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Rene Descartes "Discours de la méthode" has fizzled out

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  Rene Descartes "Discours de la méthode" has fizzled out Mild Shock <janburse@fastmail.fm> - 2025-11-14 11:27 +0100
    Philosophical Twist due to negligence (Re: Rene Descartes "Discours de la méthode" has fizzled out) Mild Shock <janburse@fastmail.fm> - 2025-11-14 11:44 +0100
      Re: Philosophical Twist due to negligence (Re: Rene Descartes "Discours de la méthode" has fizzled out) Ross Finlayson <ross.a.finlayson@gmail.com> - 2025-11-14 11:10 -0800
        NY is the next Detroit in 2035 (Was: Philosophical Twist due to negligence) Mild Shock <janburse@fastmail.fm> - 2025-11-14 23:12 +0100
          Its a little sad story with NY (Was: NY is the next Detroit in 2035) Mild Shock <janburse@fastmail.fm> - 2025-11-14 23:25 +0100
            Re: Its a little sad story with NY (Was: NY is the next Detroit in 2035) Mild Shock <janburse@fastmail.fm> - 2025-11-14 23:45 +0100
      How to not be Artificial Intelligent [Boris the Loris deeply shocked] (Re: Philosophical Twist due to negligence) Mild Shock <janburse@fastmail.fm> - 2025-11-16 11:24 +0100
        Abstraction refinement (CEGAR) etc.. [Community Blind Spot] (Was: How to not be Artificial Intelligent) Mild Shock <janburse@fastmail.fm> - 2025-11-16 12:05 +0100
          The illusion of set theories [Computational Logic Primate] (Was: Abstraction refinement (CEGAR) etc.. [Community Blind Spot]) Mild Shock <janburse@fastmail.fm> - 2025-11-16 13:08 +0100
            Not all logicians are primarily interested in "computation" (Re: The illusion of set theories [Computational Logic Primate]) Mild Shock <janburse@fastmail.fm> - 2025-11-16 16:32 +0100
    AnythingLLM QNN/ONNX: Massiv Computations versus John Sowa (Re: Rene Descartes "Discours de la méthode" has fizzled out) Mild Shock <janburse@fastmail.fm> - 2025-11-25 20:07 +0100
      Benchmark results DirectML versus QNN [Challenge for Geekbench AI] (Re: AnythingLLM QNN/ONNX: Massiv Computations versus John Sowa) Mild Shock <janburse@fastmail.fm> - 2025-11-25 20:17 +0100

#341971 — Rene Descartes "Discours de la méthode" has fizzled out

Hi,

How it started:

https://conceptbase.sourceforge.net/

How its going:

https://www.ibm.com/products/datastax

The problem with claims such as " Formal languages,
such as KAOS, are based on predicate logic and
capture additional details about an application
in a precise manner. They also provide a foundation
for reasoning with information models." is that
every thing in the quoted sentence is wrong.

Real AI systems scale by approximation,
vectorization, distributed representations,
and partial knowledge — not by globally
consistent logical models. No classical requirements
language or ontology captures the informal
cognitive machinery that makes
intelligence flexible. Intelligence needs the
whole messy cognitive spectrum.

Somehow DataStax looks like n8n married AI embedding.
I hope Amazon, Meta, Google, etc.. get the message.
I don't worry about Microsoft, they might come with

something from their Encarta corner and Copilot+ is
more Local AI. After all we need things like Wikidata
in a Robot and not in a Data Center.

LoL

Bye

[toc] | [next] | [standalone]

#341972 — Philosophical Twist due to negligence (Re: Rene Descartes "Discours de la méthode" has fizzled out)

Hi,

Descartes’ “divide problems into parts” works
only for well-behaved, linear, decomposable systems.
But its just that parts might end up as Schrödingers

equation. It could be that stable diffusion is the
new constraint solver. In a sense, stable diffusion
models (or other generative AI) are functioning as

probabilistic, fuzzy constraint solvers — but in a
very different paradigm from classical logic or
formal methods. But what was neglected?

- Cybernetics (1940s–50s)
Focused on feedback loops, control, and self-regulation
in machines and biological systems. Showed that
decomposition can fail because subparts are interdependent.

- Chaos Theory (1960s–80s)
Nonlinear deterministic systems can produce unpredictable,
sensitive dependence on initial conditions. Decomposition
into parts is tricky: small errors explode, and “solving
subparts” may not help predict the whole.

- Santa Fe Institute & Complex Systems (1980s–present)
Studied emergent behavior, networks, adaptation,
self-organization. Linear, reductionist thinking fails
to capture dynamics of economic, social, and ecological systems.

Bye

Mild Shock schrieb:
> Hi,
> 
> How it started:
> 
> https://conceptbase.sourceforge.net/
> 
> How its going:
> 
> https://www.ibm.com/products/datastax
> 
> The problem with claims such as " Formal languages,
> such as KAOS, are based on predicate logic and
> capture additional details about an application
> in a precise manner. They also provide a foundation
> for reasoning with information models." is that
> every thing in the quoted sentence is wrong.
> 
> Real AI systems scale by approximation,
> vectorization, distributed representations,
> and partial knowledge — not by globally
> consistent logical models. No classical requirements
> language or ontology captures the informal
> cognitive machinery that makes
> intelligence flexible. Intelligence needs the
> whole messy cognitive spectrum.
> 
> Somehow DataStax looks like n8n married AI embedding.
> I hope Amazon, Meta, Google, etc.. get the message.
> I don't worry about Microsoft, they might come with
> 
> something from their Encarta corner and Copilot+ is
> more Local AI. After all we need things like Wikidata
> in a Robot and not in a Data Center.
> 
> LoL
> 
> Bye

[toc] | [prev] | [next] | [standalone]

#341983 — Re: Philosophical Twist due to negligence (Re: Rene Descartes "Discours de la méthode" has fizzled out)

