The new Birthday Paradox:
The more elaborate and thoughtful a social media birthday greeting is, the more you will discount it thinking it was written by AI.
And no it's not my birthday season.
This paper nails why most algorithmically created #music is so instantly recognizable, and cheesy. The problem isn’t the composition algorithm, it is the taste discriminator (or, rather, the lack/quality thereof), and adding such a discrimination algorithm is a step forward
(from the 3rd Conference on AI Music Creativity #AIMC)
#AI #ArtificialIntelligence #Art #Creativity
https://zenodo.org/record/7088395
Human composers arrive at creative decisions on the basis of their individual musical taste. For automatic algorithmic composition, we propose to embrace that concept and encode taste as binary classification task. We identify and reconsider an implicit assumption: each and every result of a successful composing algorithm should be of great quality. In contrast, we formulate a general concept of composer-producer collaboration: an artificial music producer that filters 'good' and 'poor' results of an independent composer can improve musical quality without the need of refactoring composing strategies. That way, creative programming can be divided into independent subtasks, which allow for modular (multi-agent) system designs as well as productive team development. In a proof-of-concept experiment, we perform the discrimination of real Bach chorales from fakes generated by DeepBach using neural networks. This leads to an improvement of the overall results and provides possibilities to explain model behavior. Our concept can effortlessly be transferred to any pre-existing music generator.
This paper briefly but incisively summarizes the key reasons why #AI-generated #music distinctly lacks “human qualities”, and how artists can lean into mistakes and shortcomings to create novel artistic work, whereas technologists try to eliminate mistakes, with commensurately uninteresting artistic results
(from the 3rd Conference on AI Music Creativity #AIMC)
#ArtificialIntelligence #Art #Creativity
https://zenodo.org/record/7088311
This paper seeks to identify aesthetically productive problems. Based on Melanie Mitchell's much-discussed 2021 paper "Why AI is Harder Than We Think," it seeks to outline four areas of artistic potential that are related to the four "fallacies" in AI research identified by Mitchell. These are underlying assumptions of AI research that have contributed to overconfident predictions. The paper uses these fallacies as a point of departure to discuss the relation of AI research and artistic practice, not from a utilitarian or problem-solving point of view, but rather in order to identify how frictions and fallacies disclose aesthetically productive areas. The paper seeks to demonstrate how these fallacies are not only shortcomings with regard to our understanding of intelligence, but how they are actually at the core of what constitutes aesthetics and artistic practice.
Mor Naaman
Eric-Oliver Mächler
Mor Naaman
Timo Kissel