Hello,

More philosophy about "All models are wrong, but some are useful"..

I am a white arab and i think i am smart since i have also invented many
scalable algorithms and algorithms..

I invite you to read the following interesting article of Daniel Lemire,
he is a PhD researcher in in Engineering Mathematics and MSc in Mathematics:

All models are wrong

https://lemire.me/blog/2021/05/26/all-models-are-wrong/

You can read more about Daniel Lemire here(he is also a professor):

https://lemire.me/en/

So notice that the PhD above must be smart at around 140 or 145 IQ.

So i think i am smart and the PhD researcher above in Engineering
Mathematics is is also saying the following:

"All models are wrong, but some are useful"

So since i think i am smart i have just read rapidly the above article
and i will right now rapidly find a pattern with my fluid intelligence,
and it is the following:

Notice that a mathematical model can be a static system, and this
mathematical model can give a prediction and a result that is an
approximation that is useful, so i can say that i can like measure it
relatively or absolutely, i mean i can say like locally that since the
result of a mathematical model can be an approximation that is not the
exact result of the result of the reality, so then i can say that
locally i can say that a mathematical model is wrong on the exactitude
of the calculation of the result, but i can say more globally that since
the mathematical model can give a "useful" result that is a useful
approximation that permits us to predict, so then the functionality that
is predictive of the mathematical model is not wrong, so i can then say
globally that the mathematical model is not wrong. And this proves that
the following saying is wrong:

"All models are wrong, but some are useful"

Other than that he is saying in the above article the following:

"Pure logic, pure mathematics only works locally. It does not scale. It
does not mean that pure logic is ‘bad’, only that its application is
limited."

I then say that even if it Pure logic, pure mathematics doesn't scale,
we have to look at its weight of importance and usefulness,
so it can be that even if the mathematical model doesn't scale,
it can have a great weight of importance and a great usefulness.

Yet more philosophy about composability and the Heisenberg Uncertainty
Principle and more..

I invite you to read the following article about composability:

On Composability

https://bartoszmilewski.com/2020/05/22/on-composability/

I think the above article is not taking into account the following
new discovery about the Heisenberg Uncertainty Principle:

Evading the uncertainty principle in quantum physics

New technique gets around 100-year-old rule of quantum physics for the
first time

In quantum mechanics, the Heisenberg uncertainty principle dictates that
the position and speed of an object cannot both be known fully precisely
at the same time. Researchers now show that two vibrating drumheads, the
size of a human hair, can be prepared in a quantum state which evades
the uncertainty principle.

Read more here:

https://www.sciencedaily.com/releases/2021/05/210506142138.htm

And read the following interesting article about it:

Scientist find a loophole in Heisenberg's uncertainty principle

https://www.livescience.com/quantum-drum-duet-heisenberg-uncertainty-principle.html

More of my philosophy about inductive logic and more..

I am a white arab and i think i am smart since i have also invented many
scalable algorithms and algorithms..

I invite you to read the following article about Hume’s on inductive logic:

Problem of induction

https://www.britannica.com/topic/problem-of-induction

Read about David Hume philosopher here:

https://en.wikipedia.org/wiki/David_Hume

And i invite you to also read the following article about inductive
reasoning:

Be Humble: Black Swans and the Limits of Inductive Reasoning

https://www.datarobot.com/blog/be-humble-black-swans-and-the-limits-of-inductive-reasoning/

I will say that i am not in accordance with David Hume philosopher on
inductive logic, since notice that the above article is saying the
following about David Hume views on inductive reasoning:

"It is important to note that Hume did not deny that he or anyone else
formed beliefs on the basis of induction; he denied only that people
have any reason to hold such beliefs (therefore, also, no one can know
that any such belief is true)"

So i think that we have not to be "pessimistic" as David Hume
philosopher about inductive reasoning, since we have to distinguish
between the inductive reasoning that work and the inductive reasoning
that doesn't work correctly, so let me show you an example of inductive
reasoning that works, here it is: So to give an interesting example of
science of computing, we can ask: What is the time complexity of the
following binary search algorithm:

https://www.guru99.com/binary-search.html

And here is my mathematical calculations of its time complexity, so
notice that it uses inductive reasoning that works:

Recurrence relation of a binary search algorithm is: T(n)=T(n/2)+1

Because the "1" is like a comparison that we do in each step of
the divide and conquer method of the binary search algorithm.

