From Newsgroup: comp.lang.prolog

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
https://swi-prolog.discourse.group/t/prolog-and-llms-genai/8699

Guess what my new ThinkCenter, 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 user interface, but can dedicatedly
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

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

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
https://swi-prolog.discourse.group/t/prolog-and-llms-genai/8699

Guess what my new ThinkCenter, 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 user interface, but can dedicatedly
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

--- Synchronet 3.21a-Linux NewsLink 1.2