On 11/14/2025 02:44 AM, Mild Shock wrote:
> Hi,
>
> Descartes’ “divide problems into parts” works
> only for well-behaved, linear, decomposable systems.
> But its just that parts might end up as Schrödingers
>
> equation. It could be that stable diffusion is the
> new constraint solver. In a sense, stable diffusion
> models (or other generative AI) are functioning as
>
> probabilistic, fuzzy constraint solvers — but in a
> very different paradigm from classical logic or
> formal methods. But what was neglected?
>
> - Cybernetics (1940s–50s)
> Focused on feedback loops, control, and self-regulation
> in machines and biological systems. Showed that
> decomposition can fail because subparts are interdependent.
>
> - Chaos Theory (1960s–80s)
> Nonlinear deterministic systems can produce unpredictable,
> sensitive dependence on initial conditions. Decomposition
> into parts is tricky: small errors explode, and “solving
> subparts” may not help predict the whole.
>
> - Santa Fe Institute & Complex Systems (1980s–present)
> Studied emergent behavior, networks, adaptation,
> self-organization. Linear, reductionist thinking fails
> to capture dynamics of economic, social, and ecological systems.
>
> Bye
>
> Mild Shock schrieb:
>> Hi,
>>
>> How it started:
>>
>> https://conceptbase.sourceforge.net/
>>
>> How its going:
>>
>> https://www.ibm.com/products/datastax
>>
>> The problem with claims such as " Formal languages,
>> such as KAOS, are based on predicate logic and
>> capture additional details about an application
>> in a precise manner. They also provide a foundation
>> for reasoning with information models." is that
>> every thing in the quoted sentence is wrong.
>>
>> Real AI systems scale by approximation,
>> vectorization, distributed representations,
>> and partial knowledge — not by globally
>> consistent logical models. No classical requirements
>> language or ontology captures the informal
>> cognitive machinery that makes
>> intelligence flexible. Intelligence needs the
>> whole messy cognitive spectrum.
>>
>> Somehow DataStax looks like n8n married AI embedding.
>> I hope Amazon, Meta, Google, etc.. get the message.
>> I don't worry about Microsoft, they might come with
>>
>> something from their Encarta corner and Copilot+ is
>> more Local AI. After all we need things like Wikidata
>> in a Robot and not in a Data Center.
>>
>> LoL
>>
>> Bye
>

It seems that mostly "Open Source"
serves most people's needs these days.

What about KIF and the UMBC agents archive?

https://agents.umbc.edu/

Rather outdated, ..., so, they'd run today
on what would be a, "tiny", amount of resources.

Besides, "Information Retrieval" itself is pretty
much moribund these days.


If you're going to read Rene 'Renatus' DesCartes,
and his approach to elements, he doesn't just take
them apart, he does so in a way that the elements
put themselves back together.

"While we thus reject all of which we can entertain
the smallest doubt, and even imagine that it is false,
we easily indeed suppose that there is neither God,
nor sky, not bodies, and that we ourselves have
neither hands nor feet, nor, finally, a body;
but we cannot in the same way suppose that we are
not while we doubt of the truth of these thing;
for their is a repugnance in conceiving that what
thinks does not exist at the very time when it thinks."
-- DesCartes, "Principles of Philosophy"

Yeah, try that prompt, see if, you know
it "makes a man of it".

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#341987 — NY is the next Detroit in 2035 (Was: Philosophical Twist due to negligence)

Hi,

Although you could believe you are in the
possesion of Cartesian Thinking, you still
dont know whether NY is the next Detroit in 2035.

Let me explain what mostlikely will happen:

- Its for long no sea-line immigration hub anymore
- The millionairs don't need financial district proximity
   anymore, everything is done online anyways.
- The meat grinder of white colour jobs disappears,
   everything is done by artificial intelligence anyways.
- The new major creates a little califath, with
   the appeal of bombay
- Jeff Bezos gets his energy from space, and puts
   data centers there, and saves the florida crocodiles
- Trump still does parties in Miami, and not in NY
   and the epstain files have still not been released.

LoL

Bye

Ross Finlayson schrieb
> If you're going to read Rene 'Renatus' DesCartes,
> and his approach to elements, he doesn't just take
> them apart, he does so in a way that the elements
> put themselves back together.

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#341988 — Its a little sad story with NY (Was: NY is the next Detroit in 2035)

Hi,

Its a little sad story with NY.
Since the East Coast Logic was not
only Chicago, you found in NY

people dead and still alive:
- Raymond Smullyan
- Melvin Fitting
- Who else?

Some of them were even Prologers.

Bye

Mild Shock schrieb:
> Hi,
> 
> Although you could believe you are in the
> possesion of Cartesian Thinking, you still
> dont know whether NY is the next Detroit in 2035.
> 
> Let me explain what mostlikely will happen:
> 
> - Its for long no sea-line immigration hub anymore
> - The millionairs don't need financial district proximity
>    anymore, everything is done online anyways.
> - The meat grinder of white colour jobs disappears,
>    everything is done by artificial intelligence anyways.
> - The new major creates a little califath, with
>    the appeal of bombay
> - Jeff Bezos gets his energy from space, and puts
>    data centers there, and saves the florida crocodiles
> - Trump still does parties in Miami, and not in NY
>    and the epstain files have still not been released.
> 
> LoL
> 
> Bye
> 
> Ross Finlayson schrieb
>> If you're going to read Rene 'Renatus' DesCartes,
>> and his approach to elements, he doesn't just take
>> them apart, he does so in a way that the elements
>> put themselves back together.
> 
> 

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#341989 — Re: Its a little sad story with NY (Was: NY is the next Detroit in 2035)

Hi,

I was refering to the assumptions that FOL
is the door opener for reasoning with information:

> The problem with claims such as " Formal languages,
> such as KAOS, are based on predicate logic and
> capture additional details about an application
> in a precise manner. They also provide a foundation
> for reasoning with information models." is that
> every thing in the quoted sentence is wrong. 

I don't know, there are a lot of half century old
discoveries now in front of me. Where subtle things,
away from FOL, like recently Operational Sets by

Feferman have an appearance in my Prolog system.
What could be also interesting is Church-Frege
Ontology. It was later formalized in a book by

Melvin Fitting. You find it here:

Fitting, M. (2002). Types, Tableaus, and
Gödel’s God, Dordrecht: Kluwer.
https://link.springer.com/book/10.1007/978-94-010-0411-4

I am not yet 100% sure, it could be that I have
also a use case, or many use cases for some
of the distinctions made, it seems the FOL model,

especially the FOL= model, i.e. FOL with equality,
is much much too simplified for practical uses.
I don't know whether these problems have already

a final settlement. A great deal of Proof Assistant
development in recent years only circled around
subletities of equality. Most prominent incursion

was 2009 with Vladimir Voevodsky, also NY.

