More of my philosophy about France and about philosophy and about sex and about the Al-Israa’ and Al-Mi`raj and about my new monotheistic religion and about corruption and about Liberty and about the job and about the Return on investment (ROI) and a

[Amine Moulay Ramdane]

Hello,


More of my philosophy about France and about philosophy and about sex and about the Al-Israa’ and Al-Mi`raj and about my new monotheistic religion and about corruption and about Liberty and 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..


Je pense que le cas de la France est une chose intéressante, alors je vais me mettre à parler de la France en abordant un point très important suivant. Mais comme vous allez le remarquer, je vais parler en anglais pour me faire comprendre par le grand nombre.

I think that the case of France is an interesting thing, so I am going to talk about France by addressing a very important point. But as you will notice, I will speak in english to make myself understood by the majority.

So i will invite you first to look at the following french video of what we call the Clash of Tariq Ramadan / Eric Zemmour sur le voile - On n’est pas couché 26 septembre 2009:

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


So i think Eric Zemmour is making a mistake in the above video, since
he is not understanding the way of behaving in the Islamic culture, and the way of behaving in the Islamic culture is not that you are muslim, since it can
be that you have to be decency of Islamic culture, so i think that Eric
Zemmour is not understanding that it is about morality and about decency,
so for example classical Arabic in the Qur'aan makes us feel much more deeply the decency and morality of the Qur'aan , so it is why when you learn
arabic you will start to understand the decency and morality of Islamic culture by for example understanding it much more deeply from the Qur'aan,
so i think that decency is a good thing, so it is why i say that France
is not yet understanding this decency and morality of the Qur'aan,
so i think France has to learn classical Arabic so that to understand and feel much more deeply the decency and morality of the Qur'aan, so it is why i say
that Eric Zemmour has not learned correctly classical Arabic so that
to understand and feel much more more deeply the decency and morality of the Islamic culture.

So now i will talk more about philosophy, so as you are noticing that
i am talking about different subjects so that you understand my views,
but now i will talk about an important thing in philosophy, so i think i have
rapidly understood an important thing in philosophy, and it is related to philosophy and to religion, since we have to look at for example my new monotheistic religion from an interesting philosophical point of view,
and it is that in philosophy, it is not enough to say to people to do this or that,
since you have to have the mechanisms that correctly push them to
do this or that , so you are noticing that it is my way of doing, since for
example in philosophy, a paying job is an attractive mechanism that pushes us to be well educated and to be specialized in what we do better, so i think you are understanding this important idea in philosophy, so for example my new monotheistic religion is also using mechanisms that push people to be a kind of perfection, and you can read about my new monotheistic religion in my below thoughts, and even in economy or business you have to have those mechanisms , i mean that for example self-interest is controlled by competition so that to not lead to corruption and it is why i am explaining it in my below thoughts by talking more about it, so i invite you to reread my below thoughts so that you understand my views and so that to understand how i am doing it:

So now i will talk about sex, so i have just seen the following video
of a song about sex of George Michael:

George Michael - I Want Your Sex (Stereo Version)

https://www.youtube.com/watch?v=vldh7oQD-a4


So i think that the above video about sex is not so mature, since i think that the weakness of the above video is that it is neglecting the fact that
we have to ensure that people are correctly insightful before doing sex,
i mean that as you are noticing in my below thoughts, that i am wanting
to make people more educated in different subjects of philosophy and
in religion etc. so that they be correctly equipped so that to be responsability in sex etc., since sex has also to come with responsability, since we are noticing it today that responsability is lacking on sex, and you can also easily notice it on the above video about sex of George Michael, so then you have to reread my below thoughts so that to understand my views:


So i have just read the following Islamic article about prophet Muhammad in
Al-Israa’ and Al-Mi`raj:

https://www.islamawareness.net/Isra/fatwa_01.html


So it is saying that prophet Muhammad has talked to God in Al-Israa’ and Al-Mi`raj, but i think it is not correct to say so, since i think we have not to be confident with the writing of the Qur'aan or Hadith, since in my new monotheistic religion the Qur'aan and the Bible were also cursed by God, so they contain errors and scientific errors, so i think from what we notice, that since the revelation to prophet Muhammad has come from Angel Gabriel , so i think that God is "Holy" and he can not interact directly with impure and imperfect humans such as prophet Muhammad or Jesus Christ, so it is why you have to read about my new monotheistic religion below so that to understand my views:

