This article explores the evolving landscape of translation in the era of advanced Artificial Intelligence (AI). While neural machine translation (NMT) and large language models (LLMs) have achieved unprecedented fluency, the core of linguistic nuance remains a subject of debate. By analyzing the current capabilities of AI against human cognitive processing, this study identifies the critical gaps in automated systems — specifically regarding cultural context and pragmatic meaning — and proposes a collaborative model for the future of the profession.
AI Vs. Human: The Future of Translation in The Digital Age
DOI:
Keywords:
Artificial Intelligence
Machine Translation
Neural Networks
Abstract
References
Common Sense Advisory (CSA) Research. (2024). The State of the Linguist Supply Chain: 2024 Global Rankings and Market Trends. Retrieved from https://csa-research.com
Karaulov, Y. N. (2023). The Russian Language and the Linguistic Personality: Contemporary Interpretations in the Digital Age (Reprint ed.). Moscow: Nauka. (Original work published 1987).
Moorkens, J., & Lewis, D. (2024). Human-in-the-Loop: The Ethical and Practical Evolution of Post-Editing in Neural Machine Translation. Journal of Specialized Translation, 41(1), 112-134.
OpenAI. (2024). GPT-4 Technical Report: Capabilities and Limitations in Multilingual Processing. OpenAI Blog.
Slator. (2025). 2025 Language Industry Market Report: The Impact of Generative AI on Localization. Retrieved from https://slator.com
Vaswani, A., et al. (2023). Attention is All You Need: Five Years of Transformer Evolution in Linguistics. Computational Linguistics Review, 15(3), 45-67.
Zhu, Y., & Wang, X. (2024). The «Fluency Trap»: Analyzing Semantic Shifts in Large Language Model Translations. International Journal of AI & Philology, 12(2), 201-218.