John Boyne, the award-winning author of The Boy in the Striped Pyjamas, has acknowledged that a cursory Google led to him accidentally including monsters from the popular video game The Legend of Zelda: Breath of the Wild in his new novel. Boyne's A Traveller at the Gates of Wisdom opens in AD1 and ends 2,000 years later, following a narrator and his family. This is a thread, but it's worth it I promise. On Reddit today, user u/NoNoNo_OhOhOh posted a page from acclaimed Irish novelist John Boyne's latest book, 'The Traveller At the Gates of Wisdom. As the writer Dana Schwartz pointed out on Twitter, "if those ingredients look weird to you, it is because they are straight of out of the Zelda game Breath of the Wild".

The rest of the thread, Tell me about a paper that you found inspiring, from u/mitare is also quite interesting. This paper is a really comprehensive review detailing what exactly current ML techniques are unable to do that humans can do very well. It lays the groundwork that needs to be done to make human-level artificial intelligence.

Johannesburg-based producer HYMR's debut album, Artificial Intelligence is dripping in cinematic glory but for a handful of tracks that, while sounding good, don't add any weight to the piece. Fillers aside the record paints an intriguing portrait of the cyber-dystopia we are so rapidly heading towards where robots have the power to kill us and the environment has been tortured to within an inch of its life. 'Artificial Intelligence', a remix of a track that features, later on, sounds like the intro to a dystopian film. A young protagonist stands on the roof of a high-rise building dreaming of a better world as they look out on a 21st-century Hell-scape defined by monotonous grey buildings and never-ending rain. 'Cosmic Dreamer', an instrumental number that brings a world of tension to the album, continues this cinematic idea before'Polluted Planet' takes things in a more EDM-based direction.

This paper presents the Jazz Transformer, a generative model that utilizes a neural sequence model called the Transformer-XL for modeling lead sheets of Jazz music. Moreover, the model endeavors to incorporate structural events present in the Weimar Jazz Database (WJazzD) for inducing structures in the generated music. While we are able to reduce the training loss to a low value, our listening test suggests however a clear gap between the average ratings of the generated and real compositions. We therefore go one step further and conduct a series of computational analysis of the generated compositions from different perspectives. This includes analyzing the statistics of the pitch class, grooving, and chord progression, assessing the structureness of the music with the help of the fitness scape plot, and evaluating the model's understanding of Jazz music through a MIREX-like continuation prediction task. Our work presents in an analytical manner why machine-generated music to date still falls short of the artwork of humanity, and sets some goals for future work on automatic composition to further pursue.

Drawing an analogy with automatic image completion systems, we propose Music SketchNet, a neural network framework that allows users to specify partial musical ideas guiding automatic music generation. We focus on generating the missing measures in incomplete monophonic musical pieces, conditioned on surrounding context, and optionally guided by user-specified pitch and rhythm snippets. First, we introduce SketchVAE, a novel variational autoencoder that explicitly factorizes rhythm and pitch contour to form the basis of our proposed model. Then we introduce two discriminative architectures, SketchInpainter and SketchConnector, that in conjunction perform the guided music completion, filling in representations for the missing measures conditioned on surrounding context and user-specified snippets. We evaluate SketchNet on a standard dataset of Irish folk music and compare with models from recent works. When used for music completion, our approach outperforms the state-of-the-art both in terms of objective metrics and subjective listening tests. Finally, we demonstrate that our model can successfully incorporate user-specified snippets during the generation process.

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