This article explores the development of computer linguistics and artificial intelligence (AI) paradigms in translation studies, comparing traditional human-centered approaches with modern technological methods. It traces the evolution of machine translation from rule-based and statistical systems to neural machine translation (NMT), emphasizing how these advances have transformed the translation process. Particular attention is given to the difficulties of applying AI-based translation to typologically different languages such as English and Uzbek, including challenges related to morphology, syntax, and semantic ambiguity. The study concludes that although AI significantly broadens the scope of translation studies, it functions mainly as an assistive tool, while human translators remain essential for cultural and contextual accuracy.

computer linguistics, artificial intelligence (AI) paradigms, rule-based, statistical machine translation, neural machine translation (NMT).

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