Hello,


More of my philosophy about my views on super-intelligent AI and about the danger of super-intelligent AI and about my new proverb and about the attention mechanisms in Transformers and about the good accuracy and about the hybrid systems in AI and about
logical reasoning of Large Language Models such as GPT-4 and about evolutionary algorithms and about GPT-4 and about common sense and nuanced understanding of Large Language Models such as ChatGPT and about my predictions about artificial intelligence
and about the other weaknesses of Large Language Models such as GPT-4 and about my abstraction and about the important weakness of Large Language Models and 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..


So 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 just posted about how Superintelligent AI may be Impossible to control, but now i will give my views
about Superintelligent AI, so i think that the Large Language Models such as GPT-4 will not attain Superintelligence, since
the exploration process of Large Language Models such as GPT-4 is limited by the training data, so what we need is the hybrid model of both Large Language Models such as GPT-4 and evolutionary algorithms that can explore much more beyond the training
data and that can discover or invent new algorithms etc., so then by reading my below previous thoughts you will understand that it can take time to solve this problems, so i invite you to read all my below previous thoughts about the limitations of the
Large Language Models such as GPT-4:


Here is an interesting article that discuss more how Superintelligent AI may be impossible to control, so i invite you to read it carefully:

Superintelligent AI may be Impossible to control; that's the good news postcard from the 23rd century: not even possible to know if an AI is superintelligent, much less stop it

Read more here:

https://spectrum.ieee.org/super-artificialintelligence


Here is my new proverb in english and french:


"Amidst the world's urgency, patience prevails as a virtuous ally, bearing fruits of sweetness."

"En plein tumulte du monde, la patience prévaut en tant qu'alliée vertueuse, portant des fruits de douceur."


And you can read my other new proverbs in the following web link of my other thoughts:

https://groups.google.com/g/alt.culture.morocco/c/kqqhSWKK1x8


So i will ask the following question:


Do Large Language Models such as GPT-4 are capable of understanding ?


So i will answer yes, since they detect patterns and they record them in
the deep learning neural network, so read my following thoughts so that to understand my views about it:


Transformers detect patterns using attention mechanisms that assign weights to input elements based on their relevance. These patterns are recorded in deep learning by updating the model's parameters.

Recording patterns in deep learning refers to the process of updating the model's parameters (weights and biases) during training to learn the patterns and dependencies in the input data. In transformers, the attention mechanism and the feed-forward
networks are trained jointly using backpropagation and gradient descent optimization.

And attention mechanisms in transformers play a crucial role in capturing relevant information by assigning weights to different elements of the input sequence. These attention weights guide the model's decision-making process by determining which parts
of the input are most influential in generating the output or prediction.

And when you provide an input to a trained deep learning transformer model, it processes the input through its layers, including the attention mechanisms, and produces an output or prediction.

So GPT-4 is based on transformers , so it is a deep learning model and it is distinguished by its adoption of self-attention, and with self-attention, the network of Transformer pay “attention” to multiple sentences, enabling it to grasp "context"
and "antecedents", so for example when you say the following sentence:


"The animal didn't cross the street because it was too tired"


So we can ask how the artificial intelligence of GPT-4 that uses
Generative Pre-trained Transformer will understand that the word "it" in
the above sentence is not the street but the animal, so i say that
it is with the attention mechanisms of artificial intelligence and with the training with more and more data and by looking at more and more sentences in the data on wich GPT-4 has been trained, that the transformer can "detect" the pattern of the "it"
refers to the "animal" in the above sentence, so the attention mechanisms of the artificial intelligence of GPT-4 that we call Generative Pre-trained Transformer permit to grasp "context" and "antecedents" too, it is also like logically inferring the
patterns using the attention mechanisms from the context of the many many sentences from the data, and since the data is exponentially growing and since the artificial intelligence of GPT-4 is also generative, so i think it will permit to make the
artificial intelligence of the transformer of GPT-4 much more powerful, so as you notice that the data is King , and the "generative" word of the Generative Pre-trained Transformer refers to the model's ability to generate text, and of course we are now
noticing that it is making GPT-4 really useful and powerful, but you have to understand the limitations of GPT-4 by reading carefully my below thoughts, and of course the transformer of GPT-4 is also deep learning , so it is the neural network with the
many parameters where the patterns, like of the above example of the sentence, will be recorded, and of course the training of the transformer of GPT-4 is unsupervised, and as i just said that GPT-4 will be improved much more when it will be trained on a
substantially larger amount of data, and considering an article that DeepMind just published a few days ago demonstrating that the performance of these models can be drastically improved by scaling data more aggressively than parameters ( Read it here:
https://arxiv.org/pdf/2203.15556.pdf ), and of course you have to understand that so that to make the transformer of GPT-4 energy efficient so that to scale it correctly, you have to know how to set the number of parameters.