So i think that we say that God is "Holy" and that means that "Holy" refers to the divine attribute of being pure, perfect, and separate from all that is sinful or impure, so i think that God can not interact directly with humans that are impure or not perfect, since he is Holy, and it is why i think that God has used
Angels such as the Angel Gabriel to interact with impure and imperfect humans, and of course you are noticing that prophet Muhammad has
not interacted and talked to God, but he has interacted and talked to Angel Gabriel , so i think that it also means that God is arrogant , since he doesn't interact with impure and imperfect humans, so my new monotheistic religion says that God is arrogant , and here is what i have just said about my new monotheistic religion:

Of course we are all humans, so the smart way to do is not to divide humans by groups of blacks and arabs and europeans etc. but it is to "unite" them by transcending with good morality, so i think that we have not to be diversity, but we have to be a smart diversity, so i think it is about morality , so what remains is how to make humans follow the good morality, so then so that to make humans follow the good morality, you have also to know how to be disciplined at showing that you are the good morality so that to also construct the collective imaginary that makes you successful, so you have to know how to create the good human values and the good values that unite humans in following the good morality so that to be successful, so notice how i have created human values and values by inventing many poems of Love and poems, and you can read them below, and notice how i have created human values and values by inventing many proverbs, and you can read them below, and notice how i have created human values and values by inventing my thoughts of my philosophy , and you can read them in the below web links, and notice how for example monotheistic religions have allowed us to transcend with the idea of God so that to unite humans in a smarter way, and of course i have just explained that the idea of God is a simplistic thing , but inventing a new efficient monotheistic religion is a much more sophisticated thing, so you have to understand me, so i have quickly invented a new monotheistic religion that i think is efficient, so you have to read carefully about its ideas in the below web link, so you have to understand that the idea of God is a simplistic thing, but i think that inventing a new monotheistic religion that is efficient is a much more sophisticated thing, so you have to read about my new monotheistic religion so that to know how i have done it, so the first idea of my monotheistic religion is that God is not an evolutionary thing that comes from a type 4 or type 5 civilization, since he can self-improve and not curse the descendants of Adam and Eve and so he has to help humanity , but the idea of my new monotheistic religion is that the nature of God is "special" and God doesn't look like an evolutionary thing like a God of type 4 or 5 civilization, so then after that my second idea of my new monotheistic religion is that God has to be greatly arrogant, since if he is not greatly arrogant, he can for example self-improve and not for example curse the descendants of Adam and Eve, the other new idea of my new monotheistic religion is that not only Adam and Eve and there descendants were cursed by God, but the Bible and Qur'aan too were cursed by God, and it is why they contain scientific errors etc., so the other idea is that prophet Muhammed has been enhanced by God so that he can invent the Qur'aan, and the other idea of my new monotheistic religion is that prophet Muhammed is not the last prophet from God, since saying that he is the last prophet from God is part of the curse from God, and the other ideas of my new monotheistic religion is that the laws of my new monotheistic religion have to be universal, and of course you have to notice how i have smartly done it by coming with universal laws and by logically inferring them from the nature of God, and of course there is many new ideas in my new monotheistic religion, so i invite you to read them carefully so that to notice how i have done it, so you can read my thoughts of my new monotheistic religion in the following web link, and of course you have to notice that i am saying that i am a new prophet from God that is inspired by God, and you can read my thoughts of my new monotheistic religion in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/kFxTDu6_jus


So i have just said below that so that to make low corruption , you have to
make the private sector a powerful motor of the economy, since there is good competition in the private sector, and here is what i have just said about how competition fights efficiently corruption:

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.

So i think i am not stupid, since we can ask a question of from where comes Liberty, but i say that it is also like Large Language Models such GPT-4 that approximate, since i think that when we provide with jobs, people will get educated and will get specialized in what they do better, so i think that
the most important thing, that is attractive, is the job, but of course the other things will get much more easy when you have a job, and of course that today even if we are specialized in what we do better so that to increase much more productivity and quality, i say that you can get much more smart by also for example using Large Language Models such as GPT-4, but i repeat that the most important thing is the job, and of course that productivity is not just increased by specialization in what we do better, since it is also increased by automation and artificial intelligence, so i invite you to read my following thoughts so that you understand my views about it:

And you can read more about my thoughts about productivity and capitalism and more in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/KN4V-rGRDdU


And of course i invite you to read my following thoughts so that you understand my views:


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 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.

