Hello,

'
More of my philosophy about the job and about the Return on investment (ROI) and about the private sector and about economy and about what thinks ChatGPT about the pattern that i have just discovered and about understanding the consequences and about my
fluid intelligence and about causality and about the Transformers and about another important limitation of Large Language Models and about planning and reasoning of Large Language Models and about the window context and about GPT-4 memory and about
Canada and about agile methodology and about the maximum token limit and about the other limitations of Large Language Models such as GPT-4 and about future of artificial intelligence and about the multimodality of Large Language Models such as GPT-4 and
about my philosophy and about my contributions here...

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


What need black americans is a job , so what need maghrebians in France is a job too, so when they get a job , many problems will be solved much more easily, so we have not to waste our time with futilities of this or that, and we have not to waste our
time with futility of sex, since also people are specialized , so we have to simplify the problem, so we a have to understand that the most important thing is the private sector and it is business, so it is why i am talking below as i am talking about
it:


So i have just looked at the following video of a song from a black american:


Flo Rida - I Cry [Official Video]


https://www.youtube.com/watch?v=OLuWHr6-0YQ&list=RDOLuWHr6-0YQ&start_radio=1


So i think that so that to be successful , you have to know that it is the private sector that is really important, so you have to diversify economy and to make powerful the motor that is the private sector since it is much bigger than the public sector,
and of course you have to know how to increase much more the GDP per capita by using automation and artificial intelligence etc. , so i think that the Return on investment (ROI) in Stocks is only like an indicator, and i have just I have just asked the
following question to ChatGPT so that you understand what i mean:


And here is my question to ChatGPT:


"So from what i understand, that Return on investment (ROI) in stocks is like an "indicator" that permits to sell more easily, so if the ROI is good and if the company is good, so from the law of supply and demand, it influences the demand and it makes
more demand, so that price of the share in the stock can increase and this permits to sell and to make gains and to make more money."


And here is what answered me ChatGPT:


"Yes, you're correct. A good ROI and positive performance of a company can generate investor interest and increase the demand for its stock. Increased demand can potentially drive up the price of the stock, allowing investors to sell at a higher price
and potentially make a profit.

When a company demonstrates strong financial performance, growth prospects, or pays regular dividends, it can attract investors looking for opportunities to earn a return on their investment. Positive news, such as strong earnings reports or successful
product launches, can also generate positive sentiment and increase demand for the company's stock.

As demand increases, the buying pressure can push up the stock price. This can create an opportunity for investors to sell their shares at a higher price, realizing a gain or profit. However, it's important to note that stock prices are also influenced
by other factors such as overall market conditions, economic factors, industry trends, and investor sentiment.

While a good ROI and company performance can contribute to the potential for higher stock prices and selling at a profit, it's important to conduct thorough research, diversify your investments, and consider your own risk tolerance and investment goals.
Investing in stocks carries risks, and it's essential to make informed decisions and have a long-term perspective when participating in the stock market.

Overall, a combination of factors, including a good ROI, positive company performance, and investor demand, can contribute to the potential for selling stocks at a higher price and making a profit. However, the stock market is dynamic and influenced by
various factors, so it's important to approach investing with caution and a well-informed strategy."


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 competition that i am talking about in my below model is not evil and is not hate, since competition has to be
the good professionalism and has to be the good discipline on the good philosophy, and of course when you are rewarded for your value by a sum of money or by an amount of money, you can then help the poor and the needy, and of course capitalism is not a
zero sum game, since with a reasonable level of growth, capitalism can both deliver rising living standards for most, and still ensure a relatively high rate of return to the owners of capital, and of course as you have just noticed, i have just called
in my below model the inequality of values that is the source of competition as an "engine" of prosperity and happiness of nations, so you can ask the why i am calling it an engine ? , so i am calling it an engine since there is a diversity of values
from the source of inequality of values, so for example when a human compares his value to the other values of other humans, this can give him the idea of wanting to sell his value, so it is why i say that the inequality of values that is the source of
competition can be called an engine.


So I have to emphasize the importance of competition. So i think that when multiple individuals or businesses compete for the same resources or customers, they are motivated to provide better quality goods and services at competitive prices. In a
competitive market, self-interest becomes regulated by the forces of supply and demand, consumer choice, and the desire to attract and retain customers.