Bye

Mild Shock schrieb:
> Hi,
> 
> Its a little sad story with NY.
> Since the East Coast Logic was not
> only Chicago, you found in NY
> 
> people dead and still alive:
> - Raymond Smullyan
> - Melvin Fitting
> - Who else?
> 
> Some of them were even Prologers.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Although you could believe you are in the
>> possesion of Cartesian Thinking, you still
>> dont know whether NY is the next Detroit in 2035.
>>
>> Let me explain what mostlikely will happen:
>>
>> - Its for long no sea-line immigration hub anymore
>> - The millionairs don't need financial district proximity
>>    anymore, everything is done online anyways.
>> - The meat grinder of white colour jobs disappears,
>>    everything is done by artificial intelligence anyways.
>> - The new major creates a little califath, with
>>    the appeal of bombay
>> - Jeff Bezos gets his energy from space, and puts
>>    data centers there, and saves the florida crocodiles
>> - Trump still does parties in Miami, and not in NY
>>    and the epstain files have still not been released.
>>
>> LoL
>>
>> Bye
>>
>> Ross Finlayson schrieb
>>> If you're going to read Rene 'Renatus' DesCartes,
>>> and his approach to elements, he doesn't just take
>>> them apart, he does so in a way that the elements
>>> put themselves back together.
>>
>>
> 

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#342029 — How to not be Artificial Intelligent [Boris the Loris deeply shocked] (Re: Philosophical Twist due to negligence)

Hi,

How it started, some useless GOFAI framing and
production systems lore:

Computational Logic and Human Thinking:
How to Be Artificially Intelligent
https://www.cambridge.org/core/books/computational-logic-and-human-thinking/C2AFB0483D922944067DBC76FFFEB295

How its going, please note CodeMender from Google:

New Google Riftrunner AI (Gemini 3) Shocks Everyone
https://www.youtube.com/watch?v=F_YWQ12qQ8M

Especially note the section about CodeMender(*), and AI
built on Gemini, which does inspect and suggest changes
to OpenSource projects.

So whats the rule of predicting the future in AI. Well
just take skeptics, like Boris the Loris (**) (nah we don't
use Fuzzy Testing here, CodeMender uses this among other

methods), Linus Torwald (nah, AI for OpenSource is still
far away, CodeMender is here) etc.. Negate what they are
saying and you get a perfect prediction for 2025 / 2026.

LoL

Bye

(*) Already *old* anouncement from October 6, 2025:

Introducing CodeMender: an AI agent for code security
https://deepmind.google/blog/introducing-codemender-an-ai-agent-for-code-security/

(**) Ok, when you don't find Boris the Loris on
SWI-Prolog discourse, you might find him here:

Hello. My name is Boris and this is my family. We're
lorises and we are primates - a bit like small
monkeys. We tend to move quite slowly which is
why we are Slow Lorises. We have big eyes so we
can see well in the dark to catch insects for our dinner.

My name... is Boris
https://x.com/mrborisloris

Mild Shock schrieb:
> Hi,
> 
> Descartes’ “divide problems into parts” works
> only for well-behaved, linear, decomposable systems.
> But its just that parts might end up as Schrödingers
> 
> equation. It could be that stable diffusion is the
> new constraint solver. In a sense, stable diffusion
> models (or other generative AI) are functioning as
> 
> probabilistic, fuzzy constraint solvers — but in a
> very different paradigm from classical logic or
> formal methods. But what was neglected?
> 
> - Cybernetics (1940s–50s)
> Focused on feedback loops, control, and self-regulation
> in machines and biological systems. Showed that
> decomposition can fail because subparts are interdependent.
> 
> - Chaos Theory (1960s–80s)
> Nonlinear deterministic systems can produce unpredictable,
> sensitive dependence on initial conditions. Decomposition
> into parts is tricky: small errors explode, and “solving
> subparts” may not help predict the whole.
> 
> - Santa Fe Institute & Complex Systems (1980s–present)
> Studied emergent behavior, networks, adaptation,
> self-organization. Linear, reductionist thinking fails
> to capture dynamics of economic, social, and ecological systems.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> How it started:
>>
>> https://conceptbase.sourceforge.net/
>>
>> How its going:
>>
>> https://www.ibm.com/products/datastax
>>
>> The problem with claims such as " Formal languages,
>> such as KAOS, are based on predicate logic and
>> capture additional details about an application
>> in a precise manner. They also provide a foundation
>> for reasoning with information models." is that
>> every thing in the quoted sentence is wrong.
>>
>> Real AI systems scale by approximation,
>> vectorization, distributed representations,
>> and partial knowledge — not by globally
>> consistent logical models. No classical requirements
>> language or ontology captures the informal
>> cognitive machinery that makes
>> intelligence flexible. Intelligence needs the
>> whole messy cognitive spectrum.
>>
>> Somehow DataStax looks like n8n married AI embedding.
>> I hope Amazon, Meta, Google, etc.. get the message.
>> I don't worry about Microsoft, they might come with
>>
>> something from their Encarta corner and Copilot+ is
>> more Local AI. After all we need things like Wikidata
>> in a Robot and not in a Data Center.
>>
>> LoL
>>
>> Bye
> 

[toc] | [prev] | [next] | [standalone]

#342030 — Abstraction refinement (CEGAR) etc.. [Community Blind Spot] (Was: How to not be Artificial Intelligent)

Hi,

Something tells me the Prolog community has a
sever blind spot, in their Logic education.
Possibly never touch a book like this here,

even not with tweezers:

Undergraduate Texts in Mathematics - 1983
H .- D. Ebbinghaus et. al - Mathematical Logic
http://www.fuchs-braun.com/media/ca80d9e55f6d3bfaffff8005fffffff0.pdf

The front cover features a smiling face,
illustrating Ehrenfeucht Fraisse (EF) games.
There is a compelling relationship between

