title: We Can't Understand AI Using our Existing Vocabulary
summary: This position paper argues that, in order to understand AI, we cannot rely on
  our existing vocabulary of human words. Instead, we should strive to develop
  neologisms: new words that represent precise human concepts that we want to
  teach machines, or machine concepts that we need to learn. We start from the
  premise that humans and machines have differing concepts. This means
  interpretability can be framed as a communication problem: humans must be able
  to reference and control machine concepts, and communicate human concepts to
  machines. Creating a shared human-machine language through developing
  neologisms, we believe, could solve this communication problem. Successful
  neologisms achieve a useful amount of abstraction: not too detailed, so they're
  reusable in many contexts, and not too high-level, so they convey precise
  information. As a proof of concept, we demonstrate how a "length neologism"
  enables controlling LLM response length, while a "diversity neologism" allows
  sampling more variable responses. Taken together, we argue that we cannot
  understand AI using our existing vocabulary, and expanding it through
  neologisms creates opportunities for both controlling and understanding
  machines better.
paper_authors._id: 67b30146b02f929c82ce075e
paper_authors.name: John Hewitt
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:54:11.819Z
paper_authors._id: 646ec1161a198f8520f3c705
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paper_authors.fullname: John Hewitt
paper_authors.type: user
paper_authors.user: johnhew
paper_authors._id: 67b30146b02f929c82ce075f
paper_authors.name: Robert Geirhos
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:53:50.762Z
paper_authors._id: 673bbe0d7dfcdedd52619ec2
paper_authors.avatarUrl: /avatars/531a44f05d0c738bbe3e028c76c2e948.svg
paper_authors.fullname: Robert Geirhos
paper_authors.type: user
paper_authors.user: rgeirhos
paper_authors._id: 67b30146b02f929c82ce0760
paper_authors.name: Been Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T09:37:49.305Z
paper_authors._id: 65dec807f4cf523519261714
paper_authors.avatarUrl: /avatars/c6acb4f0c7ad715ff2c821c85be88d73.svg
paper_authors.fullname: been kim
paper_authors.type: user
paper_authors.user: Beenkim
paper_title: We Can't Understand AI Using our Existing Vocabulary
paper_summary: This position paper argues that, in order to understand AI, we cannot rely on
  our existing vocabulary of human words. Instead, we should strive to develop
  neologisms: new words that represent precise human concepts that we want to
  teach machines, or machine concepts that we need to learn. We start from the
  premise that humans and machines have differing concepts. This means
  interpretability can be framed as a communication problem: humans must be able
  to reference and control machine concepts, and communicate human concepts to
  machines. Creating a shared human-machine language through developing
  neologisms, we believe, could solve this communication problem. Successful
  neologisms achieve a useful amount of abstraction: not too detailed, so they're
  reusable in many contexts, and not too high-level, so they convey precise
  information. As a proof of concept, we demonstrate how a "length neologism"
  enables controlling LLM response length, while a "diversity neologism" allows
  sampling more variable responses. Taken together, we argue that we cannot
  understand AI using our existing vocabulary, and expanding it through
  neologisms creates opportunities for both controlling and understanding
  machines better.
paper_ai_summary: Introducing new words (neologisms) can enhance communication between humans and machines, improving understanding and control of AI.
paper_ai_keywords: neologisms interpretability communication problem human-machine language LLM length neologism diversity neologism