Hello,

Read again, i correct about more philosophy about Swarm intelligence and PSO and artificial intelligence..

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

Can we ask a philosophical question:

Is Swarm intelligence like PSO(Particle Swarm Optimization) a brute-force general problem-solving technique and what is it ?

So i invite you first to read the following interesting article about
artificial intelligence of PSO(Particle Swarm Optimization):
https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization

And read my following thoughts about artificial intelligence:

https://groups.google.com/g/alt.culture.morocco/c/BKGvkTI5FY4

So I think i am smart and i will say that since artificial intelligence of PSO(Particle Swarm Optimization) is guided by not only exploration but by the mechanism of exploitation, so this shows that this mechanism of exploitation guides the problem-solving technique, so we can
not say that Swarm intelligence of PSO is a brute-force general problem-solving technique, since brute-force general problem-solving technique and algorithmic paradigm consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem's statement, but notice that brute-force general problem-solving technique is not guided by the quality of exploitation of artificial intelligence of PSO(Particle Swarm Optimization), and i can say that the artificial intelligence of PSO(Particle Swarm Optimization) is Swarm intelligence that is more general that the
particular case of the divide and conquer algorithms, since in the
divide and conquer algorithms we are guided by the divide mechanism that is the "exploration" that finds a solution and after that there is an exploitation of this solution of this quality that is the already sorted small arrays in the case of mergesort that will permit the recursive merge algorithm to reduce the time complexity to n*log(n), so i think it is the same for PathNet in artificial intelligence that i also think uses a kind of divide and conquer algorithm.

Here is PathNet in artificial intelligence, read carefully about it here:

https://medium.com/@thoszymkowiak/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46

More philosophy about declarative programming and imperative programming and object oriented programming..

I invite you to read the following interesting webpage from wikipedia
about declarative programming:

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

I think that declarative programming is too much abstraction
since Functional programming is declarative programming that attempts to minimize or eliminate side effects by describing what the program must accomplish in terms of the problem domain, rather than describe how to accomplish it as a sequence of the programming language primitives (the how being left up to the language's implementation). This is in contrast with imperative programming, which implements algorithms in explicit steps. So declarative programming like functional programming generally uses higher levels of abstractions than imperative programs or OOP. That is, they’re further away from describing what’s actually happening in the computer or lower level layers of programming, so i think that declarative programming like functional programming is too much abstraction that lacks understanding of the lower level layers of programming that is also very important to know so that to become efficient, so i think that this way of too much abstraction or/and too much centralization of cloud computing like of Amazon or functional programming and such look like the old way of too much centralization and too much abstraction of the mainframe computer that was lacking on efficiency, so then we have to know how to "balance" so that to be efficient.

More philosophy about functional programming and more..

I think that the Delphi List Monad and the Delphi Maybe Monad that i am giving below are not slower and they are working correctly.

I think i am smart and i understand the objections of neo-Nazism and white supremacism, but i think that neo-nazis and white supremacists are not thinking correctly, because there way of thinking there racial superiority is not the correct way simply because they are not taking
correctly into account the exponential progress of our humanity,
since i am convinced that with this exponential progress of our humanity
we will soon (in about 20 years or 30 years from now) be able to enhance much more our genetics and become much more smart
or much more beautiful and we will soon be able to do much more
than that because we will soon become so powerful because of this exponential progress of our humanity, so this is why i am not in accordance with white supremacism and neo-nazism since they have to adapt to this exponential progress of our humanity.

More philosophy about the exponential progress and about artificial intelligence..

I think i am smart and i think that we are going to become so powerful soon, i mean that this exponential progress of our humanity is an amazing thing, and i think that with this exponential progress, artificial intelligence too will soon become so powerful soon, so i think that the most important thing now is not to ask if we are going to become so powerful soon, but the most important thing it is that we have to ensure high "safety" or high "reliability" of this process of becoming so powerful by for example being a sophisticated philosophy. And i invite you to look at the following video so that to understand this exponential progress of our humanity:

Exponential Progress: Can We Expect Mind-Blowing Changes In The Near Future

https://www.youtube.com/watch?v=HfM5HXpfnJQ&t=144s

More philosophy about artificial intelligence and about the objective function that guides..

I think i am smart, and i have quickly thought about the objective
function of artificial intelligence that guides, so the important philosophical question in artificial intelligence is:

Is general artificial intelligence possible and what is the the objective function that guides general artificial intelligence ?

I think that the objective function of general artificial intelligence
is not the same as a gradient descent with wich we optimize, since i think that the objective function in artificial intelligence is
that we have to build like a model that approximate the result
of the reality, i mean that the objective function of general artificial intelligence has to be that we measure the meaning with the previous meanings from the data, and this way we approximate the meaning, so we have to build much more meanings and meaning from the data and we have to measure the unknown meaning with the previous meanings and if it doesn't approximate well, so it is like a baby that doesn't know about the meaning and it can become a dangerous environment for the baby like for general artificial intelligence, and my way is also valid in a distributed environment with many artificial intelligence robots, so i think that the more localized artificial intelligence will become incrementally and rapidly a more and more generalized artificial intelligence that can approximate a general artificial intelligence, so i think that general artificial intelligence is possible.

More philosophy about Swarm intelligence and about the too much abstraction and more..

I invite you to read about Swarm intelligence here:

How Swarm Intelligence Is Making Simple Tech Much Smarter

https://singularityhub.com/2018/02/08/how-swarm-intelligence-is-making-simple-tech-much-smarter/

I think that humanity is using "specialization" and the "division" of labour that reduces complexity so that to be efficient, so our evolution looks like Swarm intelligence algorithms, since Swarm intelligence algorithms also have to both explore with a bigger size of the population of the Swarm intelligence algorithms and have to do exploitation so that to reduce complexity and be efficient, and this bigger size of of the population in Swarm intelligence algorithms that explore is the division of labour that we use and it is also a specialization and it looks like a divide and conquer algorithms, since notice in the mergesort algorithm that we are reducing the time complexity from for example a time complexity of an n^2 sorting algorithm to n*log(n), and you are noticing it clearly since the mergesort algorithm reduces much more the time complexity by recursively "dividing" the array of elements so that to obtain a small arrays that are already sorted(or we can sort the small arrays of 10 elements by using an algorithm of sorting of n^2 and it will be more efficient), and notice that when we obtain those small arrays that are already sorted, it is what reduces the complexity and brings much more efficiency, since we are recursively using the merge algorithm after that, and notice that the mergesort is also like Swarm intelligence since when we recursively divide and obtain the already sorted arrays, notice that this recursively dividing leads to many already sorted arrays, and it is like the bigger size of the population in Swarm intelligence that explore and that leads to high efficiency, so i think that artificial
intelligence must be like a divide and conquer algorithm, so you divide like in a Divide and conquer algorithm so that to obtain a high quality
or high efficiency since you reduce complexity, so it looks
like PathNet in artificial intelligence, read my following thoughts about it: