[continued from previous message]
I think i am highly smart, so i say that the Japanese Ikigai is like a Japanese philosophy that is like the right combination or "balance" of passion, vocation, and mission, and Ikigai and MTP, as concepts, urge us to align our passions with a mission to
better the world, but i think that Japanese Ikiai is a also smart since it gets the "passion" from the "mission", since the mission is also the engine, so you have to align the passion with the mission of the country or the global world so that to be
efficient, and Japanese Ikigai is also smart since so that to higher productivity and be efficient, you have to "specialize" in doing a job, but so that to higher more productivity and be more efficient you can also specialize in what you do "better",
and it is what is doing Japanese Ikigai, since i think that in Japanese Ikigai, being the passion permits to make you specialized in a job in what you do better, and here is what i have just smartly said about productivity:
I think i am highly smart, and i have passed two certified IQ tests and i have scored above 115 IQ, and i will now talk about another important idea of Adam Smith the father of economic Liberalism, and it is about "specialization" in an economic system,
since i say that in an economic system we have to be specialized in doing a job so that to be efficient and productive, but not only that, but we have to specialize in doing a job in what we do better so that to be even more efficient and productive, and
we have to minimize at best the idle time or the wasting of time doing a job, since i can also say that this average idle time or wasting time of the workers working in parallel can be converted to a contention like in parallel programming, so you have
to minimize it at best, and you have to minimize at best the coherency like in parallel programming so that to scale much better, and of course all this can create an economy of scale, and also i invite you to read my following smart and interesting
thoughts about scalability of productivity:
I will talk about following thoughts from the following PhD computer scientist:
https://lemire.me/blog/about-me/
Read more carefully here his thoughts about productivity:
https://lemire.me/blog/2012/10/15/you-cannot-scale-creativity/
And i think he is making a mistake in his above webpage about productivity:
Since we have that Productivity = Output/Input
But better human training and/or better tools and/or better human smartness and/or better human capacity can make the Parallel productivity part much bigger that the Serial productivity part, so it can scale much more (it is like Gustafson's Law).
And it looks like the following:
About parallelism and about Gustafson’s Law..
Gustafson’s Law:
• If you increase the amount of work done by each parallel
task then the serial component will not dominate
• Increase the problem size to maintain scaling
• Can do this by adding extra complexity or increasing the overall
problem size
Scaling is important, as the more a code scales the larger a machine it
can take advantage of:
• can consider weak and strong scaling
• in practice, overheads limit the scalability of real parallel programs
• Amdahl’s law models these in terms of serial and parallel fractions
• larger problems generally scale better: Gustafson’s law
Load balance is also a crucial factor.
And read my following thoughts about Evolutionary Design methodology and that is so important so that to understand:
And I invite you to look at step 4 of my below thoughts of software Evolutionary Design methodology with agile, here it is:
4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.
And you will notice that it has to be done by "prioritizing" the pieces of the software to be delivered to the customers, and here again in agile you are noticing that we are also delivering prototypes of the software, since we often associate prototypes
with nearly completed or just-before launch versions of products. However, designers create prototypes at all phases of the design process at various resolutions. In engineering, students are taught to and practitioners think deeply before setting out to
build. However, as the product or system becomes increasingly complex, it becomes increasingly difficult to consider all factors while designing. Facing this reality, designers are no longer just "thinking to build" but also "building to think." By
getting hands on and trying to create prototypes, unforeseen issues are highlighted early, saving costs related with late stage design changes. This rapid iterative cycle of thinking and building is what allows designers to learn rapidly from doing.
Creating interfaces often benefit from the "build to think" approach. For example, in trying to layout the automotive cockpit, one can simply list all the features, buttons, and knobs that must be incorporated. However, by prototyping the cabin does one
really start to think about how the layout should be to the driver in order to avoid confusion while maximizing comfort. This then allows the designer iterate on their initial concept to develop something that is more intuitive and refined. Also
prototypes and there demonstrations are designed to get potential customers interested and excited.
More of my philosophy about the Evolutionary Design methodology and more..
Here are some important steps of software Evolutionary Design methodology:
1- Taking a little extra time during the project to write solid code and
fix problems today, they create a codebase that’s easy to maintain
tomorrow.
2- And the most destructive thing you can do to your project is to build
new code, and then build more code that depends on it, and then still
more code that depends on that, leading to that painfully familiar
domino effect of cascading changes...and eventually leaving you with
an unmaintainable mess of spaghetti code. So when teams write code,
they can keep their software designs simple by creating software
designs based on small, self-contained units (like classes, modules,
services, etc.) that do only one thing; this helps avoid the domino
effect.
3- Instead of creating one big design at the beginning of the project
that covers all of the requirements, agile architects use incremental
design, which involves techniques that allow them to design a system
that is not just complete, but also easy for the team to modify as
the project changes.
4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.
5- And an iterative process in agile is one that makes progress through
successive refinement. A development team takes a first cut
at a system, knowing it is incomplete or weak in some (perhaps many)
areas. They then iteratively refine those areas until the product is
satisfactory. With each iteration the software is improved through
the addition of greater detail.
More of philosophy about Democracy and the Evolutionary Design methodology..
