Before measuring machine intelligence, we must define it rigorously — grounded in cognitive science, not marketing.
A scientific, testable framework.
Common myths and false proxies.
Turn theory into measurable dimensions.
Score and profile against human baselines.
A hard drive with Wikipedia is not intelligent. Storage is not cognition.
Calculators are fast. They are not intelligent.
Excelling at one task does not demonstrate general cognitive capability.
Autocomplete predicts the next token. Understanding requires more.
Sounding intelligent is not the same as being intelligent.
Generalize principles, update beliefs, transfer knowledge across domains.
Handle situations absent from training data.
Generate new solutions. Break assumptions when appropriate.
Notice failure, change strategies, manage resources effectively.
Know what you don't know. Calibrate confidence accurately.
Scores from 0 to 230+, calibrated to human cognitive development (σ = 15). MHIL-100 represents average adult baseline.
MHIL reports both an overall score and a multi-dimensional profile. Two systems can share the same score but have radically different capabilities and risks.