Hello,

I invite you to look at the following interesting video:

Computer Science vs Software Engineering - Which One Is A Better Major?

https://www.youtube.com/watch?v=34sxyECkEkw

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

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

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/

I invite you to read the following very interesting article:

Collective intelligence is the root of human progress

https://singularityhub.com/2017/10/16/collective-intelligence-is-at-the-root-of-human-progress/

Yet more precision about quantum computers and about artificial
intelligence..

Look at the video of IBM about there quantum computer, i think that IBM
has worked on an interface with hardware circuits that permits to
translate from your favorite programming language to the quantum
computer language, so there is no need to learn a quantum programming
language, so it will become much more easy to program a quantum
computer, so look at the video here to notice it:

https://www.ibm.com/quantum-computing/?p1=Search&p4=43700050386405608&p5=b&gclid=Cj0KCQjwp86EBhD7ARIsAFkgakiGC294e584qtmsskplAXivote6smMgZ5hzM2a9Cd8u4bX8qg6ZFSEaAu-uEALw_wcB&gclsrc=aw.ds

And read my following previous thoughts:

I invite you to read the following interesting article about quantum
computers:

https://www.mjc2.com/quantum-computing-logistics-manufacturing-optimization.htm

So as you are noticing that a quantum computer permits to do Quantum
Parallelism that is special, since a register of a quantum computer is
not the same as a register of classical computer, the register of a
quantum computer can be any combination of numbers, all at the same time
using quantum effects of a Quantum Computer. If a register can represent
any number from 0-1000, then in a quantum computer the register could be
set up so that it is a mixture of all numbers from 0-1000 at the same
time, and so quantum computer can also do Quantum Parallelism that is so
powerful on for example logistics and on artificial intelligence, but
you have for example to rewrite the energy "minimization" in deep
learning of artificial intelligence into a quantum computing way that
can be done really really fast by Quantum Parallelism , but notice that
a quantum computer can not replace a classical computer, a quantum
computer can only be used for some kinds of problems. And i invite
you to read below my way of doing energy minimization in artificial
intelligence with PSO(Particle Swarm Optimization), so
read below since i am explaining more in my thoughts below what is
artificial intelligence, so read them below:

So i invite you to learn more about quantum computers and quantum
computing in the following website of IBM and DWAVE:

https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer

And:

https://www.ibm.com/quantum-computing/?p1=Search&p4=43700050386405608&p5=b&gclid=Cj0KCQjwp86EBhD7ARIsAFkgakiGC294e584qtmsskplAXivote6smMgZ5hzM2a9Cd8u4bX8qg6ZFSEaAu-uEALw_wcB&gclsrc=aw.ds

And I invite you to read the following interesting article:

This is your brain on Quantum Computers

https://singularityhub.com/2016/10/02/this-is-your-brain-on-quantum-computers/

And read the following news:

New Passive Quantum Error Correction Could Be The Breakthrough for Large
Scale Quantum Computers

Read more here:

https://www.nextbigfuture.com/2021/02/new-passive-quantum-error-correction-could-be-the-breakthrough-for-large-scale-quantum-computers.html?fbclid=IwAR00BbqQIMw-b9FgeNrbaN-WMepzV1Y4QLOEtgs3x5WLP1nNt7rNNQJL8jU

Look at the following video:

China claims ‘quantum supremacy’ with new supercomputer | DW News

https://www.youtube.com/watch?v=E5MBAJJU9Hk

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).