Hello,
More philosophy about quantum mechanics and Quantum computers and about artificial intelligence..
I am a white arab and i think i am smart since i have also invented many scalable algorithms and algorithms..
I invite you to look at the following interesting video so
that to understand more what is happening in quantum mechanics:
Does Consciousness Create Reality? Double Slit Experiment may show the Answer
https://www.youtube.com/watch?v=h75DGO3GrF4
And it is related to my following thoughts and writing:
Quantum computers are already detangling nature’s mysteries
Practical quantum computers may be decades away – but the race to build them is already tackling thorny global problems, and unlocking the secrets of the universe
Read more the following interesting article so that to understand:
https://www.wired.co.uk/article/quantum-computing?fbclid=IwAR1X67cCtM0KHkIAgU-kVZk-iSUFOuNh6gT0PNqwNOUKUCeiF9SPWAYKZnI
More philosophy about Quantum Computing and Big Data and artificial intelligence..
Quantum computers are particularly suitable for solving certain mathematical problems. For example, they can be used to find very large prime numbers. It would therefore be possible to apply this technology to the field of cryptography to create stronger
cybersecurity systems.
Quantum computing could also allow researchers to model complex chemical reactions. At present, even the most powerful supercomputers cannot do this task. In July 2016, Google engineers managed to simulate a hydrogen molecule for the first time using a
quantum device. Similarly, IBM then managed to model the behaviour of even more complex molecules. Researchers hope to be able to create new molecules for medicine through quantum simulations.
In the field of Big Data, quantum computing allows companies to collect and analyze huge amounts of data very quickly thanks to quantum algorithms. The detection, analysis, integration and diagnosis of separate data sets can be carried out much more
easily. All the elements of a massive database can be analyzed simultaneously to discover patterns.
Read more here:
https://www.dsxhub.org/quantum-computing-and-big-data-a-revolution-in-data-analysis/
Quantum Computing Grows in Popularity with Rising Investments and Experiments
Read more here:
https://translate.google.com/translate?hl=en&sl=auto&tl=en&u=https%3A%2F%2Fhardware.developpez.com%2Factu%2F315284%2FL-informatique-quantique-gagne-en-popularite-avec-des-investissements-et-des-experiences-en-hausse-malgre-la-complexite-de-la-technologie-
et-les-limites-des-competences-selon-IDC%2F
More of my philosophy about artificial intelligence and more..
Deep Mind Assert Reinforcement Learning Could Solve Artificial General Intelligence
Read more here:
https://www.nextbigfuture.com/2021/06/deep-mind-assert-reinforcement-learning-could-solve-artificial-general-intelligence.html
I invite you to look at the following interesting video:
Future AI will REVOLUTIONIZE games
https://www.youtube.com/watch?v=nx8RPnrP9W0
So as you are noticing by looking at the above video that games
development will soon be democratized with AI, so look at the
above video carefully to notice it, so our Era has been characterized
by democratization of technology and democratization of information and democratization of finance.
AI spots critical Microsoft security bugs 97% of the time
Read more here:
https://venturebeat.com/2020/04/16/ai-spots-critical-microsoft-security-bugs-97-of-the-time/
AI enables actors to pronounce perfect lines in foreign languages
Read more here:
https://translate.google.com/translate?hl=en&sl=auto&tl=en&u=https%3A%2F%2Fintelligence-artificielle.developpez.com%2Factu%2F315303%2FUne-IA-permet-aux-acteurs-de-prononcer-des-repliques-parfaites-dans-des-langues-etrangeres-cette-technologie-peut-
traduire-le-cinema-et-la-television-sans-perdre-la-performance-originale-d-un-acteur%2F
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/
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
And read about the following interesting new discovery..
Quantum computing breakthrough could accelerate adoption by years
https://www.techradar.com/uk/news/quantum-computing-breakthrough-could-accelerate-adoption-by-years?fbclid=IwAR3e2Clzl4lynlpFZdXwQRN1PQeRDF-U48UsVBYH7YGKmoYMdQzftpl4les
About Symmetric encryption and quantum computers..
Symmetric encryption, or more specifically AES-256, is believed to be quantum resistant. That means that quantum computers are not expected to be able to reduce the attack time enough to be effective if the key sizes are large enough.
Read more here:
Is AES-256 Quantum Resistant?
https://medium.com/@wagslane/is-aes-256-quantum-resistant-d3f776163672
This is why i am using Parallel AES encryption with 256 bit keys in my powerful Parallel Archiver, you can read about it and download it from here:
https://sites.google.com/site/scalable68/parallel-archiver
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 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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