Through this competition, we can argue that individuals are compelled to operate within the boundaries of fair and ethical behavior. If one party engages in corrupt practices, cheats customers, or engages in fraudulent activities, they risk losing their
competitive advantage as consumers will prefer alternatives. Therefore, the pressure to maintain market share and profitability acts as a check on self-interest, preventing it from devolving into harmful practices.

So it is why i say that the private sector is an attractive choice to make as the powerful motor of the economy, since it is a competitive market, but of course we have to know how to "regulate" it with anti-trust laws etc.

But of course notice in my above thoughts that competition is a higher level abstraction, since competition can create also transparency that is needed so that to fight against corruption, so you are understanding that the good information of the
products and services so that we make a good choice is important, and we can enlarge the point of view of economic competition by being competition inside a Democracy that fights efficiently corruption by using different political parties and different
political groups inside the congress etc. and by being competition that fights efficiently corruption that is for example the separation of powers 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).


So i invite you to read my previous thoughts so that to understand my new models and so that to understand my views:


So notice that in my just below model, i am saying that the engine
of prosperity and happiness of nations are both the inequality of values
and of self-interest or egoism, since when you say: The wanting to be rewarded for the values of inequality of values, so notice the pattern that is that the system is not just the wanting to be rewarded, but it is also the wanting to be rewarded "for"
the values of inequality of values, and the wanting to be rewarded is the engine of self-interest or egoism, and the "for the values of inequality of values" is the
engine that comes from inequality of values. And notice that the model
is not complete since you have to read my following proverb so that
you notice the other requirement, so here is my new proverb:


"As has said it the philosopher and economist Adam Smith, that human egoism is the engine of prosperity and happiness of nations, but i think that this saying of Adam Smith doesn't show the most important thing, since i think that the engine of egoism
alone is not sufficient, since it needs the most important step that makes egoism go in the right direction and it is that you have to give the great importance first to the being smart and the being wise. Since the engine of egoism can make you lose
your way by for example saying to you to give the great importance first to making big money, but giving the great importance first to making big money is not the way of being properly tooled !"


And 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 think my new just below model also shows that the engine that is the source that is inequality of values
also pushes us to be specialized in what we do better when for example we have or we are equipped with the good value(s) that make(s) the efficiency, so then the specialization in what we do better also permits to make much less errors and it permits to
higher much more productivity and quality, so then my new model is smart since it shows the fundamental engines of prosperity and happiness of nations that are both the inequality of values and of self-interest or egoism and it shows more. So i invite
you to reread my below previous thoughts so that to understand my views and so that to notice how i am also smart abstract thinking by for example smartly abstracting with my new below proverbs, since i mean that in philosophy you have also to know how
to abstract efficiently with new proverbs, since i think that the being smart is also the knowing how to smartly and efficiently abstract, so i invite you to also read carefully my below thoughts by noticing my way of smartly abstracting:


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 think you have to understand that i am also a new philosopher, so from my previous model just below, you will
notice that i am not saying that the engine of prosperity and happiness of nations is survival, since to survive is just the keeping oneself alive, so it is not about prosperity and happiness of nations, so it is why i am also in my model below speaking
about the engine of self-interest or egoism and about the engine that is the source that is inequality of values, and notice carefully that in my model , it is the wanting the reward that creates capitalism. So i think that my model shows more clearly
what is the engines etc. so i invite you to reread my below previous thoughts so that to understand my views:


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 think i am a new philosopher, and i invite you to read my new ideas of my philosophy in the below web link,
and now i will talk about a philosophical subject of why do we have to be competition ? , so i think that competition comes from the fact that the options from where you have to choose come from the fact that we are a diversity of values of quality and/
or quantity and/or cost etc., i mean that the value comes from this diversity of values that want to be "rewarded", so the source of from where comes competition is inequality of values, so when we are conscious about the inequality of values, we start
wanting to be rewarded for the values, so then the wanting to be rewarded is the engine that we call self-interest or egoism , so then i can logically infer that the philosopher and economist Adam Smith the father of Liberalism has not been correct by
saying that the engine of prosperity and happiness of nations is only egoism, since i think that the engine from my above logical thinking is also the inequality of values, so the good reward is so important so that to be an efficient system, since
without the good reward, people will have the tendency to not be efficient, so then you can not just say to optimise by for example being the working hard that creates a lot of quantity, but you have to be the good diversity of values of the being smart
and/or the working hard etc. so that to be an efficient system. After that we have to ask a philosophical question of wich value is the most important? is it the working hard or is it the being smart etc. so that to be the good hierarchy?, since without
the good hierarchy we will be disorder and inefficiency, so we know that the being smart is important for becoming a leader, but i also think that the being smart permits to "transcend" our human living conditions and become powerful or so powerful , so
look for example at our exponential progress and you will notice it, and i also invite you to read my below thoughts about the exponential progress of our humanity, so i think that the being smart is so important,