EF and Fuzzy Testing. Just take A and B, a formal
form of a spec and of some code. This is quite
different from Lorentz Games, where the initial

set-up is different. But here if Anna plays
Player II in G(M1,M2) and Bert plays Player II
in G(M2,M1). Then if Anna has a winning strategy,

then Bert has a winning strategy. Sounds like
Bisimulation again. One of the biggest struggels
for Boris the Loris and Nazi Retart Julio of

all time. Or this complete blunder, navigating
in the dark, trying to identify an elephant:

@kuniaki.mukai
https://swi-prolog.discourse.group/t/cyclic-terms-unification-x-f-f-x-x-y-f-y-f-y-x-y/9097/72

Bye

P.S.: Mostlikely the cardinal sin of the Prolog
Community is that they don't apply Proof Theoretic
methods and Model Theoretic methods on equal

footing. They don't understand how the two
methods are related, even on the most basic
level, such as counter models, which is a level

more basic than EF games. One of the future
challenges for the community could be extending
proof theoretic methods and model theoretic

methods to (seemingly) higher order logic. This
could be quite messy, or not? I am currengly
fascinated by Feferman Operative Sets and

like Melvin Fittings work in higher order logic.

Mild Shock schrieb:
> Hi,
> 
> How it started, some useless GOFAI framing and
> production systems lore:
> 
> Computational Logic and Human Thinking:
> How to Be Artificially Intelligent
> https://www.cambridge.org/core/books/computational-logic-and-human-thinking/C2AFB0483D922944067DBC76FFFEB295 
> 
> 
> How its going, please note CodeMender from Google:
> 
> New Google Riftrunner AI (Gemini 3) Shocks Everyone
> https://www.youtube.com/watch?v=F_YWQ12qQ8M
> 
> Especially note the section about CodeMender(*), and AI
> built on Gemini, which does inspect and suggest changes
> to OpenSource projects.
> 
> So whats the rule of predicting the future in AI. Well
> just take skeptics, like Boris the Loris (**) (nah we don't
> use Fuzzy Testing here, CodeMender uses this among other
> 
> methods), Linus Torwald (nah, AI for OpenSource is still
> far away, CodeMender is here) etc.. Negate what they are
> saying and you get a perfect prediction for 2025 / 2026.
> 
> LoL
> 
> Bye
> 
> (*) Already *old* anouncement from October 6, 2025:
> 
> Introducing CodeMender: an AI agent for code security
> https://deepmind.google/blog/introducing-codemender-an-ai-agent-for-code-security/ 
> 
> 
> (**) Ok, when you don't find Boris the Loris on
> SWI-Prolog discourse, you might find him here:
> 
> Hello. My name is Boris and this is my family. We're
> lorises and we are primates - a bit like small
> monkeys. We tend to move quite slowly which is
> why we are Slow Lorises. We have big eyes so we
> can see well in the dark to catch insects for our dinner.
> 
> My name... is Boris
> https://x.com/mrborisloris
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Descartes’ “divide problems into parts” works
>> only for well-behaved, linear, decomposable systems.
>> But its just that parts might end up as Schrödingers
>>
>> equation. It could be that stable diffusion is the
>> new constraint solver. In a sense, stable diffusion
>> models (or other generative AI) are functioning as
>>
>> probabilistic, fuzzy constraint solvers — but in a
>> very different paradigm from classical logic or
>> formal methods. But what was neglected?
>>
>> - Cybernetics (1940s–50s)
>> Focused on feedback loops, control, and self-regulation
>> in machines and biological systems. Showed that
>> decomposition can fail because subparts are interdependent.
>>
>> - Chaos Theory (1960s–80s)
>> Nonlinear deterministic systems can produce unpredictable,
>> sensitive dependence on initial conditions. Decomposition
>> into parts is tricky: small errors explode, and “solving
>> subparts” may not help predict the whole.
>>
>> - Santa Fe Institute & Complex Systems (1980s–present)
>> Studied emergent behavior, networks, adaptation,
>> self-organization. Linear, reductionist thinking fails
>> to capture dynamics of economic, social, and ecological systems.
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> How it started:
>>>
>>> https://conceptbase.sourceforge.net/
>>>
>>> How its going:
>>>
>>> https://www.ibm.com/products/datastax
>>>
>>> The problem with claims such as " Formal languages,
>>> such as KAOS, are based on predicate logic and
>>> capture additional details about an application
>>> in a precise manner. They also provide a foundation
>>> for reasoning with information models." is that
>>> every thing in the quoted sentence is wrong.
>>>
>>> Real AI systems scale by approximation,
>>> vectorization, distributed representations,
>>> and partial knowledge — not by globally
>>> consistent logical models. No classical requirements
>>> language or ontology captures the informal
>>> cognitive machinery that makes
>>> intelligence flexible. Intelligence needs the
>>> whole messy cognitive spectrum.
>>>
>>> Somehow DataStax looks like n8n married AI embedding.
>>> I hope Amazon, Meta, Google, etc.. get the message.
>>> I don't worry about Microsoft, they might come with
>>>
>>> something from their Encarta corner and Copilot+ is
>>> more Local AI. After all we need things like Wikidata
>>> in a Robot and not in a Data Center.
>>>
>>> LoL
>>>
>>> Bye
>>
> 

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#342031 — The illusion of set theories [Computational Logic Primate] (Was: Abstraction refinement (CEGAR) etc.. [Community Blind Spot])

Hi,

Set theory was initially praised as a foundation
for mathematics. But the reduction of mathematics
to foundation, was also carried out in type theories.
And we see that this reduction is rather arbitrary,

we now have dozen of competing set theories and
type theories. Whats more stunning, the reduction
allows us to view Model Theory in a Proof Theory
fashion, possibly implying that Model Theory doesn't

exist or is not needed? But this is a dangerous
conclusion, since the foundation might be a theoretical
technical thing, far away from practical use.
Take naive comprehension, was it wrong?

