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Prof. BERT DE VRIES - ON ACTIVE INFERENCE

Machine Learning Street Talk (MLST)

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Robustness and Adaptation in Neural Systems

This chapter examines how sub-symbolic systems in neural networks can adapt to varying computational resources and fluctuating environments, maintaining effectiveness despite reduced connections. The speakers discuss the implications of active inference and morphological resilience in biological systems, drawing parallels between neural processing and practical engineering challenges. Additionally, they highlight the shift towards generative models in modern coding practices, advocating for a deeper understanding of domain-specific applications in engineering.

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