Hello..


More of my philosophy about exponential improvement of computation and AI(artificial intelligence)..

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

I invite you to look carefully at the following video of a jewish
AI(artificial intelligence) scientist about artificial intelligence(And
read about him here: https://rogantribe.com/who-is-lex-fridman/):

Exponential Progress of AI: Moore's Law, Bitter Lesson, and the Future
of Computation

https://www.youtube.com/watch?v=Me96OWd44q0

I think that the jewish AI(artificial intelligence) scientist that is
speaking on the video above and that is called Lex Fridman is making a
big mistake, since he focuses too much on improving Deep Learning in
artificial intelligence using exponential improvement of computation of
CPU hardware, but i think that it is a "big" mistake and you can easily
notice it by reading carefully my following thoughts and writing:

More of my philosophy about artificial intelligence and specialized
hardwares and more..

I think that specialized hardwares for deep learning in artificial
intelligence like GPUs and quantum computers are no more needed, since
you can use only a much less powerful CPU with more memory and do it efficiently, since a PhD researcher called Nir Shavit that is a jewish
from Israel has just invented a very interesting software called neural
magic that does it efficiently, and i invite you to look at the
following very interesting video of Nir Shavit to know more about it:

The Software GPU: Making Inference Scale in the Real World by Nir
Shavit, PhD

https://www.youtube.com/watch?v=mGj2CJHXXKQ

And there is not only the jewish above called Nir Shavit that has
invented a very interesting thing, but there is also the following
muslim Iranian and Postdoctoral Associate that has also invented a very interesting thing too for artificial intelligence, and here it is:

Why is MIT's new "liquid" AI a breakthrough innovation?

Read more here:

https://translate.google.com/translate?hl=en&sl=auto&tl=en&u=https%3A%2F%2Fintelligence-artificielle.developpez.com%2Factu%2F312174%2FPourquoi-la-nouvelle-IA-liquide-de-MIT-est-elle-une-innovation-revolutionnaire-Elle-apprend-continuellement-de-son-
experience-du-monde%2F

And here is Ramin Hasani, Postdoctoral Associate (he is an Iranian):

https://www.csail.mit.edu/person/ramin-hasani

And here he is:

http://www.raminhasani.com/

He is the study’s lead author of the following new study:

New ‘Liquid’ AI Learns Continuously From Its Experience of the World

Read more here:

https://singularityhub.com/2021/01/31/new-liquid-ai-learns-as-it-experiences-the-world-in-real-time/

More of my my philosophy about the Exploration/Exploitation trade off in AI(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

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.


Thank you,
Amine Moulay Ramdane.

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