• More of my philosophy of what is human self-awareness and human awarene

    From World-News2100@21:1/5 to All on Mon Sep 13 17:12:17 2021
    Hello...


    More of my philosophy of what is human self-awareness and human awareness...

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

    So i think i am really smart and i will explain:

    So i will start by asking a philosophical question of:

    Is human self-awareness and awareness an emergence and what is it ?

    So i will explain my findings:

    I think i have found the first smart pattern with my fluid intelligence
    and i found also the rest and it is the following:

    Notice that when you touch a cold water you will know about the essence
    or nature of the cold water and you will also know that it is related
    to senses of humans, so i think that the senses of a human give life
    to ideas, it is like a "reification" of an idea, i mean that an idea
    is alive since it is like reified with the senses of humans that senses
    time and space and matter, so this reification gives the correct meaning
    since you are like reifying with the human senses that gives the
    meaning, and i say that this capacity of this kind of reification with
    the human senses is an emergence that comes from the human biology, so i
    am smart and i will say that the brain is a kind of calculator that
    calaculates by using composability with the meanings that come also from
    this kind of reification with the human senses, and i think that
    self-awareness comes from the human senses that senses our ideas of our thinking, and it is what gives consciousness and self-awareness, so now
    you are understanding that what is missing in artificial intelligence is
    this kind of reification with the human senses that render the brain
    much more optimal than artificial intelligence, and i will explain more
    the why of it.

    Read my previous thoughts:

    More of my philosophy of what is it being smart..

    I think the process of smart thinking is a sophisticated divide and
    conquer (with like an algorithm) of the system that you are thinking and
    after that it is like a "calculation" of the meaning of the pattern or
    patterns to be discovered with "meanings" of the parts of the system
    that you are thinking, so i think that the divide and conquer can be a
    search of the meaning of the new pattern or patterns by also maximizing
    with a previous known meanings in the brain of the parts of the system
    that you are thinking that gives a useful global meaning of a new
    pattern or system of patterns that is like the finding of the global
    optimum, and it looks like particle swarm optimization (PSO) in
    artificial intelligence or Reinforcement Learning in artificial
    intelligence, so in my next posts i will explain more and i will speak
    about the difference between the meaning in the brain and the meaning in artificial intelligence, since i think they are not the same and i will
    explain it, and i think that the kind of meaning of the brain gives self-awareness and consciousness or awareness.

    And read more my following thoughts of my philosophy about what is
    smartness:

    https://groups.google.com/g/alt.culture.morocco/c/Wzf6AOl41xs

    And read my following thoughts:

    More of my philosophy about the poor local search ability of particle
    swarm optimization (PSO) in artificial intelligence..

    I am posting again my following thoughts since i think they are interesting:

    I will explain something important about particle swarm optimization
    (PSO) in artificial intelligence:

    In many research papers, it is proved that particle swarm optimization
    (PSO) in artificial intelligence could provide faster convergence and
    could find better solutions when compared to GA(genetic algorithm). The implementation of PSO is also simple. But the main disadvantage of PSO
    is with its poor local search ability. But you have to understand that
    the poor local search ability of PSO is much more compensated by its
    "faster" convergence, this is why i think that PSO is really useful
    since you can even guarantee the optimal convergence in PSO as i am
    learning you in my below thoughts and writing, so read them carefully.

    More of my philosophy about evolutionary algorithms and artificial intelligence..

    You can read more about my education and my way of doing here:

    Here is more proof of the fact that i have invented many scalable
    algorithms and algorithms:

    https://groups.google.com/g/comp.programming.threads/c/V9Go8fbF10k

    And you can take a look at my photo that i have just put
    here in my website(I am 53 years old):

    https://sites.google.com/site/scalable68/jackson-network-problem

    I think i am smart and I will explain more evolutionary algorithms such
    as particle swarm optimization (PSO) and the genetic algorithm(and also
    don't forget to read carefully my below new interesting proverb):