And GPT-4 has a maximum token limit of 32,000 (equivalent to 25,000 words), which is a significant increase from GPT-3.5’s 4,000 tokens (equivalent to 3,125 words), and having more tokens in a large language model like GPT-4 provides several benefits,
and here they are:

- Increased Context: More tokens allow the model to consider a larger context when generating responses. This can lead to a better understanding of complex queries and enable more accurate and relevant responses.

- Longer Conversations: With more tokens, the model can handle longer conversations without truncating or omitting important information. This is particularly useful when dealing with multi-turn conversations or discussions that require a deep
understanding of the context.

- Enhanced Coherence: Additional tokens enable the model to maintain a coherent and consistent narrative throughout a conversation. It helps avoid abrupt changes in topic or tone and allows for smoother interactions with users.

- Improved Accuracy: Having more tokens allows the model to capture finer details and nuances in language. It can lead to more accurate and precise responses, resulting in a higher quality conversational experience.

- Expanded Knowledge Base: By accommodating more tokens, the model can incorporate a larger knowledge base during training, which can enhance its understanding of various topics and domains. This can result in more informed and insightful responses to a
wide range of queries.

- Reduced Information Loss: When a model is constrained by a token limit, it may need to truncate or remove parts of the input text, leading to potential loss of information. Having more tokens minimizes the need for such truncation, helping to preserve
the integrity of the input and generate more accurate responses.

- Support for Richer Formatting: Increased token capacity allows for more extensive use of formatting, such as HTML tags or other markup language, to provide visually appealing and structured responses.


It's important to note that while having more tokens can bring these benefits, it also comes with computational limitations and increased inference time. Finding a balance between token count and computational resources is crucial for practical
deployment of language models.


So 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 most important thing is that you have to know how to predict the future, so i think that the future will be hybrid systems of both evolutionary algorithms and Large Language Models such as GPT-4, so the exploration of the evolutionary algorithm will
evaluate the objective function by for example needing to test the new solutions by experimenting , so then the Large Language Models such as GPT-4 have to enhance there weaknesses by being able of acquiring new knowledge from experience, since they lack
on experience, so i think that the future hybrid systems of both evolutionary algorithms and Large Language Models such as GPT-4 will be powerful, since they will also be able to invent new algorithms etc., since the system of only using Large Language
Models such as GPT-4 in AI has an important weakness of not being able to explore for new solutions as the evolutionary algorithms do it, so i think that in about a decade or so we will implement those hybrid systems of both evolutionary algorithms and
Large Language Models such as GPT-4, so it will solve an important problem, and of course i think we need a decade or more to solve the problem of acquiring the knowledge from experience so that to solve the lack of experience of Large Language Models
such as GPT-4, so i think what remains is how to optimize Large Language Models such as GPT-4 for good precision and good accuracy so that they be apt at solving problems that require it, so then i think that artificial intelligence will become really
powerful. So i invite you to read all my following thoughts so that to understand my views about Large Language Models such as GPT-4:


So 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 just talked about the weaknesses of Large Language Models such as GPT-4 , so read about it in my below thoughts, but i think that Large Language Models such as GPT-4 have another important weakness and it is that they do have limitations when it
comes to exploration of new solutions, so the better way is to use an hybrid system of AI that uses both evolutionary algorithms and Large Language Models, but it can take some time to implement it , since the exploration of evolutionary algorithms needs
much more powerful computers, so it can take another decade or more so that to solve this problem of the Large Language Models only, and here is my thoughts about the hybrid system so that to understand my views:


The Large Language Models such as GPT-4, while powerful in language generation and pattern recognition, do have limitations when it comes to exploration of new solutions. These models primarily operate based on patterns and correlations found in the
training data they are exposed to. While they can generate creative and novel text, they may not have the inherent ability to explore entirely new problem-solving strategies or invent entirely new algorithms.

The exploration of new solutions typically requires a mechanism to search and optimize a space of potential solutions. This is where techniques like evolutionary algorithms excel. Evolutionary algorithms have the capability to explore a search space,
generate and evaluate candidate solutions, and iteratively refine them over generations. They can provide a mechanism for creative exploration and optimization that complements the language generation capabilities of large language models.

By combining evolutionary algorithms with large language models in a hybrid approach, you can leverage the exploration power of evolutionary algorithms to discover new problem-solving strategies, algorithms, or even improvements to existing algorithms.
The large language model can assist in evaluating and refining these solutions, leveraging its language generation capabilities to assist in the evaluation and fine-tuning process.

