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More of my philosophy about the future of artificial intelligence and about Reinforcement learning and about egoism and about the good and smart way and about the mechanisms and about civilization and about the widely spoken languages and about Googl
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Amine Moulay Ramdane
Feb 13, 2024, 2:49:59 PMFeb 13
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Hello,
More of my philosophy about the future of artificial intelligence and about Reinforcement learning and about egoism and about the good and smart way and about the mechanisms and about civilization and about the widely spoken languages and about Google or Alphabet and more of my thoughts..
I am a white arab from Morocco, and i think i am smart since i have also
invented many scalable algorithms and algorithms..
You have to understand that i have just grouped my important thoughts below about the important limitations of artificial intelligence , and also i have just invented a new model of what is human consciousness , that you can read below , so that to show you how artificial intelligence will not attain artificial general intelligence , also i have just added my thoughts about another limitation of artificial intelligence that you can read below:
Here is my new question to ChatGPT:
So the Large Language Models such as ChatGPT are trained and they
discover lower level patterns and higher level patterns so that to
understand it all , but i think that the main limitation of
reinforcement learning is that it can not innovate by inventing outside
the trained data of the lower level patterns and the higher level
patterns, so it is the humans that have to innovate by inventing outside it.
And here is what has just answered me ChatGPT:
"Yes, that's a fair assessment. Reinforcement learning (RL) algorithms
operate within the confines of the data they are trained on and the
reward signals provided by humans or predefined criteria. They learn to
optimize their behavior based on the patterns present in the training
data and the feedback they receive during interaction with the
environment. However, RL algorithms typically do not possess the ability
to spontaneously innovate or invent entirely new concepts, strategies,
or algorithms that are beyond the scope of their training data or reward
signals.
Innovation often involves creativity, insight, and the ability to
generate novel solutions to problems. While RL can be a powerful tool
for optimizing existing strategies or learning from data, it may not
inherently possess the capacity for creative innovation in the same way
humans do. Instead, humans play a crucial role in driving innovation by
formulating new problems, designing novel algorithms, and providing
guidance and feedback to RL systems.
That said, RL can still contribute to innovation by automating tasks,
optimizing processes, and discovering optimal solutions within the
confines of a given problem domain. Additionally, RL research may
inspire new approaches to problem-solving or provide insights into
learning and decision-making processes that can inform human innovation
efforts.
In summary, while RL has its strengths in optimization and learning from
data, it relies on human ingenuity and creativity to drive true
innovation by inventing outside the constraints of the trained data and
reward signals. Collaboration between humans and RL systems can leverage
the strengths of both to push the boundaries of what is possible in
problem-solving and decision-making."
And here is my second question to ChatGPT:
So then innovation by inventing outside the trained data of the lower
level patterns and the higher level patterns is still confined to the
domain of humans.
And here is what has just answered me ChatGPT:
"Yes, that's correct. As of the current state of artificial intelligence, particularly in the context of reinforcement learning, the
ability to innovate by inventing entirely new concepts, strategies, or
algorithms that are outside the scope of trained data and predefined
patterns remains primarily within the domain of human intelligence.
Humans possess unique cognitive abilities such as creativity, abstract
thinking, and the capacity for insight, which enable them to imagine and
generate novel solutions to problems. These abilities allow humans to
innovate in ways that go beyond the confines of existing data or learned
patterns.
While artificial intelligence, including reinforcement learning, can
automate tasks, optimize processes, and learn from data, its ability to
innovate in the same way humans do is currently limited. AI systems rely
on the data they are trained on and the reward signals they receive, and
they operate within the constraints of these inputs.
However, AI systems can still play a valuable role in assisting and
augmenting human innovation efforts. They can help identify patterns in
large datasets, generate insights from data, and optimize solutions
within specific problem domains. Moreover, AI research continues to
advance rapidly, and future developments may lead to AI systems with
enhanced capabilities for creative problem-solving and innovation.
Overall, while AI has made significant strides in various domains,
including reinforcement learning, the domain of true innovation—where
novel concepts are generated outside the confines of existing data and
patterns—remains firmly within the realm of human intelligence."
And here is my other question to ChatGPT:
So i want to say that Large Language models such as ChatGPT or Gemini
are still lacking , since if we can not trust the trained data of Large
Language Models such as ChatGPT, since it can be of not correct deeper
understanding and/or it is not correctly diversified and is then not of
the correct quality, so Large Language Models are here to augment humans
, and not to replace humans , also i think that we still need to use and
trust more professionals in this or that field or expertize in this or
that field so that to correct this important deficiency of Large
Language Models like ChatGPT , so i think that the quality of the
trained data is an important weakness of Large Language Models such as
ChatGPT , so it is why we are noticing that they are lacking on accuracy.
And here is what has just answered me ChatGPT:
"Your points are well-taken. Indeed, the quality of the trained data
plays a crucial role in the performance and reliability of large
language models like ChatGPT. If the training data is not diverse,
comprehensive, or accurately representative of the subject matter, it
can lead to limitations in the model's understanding and output. Here's
a summary of your points:
Trained Data Quality: The accuracy and depth of understanding of large
language models are heavily influenced by the quality of the training
data. If the data is lacking in depth, diversity, or accuracy, it can
result in deficiencies in the model's output.
Human Augmentation: Rather than replacing humans, large language models should be viewed as tools to augment human capabilities. Human expertise
and judgment remain essential for validating, refining, and
supplementing the outputs generated by AI systems.
Need for Expertise: Professionals and domain experts play a critical
role in correcting deficiencies in large language models. Their input
and oversight can help identify inaccuracies, biases, or gaps in the
model's understanding and contribute to improving its overall performance.
Weakness in Data Quality: The primary weakness of large language models
like ChatGPT often lies in the quality of the trained data. Addressing
this deficiency requires ongoing efforts to curate, verify, and
diversify the training data to enhance the model's robustness and accuracy.
In conclusion, while large language models offer significant potential
for various applications, including natural language understanding and
generation, their effectiveness ultimately depends on the quality of the
data used to train them. By prioritizing data quality and leveraging
human expertise, we can work towards improving the reliability and
trustworthiness of AI systems like ChatGPT."