∃x∀y(y e x <=> phi(y))

It was fixed after Russell/Frege by ?von Neumann?:

∀z∃x∀y(y e x <=> y e z & phi(y))

But in reverse mathematics we find another refinement:

∃x∀y(y e x <=> phi(y))   for phi a certain class

So instead introducing a set like upper bound z,
where we would only look at the projection of
comprehension to z, we take the naive intuition more
seriously, born from informal usage, and say,

naive comprehension was not really wrong!

Bye

P.S.: It would be interesting to see whether
Operational Set theory as presented today, has
a developed Model Theoretic language? Or does it
also subscribe to the Computational Logic Primate?

5.3 Relativizing operational set theory
It is shown in [45] that a direct relativization
of operational reflection leads to theories that are
significantly stronger than theories formalizing the
admissible analogues of classical large cardinal axioms.
This refutes the conjecture 14(1) on p. 977 of Feferman [19].
https://home.inf.unibe.ch/ltg/publications/2018/jae18.pdf

Is relativizing the backdoor of a model theory,
that might also be useful for OST? OST is used like
Lego bricks here. Adding this or that, one gets different
set theories (or maybe type theories).

Operational set theory and small large cardinals
Solomon Feferman - 2006
Conjecture 14.
  (1) OST + (Inacc) ≡ KPi.
  (2) OST + (Mahlo) ≡ KPM.
  (3) OST + (Reg2) ≡ KPω +( 3 −Reflection).
https://math.stanford.edu/~feferman/papers/OST-Final.pdf

One gets the impression of OST being a sub-foundation toy.

Mild Shock schrieb:
> Hi,
> 
> Something tells me the Prolog community has a
> sever blind spot, in their Logic education.
> Possibly never touch a book like this here,
> 
> even not with tweezers:
> 
> Undergraduate Texts in Mathematics - 1983
> H .- D. Ebbinghaus et. al - Mathematical Logic
> http://www.fuchs-braun.com/media/ca80d9e55f6d3bfaffff8005fffffff0.pdf
> 
> The front cover features a smiling face,
> illustrating Ehrenfeucht Fraisse (EF) games.
> There is a compelling relationship between
> 
> EF and Fuzzy Testing. Just take A and B, a formal
> form of a spec and of some code. This is quite
> different from Lorentz Games, where the initial
> 
> set-up is different. But here if Anna plays
> Player II in G(M1,M2) and Bert plays Player II
> in G(M2,M1). Then if Anna has a winning strategy,
> 
> then Bert has a winning strategy. Sounds like
> Bisimulation again. One of the biggest struggels
> for Boris the Loris and Nazi Retart Julio of
> 
> all time. Or this complete blunder, navigating
> in the dark, trying to identify an elephant:
> 
> @kuniaki.mukai
> https://swi-prolog.discourse.group/t/cyclic-terms-unification-x-f-f-x-x-y-f-y-f-y-x-y/9097/72 
> 
> 
> Bye
> 
> P.S.: Mostlikely the cardinal sin of the Prolog
> Community is that they don't apply Proof Theoretic
> methods and Model Theoretic methods on equal
> 
> footing. They don't understand how the two
> methods are related, even on the most basic
> level, such as counter models, which is a level
> 
> more basic than EF games. One of the future
> challenges for the community could be extending
> proof theoretic methods and model theoretic
> 
> methods to (seemingly) higher order logic. This
> could be quite messy, or not? I am currengly
> fascinated by Feferman Operative Sets and
> 
> like Melvin Fittings work in higher order logic.
> 
> Mild Shock schrieb:
>> Hi,
>>
>> How it started, some useless GOFAI framing and
>> production systems lore:
>>
>> Computational Logic and Human Thinking:
>> How to Be Artificially Intelligent
>> https://www.cambridge.org/core/books/computational-logic-and-human-thinking/C2AFB0483D922944067DBC76FFFEB295 
>>
>>
>> How its going, please note CodeMender from Google:
>>
>> New Google Riftrunner AI (Gemini 3) Shocks Everyone
>> https://www.youtube.com/watch?v=F_YWQ12qQ8M
>>
>> Especially note the section about CodeMender(*), and AI
>> built on Gemini, which does inspect and suggest changes
>> to OpenSource projects.
>>
>> So whats the rule of predicting the future in AI. Well
>> just take skeptics, like Boris the Loris (**) (nah we don't
>> use Fuzzy Testing here, CodeMender uses this among other
>>
>> methods), Linus Torwald (nah, AI for OpenSource is still
>> far away, CodeMender is here) etc.. Negate what they are
>> saying and you get a perfect prediction for 2025 / 2026.
>>
>> LoL
>>
>> Bye
>>
>> (*) Already *old* anouncement from October 6, 2025:
>>
>> Introducing CodeMender: an AI agent for code security
>> https://deepmind.google/blog/introducing-codemender-an-ai-agent-for-code-security/ 
>>
>>
>> (**) Ok, when you don't find Boris the Loris on
>> SWI-Prolog discourse, you might find him here:
>>
>> Hello. My name is Boris and this is my family. We're
>> lorises and we are primates - a bit like small
>> monkeys. We tend to move quite slowly which is
>> why we are Slow Lorises. We have big eyes so we
>> can see well in the dark to catch insects for our dinner.
>>
>> My name... is Boris
>> https://x.com/mrborisloris
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Descartes’ “divide problems into parts” works
>>> only for well-behaved, linear, decomposable systems.
>>> But its just that parts might end up as Schrödingers
>>>
>>> equation. It could be that stable diffusion is the
>>> new constraint solver. In a sense, stable diffusion
>>> models (or other generative AI) are functioning as
>>>
>>> probabilistic, fuzzy constraint solvers — but in a
>>> very different paradigm from classical logic or
>>> formal methods. But what was neglected?
>>>
>>> - Cybernetics (1940s–50s)
>>> Focused on feedback loops, control, and self-regulation
>>> in machines and biological systems. Showed that
>>> decomposition can fail because subparts are interdependent.
>>>
>>> - Chaos Theory (1960s–80s)
>>> Nonlinear deterministic systems can produce unpredictable,
>>> sensitive dependence on initial conditions. Decomposition
>>> into parts is tricky: small errors explode, and “solving
>>> subparts” may not help predict the whole.
>>>
>>> - Santa Fe Institute & Complex Systems (1980s–present)
>>> Studied emergent behavior, networks, adaptation,
>>> self-organization. Linear, reductionist thinking fails
>>> to capture dynamics of economic, social, and ecological systems.
>>>
>>> Bye
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> How it started:
>>>>
>>>> https://conceptbase.sourceforge.net/
>>>>
>>>> How its going:
>>>>
>>>> https://www.ibm.com/products/datastax
>>>>
>>>> The problem with claims such as " Formal languages,
>>>> such as KAOS, are based on predicate logic and
>>>> capture additional details about an application
>>>> in a precise manner. They also provide a foundation
>>>> for reasoning with information models." is that
>>>> every thing in the quoted sentence is wrong.
>>>>
>>>> Real AI systems scale by approximation,
>>>> vectorization, distributed representations,
>>>> and partial knowledge — not by globally
>>>> consistent logical models. No classical requirements
>>>> language or ontology captures the informal
>>>> cognitive machinery that makes
>>>> intelligence flexible. Intelligence needs the
>>>> whole messy cognitive spectrum.
>>>>
>>>> Somehow DataStax looks like n8n married AI embedding.
>>>> I hope Amazon, Meta, Google, etc.. get the message.
>>>> I don't worry about Microsoft, they might come with
>>>>
>>>> something from their Encarta corner and Copilot+ is
>>>> more Local AI. After all we need things like Wikidata
>>>> in a Robot and not in a Data Center.
>>>>
>>>> LoL
>>>>
>>>> Bye
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#342033 — Not all logicians are primarily interested in "computation" (Re: The illusion of set theories [Computational Logic Primate])

