More of my philosophy about the relative and absolute measurement and about racism and about the style and about reputation and about the why of my new model and about social justice and about fairness and about specialization and about my new monoth

[Amine Moulay Ramdane]

Hello,


More of my philosophy about the relative and absolute measurement and about racism and about the style and about reputation and about the why of my new model and about social justice and about fairness and about specialization and about my new monotheistic religion and about the efficient way of doing and about Jihadists and about my philosophy and more of my thoughts..

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


I have just said yesterday the following about racism:

--

I have just looked at the following video from south Africa about racism:

Big Debate on Racism

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


So i think i am a philosopher and a thinker, and i have quickly looked
at the above video about racism, so i think that people on the above video
are not so smart, since they are looking at racism not from a philosophical point of view, but they are looking at it naively, so i think that the problem of racism comes from the fact that humans want to be happy, so this wanting
to be happy makes them demanding high quality, since you have to know how to make a difference between the absolute and relative measurement, so for example humans are not measuring just relatively on for example the normal distribution of IQs, since they can for example like measure absolutely or measure relatively to a larger knowledge and say that the high IQ is not "enough", so it becomes for them not a high IQ, so i think that it is the tendency of the way of measuring of humans, so humans are finding there happiness not "enough", so they are wanting to be happy , so they are demanding high quality so that to be happy, so from there demanding high quality, they are becoming like racist towards this or that human that doesn't fulfill there requirements of demanding high quality, so i think that it is the philosophical way of explaining racism, so it is why i have invented a new monotheistic religion so that to bring the necessary patience or tolerance. So i invite you to read my below thoughts so that to understand my views.

--

And here is my discussion with ChatGPT that explains more my above thoughts:


Here is my first question to ChatGPT:

"Can i say that a relative measurement is like measuring the IQs that are compared relatively to the normal distribution, so it is relative to the knowledge of the normal distribution, but if we measure relatively to a larger knowledge, we can say that a high IQ is not enough, so it can become not a high IQ ."

And here is what has answered me ChatGPT:

"Yes, you can certainly use the concept of relative measurement to describe IQ scores in relation to a normal distribution or a larger knowledge base. IQ (Intelligence Quotient) is typically measured and compared relative to a standardized population, such as a normal distribution with a mean of 100 and a standard deviation of 15.

When we say someone has a high IQ, it means their IQ score is above average compared to the reference population. However, if we were to consider a larger knowledge base or a different reference population, what might be considered a high IQ in one context could be relatively lower in another.

For example, if we compare the IQ scores of individuals from different countries, we might find that what is considered a high IQ in one country could be relatively lower in another due to variations in education systems, cultural differences, or other factors. In this case, the relative measurement allows us to understand IQ scores in relation to a specific reference point or population.

Therefore, by considering a larger knowledge base or changing the reference point, it is possible to conclude that a high IQ in one context may not be sufficient or "high" when compared to a different context or population."


And here is my second question to ChatGPT:

"So if we take a larger knowledge, we can even say by looking at our universe, that we are still weak and we can not conquer the universe, and then we can say that high IQ is not enough to conquer the universe, so it becomes that a high IQ is not a High IQ."


And here is what has answered me ChatGPT:

"If we consider the vastness and complexity of the universe, it is indeed possible to argue that even the highest IQ individuals may still have limitations or gaps in knowledge and understanding. The challenges and scope of conquering the universe are immense, involving multiple fields of knowledge, technological advancements, and unknown phenomena.

In that context, one could argue that a high IQ alone is not sufficient for such an ambitious goal. Conquering the universe would likely require not only intelligence but also expertise in various scientific disciplines, technological advancements, resources, collaboration, and many other factors.

So, using a relative measurement approach, where the reference point is the universe and its vastness, we could say that a high IQ, while still valuable, might not be enough to achieve the feat of conquering the universe. It underscores the idea that relative to the scale of the universe, even the highest IQ might not be considered "high" in that particular context."


