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Prof. BERT DE VRIES - ON ACTIVE INFERENCE

Machine Learning Street Talk (MLST)

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Adapting Active Inference: Lessons from Nature

This chapter explores the challenges of online structural adaptation in active inference systems, contrasting them with human learning experiences. It emphasizes the importance of dynamic model updates and innovative algorithm design for improving predictive accuracy in real-world applications. The discussion highlights the potential of self-organizing software and the need for a shift in engineering practices to accommodate the complexities of emergent behaviors in systems.

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