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

Machine Learning Street Talk (MLST)

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Survival and Evolution in AI

This chapter explores the concept of survival in evolution, highlighting it as a constraint instead of a primary objective, particularly in relation to artificial intelligence. The discussion emphasizes the complexities of genetic representation and the importance of energy constraints in evolutionary processes, contrasting biological evolution with traditional genetic algorithms. By examining the implications of these principles, the chapter suggests that understanding evolution can enhance our approaches to training AI systems while addressing limitations in current models.

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