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Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making
This paper investigates the prospect of developing human-interpretable, explainable artificial intelligence (AI) systems based on active inference and the free energy principle.
Rejecting Cognitivism: Computational Phenomenology for Deep Learning
We propose a non-representationalist framework for deep learning relying on a novel method: computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models.
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