But i think that the other important limitation of artificial
intelligence that also shows that artificial intelligence will not
replace humans , but will mainly augment humans , is the following:
Human intelligence and artificial intelligence do approach pattern
discovery in distinct ways.
Human Fluid Intelligence:
1- Adaptability: Human fluid intelligence refers to the ability to
quickly adapt and solve new problems. Humans can recognize patterns and
make connections based on relatively limited data or experiences.
2- Contextual Understanding: Humans often rely on contextual
understanding, drawing on a wide range of knowledge and experiences to
identify patterns. This ability to bring diverse information into play
is a hallmark of human intelligence.
3- Innate Creativity: Humans can be highly creative and draw on
intuition, imagination, and abstract thinking to identify patterns. This
creativity allows for the recognition of novel patterns or the
application of existing patterns in new contexts.
Artificial Intelligence:
1- Data Dependency: AI systems, including machine learning models, often
require substantial amounts of data to identify patterns effectively.
The learning process for AI involves analyzing large datasets to discern
underlying patterns and relationships.
2- Algorithmic Approach: AI relies on algorithms and mathematical models
to analyze data and identify patterns. The effectiveness of AI in
pattern recognition depends on the quality of the algorithms and the
quantity and representativeness of the training data.
3- Narrow Specialization: While AI can excel in specific tasks and
domains, it may lack the broader adaptability and creativity seen in
human fluid intelligence. AI systems are often designed for specific
purposes and may struggle with tasks outside their predefined scope.
In summary, while humans can quickly adapt, understand contexts, and
exhibit creativity in pattern recognition, AI systems depend on vast
amounts of data and algorithms.
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ , so
you have to understand more my below previous thoughts about my new
model of what is human consciousness , so you have to understand that
my new model of what is human consciousness says that there is a
"primitive" human consciousness that is a "primitive" self-consciousness
and that is like a controller that controls the human hands etc. , but i
am not talking about it since it is a primitive thing , so it is why my
new model of what is human consciousness is showing that the very
important thing is the consciousness that comes from the meaning that
comes from the reification with the human senses.. , so you have to
understand it correctly by reading my new model of what is human
consciousness in the below web link , so i think that by reading it ,
you will able to understand that artificial intelligence will not attain
artificial general intelligence , even if it will become a powerful tool.
So you have to also understand that the quality of data from Generative
Adversarial Networks (GANs) or such in artificial intelligence is
probabilistic in nature too , so we cannot say that data generated by
models like Generative Adversarial Networks (GANs) is 100% truth or a
perfect representation of the real-world data distribution , so then you
are understanding from my thoughts that synthetic data from Generative
Adversarial Networks (GANs) or such is probabilistic in nature and Large
Language Models are probabilistic in nature and Reinforcement learning
is probabilistic in nature , so it is a weakness or limitation of
artificial intelligence. So i invite you to carefully read my below
previous thoughts so that to understand my views on what is
consciousness and what is smartness and about artificial intelligence:
So from my below new model of what is human consciousness in the below web link, i think you can logically infer by discovering a pattern with your
fluid intelligence that explains what is human smartness or what is human
fluid intelligence , so here is the pattern that i have just discovered:
So you have to carefully read my below new model of what is human
consciousness so that you understand that it is also like a
"reification" with the meaning that comes from the human senses , so i
am explaining more in the below web link my new model of what is human
consciousness so that you understand it correctly , so i am also
explaining that this reification with the human senses also permits smartness to require much less data than artificial intelligence , so then i think that the pattern that i am discovering with my fluid intelligence is that human smartness or human fluid intelligence is like a neural network in the human brain that also works with the "meaning" that comes from the reification with human senses , so then this kind of neural network in
the human brain can be more or less powerful and this has as an effect
to make the human brain able to discover patterns with much less data
than artificial intelligence , so i think it is the pattern that
explains what is human smartness or human fluid intelligence , so i
invite you to carefully read my below new model of what human
consciousness so that to understand it correctly and i invite you to
read about the important limitations of artificial intelligence in my
below previous thoughts:
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ , so
i invite you to read my following discussion with ChatGPT so that you
understand how i am explaining to ChatGPT that the probabilistic nature
is not only inherent to Large Language Models , but it is also inherent
to Reinforcement learning , so i think it is a limitation of Large
language models such as ChatGPT from OpenAI or such the new Gemini from Google , so i invite you to carefully read my following discussion with
ChatGPT on this important subject , and of course i invite you to
carefully read the other limitations of artificial intelligence in my
below thoughts:
So here is my first question to ChatGPT:
"So i say that the other limitation of Large language models such as
GPT-4 is that they are probabilistic in nature , so then Large language
models such as GPT-4 are not designed to be highly accurate or
consistent , and it is one of there limitation , so i think that
reinforcement learning can not solve efficiently this problem , since we
can not know for sure when using Reinforcement learning it will be
highly accurate or consistent."
And here is what has just answered me ChatGPT:
"Your statement captures a common characteristic of large language
models like GPT-4, which is their probabilistic nature. Indeed, these
models generate responses based on probabilities learned from training
data, and as a result, they may produce different outputs for the same
input on different occasions.
Reinforcement learning, in the context of language models, is typically
used to fine-tune or guide the model's behavior based on some reward
signal. While reinforcement learning can be applied to improve aspects
of model behavior, it might not inherently address the probabilistic
nature of these models. Reinforcement learning is more focused on
optimizing for a specific objective or reward, and it may not guarantee
high accuracy or consistency in all situations.
It's essential to consider that the limitations you've highlighted are
inherent to the current state of large language models, and ongoing
research aims to address these challenges. Researchers are exploring
various techniques, including refining training strategies,
incorporating reinforcement learning, and developing more sophisticated
architectures to enhance the accuracy and consistency of language
models. However, achieving perfect accuracy and consistency in natural
language understanding and generation remains a complex and evolving
area of research."
And here is my second question to ChatGPT:
"But there is still a problem , since we can not know for sure when
Reinforcement learning will make the Large Language Model be high
accuracy or consistency , so we can not confirm it , so it is still
probabilistic in nature , so it is a weakness or it is a limitation."