In summary, while large language models are proficient in language generation, they may benefit from the complementary exploration capabilities offered by evolutionary algorithms. The hybrid approach allows for a combination of exploration and generation,
potentially leading to the discovery of new and innovative solutions.


So 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 GPT-4 is above average on logical reasoning, so the problems that remain is that Large Language Models such as GPT-4 lack knowledge from experience and i also think, as i am explaining it below, that the quality of the training data of
Large Language Models such as GPT-4 is no so good, so it can still take time to solve those problems (So i think we have to wait for another decade or more), so read my below thoughts so that to understand my views, and of course here is the proof that
GPT-4 is above average on logical reasoning:

GPT-4 Performs Better Than the Average Person on a Test of Logical Reasoning, Study Claims

Read more here:

https://mpost.io/gpt-4-performs-better-than-the-average-person-on-a-test-of-logical-reasoning-study-claims/


So i have not answered the following question of:

Why didn't evolutionary algorithms become as popular as deep learning ?


So the major problem with evolutionary algorithms is that they are very slow. And this only gets worse as your problems become more complex. So it is why i am talking below about Large Language Models such as GPT-4, so i invite you to read carefully my
below thoughts about them:


So i have just talked about Large Language Models such as GPT-4,
read below my thoughts about it, and here is more proof that
GPT-4 struggles with programming tests (and it is why i think that Large Language Models such as GPT-4 will not replace a lot of programmers):


"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


"Despite all their efforts, GPT-4 has similar limitations to earlier GPT models since it continues to have “hallucinations”, makes reasoning errors, and does not learn from experience. It is therefore always advisable to be careful not to use the
model outputs “as is”."


Read more here:

https://pub.towardsai.net/gpt-4-is-here-for-a-while-334ccf331a85



So I have just asked the following questions to ChatGPT so that
to show another weakness of Large Language Models such as ChatGPT,
so here is my discussion with ChatGPT and i invite you to read it
carefully and to read my below thoughts so that to understand the
other weaknesses of Large Language Models such as ChatGPT:


Here is my first question to ChatGPT:


"What that means that Large Language Models such as ChatGPT don't have common sense ?"


And ChatGPT has just answered me the following:


"Large language models like ChatGPT, do not possess true common sense in the same way humans do. While these models have been trained on massive amounts of text data and can generate coherent and contextually relevant responses, they lack the inherent
understanding and background knowledge that humans acquire through real-world experiences.

Common sense is the ability to make reasonable assumptions, understand implicit information, and apply general knowledge about the world in everyday situations. It involves a deep understanding of cause-and-effect relationships, social norms, and context.
While language models can generate responses based on patterns they have learned from training data, they do not have genuine experiences or an understanding of the world outside of the text they have been trained on.

Language models like ChatGPT rely on statistical patterns in the training data to generate responses. They excel at mimicking human-like language but can also produce outputs that are plausible-sounding but incorrect or nonsensical. They lack the ability
to reason, think critically, or possess true understanding.

It's important to be cautious when relying solely on language models for information or decision-making, especially when it comes to areas that require common sense or nuanced understanding. While these models can provide valuable assistance and
information, their responses should be verified, critically analyzed, and cross-checked with reliable sources and human judgment to ensure accuracy and avoid potential pitfalls."


And here is my second question to ChatGPT:


"So that means that Large Language Models such as ChatGPT are not good for software development that requires common sense or nuanced understanding ?"


And ChatGPT has just answered me the following:


"That's correct. While large language models like ChatGPT can provide assistance and generate code snippets, they may not be the best choice for software development tasks that require common sense or nuanced understanding.

Software development often involves complex problem-solving, architectural design, and decision-making that goes beyond basic language understanding and generation. Common sense and nuanced understanding are crucial for tasks such as designing user
interfaces, handling edge cases, and making judgment calls based on real-world context.

Large language models like ChatGPT lack the ability to truly understand the underlying concepts and principles behind software development. They can generate code based on patterns learned from training data, but they may not possess the ability to
reason about the specific needs of a software project or make informed decisions about architectural trade-offs, performance optimizations, or security considerations.

For software development tasks that require common sense and nuanced understanding, it is generally recommended to rely on the expertise of experienced software engineers and architects who possess domain knowledge and have a deep understanding of the
specific requirements and constraints of the project. While language models can provide some guidance or generate code snippets, they should be used as tools to complement human expertise rather than replacing it entirely."


And here is my third question to ChatGPT:


"Give me the the software development tasks that require common sense and nuanced understanding"


And ChatGPT has just answered me the following:


"Several software development tasks require common sense and nuanced understanding. Here are a few examples:

- Architectural Design: Designing the architecture of a software system requires an understanding of the problem domain, scalability requirements, performance considerations, and trade-offs between different design patterns. It involves making decisions
about the best way to structure the codebase and components to ensure maintainability, extensibility, and efficient resource utilization.