And you can read my following new thoughts so that to understand my philosophical views about the style and about reputation:


So i think that morality also comes from the rule of supply and demand in
economics as i am explaining just below, but not only that, but we can notice that most of humans demand high quality even if they are not of high quality, so then it pushes us to be competition of who is of higher and higher quality, so it becomes that it is like a pressure that creates higher and higher quality, so then i say that the system has the tendency to push us so that we be of higher and higher quality, so i think that we have not to be pessimistic about morality since it is constrained in this kind of way. And i invite you to read my following new thoughts so that to understand more about it and so that to understand my views about reputation and Love etc.:


Here is another interesting proverb that shows an important thing that i am also talking about below:

"Reputation is the currency of trust, its value earned through integrity and tested through time."


So as you are noticing that reputation is an important thing, so it is a matter
of trust, and as you are noticing that in my below thoughts i am also showing that Love has requirements such as mutual respect, but of course
the mutual respect is an abstraction, since respect or trust follows the rule of supply and demand, and it is inherent to the rule of supply and demand that the most important thing that permits you to transcend is the being smart and the being wise, so i think that when we judge the lifestyle or the style, it is also, i think, that most humans demand high quality even if they are not of high quality, so we have not to be naive, so it is why i say that the lifestyle and the style are judged by this demanding high quality, since also humans want to be happy, so it is why you are noticing that i am not naive by
talking to you in a kind of sophisticated manner by inventing poems of Love and poems that you can read in my below thoughts and in the below web link, and by my inventing new smart proverbs that you can read in my below web link and by inventing the ideas of my philosophy that you can read in the below web links:

So here is a proverb that abstract more what i want to say:

"Reputation is like a shadow. It's the reflection of your actions and follows you wherever you go."


So as you are noticing , it is why i am showing my kind of style that is also genetical in me, so that to show how my personality looks like and so that to show how i behave in the everyday life, so read my below thoughts so that to understand more my views about the style and about Love etc.:

Here is my new poem in english and notice again my kind of style,
and notice that "my profile" in my new poem below can mean a much deeper information about me and my personality, so i invite you to read my new poem in english:


---

I am showing you my style

So it's not like a simple flat file

I am showing you my style

And it's not like the hostile

Since how do you think we should reconcile?

Is it by being only a francophile?

Or is it by being juvenile?

I am showing you my style

So that we can compare lifestyles

I am showing you my style

Because good morality should always be worthwhile

I am showing you my style,

That's why I present my poems and proverbs to reveal my profile

I am showing you my style

Don't you notice that in software, when you want to compile

It's also about the product or service that must be worthwhile!

--


So i think i am not like the terrorists of the 9/11 like Mohamed Atta,
so i think i am also a philosopher and a thinker, so now i will answer
why i am saying the following in my below thoughts:


"...Since how to tune correctly this fight for life so that to not be too much savagery ? so then you will say that you have to tune correctly the fight for life by being fight for life, and it becomes circular logic that is not good and that doesn't solve the problem of how to tune correctly the fight for life"


So you will ask what do i mean by the above, so i say that it is related to what we say in french: "Il faudrait du savoir vivre ensemble" , that we can translate in english by: "We have to know how to live in harmony", and i have also just invented a new proverb in english and french that also shows it more clearly, and here it is:

"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."

So you are now seeing or understanding more clearly the problem, so how can you bring this necessary patience ? so i think that now you are also understanding the why i have invented my new monotheistic religion, so i invite you to read my below thoughts of my new model:

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 have
to make you understand my new model, so my new model comes in two parts, so there is my new economic model below that i think enhances the economic model of the philosopher and economist Adam Smith the father of Liberalism, and there is also my new monotheistic religion below that i think is efficient, since you have also to understand what is an Ideal, so i think that an Ideal can not be that we have to return to the archaic model that is to be nationalism, so it is why we have to have something that unite
people in a kind of way that is satisfactory, and it is why i am coming
with my new monotheistic religion that also has as a goal to unite people
in a kind of way that is satisfactory and to bring decency, other than that i think my new model also solves an important problem and it is the following:

I have just looked at the following video of the indian philosopher Jiddu Krishnamurti, i invite you to look at it carefully:

Does God exist? | J. Krishnamurti

https://www.youtube.com/watch?v=tYjYL448-yY

And you can read about Jiddu Krishnamurti here:

https://en.wikipedia.org/wiki/Jiddu_Krishnamurti


So i think i am highly smart, and notice the approach of the above indian philosopher in the above video, so he wants humans to not believe
in God , but notice how he is saying that you have to go out the belief in God and not believe in God and then you have to fight for life, and here again there is like a logical contradiction , since how to tune correctly this fight for life so that to not be too much savagery ? so then you will say that you have to tune correctly the fight for life by being fight for life, and it becomes circular logic that is not good and that doesn't solve the problem of how to tune correctly the fight for life, and it is the big weakness of philosophy and of the philosophers, and it is why i have constructed quickly my new monotheistic religion that solves the problems and that solves the problems of previous monotheistic religions.


So i invite you to read my thoughts of my new economic model below,
and of my new monotheistic religion below:


So as you have just noticed , i have not talked about social justice , since
it is like inherent, since here is my discussion with ChatGPT so that to understand my views:


Here is my first question to ChatGPT:

"I mean that we can go from capitalism that is not a zero sum game to create peace, and social justice is inherent to the system , since without social justice we can have violence and disorder, so it is like inherent that we have to have it ?"


And here is what answered me ChatGPT:

"I understand your point now. Yes, social justice is indeed crucial for maintaining peace and order within a society. While capitalism is not inherently designed to achieve social justice, it can provide the framework for economic growth and prosperity. By ensuring that social justice principles are integrated into the functioning of capitalism, it becomes more conducive to fostering peace and stability.

When social justice is prioritized within a capitalist system, it helps mitigate the potential negative consequences of inequality and exploitation. By implementing policies that promote equal opportunities, fair distribution of resources, and protection of individual rights, a more just and inclusive society can be created.

Social justice within a capitalist framework involves addressing issues such as income inequality, access to education and healthcare, fair wages, worker rights, and environmental sustainability. By actively working towards these goals, capitalism can contribute to reducing social tensions, enhancing social cohesion, and minimizing the risk of violence or disorder arising from unjust conditions.

It is important to note that achieving social justice within a capitalist system requires proactive efforts, as market forces alone may not necessarily lead to equitable outcomes. Governments, institutions, and civil society play crucial roles in implementing policies, regulations, and social programs that ensure social justice is upheld.

By recognizing the importance of social justice within capitalism and actively working towards it, societies can strive to create a more peaceful, inclusive, and harmonious environment for all individuals."


And here is my second question to ChatGPT:


"But i think that competition between capitalism and governance creates the conditions that makes capitalism fair"

And here is what has answered me ChatGPT:

"The interplay between capitalism and governance is indeed important for creating conditions that foster fairness within the system. Competition can be a driving force for innovation, productivity, and economic growth, while governance provides the necessary framework to ensure that competition remains fair and equitable.

Governance mechanisms, such as regulations, laws, and oversight, help prevent anti-competitive practices, exploitation, and the concentration of power within capitalism. They set the rules of the game, establish safeguards for workers' rights, protect consumers, and promote a level playing field for businesses.

By creating and enforcing regulations, governments can address market failures, promote social welfare, and ensure that capitalism operates within ethical boundaries. For example, anti-trust laws can prevent monopolistic behavior, labor regulations can protect workers' rights, and environmental regulations can promote sustainability.

In addition to regulations, governance can also encompass social policies that aim to reduce inequality, provide social safety nets, and ensure access to basic services such as education, healthcare, and housing. These policies help counterbalance the potential negative consequences of a purely market-driven system.

The relationship between capitalism and governance is dynamic and complex, and striking the right balance is essential. Too little regulation can lead to unfair practices, inequality, and social instability. On the other hand, excessive regulation can stifle innovation, economic growth, and individual freedoms.

Finding the right equilibrium between competition and governance is an ongoing challenge. It requires a continuous evaluation and adjustment of policies to ensure that capitalism operates in a way that promotes both efficiency and fairness. By creating a supportive regulatory environment and implementing effective social policies, societies can harness the potential of capitalism while upholding principles of social justice and fairness."


