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Explainability, Reasoning, Priors and GPT-3

Machine Learning Street Talk (MLST)

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Rethinking Intelligence: Transformational Learning

This chapter explores transformationally invariant learning and its impact on intelligence testing, arguing that traditional tests may overlook essential problem-solving variations. It emphasizes the significance of integrating core knowledge in neural networks to enhance reasoning capabilities and efficiency in learning across various applications.

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