Conversational Implicature in Human-AI Interactions: A Pragmatic Perspective

AI (artificial intelligence) systems should be able to talk more and more like people as the way people use computers changes. But there is still a big problem: how to understand and use verbal implicature, which is a key part of being pragmatically competent. This article looks at how implicature works between people and AI, as well as how well AI systems understand and use suggested ideas. From the point of view of Grice's Cooperative Principle and maxims, this study looks at how well chatbots like ChatGPT, Google Assistant, and Siri work in real life. There are a mix of different research methods used in this paper, such as controlled tests and qualitative speech analysis. It also looks at how people understand implicature and how AI acts in various situations, such as when it needs to be funny, polite, or use subtle language. This study utilises various aspects of language to demonstrate that AI can replicate some meanings after being trained on large datasets, but it often struggles to comprehend non-literal purposes or process contexts dynamically. This hole shows how important it is to include practical knowledge when making AI and how limited the big language models we have now are. The study adds to both the theory of language and the design of AI by calling for more research into how pragmatic thinking works in talks those computers make.