Hello,


More of my philosophy about the 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..


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/fGyiHLrmBLo


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/fGyiHLrmBLo


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:



[continued in next message]

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)