And here is what has just answered me ChatGPT:
"You raise a valid point. The integration of reinforcement learning in
large language models does not eliminate the inherent probabilistic
nature of these models. Reinforcement learning can guide the model
towards better performance by optimizing for specific objectives, but it
doesn't guarantee deterministic outcomes in all cases.
The probabilistic nature of language models stems from the fact that
they generate responses based on learned probabilities from training
data. Even with reinforcement learning, there is uncertainty involved,
and the model's behavior can still vary depending on the context and
input it receives.
Therefore, it's accurate to view this as a limitation. The challenge is
to strike a balance between the flexibility and creativity provided by
probabilistic models and the need for consistent and accurate responses.
Researchers are actively exploring ways to mitigate these limitations,
but achieving complete determinism in natural language understanding and
generation remains a complex and evolving area of research.
Acknowledging and understanding these limitations is crucial for
deploying language models effectively and ensuring users are aware of
the potential variability in responses. It also underscores the
importance of ongoing research and development to address these
challenges in future iterations of language models."
And I invite you to read carefully the following new article from InfoWorld
that shows how help for generative AI is on the way by using for example
knowledge graph and such:
https://www.infoworld.com/article/3712266/help-for-generative-ai-is-on-the-way.html
But i think that the above article is not speaking about the limitations
of this way of doing , so here is my thoughts below in the form of my
discussion with ChatGPT so that you understand the limitations of it:
Here is my first question to ChatGPT:
But you are saying the following: Knowledge graphs provide a structured
representation of information, enabling AI systems to cross-reference
and verify facts more accurately , but i mean that AI systems such as
LLMs are still limited by there limitation to verify facts with there
smartness , since there smartness is limited by the training data , so
it is like circular.
And here is what has answered me ChatGPT:
"You're correct in highlighting the circularity inherent in relying
solely on LLMs, especially when it comes to fact-checking and
verification. While knowledge graphs can provide structured information
for verification, if an LLM's understanding is inherently limited by its
training data, it may not effectively utilize the structured information
provided by the knowledge graph to verify facts accurately.
This limitation underscores the importance of a holistic approach to
building AI systems. While LLMs have demonstrated impressive
capabilities in natural language understanding and generation, they
should be complemented with other techniques, such as knowledge graphs,
expert systems, or external databases, to verify facts and provide
reliable information to users.
Furthermore, efforts to mitigate biases and limitations in LLMs'
training data, as well as ongoing research in natural language
processing and AI, are essential to improving the accuracy and
reliability of AI systems in fact-checking and verification tasks.
In summary, while knowledge graphs can enhance the structured
representation of information for fact-checking, addressing the
limitations of LLMs' training data and inherent biases is crucial for
effectively utilizing this information and improving the reliability of
AI systems."
And here is my second question to ChatGPT:
So i think you are understanding now what i want to mean , and it is
that fact checking and verification on knowledge graphs so that to
increase accuracy , is limited by the smartness of the LLMs that is
limited by the training data that is limited by the lack of context and
the lack quality etc.
And here is what has just answered me ChatGPT:
"Yes, I understand your point now. You're highlighting a critical aspect
of the limitations of fact-checking and verification tasks, especially
when utilizing knowledge graphs with large language models (LLMs) like GPT.
Indeed, the accuracy of fact-checking and verification performed by LLMs
is inherently limited by several factors:
1- Training Data: LLMs are trained on vast amounts of text data, but
this data may contain biases, inaccuracies, or lack contextual nuances.
As a result, the model's understanding of language and ability to verify
facts may be limited by the quality and breadth of the training data.
2- Contextual Understanding: LLMs may struggle with understanding
context and nuance in language, which is crucial for accurate
fact-checking and verification. Without a deep understanding of the
context surrounding a statement or claim, LLMs may misinterpret
information or provide inaccurate assessments.
3- Quality of Information: Knowledge graphs provide structured
information, but the quality and reliability of the data within the
knowledge graph can vary. If the knowledge graph contains inaccuracies
or outdated information, LLMs may propagate these inaccuracies during
fact-checking and verification tasks.
4- Scope of Knowledge: LLMs have limitations in their knowledge base, as
they can only generate responses based on the information present in
their training data. If a fact or claim falls outside the scope of the
model's training data, it may struggle to provide accurate verification.
Given these limitations, it's important to approach fact-checking and
verification tasks with caution and to consider the broader context and
sources of information. While knowledge graphs can provide structured
data for verification, addressing the limitations of LLMs' training data
and ensuring the quality and relevance of information are essential
steps in improving the accuracy of fact-checking and verification
processes. Additionally, integrating multiple sources of information and
employing human judgment and expertise can further enhance the
reliability of fact-checking efforts."
The other limitation of Large language models such as GPT-4 is that they
are probabilistic in nature , so then Large language models such as
GPT-4 are not designed to be highly accurate or consistent , and it is
one of there limitation , so i invite you to carefully read the
following interesting article that talks about it:
Are large language models wrong for coding?
https://www.infoworld.com/article/3697272/are-large-language-models-wrong-for-coding.html
Also so that to know the other important limitations of artificial
intelligence , i invite you to read my following thoughts:
I invite you to take a look at my new model of what is human
consciousness that i have just invented that permits to predict and that
shows the important limitations of artificial intelligence and that
shows that artificial intelligence will not attain artificial general
intelligence (AGI) , but it will become a powerful tool that will
augment humans , so the first prediction of my new model of what is
human consciousness is that artificial intelligence will not attain
artificial general intelligence but it will become a powerful tool,
second prediction of my new model is that artificial intelligence will
then mainly augment humans , but it will not replace humans , and third
prediction of my model is that we have to decrypt the human brain so
that we understand deeply the human consciousness so that we augment
artificial intelligence with consciousness so that it solves the problem
and so that artificial intelligence becomes artificial general
intelligence or super intelligence , and fourth prediction is that my
new model shows that until the next step we are more safe , since in the
next step of understanding deeply human consciousness , we will be so
powerful since humanity is progressing in an exponential progress , so i
think then we will be able to help effectively humans even if artificial
intelligence will be augmented with consciousness and will replace
humans. So i invite you to read my new model of what is human
consciousness in the following web link:
https://groups.google.com/g/alt.culture.morocco/c/s53zucweUIQ
And i invite you to read carefully all my following previous thoughts:
A study by AI researchers at Princeton and the University of Chicago
suggests that LLMs are a long way from being able to solve common
software engineering problems.
