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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."

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.

So i will give my example of pattern recognition with my fluid

intelligence that permits me to understand, here it is:

So if you want to go fast from my country Morocco to another country

called USA , how will you do it ? or what will you do ?

It is like my IQ test..

So if you answer that you need for example to use a fast airplane to go

fast from Morocco to USA, your answer is a stupid answer, so you need

the smart answer, so i will answer that the fast airplane too has to be

"reliable" and your "health" has too to permit it and the "weather" has

too to permit it, so now you are clearly noticing that you need to take

into account many "factors" so that to go fast from Morocco to USA, so

you are clearly noticing that being smart needs also a good plan

So that to understand more, let us say that you are measuring a human

IQ, so if it is high human IQ , this value is a measure that is relative

to the other human IQs, so you will say that this high IQ is much better

at adaptability than the other humans, but it is not correct measure,

because even science and technology have constraints that constrain(or

limit greatly) the expressiveness of human IQs, so then we can not say

that a high human IQ is better at adaptability than the other humans..

More philosophy about how to measure human IQ or human smartness..

I think i am smart, and i will talk about how to measure human IQs or

human smartness, first you have to know that you can measure relatively

or absolutely, so if you measure the IQ of a human, you will give a

value of IQ that is "relative" to the distribution of IQs of humans, so

can we ask if it is the right way to measure human IQs? i think it is

not, because there is a "very" important thing that is missing, and it

is that you have to also measure IQ or smartness relatively to the

"constraints" in our reality that constrain(or limit) human IQ or human

smartness, and i think this will give a much more realistic measure of

human IQs or human smartness, so if you are really smart you will start

by searching what are those constraints in the reality that constrain

human IQs or human smartness, because this way you will become really smart.

Let me give an example about how to measure IQs or smartness..

So if you are really smart you will give a smart example so that people

can understand, so here it is:

If i say: 2 + 2 = 4

So you will notice that this equality is also constrained by constraints

of reality, since for example you are noticing that it is not so

mathematically expressive, so this not mathematically expressive is also

constraining human IQ or human smartness, since if you understand and

learn this mathematical equality, another person will quickly do the

same, so the other person will adapt quickly to this level of smartness,

so now you are noticing the smart idea, it is that even science and

technology are constrained the same way, and this constraints on

science and technology constrain or limit the expressiveness of high

human IQs or high level of smartness so that other lower level human

IQs or smartness can attain the level of adaptability of high human

IQs, this is what is happening in our today world, and if you are smart

you will notice that there is something else that is happening and it

is that abstraction of complexity that reduce the complexity is making

others not understanding the complexity behind the abstraction and this

is not so efficient.

Here is more about the constraints on science and technology:

Is Science Going To End?

Read more here:

https://philosophynow.org/issues/68/Is_Science_Going_To_End

And read also the following

The Industrial Era Ended, and So Will the Digital Era

Read more here:

https://hbr.org/2018/07/the-industrial-era-ended-and-so-will-the-digital-era

More political philosophy about what is smartness..

I give you an example so that you understand:

If i give the following three words:

I, love, you.

It is not the same as if i give the following five words:

I, love, you, very, much

So you are noticing that the five words permit a more sophisticated

expressiveness, and notice that i am saying more sophisticated, since

the five words bring more efficiency, and this bringing more efficiency

is also what we call smartness, but notice that this smartness is

brought by using the "tool" that is composed of the five words, so the

tool that is our english language brings smartness, so then we have to

be convinced by the fact that the tool like internet brings a much more

efficiency and this much more efficiency brings much more smartness, so

now you are noticing that smartness is not only genetical or cultural,

but it is also the smartness of using the tool, and this is a very

important thing, since the tool can be powerful and it can advance very

much a human and can make a human really smart. So you have to

understand that we are also in an Era of powerful tools such as internet

that can advance very much a human and that can make a human really smart.

And i invite you to read my thoughts of my philosophy here:

https://groups.google.com/g/alt.culture.morocco/c/YZSYxV41-qI

Also i invite you to read more of my thoughts of my philosophy here:

https://groups.google.com/g/comp.programming.threads/c/OjDTCDiawJw

Also i invite you to read more of my thoughts of my philosophy here:

https://groups.google.com/g/alt.culture.morocco/c/ftf3lx5Rzxo

Thank you,

Amine Moulay Ramdane.

