Paper title: We Can’t Understand AI Using our Existing Vocabulary
https://arxiv.org/pdf/2502.07586

This research posits that current vocabularies are insufficient for understanding AI, hindering effective human-machine communication. To address this, the authors advocate for creating "neologisms," which are new words representing human concepts for machines or machine concepts for humans. These neologisms aim to bridge the conceptual gap by providing a shared language, improving AI interpretability and control. As a proof of concept, they introduce "neologism embedding learning," a method for encoding these new words, and demonstrate its potential through experiments involving length, diversity and quality control of language model responses. The study argues that neologisms can facilitate more precise communication and mitigate biases, ultimately leading to a more collaborative human-AI relationship. The authors acknowledge potential misuse but emphasize the overall aim of aligning AI with human intentions through enhanced communication.