On the stochastics of human and artificial creativity

TL;DR

This paper develops a statistical model of human creativity based on stochastic theory and uses it to evaluate AI systems, concluding that current AI lacks human-level autonomous creativity.

Contribution

It introduces a novel stochastic representation of human creativity and applies it to assess modern AI systems' creative capabilities.

Findings

Current AI systems do not exhibit human-level autonomous creativity.

The stochastic model highlights the randomness and bias in human creative processes.

Assessment framework can compare AI creativity to human benchmarks.

Abstract

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.