Hello,


More of my philosophy 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..


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 gitub in the training process, it can not understand causality in it , so it can not understand correctly the code on Gitub ?"


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 gitub 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:


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..


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