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.
Barto, A. G.; Sutton, R. S.; Anderson, C. W. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics Vol. SMC-13, No. 5, 834–846, 1983.
Narendra, K. S.; Parthasarathy, K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks Vol. 1, No. 1, 4–27, 1990.
Schmidhuber, J. Deep learning in neural networks: An overview. Neural Networks Vol. 61, 85–117, 2015.
Schmidhuber, J. Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science Vol. 18, No. 2, 173–187, 2006.
Schmidhuber, J. Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Transactions on Autonomous Mental Development Vol. 2, No. 3, 230–247, 2010.
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A. C.; Bengio, Y. Generative adversarial nets. Communications of the ACM Vol. 63, No. 11, 139–144, 2020.
Schmidhuber, J. Generative Adversarial Networks are special cases of Artificial Curiosity (1990) and also closely related to Predictability Minimization (1991). Neural Networks Vol. 127, 58–66, 2020.
Solomonoff, R. J. A formal theory of inductive inference. Part Ⅰ. Information and Control Vol. 7, No. 1, 1–22, 1964.
Kolmogorov, A. N. Three approaches to the quantitative definition of information. International Journal of Computer Mathematics Vol. 2, 157–168, 1968.
Chaitin, G. J. On the length of programs for computing finite binary sequences. Journal of the ACM Vol. 13, No. 4, 547–569, 1966.
Levin, L. A. On the notion of a random sequence. Soviet Math. Dokl. Vol. 14, No. 5, 1413–1416, 1973.
Solomonoff, R. Complexity-based induction systems: Comparisons and convergence theorems. IEEE Transactions on Information Theory Vol. 24, No. 4, 422–432, 1978.
Li, M.; Vitányi, P. An Introduction to Kolmogorov Complexity and Its Applications. New York: Springer, 1997.
Schmidhuber, J. Hierarchies of generalized Kolmogorov complexities and nonenumerable universal measures computable in the limit. International Journal of Foundations of Computer Science Vol. 13, No. 4, 587–612, 2002.
Schmidhuber, J. Optimal ordered problem solver. Machine Learning Vol. 54, No. 3, 211–254, 2004.
Schmidhuber, J. Discovering neural nets with low Kolmogorov complexity and high generalization capability. Neural Networks Vol. 10, No. 5, 857–873, 1997.
Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; Neubig, G. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys Vol. 55, No. 9, 1–35, 2023.
Jordan, S. R. A natural language understander based on a freely associated learned memory net. International Journal of Computer & Information Sciences Vol. 6, 9–25, 1977.
Kloumann, I. M.; Danforth, C. M.; Harris, K. D.; Bliss, C. A.; Dodds, P. S. Positivity of the English language. PLoS One Vol. 7, No. 1, e29484, 2012.
Wierstra, D.; Forster, A.; Peters, J.; Schmidhuber, J. Recurrent policy gradients. Logic Journal of IGPL Vol. 18, No. 5, 620–634, 2010.
Schmidhuber, J. A local learning algorithm for dynamic feedforward and recurrent networks. Connection Science Vol. 1, No. 4, 403–412, 1989.
Wilson, S. W. ZCS: A zeroth level classifier system. Evolutionary Computation Vol. 2, No. 1, 1–18, 1994.
Baum, E. B.; Durdanovic, I. Toward a model of mind as an economy of agents. Machine Learning Vol. 35, No. 2, 155–185, 1999.
Brush, S. G. History of the Lenz–Ising model. Reviews of Modern Physics Vol. 39, No. 4, 883, 1967.
Gibbard, A. Manipulation of voting schemes: A general result. Econometrica: Journal of the Econometric Society Vol. 41, 587–601, 1973.
Satterthwaite, M. A. Strategy-proofness and Arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory Vol. 10, No. 2, 187–217, 1975.
Von Neumann, J.; Morgenstern, O. Theory of Games and Economic Behavior. Princeton: Princeton University Press, 1947.
Zhang, S.; Peng, H.; Fu, J.; Luo, J. Learning 2D temporal adjacent networks for moment localization with natural language. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 7, 12870–12877, 2020.
Li, K.; Guo, D.; Wang, M. Proposal-free video grounding with contextual pyramid network. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, No. 3, 1902–1910, 2021.
Azpúrua, H.; Saboia, M.; Freitas, G. M.; Clark, L.; Agha-mohammadi, A. A.; Pessin, G.; Campos, M. F. M.; Macharet, D. G. A survey on the autonomous exploration of confined subterranean spaces: Perspectives from real-word and industrial robotic deployments. Robotics and Autonomous Systems Vol. 160, 104304, 2023.