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The Fractured Entangled Representation Hypothesis (Kenneth Stanley, Akarsh Kumar)

Machine Learning Street Talk (MLST)

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Challenges of AI Representation

This chapter discusses the limitations of neural networks in modeling complex realities, introducing the Fractured Entangled Representation Hypothesis as a new perspective. It highlights the importance of understanding representation in AI systems, emphasizing that benchmark performance alone does not equate to a healthy model and touching on the philosophical aspects of measurement in research.

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