Generativity

Generativity is a concept that describes the capacity of a system to produce novel and coherent outputs. In artificial intelligence, it often refers to the ability of a model to generate new content, such as text, images, or code, that is not explicitly present in its training data. This generation is based on patterns and structures learned during training.

The idea of generativity is central to many advancements in creative AI. Generative models, like large language models (LLMs) and diffusion models for image synthesis, are designed to be generative. They can create original pieces of art, write stories, compose music, or even develop software based on prompts or existing data. The quality and novelty of these generated outputs are key metrics for evaluating generative systems.

Beyond AI, generativity can be observed in other complex systems. For example, biological organisms exhibit generativity through reproduction and evolution, creating new individuals and species over time. In human creativity, artists, writers, and musicians demonstrate generativity by producing original works. The underlying principle is the creation of something new from learned components or rules, allowing for adaptation and expansion.