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

Machine Learning Street Talk (MLST)

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Deceptive Search Spaces and Evolutionary Discoveries

This chapter explores the concept of deceptive search spaces in optimization processes, illustrated through the metaphor of a butterfly representing unexpected discoveries. It emphasizes the significance of novelty search algorithms and contrasts traditional methods like stochastic gradient descent with more open-ended approaches, using examples from the Pick Breeder system. The discussion also delves into the complexities of representation in AI and human creativity, highlighting the interplay between intuition and optimization in generating new insights.

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