Mindstorms in Natural Language-Based Societies of Mind

Inspired by Minsky's Society of Mind, Schmidhuber's Learning to Think, and other more recent works, this paper proposes and advocates for the concept of natural language-based societies of mind (NLSOMs). We imagine these societies as consisting of a collection of multimodal neural networks, including large language models, which engage in a “mindstorm” to solve problems using a shared natural language interface. Here, we work to identify and discuss key questions about the social structure, governance, and economic principles for NLSOMs, emphasizing their impact on the future of AI. Our demonstrations with NLSOMs-which feature up to 129 agents-show their effectiveness in various tasks, including visual question answering, image captioning, and prompt generation for text-to-image synthesis.

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Publication Details

Journal
Computational Visual Media
Published
2025-02-01
DOI
https://doi.org/10.26599/cvm.2025.9450460
Citations
4
Primary Topic
Cognitive Science and Mapping
Type
article
Field-Weighted Citation Impact
7.44
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article

Mindstorms in Natural Language-Based Societies of Mind

Kazuki Irie, Louis Kirsch, Francesco Faccio, Dylan R. Ashley et al.
4 citations
Computational Visual Media
Cognitive Science and Mapping
7.44
article

Mindstorms in Natural Language-Based Societies of Mind

Kazuki Irie, Louis Kirsch, Francesco Faccio, Dylan R. Ashley, Piotr Piękos, Jinjie Mai, Vincent Herrmann, Imanol Schlag, Bernard Ghanem, Hasan Abed Al Kader Hammoud, Mingchen Zhuge, Abdullah Hamdi, Weimin Shi, Róbert Csordás, Deng-Ping Fan, Jürgen Schmidhuber, Aleksandar Stanić, Yuhui Wang, Wenyi Wang, Bing Li, Shuming Liu, Guohao Li, Aditya A. Ramesh, Haozhe Liu, Mengmeng Xu, Anand Gopalakrishnan
article en
4 citations

Abstract

Inspired by Minsky's Society of Mind, Schmidhuber's Learning to Think, and other more recent works, this paper proposes and advocates for the concept of natural language-based societies of mind (NLSOMs). We imagine these societies as consisting of a collection of multimodal neural networks, including large language models, which engage in a “mindstorm” to solve problems using a shared natural language interface. Here, we work to identify and discuss key questions about the social structure, governance, and economic principles for NLSOMs, emphasizing their impact on the future of AI. Our demonstrations with NLSOMs-which feature up to 129 agents-show their effectiveness in various tasks, including visual question answering, image captioning, and prompt generation for text-to-image synthesis.

Computational Visual MediaVol. 11(1)
Nankai University (CN), Dalle Molle Institute for Artificial Intelligence Research (CH), Harvard University Press (US), ETH Zurich (CH), University of Oxford (GB), Science Oxford (GB), King Abdullah University of Science and Technology (SA), Beihang University (CN), Stanford University (US)
Quality Education
Openalex Percentile: Top 4%
Cognitive Science and Mapping
7.44
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