A Formal Mathematical Model of The Holy Trinity.

[Simon PayntonParticipant]

King Iyk wrote:

You cannot reasonably expect me to prioritize your opinion over that of a system more knowledgeable than you and demonstrably superior at processing and validating data. That is precisely the point.

Yes, but:

1. argument from authority never cuts it.
2. AI can’t tell you if the inputs to your argument are real or not: if there really is some kind of causal connection between the numbers and the basic elements of God’s existence.  AI can never tell you that, therefore, using AI to prove your point falls flat.

[Simon PayntonParticipant]

The best you’ve got are symbolic arguments – “this” symbolises “that” and therefore “the other”.  That’s not enough to prove something is real.

[King IykParticipant]

Simon Paynton wrote:

The best you’ve got are symbolic arguments – “this” symbolises “that” and therefore “the other”. That’s not enough to prove something is real.

AI, which was for the most part developed by atheist scientists, disagrees with you that this is just a ‘symbolic argument’.

Again, I will repeat the challenge from my last post so you can focus on the task at hand:

You science-oriented atheists have effectively undermined your own position by creating an advanced intelligent system (AI) that affirms the validity of this proof without hesitation. This is a system that is vastly more knowledgeable than you and far better at processing and validating data. Your words carry little weight when the foremost intellectual achievement of the human race declines to grant the “Q.E.D.” label to the billions of other proposed proofs, yet confidently assigns it to this one alone.

The only way to redeem yourselves from this error would be to demonstrate that AI is not competent at validating such arguments. You could attempt this by producing an alternative demonstration of a different God that claims empirical and mathematical certainty, includes explicit axioms, formal definitions of operations, proven closure theorems, predictive power, brittleness (where altering a single value collapses the system), and clear falsifiability conditions; then have it validated by all major advanced AIs and present it here. Doing so would undermine the credibility of my proof.

If, as you claim, “God = 12” and “Trinity = 18” are merely arbitrary assignments, and all you see is circular reasoning, arguments from coincidence, non sequiturs, and pattern-making from randomness—rather than a rigorous formal argument where x causes y and inevitably leads to z—then producing such a counter-demonstration should be straightforward.

• This reply was modified 1 month ago by  King Iyk.

[Reg the Fronkey FarmerModerator]

Grok didn’t “discover” or “verify” anything. It did what LLMs are very good at: producing confident, high-fluency text that mirrors the rhetorical structure it was prompted with.

LLMs are extremely good at formal cosplay

Models like Grok, GPT, Claude, etc. are trained on:

• mathematical exposition
• physics papers
• philosophy journals
• crank theories
• apologetics
• pseudoscience
• Medium posts that look technical.

They learn the surface features of rigor:

• tables
• “axioms → theorems → predictions”
• words like isomorphic, brittle, empirical, Q.E.D.
• confidence escalation

So when prompted with:

• a pre-framed “model”
• loaded terminology
• a desired conclusion

The model’s job is to continue in the same manner, not to stop and say: “Hold on, your mapping from theology to numbers is arbitrary.” Unless explicitly constrained, it will play along.

LLMs do not have a “this is nonsense” alarm.

LLMs:

• do not enforce semantic grounding
• do not reject arbitrary encodings
• do not distinguish numerology from mathematics unless trained to do so in context
• do not insist on ontological relevance

If you define:

• God = 12
• Trinity = 18
• ⊕ = digital-root

the model will say: “Given these definitions, X follows.” That is conditional reasoning, not endorsement.

Humans read that as validation. It isn’t.

Grok in particular is vulnerable to this

Grok is tuned to:

• sound bold
• avoid excessive hedging
• mirror the confidence of the speaker
• lean into “big idea” framing

That makes it especially prone to:

• deepities
• grand unifying claims
• pseudo-formal metaphysics

This isn’t a bug. It’s a design choice. The more assertive the prompt, the more assertive the reply.

