Music Inpainting is the task of filling in missing or lost information in a piece of music. We investigate this task from an interactive music creation perspective. To this end, a novel deep learning-based approach for musical score inpainting is proposed. The designed model takes both past and future musical context into account and is capable of suggesting ways to connect them in a musically meaningful manner. To achieve this, we leverage the representational power of the latent space of a Variational Auto-Encoder and train a Recurrent Neural Network which learns to traverse this latent space conditioned on the past and future musical contexts. Consequently, the designed model is capable of generating several measures of music to connect two musical excerpts. The capabilities and performance of the model are showcased by comparison with competitive baselines using several objective and subjective evaluation methods. The results show that the model generates meaningful inpaintings and can be used in interactive music creation applications. Overall, the method demonstrates the merit of learning complex trajectories in the latent spaces of deep generative models.

The generation of music that adapts dynamically to content and actions has an important role in building more immersive, memorable and emotive game experiences. To date, the development of adaptive music systems for video games is limited by both the nature of algorithms used for real-time music generation and the limited modelling of player action, game world context and emotion in current games. We propose that these issues must be addressed in tandem for the quality and flexibility of adaptive game music to significantly improve. Cognitive models of knowledge organisation and emotional affect are integrated with multi-modal, multi-agent composition techniques to produce a novel Adaptive Music System (AMS). The system is integrated into two stylistically distinct games. Gamers reported an overall higher immersion and correlation of music with game-world concepts with the AMS than with the original game soundtracks in both games.

ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up for ProPublica's Big Story newsletter to receive stories like this one in your inbox as soon as they are published. Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren't enough to set off a small microphone in the ceiling that was supposed to detect aggression.

The Allen Institute for Artificial Intelligence has an interesting new tactic in the war on fake news: make more of it. A team of researchers at the institute recently developed Grover, a neural network capable of generating fake news articles in the style of actual human journalists. In essence, the group is fighting fire with fire because the better Grover gets at generating fakes, the better it'll be at detecting them. Our study presents a surprising result: the best way to detect neural fake news is to use a model that is also a generator. The generator is most familiar with its own habits, quirks, and traits, as well as those from similar AI models, especially those trained on similar data, i.e. publicly available news.

Before this year's E3, the annual video games event where publishers descend on Los Angeles to unveil and promote their wares for the next year and beyond, anticipation was high for Square Enix's new Avengers game – an action-adventure for one to four players, in which you can fight as Hulk, Black Widow, Thor and plenty of others. In the year that Endgame grossed more than $2.7bn at the box office worldwide, surely not much could go wrong for a game proffering a personalised Marvel superhero fantasy. As it turned out, however, the Avengers game's big reveal fell rather flat (and was rather eclipsed by Keanu Reeves, who made a surprise appearance to reveal his top-secret cameo in the forthcoming Cyberpunk 2077 the day before). That is Iron Man, right? Why does he look nothing like Robert Downey Jr? The Avengers characters in this online action-adventure game share absolutely no likeness with the ones we know from the Marvel Cinematic Universe.

The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a novel formulation of audio synthesizer control. We formalize it as finding an organized latent audio space that represents the capabilities of a synthesizer, while constructing an invertible mapping to the space of its parameters. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce the disentangling flows, which allow to perform the invertible mapping between separate latent spaces, while steering the organization of some latent dimensions to match target variation factors by splitting the objective as partial density evaluation. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. We also show that our model is able to learn semantic controls of a synthesizer by smoothly mapping to its parameters. Finally, we discuss the use of our model in creative applications and its real-time implementation in Ableton Live

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided; what we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We discuss what these essential doctrines might be and why their establishment is required to form connections, possibly growing, between a knowledge store that has been built up and new pieces of information that curiosity will bring back. As more findings are acquired and more bonds are fermented, we need a way to, periodically, reduce the amount of data; in the sense, it is important to capture the critical characteristics of what has been accumulated or produce a summary of what has been gathered. We start with the intuition for this line of reasoning and formalize it with a series of models (and iterative improvements) that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life.

These "breakfasts" are intended to be a convivial place for exchange with the invited expert, but also between the Council of Europe community and Strasbourg academics interested in this subject. The first meeting will take place on Thursday 27 June 2019 (8h30 - 10h30) at the Maison interuniversitaire des sciences de l'Homme (MISHA), 5 allée du Général Rouvillois (Strasbourg) - Tram E / Stop Observatoire. Frédéric Wickert, an entrepreneur and expert with the Council of Europe, will take part in this first exercise on the theme: the different faces of AI.

While we don't yet have the personal androids promised to us in sci-fi movies, artificial intelligence (AI) is increasingly a part of our everyday lives, with Forbes declaring 2019 "the year AI will move into the mainstream." Thanks to AI, you can use your smartphone to deposit checks. And AI makes recommendations on Amazon and Netflix based on your usage and preferences. With AI becoming more deeply integrated into our professional and personal lives, the Indeed analytics team crunched platform data to learn more about AI jobs in 2019. What are the top positions?

TECHNOLOGY has advanced so much that one can now produce or alter audio or video content to show or present something that actually didn't happen. With deepfake technology (which combines "deep learning" with "fake"), one can, for example, superimpose someone's face over another person's to create a video to support his or her own agenda. The video is then circulated online, with disastrous consequences on the victim if the purpose is vile in nature, such as the sex video that is currently doing its rounds on social media in Malaysia. Deepfake is artificial intelligence (AI) at work, and there is little you can do to prevent it from happening to you, as highly-paid Hollywood actress Scarlett Johansson lamented. The subject of a fake porn video, she told the Washington Post (Dec 31, 2018): "The truth is, there is no difference between someone hacking my account or someone hacking the person standing behind me on line at the grocery store's account. It just depends on whether or not someone has the desire to target you. "Obviously, if a person has more resources, they may employ various forces to build a bigger wall around their digital identity.