However, it's worth mentioning that transformers can still be useful in programming tasks, especially for tasks that involve natural language processing, code summarization, or generating code documentation. They can assist with generating human-readable explanations, providing code examples, or even aiding in code completion.

To address the limitations of causality in programming, specialized programming language models and frameworks have been developed that explicitly incorporate temporal and causal relationships in their architectures. Some examples include models like Gated Graph Neural Networks (GGNNs) or frameworks like Neural Abstract Syntax Trees (NASTs), which aim to better capture the semantics and causal dependencies in programming languages.

Researchers are actively exploring ways to combine the power of transformers with these programming-specific models to enhance their ability to reason about causality and temporal dependencies in the context of programming tasks. These advancements aim to overcome the limitations and improve the performance of large language models in programming-related scenarios."


And here is my third question:


"But this limitation is present in ChatGPT ?"


And here is what answered ChatGPT:


"Yes, the limitation of lack of causality is present in ChatGPT and other transformer-based language models like it. Transformers, including ChatGPT, do not have an explicit mechanism to model causality or capture temporal dependencies in their architecture. As a result, these models may struggle with tasks that require understanding and reasoning about cause-effect relationships or chronological ordering of events.

ChatGPT processes input sequences in a parallel manner using self-attention mechanisms, treating all positions equally without inherent knowledge of temporal or causal relationships. While the model can learn statistical patterns from large-scale training data, it lacks a built-in mechanism to explicitly model or reason about causality.

When it comes to programming-related tasks, ChatGPT may face challenges in accurately understanding and generating code that relies on temporal dependencies or causal relationships. Writing complex algorithms, implementing conditional logic, or dealing with recursive functions could be particularly challenging for ChatGPT due to the lack of causality in its architecture.

It's important to note that while ChatGPT can still provide useful responses and suggestions for programming-related queries, its ability to handle intricate causal and temporal aspects in programming may be limited compared to specialized programming language models or frameworks that explicitly incorporate causality and temporal reasoning."


And here is my fourth question to ChatGPT:


"So it is why ChatGPT can be not good at programming too ?"



And here is what answered ChatGPT:



"Yes, the limitations of transformers, including the lack of causality and difficulty in capturing temporal dependencies, can contribute to ChatGPT not being as good at programming-related tasks compared to specialized programming language models or frameworks.

Programming tasks often involve complex logic, control flow, and understanding the cause-effect relationships between different code statements and variables. These tasks require reasoning about the order of execution, the impact of one code statement on subsequent statements, and the ability to handle conditional statements, loops, and recursion.

Due to the lack of an explicit mechanism to model causality, ChatGPT may struggle to accurately reason about these causal relationships and temporal dependencies. The model may produce responses that are syntactically correct but lack an accurate understanding of the underlying programming concepts or the desired cause-effect relationships.

Furthermore, programming often requires precise and unambiguous instructions, as well as knowledge of specific programming language syntax and conventions. While ChatGPT has been trained on a vast amount of data, including programming-related content, its responses may still be limited in terms of providing accurate and reliable programming guidance or generating code that adheres to best practices.

However, it's important to note that ChatGPT can still be helpful in providing general programming advice, suggesting code snippets, or assisting with code understanding at a high level. It can serve as a useful tool for brainstorming ideas or getting initial guidance. But for more complex programming tasks that rely heavily on causality, temporal dependencies, and intricate language-specific details, specialized programming language models or frameworks designed explicitly for programming may be more suitable."


And here is my fifth question to ChatGPT:


"But does it affect only the questions from the prompt of ChatGPT or it also affect the training process of ChatGPT ?"



And here what answered ChatGPT:


"The limitation of lack of causality in transformers, including ChatGPT, primarily affects the model's ability to reason about causality and temporal dependencies during the training process and when generating responses to queries.

During the training process, the lack of an explicit mechanism to model causality can impact the model's understanding and representation of causal relationships in the data it is trained on. Transformers, including ChatGPT, learn from large amounts of text data, but they do not inherently capture causality unless it is implicitly encoded in the training data.

When it comes to generating responses to specific queries, the lack of causality in the model's architecture can limit its ability to reason about causality in real-time interactions. This limitation can affect the accuracy and coherence of the model's responses, particularly for tasks that require understanding causal relationships, temporal dependencies, or the sequential order of events.