Other than that i invite you to read carefully my following interesting thoughts about productivity and capitalism and more in the following web link:

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


So now i will talk about the division of Labour or specialization in capitalism, so in capitalism , specialization in what we do better lowers the errors in production , so this reduces the waste that occurs during production processes, other than that, specialization in what we do better increases output, so it can take less hours to produce, so this lower unit costs, so other than that you have to understand my new model below that enhances the Adam Smith economic model, so i think my new model below creates diversification of economy, since diversification of economy creates resilience of the economy, since i am saying in my below model that the engine of prosperity and happiness of nations are both the inequality of values and of self-interest or egoism, and both inequality of values and self-interest or egoism diversifies economy, 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 !"


Other than that i invite you to read carefully my following interesting thoughts about productivity and capitalism and more in the following web link:

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


So now i will make something clear about my new monotheistic religion,
and it is when i am saying that the Qur'aan is not the words of God, i mean that Angel Gabriel has enhanced to a certain level prophet Muhammad so that he also invent many parts of the Qur'aan, i mean that Angel Gabriel has put ideas of him in the Qur'aan, and Angel Gabriel has to certain level controlled prophet Muhammad so that he can put ideas of him in the Qur'aan, so you are now understanding how my new monotheistic religion views Qur'aan and Islam, other than that, my new monotheistic religion says that not only Adam and Eve and there descendants have been cursed by God, but the Qur'aan and the Bible too have been cursed by God, and it is why we find errors and scientific errors in the Qur'aan, and the fact that Angel Gabriel has controlled and let prophet Muhammad put his ideas in the Qur'aan, is a curse from God, and you can read my below thoughts so that to understand my new monotheistic religion and so that to understand my views on different subjects:

So you have to understand my way of doing, so i will be more clear, so i have just looked more at the Jihadist like Mohamed Atta , and you have to know that i am not like those terrorists of the 9/11, since i think that they are idiotic, since i think that they don't know how to think correctly, and there way of doing shows clearly that they are not well educated, so i think that the weakness of there ideology is that they are not well educated, as most of people are not well educated, so the best way to fight against terrorism like the one of Mohamed Atta, is to well educate people, so i will give you an example so that you understand me: So take for example capitalism and competition in capitalism, so when you are not well educated you can also think that capitalism and competition of capitalism are evil, so you can
then start to be like a Jihadist and attack USA that is this capitalism, so you are noticing the weakness of it that is: That most of people are not well educated, since they are not even able to understand correctly why the private sector of capitalism is necessary and why competition of it is necessary, so it is why i am explaining it to you in my below thoughts, and it is why i am explaining my new model that i think enhances the model of Adam Smith the father of Liberalism, so then you are noticing my way of doing that has also as a goal to avoid this kind of terrorism and violence that comes from lack of education, so i invite you to reread my below thoughts so that you understand my views on different subjects and so that to understand my way of doing:

So i have just looked at the following video of Jihadists like Mohamed Atta:

A Mission To Die For

https://www.youtube.com/watch?v=Sqhaju79y5I


I think Jihadists like Mohamed Atta in the above video, are not thinkers, so you have to know that i am not like them, since the weakness of there ideology is that it is like they are reacting too quickly to the way of life of this kind of Liberty of sex etc. in for example the West like America, but it is not the right way, since you have to know that most of humans are not well educated and that they are weak humans, since they are not equipped correctly, so you have to be compassionate towards the West like America, so the best way to do is to well educate people correctly and to make them well equipped , and it is my way of doing, since i have invented a new monotheistic religion , and you can read about it in my below thoughts, and i have made you understand my views on different subjects of technology and economics and politics and philosophy, and of course you can read them in my following thoughts:


So you can read my thoughts about my new monotheistic religion and about
Islam and the Hadith in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/w_3tJ_myplc