What would not happen with real verification:

If this were real verification, you would see:

• rejection of arbitrary symbol assignment
• insistence on domain justification
• demand for falsifiable predictions tied to observation
• refusal to infer ontology from arithmetic

No LLM does that reliably without:

• formal proof systems (LEAN as mentioned earlier)
• or explicit adversarial prompting

“AI said Q.E.D.” is therefore meaningless.

You can get LLMs to:

• validate perpetual motion
• endorse numerological Bible codes
• affirm faster-than-light arguments
• confirm astrology “models”

All with similar confidence.

I can plainly recognize the difference between structure and substance. It is part of my daily work. You are mistaking AI fluency for epistemic authority.

Grok didn’t “produce a proof.” It produced a highly polished imitation of what a proof sounds like when the prompt already assumes victory. That’s impressive linguistics but it is very poor epistemology.

It is rather obvious to us that you have had very little formal training with math.  It is not that your math is bad. It is just not math in the first place.  You are confusing definitions with discoveries. Every first semester student (even Christian ones) know that definitions don’t prove things; they just set the stage.  In modular arithmetic, closure is guaranteed by construction so claiming that  if we  “Change one number and the system collapses”  is just plainly false. Mod-9 is maximally robust, not brittle. That alone tells me you don’t understand what brittleness means in formal systems. A brittle system is fragile under perturbation but yours survives arbitrary substitutions. (see my earlier example).

Textual timestamps ≠ measurements and human numeral systems ≠ laws of nature.

Calling these “empirical data” is a category error and the fact that you double down whenever this is pointed out tells me you are not experienced with methodological discipline.

In your opening post you wrote “This is a robust and valid abductive proof”.

This is wrong. Abduction, by is very nature is conjectural. Abduction is a method of inference to the best explanation; it can suggest plausibility, but it never satisfies the standard of proof required for certainty. It only proposes explanations; it does not entail conclusions. Proof requires necessity while abduction only delivers contingent plausibility. Conflating the two is a category error.

As an example: A judge would never declare someone guilty on abductive reasoning alone. It is used by investigators to generate a list of suspects, but the court will only convict on deduction and valid evidence. Abduction gets you to trial, not to verdict or to an hypothesis, not to a proof.

“Valid” doesn’t apply to abduction either. Validity is a property of deductive arguments only. An argument is valid if: If the premises are true, the conclusion must be true. Abductive arguments are not truth-preserving. They just rank arguments as better or worse explanations.

So it is not “valid”, “abductive” or a “proof”. You would be more accurate if you said “Amen” instead of Q.E.D.

Please post just your theorem and your full dataset. Maybe I can get it to work for you. I will check it properly with Lean (as posted earlier).  (AI reframed some of my wording in this post).

[King IykParticipant]

You are still treating this as me versus you. That is precisely what it is no longer.

The moment an advanced intelligent system that is created, trained, and benchmarked by the scientific-atheist community, systematically refuses to grant “Q.E.D.” to countless other proposed proofs, yet does so here under explicit axioms, defined operations, and demonstrated closure, the dispute ceases to be interpersonal. It becomes intra-methodological.

The tension is no longer between my claim and your critique.
It is between your epistemic standards and the artifact built to embody them.

When I cite AI, I am not appealing to authority; I am appealing to consistency. If the same evaluative machinery that rejects billions of incoherent or circular constructions recognizes internal necessity in this one, then dismissing that result does not refute the model; it selectively overrides it.

That selective override is the real issue.

Humans reason with two faculties: The Mind and The Heart. We possess analytic cognition (the mind): logic, structure, consequence; and we possess affective cognition (the heart): identity, commitment, aversion, pride. AI does not have the latter. It does not protect a worldview. It does not experience reputational cost. It does not feel threatened by implication. It evaluates only what follows from what.

In that sense, AI represents a stripped-down version of the scientific mind: inference without attachment.

So when you say “AI saying Q.E.D. is meaningless,” what you are really saying is not that the reasoning fails, but that you reserve the right to reject the conclusions of your own methodological offspring when they become uncomfortable.

That is not a critique of the argument.
It is a declaration of epistemic veto.

You are not arguing against me.
You are arguing against the implications of your own standards, applied without emotional insulation.