It's important to note that transformers, including ChatGPT, excel in capturing statistical patterns and dependencies from the training data. They can learn to generate coherent and contextually appropriate responses based on the patterns present in the data. However, without an explicit mechanism to model causality, the model may struggle to handle tasks that require explicit reasoning about causality or precise temporal ordering.

To mitigate these limitations, researchers are actively exploring various approaches to incorporate causality and improve the models' ability to reason about temporal dependencies. These efforts aim to enhance the training process and enable models like ChatGPT to capture and reason about causality more effectively, potentially improving their performance in programming and other tasks that involve causal relationships."


And here is my sixth question to ChatGPT:

"So when Large Language Models such as ChatGPT read the code from GitHub in the training process, it can not understand causality in it , so it can not understand correctly the code on GitHub ?"


And here is what answered ChatGPT:


"That's correct. When ChatGPT reads code from GitHub or any other source, it does not inherently understand the causality or temporal dependencies present in the code. Transformers, including ChatGPT, treat the input as a sequence of tokens and do not have an explicit mechanism to model the causal relationships within the code.

As a result, ChatGPT may struggle to fully comprehend and reason about the complex causal dependencies that exist in the code. It may lack the ability to understand the cause-effect relationships between different code statements, the impact of one statement on subsequent statements, or the temporal ordering of actions within the code.

While ChatGPT may be able to provide some assistance or generate code snippets based on statistical patterns learned during training, its understanding may be limited compared to specialized programming language models or frameworks that explicitly incorporate causality and temporal reasoning.

It's important to note that code comprehension, understanding the causality within the code, and accurately predicting the behavior of a codebase are challenging tasks even for human programmers. While ChatGPT can provide general guidance or suggestions, relying solely on ChatGPT for understanding complex codebases or making critical programming decisions may not yield optimal results. Human expertise and specialized tools designed for code analysis and understanding remain crucial in the field of software development."


And here is my seventh question to ChatGPT:


"So from what i understand is that causality has to be present clearly in the text so that that ChatGPT discover it, but on GitHub it is not the same ?"


And here is what answered ChatGPT:


"Yes, you're correct. Transformers like ChatGPT primarily learn from large amounts of text data, including code and natural language. They capture statistical patterns and dependencies present in the training data, which allows them to generate coherent and contextually appropriate responses. However, transformers do not inherently discover or understand causality or temporal dependencies unless they are explicitly encoded in the training data.

When it comes to code on GitHub, the lack of explicit causality annotations or explicit encoding of temporal dependencies within the code makes it challenging for ChatGPT or any transformer-based model to understand and reason about causality accurately. Transformers process code as a sequence of tokens without inherent knowledge of the underlying code structure or the causal relationships within it.

While ChatGPT can learn some code-related patterns during training, its understanding of code is limited to what it has observed in the training data. The model may struggle to grasp the complex cause-effect relationships and temporal dependencies present in the code, making it challenging to accurately comprehend the behavior or intentions of a codebase.

Understanding code, including causality and temporal dependencies, requires domain-specific knowledge, expertise, and a deeper understanding of programming concepts beyond statistical patterns. Specialized programming language models or frameworks explicitly designed for code analysis and understanding can provide better insights into codebases by incorporating domain-specific knowledge and encoding causal relationships.

Therefore, when working with code on GitHub or other code repositories, it's advisable to rely on specialized tools, code analysis techniques, and human expertise to gain a more accurate understanding of the codebase, its causal relationships, and its behavior."


So here is my just new questions that shows another important limitation of ChatGPT:


So i have just asked the following question to ChatGPT:

"So when you present Large Language Models such as ChatGPT with a question , they are limited by the question and they are not able to plan beyond the question, so they are like supervised by the questions from the prompt"

And ChatGPT has just answered:

"Yes, you are correct. Large language models like ChatGPT are limited by the input they receive, including the prompt or question. They generate responses based on the context provided in the input and the patterns they have learned from the training data.

The model's output is supervised by the input it receives, meaning it tries to generate a response that is relevant and coherent within the context of the given prompt or question. However, the model does not have the ability to actively expand its reasoning or plan beyond the provided input.

The responses generated by these models are influenced by the patterns and associations found in the training data, and they strive to generate outputs that are similar to human-generated text. But their responses are not the result of a conscious thought process or independent planning.

It's important to note that language models are highly effective at processing and generating text, but they lack true understanding, consciousness, and the ability to reason and plan in the same way humans do."