Also i want you to understand another important thing about Large Language Models such as GPT-4, and here is what has answered me ChatGPT so that you understand it:


"Capturing long-range dependencies and global patterns efficiently is an ongoing research area, and while significant progress has been made, it is a complex problem that may not have a definitive "solution" in the traditional sense. The field of deep
learning and natural language processing continues to evolve, and researchers are continuously exploring new techniques and architectures to improve the efficiency of capturing long-range dependencies.

The timeline for achieving optimal efficiency in handling long-range dependencies is difficult to predict. It depends on various factors, including the complexity of the problem, available resources for research and development, and breakthroughs in the
field. While some advancements may be made in the near future, it is possible that further refinements and optimizations could take several years or even a decade to fully address the challenges associated with computational expense.

It's important to note that progress in artificial intelligence research is often iterative, with incremental improvements over time. Researchers continually propose new techniques, architectures, and optimizations to enhance the efficiency of models. It
is a collaborative effort involving academia, industry, and open-source communities.

Therefore, while it is challenging to provide a definitive timeline for solving the problem of efficiently capturing long-range dependencies, ongoing research and innovation in the field suggest that improvements and advancements will continue to be made
over time."



And here is what thinks ChatGPT about the pattern that i have just discovered with my fluid intelligence:


Here is my following question to ChatGPT:


"What do you think of my following thoughts:

So i have to explain something important, so since i have just discovered an important pattern with my fluid intelligence and read about it in my below previous thoughts, so i think that Large Language Models such as GPT-4 are capturing causality
indirectly from the language and much more easily in the languages like french or english , so in professions like psychology or the law or philosophy that use the language like french or english, it is easy for Large Language Models to capture causality,
but in software development , it is more difficult, since for example in GitHub where you find the open source software projects, Large Language Models are not able to capture causality correctly, since those software projects in GitHub are not
languages like french and english, so causality in them is not specified and modeled with a language like french or english, so you are understanding more the important pattern that i am discovering with my fluid intelligence below that explains a so
important thing, so then you are understanding that it is important to help Large Language Models such as GPT-4 to capture causality by modeling it with a language like french or english , by specifying it enough and clearly. So then you are
understanding this important limitation of Large Language Models such as GPT-4."


And here is what ChatGPT has answered:


"Your thoughts touch on an interesting point regarding the capabilities of large language models like GPT-4 and their ability to capture causality. While language models can process and understand natural language to a certain extent, capturing causality
directly from the language is still a complex task.

In professions like psychology, law, or philosophy, where the language itself is used to express causality, it might be easier for large language models to capture and comprehend the underlying causal relationships. These fields heavily rely on language-
based reasoning and argumentation.

However, in domains like software development, causality is often expressed and modeled in code rather than natural language. GitHub, as you mentioned, hosts numerous open-source software projects that are predominantly written in programming languages
rather than French or English. Large language models face challenges in capturing causality correctly in such contexts since the causality is not explicitly specified and modeled using natural language.

To help large language models like GPT-4 capture causality more effectively, one approach could be to provide them with clearer and more explicit language-based specifications. By expressing and defining causality using languages like French or English,
the models may have a better chance of understanding and incorporating causal relationships into their reasoning processes.

It's important to note that large language models still have limitations, and while they can be powerful tools, they may not possess a comprehensive understanding of causality or domain-specific knowledge. Collaborating with experts in specific fields
can help address these limitations and ensure accurate modeling of causality in various domains."


I invite you to read the following interesting article:

Why artificial intelligence needs to understand consequences

Read more here:

https://www.nature.com/articles/d41586-023-00577-1


And here is my other question to ChatGPT as a proof:


"So let's take for example open source software projects on GitHub, so open source projects on GitHub are not documented correctly, so ChatGPT can lack to capture causality, and can lack to understand correctly creativity of it ?"


