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.

Macau casinos have been rolling out artificial intelligence (AI) technologies in order to monitor habits of patrons and quickly identify those individuals who are likely to spend, and lose, big. The eyes in the skies of Macau casinos are upgrading their capabilities with artificial intelligence. Make no mistake, the AI implementations aren't tailored to protect gamblers. Instead, the technology is to aid the casino in pinpointing its VIPs and making sure they're being catered to. Bloomberg journalist Jinshan Hong says Las Vegas Sands and MGM Resorts' Macau casinos have already installed such monitoring equipment. Hong reports that hidden cameras use facial recognition technologies to identify guests, and digitally-enabled poker chips and baccarat tables allow the house to keep close tabs on guests.

"Machine Learning: Living in the Age of AI," examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.

This article is reprinted by permission from NextAvenue.org. New technology devices and apps pop up as abundantly as summer weeds here in Silicon Valley. Chip-enhanced products offer to satisfy almost every need imaginable. Prompts from your smart refrigerator tell you to buy more milk. With a voice command, music plays to facilitate meditation, thanks to your smart -- always on -- helper who listens for your next query from a canister on your kitchen counter; you know, the one with a woman's voice and name.

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we propose an adaptive deep learning strategy for cross-domain recommendation, referred to as ADC. We design a neural architecture and formulate a cross-domain loss function, to compute the non-linearity in user preferences across domains and transfer the knowledge of users' multiple behaviors, accordingly. In addition, we introduce an efficient algorithm for cross-domain loss balancing which directly tunes gradient magnitudes and adapts the learning rates based on the domains' complexities/scales when training the model via backpropagation. In doing so, ADC controls and adjusts the contribution of each domain when optimizing the model parameters. Our experiments on six publicly available cross-domain recommendation tasks demonstrate the effectiveness of the proposed ADC model over other state-of-the-art methods. Furthermore, we study the effect of the proposed adaptive deep learning strategy and show that ADC can well balance the impact of the domains with different complexities.

Until recently, artificial intelligence was a thing for science fiction movies and books. However, we are now in the midst of an ever-changing tech world where we are advancing faster than ever before. The future of AI is unknown, but that doesn't stop people from contemplating. Here are some of the best quotes about the future of AI. "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks."