Generating art

Some more generative art. First, here’s Thomas Lin Pedersen, a “former bioinformatician / computational biologist turned data scientist turned software engineer”. Quite a mouthful.

Generative art by Thomas Lin Pedersen
I’m a generative artist focusing mainly on exploring the beauty of dynamic systems. For me, the sweet spot of generative art lies in creating a system that you know well enough to set it up for success, but is so complex that you still get surprised when you see the result. The more I become familiar with a system I’ve developed, the more it feels like a (slightly unpredictable) brush to paint with.

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I can’t begin to understand how he’s using R, software normally used for data analysis and statistics, to create such images.

A more traditional approach would be through the use of GANs, as we’ve seen before. (Strange to use the word ‘traditional’ with such a new and emerging field.) Here’s something from Joel Simon, who also takes inspiration from the systems of biology computation and creativity.

Artbreeder — create beautiful, wild and weird images
Simply keep selecting the most interesting image to discover totally new images. Infinitely new random ‘children’ are made from each image. Artbreeder turns the simple act of exploration into creativity …

Artbreeder started as an experiment in using breeding and collaboration as methods of exploring high complexity spaces. GAN’s are the engine enabling this. Artbreeder is very similar to, and named after, Picbreeder. It is also inspired by an earlier project of mine Facebook Graffiti which demonstrated the creative capacity of crowds.

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