Category: AI Worlds

  • … The Neurodivergent View

    … which is really: I don’t have to be that.

    This is an interesting realization: Many neurodivergent people are unhappy with who they are because they have been conditioned socially to believe they should be something else.

    I think that’s sad. But my research has shown that it is nearly invisible. Where does it live? Where does it come from? How does it work?

    Those are the big questions.

    And that’s what I do here, what We do here. We look at the baseline data and follow it through the different cultural changes that contributed and are probably still contributing to the existing complex of “cultural inbreeding” that we see today.

    Research of historical and research publications using ChatGPT showed no direct investigation of cultural inbreeding with a linguistic basis. Several references provided threads:

    • A ScienceDirect topic overview uses “cultural inbreeding” for extreme broadcast transmission (one teacher/source → everyone), i.e., rapid homogenization via narrowed model diversity—this can map cleanly onto language standardization and register convergence under mass media.
    • “Inbreeding” used literally via language boundaries (medical genetics context)
    • A human genetics paper about the Basque population references “cultural inbreeding” tied to the Basque language’s role in endogamy patterns (language as a boundary marker). Not linguistics per se, but it’s one of the places where language is explicitly named as the mechanism of social closure.

    This produced the standard Skinnerian interpretation.

    … and got something really good.

    Right. My last pass started from transmission and copying (who learns from whom), which is the classic “watch the dog, count the bites” move. You’re asking for the opposite direction:

    Language isn’t a label on behavior. Language is a causal instrument that reshapes the space of possible behavior, possible thought, and possible social reality. So: what language does to the dog, the owners, the friends, the whole scene.

    Here’s a cleaner footing.

    Language as an operator on reality, not a report about reality

    Think of “Language in Service of Goal” as teleology baked into grammar, lexicon, and genre—not just “people choose words.”

    Language does causal work by altering:

    • What entities exist socially (what is countable, nameable, actionable)
    • What relations are legitimate (duties, permissions, rights, blame)
    • What explanations are acceptable (what counts as “because”)
    • Who has standing to speak (authority, credibility, admissible testimony)
    • What futures are imaginable (plans, norms, “solutions”)

    That’s episteme-level shaping.

    The main “causal levers” language uses

    1) Ontology injection = Teleology: make intervention possible

    Language creates stable things out of fluid experience.

    Mechanisms:

    • Nominalization (“X is a disorder/trait/issue”) turns processes into objects.
    • Kind terms (“type of person,” “breed,” “risk group”) create naturalized categories.
    • Reification via definitional frames (“by definition…”) freezes meaning.

    Effect on the “dog scene”:

    • The dog becomes not “acting” but being: aggressive, reactive, dominant, unsafe, therapy dog material, etc.
    • Owners become responsible, negligent, victims, handlers—each with a different action-space.

    Teleology: make intervention possible (manage, treat, regulate, punish, insure).

    2) Deontic control – Teleology: coordinate group safety and status

    Language installs obligation and permission into the scene.

    Mechanisms:

    • Deontic modals: must, should, allowed, prohibited
    • Policy/legal idioms: compliance, liability, reportable, duty of care
    • Moral registers: good owner, irresponsible, dangerous

    Effect:

    • Social reality reorganizes around norm enforcement rather than description.
    • The friends become enforcement nodes (“you can’t bring that dog here”).

    Teleology: coordinate group safety and status (and often institutional risk).

    3) Causal grammar = Teleology: stabilize a governing narrative

    Language doesn’t just explain; it selects allowable causes.

    Mechanisms:

    • Etiology templates (“because trauma,” “because training,” “because temperament”)
    • Agent deletion (passives: “mistakes were made,” “the dog was triggered”)
    • Single-cause magnetism (one story that makes alternatives illegible)

    Effect:

    • You don’t just get different conclusions—you get different worlds where different actions are sensible.

    Teleology: stabilize a governing narrative (so action can proceed without reopening reality every time).

    4) Epistemic gating – Teleology: protect authority structures

    Language controls what counts as knowledge and who can supply it.

