Hello,

More of my philosophy 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 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.