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interests / soc.culture.china / More philosophy about composability and the Heisenberg Uncertainty Principle and more..

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o More philosophy about composability and the Heisenberg UncertaintyWorld90

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

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From: m...@m.com (World90)
Newsgroups: soc.culture.china
Subject: More philosophy about composability and the Heisenberg Uncertainty
Principle and more..
Date: Wed, 26 May 2021 11:18:27 -0400
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 by: World90 - Wed, 26 May 2021 15:18 UTC

Hello,

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

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."

So i think i am smart and i will explain my new proverb above:

With pattern recognition of fluid intelligence we are finding
patterns that are the tools, and we are understanding and applying those
patterns that we are finding with fluid intelligence to
other many other new problems, so then we are not finding the patterns
again and again, so then we are not finding the tools that are those
patterns again and again, so pattern recognition of fluid intelligence
is a minimization process that permits to find a small number of tools
that permit to solve great number of problems. I think i am smart, and I
have to be more precise, so you have to understand that the minimization
process above is on the "finding", so when i say in my proverb above:
"Human smartness is finding a small number of tools that permit to solve
a great number of problems", the "finding a small number" is my good
abstract thinking and it means that it is a minimization process on the
"finding", since we are not "finding" the tools that are the patterns
again and again.

More of my philosophy about human fluid intelligence and smartness..

Human fluid intelligence involves being able to think and reason
abstractly and solve problems, so it needs abstract thinking and pattern
recognition, but we have to ask a philosophical question of:

What is abstract thinking in human fluid intelligence ?

So i will rapidly answer that abstract thinking is like
when in software programming we construct a "class" (that leads to an
object in runtime) and its attributes, it is like the way of
constructing a "concept" and knowing about what is its characteristics,
so abstract thinking is not finding a thing of a particular Husky Dog
and what is its characteristics, but it is finding the general concept
of a Dog and its attributes or characteristics, so i think that
good abstract thinking is much more powerful, so now i will ask a
philosophical question of:

What is the "relation" in human fluid intelligence between pattern
recognition and abstract thinking ?

I think that i have to define what is pattern recognition in human fluid
intelligence, so i will say the following:

Pattern recognition in human fluid intelligence is to recognize a
particular way in which something is done, is organized, or happens, so
i think that it is with this pattern recognition that we are able to
incrementally understand and we are able to construct concepts etc. so
this also permits to do abstract thinking in human fluid intelligence.


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interests / soc.culture.china / More philosophy about composability and the Heisenberg Uncertainty Principle and more..

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