I will make a logical analogy between software projects and Democracy,
first i will say that because of the today big complexity of software
projects, so the "requirements" of those complex software projects are
not clear and a lot could change in them, so this is
why we are using an Evolutionary Design methodology with different tools
such as Unit Testing, Test Driven Development, Design Patterns,
Continuous Integration, Domain Driven Design, but we have to notice
carefully that an important thing in Evolutionary Design methodology is
that when those complex software projects grow, we have first to
normalize there growth by ensuring that the complex software projects
grow "nicely" and "balanced" by using standards, and second we have to
optimize growth of the complex software projects by balancing between
the criteria of the easy to change the complex software projects and the performance of the complex software projects, and third you have to
maximize the growth of the complex software projects by making the most
out of each optimization, and i think that by logical analogy we can
notice that in Democracy we have also to normalize the growth by not
allowing "extremism" or extremist ideologies that hurt Democracy, and we
have also to optimize Democracy by for example well balancing between "performance" of the society and in the Democracy and the "reliability"
of helping others like the weakest members of the society among the
people that of course respect the laws.
More of my philosophy about the the importance of randomness in
the genetic algorithm and in the evolutionary algorithms and more
of my thoughts..
More of my philosophy about the genetic algorithm and about artificial intelligence and more of my thoughts..
I think i am highly smart, so i will ask the following philosophical question about the genetic algorithm:
Is the genetic algorithm a brute-force search and if it is
not, how is it different than the brute-force search ?
So i have just quickly took a look at some example of a minimization problem with a genetic algorithm, and i think that the genetic algorithm is not a brute-force search, since i think that when in a minimization
problem with a genetic algorithm you do a crossover, also called recombination, that is a genetic operator used to combine the genetic information of two parents to generate new offspring, the genetic algorithm has this tendency to also explore locally
and we call it exploitation, and when the genetic algorithm does genetical mutations with a level of probability, the genetic algorithm has this tendency to explore globally and we call it exploration, so i think a good genetic algorithm is the one that
balance efficiently exploration and exploitation so that to avoid premature convergence, and notice that when you explore locally and globally you can do it with a bigger population that makes it search faster, so it is is why i think the genetic
algorithm has this kind of patterns that makes it a much better search than brute-force search. And so that to know more about this kind of artificial intelligence , i invite you to read my following thoughts in the following web link about evolutionary
algorithms and artificial intelligence so that to understand more:
https://groups.google.com/g/alt.culture.morocco/c/joLVchvaCf0
More of my philosophy about the other conditions of the genetic algorithm and about artificial intelligence and more of my thoughts..
.
I think i am highly smart, and i think that the genetic algorithm
is interesting too, but i have to speak about one other most important thing about the genetic algorithm, so i will ask a philosophical question about it:
Since as i just said previously, read it below, that a good genetic algorithm has to efficiently balance between global(exploration) and local(exploitation) search , but how can you be sure that you have found a global optimum ?
I think i am smart, and i will say that it also depends on the kind of problem, so if for example we have a minimization problem, you can
rerun a number of times the genetic algorithm so that to select the best minimum among all the results and you can also give more time to
the exploration so that to find the a better result, also you have to know that the genetic algorithm can be more elitist in the crossover steps, but i think that this kind of Elitism can has the tendency to not efficiently higher the average best of the
average members of the population, so then it depends on wich problem you want to use the genetic algorithm, also i think that the genetic algorithm is
a model that explains from where comes humans, since i also think
that the genetic mutations of humans, that happens with a probability, has also not only come from the inside body from the chromosomes and genes, but they also were the result of solar storms that, as has said NASA, that may have been key to life on
Earth, read here so that to notice it:
https://www.nasa.gov/feature/goddard/2016/nasa-solar-storms-may-have-been-key-to-life-on-earth
I think i am highly smart, and i will invite you to read my following
smart thoughts about evolutionary algorithms and artificial intelligence so that you notice how i am talking about the so important thing that we call "randomness":
https://groups.google.com/g/alt.culture.morocco/c/joLVchvaCf0
So i think i am highly smart, and notice that i am saying in the above web link the following about evolutionary algorithms:
"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."
So i think that in the genetic algorithm, there is a part that is hard coded, like selecting the best genes, and i think that it is what
we call regularity, since it is hard coded like that, but there is
a so important thing in that genetic algorithm that we call randomness,
and i think that it is the genetic mutations that happen with a
probability and that give a kind of diversity, so i think that this
genetic mutations are really important, since i can for example
say that if the best genes are the ones that use "reason", so then reason too can make the people that has the tendency to use reason do
a thing that is against there survival, like going to war when we
feel that there is too much risk, but this going to war can make
the members or people that use reason so that to attack the other enemy
be extinct in a war when they loose a war, and it is the basis of randomness in a genetic algorithm, since even when there is a war
between for example two Ant colonies, there are some members that do not make war and that can survive if other are extinct by making war, and i say it also comes from randomness of the genetics.
More of my philosophy about student performance and about artificial intelligence and more of my thoughts..
I have just read the following interesting article from McKinsey, and
i invite you to read it carefully:
Drivers of student performance: Asia insights
https://www.mckinsey.com/industries/education/our-insights/drivers-of-student-performance-asia-insights
And i think i am smart, and i think that the following factors in the above article that influence student performance are not so difficult to implement:
1- Students who receive a blend of inquiry-based and teacher-directed instruction have the best outcomes
2- School-based technology yields the best results when placed in the
hands of teachers
3- Early childhood education has a positive impact on student scores,
but the quality and type of care is important
But i think that the factor that is tricky to implement (since it needs good smartness) is good motivation calibration that permits to score 8 to 14 percent higher on the science test than poorly calibrated one, and the high self-identified motivation
that permits to score 6 to 8 percent higher.
Thank you,
Amine Moulay Ramdane.
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