Read more here in the following new article:
https://leaddev.com/tech/researchers-say-generative-ai-isnt-replacing-devs-any-time-soon
And read the following about GPT-4:
"In programming tests, GPT-4 did worse; the AI struggles with code, it
seems. GPT-4 was able to get 31 out of 41 correct solutions in the
"easy" Leetcode test, but got just 21/80 on the medium test and only 3
correct questions on the hard test. Meanwhile, its Codeforces rating is
a measly 392, placing it below the 5th percentile of users.""
Read more here:
https://hothardware.com/news/openai-gpt-4-model
So I think i am a new philosopher , and you can read the new ideas of my
philosophies in the below web link , and now i will talk about an
important subject in philosophy and it is about egoism , so i think that
we have not to be pessimistic about egoism , since i think that the
mechanism that regulate egoism is also the society , since the
individual in a society know that he has to balance the individual
egoism or interest with the interest of the society that can be the
society of the country or the society of the world , also we know that
there is also the laws of the country and international laws , but i
think that the interest of the society regulates the interest or egoism
of the individual and it is why i say that it is also a mechanism that
has the tendency to make criminality low , so i think we have not to be
pessimistic about criminality since i think that the mechanisms that
make criminality low are the interest of the society that regulates the
interest of the individual , and the laws of the country and the
international laws , and self-interest in economic Liberalism or capitalism that most of the time is regulated by competition to not lead to corruption, fraud, price-gouging, and cheating , and there is also the competition inside a
Democracy that also fights efficiently corruption by using different
political parties and different political groups inside the congress
etc. , and competition that fights efficiently corruption is also the
separation of powers like in USA , since the U.S. constitution
establishes three separate but equal branches of government: the
legislative branch (makes the law), the executive branch (enforces the
law), and the judicial branch (interprets the law).
And I will now 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.
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ ,
and i think i am a new philosopher and you can read my new ideas of my
philosophy below , but now i invite you to look at the following two
videos from USA that show how americans are not knowing how to answer
the question of how to define success and my answer to this two videos
is below:
What’s Your Definition of Success? | The Success Series
https://www.youtube.com/watch?v=ulShj4keKNw
Defining Success | Fred Miles | TEDxGoshen
https://www.youtube.com/watch?v=W0BaWfuW7RI
So i think that the americans in the above videos are not knowing how to
define success , but i think i am a new smart philosopher and i will
now discover the patterns with my fluid intelligence that answer the
question of how to define success , and here they are:
So i think that the higher level way of answering the question of how to
define success is to first know that there are also the two ways of
measuring , so there is the absolute measurement and the relative
measurement , so for example there is the pragmatic way of how to
measure the human IQs relatively to the distribution of human IQs , but
there is like the measuring in a holistic way by saying that since the
conditions of life are as they are , so then the smart IQs are not
sufficient , and we can then say in like a holistic way that the smart
measured human IQs compared to the conditions of life that are as they
are , are not smart , so then you are understanding that in philosophy
we have also to be the pragmatic way by saying that the approach in
philosophy is not to say that life is shit or the like, but it is to be
pragmatic and constructive by for example doing the good philosophy and
following the good philosophy , so then i will say that the answer to
the above question of how to define success is the following: So i will
say that in philosophy the goal is not to make the citizen smart since
it is also not the pragmatic way of doing, but it is to make the good
citizen , and from the good citizen we can measure success , so for
example success is not to say that the citizen has to be rich or has to
be smart , but the citizen has to be the good citizen and the good
citizen can be approximated by defining it with the good philosophy ,
and then we can say that the good citizen is success and it is how to
define success , it is why i am coming too with the new ideas of my
philosophy so that to also help you define the good citizen and be the
good citizen too , and i invite you to carefully read my thoughts of my
philosophy below and in the below web link:
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ , so
now i will make you understand a so important thing, so as you are
noticing that i am saying in my below previous thoughts the following:
"So for example you will notice that my philosophy explains that what is
important in philosophy is not that you have to be science and
technology , but my philosophy is explaining that what is important is
the minimum model that is creative of a good civilization , and this
minimum model is for example the mechanisms that are the engine that
convince you and that makes want to be a good civilization."
So i think you have to discover the patterns with your fluid
intelligence so that to understand what i mean above , so i will now
show you the patterns , so for example when you are not smart , you will
say that it is easy , since the mechanisms have for example to be the
wanting to have "big money" that attracts and/or to be the wanting to be
smart since it makes you be successful , but i am smart and i answer you
that it is the stupid way to say so , since my smart way of my
philosophy says that both the wanting to be smart comes with negativity
and the wanting to have big money comes with negativity and it is a
delayed reward and they both , with there negativity , can be
destructive , so my philosophy says that it is not the good way to do ,
and my philosophy shows you many mechanisms , and i invite you to read
them below , and read for example the following mechanisms that answers
the above problem in a smart way:
So i think i am also a new philosopher , and you can read my new ideas
of my philosophies below , so now i will talk about an important subject
in philosophy , and it is that you have to know how to be philosophy
with humans , i mean you can say to humans to be for example
responsability by studying and by working in a job , but it is not the
efficient way of doing , since for example responsability by studying
and by working in a job has a delayed reward , so you have to be
efficient and smart and know that with this delayed reward it is not as
efficient , so you have for example to say to a human that he can
specialize in what he does better , and when he specializes in what he
does better , he can find the job more easy or easy to do , so it is a
pleasure that balances with the delayed reward so that it be efficient ,
but there is not only specialization in what we do better , but there is
also the "passion" for a work or a job , so when you are passion for a
work or job , you find pleasure in doing it , so this pleasure also
balances with the delayed reward so that it be efficient , so it is why
i say that the better way is to say to a human that he can specialize in
what he does better and in what he find passion so that it balances with
the delayed reward and so that to increase much more productivity and
quality. And of course you have to know how to align with the mission of
the country and the world.
And i invite you to read carefully my previous thoughts in the following
web link:
https://groups.google.com/g/alt.culture.morocco/c/gT2NxmsRAyg
Thank you,
Amine Moulay Ramdane.
