  What constitutes human creativity, and is it possible for computers to
exhibit genuine creativity? We argue that achieving human-level intelligence in
computers, or so-called Artificial General Intelligence, necessitates attaining
also human-level creativity. We contribute to this discussion by developing a
statistical representation of human creativity, incorporating prior insights
from stochastic theory, psychology, philosophy, neuroscience, and chaos theory.
This highlights the stochastic nature of the human creative process, which
includes both a bias guided, random proposal step, and an evaluation step
depending on a flexible or transformable bias structure. The acquired
representation of human creativity is subsequently used to assess the
creativity levels of various contemporary AI systems. Our analysis includes
modern AI algorithms such as reinforcement learning, diffusion models, and
large language models, addressing to what extent they measure up to human level
creativity. We conclude that these technologies currently lack the capability
for autonomous creative action at a human level.