So the calculation of the recurrence equation gives:

1st step=> T(n)=T(n/2) + 1

2nd step=> T(n/2)=T(n/4) + 1 ……[ T(n/4)= T(n/2^2) ]

3rd step=> T(n/4)=T(n/8) + 1 ……[ T(n/8)= T(n/2^3) ]

..

..

kth step=> T(n/2^k-1)=T(n/2^k) + 1*(k times)

Adding all the equations we get, T(n) = T(n/2^k) + k times 1

This is the final equation.

So how many times we need to divide by 2 until we have only one element
left?

So it must be:

n/2^k= 1

This gives: n=2^k

this give: log n=k [taken log(base 2) on both sides ]

Put k= log n in the final equation above and it gives:

T(n) = T(1) + log n

T(n) = 1 + log n [we know that T(1) = 1 , because it’s a base condition
as we are left with only one element in the array and that is the
element to be searched so we return 1]

So it gives:

T(n) = O(log n) [taking dominant polynomial, which is n here)

This is how we got “log n” time complexity for binary search.

More philosophy of what is philosophy..

I think i am a philosopher that is smart, so i will explain what is
philosophy, philosophy is by logical analogy like software engineering
(and read about software engineering in my thoughts below), i mean that
it is a high level knowledge and a high level view of the "way", for
example philosophy is the "way" of how do we have to behave as a society
or a global world, also you will notice that philosophy doesn't get into
the details as is getting science into the the much details, so this
proves that it is a high level knowledge, but more than that philosophy
can also give the high level way to science so that science gets into
the much details, so i think i am a philosopher that is smart and i am
like feeling more deeply philosophy and finding patterns of philosophy
with my fluid intelligence, so i am still inventing thoughts of
philosophy, so i invite you to read all my thoughts of my philosophy
below so that to understand my philosophy:

More philosophy about software engineering and about computer science..

I will ask a philosophical question of:

What is software engineering and what is computer science ?

I think i am smart and i will answer that it is related to abstract
thinking and pattern recognition of human fluid intelligence,
since software engineering is about the high level knowledge,
i mean that it deals with such high level things as concepts
and there relationships, connections, and context..., so in software
engineering the most important thing is like abstract thinking , but
it can use sophisticated pattern recognition of fluid intelligence, so
it also uses high pure smartness, and this abstract thinking of software
engineering doesn't get into the "details" as is getting computer
science, so computer science gets into the much details, so software
engineering is like mathematical modeling that is also a science, but
computer science is "hard" science.

And to know more about mathematical modeling, i invite you to
look at my following software project of PDQ for Delphi and Freepascal
and it is my port of PDQ version 6.2.0 to Delphi on Windows and to
Freepascal on both Windows and Linux, i have also provided you with two
demos, one queuing MM1 demo, and another Jackson network demo. Also i
have provided you with my html tutorial on how to solve analytically(by
using mathematical modeling) the Jackson network problem provided to you
as a PDQ demo, and here it is so that to know what is mathematical modeling:

https://sites.google.com/site/scalable68/pdq-for-delphi-and-freepascal

More of my explanation of my just new proverb about fluid intelligence
and smartness..

Here is my just new proverb:

"Human smartness is finding a small number of tools that permit to
solve a great number of problems, so when you look carefully at what is
human smartness you will notice that it is not about great quantity, it
is about a small quantity of good quality that permits us to be so
powerful. Being smart is not about quantity, it is much more about quality."