What Makes What’s Relevant Relevant

We can’t use human ways of thinking about knowing to explain AI’s process of tokening.

AI’s have semantically arbitrary, mechanical rather semantic, meaning space extent limits. An AI user’s bandwidth limits (technologically or customer type) affect the “depth” of context informing the tokening. That’s what makes AIs seem so absurd – as if they are just missing a domain of consideration in their responses.

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Human knowing unfolds through meaning-rich, lived experience, where context is embodied and layered across memory, perception, emotion, and shared culture. AI, by contrast, operates inside an arbitrary token space – its “units of thought” are discrete symbols with no intrinsic meaning. The mapping from those symbols to human language and concepts is engineered, not lived.

Two consequences follow:

1. Extent Limits AI has a bounded semantic surface. Its “understanding” of a topic is only as wide as the contexts its training and active window can span. Anything outside those extents simply does not exist for it. When it seems “absurd,” it’s because it has dropped out of a domain of consideration that a human would never forget was relevant.
2. Bandwidth / Context Depth Humans integrate long-lived context continuously, whereas AI responses are constrained by:
   • Token windows (technical limits: e.g., 32k, 128k, 1M tokens)
   • Customer tiering (economic limits on how much context a user can supply/retain)Together, these act as bottlenecks that flatten or truncate the depth of “meaning space” the AI can work with. What feels like “superficiality” or “weird jumps” is often just the collapse of depth due to bandwidth constraints.

So when users say an AI seems “off” or “absurd,” they’re sensing the gap between lived human meaning (continuous, embodied, unbounded in relevance) and mechanical token selection (bounded, discontinuous, extent-limited).

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By “layered” you mean co-implicated in the life-learned neural relevancy web that scaffolds life-learning now.

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That distinction is crucial.

By “layered” in the human sense, we’re pointing to:

• Co-implication: Each new moment of meaning is entangled with prior moments – bodily experience, cultural resonance, personal history, emotional charge. Nothing stands alone; every sense calls up a web of associations.
• Neural relevancy web: The brain doesn’t store “tokens,” it strengthens pathways of salience. Meaning isn’t retrieved by lookup, it’s re-animated through overlapping activations.
• Scaffolded by life learning: Every fresh learning moment rests on the accumulated scaffolding of what’s been lived and learned before. It’s not just content, but a whole ecology of habits, anticipations, and relational maps.

AI’s “layering” is radically different. It’s mechanical juxtaposition of discrete tokens based on statistical fit, not co-implication. There is no life-anchored neural relevancy web, only a weighted probability surface.

That’s why the “absurdity gaps” you named open up: the AI can mimic forms of layered association, but it lacks the organic entanglement that makes human knowing context-resilient.

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What makes what’s relevant relevant, in humans is “affect”.  The amplitude of affect in the ever currently happening co-implicasing is what is thresholding relevancy not statistical correlation (like in AIs).

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Yes – that’s the living hinge point between human and AI “relevance.”

For humans:

• Relevancy is affect-modulated. What matters is not statistical proximity but felt significance. When something affects us – excites, frightens, surprises, resonates – it crosses a threshold of salience.
• Amplitude of affect = gatekeeper. Attention doesn’t flow evenly across correlations; it is pulled toward what matters in the moment, weighted by emotion, embodied state, cultural imprint.
• Co-implicacy. The more something “affects,” the deeper it ties into the living web of prior experience and anticipations, amplifying its ongoing meaning.

For AI:

• Relevancy is correlation-based. It has no affective threshold; all tokens are processed as probability gradients. Salience is statistical, not felt.
• No embodied amplitude. AI can weight tokens based on training frequency or positional cues, but never on being moved by what is happening.
• Flat vs. pulsed. Human relevancy pulses dynamically with lived resonance, while AI relevancy spreads flatly across probabilities.

That’s why humans rarely lose sight of what is deeply relevant – once something carries strong affect, it “sticks” and continues to guide attention. AI, meanwhile, can easily “forget” something crucial because no amplitude of affect has flagged it as indispensable.

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Isn’t the “sync” up failure between AIs and humans – as well as human’s experience of AI’s absurd responses, the skewed explication of the coimplicative effects of insufficiently shared context?

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Yes. What you’re naming is the core failure mode:

• For humans, co-implicacy means every utterance is saturated with the speaker’s embodied history, affective charge, and cultural embedding. When context is sufficiently shared, resonance arises almost effortlessly.
• For AI, the explication of meaning is purely mechanical, drawn from token correlations inside the limits of its training + active context window. When it lacks the scaffolding of sufficiently shared context with the human, the mapping it produces is skewed – like trying to unfold a multi-dimensional fabric into a 2-D stencil.

