been a matter of much debate among fans, however. Now, researchers have used AI to work out which Beatle penned the music for a number of their most disputed songs. Scientists from Harvard and Dalhousie University in Canada trained their software using the Beatles back catalogue to recognise 137 unique musical patterns. These patterns, including notes, chords, and other musical motifs, were applied to eight disputed pieces of music to work out which of the Beatles wrote them.

Artificial Intelligence (AI) is poised to improve people's lives worldwide and accelerate progress on the United Nations Sustainable Development Goals (SDGs). Yet, AI can also bring with it a host of unintended consequences. One of the most pernicious areas could be AI's ability to spread misinformation at a pace and scale not seen before. At the recent AI for Good Global Summit, participants from academia, the United Nations, major media outlets and the private sector gathered to discuss the unintended consequences of AI and AI-powered misinformation. To be sure, AI has provided a wealth of task-facilitating tools for media and the field of journalism, where its impact can be seen in everything from the emergence of voice-recognition transcription tools to automatically generated content.

Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading software technology company, was selected by Microsoft (MSFT) to join its elite group of global independent software vendors for intensive joint sales, support and go-to-market initiatives, according to an announcement made today by Jay Hutton, VSBLTY co-founder and CEO. Launched in 2016, Microsoft created its co-sell ready initiative under its Microsoft One program to provide comprehensive sales and marketing support for select partners. To be eligible for the go-to-market program, independent software vendors must submit customer references that demonstrate successful projects and meet a performance commitment in addition to passing sales and technology assessments. VSBLTY technology provides customer audience measurement using the power of machine learning through computer vision. Its industry leading VisionCaptor and DataCaptor combine motion graphics and interactive brand messaging with first of its kind Facialanalytics .

Earlier this month, an unexceptional thread appeared on Reddit announcing that there is a new way "to cook egg white[s] without a frying pan". As so often happens on this website, which calls itself "the front page of the internet", this seemingly banal comment inspired a slew of responses. "I've never heard of people frying eggs without a frying pan," one incredulous Redditor replied. "I'm gonna try this," added another. One particularly enthusiastic commenter even offered to look up the scientific literature on the history of cooking egg whites without a frying pan. Every day, millions of these unremarkable conversations unfold on Reddit, spanning from cooking techniques to geopolitics in the Western Sahara to birds with arms.

In 1983, millions of unsold cartridges of the Atari game ET The Extra Terrestrial were secretly buried in a concrete-covered landfill in Alamogordo, New Mexico. Thanks to extremely rushed development and alleged interference from Universal Pictures, the movie tie-in was one of the worst video games ever made, and a mass grave was the only option for the poor, unwanted cartridges. Unfortunately, like movies adapted from games, games adapted from movies are often bad. Historically, this has not been the result of too much interference from the movie studios that own the licences, but too little. Promising pop-culture properties such as Transformers, Robocop and Harry Potter have often been farmed out to contracted development studios that are then given far too little time to make anything half decent. The forthcoming Star Wars Jedi: Fallen Order, however, has better prospects than most.

When you're watching a modern blockbuster such as The Avengers, it's hard to escape the feeling that what you're seeing is almost entirely computer-generated imagery, from the effects to the sets to fantastical creatures. But if there's one thing you can rely on to be 100% real, it's the actors. We might have virtual pop stars like Hatsune Miku, but there has never been a world-famous virtual film star. Even that link with corporeal reality, though, is no longer absolute. You may have already seen examples of what's possible: Peter Cushing (or his image) appearing in Rogue One: A Star Wars Story more than 20 years after his death, or Tupac Shakur performing from beyond the grave at Coachella in 2012.

The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, user specific preferences over aesthetics in photography, while surpassing the state-of-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.

Emotional aspects play an important part in our interaction with music. However, modelling these aspects in MIR systems have been notoriously challenging since emotion is an inherently abstract and subjective experience, thus making it difficult to quantify or predict in the first place, and to make sense of the predictions in the next. In an attempt to create a model that can give a musically meaningful and intuitive explanation for its predictions, we propose a VGG-style deep neural network that learns to predict emotional characteristics of a musical piece together with (and based on) human-interpretable, mid-level perceptual features. We compare this to predicting emotion directly with an identical network that does not take into account the mid-level features and observe that the loss in predictive performance of going through the mid-level features is surprisingly low, on average. The design of our network allows us to visualize the effects of perceptual features on individual emotion predictions, and we argue that the small loss in performance in going through the mid-level features is justified by the gain in explainability of the predictions.

Multiple sequence to sequence models were used to establish an end-to-end multi-turns proactive dialogue generation agent, with the aid of data augmentation techniques and variant encoder-decoder structure designs. A rank-based ensemble approach was developed for boosting performance. Results indicate that our single model, in average, makes an obvious improvement in the terms of F1-score and BLEU over the baseline by 18.67% on the DuConv dataset. In particular, the ensemble methods further significantly outperform the baseline by 35.85%.