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Deep Learning as a Socio-Technical General-Purpose Technology: Architectural Evolution, Market Dynamics, and Cross-Domain Transformations
Issue Vol. 2 No. 01 (2025): Volume 02 Issue 01 --- Section Articles
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Abstract
Deep learning has emerged as one of the most transformative general-purpose technologies of the twenty-first century, reshaping not only computational practice but also economic organization, scientific inquiry, and socio-technical systems across diverse domains. From its early theoretical foundations in neural computation to its contemporary instantiations in large-scale transformer architectures, deep learning represents a convergence of algorithmic innovation, data availability, hardware acceleration, and market-driven adoption. This article presents a comprehensive and critical examination of deep learning as both a technical paradigm and an economic force, situating architectural developments within broader industrial, institutional, and societal contexts. Drawing exclusively on the provided literature, the study integrates perspectives from computer vision, natural language processing, reinforcement learning, healthcare, bioinformatics, finance, smart cities, recommendation systems, and neuromorphic computing to articulate a unified analytical narrative. Particular attention is paid to the co-evolution of model architectures and market structures, highlighting how advances such as convolutional neural networks, transformers, and reinforcement learning systems have catalyzed new applications while simultaneously being shaped by commercial incentives and infrastructural constraints.
The analysis foregrounds the role of deep learning markets, including hardware, software, and services, as articulated in contemporary industry assessments, to contextualize academic innovation within real-world deployment trajectories (MarketsandMarkets, 2023). Rather than treating market growth as an external outcome, the article argues that economic forces actively influence research priorities, architectural choices, and evaluation norms within the deep learning community. Methodologically, the study adopts a qualitative, integrative research design grounded in interpretive synthesis of existing scholarly and industry sources, enabling a richly elaborated discussion without reliance on mathematical formalism or empirical experimentation. The results section presents a descriptive analysis of thematic patterns emerging across application domains, while the discussion section offers an extended theoretical interpretation that engages scholarly debates on generalization, scalability, efficiency, and ethical governance.
By emphasizing depth over brevity and interpretation over summary, this article contributes a holistic, publication-ready account of deep learning’s current state and future trajectories. It positions deep learning not merely as a collection of algorithms, but as an evolving socio-technical system whose implications extend far beyond computation into the fabric of modern society (Krizhevsky et al., 2012; Brown et al., 2020; Devlin et al., 2019). In doing so, it provides a foundation for future interdisciplinary research that critically interrogates both the promises and the limitations of deep learning as a dominant technological paradigm (MarketsandMarkets, 2023).
Keywords
References
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