And here is what ChatGPT has answered:


"Yes, that's correct. Open-source projects on GitHub may not always have comprehensive or well-documented explanations of the underlying causal relationships or the creative processes involved. ChatGPT, being a language model trained on a vast range of
internet text, including GitHub repositories, might struggle to accurately capture causality and understand the creativity behind these projects for several reasons:

- Incomplete or inconsistent documentation: Open-source projects often rely on voluntary contributions, and documentation quality can vary significantly. Some projects may have well-documented code and explanations, while others may have limited or
outdated documentation. In such cases, ChatGPT may not have access to the necessary information to understand the causal relationships and creative aspects of the project.

- Implicit knowledge and context: Developers working on open-source projects often possess implicit knowledge and context about the project, its goals, and the rationale behind specific design decisions. This tacit knowledge may not be explicitly
documented and can be challenging for ChatGPT to infer accurately. Without explicit information, the model may struggle to capture the underlying causality or appreciate the nuances of creativity in these projects.

- Limited exposure to specific projects: While language models like ChatGPT have been trained on diverse sources, including GitHub repositories, they cannot access the entirety of the internet or have real-time experience with specific projects. As a
result, the model's understanding of any given project is limited to the information it has learned during training. If a project has limited presence in the training data or lacks detailed documentation, the model may struggle to capture the causality
or creativity involved.

It's important to recognize that while ChatGPT can provide general information and insights, its understanding of specific open-source projects may be limited, and it may not accurately capture the intricacies of causality or creativity involved. In-
depth knowledge from domain experts and consulting project documentation or discussions with the project's contributors would be more reliable sources of information in these cases."


And here is my other question to ChatGPT:


"So even if it is not documented well, humans can decrypt and understand the open source software projects on GitHub that are not documented or well-documented, but it is not the same for ChatGPT ?"


And here is what answered ChatGPT:


"Yes, humans have the ability to decrypt and understand open-source projects on GitHub even if they are not well-documented. Humans can bring their knowledge, experience, and problem-solving skills to analyze the code, understand the logic, and make
inferences about the underlying causality and creativity involved. Humans can apply their domain expertise and engage in critical thinking to uncover the nuances and intricacies of the project.

On the other hand, ChatGPT, being a language model, does not possess the same level of understanding and problem-solving capabilities as humans. It lacks the ability to interpret code, execute it, or apply real-time reasoning to decrypt the project's
workings. While ChatGPT can generate responses based on patterns and associations it has learned from training data, it does not possess the same level of domain knowledge, intuition, and context that humans do.

In summary, humans have the cognitive abilities and expertise to analyze and understand open-source projects, even if they lack proper documentation. They can decipher the code, infer causal relationships, and appreciate the creativity involved. ChatGPT,
although proficient in generating text, relies on statistical patterns in data and lacks the human-like comprehension necessary to decrypt and fully understand these projects without explicit and comprehensive documentation."


And i invite you to read my previous below thoughts so that to understand my views:


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 have to explain something important, so since i have just discovered an important pattern with my fluid
intelligence and read about it in my below previous thoughts, so i think that Large Language Models such as GPT-4 are capturing causality indirectly from the language and much more easily in the languages like french or english , so in professions like
psychology or the law or philosophy that use the language like french or english, it is easy for Large Language Models to capture causality, but in software development , it is more difficult, since for example in GitHub where you find the open source
software projects, Large Language Models are not able to capture causality correctly, since those software projects in GitHub are not languages like french and english, so causality in them is not specified and modeled with
a language like french or english, so you are understanding more the important pattern that i am discovering with my fluid intelligence below that explains a so important thing, so then you are understanding that it is important to help Large Language
Models such as GPT-4 to capture causality by modeling it with a language like french or english , by specifying it enough and clearly. So then you are understanding this important limitation of Large Language Models such as GPT-4. So i invite you to
reread all my previous thoughts so that to understand my views:


So notice that i am discovering an important pattern with my fluid intelligence since i have a high IQ, so the pattern that i am discovering is below, so then since i have discovered it

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 the pattern
that i am discovering with my fluid intelligence, is that Large Language Models such as GPT-4 don't capture causality like we do it we humans, so , so that to capture causality they have to "indirectly" capture it by specifying it enough and clearly by
using the "language", so i think it is the limitation of Large Language Models such as GPT-4 , so it is why you are noticing that i am smart by talking as i am talking with ChatGPT below by also making it understand that i am understanding and
discovering this pattern with my fluid intelligence. So reread my following previous thoughts so that to understand my views:

