I Feel You: What makes algorithmic experience personal?

Abstract

Since artificial neural networks are excelling in stylization of visual, textual and acoustic objects, we discuss the potential of such technologies in context of human-to-machine interactions in general and in an artistic context specifically. We discuss a paradigm of an uncanny-valley that was originally proposed for human-robot interaction and look at it at a new angle working with algorithms rather than with robots. We propose an exploration framework for human-algorithm interaction and illustrate basic principles of such interactions with two case studies on the verge of art and computer science. Finally, we discuss how exploration paradigm outlines the vector of further humanalgorithm collaborations.Artificial intelligence. Generative art. Data exploration. Human-algorithm interaction.

Music generation with variational recurrent autoencoder supported by history

“…After a subjective assessment of the tracks produced by different algorithms, we find that VRASH yields the highest percentage of tracks with qualitative interesting temporal and melodic structures. In [24] the artistic applications of the VRASH architecture are highlighted along with positive feedback from listeners as well as from professional musicians.…”

“…The rapid progress of artificial neural networks is gradually erasing the border between the arts and the sciences. A significant number of results demonstrate how areas previously regarded as entirely human due to their creative or intuitive nature are now being opened up for algorithmic approaches [24]. Music is one of these areas.…”