Artificial Intelligence in Interdisciplinary Linguistics

Abstract

Artificial intelligence (AI) is becoming an integral part of various scientific disciplines, industries, and everyday life. AI studies cover quite a number of scientific fields, and the topic needs an integrated and convergent approach to address its multifaceted challenges. This paper provides an extensive survey of existing approaches to define and interpret the AI concept. The research objective was to identify the invariant characteristics of AI that underscore its interdisciplinary nature. The article categorizes the primary drivers, technologies, and key research models that fuel the advancement of AI, which possesses a unique capability to leverage knowledge, acquire additional insights, and attain human-like intellectual performance by analyzing expressions and methods of human cognition. The emulation of human intellectual activity and inherent propensity for continual evolution and adaptability both unlock novel research prospects and complicate the understanding of these processes. Algorithms, big data processing, and natural language processing are crucial for advancing the AI learning technologies. A comprehensive analysis of the existing linguistic research revealed an opportunity to unify various research approaches within this realm, focusing on pivotal tasks, e.g., text data mining, information retrieval, knowledge extraction, classification, abstracting, etc. AI studies make it possible to comprehend its cognitive potential applications across diverse domains of science, industry, and daily life.