More of my philosophy about the future of artificial intelligence and about Reinforcement learning and about egoism and about the good and smart way and about the mechanisms and about civilization and about the widely spoken languages and about Google or Alphabet and more of my thoughts..
I am a white arab from Morocco, and i think i am smart since i have also
invented many scalable algorithms and algorithms..
You have to understand that i have just grouped my important thoughts below about the important limitations of artificial intelligence , and also i have just invented a new model of what is human consciousness , that you can read below , so that to show you how artificial intelligence will not attain artificial general intelligence , also i have just added my thoughts about another limitation of artificial intelligence that you can read below:
Here is my new question to ChatGPT:
So the Large Language Models such as ChatGPT are trained and they
discover lower level patterns and higher level patterns so that to
understand it all , but i think that the main limitation of
reinforcement learning is that it can not innovate by inventing outside
the trained data of the lower level patterns and the higher level
patterns, so it is the humans that have to innovate by inventing outside it.
And here is what has just answered me ChatGPT:
"Yes, that's a fair assessment. Reinforcement learning (RL) algorithms
operate within the confines of the data they are trained on and the
reward signals provided by humans or predefined criteria. They learn to
optimize their behavior based on the patterns present in the training
data and the feedback they receive during interaction with the
environment. However, RL algorithms typically do not possess the ability
to spontaneously innovate or invent entirely new concepts, strategies,
or algorithms that are beyond the scope of their training data or reward
signals.
Innovation often involves creativity, insight, and the ability to
generate novel solutions to problems. While RL can be a powerful tool
for optimizing existing strategies or learning from data, it may not
inherently possess the capacity for creative innovation in the same way
humans do. Instead, humans play a crucial role in driving innovation by
formulating new problems, designing novel algorithms, and providing
guidance and feedback to RL systems.
That said, RL can still contribute to innovation by automating tasks,
optimizing processes, and discovering optimal solutions within the
confines of a given problem domain. Additionally, RL research may
inspire new approaches to problem-solving or provide insights into
learning and decision-making processes that can inform human innovation
efforts.
In summary, while RL has its strengths in optimization and learning from
data, it relies on human ingenuity and creativity to drive true
innovation by inventing outside the constraints of the trained data and
reward signals. Collaboration between humans and RL systems can leverage
the strengths of both to push the boundaries of what is possible in
problem-solving and decision-making."
And here is my second question to ChatGPT:
So then innovation by inventing outside the trained data of the lower
level patterns and the higher level patterns is still confined to the
domain of humans.
And here is what has just answered me ChatGPT:
"Yes, that's correct. As of the current state of artificial intelligence, particularly in the context of reinforcement learning, the
ability to innovate by inventing entirely new concepts, strategies, or
algorithms that are outside the scope of trained data and predefined
patterns remains primarily within the domain of human intelligence.
Humans possess unique cognitive abilities such as creativity, abstract
thinking, and the capacity for insight, which enable them to imagine and
generate novel solutions to problems. These abilities allow humans to
innovate in ways that go beyond the confines of existing data or learned
patterns.
While artificial intelligence, including reinforcement learning, can
automate tasks, optimize processes, and learn from data, its ability to
innovate in the same way humans do is currently limited. AI systems rely
on the data they are trained on and the reward signals they receive, and
they operate within the constraints of these inputs.
However, AI systems can still play a valuable role in assisting and
augmenting human innovation efforts. They can help identify patterns in
large datasets, generate insights from data, and optimize solutions
within specific problem domains. Moreover, AI research continues to
advance rapidly, and future developments may lead to AI systems with
enhanced capabilities for creative problem-solving and innovation.
Overall, while AI has made significant strides in various domains,
including reinforcement learning, the domain of true innovation—where
novel concepts are generated outside the confines of existing data and
patterns—remains firmly within the realm of human intelligence."
And here is my other question to ChatGPT:
So i want to say that Large Language models such as ChatGPT or Gemini
are still lacking , since if we can not trust the trained data of Large
Language Models such as ChatGPT, since it can be of not correct deeper
understanding and/or it is not correctly diversified and is then not of
the correct quality, so Large Language Models are here to augment humans
, and not to replace humans , also i think that we still need to use and
trust more professionals in this or that field or expertize in this or
that field so that to correct this important deficiency of Large
Language Models like ChatGPT , so i think that the quality of the
trained data is an important weakness of Large Language Models such as
ChatGPT , so it is why we are noticing that they are lacking on accuracy.
And here is what has just answered me ChatGPT:
"Your points are well-taken. Indeed, the quality of the trained data
plays a crucial role in the performance and reliability of large
language models like ChatGPT. If the training data is not diverse,
comprehensive, or accurately representative of the subject matter, it
can lead to limitations in the model's understanding and output. Here's
a summary of your points:
Trained Data Quality: The accuracy and depth of understanding of large
language models are heavily influenced by the quality of the training
data. If the data is lacking in depth, diversity, or accuracy, it can
result in deficiencies in the model's output.
Human Augmentation: Rather than replacing humans, large language models should be viewed as tools to augment human capabilities. Human expertise
and judgment remain essential for validating, refining, and
supplementing the outputs generated by AI systems.
Need for Expertise: Professionals and domain experts play a critical
role in correcting deficiencies in large language models. Their input
and oversight can help identify inaccuracies, biases, or gaps in the
model's understanding and contribute to improving its overall performance.
Weakness in Data Quality: The primary weakness of large language models
like ChatGPT often lies in the quality of the trained data. Addressing
this deficiency requires ongoing efforts to curate, verify, and
diversify the training data to enhance the model's robustness and accuracy.
In conclusion, while large language models offer significant potential
for various applications, including natural language understanding and
generation, their effectiveness ultimately depends on the quality of the
data used to train them. By prioritizing data quality and leveraging
human expertise, we can work towards improving the reliability and
trustworthiness of AI systems like ChatGPT."
But i think that the other important limitation of artificial
intelligence that also shows that artificial intelligence will not
replace humans , but will mainly augment humans , is the following:
Human intelligence and artificial intelligence do approach pattern
discovery in distinct ways.