And here is my second question to ChatGPT:


"So then Large Language Models such as ChatGPT are not as smart as humans by not being able to autonomously plan and reason like i am explaining above"

And here is what has just answered ChatGPT:

"Correct, language models like ChatGPT are not as smart as humans in terms of planning and reasoning. While they can generate coherent and contextually relevant text based on patterns learned from training data, they lack the depth of understanding, intuition, and cognitive abilities that humans possess.

Humans have the ability to actively plan, reason, and think through problems, considering various factors, long-term goals, and potential outcomes. Humans can also use prior knowledge and experiences to make informed decisions and navigate complex situations. Large language models, on the other hand, do not possess these higher-level cognitive abilities.

While language models can provide impressive responses and simulate human-like conversation, they are ultimately limited by the data they were trained on and the patterns they learned during that training. They lack true comprehension and consciousness and are unable to engage in autonomous planning or reasoning beyond the immediate input they receive."


And here is my third question to ChatGPT:


"So then we can think Large Language Models such as ChatGPT as needing to be supervised from the prompt like in ChatGPT , so they are not able to plan and reason in a more unsupervised or autonomous way like humans"


And here is what has just answered ChatGPT:

"Yes, that's a reasonable way to think about it. Large language models like ChatGPT are typically supervised by the input they receive, such as a prompt or a question. They generate responses based on the patterns and associations they have learned from their training data.

Unlike humans, who can plan and reason in a more unsupervised way, language models rely on explicit input and guidance to generate responses. They do not possess the same level of autonomy, creativity, or independent thinking that humans have.

While language models can produce impressive outputs and mimic human-like conversation, their responses are ultimately driven by the patterns in the training data and the input they receive. They lack the ability to autonomously plan and reason beyond the limitations of the given prompt or question.

However, it's worth noting that ongoing research aims to explore ways to enhance the capabilities of language models, potentially incorporating more unsupervised learning and reasoning techniques. But as of now, large language models are primarily supervised by the prompt and do not possess the same level of unsupervised planning and reasoning abilities as humans."


And here is the answer to my another question to ChatGPT that shows another limitation of ChatGPT:


Here is what has just answered me ChatGPT:


"The window context during the training process refers to the number of preceding words or tokens that the language model considers while learning from the training data. A larger window context would allow the model to capture longer-range dependencies and patterns in the data.

While future powerful quantum computers may provide computational advantages for certain tasks, they do not directly address the challenge of enlarging the window context during the training process. The window context is primarily determined by the architectural design and computational constraints of the language model itself, rather than the specific computing substrate.

Increasing the window context in the training process involves managing memory requirements, computational resources, and optimization techniques specific to language models. These considerations are related to the model's architecture, available memory capacity, and training efficiency. It requires innovations in model design, training algorithms, and memory management techniques.

While quantum computers may offer computational benefits for certain aspects of training or inference tasks, such as optimization or sampling, they do not inherently solve the challenges associated with enlarging the window context during the training process. The solution lies in advancements in language model design, training methodologies, and memory optimization, which are independent of the specific computing substrate used."


I invite you to read the following article so that to understand an important limitation of Large Language Models such as GPT-4:

GPT-4 has the memory of a Goldfish

https://www.theatlantic.com/technology/archive/2023/03/gpt-4-has-memory-context-window/673426/


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 the above article is not explaining correctly , so i will explain: So another important problem is how to solve the above problem, since Large Language Models such as GPT-4 can only discover patterns with the attention mechanisms etc. only inside a window of the maximum limit of tokens when they are trained , so the problem remains of how to ensure that the global patterns are discovered, since for example an average book typically contains around 80,000 to 100,000 words, so GPT-4 has a window of maximum token limit of 32,000 (equivalent to 25,000 words), so then there is local patterns that require a small window , but there is also global patterns that require a large window, so for example discovering the global patterns in the training data can require one book or two books or more, so then you are noticing that it is an important limitation of Large Language Models such as GPT-4, since the size of the context window can indeed impact the capacity of a language model to understand nuanced concepts and leverage common sense knowledge, since a smaller window may limit the model's ability to capture long-range dependencies, complex relationships, and broader contextual information that are also crucial for nuanced understanding, but making the window of maximum token limit bigger than that of GPT-4, so that to solve efficiently the problem, comes with too much computational limitations and with too much increased inference. So i invite you to read my below previous thoughts so that to understand my views on it and on artificial intelligence:


And here is what has anwered me ChatGPT:


"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."



