Generative art

Generative art refers to art that has been generated algorithmically rather than manually crafted. Generative art utilizes rule-based algorithms, randomness, and machine learning models to produce original works without direct human input. The artist creates the system or process which then autonomously generates unique images, sounds, sculptures, or other outputs.

Some methods used in generative art include:

Procedural generation - Applying rules systematically to generate content
Mathematical visualization - Visualizing mathematical formulas, data, or concepts
Physics simulations - Simulating natural physical processes digitally
Machine learning - Using AI models like GANs and diffusion models to synthesize novel content

Generative art explores ideas of creativity, emergence, computation, randomness, and autonomy. By ceding some creative control to algorithms, artists can discover new aesthetics and given enough complexity, generative systems can produce highly original works. Early pioneers of generative art include artists like John Whitney, Frieder Nake, and Vera Molnár. Recent advances in AI are revolutionizing generative art.

Key concepts:

Randomness - Adding randomness introduces unpredictability and variety
Iterations - Running the generative process repeatedly
Emergence - Complex generative artworks exhibiting emergent patterns
Autonomy - The art process functions independently without human guidance