Human Fluid Intelligence:
1- Adaptability: Human fluid intelligence refers to the ability to
quickly adapt and solve new problems. Humans can recognize patterns and
make connections based on relatively limited data or experiences.
2- Contextual Understanding: Humans often rely on contextual
understanding, drawing on a wide range of knowledge and experiences to
identify patterns. This ability to bring diverse information into play
is a hallmark of human intelligence.
3- Innate Creativity: Humans can be highly creative and draw on
intuition, imagination, and abstract thinking to identify patterns. This
creativity allows for the recognition of novel patterns or the
application of existing patterns in new contexts.
Artificial Intelligence:
1- Data Dependency: AI systems, including machine learning models, often
require substantial amounts of data to identify patterns effectively.
The learning process for AI involves analyzing large datasets to discern
underlying patterns and relationships.
2- Algorithmic Approach: AI relies on algorithms and mathematical models
to analyze data and identify patterns. The effectiveness of AI in
pattern recognition depends on the quality of the algorithms and the
quantity and representativeness of the training data.
3- Narrow Specialization: While AI can excel in specific tasks and
domains, it may lack the broader adaptability and creativity seen in
human fluid intelligence. AI systems are often designed for specific
purposes and may struggle with tasks outside their predefined scope.
In summary, while humans can quickly adapt, understand contexts, and
exhibit creativity in pattern recognition, AI systems depend on vast
amounts of data and algorithms.
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ , so
you have to understand more my below previous thoughts about my new
model of what is human consciousness , so you have to understand that
my new model of what is human consciousness says that there is a
"primitive" human consciousness that is a "primitive" self-consciousness
and that is like a controller that controls the human hands etc. , but i
am not talking about it since it is a primitive thing , so it is why my
new model of what is human consciousness is showing that the very
important thing is the consciousness that comes from the meaning that
comes from the reification with the human senses.. , so you have to
understand it correctly by reading my new model of what is human
consciousness in the below web link , so i think that by reading it ,
you will able to understand that artificial intelligence will not attain
artificial general intelligence , even if it will become a powerful tool.
So you have to also understand that the quality of data from Generative
Adversarial Networks (GANs) or such in artificial intelligence is
probabilistic in nature too , so we cannot say that data generated by
models like Generative Adversarial Networks (GANs) is 100% truth or a
perfect representation of the real-world data distribution , so then you
are understanding from my thoughts that synthetic data from Generative
Adversarial Networks (GANs) or such is probabilistic in nature and Large
Language Models are probabilistic in nature and Reinforcement learning
is probabilistic in nature , so it is a weakness or limitation of
artificial intelligence. So i invite you to carefully read my below
previous thoughts so that to understand my views on what is
consciousness and what is smartness and about artificial intelligence:
So from my below new model of what is human consciousness in the below web link, i think you can logically infer by discovering a pattern with your
fluid intelligence that explains what is human smartness or what is human
fluid intelligence , so here is the pattern that i have just discovered:
So you have to carefully read my below new model of what is human
consciousness so that you understand that it is also like a
"reification" with the meaning that comes from the human senses , so i
am explaining more in the below web link my new model of what is human
consciousness so that you understand it correctly , so i am also
explaining that this reification with the human senses also permits smartness to require much less data than artificial intelligence , so then i think that the pattern that i am discovering with my fluid intelligence is that human smartness or human fluid intelligence is like a neural network in the human brain that also works with the "meaning" that comes from the reification with human senses , so then this kind of neural network in
the human brain can be more or less powerful and this has as an effect
to make the human brain able to discover patterns with much less data
than artificial intelligence , so i think it is the pattern that
explains what is human smartness or human fluid intelligence , so i
invite you to carefully read my below new model of what human
consciousness so that to understand it correctly and i invite you to
read about the important limitations of artificial intelligence in my
below previous thoughts:
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ , so
i invite you to read my following discussion with ChatGPT so that you
understand how i am explaining to ChatGPT that the probabilistic nature
is not only inherent to Large Language Models , but it is also inherent
to Reinforcement learning , so i think it is a limitation of Large
language models such as ChatGPT from OpenAI or such the new Gemini from Google , so i invite you to carefully read my following discussion with
ChatGPT on this important subject , and of course i invite you to
carefully read the other limitations of artificial intelligence in my
below thoughts:
So here is my first question to ChatGPT:
"So i say that the other limitation of Large language models such as
GPT-4 is that they are probabilistic in nature , so then Large language
models such as GPT-4 are not designed to be highly accurate or
consistent , and it is one of there limitation , so i think that
reinforcement learning can not solve efficiently this problem , since we
can not know for sure when using Reinforcement learning it will be
highly accurate or consistent."
And here is what has just answered me ChatGPT:
"Your statement captures a common characteristic of large language
models like GPT-4, which is their probabilistic nature. Indeed, these
models generate responses based on probabilities learned from training
data, and as a result, they may produce different outputs for the same
input on different occasions.
Reinforcement learning, in the context of language models, is typically
used to fine-tune or guide the model's behavior based on some reward
signal. While reinforcement learning can be applied to improve aspects
of model behavior, it might not inherently address the probabilistic
nature of these models. Reinforcement learning is more focused on
optimizing for a specific objective or reward, and it may not guarantee
high accuracy or consistency in all situations.
It's essential to consider that the limitations you've highlighted are
inherent to the current state of large language models, and ongoing
research aims to address these challenges. Researchers are exploring
various techniques, including refining training strategies,
incorporating reinforcement learning, and developing more sophisticated
architectures to enhance the accuracy and consistency of language
models. However, achieving perfect accuracy and consistency in natural
language understanding and generation remains a complex and evolving
area of research."
And here is my second question to ChatGPT:
"But there is still a problem , since we can not know for sure when
Reinforcement learning will make the Large Language Model be high
accuracy or consistency , so we can not confirm it , so it is still
probabilistic in nature , so it is a weakness or it is a limitation."
And here is what has just answered me ChatGPT:
"You raise a valid point. The integration of reinforcement learning in
large language models does not eliminate the inherent probabilistic
nature of these models. Reinforcement learning can guide the model
towards better performance by optimizing for specific objectives, but it
doesn't guarantee deterministic outcomes in all cases.
