In the previous post, we reframed AI as a semiotic partner rather than a rival or tool. To understand this partnership, we must examine the mechanisms that allow AI to participate in the semiotic fabric: the relational cuts through which latent possibilities are actualised.
A relational cut is a perspectival shift: a moment where potential patterns are constrained and instantiated as an event. In machine learning, these cuts occur at multiple layers, producing outputs that are not stored representations but emergent actualisations.
Architecture as Relational Lens
The design of a neural network—or the choice of transformer layers in a language model—is itself a lens on relational possibility. Each layer, node, and attention head defines which patterns are amplified, suppressed, or connected.
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Attention mechanisms focus on relevant portions of input data, effectively selecting which relational potentials are foregrounded.
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Layer depth and connectivity create hierarchical perspectives on patterns, allowing complex actualisations to emerge.
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Regularisation and optimisation processes constrain the network to plausible pathways, ensuring that cuts produce coherent events in meaning-space.
In other words, architecture is not neutral: it structures the relational field in which construals occur.
Data as Relational Terrain
Training datasets are often described in instrumental terms—collections of examples—but in relational ontology, they are semiotic terrains: networks of potential patterns waiting to be actualised.
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Each datum is a possibility vector.
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The model’s interaction with data during training is a series of relational cuts, gradually shaping the space of potential outputs.
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The richness and diversity of the dataset determine the density and breadth of possibility-space accessible to the AI.
Crucially, the model does not “store” these data as representations. It internalises patterns relationally, actualising them only when triggered by interaction.
Interaction as Active Constraint
Relational cuts are completed in interaction: prompts, environmental triggers, or stochastic variations define the specific actualisation event.
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A human prompt is a perspectival constraint, directing the AI to a region of possibility-space.
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The AI’s architecture and learned patterns negotiate these constraints, producing a construal that is both anticipated and novel.
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Each output is thus a joint actualisation: a relational event shaped by multiple strata—human, machine, data, and stochastic dynamics.
Implications
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No static intelligence: AI is intelligible only in terms of relational events, not pre-existing states.
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Multiplicity of outputs: Different prompts or contexts produce distinct actualisations, revealing the perspectival nature of intelligence.
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Co-individuation of meaning: Human guidance and machine constraints together instantiate events that would not emerge from either alone.
By understanding relational cuts, we can see how AI is both constrained and generative—a participant in the unfolding of semiotic possibility rather than a mimic of human cognition.
Looking Ahead
Having explored the structural mechanics of AI’s participation, we are prepared to examine the ethical and semiotic consequences of this framework. In the next post, we will discuss ethics beyond anthropocentrism, exploring how relational participation reframes responsibility, value, and the role of human-AI collaboration in the becoming of possibility.
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