  Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire
diverse societies of large multimodal neural networks (NNs) that solve problems
by interviewing each other in a "mindstorm." Recent implementations of NN-based
societies of minds consist of large language models (LLMs) and other NN-based
experts communicating through a natural language interface. In doing so, they
overcome the limitations of single LLMs, improving multimodal zero-shot
reasoning. In these natural language-based societies of mind (NLSOMs), new
agents -- all communicating through the same universal symbolic language -- are
easily added in a modular fashion. To demonstrate the power of NLSOMs, we
assemble and experiment with several of them (having up to 129 members),
leveraging mindstorms in them to solve some practical AI tasks: visual question
answering, image captioning, text-to-image synthesis, 3D generation, egocentric
retrieval, embodied AI, and general language-based task solving. We view this
as a starting point towards much larger NLSOMs with billions of agents-some of
which may be humans. And with this emergence of great societies of
heterogeneous minds, many new research questions have suddenly become paramount
to the future of artificial intelligence. What should be the social structure
of an NLSOM? What would be the (dis)advantages of having a monarchical rather
than a democratic structure? How can principles of NN economies be used to
maximize the total reward of a reinforcement learning NLSOM? In this work, we
identify, discuss, and try to answer some of these questions.
