We Can't Understand AI Using our Existing Vocabulary
Authors: name: John Hewitt, name: Robert Geirhos, name: Been Kim

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.