Hello,



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

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


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.


[continued in next message]

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