    I think that Modern trends in solving tough optimization problems tend
    to use evolutionary algorithms and nature-inspired metaheuristic
    algorithms, especially those based on swarm intelligence (SI), two major characteristics of modern metaheuristic methods are nature-inspired, and
    a balance between randomness and regularity. And notice that i am
    talking smartly below about the powerful modern evolutionary algorithm
    that we call particle swarm optimization (PSO), and i think that the
    powerful modern evolutionary algorithm that we call particle swarm
    optimization (PSO) is also a balanced use of randomness with a proper combination with certain deterministic components that is in fact the
    essence of making such algorithms so powerful and effective, and notice
    that the randommness in a genetic algorithm (GA) comes from the
    randomness of mutations of chromosomes or in PSO it comes from the size
    of the population that is constituted with the members that search also randomly, and this randomness in artificial intelligence like PSO
    and Reinforcement learning permits to move forward towards a better
    global optimum of efficiency, and if the randomness in an algorithm is
    too high, then the solutions generated by the algorithm do not converge
    easily as they could continue to "jump around" in the search space. If
    there is no randomness at all, then they can suffer the same
    disadvantages as those of deterministic methods (such as the
    gradient-based search). Therefore, a certain tradeoff is needed.

    More of my my philosophy about the Exploration/Exploitation trade off in AI(artificial intelligence)..

    In Reinforcement Learning in AI(artificial intelligence), for each
    action (i.e. lever) on the machine, there is an expected reward. If this expected reward is known to the Agent, then the problem degenerates into
    a trivial one, which merely involves picking the action with the highest expected reward. But since the expected rewards for the levers are not
    known, we have to collate estimates to get an idea of the desirability
    of each action. For this, the Agent will have to explore to get the
    average of the rewards for each action. After, it can then exploit its knowledge and choose an action with the highest expected rewards (this
    is also called selecting a greedy action). As we can see, the Agent has
    to balance exploring and exploiting actions to maximize the overall
    long-term reward. So as you are noticing i am posting below my
    just new proverb that talks about the Exploration/Exploitation trade off
    in AI(artificial intelligence), and you also have to know how to build correctly "trust" between you and the others so that to optimize
    correctly, and this is why you are seeing me posting my thoughts like i
    am posting.

    You have to know about the Exploration/Exploitation trade off in
    Reinforcement Learning and PSO(Particle Swarm Optimization) in AI by
    knowing the following and by reading my below thoughts about artificial intelligence:

    Exploration is finding more information about the environment.

    Exploitation is exploiting known information to maximize the reward.

    This is why i have just invented fast the following proverb that also
    talks about this Exploration/Exploitation trade off in AI (artificial intelligence):

    And here is my just new proverb:

    "Human vitality comes from intellectual openness and intellectual
    openness also comes from divergent thinking and you have to well balance divergent thinking with convergent thinking so that to converge towards
    the global optimum of efficiency and not get stuck on a local optimum of efficiency, and this kind of well balancing makes the good creativity."

    And i will explain more my proverb so that you understand it:

    I think that divergent thinking is thought process or method used to
    generate creative ideas by exploring many possible solutions, but notice
    that we even need openness in a form of economic actors that share ideas
    across nations and industries (and this needs globalization) that make
    us much more creative and that's good for economy, since you can easily
    notice that globalization also brings a kind of optimality to divergent thinking, and also you have to know how to balance divergent thinking
    with convergent thinking, since if divergent thinking is much greater
    than convergent thinking it can become costly in terms of time, and if
    the convergent thinking is much greater than divergent thinking you can
    get stuck on local optimum of efficiency and not converge to a global
    optimum of efficiency, and it is related to my following thoughts about
    the philosopher and economist Adam Smith, so i invite you to read them:

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

    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.

    And read my following thoughts of my philosophy about what is smartness:

    https://groups.google.com/g/alt.culture.morocco/c/Wzf6AOl41xs

    And read my following thoughts of my philosophy about my new proverbs
    and about dignity:

    https://groups.google.com/g/alt.culture.morocco/c/72FdpcFe9Vk


    Thank you,
    Amine Moulay Ramdane.

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