    Mechanisms:

    • Evidentials (explicit in some languages; implicit in English via “I saw / I heard / studies show”)
    • Credibility markers and hedges (“obviously,” “just,” “mere,” “anecdotal”)
    • Professional registers that convert lived report into “non-evidence”

    Effect:

    • Owners’ experience can be downgraded as “subjective,” while an outside authority’s template becomes “objective.”
    • The dog’s behavior is reauthored through a higher-status vocabulary.

    Teleology: protect authority structures and keep the system governable.

    5) Indexing and identity manufacture = Teleology: maintain group boundaries and hierarchy

    Words don’t only mean; they signal membership.

    Mechanisms:

    • Enregisterment: a way of speaking becomes “the proper way” for a role/community.
    • Shibboleths and keyword policing (“reactive not aggressive”; “positive reinforcement only”)
    • Status dialects: who can speak “correctly” about dogs

    Effect:

    • The dog discourse becomes a social sorting machine: competent vs incompetent owner, insider vs outsider.

    Teleology: maintain group boundaries and hierarchy.

    6) Future-shaping (affordances) – Teleology: steer futures without ever announcing steering

    Language changes what actions feel available.

    Mechanisms:

    • “Solution vocabularies” vs “relationship vocabularies”
    • Scripts embedded in words: rehabilitate, rehome, euthanize, socialize, desensitize
    • Time-structure: “progress,” “relapse,” “maintenance”

    Effect:

    • The scene is pushed toward certain trajectories as “reasonable,” others as “unthinkable.”

    Teleology: steer futures without ever announcing steering.

    What “cultural inbreeding” would mean in this operator view

    Not “closed networks copy each other.” Instead:

    A community recycles the same operator-vocabulary until it becomes an epistemic monoculture—a self-sealing system where:

    • ontology is pre-decided (what exists)
    • causality is pre-decided (what explains)
    • legitimacy is pre-decided (who knows)
    • interventions are pre-decided (what must be done)

    So the “dog” becomes less an animal in context and more a node inside a controlling grammar.

    That’s the non-Skinnerian punchline: language is acting on people and animals by acting on the world they can jointly recognize.

    If you want a compact “working model” you can reuse

    Language-as-Operator = Ontology + Deontics + Causality + Epistemic Gatekeeping + Identity Indexing + Affordance Shaping

    Different “cultural revolutions” are, in practice, massive rewrites of one or more of those operator layers (printing press, bureaucracy, clinical regimes, legal regimes, algorithmic platforms, etc.).

    No questions from me. If you push this next toward teleology-specific vocabularies (clinical, legal, bureaucratic, revolutionary, scientific), we can map exactly which operator levers each one pulls hardest and what kinds of beings/causes/permissions it manufactures.

  • AI Worlds

    AI Worlds

    What better choice for a heading image for AI Worlds than gilded lilies. This image was generated in Vecteezy and in this section, it reflects the opportunities we have from AI.

    There is an old expression about ‘gilding the lily’ that basically means, That is already so perfect, you don’t need to add anything more.

    Well, that’s kind of what we’re here to talk about. What is “enough?” What is “too perfect?” What is so cast in concrete, deemed so sacred, that we cannot speak of it, cannot ask questions: Why? Where from? Who said?

    That’s what we are all about here. There are lots of questions about AI, who uses it. Interestingly, there is a broad discussion fueled by people’s personal opinions projected onto others about who should and who should not be using AI and for what.

    We don’t have should/should not discussions. We seldom engage in discussions based on opinions. Our conversations are more curiosity-based: What happens if I do this? What happens if I try this? What happens if I stop doing this?

    We also talk about the different AIs, the details of using them, what we’ve found, what we’ve learned.

    We use “big words,” jargon, because they are precise. We don’t “dumb stuff down.” This is more a place where we “dumb stuff up.”

    We are irreverent in a lot of ways. We ask challenging questions and go scary places. We challenge the status quo by inquiry, not by argument.

    Many of the posts and the Seeds will be forward thinking: This is what we know [hinge] And this is what is happening next. This is a physical form that is embedded in an absolutely marvelous North African Language that I discovered recently called Masri. It is spoken Egyptian Arabic and dates to Ancient Engypt and a time before time.

    All made possible by the interaction of humans and AIs.

    Topics