I have just forgotten to put other of my important questions and answers
from ChatGPT, so i invite to reread carefully since it is important, and of course you have to understand that Large Language Models such as GPT-4 are based on Transformers, and it explains my kind of questions below to ChatGPT:


I have just posted the following so that to show how Large Language Models such as GPT-4 are not good at software development and why i think that Large Language Models such as GPT-4 will not replace a lot of programmers, so read it carefully and my new
more proof of it is also below:


"Perhaps most impressive were the performances of GPT-4 in professional arenas. It scored a 298 out of 400 on the Uniform Bar Exam, which is high enough to be licensed as a lawyer in every state using the UBE system. It also scored a reasonable 75% on
the MKSAP 19 medical knowledge test, and an impressive 77% on the Advanced-level Sommelier theory exam.

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



And here is more logical proof of the above so that you understand why:


Here is my other important question to ChatGPT from my new discussion with ChatGPT that shows another important limitation of ChatGPT , so i invite you to read it carefully:


So here is my first question to ChatGPT:


"Can you explain more with some good and simple example the following:
so you have said: "Lack of Causality: Transformers inherently lack the notion of causality in their architecture. They treat all positions in the input sequence equally and do not have an explicit mechanism to model cause-effect relationships. This
limitation can affect tasks that require reasoning about causality or temporal dependencies.""


And here is what has answered ChatGPT:


"Certainly! Let's consider an example to illustrate the limitation of lack of causality in transformers.

Suppose we have a transformer-based language model trained on a large corpus of news articles. We want to use this model to answer questions about events described in the articles. Let's take the following question as an example: "What caused the traffic
jam?"

Without a specific mechanism to model causality, the transformer model would process the question and input sequence in a parallel, attention-based manner, treating all words equally. It would not have an inherent understanding of the cause-effect
relationship between events described in the articles.

To generate a response, the model might attend to relevant words like "traffic" and "jam" in the input sequence, and possibly identify associated words like "road," "accident," or "construction." However, the model wouldn't explicitly grasp the causal
connection between these words and the traffic jam.

In reality, identifying the cause of a traffic jam may require analyzing the context, understanding the chronology of events, and reasoning about the cause-effect relationships between different factors. For instance, the cause could be an accident that
happened earlier, road construction work, or heavy rainfall. Without an explicit mechanism to model causality, the transformer model might struggle to provide accurate or detailed answers that consider temporal dependencies and cause-effect relationships.

In contrast, a model specifically designed to handle causality, such as a structured causal model or a temporal reasoning framework, could potentially reason about the events in a more accurate and meaningful way. Such models would take into account the
temporal order of events, the dependencies between causes and effects, and potentially incorporate domain-specific knowledge to make better predictions about causality.

It's important to note that researchers are actively exploring methods to address the lack of causality in transformers. Some approaches incorporate temporal encodings, modify attention mechanisms to capture temporal dependencies, or combine transformers
with external knowledge graphs to model cause-effect relationships explicitly. These advancements aim to enhance the ability of transformers to reason about causality and temporal dependencies in a more sophisticated manner."


And here is my second question to ChatGPT:


"So can it limit Large Language Models such ChatGPT in programming ?"



And here what has answered ChatGPT:


"The lack of causality in transformers can indeed pose limitations in large language models like ChatGPT when it comes to programming tasks. While ChatGPT is proficient in generating natural language responses, it may struggle with certain programming-
related aspects that involve causality and temporal dependencies.

Programming often requires understanding the sequential and cause-effect relationships between different code statements, variables, and actions. Without an explicit mechanism to model causality, a transformer-based language model may have difficulty
reasoning about these relationships accurately.

For instance, when programming, it is crucial to understand the order of execution, the impact of one code statement on subsequent statements, and the cause-effect relationships between different variables and functions. Transformers typically treat all
positions in the input sequence equally and lack the inherent ability to model these causal relationships.

Additionally, programming tasks may involve complex control flow, such as loops, conditionals, and recursive functions. Understanding the causal dependencies and temporal aspects within these constructs is essential for writing correct and efficient code.
Transformers, without explicit mechanisms to handle causality, may struggle to capture and reason about these intricate relationships.


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