  With state-of-the-art models achieving high performance on standard
benchmarks, contemporary research paradigms continue to emphasize general
intelligence as an enduring objective. However, this pursuit overlooks the
fundamental disparities between the high-level data perception abilities of
artificial and natural intelligence systems. This study questions the Turing
Test as a criterion of generally intelligent thought and contends that it is
misinterpreted as an attempt to anthropomorphize computer systems. Instead, it
emphasizes tacit learning as a cornerstone of general-purpose intelligence,
despite its lack of overt interpretability. This abstract form of intelligence
necessitates contextual cognitive attributes that are crucial for human-level
perception: generalizable experience, moral responsibility, and implicit
prioritization. The absence of these features yields undeniable perceptual
disparities and constrains the cognitive capacity of artificial systems to
effectively contextualize their environments. Additionally, this study
establishes that, despite extensive exploration of potential architecture for
future systems, little consideration has been given to how such models will
continuously absorb and adapt to contextual data. While conventional models may
continue to improve in benchmark performance, disregarding these contextual
considerations will lead to stagnation in human-like comprehension. Until
general intelligence can be abstracted from task-specific domains and systems
can learn implicitly from their environments, research standards should instead
prioritize the disciplines in which AI thrives.
