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

Machine Learning Street Talk (MLST)

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Creativity and Neural Networks

This chapter explores the intersection of creativity and problem-solving within the context of large language models. The speakers discuss the limitations of current AI models in achieving true creativity, emphasizing the importance of human intuition and evolutionary methods in enhancing neural network development. Through contrasting conventional training approaches with more flexible evolutionary algorithms, the chapter highlights the need for innovative strategies to unlock higher levels of abstraction and adaptability in artificial intelligence.

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