This chapter delves into how language models and biology share similarities in learning higher order concepts beyond training data, discussing the application of AI in predicting protein structures. It highlights the efficiency of models like alphafold two and eosim fold, drawing parallels between attention maps in language models and protein contact maps. The conversation emphasizes the importance of proper data splitting to avoid overfitting and explores methods like confidence bootstrapping and domain-based data splitting for improved model performance and generalization.

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