And I will now explain an important thing, so in economy and in business, you have to look at the economic model from a higher level point of view, i mean when for example there is competition in the private sector in the low level layer between individuals around the world or inside a country , you have not to look at it pessimistically, since you have to look at the model from a higher level point of view that says that capitalism is not a zero sum game, and 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, so then it shows that we have to be optimistic, since even if americans in USA buy from arabs in arab countries or arabs in arab countries buy from americans in USA, so i think it is a win-win situation, since capitalism is not a zero sum game, and 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, so then i think we have to be optimistic about capitalism since i think that capitalism brings Liberty, other than that we have to be optimistic since i have also invented a new monotheistic religion that i think is efficient, so i think that it also brings Liberty , and you can read about it in the following web link:


https://groups.google.com/g/alt.culture.morocco/c/w_3tJ_myplc


Other than that , i have just said that self-interest is controlled by competition so that to not lead to corruption, so then the good
idea is to make the competitive market of the private sector the powerful
motor of the economy, and speaking about competition , i invite you
to read about my new economic model below so that to understand my views:

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,


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


So i also invite you to look at the following web link with my thoughts about the new and future technologies:

https://groups.google.com/g/alt.culture.morocco/c/lfnlD52jDzI


And of course, i invite you to read all my below thoughts about artificial intelligence and about technology and more , so that to understand my views:

I invite you to read the following new article about the new DeepMind AI model called Gemimi:

After Bard, Google’s DeepMind hopes Gemini will truly surpass ChatGPT

https://indianexpress.com/article/technology/artificial-intelligence/deepmind-claims-gemini-will-rival-chatgpt-8688846/


And here is my questions to ChatGPT about the limitations of the above new DeepMind AI model called Gemimi:


And here is my first question to ChatGPT:


"So the hybrid model of AlphaGO techniques and large language models such ChatGPT, used by new DeepMind AI model called Gemimi,
can still lack on creativity , since the exploration is limited by the data ?"


And here is what answered me ChatGPT:


"Yes, the hybrid model of combining AlphaGo techniques with large language models like ChatGPT may still face limitations when it comes to creativity, and one of the factors contributing to this limitation is the reliance on available data.

Creativity often involves thinking beyond the existing patterns and generating novel, innovative solutions. While large language models like ChatGPT have shown impressive language generation capabilities, they are fundamentally trained on vast amounts of existing text data. The model's responses are primarily influenced by the patterns, examples, and language conventions present in the training data.

If the training data does not sufficiently capture a wide range of creative or unconventional approaches, the model may struggle to generate truly creative outputs. The model's exploration is indeed limited by the data it has been exposed to during training, which can constrain its ability to think "outside the box" and produce highly imaginative or innovative responses.

To enhance creativity, it may be necessary to provide the model with additional diverse and creative training data or employ techniques like reinforcement learning with suitable reward mechanisms that encourage creative outputs. However, achieving truly human-level creativity in language generation remains a challenging goal for current AI models, and they may not match the level of inventive thinking and imagination exhibited by human beings."


And here is my second question to ChatGPT:


"And what you are saying is also true for the hybrid model of AlphaGO techniques and large language models such ChatGPT ?"


"Yes, the limitations I mentioned regarding creativity and the influence of available data still apply to the hybrid model that combines AlphaGo techniques with large language models like ChatGPT.

The hybrid model inherits the data limitations from the underlying large language model component. It heavily relies on the patterns and examples present in the training data to generate responses. Therefore, if the training data does not contain diverse and creative examples, the hybrid model may struggle to exhibit high levels of creativity in its outputs.

While integrating AlphaGo techniques can enhance decision-making and exploration capabilities, it does not fundamentally change the limitations related to creativity imposed by the available training data. The hybrid model may still face challenges in generating highly creative or innovative responses that go beyond what it has seen during training.

Achieving true human-level creativity in AI models remains an ongoing research challenge."


And so that to understand more the limitations of Large Language Models such as GPT-4 and of the next new Google AI model called
Gemimi, i invite you to read my following thoughts:


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


So then i also invite you to read my beautiful poems of Love and
poems that are in accordance with my above new proverb in the following web link:

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


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


https://groups.google.com/g/alt.culture.morocco/c/mb6EO-h7ZXU


And here:


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.