From Newsgroup: sci.logic

Hi,

No joke, I have a new CLP heuristic coded,
and made a blind test. Explaind to the AI
what I coded, but not how coded it,

omitted the crucial scoring function, and
the AI was exactly proposing this scoring
funcion out of the blue.

I was quite bamboozled. The AI could not tell
me how it recalled or halucinated the formula,
I spent like 1-2 hours trying to find a

precedent paper. Will blog about it later.
So whats going on . Why can AI suddently
propose Heuristics. Because they are now

differently trained:

Phase 1: Early LLMs (~2018–2020)
Phase 2: LLMs with Prompted Reasoning (~2020–2022)
Phase 3: Scaling & Reasoning Engineering (~2023–2025)

Training an AI to look for heuristics,
can help in self programming. The generative
part of the AI would generate some code,

which then the AI would use for itself. See also:

How it started:

AI for Low-Code for AI
Nikitha Rao, Jason Tsay, Kiran Kate,
Vincent J. Hellendoorn, Martin Hirzel - 2023
https://arxiv.org/abs/2305.20015

How its going:

Successor-Generator Planning with LLM-generated Heuristics
Alexander Tuisov1, Yonatan Vernik2,
Alexander Shleyfman - 2024
https://arxiv.org/abs/2501.18784v4

Bye

Mild Shock schrieb:

Hi,

Good Morning Vietnam, the HPC-AI Convergence
doesn't sleep. Here a friendly reminder of
the Sudoku leader board (Topn87 Challenge):

#1: jczsolve / Rust WASM
    Solving 87 Sudokus in 0.006 seconds (0 sec / Sudoku)
    https://emerentius.github.io/sudoku_web/

#2: Kudoku / JavaScript
    Solving 87 Sudokus in 0.043 seconds (0.0004 sec / Sudoku)
    https://attractivechaos.github.io/plb/kudoku.html

#3: Picat / import cp. solve([ff],L)
    CPU time 0.175 seconds
    https://picat-lang.org/

#4: Picat / import sat. solve(L)
    CPU time 0.373 seconds
    https://fmv.jku.at/kissat/

Tested on Windows 11, with a AMD Ryzen AI 350

Didn't test yet GNU Prolog, ECLiPSe Prolog or
Ciao Prolog. So whats next? Well beat #1 by
tapping into an NPU of Copilot+ PC.

Have Fun!

Its Winner Winner Chicken Dinner time again...

Bye

--- Synchronet 3.21b-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

How to spot a failed coping with AI?
Very easy, if somebody starts some gibberish
about consciousness you can be sure he is clueless:

Are AI minds genuine minds?
A genuine mind is typically associated with
consciousness, self-awareness, intentionality,
and the capacity to experience mental states such as emotions. https://events.rice.edu/event/416255-moshe-vardi-world-logic-day-lecture

What will be Moshe Vardi conclusion nowadays,
that self programming is a first form of
inner monologue? Well I guess the GPUs have

termal activity either way, who cares?

LoL

Bye

P.S.: Just observe how clever Alan Turing was:
He writes near the beginning of the paper:

“I propose to consider the question, ‘Can machines
think?’ This should begin with definitions of the
meaning of the terms ‘machine’ and ‘think.’”

To avoid endless philosophical debate, he replaces
it with the imitation game.

Key points:
- Word usage — Turing does not use the word “consciousness”
in the paper as a criterion for the test.

- Intent — He wanted to sidestep debates about
inner experience and focus on observable behavior.

- Later interpretations — Many later philosophers
and AI critics have connected the Turing Test to
questions of consciousness, but this is a re-
interpretation or extension of Turing’s original proposal.

So, while the Turing Test is often brought into
discussions of machine consciousness, Turing himself
did not mention it in that context in his 1950 paper.

Mild Shock schrieb:

Hi,

No joke, I have a new CLP heuristic coded,
and made a blind test. Explaind to the AI
what I coded, but not how coded it,

omitted the crucial scoring function, and
the AI was exactly proposing this scoring
funcion out of the blue.

