From Newsgroup: sci.physics

Hi,

The English had Aristoteles (*), the French had
Descartes, and the Dutch have their national
Flag. The culmination of the Enlighment was

the distinction between analytic and synthetic
truth. But this doesn't help to understand
Generative AI, which produces a mish mash

of the factual and the plausible. But the logical
and non-logical distinction lead to abominations
like ascribing to Wittgenstein the maxim,

"All logical differences are big differences", with
the even worse conjecture "All nonlogical differences
are small differences". But an early conceptual

prototype of ChatGPT was given by:

"Mirror (**) Mirror on the Wall who is the Fairest of them All?"
- Snow White, Brothers Grim

So its all about retrieving mirror texts and images and
transforming them, the retrieval having good old metrics like
recall and precision, and the transformation having also metrics,

metrics all relative to a group preferences assumption of
the end-user, so that the end-user can more cost effictively
and more market penetratingly act, in a totally

new AI Boom infected environment.

Bye

(*)
we have powers and faculties fitted to deal with
them, and are **happy or miserable** in proportion
as we know how to **frame** a right judgment of things
The elements of logic. In four books
by Duncan, William, 1717-1760 https://archive.org/details/elementsoflogic00dunc/page/n5/mode/2up

(**)

An earlier version of "Mirrors" (Chapter 7) was written for a
volume in honor of Thomas A. Sebeok (He was among the
founders of biosemiotics, and coined the term "zoosemiotics"
in 1963 to describe the development of signals and signs by
non-human animal species) for his sixty-fifth birthday.
Umberto Eco, ''Semiotics and the Philosophy of Language'',
Bloomington: Indiana U.P., 1984
https://monoskop.org/images /b/b3/Eco_Umberto_Semiotics_and_the_Philosophy_of_Language_1986.pdf
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

Hi,

Current AI is split between two worlds that don't play well together:

Deep Learning (neural networks, transformers, ChatGPT) - great at
learning from data, terrible at logical reasoning
Symbolic AI (logic programming, expert systems) - great at logical
reasoning, terrible at learning from messy real-world data

Tensor Logic unifies both. It's a single language where you can:
Write logical rules that the system can actually learn and modify
Do transparent, verifiable reasoning (no hallucinations)
Mix "fuzzy" analogical thinking with rock-solid deduction

The Killer Feature: The Temperature Knob

https://www.youtube.com/watch?v=4APMGvicmxY

Bye

Mild Shock schrieb:

Hi,

The English had Aristoteles (*), the French had
Descartes, and the Dutch have their national
Flag. The culmination of the Enlighment was

the distinction between analytic and synthetic
truth. But this doesn't help to understand
Generative AI, which produces a mish mash

of the factual and the plausible. But the logical
and non-logical distinction lead to abominations
like ascribing to Wittgenstein the maxim,

"All logical differences are big differences", with
the even worse conjecture "All nonlogical differences
are small differences". But an early conceptual

prototype of ChatGPT was given by:

"Mirror (**) Mirror on the Wall who is the Fairest of them All?"
- Snow White, Brothers Grim

So its all about retrieving mirror texts and images and
transforming them, the retrieval having good old metrics like
recall and precision, and the transformation having also metrics,

metrics all relative to a group preferences assumption of
the end-user, so that the end-user can more cost effictively
and more market penetratingly act, in a totally

new AI Boom infected environment.

Bye

(*)
we have powers and faculties fitted to deal with
them, and are **happy or miserable** in proportion
as we know how to **frame** a right judgment of things
The elements of logic. In four books
by Duncan, William, 1717-1760 https://archive.org/details/elementsoflogic00dunc/page/n5/mode/2up

(**)

An earlier version of "Mirrors" (Chapter 7) was written for a
volume in honor of Thomas A. Sebeok (He was among the
founders of biosemiotics, and coined the term "zoosemiotics"
in 1963 to describe the development of signals and signs by
non-human animal species) for his sixty-fifth birthday.
Umberto Eco, ''Semiotics and the Philosophy of Language'',
Bloomington: Indiana U.P., 1984
https://monoskop.org/images /b/b3/Eco_Umberto_Semiotics_and_the_Philosophy_of_Language_1986.pdf

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

On 12/08/2025 06:54 AM, Mild Shock wrote:

Hi,

Current AI is split between two worlds that don't play well together:

Deep Learning (neural networks, transformers, ChatGPT) - great at
learning from data, terrible at logical reasoning
Symbolic AI (logic programming, expert systems) - great at logical
reasoning, terrible at learning from messy real-world data

Tensor Logic unifies both. It's a single language where you can:
Write logical rules that the system can actually learn and modify
Do transparent, verifiable reasoning (no hallucinations)
Mix "fuzzy" analogical thinking with rock-solid deduction

The Killer Feature: The Temperature Knob

https://www.youtube.com/watch?v=4APMGvicmxY

Bye

Mild Shock schrieb:

Hi,

The English had Aristoteles (*), the French had
Descartes, and the Dutch have their national
Flag. The culmination of the Enlighment was

the distinction between analytic and synthetic
truth. But this doesn't help to understand
Generative AI, which produces a mish mash

of the factual and the plausible. But the logical
and non-logical distinction lead to abominations
like ascribing to Wittgenstein the maxim,

"All logical differences are big differences", with
the even worse conjecture "All nonlogical differences
are small differences". But an early conceptual

prototype of ChatGPT was given by:

"Mirror (**) Mirror on the Wall who is the Fairest of them All?"
- Snow White, Brothers Grim

So its all about retrieving mirror texts and images and
transforming them, the retrieval having good old metrics like
recall and precision, and the transformation having also metrics,

metrics all relative to a group preferences assumption of
the end-user, so that the end-user can more cost effictively
and more market penetratingly act, in a totally

new AI Boom infected environment.

Bye

(*)
we have powers and faculties fitted to deal with
them, and are **happy or miserable** in proportion
as we know how to **frame** a right judgment of things
The elements of logic. In four books
by Duncan, William, 1717-1760
https://archive.org/details/elementsoflogic00dunc/page/n5/mode/2up

(**)

An earlier version of "Mirrors" (Chapter 7) was written for a
volume in honor of Thomas A. Sebeok (He was among the
founders of biosemiotics, and coined the term "zoosemiotics"
in 1963 to describe the development of signals and signs by
non-human animal species) for his sixty-fifth birthday.
Umberto Eco, ''Semiotics and the Philosophy of Language'',
Bloomington: Indiana U.P., 1984
https://monoskop.org/images
/b/b3/Eco_Umberto_Semiotics_and_the_Philosophy_of_Language_1986.pdf

Wittgenstein's an inconstant flake. He'll say anything.


Tensors are a cop-out, it's like "what are tensors, really",
and getting "what do you want them to be". Then, making
all manner or projections and perspective and the affine
and the linear and showing they implement tensors tends
to get "I don't know those". "How about matroids,
how about the convolutive, how about all these implementations
of tensorial products and about their inner and outer forms",
and getting "can you write it in squares and triangles".

Then the linear quite simply and the various examples
that bridge the analytical bridges the linear and non-linear,
and real-valued and not-the-complete-ordered-field-real-valued,
about the "un-linear", has Lagrange laughing up his sleeve,
saying "my anti-reductionism is an effective reductionism".


And it's like, yeah, Lagrange, here's a monomode process,
now it's "tensors" and it's a Lagrangian. Then he's like
"I can't not do that".


Tensorial products just resulting the vectorial somehow,
_that being their definition_, isn't that un-usual a thing.


--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

Hi,

Since AI passed the Turing Test in 2022.
Mathematician are devicing the Birch++-Test:

Professor Yang-Hui He discusses the murmuration
conjecture, shows how DeepMind, OpenAI, and EpochAI
are rewriting the rules of pure math, and reveals
what happens when machines start making research-
level discoveries faster than any human could. AI
is taking us beyond proof straight into the future
of discovery. Get ready to witness a turning point
in mathematical history: in this episode, we dive
into the AI breakthroughs that stunned number
theorists worldwide.