How Canada could become the third or fourth largest economy in the world by the 2040s

Read more here:

https://www.nextbigfuture.com/2023/06/how-canada-could-become-the-third-or-fourth-largest-economy-in-the-world-by-the-2040s.html#more-183725


So as you have just noticed, i have just talked yesterday about my new thoughts on programming by saying the following:


"So in programming so that to not make the system harder to understand, test, and maintain, you have to implement what you need and you have to minimize at best complexity and you should avoid the duplication of code in your application and you should encapsulate data and behavior in your classes and objects, and take advantage of object-oriented programming (OOP) concepts such as inheritance, composition, and polymorphism to create modular, manageable, and organized code, and of course you have to minimize at best coupling and maximize at best cohesion, and you should well document code so that it be much easier to manage, maintain, and debug and you should run unit tests often, and you have to use meaningful names, and of course you should refactor your code regularly by improving code quality since refactoring makes the code far easier to maintain over time."


But i think i have also to talk about the most important ideas of agile methodology, so of course agile methodology is used so that to adapt efficiently to the changing environment or so that to adapt efficiently to change, so here is my interesting thoughts about agile methodology , so read them carefully:

Here are some important steps of software Evolutionary Design methodology with agile:


1- Taking a little extra time during the project to write solid code and
fix problems today, they create a codebase that’s easy to maintain
tomorrow.

2- And the most destructive thing you can do to your project is to build
new code, and then build more code that depends on it, and then still
more code that depends on that, leading to that painfully familiar
domino effect of cascading changes...and eventually leaving you with
an unmaintainable mess of spaghetti code. So when teams write code,
they can keep their software designs simple by creating software
designs based on small, self-contained units (like classes, modules,
services, etc.) that do only one thing; this helps avoid the domino
effect.

3- Instead of creating one big design at the beginning of the project
that covers all of the requirements, agile architects use incremental
design, which involves techniques that allow them to design a system
that is not just complete, but also easy for the team to modify as
the project changes.

4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.

5- And an iterative process in agile is one that makes progress through
successive refinement. A development team takes a first cut
at a system, knowing it is incomplete or weak in some (perhaps many)
areas. They then iteratively refine those areas until the product is
satisfactory. With each iteration the software is improved through
the addition of greater detail.


And I invite you to look at step 4 of my below thoughts of software Evolutionary Design methodology with agile, here it is:

4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.

And you will notice that it has to be done by "prioritizing" the pieces of the software to be delivered to the customers, and here again in agile you are noticing that we are also delivering prototypes of the software, since we often associate prototypes with nearly completed or just-before launch versions of products. However, designers create prototypes at all phases of the design process at various resolutions. In engineering, students are taught to and practitioners think deeply before setting out to build. However, as the product or system becomes increasingly complex, it becomes increasingly difficult to consider all factors while designing. Facing this reality, designers are no longer just "thinking to build" but also "building to think." By getting hands on and trying to create prototypes, unforeseen issues are highlighted early, saving costs related with late stage design changes. This rapid iterative cycle of thinking and building is what allows designers to learn rapidly from doing. Creating interfaces often benefit from the "build to think" approach. For example, in trying to layout the automotive cockpit, one can simply list all the features, buttons, and knobs that must be incorporated. However, by prototyping the cabin does one really start to think about how the layout should be to the driver in order to avoid confusion while maximizing comfort. This then allows the designer iterate on their initial concept to develop something that is more intuitive and refined. Also prototypes and there demonstrations are designed to get potential customers interested and excited.


More of philosophy about Democracy and the Evolutionary Design methodology..

I will make a logical analogy between software projects and Democracy,
first i will say that because of the today big complexity of software
projects, so the "requirements" of those complex software projects are
not clear and a lot could change in them, so this is why we are using an Evolutionary Design methodology with different tools
such as Unit Testing, Test Driven Development, Design Patterns,
Continuous Integration, Domain Driven Design, but we have to notice
carefully that an important thing in Evolutionary Design methodology is
that when those complex software projects grow, we have first to
normalize there growth by ensuring that the complex software projects
grow "nicely" and "balanced" by using standards, and second we have to
optimize growth of the complex software projects by balancing between
the criteria of the easy to change the complex software projects and the
performance of the complex software projects, and third you have to
maximize the growth of the complex software projects by making the most
out of each optimization, and i think that by logical analogy we can
notice that in Democracy we have also to normalize the growth by not
allowing "extremism" or extremist ideologies that hurt Democracy, and we
have also to optimize Democracy by for example well balancing between
"performance" of the society and in the Democracy and the "reliability"
of helping others like the weakest members of the society among the
people that of course respect the laws.


