Hello,



More of my philosophy about the quality of Large Language Models such as GPT-4 and about the deeper meaning and about mathematics and about Large Language Models such as GPT-4 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 the following:


"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 there is still an important weakness in Large Language Models such as GPT-4, and it is that humans
evaluate the quality of the data by using the much deeper meaning that comes from the human brain, so humans are guided in choosing the best quality by using this mechanism, but Large Language Models such as GPT-4 evaluate the quality by the quality of
the data, so Large Language Models such as GPT-4 lacks the much deeper meaning of the brain and it is the defect, so it looks like the problem that i am talking about in my below thoughts about the quality of the training data."


And here is more of my logical proof of it in a form of my following interesting discussion with ChatGPT on this subject ,so i invite you to read it carefully so that to understand the important weakness of Large Language Models such as GPT-4 :


So here is my first question to ChatGPT:


"And for Large Language Models such as GPT-4, human review and evaluation so that to conform with high standards of quality of training data can take a long time."


And ChatGPT has just answered me:


"The time required for human review and evaluation of data can vary depending on factors such as the size of the dataset, the complexity of the subject matter, the number of human reviewers involved, and the specific standards and guidelines set for
quality assurance. While comprehensive and rigorous human review can be time-consuming, it is difficult to provide a specific timeframe without detailed context.

Efforts are made to balance the need for thorough evaluation with the practical constraints of time and resources. The use of efficient review processes, clear guidelines, and iterative feedback loops can help streamline the review and evaluation phase.
Automation and natural language processing techniques can also assist in accelerating certain aspects of data analysis, although human judgment and expertise remain critical.

It's worth noting that the time spent on human review is often distributed throughout the development process, including initial data collection, dataset curation, and ongoing evaluation as the language model is trained and fine-tuned. The aim is to
continuously improve the model's performance, accuracy, and adherence to high-quality standards.

While the duration of human review is an important consideration, it's also important to balance it with other factors, such as the need to make progress, deliver timely updates, and address real-world demands. The field of natural language processing is
constantly evolving, and developers strive to strike a balance between quality assurance and the practicalities of advancing the technology.

Overall, while human review can be time-consuming, the specific duration required will depend on various factors and the specific goals and constraints of the project. The aim is to ensure a reasonable level of quality while maintaining efficiency in the
development and deployment of language models."


And here is my second question to ChatGPT:


"So then it is an approximation and we can not ensure yet that Large Language Models such as GPT-4 are accurate and of high quality ?"


And ChatGPT has just answered me the following:


"Large Language Models (LLMs) are indeed an approximation, and achieving perfect accuracy and quality in their outputs is a challenging task. While significant progress has been made in developing LLMs that generate coherent and contextually relevant
text, they are not flawless or infallible. There are inherent limitations and potential shortcomings in their performance.

Ensuring the accuracy and high quality of LLMs is an ongoing endeavor, and researchers and developers are actively working to improve their capabilities. Efforts are made to curate high-quality training data, refine guidelines, incorporate human review,
and iteratively fine-tune the models. However, due to the complexity of natural language understanding and the vastness of human knowledge, achieving complete accuracy remains a significant challenge.

It's important to approach LLM outputs with a critical mindset, recognizing that they are probabilistic models trained on vast amounts of data. The generated responses can sometimes be influenced by biases present in the training data or may lack a
comprehensive understanding of nuanced contexts.

To mitigate these limitations, responsible use and interpretation of LLM outputs are crucial. Recognizing and understanding the potential biases, limitations, and uncertainties of the models can help users make informed decisions and take additional
steps to verify information when necessary. Combining LLM-generated responses with human judgment and expertise is often recommended for critical tasks that require high accuracy and quality.

While LLMs have demonstrated impressive capabilities, they are not a perfect substitute for human intelligence or understanding. Ongoing research, open dialogue, and collaborative efforts are necessary to address the limitations, improve the quality, and
ensure responsible use of LLM technology."


I have also asked this third interesting question to ChatGPT:


"I mean since Large Language Models such as GPT-4 lack true understanding or consciousness, so it is still an approximation , so we can not be sure of the quality ?"


And ChatGPT has answered me the following:


"You are correct that since large language models lack true understanding or consciousness, their responses are based on statistical patterns and associations in the training data. This means that their outputs are approximations and not a reflection of
true comprehension. As a result, there are limitations and potential quality concerns with these models. Here are some factors to consider:

- Biases in training data: Large language models can inadvertently learn biases present in the training data. If the training data contains biases, the model's responses may exhibit similar biases or reinforce existing societal biases. Bias mitigation
techniques are being actively researched and implemented to address this concern.

