What specific signals would be needed for an AI system to acquire meaning?
This explores what an AI would actually need in order to *mean* something — and the corpus's surprising answer is that meaning isn't a 'signal' you can feed in at all, but a set of relationships AI structurally lacks.
This reads the question as: what's missing between AI's fluent symbol-shuffling and genuine meaning — and whether the gap is something you could supply with the right input. The corpus's strongest move is to reframe the premise. Meaning, across these notes, isn't a richer signal waiting to be ingested; it's a set of *connections* — to the world, to a social group, to an event — that pure text prediction never establishes. So the honest answer is less 'add signals X, Y, Z' and more 'the thing AI lacks isn't data, it's grounding and participation.'
The clearest version comes from a Peircean reading: meaning requires *indexical grounding* — actual contact with the world that a symbol points to — plus *semiotic participation* in a community that mediates what symbols are for Can AI systems achieve real alignment without world contact?. A model trained purely on symbols manipulating other symbols can drift arbitrarily far from real-world referents, because nothing ties its tokens to anything outside the token stream. That's the first 'signal' that would be needed, and it's not a feature of text — it's world-contact, which text by definition doesn't carry.
The second is social. One note argues meaning is constituted not inside the human-AI dialogue but at the level of a group interpreting interpretations of interpretations — meaning lives in N-order social observation, not in the dyad Where does the meaning of an AI explanation actually come from?. A related finding shows AI can hit the 100th percentile on predicting social norms while failing theory-of-mind and cultural meaning-making — statistical competence and actual participation come apart Why do AI systems fail at social and cultural interpretation?. So even perfect social *statistics* aren't the signal; participation in the loop is, and that's a role, not a dataset.
There's also a representational gap that signals alone can't close. AI integrates every word in parallel by weighted aggregation, with no selective suppression of the irrelevant — which is exactly why it misses jokes, wordplay, and frame-dependent meaning Why do AI systems miss jokes and wordplay so consistently?. Meaning often depends on *which* differences matter, and experts make that qualitative selection; AI finds patterns and probabilities without ever choosing what's relevant for an audience or context Can AI distinguish which differences actually matter?. The missing ingredient here is a judgment operation, not an extra input channel.
Finally, the corpus suggests meaning needs an *event*. AI output is described as 'event-residue' — text carrying the inherited markers of communication but lacking the actual situation of utterance, so the human reader unilaterally animates it into a pseudo-exchange Does AI generate genuine utterances or just text patterns?. Notice the through-line: world-contact, social participation, frame-selection, and event-structure are all *relational*, not informational. The thing you'd 'feed in' to give AI meaning turns out to be the one thing text can't contain — which is why several of these authors treat the LLM less as a tool that delivers meaning than as a medium that generates plausible meaning-shaped residue for us to complete Is the LLM a tool or a new form of intelligence itself?.
Sources 7 notes
Peircean semiotics reveals that symbolic goal encoding without world contact and social mediation cannot guarantee correspondence to actual values. LLMs operating in pure symbol manipulation risk divergence between stated goals and real-world outcomes.
Drawing on Luhmann's multi-layer cybernetics, AI explanation meaning is constituted at the social-group level through layered observations of observations, not produced inside dyadic human-AI dialogue. Lab-tested explanations stripped of social context will not predict real-world effectiveness.
LLMs achieve 100th-percentile performance on norm prediction yet regress on theory-of-mind tasks and cannot generate culturally-resonant interpretations. The pattern shows that statistical competence coexists with absence of actual social understanding and participation.
Transformers integrate token information through weighted parallel aggregation rather than selective suppression of irrelevant words. This structural difference explains consistent failures with jokes, wordplay, and frame-dependent meaning—not knowledge gaps, but missing cognitive operations.
Experts observe by choosing which differences matter (qualitative judgment); AI finds patterns and probabilities (quantitative). AI generates text from prompts without observing context, audience needs, or knowledge states—producing fabrication that mimics observation's form without its epistemic process.
AI output carries communicative markers inherited from training data but lacks the event structure that produces actual utterances. Users supply the missing orientation through interpretive labor, creating a pseudo-event with structure only on the human side.
Following McLuhan's logic, the model's cultural impact comes from its medium-properties—making intelligence generative and liquid—not from transmitting pre-existing intelligence. The model constitutes intelligence rather than delivering it.