So GPT-4 has a window of maximum token limit of 32,000 (equivalent to 25,000 words), but it can still be not sufficient to detect or discover global patterns as i am explaining it below, and it's important to note that while having more tokens can bring benefits, it also comes with computational limitations and increased inference time. So finding a balance between token count and computational resources is crucial for practical deployment of language models such GPT-4.


So here is another limitation of Large Language Models such as GPT-4:


So here is my question to ChatGPT:


"So from what i understand , discovering patterns of the language can require the discovering of local or global patterns, but the global patterns can be difficult to detect since they can require the scanning of multiple documents or papers, so then even a large window of the maximum tokens can not be sufficient ?"


And here is the answer of ChatGPT:


"Yes, you have summarized it correctly. Discovering patterns in language involves identifying both local and global patterns. Local patterns refer to dependencies and relationships within a smaller context window, while global patterns span across larger sections of text.

While a larger window size, such as a larger maximum token limit, can potentially capture more extensive context and aid in detecting some global patterns, it may not be sufficient on its own. The discovery of global patterns often requires the analysis of multiple documents or papers, which can be challenging and time-consuming.

Even with a larger window size, there are limitations to how much context can be considered, and there may still be relevant information outside the window that influences the global patterns. Additionally, the sheer volume of data, such as vast collections of documents or papers, makes it impractical to analyze every piece of information exhaustively.

Therefore, despite the advantages of a larger window size, the discovery of global patterns can still be challenging and may require additional strategies such as sampling techniques, prioritization, collaboration, or meta-analyses.

It's important to recognize that language models, including those with large windows, have limitations in their ability to comprehensively capture all global patterns. Balancing the available computational resources, practical considerations, and research methodologies is crucial in effectively discovering and interpreting patterns in language."



And here is the other information from ChatGPT about its limitations:



"Technically, large language models like GPT-3.5 lack true comprehension and struggle with common sense for a few reasons:

Statistical patterns: Language models are trained using a process called unsupervised learning, where they predict the next word in a sentence based on the patterns observed in the training data. They learn to generate text that is statistically similar to the training data, but this process does not involve true understanding of the meaning behind the words. The models primarily rely on statistical associations and surface-level patterns, rather than deep semantic understanding.

Lack of external knowledge: Language models typically do not possess external knowledge beyond what is present in their training data. They lack a comprehensive understanding of the world, common knowledge, or access to real-time information. Without this broader context, they may struggle to reason effectively or demonstrate common-sense understanding.

Absence of reasoning abilities: While attention mechanisms help language models capture relationships between words or tokens in a sequence, they do not provide explicit reasoning capabilities. Language models cannot engage in logical reasoning, perform deductive or inductive reasoning, or make use of background knowledge to draw accurate conclusions. They are limited to what they have learned from the training data.

Lack of grounding in the physical world: Language models like GPT-3.5 do not have direct sensory perception or embodied experiences. They operate purely on text input and lack the ability to perceive the world through visual, auditory, or tactile modalities. This lack of grounding in the physical world further limits their comprehension and common-sense reasoning abilities.

Overall, the current architecture and training methods of large language models are primarily focused on generating text that mimics human-like responses based on statistical patterns. While they can produce impressive outputs, they lack the deeper understanding and reasoning capabilities that come naturally to human language comprehension."



And here is the other information from ChatGPT about its limitations:


"Certainly! The weakness of relying solely on statistical associations and surface-level patterns in language models like GPT-3.5 is that they can lead to limitations and inaccuracies in understanding and generating text. Here are some key aspects to consider:

- Lack of semantic understanding: Statistical associations and surface-level patterns do not capture the underlying meaning and semantics of language. Language models primarily learn to associate certain words or phrases with others based on their co-occurrence in the training data. While this enables them to generate text that appears similar to the training data, it does not reflect a deep comprehension of the concepts and relationships expressed by the words.

