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Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

Machine Learning Street Talk (MLST)

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Harnessing Collective Intelligence for Innovation

The integration of multiple models into a meta-architecture enables sophisticated problem-solving through collaborative learning. In this system, diverse foundation models interact as a 'society of mind,' prompting each other to generate innovative solutions collectively. This collaboration can take various forms, such as monarchies or democracies, influencing the decision-making process. The dynamics of these interactions raise questions about the effectiveness of different governance styles in knowledge acquisition and problem-solving. The essence of innovation lies not only in individual flashes of insight but also in the iterative process of trial and error, where shared knowledge and diverse perspectives converge to spark creative breakthroughs. This continuous cycle of exploration and collaboration enriches the understanding and capabilities of the models, ultimately driving significant advancements in technology and problem resolution.

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