Transcending Concretization: Rethinking Simondon’s Theory of Technological Evolution—An Investigation Based on Convolutional Neural Networks

人工智能技术的迅速发展使思考技术演进之逻辑愈发必要。西蒙东的具体化理论主张技术演化在于功能与结构的逐步协调，在这一过程中技术趋向于有机。然而，该理论未能有效地解释现代人工智能技术的发展。本文以卷积神经网络为考察对象，梳理了早期LeNet至深度学习时代AlexNet的演进路径。研究表明，人工智能技术的演进往往优先考虑计算效率、并行性、软硬件协同等性能提升，而非单纯的具体化，有时甚至表现出“去具体化”趋势。由此，我们将超越西蒙东的具体化路径并试图揭示出一种超越“人类中心主义”的技术演进逻辑。The rapid development of artificial intelligence technology makes it increasingly necessary to contemplate the logic of technological evolution. Simondon’s concretization theory posits that technological evolution lies in the gradual coordination of function and structure, during which technology tends to become organic. However, this theory fails to adequately explain the development of modern artificial intelligence technology. This paper examines convolutional neural networks by tracing the evolutionary path from the early LeNet to AlexNet in the deep learning era. The study shows that the evolution of artificial intelligence technology often prioritizes improvements in performance—such as computational efficiency, parallelism, and the synergy between software and hardware—rather than mere concretization, sometimes even exhibiting a trend toward “deconcretization.” Consequently, we aim to go beyond Simondon’s concretization pathway and attempt to unveil a logic of technological evolution that transcends anthropocentrism.