From Newsgroup: sci.physics

Hi,

Notably we try to do something with AI laptops
that came out in 2025. Tapping into their NPU.
Back in the late 80's GigaLIP were rather

hypothetical, small machines could hardly
do KLIPs. Today small machines do easily MLIPs.
But hardly any popular Prolog systems already taps

into the AI Boom, they are all Sleepy Joes.
Not to mention populate by morons like Boris the
Loris and Nazi Retard Julio.

They simply cannot connect the dots.

Bye

P.S.: Nice travel to the past is this paper:

Is a GigaLIP Fast Enough?
TOM W. KELLER - December 1988
DOI: 10.1007/BF00436711

Mild Shock schrieb:

Hi,

I am 100% serious about Giga Logical Inferences
per Second (GLIPS). Leaving behind the sequential
constraint solving world:

The Complexity of Constraint Satisfaction Revisited https://www.cs.ubc.ca/~mack/Publications/AIP93.pdf

Only I have missed the deep learning bandwagon,
never programmed with PyTorch or Keras. So even
for the banal problem of coding some

ReLU networks and shipping them to a GPU or NPU,
or a hybrid, I don't have much experience. So
I am marveling at papers such as:

Learning Variable Ordering Heuristics
for Solving Constraint Satisfaction Problems
https://arxiv.org/abs/1912.10762

Given that the AI Boom started after 2019,
the above paper is already old, and it has
currious antique terminology like Multilayer

Perceptron, which is not so common anymore?
It does also more than what I want to demonstrate,
it does also do policy learning.

Bye

Mild Shock schrieb:

Hi,

I am spekulating an NPU could give 1000x more LIPS.
For certain combinatorial search problems. It all
boils down to implement this thingy:

In June 2020, Stockfish introduced the efficiently
updatable neural network (NNUE) approach, based
on earlier work by computer shogi programmers
https://en.wikipedia.org/wiki/Stockfish_%28chess%29

There are varying degrees what gets updated of
a neural network. But the specs of an NPU tell
me very simply the following:

- An NPU can make 40 TFLOPS, all my AI Laptops
-a-a from 2025 can do that right now. The brands
-a-a are Intel Ultra, AMD Ryzen and Snapdragon X,

-a-a but I guess there might be more brands around,
-a-a which can do that with a price tag less
-a-a than 1000.- USD.

- SWI Prolog can make 30 MLIPS, Dogelog Player
-a-a runs similar, some Prolog systems are faster.

Now thats is 10^12 versus 10^6. If some of the
LIPS can be delegated to a NPU, and if we assume
for example less locality or more primitive

operations that require a layering. Would could assume
that from the NPU 10^12 a factor of 1000 goes
away. So we might still see 10'9 LIPS emerge.

Now make the calculation:

- Without NPU: MLIPS
- With NPU: GLIPS
- Ratio: 1000x times faster

Have fun!

Bye

Mild Shock schrieb:

Hi,

So Boris the Loris and Nazi Retartd Julio are
not alone. There is now a mobilization of the
kind of rage against the machine,

fighting for methods without randomness. Its
almost like-a Albert Einstein ascendet from his
grave and is now preaching,

"God does not play dice"

So how it started:

PIVOT was an interactive program verifier designed by
L. Peter Deutsch for his Ph.D. dissertation.
Posted here by permission of L. Peter Deutsch.
https://softwarepreservation.computerhistory.org/pivot/

How its going:

Formal Methods: Whence and Whither?
The text also highlights the evolving role of formal
methods amidst technological advancements, such as
AI, and explores educational and standardization issues
related to their adoption.
https://de.slideshare.net/slideshow/formal-methods-whence-and-whither-keynote/273708245


Can the Don Quijotes win, and fight the AI windmills?

LoL

Bye

Mild Shock schrieb:

Hi,

Boris the Loris and Julio Di Egidio the Nazi Retard,
are going for an afterwork beer. They are still
highly confused by Fuzzy Testing:

Star Trek - The 70's Disco Generation
https://www.youtube.com/watch?v=505zvAvnreg

The favorite hangout is Spock's Logic Dancefloor,
which is known for its sharp unfuzzy wit. They
have-a a chat with Data about Disco Math,

the only Math which has no Fuzzy Logic in it.

Bye

Mild Shock schrieb:

Hi,

Candidate Recommendation Draft - 30 September 2025
https://www.w3.org/TR/webnn

WebNN samples by Ningxin Hu, Intel, Shanghai
https://github.com/webmachinelearning/webnn-samples

Bye

Mild Shock schrieb:

Hi,

It seems I am having problems pacing with
all the new fancy toys. Wasn't able to really
benchmark my NPU from a Desktop AI machine,

picked the wrong driver. Need to try again.
What worked was benchmarking Mobile AI machines.
I just grabbed Geekbench AI and some devices:

USA Fab, M4:

-a-a-a-a sANN-a-a-a hANN-a-a-a qANN
iPad CPU-a-a-a 4848-a-a-a 7947-a-a-a 6353
iPad GPU-a-a-a 9752-a-a-a 11383-a-a-a 10051
iPad NPU-a-a-a 4873-a-a-a 36544-a-a-a *51634*

China Fab, Snapdragon:

-a-a-a-a sANN-a-a-a hANN-a-a-a qANN
Redmi CPU-a-a-a 1044-a-a-a 950-a-a-a 1723
Redmi GPU-a-a-a 480-a-a-a 905-a-a-a 737
Redmi NNAPI-a-a-a 205-a-a-a 205-a-a-a 469
Redmi QNN-a-a-a 226-a-a-a 226-a-a-a *10221*

Speed-Up via NPU is factor 10x. See the column
qANN which means quantizised artificial neural
networks, when NPU or QNN is picked.

The mobile AI NPUs are optimized using
mimimal amounts of energy, and minimal amounts
of space squeezing (distilling) everything

into INT8 and INT4.

Bye

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