The probabilistic nature of language models stems from the fact that
they generate responses based on learned probabilities from training
data. Even with reinforcement learning, there is uncertainty involved,
and the model's behavior can still vary depending on the context and
input it receives.
Therefore, it's accurate to view this as a limitation. The challenge is
to strike a balance between the flexibility and creativity provided by
probabilistic models and the need for consistent and accurate responses.
Researchers are actively exploring ways to mitigate these limitations,
but achieving complete determinism in natural language understanding and
generation remains a complex and evolving area of research.
Acknowledging and understanding these limitations is crucial for
deploying language models effectively and ensuring users are aware of
the potential variability in responses. It also underscores the
importance of ongoing research and development to address these
challenges in future iterations of language models."
And I invite you to read carefully the following new article from InfoWorld
that shows how help for generative AI is on the way by using for example
knowledge graph and such:
https://www.infoworld.com/article/3712266/help-for-generative-ai-is-on-the-way.html
But i think that the above article is not speaking about the limitations
of this way of doing , so here is my thoughts below in the form of my
discussion with ChatGPT so that you understand the limitations of it:
Here is my first question to ChatGPT:
But you are saying the following: Knowledge graphs provide a structured
representation of information, enabling AI systems to cross-reference
and verify facts more accurately , but i mean that AI systems such as
LLMs are still limited by there limitation to verify facts with there
smartness , since there smartness is limited by the training data , so
it is like circular.
And here is what has answered me ChatGPT:
"You're correct in highlighting the circularity inherent in relying
solely on LLMs, especially when it comes to fact-checking and
verification. While knowledge graphs can provide structured information
for verification, if an LLM's understanding is inherently limited by its
training data, it may not effectively utilize the structured information
provided by the knowledge graph to verify facts accurately.
This limitation underscores the importance of a holistic approach to
building AI systems. While LLMs have demonstrated impressive
capabilities in natural language understanding and generation, they
should be complemented with other techniques, such as knowledge graphs,
expert systems, or external databases, to verify facts and provide
reliable information to users.
Furthermore, efforts to mitigate biases and limitations in LLMs'
training data, as well as ongoing research in natural language
processing and AI, are essential to improving the accuracy and
reliability of AI systems in fact-checking and verification tasks.
In summary, while knowledge graphs can enhance the structured
representation of information for fact-checking, addressing the
limitations of LLMs' training data and inherent biases is crucial for
effectively utilizing this information and improving the reliability of
AI systems."
And here is my second question to ChatGPT:
So i think you are understanding now what i want to mean , and it is
that fact checking and verification on knowledge graphs so that to
increase accuracy , is limited by the smartness of the LLMs that is
limited by the training data that is limited by the lack of context and
the lack quality etc.
And here is what has just answered me ChatGPT:
"Yes, I understand your point now. You're highlighting a critical aspect
of the limitations of fact-checking and verification tasks, especially
when utilizing knowledge graphs with large language models (LLMs) like GPT.
Indeed, the accuracy of fact-checking and verification performed by LLMs
is inherently limited by several factors:
1- Training Data: LLMs are trained on vast amounts of text data, but
this data may contain biases, inaccuracies, or lack contextual nuances.
As a result, the model's understanding of language and ability to verify
facts may be limited by the quality and breadth of the training data.
2- Contextual Understanding: LLMs may struggle with understanding
context and nuance in language, which is crucial for accurate
fact-checking and verification. Without a deep understanding of the
context surrounding a statement or claim, LLMs may misinterpret
information or provide inaccurate assessments.
3- Quality of Information: Knowledge graphs provide structured
information, but the quality and reliability of the data within the
knowledge graph can vary. If the knowledge graph contains inaccuracies
or outdated information, LLMs may propagate these inaccuracies during
fact-checking and verification tasks.
4- Scope of Knowledge: LLMs have limitations in their knowledge base, as
they can only generate responses based on the information present in
their training data. If a fact or claim falls outside the scope of the
model's training data, it may struggle to provide accurate verification.
Given these limitations, it's important to approach fact-checking and
verification tasks with caution and to consider the broader context and
sources of information. While knowledge graphs can provide structured
data for verification, addressing the limitations of LLMs' training data
and ensuring the quality and relevance of information are essential
steps in improving the accuracy of fact-checking and verification
processes. Additionally, integrating multiple sources of information and
employing human judgment and expertise can further enhance the
reliability of fact-checking efforts."
The other limitation of Large language models such as GPT-4 is that they
are probabilistic in nature , so then Large language models such as
GPT-4 are not designed to be highly accurate or consistent , and it is
one of there limitation , so i invite you to carefully read the
following interesting article that talks about it:
Are large language models wrong for coding?
https://www.infoworld.com/article/3697272/are-large-language-models-wrong-for-coding.html
Also so that to know the other important limitations of artificial
intelligence , i invite you to read my following thoughts:
I invite you to take a look at my new model of what is human
consciousness that i have just invented that permits to predict and that
shows the important limitations of artificial intelligence and that
shows that artificial intelligence will not attain artificial general
intelligence (AGI) , but it will become a powerful tool that will
augment humans , so the first prediction of my new model of what is
human consciousness is that artificial intelligence will not attain
artificial general intelligence but it will become a powerful tool,
second prediction of my new model is that artificial intelligence will
then mainly augment humans , but it will not replace humans , and third
prediction of my model is that we have to decrypt the human brain so
that we understand deeply the human consciousness so that we augment
artificial intelligence with consciousness so that it solves the problem
and so that artificial intelligence becomes artificial general
intelligence or super intelligence , and fourth prediction is that my
new model shows that until the next step we are more safe , since in the
next step of understanding deeply human consciousness , we will be so
powerful since humanity is progressing in an exponential progress , so i
think then we will be able to help effectively humans even if artificial
intelligence will be augmented with consciousness and will replace
humans. So i invite you to read my new model of what is human
consciousness in the following web link:
https://groups.google.com/g/alt.culture.morocco/c/s53zucweUIQ
And i invite you to read carefully all my following previous thoughts:
A study by AI researchers at Princeton and the University of Chicago
suggests that LLMs are a long way from being able to solve common
software engineering problems.