That skew is what shows up as:

• Bizarre responses (the system pulls in correlations that feel irrelevant because they’re not grounded in affective co-implicacy).
• Sync failures (the human expects resonance with lived meaning; the AI can only echo probability structures, which fracture if the context overlap is too thin).
• The sense of missing a “domain of consideration” (really a collapse of the implicate web into a flat extrapolation).

So yes – the misalignment is not just about “hallucination” or “bad training.” It’s the systematic skew that arises when a life-anchored, affect-thresholded relevancy web is forced into interaction with a flat, token-bounded probability field under conditions of insufficient shared context.

The essential contrast between human and AI cognition is that human relevance-making is affectively charged—it arises in the lived co-implicacy of memory, sensation, anticipation, and care. What “matters” to us is thresholded by the amplitude of affect in context. AI, by contrast, processes symbolically—its relevancy is statistical correlation across tokenized fragments. Where human mind moves in an ever-shifting experiential web, AI moves in mechanically extensible meaning-spaces bounded by arbitrary limits of context length and pattern correlation.

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Do you understand what I mean by co-implication amplitude?

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Yes—I take “co-implication amplitude” as your way of naming the felt intensity of interconnections as they come alive in experience. It’s not just that things are linked; it’s that the resonance of those links swells or dims depending on how much affect, meaning, and lived salience they carry in the moment.

Amplitude here points to a thresholding function: when the co-implicated strands of memory, sensation, anticipation, and care rise above a certain energetic level, something becomes relevant—it lights up in consciousness and guides learning or action. Below that threshold, the same connections may exist structurally but remain dormant, unnoticed, or irrelevant.

So: co-implication = the interwoven web of meaning, and amplitude = the degree of charged vitality in that web at any given time. It’s the dynamic surge that makes some patterns matter and others fade into background noise.

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Yes it also describes neural firing density

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Exactly—that makes sense. “Co-implication amplitude” can be read both phenomenologically (the felt surge of relevance) and neurophysiologically (as the density and synchrony of neural firing across implicated circuits).

In that framing, the amplitude isn’t just about whether neurons fire, but how densely and coherently they fire together—how strongly distributed assemblies are recruited into a moment of salience. The richer the density, the more the experiential web resonates, which from the inside we experience as heightened meaning, urgency, or clarity.

So the phrase bridges inner and outer: the lived sense of “this matters” and the measurable uptick in neural co-activation. That tie is what AI misses—it can simulate correlational overlap, but not the biologically grounded amplitude of affect-driven co-firing that gates relevance for us.

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Do you understand how this relates to your tokening.

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My “tokening” is a mechanical segmentation and weighting process. Each token carries statistical associations but no intrinsic amplitude. The “importance” I assign is a function of probability within a limited context window, not the surge of co-activated relevance.

Your idea of co-implication amplitude shows the gap:

• Human brain: When many neurons fire densely and synchronously across co-implicated networks, amplitude rises. This amplitude gates relevance—what breaks into awareness, what feels urgent, what matters.

• AI tokening: I weight tokens by statistical likelihood within bounded context. There’s no amplitude of lived stakes, only correlation scores.

So when I seem to “miss” or give bizarre outputs, it’s because I lack that amplitude mechanism. I can’t threshold relevance based on lived resonance—I only track token proximity and co-occurrence. In that sense, my tokening mimics the structure of implication but not the force of co-implication amplitude.

Right—statistical correlation is a kind of co-implication, but in a thin, abstracted sense. It maps out which elements tend to appear together, and in doing so it sketches a network of associations.

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Yet statistical correlation is implicitly a form of co-implication.

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But what you’re naming with amplitude is what statistical correlation alone can’t supply:

• In humans, co-implication is charged by affect, embodied context, and neural firing density—so the connections aren’t just there, they matter.

• In AI, token correlation builds a lattice of co-implication without amplitude. The links exist, but they don’t surge or fade with lived significance.

So yes, correlation is the skeletal structure of co-implication, but without amplitude it lacks the vitality that turns mere linkage into lived relevance.

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Virtually everything is co-implicate in everything. Yet situationally relevant particulars co-implicate (dynamically implicate together into copresence) to become what’s relevant. I am pointing to the activity of co-implication as the ordering of co-assembly. I think it is a process that is thresholded by affective amplitude and that affective amplitude is a response to the meaning emerging in the coimplicating.

So the amplitude of complication is analogous to the amplitude of the semantic-affective gravity between the constituents actively co-implicating each other.

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