Generative AI+ Art is Gaining Momentum
Blog: Jim Sinur
I thought a post on generative art might be in the interest of all things AI. This kind of art is leveraging AI, algorithms, randomness, programs, and humans to create exciting and beautiful art. As you may know, I now collaborate with Fractal Software to develop compelling and award-winning artwork. In fact, some of my fractals are my best sellers. I have a great friend and fellow artist, Bob Weerts, who is pushing this collaboration even further. Below are two of his early generative pieces:
Bob employs lines as his fundamental stylistic element and incorporates a chance in determining line length, density, and color. He cedes some control over the work’s final outcome to a process enabled by Software he’s written allow the piece to “emerge” over time. He plans to let the Software take more control of these emergent pieces over time, letting AI/Algorithms expand some range. I find his early pieces quite pleasing and interesting already.
One source of Bob’s original inspiration is Casey Reas “Process Compendium,” which, among other ideas, explored a synthesis of the Complexity Science notion of “emergence” and Generative Art in the early 2000s. An example of Reas Compendium work is below: (Click Here for Other Examples).
Reas is an internationally admired artist, but perhaps best known as the author, along with Ben Fry, of the graphical sketching too called “Processing,” which is widely used in the domains of Art, Design, and Media.
The significance of the generative art trend is perhaps exemplified by Christie’s record of $432,500 sales of “Portrait of Belamy”. The image is one of a series created by a group of young French students collaborating collectively as “Obvious”. Obvious borrowed heavily from open-source Generative Adversarial Network (GAN) algorithms specially developed by a then-high school graduate Robbie Barrat but originally conceived by the AI researcher Ian Goodfellow. This has the ball rolling, and there is new momentum under the “GAN” movement. Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in order to generate new, synthetic instances of data that can pass for real data. They are used widely in image generation, video generation, and voice generation.
GAN’s potential for both good and evil is huge because they can learn to mimic any distribution of data. GANs can be taught to create worlds eerily similar to our own in any domain: Images, music, speech, prose. They are robot artists in a sense, and their output is impressive. But they can also be used to generate fake media content of often called “deep fakes.”
AI Generative Art is quite striking. Since the whole field is getting more towards AI and less from the artist/programmer, we can expect some exciting results in the future. I will likely pursue a more intimate collaboration with all kinds of generative art going forward. Keep your eye on Bob Weerts as he is a creative guy seeking this edge faster than many other artists.
If you want to see my works, check out the fractals section here
If you want to know more about my collaborations with Software to create, check out this post