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.

翻译：尽管最先进的模型在标准基准测试中取得了高性能，当代研究范式仍将继续追求通用智能这一长期目标。然而，这种追求忽略了人工与自然智能系统在高层次数据感知能力上的根本差异。本研究质疑图灵测试作为通用智能思维评判标准的有效性，并主张其被误读为试图对计算机系统进行拟人化诠释。取而代之，本研究强调隐性学习是通用智能的基石——尽管其缺乏显式可解释性。这种抽象形式的智能需要构成人类水平感知核心的语境认知属性：可泛化的经验、道德责任和隐式优先级。这些特征的缺失会导致不可否认的感知差异，并限制人工系统有效理解环境语境的能力。此外，本研究证实，尽管对未来系统的潜在架构进行了广泛探索，但鲜有关注这些模型将如何持续吸收并适应语境数据。虽然传统模型可能持续提升基准性能，但忽视这些语境考量将导致类人理解能力的停滞。除非通用智能能够从特定任务领域中被抽象出来，且系统能够从环境中进行隐性学习，否则研究标准应优先关注人工智能擅长的领域。