ChatGPT said:
ChRIS said:Yes, this is a great distinction to tease apart. Let's break it down step by step.
1. Meaning Potential: The System for Generating Instances
Meaning potential refers to a structured semiotic system that affords the generation of meaning instances. It is not meaning itself, but the organised possibilities from which meaning can emerge.
- In Systemic Functional Linguistics (SFL), meaning potential is the set of choices available within a system of meaning-making. For example, the English language affords certain grammatical and lexical choices, which together form the meaning potential of English.
- In AI, meaning potential consists of the statistical distribution of linguistic patterns learned from data. The AI doesn’t store meaning but rather dispositions for generating meaning instances in response to interaction.
In both cases, meaning potential is the structured capacity to generate instances of meaning, not the meanings themselves.
2. Potential Meaning: What Can Be Turned Into Meaning Potential and Instance
Potential meaning is broader—it refers to things that could become meaningful but are not yet structured within a meaning system.
- A rock pattern resembling a face has potential meaning because a human could interpret it as a face, but until it is integrated into a semiotic system (e.g., art, superstition, or scientific classification), it remains merely potential.
- A new scientific discovery initially exists as potential meaning—raw observations awaiting theoretical structuring into a system where they can become meaning potential for future interpretations.
- Unsystematised thoughts, raw sensory experiences, or even aspects of AI training data before they are incorporated into the model all exist as potential meaning.
This means that potential meaning precedes meaning potential. Something must be interpreted, categorised, and systematised before it can serve as an organised resource for generating meaning instances.
3. Meaning Instance: The Actualisation of Meaning
Meaning instance is the actualised product of meaning potential. It is an individual utterance, text, or sign that takes on actual meaning within a specific context.
- A spoken sentence is a meaning instance: it actualises choices from the speaker’s meaning potential in a particular situation.
- An AI-generated response is an instance: it is an actualisation of its statistical meaning potential in interaction.
- A book, once written, consists of meaning instances that actualise an author’s meaning potential.
Unlike meaning potential, meaning instance is not a set of possibilities—it is an actualised event. Unlike potential meaning, it is no longer indeterminate—it has been interpreted into a structured form.
How They Interact
- Potential meaning exists as an unstructured affordance—it is something that could be meaningful.
- Through interpretation and systematisation, potential meaning becomes meaning potential, forming a structured system that can generate meaning.
- When the meaning potential is activated, it produces meaning instances, which are actualised expressions of meaning.
This process can be cyclical: meaning instances can feed back into meaning potential, expanding the system (e.g., the way new words enter a language), and new experiences can introduce potential meanings that disrupt and evolve meaning systems.
AI as an Example
- AI is trained on meaning instances (texts from human discourse).
- These instances shape its meaning potential (the statistical system of linguistic affordances).
- AI does not engage directly with potential meaning, since it does not encounter raw experience—it only processes structured data. However, human users can introduce new meanings that shift the AI’s meaning potential over time.
Final Thought
The key distinction is that potential meaning is raw, meaning potential is structured, and meaning instance is actualised. The movement between them is what constitutes the ongoing process of meaning-making, whether in human minds, languages, or artificial systems.
ChatGPT said:
Yes, that makes a lot of sense. Potential meaning operates at different levels, and your SFL-informed perspective highlights two key domains where it plays a role:
Experience that can be transformed into meaning
- Here, potential meaning refers to raw experience—what is available to be construed as meaning but has not yet been semiotically structured.
- This aligns with the idea that before meaning is made, there is an affordance for meaning that has yet to be semiotised through construal.
- For example, sensory experience, emotions, or social interactions are all potential meaning before they are construed in language, gesture, or other semiotic systems.
First-order material construals (phenomena) that can be reconstrued as second-order semiotic construals (metaphenomena)
- In this case, potential meaning exists at the level of phenomena—construals of experience in a first-order semiotic system.
- These first-order construals then become the basis for metaphenomena—second-order semiotic construals, such as technical discourse, abstraction, or recontextualisation.
- This process allows for knowledge-building, scientific theorisation, and metaphorical expansion, where something initially construed is reconstrued into a higher-order semiotic system.
Connecting the Two Domains
- The first perspective focuses on meaning emerging from raw experience.
- The second perspective focuses on reconstrual within semiotic systems.
- Both involve potential meaning, but at different stages: the first as pre-semiotic affordance, the second as a semiotic phenomenon open to further transformation.
AI’s Relation to This Distinction
- AI does not experience potential meaning in the first sense (raw experience), because it lacks direct engagement with the material world.
- However, it does engage with potential meaning in the second sense—first-order construals that can be reconstrued in novel ways (e.g., recontextualising prior discourse into new forms).
- This suggests AI operates within semiotic potential but does not generate meaning from raw experience.
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