# On the stochastics of human and artificial creativity

arXiv: https://arxiv.org/abs/2403.06996 · HTML: https://mlsanity.com/paper/2403.06996/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 2 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis](https://mlsanity.com/paper/2505.17241/) (2025)
- [The creative psychometric item generator: a framework for item generation and validation using large language models](https://mlsanity.com/paper/2409.00202/) (2024)

## Abstract (arXiv)

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.