- Difficulty with context-dependent interpretations: Language models struggle with context-dependent interpretations because they primarily rely on local context within a limited window of surrounding words. They may miss out on broader contextual cues or fail to consider the discourse or conversation history, leading to potential misinterpretations or inadequate responses.

- Sensitivity to training data biases: Language models tend to inherit biases present in the training data. If the data contains biased language or stereotypes, the models may inadvertently learn and reproduce these biases in their outputs. This can perpetuate social, gender, or racial biases, and result in unfair or discriminatory responses.

- Inability to handle rare or novel patterns: Language models trained solely on statistical associations may struggle with rare or novel patterns that were not sufficiently represented in the training data. If the model encounters phrases or concepts that deviate significantly from the training data's patterns, it may produce inaccurate or nonsensical responses, as it lacks a deeper understanding of the underlying meaning.

- Vulnerability to adversarial examples: Adversarial examples are carefully crafted inputs designed to mislead or confuse a model. Language models relying on statistical associations can be susceptible to adversarial attacks. By exploiting the models' tendency to generate responses based on statistical patterns, adversaries can craft inputs that lead to unexpected or undesired outputs, potentially compromising the model's reliability and trustworthiness."

---


So 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 that
Large Language Models such as GPT-4 have inputs and outputs to the outside world, so they can be connected to the outside world , so then you have to understand my below logical proof, so my logical proof below doesn't mean that artificial intelligence will not become powerful, but it just means that something is missing that is the meaning from human consciousness that also allows to learn with much less data than artificial intelligence, so then you are noticing that artificial intelligence will become powerful even if it will not attain artificial general intelligence ,
for the simple reason that the language that is an abstraction of the real world is also really interesting, since it permits to be translated from human to human by adding the meaning from human consciousness to it, so i think that multimodality with images and videos are also abstractions that will permit to make it more smart, so then with my logical proof below we can also consider that artificial intelligence is something different than intelligence of humans, and of course you can read 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 will make other predictions about artificial intelligence , so as i am explaining in the below web link of my thoughts about artificial intelligence , that artificial intelligence will not attain artificial general intelligence , so it will not attain artificial superintelligence, so i think that Large Language Models such as GPT-4, even with multimodality with text and videos and images, are still lacking something important, and it is that text or videos or images are still abstractions of the real world, so it is like the abstract language, since it is lacking the meaning from consciousness that explains my new model of what is consciousness in the below web link, so i think that the solution to this problem is to create consciousness that gives the meaning that also create an efficient objective function, so then i predict that Large Language Models such as GPT-4 will permit to augment humans, but they will not attain artificial general intelligence , so they will not attain artificial superintelligence, so then i invite you to read carefully my new model of what is consciousness in the below web link about artificial intelligence:


So you have to understand my way of doing here, so with my contributions below, i want people understand more so that to easy the job for them , so for example i will also help the poor and the needy when i will make more money from my business, so as you are noticing , i am a gentleman type of person, and so that to understand more about my personality, i invite you to read about my personality and about my Diploma in the below web link, and i have also just again grouped the following web links of my contributions here of my previous thoughts and of my new thoughts and i invite you to read them carefully:


So you can read my new thoughts about artificial intelligence and about the limitations of Large Language Models such as GPT-4 in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/HualRt6YYJI


And you can read my poems of Love and poems and you can read about my personality and about my Diploma and about my other thoughts in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/k6zzuoraFw8


And you can read more about my thoughts about productivity and capitalism and more in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/KN4V-rGRDdU


And you can read my thoughts in the following web link about cancer and about new interesting medical treatments and drugs:

https://groups.google.com/g/alt.culture.morocco/c/3HwdSeO3esc


And i invite you to read carefully my thoughts on different subjects in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/kqqhSWKK1x8


And i invite you to read carefully my following thoughts on different subjects and you can read my new proverbs in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/kqqhSWKK1x8


And here is my just new proverb in english and french:

"Amidst the world's urgency, patience prevails as a virtuous ally, bearing fruits of sweetness."

"En plein tumulte du monde, la patience prévaut en tant qu'alliée vertueuse, portant des fruits de douceur."


And of course here is more of my philosophy about the mathematical modeling of the Jackson network and about mathematics and more of my thoughts, you can read it in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/yEMTeWhBmQA


And read more of my following thoughts about cancer and about health and about new interesting medical treatments and drugs etc.:

https://groups.google.com/g/alt.culture.morocco/c/k6_-3RpoSOA



Thank you,
Amine Moulay Ramdane.