Explainability, Reasoning, Priors and GPT-3
Machine Learning Street Talk (MLST)
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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