Hi,

While mathematics is possibly not subject to "deflationism":

"According to deflationists, such suggestions are
mistaken, and, moreover, they all share a common mistake.
The common mistake is to assume that truth has a nature
of the kind that philosophers might find out about and
develop theories of."
https://plato.stanford.edu/entries/truth-deflationary/

Its the other way around for "computation", since it is more real?

For example Roberts Kowalski AI booklet is a master class
in snake oil selling, only looking at the "Logic" in
"Computational Logic", communicating a certain fascination
in logic representation, including chain of thoughts and
complex dynamic situations. Largely ignoring serious

discussion of Algorithm = Logic + Control. So maybe besides
a chapter " The grass is wet ", adding a chapter " If
we were God " would have helped in putting "Computation" back
into the picture. After all solving a connection graph is
only a small part of reasoning. Its only the Matrix in

Herbrand's original method, but there is also another more
creative step related to quantifiers in Herbrand's original
method. See also Jens Ottens implementation of leanCoP.

Bye

See also here:

The Pocket Reasoner– Automatic Reasoning on Small Devices
https://www.ntnu.no/ojs/index.php/nikt/article/download/5368/4844/20629

Mild Shock schrieb:
> Hi,
> 
> Set theory was initially praised as a foundation
> for mathematics. But the reduction of mathematics
> to foundation, was also carried out in type theories.
> And we see that this reduction is rather arbitrary,
> 
> we now have dozen of competing set theories and
> type theories. Whats more stunning, the reduction
> allows us to view Model Theory in a Proof Theory
> fashion, possibly implying that Model Theory doesn't
> 
> exist or is not needed? But this is a dangerous
> conclusion, since the foundation might be a theoretical
> technical thing, far away from practical use.
> Take naive comprehension, was it wrong?
> 
> ∃x∀y(y e x <=> phi(y))
> 
> It was fixed after Russell/Frege by ?von Neumann?:
> 
> ∀z∃x∀y(y e x <=> y e z & phi(y))
> 
> But in reverse mathematics we find another refinement:
> 
> ∃x∀y(y e x <=> phi(y))   for phi a certain class
> 
> So instead introducing a set like upper bound z,
> where we would only look at the projection of
> comprehension to z, we take the naive intuition more
> seriously, born from informal usage, and say,
> 
> naive comprehension was not really wrong!
> 
> Bye
> 
> P.S.: It would be interesting to see whether
> Operational Set theory as presented today, has
> a developed Model Theoretic language? Or does it
> also subscribe to the Computational Logic Primate?
> 
> 5.3 Relativizing operational set theory
> It is shown in [45] that a direct relativization
> of operational reflection leads to theories that are
> significantly stronger than theories formalizing the
> admissible analogues of classical large cardinal axioms.
> This refutes the conjecture 14(1) on p. 977 of Feferman [19].
> https://home.inf.unibe.ch/ltg/publications/2018/jae18.pdf
> 
> Is relativizing the backdoor of a model theory,
> that might also be useful for OST? OST is used like
> Lego bricks here. Adding this or that, one gets different
> set theories (or maybe type theories).
> 
> Operational set theory and small large cardinals
> Solomon Feferman - 2006
> Conjecture 14.
>   (1) OST + (Inacc) ≡ KPi.
>   (2) OST + (Mahlo) ≡ KPM.
>   (3) OST + (Reg2) ≡ KPω +( 3 −Reflection).
> https://math.stanford.edu/~feferman/papers/OST-Final.pdf
> 
> One gets the impression of OST being a sub-foundation toy.
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Something tells me the Prolog community has a
>> sever blind spot, in their Logic education.
>> Possibly never touch a book like this here,
>>
>> even not with tweezers:
>>
>> Undergraduate Texts in Mathematics - 1983
>> H .- D. Ebbinghaus et. al - Mathematical Logic
>> http://www.fuchs-braun.com/media/ca80d9e55f6d3bfaffff8005fffffff0.pdf
>>
>> The front cover features a smiling face,
>> illustrating Ehrenfeucht Fraisse (EF) games.
>> There is a compelling relationship between
>>
>> EF and Fuzzy Testing. Just take A and B, a formal
>> form of a spec and of some code. This is quite
>> different from Lorentz Games, where the initial
>>
>> set-up is different. But here if Anna plays
>> Player II in G(M1,M2) and Bert plays Player II
>> in G(M2,M1). Then if Anna has a winning strategy,
>>
>> then Bert has a winning strategy. Sounds like
>> Bisimulation again. One of the biggest struggels
>> for Boris the Loris and Nazi Retart Julio of
>>
>> all time. Or this complete blunder, navigating
>> in the dark, trying to identify an elephant:
>>
>> @kuniaki.mukai
>> https://swi-prolog.discourse.group/t/cyclic-terms-unification-x-f-f-x-x-y-f-y-f-y-x-y/9097/72 
>>
>>
>> Bye
>>
>> P.S.: Mostlikely the cardinal sin of the Prolog
>> Community is that they don't apply Proof Theoretic
>> methods and Model Theoretic methods on equal
>>
>> footing. They don't understand how the two
>> methods are related, even on the most basic
>> level, such as counter models, which is a level
>>
>> more basic than EF games. One of the future
>> challenges for the community could be extending
>> proof theoretic methods and model theoretic
>>
>> methods to (seemingly) higher order logic. This
>> could be quite messy, or not? I am currengly