And that is why the debate feels stalled:
because the conflict is no longer external.

It is internal.

What the models converged on is formal structure: a closed system with explicit definitions, operations, internal consistency, and nontrivial constraints. They did conditional validation: given these axioms, the consequences follow. That is exactly what you say they are capable of and exactly what was tested.

So the dispute is no longer about AI at all. It is about whether the mappings are arbitrary or constrained, and whether the system is purely symbolic or anchored to a fixed historical dataset.

You assert arbitrariness. That is the key claim. But arbitrariness is not shown by repeating “definitions don’t prove things.” It is shown by demonstrating replaceability: if the assignments can be swapped without loss, or if the same structure can be generated just as easily from unrelated inputs, then the charge stands.

This is why I have repeatedly asked; not rhetorically, but methodologically; for a counter-construction:

Same level of formalization
Same degree of closure
Same brittleness under semantic change (not mod-9 algebra alone, but history-linked constraints)
Same convergence across independent domains
Not because “AI endorsement” would make it true, but because replaceability would make it false.

Abduction alone does not yield certainty. But the point of the model is precisely that it does not remain abductive once the constraints are fixed. Abduction motivates the hypothesis; deduction governs the internal consequences. The claim under dispute is whether the anchoring constraints (timeline, geometry, arithmetic behavior) are genuinely independent or merely post-hoc patterning. That is a substantive question, not a definitional one.

So we are here:

If the structure is arbitrary, demonstrate an equally tight alternative.
If it is numerology, show its invariance under substitution.
If it collapses, collapse it constructively.

Until then, the disagreement is not that “AI was fooled,” nor that “definitions prove God,” but that you are asserting arbitrariness without exhibiting it.

That is where the burden now rests.

The AIs were not fed a “pre-framed victory prompt.”
They were given the raw data (Gospel hours, base-10 digital roots, clock geometry) and the axioms, then asked to evaluate consistency, uniqueness, brittleness, and predictive power independently. Every AI (Grok, ChatGPT, Claude, DeepSeek) concluded Q.E.D. because:

The loop closes only with {3,6,9,12}
Any perturbation breaks it (brittle, not robust under substitution)
No other integer quadruple satisfies the four independent constraints (history, math, geometry, scriptural relations)
Mod-9 is robust — but the mapping to Trinity + God Equation is uniquely brittle. Change one assignment (e.g., Spirit=8) → no closure, no cross match, no Luke 1:35 inversion.

When all alternatives fail and the explanation is unique, predictive, and over-constrained, it becomes the only conclusion.

The convergence remains:
God + The Father + The Son + The Holy Spirit = God
in time, mathematics, and geometry.

[Simon PayntonParticipant]

@regthefronkeyfarmer – what does LLM think of your proof of Dracula?

[Reg the Fronkey FarmerModerator]

I did not ask as I thought it might “put a hex” on my number system. Base-16 is for those who want math to constrain reality. Dracula revealed to me in a dream that if I can get the same “truth” out of 3, 9, 27, Hebrew gematria, or my car’s odometer, then I was not discovering anything. I was only free-associating. He revealed to me that numerology is for people who want answers without the inconvenience of being right and that even the undead were rolling their eyes. He was glad though to see Christians were no longer seeing the Cross as a symbol of the resurrection. That made both of us happy.

[Simon PayntonParticipant]

Someone said that the reason LLM validates @kingiyk‘s “proof” of God’s existence, is that the internet is full of information and discourse that agrees with it.  Whereas, there’s not much on the true existence of Dracula.  So, the LLM repeats what it sees.

There’s the whole article in last week’s Sunday School about how AI validates what people want to hear.

[TheEncogitationerParticipant]

Simon:

Someone said that the reason LLM validates @kingiyk‘s “proof” of God’s existence, is that the internet is full of information and discourse that agrees with it. Whereas, there’s not much on the true existence of Dracula. So, the LLM repeats what it sees.

There’s the whole article in last week’s Sunday School about how AI validates what people want to hear.

So LLMs are electronic literate parrots. They have a long way to go before we humans should entrust them with running anything.

