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<p>This paper introduces uncertainty theory, an epistemological framework arising directly from contemporary AI practices. Its theoretical insights, motivations, and relevance are deeply intertwined with the irreversible trajectory of modern AI development. Specifically, this theory emerges from three interconnected conditions: first, only under current AI advancements—characterized by extensive information availability and powerful modeling capabilities—has the conventional concern of underfitting data transitioned to a deeper epistemological inquiry into why AI continues to encounter fundamental limitations in fully capturing the complexities of human reality. The widely discussed phenomenon of overfitting thus signals a shift from concerns about insufficient information to a philosophical questioning of intrinsic epistemic boundaries. Second, contemporary AI research benefits from unprecedentedly idealized computational environments (e.g., GPUs, supercomputing platforms), paradoxically accentuating rather than mitigating uncertainties encountered during modeling processes. Such hyper-controlled settings make evident that uncertainty is not merely an incidental computational artifact but rather an essential methodological challenge for AI. Lastly, the ongoing saturation of AI technologies—exemplified by GPT-4—coupled with a rising emphasis on ethical governance, AI safety, and personalized applications, creates a unique socio-technological context wherein uncertainty theory gains both intelligibility and urgency. In this era, understanding uncertainty becomes critical not only technologically but philosophically.</p>

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Uncertainty theory

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Gedi Liu

摘要

This paper introduces uncertainty theory, an epistemological framework arising directly from contemporary AI practices. Its theoretical insights, motivations, and relevance are deeply intertwined with the irreversible trajectory of modern AI development. Specifically, this theory emerges from three interconnected conditions: first, only under current AI advancements—characterized by extensive information availability and powerful modeling capabilities—has the conventional concern of underfitting data transitioned to a deeper epistemological inquiry into why AI continues to encounter fundamental limitations in fully capturing the complexities of human reality. The widely discussed phenomenon of overfitting thus signals a shift from concerns about insufficient information to a philosophical questioning of intrinsic epistemic boundaries. Second, contemporary AI research benefits from unprecedentedly idealized computational environments (e.g., GPUs, supercomputing platforms), paradoxically accentuating rather than mitigating uncertainties encountered during modeling processes. Such hyper-controlled settings make evident that uncertainty is not merely an incidental computational artifact but rather an essential methodological challenge for AI. Lastly, the ongoing saturation of AI technologies—exemplified by GPT-4—coupled with a rising emphasis on ethical governance, AI safety, and personalized applications, creates a unique socio-technological context wherein uncertainty theory gains both intelligibility and urgency. In this era, understanding uncertainty becomes critical not only technologically but philosophically.