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

Machine Learning Street Talk (MLST)

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Reimagining Neural Representations

This chapter examines the challenges to traditional views of neural network representations, emphasizing the existence of modular internal structures that can enhance machine learning. Through examples from the Picbreeder platform, it discusses the role of human intuition in guiding algorithmic learning and the implications for creative processes. The dialogue highlights the importance of understanding the journey of representation development rather than merely the final outcomes, exploring the deeper psychological aspects of learning and discovery.

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