Abstract：In view that artificial intelligence (AI), with deep learning as its core technology, is bringing about the fifth paradigm in scientific research, this paper argues that linguistic studies also need to embark upon the fourth or the fifth paradigm for pursuing a data-intensive and computation-intensive approaches. To further this suggestion, insights from linguistic experts on linguistics-related issues in AI study, which are often encountered in the scientific research and technical development of AI, are introduced and examined with an aim to support my argument. To engage with this topic, the current paper advocates the following points of view, namely: (1) While language functions primarily to communicate, thoughts usually happen in form of dialoguing and questioning-answering in the mind. (2) As language and reasoning are not totally equivalent, the Turing test cannot be fully valid. (3) Grammaticalization is not an intelligence unique to mankind, so the functional principles of grammaticalization also apply to composite systems other than language, including bodies, objects, and images. (4) Any existing super-capacity large language model (LLM) is not adequate to be used as a technological interface for the communication between human beings and robots, because any effective use of language should be an embodied intelligence, including lexical grounding and environmental affordance in embodied cognition; accordingly, different types of embodied Turing tests should be taken into consideration. (5) The success of ChatGPT shows that large models, big data and powerful computability work to capture statistical rules and operating modes of language. In this sense, linguistic studies should adopt data- and computation-intensive methodologies as a fourth or a fifth paradigm to probe into the algorithmic structure of language.
袁毓林. 人工智能大飞跃背景下的语言学理论思考[J]. 语言战略研究, 2023, 8(4): 7-18.
Yuan Yulin. Theoretical Reflections on Linguistic Studies Against the Background of AI Great Leap Forward. , 2023, 8(4): 7-18.
