AI Masterpieces: But is it Art?

For a more detailed re-telling of the history of generative art, see Why love generative art?An example of how a rich result can be achieved from very little programming is in Matt Pearson’s Wave Clock (2009) as described in his book Generative Art (Manning, 2011), which renders images like:The exciting point here is that there is no way by inspection of the source code alone to predict the emergent complexity of the final piece..Check out Generative art finds its prodigy for some seriously wonderful images from a Processing master—Manolo Gamboa Naon from Argentina—that might persuade you otherwise.Heres a Processing reimagining of the famous early piece called Schotter and created by Georg Nees in the 1960s, alongside the original:And here’s the Processing source code, which we’ve adapted very slightly from the original that was written by Jim Plaxco at Artsnova.Even if you’re not a developer, it’s fairly evident that the program involves a setup function and a draw function, and that the draw function integrates some random elements with more structured patterns..p5.js is probably even more accessible to beginners than Processing itself, as JavaScript is (perhaps notoriously) much less picky about being rigorous with variable types, among other misdemeanors.You can find Processing here—and if interested, create a sketch using the source code above and obtain your own reimagining of Schotter within a few minutes.If you’re interested in p5.js—check it out here.   If art nerds had become a little smug at the embarrassing amounts of processing power available to them for conventional computer art, they were in for a rude awakening when they began to explore how they could exploit modern AI techniques like neural networks and machine learning in their work..His algorithm centered around the idea of mathematical optimization—seeking to minimize the difference between a synthesized image, the content of a source image, and the stylistic features of a style-contributing image.The images below, adapted from Gatys’ paper, vividly illustrate the idea.Gatys’ work was a breakthrough in the field of deep learning, providing the first neural-network-based method for style transfer.. More details

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