We Can't Understand AI Using our Existing Vocabulary
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.
67b30146b02f929c82ce075e
John Hewitt
admin_assigned
2025-02-19T15:54:11.819Z
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John Hewitt
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johnhew
67b30146b02f929c82ce075f
Robert Geirhos
admin_assigned
2025-02-19T15:53:50.762Z
673bbe0d7dfcdedd52619ec2
/avatars/531a44f05d0c738bbe3e028c76c2e948.svg
Robert Geirhos
user
rgeirhos
67b30146b02f929c82ce0760
Been Kim
claimed_verified
2025-04-16T09:37:49.305Z
65dec807f4cf523519261714
/avatars/c6acb4f0c7ad715ff2c821c85be88d73.svg
been kim
user
Beenkim
We Can't Understand AI Using our Existing Vocabulary
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.
Introducing new words (neologisms) can enhance communication between humans and machines, improving understanding and control of AI.
neologisms interpretability communication problem human-machine language LLM length neologism diversity neologism