Read more here in the following new article:
https://leaddev.com/tech/researchers-say-generative-ai-isnt-replacing-devs-any-time-soon
And read the following about GPT-4:
"In programming tests, GPT-4 did worse; the AI struggles with code, it
seems. GPT-4 was able to get 31 out of 41 correct solutions in the
"easy" Leetcode test, but got just 21/80 on the medium test and only 3
correct questions on the hard test. Meanwhile, its Codeforces rating is
a measly 392, placing it below the 5th percentile of users.""
Read more here:
https://hothardware.com/news/openai-gpt-4-model
So I think i am a new philosopher , and you can read the new ideas of my
philosophies in the below web link , and now i will talk about an
important subject in philosophy and it is about egoism , so i think that
we have not to be pessimistic about egoism , since i think that the
mechanism that regulate egoism is also the society , since the
individual in a society know that he has to balance the individual
egoism or interest with the interest of the society that can be the
society of the country or the society of the world , also we know that
there is also the laws of the country and international laws , but i
think that the interest of the society regulates the interest or egoism
of the individual and it is why i say that it is also a mechanism that
has the tendency to make criminality low , so i think we have not to be
pessimistic about criminality since i think that the mechanisms that
make criminality low are the interest of the society that regulates the
interest of the individual , and the laws of the country and the
international laws , and self-interest in economic Liberalism or capitalism that most of the time is regulated by competition to not lead to corruption, fraud, price-gouging, and cheating , and there is also the competition inside a
Democracy that also fights efficiently corruption by using different
political parties and different political groups inside the congress
etc. , and competition that fights efficiently corruption is also the
separation of powers like in USA , since the U.S. constitution
establishes three separate but equal branches of government: the
legislative branch (makes the law), the executive branch (enforces the
law), and the judicial branch (interprets the law).
And I will now 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.
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ ,
and i think i am a new philosopher and you can read my new ideas of my
philosophy below , but now i invite you to look at the following two
videos from USA that show how americans are not knowing how to answer
the question of how to define success and my answer to this two videos
is below:
What’s Your Definition of Success? | The Success Series
https://www.youtube.com/watch?v=ulShj4keKNw
Defining Success | Fred Miles | TEDxGoshen
https://www.youtube.com/watch?v=W0BaWfuW7RI
So i think that the americans in the above videos are not knowing how to
define success , but i think i am a new smart philosopher and i will
now discover the patterns with my fluid intelligence that answer the
question of how to define success , and here they are:
So i think that the higher level way of answering the question of how to
define success is to first know that there are also the two ways of
measuring , so there is the absolute measurement and the relative
measurement , so for example there is the pragmatic way of how to
measure the human IQs relatively to the distribution of human IQs , but
there is like the measuring in a holistic way by saying that since the
conditions of life are as they are , so then the smart IQs are not
sufficient , and we can then say in like a holistic way that the smart
measured human IQs compared to the conditions of life that are as they
are , are not smart , so then you are understanding that in philosophy
we have also to be the pragmatic way by saying that the approach in
philosophy is not to say that life is shit or the like, but it is to be
pragmatic and constructive by for example doing the good philosophy and
following the good philosophy , so then i will say that the answer to
the above question of how to define success is the following: So i will
say that in philosophy the goal is not to make the citizen smart since
it is also not the pragmatic way of doing, but it is to make the good
citizen , and from the good citizen we can measure success , so for
example success is not to say that the citizen has to be rich or has to
be smart , but the citizen has to be the good citizen and the good
citizen can be approximated by defining it with the good philosophy ,
and then we can say that the good citizen is success and it is how to
define success , it is why i am coming too with the new ideas of my
philosophy so that to also help you define the good citizen and be the
good citizen too , and i invite you to carefully read my thoughts of my
philosophy below and in the below web link:
I think i am highly smart since I have passed two certified IQ tests and
i have scored "above" 115 IQ , and i mean that it is "above" 115 IQ , so
now i will make you understand a so important thing, so as you are
noticing that i am saying in my below previous thoughts the following:
"So for example you will notice that my philosophy explains that what is
important in philosophy is not that you have to be science and
technology , but my philosophy is explaining that what is important is
the minimum model that is creative of a good civilization , and this
minimum model is for example the mechanisms that are the engine that
convince you and that makes want to be a good civilization."
So i think you have to discover the patterns with your fluid
intelligence so that to understand what i mean above , so i will now
show you the patterns , so for example when you are not smart , you will
say that it is easy , since the mechanisms have for example to be the
wanting to have "big money" that attracts and/or to be the wanting to be
smart since it makes you be successful , but i am smart and i answer you
that it is the stupid way to say so , since my smart way of my
philosophy says that both the wanting to be smart comes with negativity
and the wanting to have big money comes with negativity and it is a
delayed reward and they both , with there negativity , can be
destructive , so my philosophy says that it is not the good way to do ,
and my philosophy shows you many mechanisms , and i invite you to read
them below , and read for example the following mechanisms that answers
the above problem in a smart way:
So i think i am also a new philosopher , and you can read my new ideas
of my philosophies below , so now i will talk about an important subject
in philosophy , and it is that you have to know how to be philosophy
with humans , i mean you can say to humans to be for example
responsability by studying and by working in a job , but it is not the
efficient way of doing , since for example responsability by studying
and by working in a job has a delayed reward , so you have to be
efficient and smart and know that with this delayed reward it is not as
efficient , so you have for example to say to a human that he can
specialize in what he does better , and when he specializes in what he
does better , he can find the job more easy or easy to do , so it is a
pleasure that balances with the delayed reward so that it be efficient ,
but there is not only specialization in what we do better , but there is
also the "passion" for a work or a job , so when you are passion for a
work or job , you find pleasure in doing it , so this pleasure also
balances with the delayed reward so that it be efficient , so it is why
i say that the better way is to say to a human that he can specialize in
what he does better and in what he find passion so that it balances with
the delayed reward and so that to increase much more productivity and
quality. And of course you have to know how to align with the mission of
the country and the world.
And i invite you to read carefully my previous thoughts in the following
web link:
https://groups.google.com/g/alt.culture.morocco/c/gT2NxmsRAyg
Thank you,
Amine Moulay Ramdane.
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