Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning

2024-12-10

arXiv.org

AI Summary

This work investigates the mechanisms and conditions under which Hopfield networks exhibit emergent creativity in self-optimizing (SO) mode via unsupervised learning. Method: We introduce the first systematic theoretical and empirical framework to rigorously verify genuine creativity in SO models, formally defining cognitive and computational criteria for creative processes. Using dynamical systems analysis, we identify four distinct learning-parameter-driven dynamical regimes that unify the generation of creative outputs and stochasticity. We further construct an interpretable parameter–behavior mapping model. Contribution/Results: We prove that learning is a necessary condition for increasing the probability of creative output; demonstrate that learning enables creativity beyond random baselines; and reveal how specific parametric configurations govern transitions between deterministic, chaotic, and creative dynamics. These findings establish a novel theoretical paradigm and a controllable pathway for creativity emergence in artificial life and generative AI.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Evolutionary Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of artificial life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.

Problem

• Exploring creativity in Self-Optimization of Hopfield Networks
• Identifying conditions for creative processes in unsupervised learning
• Analyzing learning parameters to understand creative outcomes in AI

Innovation

• Self-Optimization model enhances Hopfield Network performance
• Modifies learning parameters for creative outcomes
• Framework for studying creativity in artificial systems