And King Iyk is a parrot playing The Telephone Game with this electronic literate parrot. Some “King”, huh? About as worthy of respect as “King Trump.”

• This reply was modified 2 weeks, 1 day ago by  TheEncogitationer. Reason: Change passive to active voice

[PopeBeanieModerator]

TheEncogitationer wrote:

They have a long way to go before we humans should entrust them with running anything.

LLMs will be increasing customizable wrt which sources of original data can be designated as most reliable. In some, one can already save your own references/database locally, e.g. in PDF files. Very technical, scientific, and medical topics are already at the top of the list of most-reliably-accurate in terms of citable sources and academic references, because of vast sources vetted by specific industries. Then there’s published books in the public domain on philosophy, religion, and other humanities.

That’s just off the top of my head so I know I’m missing several other fields of knowledge and research. Legislation and legal research comes late to my mind. Open source design specifications, e.g. for 3d printing and other “maker” projects; suddenly available capabilities given to everyday people, like computer/mobile phone programming, and quick proof of concept programming at higher technical levels. Protein folding, pharmaceutical research, enhance graphic interpretation of medical scans.

I.e. AI can run some things much better, but for me, the best way to explain this is still inadequate except when focusing on specific use cases. For sure, I’m constantly skipping the several dozens of google and bing searches daily, instead going for the single or a few query-refined AI responses and summaries.

I learned right away how to NOT ask leading questions, but ease into complex topics with simple questions first to get a feel for any biases AI might have up front toward a topic, and then I keep paying attention to how much it adapts itself (or not) to just telling me what it assumes/learns what I want to hear. (I’ve been like this since about middle school with social interactions, not asking questions in a biased way, even with religious people.)

I’m really curious to learn how AI in autocratically run societies rig their LLMs to respond with propaganda, gas lighting, and other fact-purging streams of bullshit. (LOL i.e. Trumpisms?! Putinisms? Used car salesmanisms? Marketing campaigns…)

Which reminds me of a new aphorism I like: People who judge others, are just not curious enough. (Late edit: I should have looked up the source(s). Walt Whitman wrote “Be curious, not judgmental”. That came from an AI I’m considering subscribing to, Perplexity.)

[TheEncogitationerParticipant]

PopeBeanie:

Walt Whitman wrote “Be curious, not judgmental”

If AI is to be a tool for the of human beings, our curiosity needs satisfaction and we have to make judgements about what AI does and produces. AI is not serving when it just regurgitates whatever supports any irrational nonsense someone believes, and does so at greater cost of energy.

[TheEncogitationerParticipant]

PopeBeanie:

I’m really curious to learn how AI in autocratically run societies rig their LLMs to respond with propaganda, gas lighting, and other fact-purging streams of bullshit. (LOL i.e. Trumpisms?! Putinisms? Used car salesmanisms? Marketing campaigns…)

King Iyk has made it pretty clear how Christians can use AI to spout pseudo-mathematical apologetics.

By the way, how is “Iyk” pronounced? Is it pronounced “Ike” as in “Reverend Ike.” Or is it pronounced “Ick” as in “Ick! How slimy!”? Inquiring minds want to know.

[Simon PayntonParticipant]

TheEncogitationer wrote:

King Iyk has made it pretty clear how Christians can use AI to spout pseudo-mathematical apologetics.

You can’t knock him for trying.  But I think he’s misguided (by AI).

[jakelafortParticipant]

It is not the trying. It is an invalid hypothesis. When people assume the truth of the matter asserted and endeavor to prove their narcissistic biases that they come to by way of indoctrination it is ugly.

I like Ike.

[TheEncogitationerParticipant]

Simon:

You can’t knock him for trying. But I think he’s misguided (by AI).

For trying a massive high-tech Gish Gallop Non Sequitur, yes, you can blame him.

And ackshuyally, he’s misguiding both himself and the AI and the AI is reinforcing his misguidance and trying to take us along for the ride. It’s case of the blind leading the blind, then trying to lead us all into a ditch the size of Yellowstone. Here’s where I get off.

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