I was quite bamboozled. The AI could not tell
me how it recalled or halucinated the formula,
I spent like 1-2 hours trying to find a

precedent paper. Will blog about it later.
So whats going on . Why can AI suddently
propose Heuristics. Because they are now

differently trained:

Phase 1: Early LLMs (~2018–2020)
Phase 2: LLMs with Prompted Reasoning (~2020–2022)
Phase 3: Scaling & Reasoning Engineering (~2023–2025)

Training an AI to look for heuristics,
can help in self programming. The generative
part of the AI would generate some code,

which then the AI would use for itself. See also:

How it started:

AI for Low-Code for AI
Nikitha Rao, Jason Tsay, Kiran Kate,
Vincent J. Hellendoorn, Martin Hirzel - 2023
https://arxiv.org/abs/2305.20015

How its going:

Successor-Generator Planning with LLM-generated Heuristics
Alexander Tuisov1, Yonatan Vernik2,
Alexander Shleyfman - 2024
https://arxiv.org/abs/2501.18784v4

Bye

Mild Shock schrieb:

Hi,

Good Morning Vietnam, the HPC-AI Convergence
doesn't sleep. Here a friendly reminder of
the Sudoku leader board (Topn87 Challenge):

#1: jczsolve / Rust WASM
     Solving 87 Sudokus in 0.006 seconds (0 sec / Sudoku)
     https://emerentius.github.io/sudoku_web/

#2: Kudoku / JavaScript
     Solving 87 Sudokus in 0.043 seconds (0.0004 sec / Sudoku)
     https://attractivechaos.github.io/plb/kudoku.html

#3: Picat / import cp. solve([ff],L)
     CPU time 0.175 seconds
     https://picat-lang.org/

#4: Picat / import sat. solve(L)
     CPU time 0.373 seconds
     https://fmv.jku.at/kissat/

Tested on Windows 11, with a AMD Ryzen AI 350

Didn't test yet GNU Prolog, ECLiPSe Prolog or
Ciao Prolog. So whats next? Well beat #1 by
tapping into an NPU of Copilot+ PC.

Have Fun!

Its Winner Winner Chicken Dinner time again...

Bye

--- Synchronet 3.21b-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Now replace "machine" by "bioform", and "think"
by "life" in the Turing Test, and you get the
Gladiator Test. The judgement is not by a single

counterpart behind a teletype, but a crowd,
we could invoke the image of a Hollywood thumbs-up or
thumbs-down gesture as signalling a yes or no.

Source:

Adrian Tchaikovsky
Dogs of War
https://en.wikipedia.org/wiki/Dogs_of_War_(2017_novel)

How Tchaikovsky’s deepens the Turing Analogy

- From "Can it think?" to "Does it suffer? Does
it deserve to live?" Turing’s test is about cognitive
performance. The bioforms’ struggle is about moral
standing. Their "Turing Test" isn’t to prove
intelligence (they are clearly intelligent), but
to prove consciousness, sentience, and intrinsic
value to a society that legally defines them as "equipment."

- The Crowd is the Global Public & Legal System
In the novel, the "crowd" isn't just a Roman audience—it's
the court of public opinion, international law, media,
and corporate boardrooms. The question is whether this
"crowd" will show mercy (granting personhood) or
condemn them to remain weapons.

Etc.. Etc..

Have Fun!

Bye

Mild Shock schrieb:

Hi,

How to spot a failed coping with AI?
Very easy, if somebody starts some gibberish
about consciousness you can be sure he is clueless:

Are AI minds genuine minds?
A genuine mind is typically associated with
consciousness, self-awareness, intentionality,
and the capacity to experience mental states such as emotions. https://events.rice.edu/event/416255-moshe-vardi-world-logic-day-lecture

What will be Moshe Vardi conclusion nowadays,
that self programming is a first form of
inner monologue? Well I guess the GPUs have

termal activity either way, who cares?

LoL

Bye

P.S.: Just observe how clever Alan Turing was:
He writes near the beginning of the paper:

“I propose to consider the question, ‘Can machines
think?’ This should begin with definitions of the
meaning of the terms ‘machine’ and ‘think.’”

