ml-foundationally-bounded-and-task-relative

IN derived (depth 1)

Created 2026-06-21T14:21:09+00:00 · Reviewed 2026-06-21T15:37:01+00:00

ML is foundationally bounded to task-relative operation — Mitchell's definition structurally requires domain-specific choices (task T, experience E, measure P) and the No Free Lunch theorem mathematically proves no universal escape exists, jointly establishing task-relativity as an invariant of the field, not a limitation to overcome.

Justifications

SL — Two independent foundational constraints (definitional and mathematical) converge on the same conclusion about ML's inherent task-specificity

Antecedents (all must be IN):

IN ml-mitchell-1997-formal-definition — Tom Mitchell's (1997) formal definition: a computer program learns from experience E with respect to task T and performance measure P if its performance on T as measured by P improves with experience E
IN ml-no-free-lunch-theorem — The No Free Lunch theorem states that no single machine learning algorithm works best for all problems