用海德格尔哲学解析机器学习，揭示其隐含的世界观。
The Metaphysics We Train: A Heideggerian Reading of Machine Learning
- 用海德格尔概念分析算法的隐性世界观
- 指出技术进步仍受制于计算至上思维
- 强调AI缺爱与焦虑，无法自省优化目标
本文通过海德格尔哲学概念对当代机器学习进行现象学解读，旨在提升从业者对其实践的反思性理解。我们提出，这一哲学视角揭示了三种技术分析无法触及的洞见：首先，算法的‘投影’（Entwurf）具有自动化、不透明和涌现性特征——一种无需明言或辩论即在梯度下降中隐含成形的本体论；其次，即使先进的技术突破也仍处于‘座架’（Gestell）的支配下，仅提升计算效率而未质疑计算本身的优先地位；第三，人工智能缺乏存在结构，特别是‘关怀’（Sorge）的缺失，真实解释了为何其无法质疑自身优化目标，也无法体验引发人类自我反思的焦虑（Angst）。最后，文章探讨该视角的教育价值，主张数据科学教育应培养技术能力与本体论素养并重的能力，使人能识别工具所内嵌的世界观，并判断计算是否为恰当的互动方式。
原文摘要 · Abstract (English)
This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of their own practice. We argue that this philosophical lens reveals three insights invisible to purely technical analysis. First, the algorithmic Entwurf (projection) is distinctive in being automated, opaque, and emergent--a metaphysics that operates without explicit articulation or debate, crystallizing implicitly through gradient descent rather than theoretical argument. Second, even sophisticated technical advances remain within the regime of Gestell (Enframing), improving calculation without questioning the primacy of calculation itself. Third, AI's lack of existential structure, specifically the absence of Care (Sorge), is genuinely explanatory: it illuminates why AI systems have no internal resources for questioning their own optimization imperatives, and why they optimize without the anxiety (Angst) that signals, in human agents, the friction between calculative absorption and authentic existence. We conclude by exploring the pedagogical value of this perspective, arguing that data science education should cultivate not only technical competence but ontological literacy--the capacity to recognize what worldviews our tools enact and when calculation itself may be the wrong mode of engagement.
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