To avoid endless philosophical debate, he replaces
it with the imitation game.

Key points:
- Word usage — Turing does not use the word “consciousness”
in the paper as a criterion for the test.

- Intent — He wanted to sidestep debates about
inner experience and focus on observable behavior.

- Later interpretations — Many later philosophers
and AI critics have connected the Turing Test to
questions of consciousness, but this is a re-
interpretation or extension of Turing’s original proposal.

So, while the Turing Test is often brought into
discussions of machine consciousness, Turing himself
did not mention it in that context in his 1950 paper.

Mild Shock schrieb:

Hi,

No joke, I have a new CLP heuristic coded,
and made a blind test. Explaind to the AI
what I coded, but not how coded it,

omitted the crucial scoring function, and
the AI was exactly proposing this scoring
funcion out of the blue.

I was quite bamboozled. The AI could not tell
me how it recalled or halucinated the formula,
I spent like 1-2 hours trying to find a

precedent paper. Will blog about it later.
So whats going on . Why can AI suddently
propose Heuristics. Because they are now

differently trained:

Phase 1: Early LLMs (~2018–2020)
Phase 2: LLMs with Prompted Reasoning (~2020–2022)
Phase 3: Scaling & Reasoning Engineering (~2023–2025)

Training an AI to look for heuristics,
can help in self programming. The generative
part of the AI would generate some code,

which then the AI would use for itself. See also:

How it started:

AI for Low-Code for AI
Nikitha Rao, Jason Tsay, Kiran Kate,
Vincent J. Hellendoorn, Martin Hirzel - 2023
https://arxiv.org/abs/2305.20015

How its going:

Successor-Generator Planning with LLM-generated Heuristics
Alexander Tuisov1, Yonatan Vernik2,
Alexander Shleyfman - 2024
https://arxiv.org/abs/2501.18784v4

Bye

Mild Shock schrieb:

Hi,

Good Morning Vietnam, the HPC-AI Convergence
doesn't sleep. Here a friendly reminder of
the Sudoku leader board (Topn87 Challenge):

#1: jczsolve / Rust WASM
     Solving 87 Sudokus in 0.006 seconds (0 sec / Sudoku)
     https://emerentius.github.io/sudoku_web/

#2: Kudoku / JavaScript
     Solving 87 Sudokus in 0.043 seconds (0.0004 sec / Sudoku)
     https://attractivechaos.github.io/plb/kudoku.html

#3: Picat / import cp. solve([ff],L)
     CPU time 0.175 seconds
     https://picat-lang.org/

#4: Picat / import sat. solve(L)
     CPU time 0.373 seconds
     https://fmv.jku.at/kissat/

Tested on Windows 11, with a AMD Ryzen AI 350

Didn't test yet GNU Prolog, ECLiPSe Prolog or
Ciao Prolog. So whats next? Well beat #1 by
tapping into an NPU of Copilot+ PC.

Have Fun!

Its Winner Winner Chicken Dinner time again...

Bye

--- Synchronet 3.21b-Linux NewsLink 1.2

From Newsgroup: sci.logic

On 10/01/2026 02:04, Mild Shock wrote:

P.S.: Just observe how clever Alan Turing was:
He writes near the beginning of the paper:

“I propose to consider the question, ‘Can machines
think?’ This should begin with definitions of the
meaning of the terms ‘machine’ and ‘think.’”

That shows what a fool he was.

Can flippos parble?

This should begin with definitions of the meaning of the terms 'flippos'
and 'parble'.

/That/ I would agree with.
--
Tristan Wibberley

The message body is Copyright (C) 2026 Tristan Wibberley except
citations and quotations noted. All Rights Reserved except that you may,
of course, cite it academically giving credit to me, distribute it
verbatim as part of a usenet system or its archives, and use it to
promote my greatness and general superiority without misrepresentation
of my opinions other than my opinion of my greatness and general
superiority which you _may_ misrepresent. You definitely MAY NOT train
any production AI system with it but you may train experimental AI that
will only be used for evaluation of the AI methods it implements.

--- Synchronet 3.21b-Linux NewsLink 1.2