>> fascinated by Feferman Operative Sets and
>>
>> like Melvin Fittings work in higher order logic.
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> How it started, some useless GOFAI framing and
>>> production systems lore:
>>>
>>> Computational Logic and Human Thinking:
>>> How to Be Artificially Intelligent
>>> https://www.cambridge.org/core/books/computational-logic-and-human-thinking/C2AFB0483D922944067DBC76FFFEB295 
>>>
>>>
>>> How its going, please note CodeMender from Google:
>>>
>>> New Google Riftrunner AI (Gemini 3) Shocks Everyone
>>> https://www.youtube.com/watch?v=F_YWQ12qQ8M
>>>
>>> Especially note the section about CodeMender(*), and AI
>>> built on Gemini, which does inspect and suggest changes
>>> to OpenSource projects.
>>>
>>> So whats the rule of predicting the future in AI. Well
>>> just take skeptics, like Boris the Loris (**) (nah we don't
>>> use Fuzzy Testing here, CodeMender uses this among other
>>>
>>> methods), Linus Torwald (nah, AI for OpenSource is still
>>> far away, CodeMender is here) etc.. Negate what they are
>>> saying and you get a perfect prediction for 2025 / 2026.
>>>
>>> LoL
>>>
>>> Bye
>>>
>>> (*) Already *old* anouncement from October 6, 2025:
>>>
>>> Introducing CodeMender: an AI agent for code security
>>> https://deepmind.google/blog/introducing-codemender-an-ai-agent-for-code-security/ 
>>>
>>>
>>> (**) Ok, when you don't find Boris the Loris on
>>> SWI-Prolog discourse, you might find him here:
>>>
>>> Hello. My name is Boris and this is my family. We're
>>> lorises and we are primates - a bit like small
>>> monkeys. We tend to move quite slowly which is
>>> why we are Slow Lorises. We have big eyes so we
>>> can see well in the dark to catch insects for our dinner.
>>>
>>> My name... is Boris
>>> https://x.com/mrborisloris
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> Descartes’ “divide problems into parts” works
>>>> only for well-behaved, linear, decomposable systems.
>>>> But its just that parts might end up as Schrödingers
>>>>
>>>> equation. It could be that stable diffusion is the
>>>> new constraint solver. In a sense, stable diffusion
>>>> models (or other generative AI) are functioning as
>>>>
>>>> probabilistic, fuzzy constraint solvers — but in a
>>>> very different paradigm from classical logic or
>>>> formal methods. But what was neglected?
>>>>
>>>> - Cybernetics (1940s–50s)
>>>> Focused on feedback loops, control, and self-regulation
>>>> in machines and biological systems. Showed that
>>>> decomposition can fail because subparts are interdependent.
>>>>
>>>> - Chaos Theory (1960s–80s)
>>>> Nonlinear deterministic systems can produce unpredictable,
>>>> sensitive dependence on initial conditions. Decomposition
>>>> into parts is tricky: small errors explode, and “solving
>>>> subparts” may not help predict the whole.
>>>>
>>>> - Santa Fe Institute & Complex Systems (1980s–present)
>>>> Studied emergent behavior, networks, adaptation,
>>>> self-organization. Linear, reductionist thinking fails
>>>> to capture dynamics of economic, social, and ecological systems.
>>>>
>>>> Bye
>>>>
>>>> Mild Shock schrieb:
>>>>> Hi,
>>>>>
>>>>> How it started:
>>>>>
>>>>> https://conceptbase.sourceforge.net/
>>>>>
>>>>> How its going:
>>>>>
>>>>> https://www.ibm.com/products/datastax
>>>>>
>>>>> The problem with claims such as " Formal languages,
>>>>> such as KAOS, are based on predicate logic and
>>>>> capture additional details about an application
>>>>> in a precise manner. They also provide a foundation
>>>>> for reasoning with information models." is that
>>>>> every thing in the quoted sentence is wrong.
>>>>>
>>>>> Real AI systems scale by approximation,
>>>>> vectorization, distributed representations,
>>>>> and partial knowledge — not by globally
>>>>> consistent logical models. No classical requirements
>>>>> language or ontology captures the informal
>>>>> cognitive machinery that makes
>>>>> intelligence flexible. Intelligence needs the
>>>>> whole messy cognitive spectrum.
>>>>>
>>>>> Somehow DataStax looks like n8n married AI embedding.
>>>>> I hope Amazon, Meta, Google, etc.. get the message.
>>>>> I don't worry about Microsoft, they might come with
>>>>>
>>>>> something from their Encarta corner and Copilot+ is
>>>>> more Local AI. After all we need things like Wikidata
>>>>> in a Robot and not in a Data Center.
>>>>>
>>>>> LoL
>>>>>
>>>>> Bye
>>>>
>>>
>>
> 

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#342279 — AnythingLLM QNN/ONNX: Massiv Computations versus John Sowa (Re: Rene Descartes "Discours de la méthode" has fizzled out)

Hi,

Ha Ha, remember this post on SWI-Prolog
discourse, the primary source for morons such
as Boris the Loris and Nazi Retard Julio:

"The idea that LLM-based methods can become
more intelligent by using massive amounts
of computation is false. They can generate
more kinds of BS, but at an enormous cost in
hardware and in the electricity to run that
massive hardware. But without methods of
evaluation, the probability that random mixtures
of data are true or useful or worth the cost
of generating them becomes less and less likely."
- John Sowa, 10 Jul 2024
https://swi-prolog.discourse.group/t/prolog-and-llms-genai/8699

Guess what my new ThinkCentre, that just arrived
via Lenovo, China, with a Snapdragon X, for around
700.- USD could easily run locally some inferencing.

