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May 30, 2021, 7:47:54 PM5/30/21

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Hello,

More philosophy about what is artificial intelligence and more..

I am a white arab, and i think i am smart since i have also invented

many scalable algorithms and algorithms, and when you are smart you will

easily understand artificial intelligence, this is why i am finding

artificial intelligence easy to learn, i think to be able to understand

artificial intelligence you have to understand reasoning with energy

minimization, like with PSO(Particle Swarm Optimization), but

you have to be smart since the Population based algorithm has to

guarantee the optimal convergence, and this is why i am learning

you how to do it(read below), i think that GA(genetic algorithm) is

good for teaching it, but GA(genetic algorithm) doesn't guarantee the

optimal convergence, and after learning how to do reasoning with energy

minimization in artificial intelligence, you have to understand what is

transfer learning in artificial intelligence with PathNet or such, this

transfer learning permits to train faster and require less labeled data,

also PathNET is much more powerful since also it is higher level

abstraction in artificial intelligence..

Read about it here:

https://mattturck.com/frontierai/

And read about PathNet here:

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

More about artificial intelligence..

I think one of the most important part in artificial intelligence is

reasoning with energy minimization, it is the one that i am working on

right now, see the following video to understand more about it:

Yann LeCun: Can Neural Networks Reason?

https://www.youtube.com/watch?v=YAfwNEY826I&t=250s

I think that since i have just understood much more artificial

intelligence, i will soon show you my next Open source software project

that implement a powerful Parallel Linear programming solver and a

powerful Parallel Mixed-integer programming solver with Artificial

intelligence using PSO, and i will write an article that explain

much more artificial intelligence and what is smartness and what is

consciousness and self-awareness..

And in only one day i have just learned "much" more artificial

intelligence, i have read the following article about Particle Swarm

Optimization and i have understood it:

Artificial Intelligence - Particle Swarm Optimization

https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization

But i have just noticed that the above implementation doesn't guarantee

the optimal convergence.

So here is how to guarantee the optimal convergence in PSO:

Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient

parameter selection guidelines in order to guarantee the optimal

convergence, here is how to do it with PSO:

v(t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))]

x(t+1) = x(t) + v(t+1)

constriction coefficient parameter is:

k = 2/abs(2-phi-sqrt(phi^2-(4*phi)))

k:=2/abs((2-4.1)-(0.640)) = 0.729

phi = c1 + c2

To guarantee the optimal convergence use:

c1 = c2 = 2.05

phi = 4.1 => k equal to 0.729

w=0.7298

Population size = 60;

Also i have noticed that GA(genetic algorithm) doesn't guarantee the

optimal convergence, and SA(Simulated annealing) and Hill Climbing are

much less powerful since they perform only exploitation.

In general, any metaheuristic should perform two main searching

capabilities (Exploration and Exploitation). Population based algorithms

( or many solutions ) such as GA, PSO, ACO, or ABC, performs both

Exploration and Exploitation, while Single-Based Algorithm such as

SA(Simulated annealing), Hill Climbing, performs the exploitation only.

In this case, more exploitation and less exploration increases the

chances for trapping in local optima. Because the algorithm does not

have the ability to search in another position far from the current best

solution ( which is Exploration).

Simulated annealing starts in one valley and typically ends in the

lowest point of the same valley. Whereas swarms start in many different

places of the mountain range and are searching for the lowest point in

many valleys simultaneously.

And in my next Open source software project i will implement a powerful

Parallel Linear programming solver and a powerful Parallel Mixed-integer

programming solver with Artificial intelligence using PSO.

Thank you,

Amine Moulay Ramdane.

More philosophy about what is artificial intelligence and more..

I am a white arab, and i think i am smart since i have also invented

many scalable algorithms and algorithms, and when you are smart you will

easily understand artificial intelligence, this is why i am finding

artificial intelligence easy to learn, i think to be able to understand

artificial intelligence you have to understand reasoning with energy

minimization, like with PSO(Particle Swarm Optimization), but

you have to be smart since the Population based algorithm has to

guarantee the optimal convergence, and this is why i am learning

you how to do it(read below), i think that GA(genetic algorithm) is

good for teaching it, but GA(genetic algorithm) doesn't guarantee the

optimal convergence, and after learning how to do reasoning with energy

minimization in artificial intelligence, you have to understand what is

transfer learning in artificial intelligence with PathNet or such, this

transfer learning permits to train faster and require less labeled data,

also PathNET is much more powerful since also it is higher level

abstraction in artificial intelligence..

Read about it here:

https://mattturck.com/frontierai/

And read about PathNet here:

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

More about artificial intelligence..

I think one of the most important part in artificial intelligence is

reasoning with energy minimization, it is the one that i am working on

right now, see the following video to understand more about it:

Yann LeCun: Can Neural Networks Reason?

https://www.youtube.com/watch?v=YAfwNEY826I&t=250s

I think that since i have just understood much more artificial

intelligence, i will soon show you my next Open source software project

that implement a powerful Parallel Linear programming solver and a

powerful Parallel Mixed-integer programming solver with Artificial

intelligence using PSO, and i will write an article that explain

much more artificial intelligence and what is smartness and what is

consciousness and self-awareness..

And in only one day i have just learned "much" more artificial

intelligence, i have read the following article about Particle Swarm

Optimization and i have understood it:

Artificial Intelligence - Particle Swarm Optimization

https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization

But i have just noticed that the above implementation doesn't guarantee

the optimal convergence.

So here is how to guarantee the optimal convergence in PSO:

Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient

parameter selection guidelines in order to guarantee the optimal

convergence, here is how to do it with PSO:

v(t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))]

x(t+1) = x(t) + v(t+1)

constriction coefficient parameter is:

k = 2/abs(2-phi-sqrt(phi^2-(4*phi)))

k:=2/abs((2-4.1)-(0.640)) = 0.729

phi = c1 + c2

To guarantee the optimal convergence use:

c1 = c2 = 2.05

phi = 4.1 => k equal to 0.729

w=0.7298

Population size = 60;

Also i have noticed that GA(genetic algorithm) doesn't guarantee the

optimal convergence, and SA(Simulated annealing) and Hill Climbing are

much less powerful since they perform only exploitation.

In general, any metaheuristic should perform two main searching

capabilities (Exploration and Exploitation). Population based algorithms

( or many solutions ) such as GA, PSO, ACO, or ABC, performs both

Exploration and Exploitation, while Single-Based Algorithm such as

SA(Simulated annealing), Hill Climbing, performs the exploitation only.

In this case, more exploitation and less exploration increases the

chances for trapping in local optima. Because the algorithm does not

have the ability to search in another position far from the current best

solution ( which is Exploration).

Simulated annealing starts in one valley and typically ends in the

lowest point of the same valley. Whereas swarms start in many different

places of the mountain range and are searching for the lowest point in

many valleys simultaneously.

And in my next Open source software project i will implement a powerful

Parallel Linear programming solver and a powerful Parallel Mixed-integer

programming solver with Artificial intelligence using PSO.

Thank you,

Amine Moulay Ramdane.

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