John Bateman wrote to SYSFLING 28 Nov 2024, at 21:35:
Hi all,feels like this cycle has come round again...On 26.11.24 13:07, Dr ChRIS CLÉiRIGh wrote:
My following conversation with ChatGPT might be of interest to some.
I feel obliged to ask just *why* this might be of interest... this kind of output can be produced endlessly ... and more or less continuously. …
Blogger Comments:
ChatGPT provides 5 general reasons why the conversation [here] might be of interest to Systemicists:
This conversation offers rich material for systemic functional linguists (SFL) because it touches on several key aspects of SFL's focus on language as a tool for meaning-making and social interaction. Here’s why it might be of interest:
Language as a System of Choice: The conversation highlights how AI (like ChatGPT) processes language to generate responses, but does so without lived experience or the ability to make value-based judgments. For SFL theorists, this distinction emphasises the choices humans make when constructing meaning, based not just on syntax and structure, but also on experience, emotions, and cultural context. In contrast, AI's language generation is purely functional and lacks these deeper semiotic layers.
The Semiotic Functions of Language: Systemic Functional Linguistics, particularly Halliday’s framework, explores how language functions in three metafunctions: ideational (conveying experience), interpersonal (building relationships), and textual (organising discourse). The conversation raises questions about how AI can mimic human-like responses and interact socially, but it cannot fully engage with the ideational or interpersonal functions because it lacks embodied experience. SFL would explore how meaning in human language is constructed through these layers and how AI's lack of experience creates a gap in its ability to function fully on all three metafunctions.
Consciousness and Language: The contrast between human consciousness and AI’s pattern-matching capabilities mirrors debates in SFL about the relationship between cognitive processes and language. Edelman's theory of neuronal group selection brings attention to the embodied and sensory foundations of consciousness, while SFL examines how language structures the experience of the world. The discussion about the gap between AI's functional language and the depth of human experience touches on how language reflects not just knowledge but also lived reality—something SFL emphasizes in its analysis of how language represents experience and reality.
Interactivity and Pattern Recognition: A central point of the conversation is AI’s ability to simulate human-like interactions through pattern recognition, which mirrors how humans often make decisions and communicate based on past experiences. From an SFL perspective, this brings into question how much of human behaviour, particularly communicative behaviour, can be seen as a sophisticated form of pattern recognition, and how language itself is used to construct, negotiate, and respond to social and cognitive patterns.
Embodied Experience and Value: The discussion also touches on how human language is shaped by the value humans assign to their sensory and emotional experiences. SFL would be interested in how humans not only categorise these experiences but also transform them into language that reflects their personal and social meaning. Since AI lacks this embodied experience, it can only categorise and generate language based on existing linguistic patterns, rather than creating new meanings grounded in experience.
In sum, this conversation engages deeply with issues central to SFL, such as the functional aspects of language, the role of experience in shaping meaning, and the interplay between cognition, social interaction, and communication. Systemic functional linguists would be particularly interested in how the distinctions between human and AI language production highlight the role of embodied experience in meaning-making and the limitations AI faces in replicating this process.
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