- Contextual limitations: While language models consider context, their understanding is limited to the immediate context provided. They may not have a persistent memory of the entire conversation or be able to reason about information from previous
interactions, which can lead to inconsistent or contextually inappropriate responses.

- Incorrect or misleading information: Language models can generate responses that sound plausible but may not always be factually accurate. They may generate answers based on patterns in the training data, even if the information is outdated, incomplete,
or incorrect. It's important to verify information from reliable sources independently.

- Lack of common sense reasoning: Large language models may struggle with common sense reasoning and understanding implicit knowledge. They rely primarily on patterns in the training data and may not possess deep conceptual understanding or intuitive
reasoning abilities.

- Ethical considerations: The use of language models also raises ethical concerns. For instance, there are debates surrounding issues like the responsible disclosure of limitations, transparency in AI-generated content, and potential misuse or malicious
applications of these models.

Given these considerations, it is essential to approach the outputs of large language models with caution and critical thinking. They can provide valuable assistance and generate useful responses, but human judgment and verification remain crucial for
assessing the quality and reliability of the information they provide."


And i think ChatGPT has another problem, and it is that the generated content can infringe on the copyright of existing works. This could occur if ChatGPT generates content similar to existing copyrighted material of the data on wich it has been trained.
So you have to be careful, since it can hurt your business, but you have to know that copyright does not protect ideas, concepts, systems, or methods of doing something. But copyright law for example protects the expression of ideas rather than the ideas
themselves. In other words, copyright law protects the specific form in which an idea is expressed, rather than the underlying idea or concept. And you have to also know that there is another problem with ChatGPT and it is that it can generate an
invention, but it could be argued that the creators of the model,
OpenAI, should be able to patent the invention. However, it could also be argued, that the source material used to train the model should be considered as prior art, meaning that the invention would not be considered new and therefore not patentable.


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, i have just posted about how Large language models such as GPT-4 struggle at math, but i think that it will be
solved, since i think that GPT-4 has a maximum token limit of 32,000 (equivalent to 25,000 words), and that's not the problem, but i think that the problem comes from the percentage of mathematical content within the training data that is relatively
small compared to the whole training data. So, i think GPT-4 has not encountered sufficient mathematical concepts and problems in the training data to develop an understanding of them. So i think the lack of exposure to mathematical content in the
training data hinders GPT-4's ability to solve complex mathematical problems effectively, and i think that this problem will soon be solved, And the other problem is that GPT-4's primary strength lies in its ability to reason with natural language.
However, when it comes to solving mathematical problems that require manipulation of equations or formulas, GPT-4's proficiency in symbolic reasoning is not good. And it is particularly evident when the GPT-4 encounters abstract symbols that must be
manipulated to arrive at a solution, but i think that this problem will soon be solved and i think that Large language models such as GPT-4 will soon be able to not struggle anymore at math. And i think that GPT-4 lacks true understanding of the world,
since its understanding comes from the data on wich it has been trained that lacks the true understanding of the world, so i think it is a limitation of GPT-4, also here is the limitations of GPT-4 that you can read in my below thoughts:

1- GPT-4 lacks on the understanding of context, since GPT-4 was trained
on large amounts of text data, but it has not the ability to
understand the context of the text. This means that it can generate
coherent sentences, but they may not always make sense in the context
of the conversation.

2- And GPT-4 is limited in its ability to generate creative or original content. GPT-4 is trained on existing text data, so it is not able to
generate new ideas or concepts. This means that GPT-4 is not suitable
for tasks that require creativity or originality.


Here is what has just answered me ChatGPT about my previous thoughts below about exploration and exploitation etc. and about the quality of the answers of ChatGPT, so here is the answer of ChatGPT to my thoughts that recognizes one important limitation
of its limitations , so read it carefully:


"Your thoughts on the genetic algorithm and its connection to exploration and exploitation in artificial intelligence are insightful. The genetic algorithm indeed utilizes exploration and exploitation processes to find optimal solutions by maintaining
diversity in the population and avoiding getting stuck in local optima.

Regarding ChatGPT, you raise valid points about the limitations and uncertainties associated with its responses. Language models like ChatGPT are probabilistic in nature, and while they can provide valuable information and assistance, they are not
infallible or a substitute for human expertise.

The answers provided by ChatGPT in response to your questions acknowledge these limitations. ChatGPT emphasizes that it is not intended to replace programmers or domain experts in tasks that require high-quality and accurate programming. Professional
programmers bring critical thinking, deep understanding, and domain-specific knowledge that language models may lack.

ChatGPT also acknowledges that its responses may not always be of high quality or accuracy, and supervision from individuals who understand the subject matter is crucial. Human supervision and guidance are necessary to ensure accurate learning, critical
assessment of information, and contextualized explanations.