The AI Math That Left Number Theorists Speechless https://www.youtube.com/watch?v=spIquD_mBFk

Bye

Mild Shock schrieb:

Hi,

Current AI is split between two worlds that don't play well together:

Deep Learning (neural networks, transformers, ChatGPT) - great at
learning from data, terrible at logical reasoning
Symbolic AI (logic programming, expert systems) - great at logical reasoning, terrible at learning from messy real-world data

Tensor Logic unifies both. It's a single language where you can:
Write logical rules that the system can actually learn and modify
Do transparent, verifiable reasoning (no hallucinations)
Mix "fuzzy" analogical thinking with rock-solid deduction

The Killer Feature: The Temperature Knob

https://www.youtube.com/watch?v=4APMGvicmxY

Bye

Mild Shock schrieb:

Hi,

The English had Aristoteles (*), the French had
Descartes, and the Dutch have their national
Flag. The culmination of the Enlighment was

the distinction between analytic and synthetic
truth. But this doesn't help to understand
Generative AI, which produces a mish mash

of the factual and the plausible. But the logical
and non-logical distinction lead to abominations
like ascribing to Wittgenstein the maxim,

"All logical differences are big differences", with
the even worse conjecture "All nonlogical differences
are small differences". But an early conceptual

prototype of ChatGPT was given by:

"Mirror (**) Mirror on the Wall who is the Fairest of them All?"
- Snow White, Brothers Grim

So its all about retrieving mirror texts and images and
transforming them, the retrieval having good old metrics like
recall and precision, and the transformation having also metrics,

metrics all relative to a group preferences assumption of
the end-user, so that the end-user can more cost effictively
and more market penetratingly act, in a totally

new AI Boom infected environment.

Bye

(*)
we have powers and faculties fitted to deal with
them, and are **happy or miserable** in proportion
as we know how to **frame** a right judgment of things
The elements of logic. In four books
by Duncan, William, 1717-1760
https://archive.org/details/elementsoflogic00dunc/page/n5/mode/2up

(**)

An earlier version of "Mirrors" (Chapter 7) was written for a
volume in honor of Thomas A. Sebeok (He was among the
founders of biosemiotics, and coined the term "zoosemiotics"
in 1963 to describe the development of signals and signs by
non-human animal species) for his sixty-fifth birthday.
Umberto Eco, ''Semiotics and the Philosophy of Language'',
Bloomington: Indiana U.P., 1984
https://monoskop.org/images
/b/b3/Eco_Umberto_Semiotics_and_the_Philosophy_of_Language_1986.pdf

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

Hi,

Its seems I shocked people when I was talking about
neurons on the head of a Turing Machine. Did
I go mad? Halcuinating like on LSD?

Unfortunately not:

A provably stable neural network Turing Machine https://arxiv.org/abs/2006.03651

Currently I would like to be able to add a simple
stack to a NPU, for a problem I have. Somehow
I have the feeling its doable via softmax,

in a limited way. Any ideas?

Bye

P.S.: A stack is possibly the simples form
of a turing machine tape. The turing machine
would be restricted so that head movements

only correspond to push and pop. Quiet amazing!

Mild Shock schrieb:

Hi,

Since AI passed the Turing Test in 2022.
Mathematician are devicing the Birch++-Test:

Professor Yang-Hui He discusses the murmuration
conjecture, shows how DeepMind, OpenAI, and EpochAI
are rewriting the rules of pure math, and reveals
what happens when machines start making research-
level discoveries faster than any human could. AI
is taking us beyond proof straight into the future
of discovery. Get ready to witness a turning point
in mathematical history: in this episode, we dive
into the AI breakthroughs that stunned number
theorists worldwide.

The AI Math That Left Number Theorists Speechless https://www.youtube.com/watch?v=spIquD_mBFk

Bye

Mild Shock schrieb:

Hi,

Current AI is split between two worlds that don't play well together:

Deep Learning (neural networks, transformers, ChatGPT) - great at
learning from data, terrible at logical reasoning
Symbolic AI (logic programming, expert systems) - great at logical
reasoning, terrible at learning from messy real-world data

Tensor Logic unifies both. It's a single language where you can:
Write logical rules that the system can actually learn and modify
Do transparent, verifiable reasoning (no hallucinations)
Mix "fuzzy" analogical thinking with rock-solid deduction

The Killer Feature: The Temperature Knob

https://www.youtube.com/watch?v=4APMGvicmxY

Bye

Mild Shock schrieb:

Hi,

The English had Aristoteles (*), the French had
Descartes, and the Dutch have their national
Flag. The culmination of the Enlighment was

the distinction between analytic and synthetic
truth. But this doesn't help to understand
Generative AI, which produces a mish mash

of the factual and the plausible. But the logical
and non-logical distinction lead to abominations
like ascribing to Wittgenstein the maxim,

"All logical differences are big differences", with
the even worse conjecture "All nonlogical differences
are small differences". But an early conceptual

prototype of ChatGPT was given by:

"Mirror (**) Mirror on the Wall who is the Fairest of them All?"
- Snow White, Brothers Grim

So its all about retrieving mirror texts and images and
transforming them, the retrieval having good old metrics like
recall and precision, and the transformation having also metrics,

metrics all relative to a group preferences assumption of
the end-user, so that the end-user can more cost effictively
and more market penetratingly act, in a totally

new AI Boom infected environment.

Bye

(*)
we have powers and faculties fitted to deal with
them, and are **happy or miserable** in proportion
as we know how to **frame** a right judgment of things
The elements of logic. In four books
by Duncan, William, 1717-1760
https://archive.org/details/elementsoflogic00dunc/page/n5/mode/2up

(**)

An earlier version of "Mirrors" (Chapter 7) was written for a
volume in honor of Thomas A. Sebeok (He was among the
founders of biosemiotics, and coined the term "zoosemiotics"
in 1963 to describe the development of signals and signs by
non-human animal species) for his sixty-fifth birthday.
Umberto Eco, ''Semiotics and the Philosophy of Language'',
Bloomington: Indiana U.P., 1984
https://monoskop.org/images
/b/b3/Eco_Umberto_Semiotics_and_the_Philosophy_of_Language_1986.pdf

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

On 12/10/2025 2:55 PM, Mild Shock wrote:

Hi,

Since AI passed the Turing Test in 2022.
Mathematician are devicing the Birch++-Test:

Professor Yang-Hui He discusses the murmuration
conjecture, shows how DeepMind, OpenAI, and EpochAI
are rewriting the rules of pure math, and reveals
what happens when machines start making research-
level discoveries faster than any human could. AI
is taking us beyond proof straight into the future
of discovery. Get ready to witness a turning point
in mathematical history: in this episode, we dive
into the AI breakthroughs that stunned number
theorists worldwide.

The AI Math That Left Number Theorists Speechless https://www.youtube.com/watch?v=spIquD_mBFk

Bye

30 years ago I didn't expect to see
computers passing the test - well,
it is passed. But leaving theorists
speechless or rewriting the rules
of pure math - is a bold exaggeration.

Thinking is not a mathematical process,
it never was and ai hasn't changed that.

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

Hi,

Thinking is not a mathematical process,
it never was and ai hasn't changed that.

Thats not very deep. Could you elobarate?

Bye

Maciej Wo+|niak schrieb:

On 12/10/2025 2:55 PM, Mild Shock wrote:

Hi,

Since AI passed the Turing Test in 2022.
Mathematician are devicing the Birch++-Test:

Professor Yang-Hui He discusses the murmuration
conjecture, shows how DeepMind, OpenAI, and EpochAI
are rewriting the rules of pure math, and reveals
what happens when machines start making research-
level discoveries faster than any human could. AI
is taking us beyond proof straight into the future
of discovery. Get ready to witness a turning point
in mathematical history: in this episode, we dive
into the AI breakthroughs that stunned number
theorists worldwide.

The AI Math That Left Number Theorists Speechless
https://www.youtube.com/watch?v=spIquD_mBFk

Bye

30 years ago I didn't expect to see
computers passing the test - well,
it is passed. But leaving theorists
speechless-a or rewriting the rules
of pure math - is a bold exaggeration.

Thinking is not a mathematical process,
it never-a was and ai hasn't changed that.

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.physics

On 12/10/2025 3:16 PM, Mild Shock wrote:

Hi,

Thinking is not a mathematical process,
it never-a was and ai hasn't changed that.

Thats not very deep. Could you elobarate?

Generally thinking (in its usual, human
specific meaning) is processing text,
mathematics is a special case of thinking,
only working with the simplest terms and
simplified rules. Thanks to simplifying
a single brain can both comprehend it and
handle it successfully. With thinking
it's not that easy, it's designed for
parallel processing by many.

--- Synchronet 3.21a-Linux NewsLink 1.2