The Opacity of Descent: Optimization, Epistemic Asymmetry, and the Semantics of Convergence in Deep Learning

Mahdi Ghaznavi

Keywords: epistemic opacity, deep learning optimization, stochastic gradient descent, feature learning, philosophy of machine learning, Heideggerian tool analysis

Abstract: Deep learning research is currently characterized by an epistemic asymmetry. While the design of neural architectures and loss functions is guided by *a priori* structural intuitions—concepts that are cognitively accessible to researchers and articulable within established theoretical frameworks—the optimization process remains a regime of essential opacity. This paper argues that optimization is not merely technically complex but structurally resistant to predictive intuition: we cannot foresee the qualitative nature of the minima found by Stochastic Gradient Descent (SGD), understanding its feature-learning properties only *post-hoc*. By synthesizing Heideggerian tool and recent work on epistemic opacity in computational science with technical phenomena, we elaborate on the transition from intentional design to algorithmic discovery. We conclude with a brief notice on how this epistemic gap necessitates a degradation of human agency in the design process, and suggest a path toward "semantic oversight".

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Submission Number: 125