Understanding Limitations in Models

This chapter explores the limitations of large language models and neural networks in their interpretation of data, particularly in language and vision. The discussion highlights inherent flaws in modeling approaches across neuroscience and finance, emphasizing the complexity of real-world systems and the challenges posed by noisy data. Through a comparative lens, the chapter underscores the intricate dynamics of biological systems versus artificial models, revealing both parallels and limitations in understanding intelligence.

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