Neural nets can represent nearby concepts in addition to the main topic they activate on. Features like Amelia Earhart, immunology, book of Titus, gender bias, and code errors showcase the diverse findings in neural nets. By personalizing neural nets to embody certain characters or concepts, the model becomes much more powerful than using a generalized approach. This ability to personalize neural nets opens up new functional uses and enhances their capabilities significantly.

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