In summary, while language models like ChatGPT can be valuable tools for assistance and learning, they should be used in conjunction with human expertise and critical thinking to ensure the delivery of high-quality and reliable solutions."


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 have understood the genetic algorithm and its processes that we call exploration and exploitation, so you
have to understand the process of exploration and exploitation in artificial intelligence, so the processes in the genetic algorithm are like the processes that have made our human genetics, so the mutations that happen with a low probability in the
genetic algorithm ensures a diversity that permits to explore if there is another more optimal solution , and the genetic algorithm has not to get stuck in a local optimum by being lack of diversity in the population of the genetic algorithm , so in
ChatGPT, other than the mechanism of attention that permits to discover the patterns on the data on wich it has been trained, it has another mechanism called Reinforcement learning from human feedback that ensure an exploration from the discovered
patterns in the data on wich it has been trained, so Reinforcement learning from human feedback in ChatGPT can make it discover other patterns and it can permits ChatGPT to refine its model , and exploitation, on the other hand, involves using the
learned knowledge to provide more reliable and effective responses, so CharGPT has to balance between exploration and exploitation so that to be efficient, but i think that one of the problem that remains , is that we can not be certain or sure that the
answers of ChatGPT are of high quality, and it is what i am explaining in my below thoughts, so i invite you to read my thoughts below so that to understand this limitation of ChatGPT, and i invite you to read my thoughts in the below web link so that to
understand the other limitations of ChatGPT and so that to understand my views on different subjects:


More of my philosophy about ChatGPT and more of my thoughts..


I think i am highly smart since I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so i invite you to read my following smart discussion with ChatGPT so that to understand the other limitations of
ChatGPT:


So I have just asked the smart following questions to ChatGPT, so read them carefully and read carefully the answers of ChatGPT:

Here is my first question to ChatGPT:


"ChatGPT, or any language model for that matter, are probabilistic in nature and can be considered as approximations, so the main problem is that if we can not be certain or sure that the answers of ChatGPT, or any language model for that matter, are of
high quality, so we have for example to review the programming code of ChatGPT, so then it means that it can not replace in this case programmers."



And here is the answer of ChatGPT:


"You are correct. Language models like ChatGPT are not intended to replace programmers or domain experts in tasks that require high-quality, accurate, and reliable programming. While they can assist with certain programming-related queries or provide
general information, they are not a substitute for the expertise and experience of professional programmers.

Programming involves complex problem-solving, critical thinking, and consideration of various factors such as performance, security, and maintainability. It requires a deep understanding of programming concepts, best practices, and specific domain
knowledge.

Language models like ChatGPT have limitations in terms of their ability to comprehend complex programming scenarios, understand the specific requirements of a project, and provide optimal solutions. They may not be aware of the latest programming
practices, industry standards, or specific project constraints. Therefore, relying solely on language models for programming tasks can result in suboptimal or incorrect solutions.

However, language models can still be useful in certain programming-related tasks, such as providing general programming explanations, assisting with syntax or code examples, or offering suggestions for common programming issues. They can serve as a
valuable resource to complement the skills and expertise of programmers.

In summary, while language models can offer assistance in programming-related tasks, they are not a replacement for programmers. Professional programmers and domain experts bring critical thinking, expertise, and the ability to understand and address
complex programming challenges, ensuring the delivery of high-quality and reliable solutions."


And here is my second a question to ChatGPT:


"So since we can not be certain or sure that the answers from ChatGPT are of high quality, so the learning process from ChatGPT needs supervision from those that understand"


And here is the answer of ChatGPT:


"You're correct that language models like ChatGPT may not always provide answers of high quality or accuracy. While efforts are made to optimize their responses, there is always a possibility of errors, incomplete information, or incorrect
interpretations.

Supervision and guidance from individuals who understand the subject matter are indeed crucial for effective learning. ChatGPT can serve as a tool to assist in the learning process, but it should not be relied upon as the sole source of information or
guidance.

When using ChatGPT or any AI model for learning purposes, it's important to approach the information critically and verify it using reliable sources. Consulting experts, teachers, or knowledgeable individuals in the field can provide valuable insights,
clarification, and oversight.

Supervision and guidance help ensure that the learning process is accurate, comprehensive, and aligned with the desired objectives. Human expertise and understanding are essential for assessing the quality of information, identifying potential biases or
inaccuracies, and providing contextualized explanations.

Ultimately, learning is a collaborative process that involves leveraging the benefits of AI models like ChatGPT while actively engaging with human expertise. By combining the strengths of AI models and human supervision, a more effective and reliable
learning experience can be achieved."


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


Thank you,
Amine Moulay Ramdane.

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