I was using AnythingLLM, it has little idioctic
electron user interface, but can support
Snapdragon X NPU and models, via QNN/ONNX:

The all-in-one AI application
https://anythingllm.com/

Tested a LLama Model, a little bit chatty to
be honest, and a Phi Silica model, not yet that
good in coding. Where did the massive computation

come from? From the SOC and the unified memory
of the Snapdragon. I had 32 GB, and 16 GB was
shared with the NPU. So you don't need to

buy an Aura Yoga laptop, which has separate
NVIDIA Graphics card, with only 8 GB. This
graphic card will be useless, many interesting

models are above 8 GB. And yes the massive
computation obviously leads to more intelligence.
The later is a riddle for every Prologer, how

could more LIPS (logical inference per second)
lead to more intelligence?

Bye

Mild Shock schrieb:
> Hi,
> 
> How it started:
> 
> https://conceptbase.sourceforge.net/
> 
> How its going:
> 
> https://www.ibm.com/products/datastax
> 
> The problem with claims such as " Formal languages,
> such as KAOS, are based on predicate logic and
> capture additional details about an application
> in a precise manner. They also provide a foundation
> for reasoning with information models." is that
> every thing in the quoted sentence is wrong.
> 
> Real AI systems scale by approximation,
> vectorization, distributed representations,
> and partial knowledge — not by globally
> consistent logical models. No classical requirements
> language or ontology captures the informal
> cognitive machinery that makes
> intelligence flexible. Intelligence needs the
> whole messy cognitive spectrum.
> 
> Somehow DataStax looks like n8n married AI embedding.
> I hope Amazon, Meta, Google, etc.. get the message.
> I don't worry about Microsoft, they might come with
> 
> something from their Encarta corner and Copilot+ is
> more Local AI. After all we need things like Wikidata
> in a Robot and not in a Data Center.
> 
> LoL
> 
> Bye

[toc] | [prev] | [next] | [standalone]

#342280 — Benchmark results DirectML versus QNN [Challenge for Geekbench AI] (Re: AnythingLLM QNN/ONNX: Massiv Computations versus John Sowa)

Hi,

This super cute Snapdragon X box, has massive
benchmark score for quantisized neural networks (qANN):

     sCPU    mCPU    GPU    sANN    hANN    qANN
AcerSwift    2835    13393    25395    6744    10167    5175
YogaUltra    2785    9844    30545    7270    13936    4830
ThinkCentre    2145    9754    13782    1414    16456    40721

AcerSwift    OpenCL    DirectML
YogaUltra    OpenCL    DirectML
ThinkCentre    Vulkan    QNN

But maybe the low qANN numbers are a problem of
Geekbench AI, and how it uses DirectML, that it cannot
yet address teh full potential of the NPUs on the

other two Local AI machines. But impressively the
QNN API and the ONNX format, goes very smooth on
the ThinkCentre. For machine translation via quantisized

neural networks (qANN). I see that the ThinkCentre
is 10 times faster than the other two machines. But I
guess with a suitable version of Geekbench AI,

the gap between to the other two machines will close.
They are just too new, so that Geekbench AI is
lacking behind.

Bye

Mild Shock schrieb:
> Hi,
> 
> Ha Ha, remember this post on SWI-Prolog
> discourse, the primary source for morons such
> as Boris the Loris and Nazi Retard Julio:
> 
> "The idea that LLM-based methods can become
> more intelligent by using massive amounts
> of computation is false. They can generate
> more kinds of BS, but at an enormous cost in
> hardware and in the electricity to run that
> massive hardware. But without methods of
> evaluation, the probability that random mixtures
> of data are true or useful or worth the cost
> of generating them becomes less and less likely."
> - John Sowa, 10 Jul 2024
> https://swi-prolog.discourse.group/t/prolog-and-llms-genai/8699
> 
> Guess what my new ThinkCentre, that just arrived
> via Lenovo, China, with a Snapdragon X, for around
> 700.- USD could easily run locally some inferencing.
> 
> I was using AnythingLLM, it has little idioctic
> electron user interface, but can support
> Snapdragon X NPU and models, via QNN/ONNX:
> 
> The all-in-one AI application
> https://anythingllm.com/
> 
> Tested a LLama Model, a little bit chatty to
> be honest, and a Phi Silica model, not yet that
> good in coding. Where did the massive computation
> 
> come from? From the SOC and the unified memory
> of the Snapdragon. I had 32 GB, and 16 GB was
> shared with the NPU. So you don't need to
> 
> buy an Aura Yoga laptop, which has separate
> NVIDIA Graphics card, with only 8 GB. This
> graphic card will be useless, many interesting
> 
> models are above 8 GB. And yes the massive
> computation obviously leads to more intelligence.
> The later is a riddle for every Prologer, how
> 
> could more LIPS (logical inference per second)
> lead to more intelligence?
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> How it started:
>>
>> https://conceptbase.sourceforge.net/
>>
>> How its going:
>>
>> https://www.ibm.com/products/datastax
>>
>> The problem with claims such as " Formal languages,
>> such as KAOS, are based on predicate logic and
>> capture additional details about an application
>> in a precise manner. They also provide a foundation
>> for reasoning with information models." is that
>> every thing in the quoted sentence is wrong.
>>
>> Real AI systems scale by approximation,
>> vectorization, distributed representations,
>> and partial knowledge — not by globally
>> consistent logical models. No classical requirements
>> language or ontology captures the informal
>> cognitive machinery that makes
>> intelligence flexible. Intelligence needs the
>> whole messy cognitive spectrum.
>>
>> Somehow DataStax looks like n8n married AI embedding.
>> I hope Amazon, Meta, Google, etc.. get the message.
>> I don't worry about Microsoft, they might come with
>>
>> something from their Encarta corner and Copilot+ is
>> more Local AI. After all we need things like Wikidata
>> in a Robot and not in a Data Center.
>>
>> LoL
>>
>> Bye
> 

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