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Creating Computer Vision and Machine Learning Algorithms that Can Analyze Works of Art

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When you study a painting, chances are that you can make several inferences about it. In addition to understanding the subject matter, for example, you may be able to classify it by period, style, and artist. Could a computer algorithm "understand" a painting well enough to perform these classification tasks as easily as a human being? We also addressed two other intriguing questions about the capabilities and limitations of AI algorithms: whether they can identify which paintings have had the greatest influence on later artists, and whether they can measure a painting's creativity using only its visual features. We wanted to develop algorithms capable of classifying large groups of paintings by style (for example, as Cubist, Impressionist, Abstract Expressionist, or Baroque), genre (for example, landscape, portrait, or still life), and artist.


Can AI Create True Art?

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As AI becomes an unstoppable force, it raises some difficult questions about the future role of humans in an increasingly automated world. Initial studies are showing that we can add the most value by focusing on four key areas: critical thinking, problem solving, managing human interactions, and above all else, expressing creativity. In short, our future role involves embracing these last bastions of human exclusivity and becoming more "human." But just last month, AI-generated art arrived on the world auction stage under the auspices of Christie's, proving that artificial intelligence can not only be creative but also produce world class works of art--another profound AI milestone blurring the line between human and machine. Naturally, the news sparked debates about whether the work produced by Paris-based art collective Obvious could really be called art at all.


A never-ending stream of AI art goes up for auction

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Training algorithms to generate art is, in some ways, the easy part. You feed them data, they look for patterns, and they do their best to replicate what they've seen. But like all automatons, AI systems are tireless and produce a never-ending stream of images. The tricky part, says German AI artist Mario Klingemann, is knowing what to do with it all. "For me, this potential is what makes it both interesting and difficult," Klingemann tells The Verge.


Autonomy, Authenticity, Authorship and Intention in computer generated art

arXiv.org Artificial Intelligence

This paper examines five key questions surrounding computer generated art. Driven by the recent public auction of a work of "AI Art" we selectively summarise many decades of research and commentary around topics of autonomy, authenticity, authorship and intention in computer generated art, and use this research to answer contemporary questions often asked about art made by computers that concern these topics. We additionally reflect on whether current techniques in deep learning and Generative Adversarial Networks significantly change the answers provided by many decades of prior research.


A.I. musicians are a growing trend. What does that mean for the music industry? Digital Trends

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The most prolific musical artists manage to release one, maybe two, studio albums in a year. Rappers can sometimes put out three or four mixtapes during that same time. However, Auxuman plans to put out a new full-length album, featuring hot up-and-coming artists like Yona, Mony, Gemini, Hexe, and Zoya, every single month. Before this goes any further, don't worry: You're not hopelessly out of touch with today's pop music. Well, at least not in the sense that you could meet them and shake their hands.