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."

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.

So i will give my example of pattern recognition with my fluid

intelligence that permits me to understand, here it is:

So if you want to go fast from my country Morocco to another country

called USA , how will you do it ? or what will you do ?

It is like my IQ test..

So if you answer that you need for example to use a fast airplane to go

fast from Morocco to USA, your answer is a stupid answer, so you need

the smart answer, so i will answer that the fast airplane too has to be

"reliable" and your "health" has too to permit it and the "weather" has

too to permit it, so now you are clearly noticing that you need to take

into account many "factors" so that to go fast from Morocco to USA, so

you are clearly noticing that being smart needs also a good plan

So that to understand more, let us say that you are measuring a human

IQ, so if it is high human IQ , this value is a measure that is relative

to the other human IQs, so you will say that this high IQ is much better

at adaptability than the other humans, but it is not correct measure,

because even science and technology have constraints that constrain(or

limit greatly) the expressiveness of human IQs, so then we can not say

that a high human IQ is better at adaptability than the other humans..

More philosophy about how to measure human IQ or human smartness..

I think i am smart, and i will talk about how to measure human IQs or

human smartness, first you have to know that you can measure relatively

or absolutely, so if you measure the IQ of a human, you will give a

value of IQ that is "relative" to the distribution of IQs of humans, so

can we ask if it is the right way to measure human IQs? i think it is

not, because there is a "very" important thing that is missing, and it

is that you have to also measure IQ or smartness relatively to the

"constraints" in our reality that constrain(or limit) human IQ or human

smartness, and i think this will give a much more realistic measure of

human IQs or human smartness, so if you are really smart you will start

by searching what are those constraints in the reality that constrain

human IQs or human smartness, because this way you will become really smart.

Let me give an example about how to measure IQs or smartness..

So if you are really smart you will give a smart example so that people

can understand, so here it is:

If i say: 2 + 2 = 4

So you will notice that this equality is also constrained by constraints

of reality, since for example you are noticing that it is not so

mathematically expressive, so this not mathematically expressive is also

constraining human IQ or human smartness, since if you understand and

learn this mathematical equality, another person will quickly do the

same, so the other person will adapt quickly to this level of smartness,

so now you are noticing the smart idea, it is that even science and

technology are constrained the same way, and this constraints on

science and technology constrain or limit the expressiveness of high

human IQs or high level of smartness so that other lower level human

IQs or smartness can attain the level of adaptability of high human

IQs, this is what is happening in our today world, and if you are smart

you will notice that there is something else that is happening and it

is that abstraction of complexity that reduce the complexity is making

others not understanding the complexity behind the abstraction and this

is not so efficient.

Here is more about the constraints on science and technology:

Is Science Going To End?

Read more here:

https://philosophynow.org/issues/68/Is_Science_Going_To_End

And read also the following

The Industrial Era Ended, and So Will the Digital Era

Read more here:

https://hbr.org/2018/07/the-industrial-era-ended-and-so-will-the-digital-era

More political philosophy about what is smartness..

I give you an example so that you understand:

If i give the following three words:

I, love, you.

It is not the same as if i give the following five words:

I, love, you, very, much

So you are noticing that the five words permit a more sophisticated

expressiveness, and notice that i am saying more sophisticated, since

the five words bring more efficiency, and this bringing more efficiency

is also what we call smartness, but notice that this smartness is

brought by using the "tool" that is composed of the five words, so the

tool that is our english language brings smartness, so then we have to

be convinced by the fact that the tool like internet brings a much more

efficiency and this much more efficiency brings much more smartness, so

now you are noticing that smartness is not only genetical or cultural,

but it is also the smartness of using the tool, and this is a very

important thing, since the tool can be powerful and it can advance very

much a human and can make a human really smart. So you have to

understand that we are also in an Era of powerful tools such as internet

that can advance very much a human and that can make a human really smart.

And i invite you to read my thoughts of my philosophy here:

https://groups.google.com/g/alt.culture.morocco/c/YZSYxV41-qI

Also i invite you to read more of my thoughts of my philosophy here:

https://groups.google.com/g/comp.programming.threads/c/OjDTCDiawJw

Also i invite you to read more of my thoughts of my philosophy here:

https://groups.google.com/g/alt.culture.morocco/c/ftf3lx5Rzxo

Thank you,

Amine Moulay Ramdane.

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