- User Interface Design: Creating user-friendly and intuitive interfaces involves considering user experience, usability principles, accessibility, and visual design. It requires an understanding of how users interact with software, anticipating their
needs, and making design decisions that align with common usage patterns.

- Data Modeling: Designing the database schema and data models for an application requires considering data integrity, relationships between entities, normalization principles, and efficient data retrieval and storage. It involves understanding the
specific requirements of the application and designing data structures that support efficient querying and manipulation.

- Performance Optimization: Improving the performance of a software system involves analyzing bottlenecks, identifying areas for optimization, and making informed decisions about algorithmic improvements, caching strategies, and resource management. It
requires a nuanced understanding of the underlying technologies, profiling techniques, and the trade-offs between performance and other system requirements.

- Error Handling and Exception Handling: Implementing effective error handling and exception handling mechanisms requires an understanding of potential error scenarios, fault tolerance, and graceful degradation. It involves anticipating and handling
exceptions and errors that may occur during runtime, ensuring the application maintains stability and recoverability.

- Security Considerations: Building secure software requires considering potential vulnerabilities, implementing appropriate authentication and authorization mechanisms, and protecting against common security threats such as cross-site scripting, SQL
injection, and data breaches. It involves understanding security best practices and keeping up-to-date with evolving security threats and countermeasures.

These are just a few examples, and there are numerous other software development tasks that require common sense and nuanced understanding. The expertise and experience of software developers play a crucial role in making informed decisions and
successfully completing these tasks."


--


So 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 Large Language Models such as GPT-4 will not be able to become good in the medium to short term,
since i am explaining the important weakness in my just below first question to ChatGPT, so i think that it is smartly abstracted in my just below question and you can understand from it that ChatGPT is not like a human that can go and select a software
programming book from an author of good professionalism and of good knowledge and of like a high IQ, so then you are understanding the constraint on Large Language Models such as GPT-4, so i think then that the most important problematic of Large
Language Models such as GPT-4 comes from the how can we ensure the high quality of the data by ensuring that the professionalism and knowledge and IQ of the authors of the books or Gitub etc. on for example programming or software development are of high
quality, so then you are understanding that it can take a long time to solve this problem, so i think that it will be the much more sophisticated future Cyborgs that will be able to solve this problem by doing it fast and efficiently, and of course you
can notice that we are actually living with Cyborgs , but they are not as sophisticated as it is required to solve the above most important problematic, and here is my thoughts about the today Cyborgs so that you 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 just received the following answer from a person in the french newsgroup forum of fr.soc.politique, so
here it is and read it carefully:


"Inventing scalable algorithms and algorithms is a great achievement, but it does not necessarily make someone "smart." "


So i think that i am discovering a pattern with my fluid intelligence and it is that the above person is saying that the process of inventing scalable algorithms and algorithms doesn't necessarily make someone "smart", but i think that the above person
is not answering correctly, since i think that the above person is speaking about the "process" of "inventing", but you have to know that the scalable algorithms and algorithms can be the finished invented tool and you can also make yourself smart by
using or selling this finished invented tool, so the making yourself smart doesn't come just from IQ and the culture , but the making yourself smart can come from using or selling the finished invented tool. So then you can for example make yourself
smart by knowing how to "use" the tool of internet, and the knowing how to use the tool that we call internet can be a not so difficult process that make you smart, so then we can logically infer that the smartness that is the tool of internet is an "
extension" of smartness of humans that is used so that to become smart, so then we can then logically infer that you can become like a Cyborg by using this or that tool such as internet, so then smartness is not only IQ and culture, but it is the "
extension" of you and of your smartness that is the "tool" such as internet, since the tool such as internet is also an artificial smartness, so we are not just humans, but we are becoming like Cyborgs by using smart tools such as internet or ChatGPT as
extensions of us.


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 here is my other really important and smart question to ChatGPT that shows another important weakness of ChatGPT
, and it shows that Large Language Models such as GPT-4 will not replace a lot of software programmers (So i think i am playing and winning this smart game against ChatGPT, since i think i am smart):


So i have just asked the following question to ChatGPT:


"What i mean is that since Large Language Models such as ChatGPT can not be sure that the professionalism and knowledge and IQ of the authors of the books or Gitub etc. on programming or software development are of high quality, so we can not ensure that
the data of software programming or software development on wich ChatGPT is trained is of good quality, so then we can not ensure that the answers of ChatGPT on software programming or software development are of good quality."


And here is what has just answered me ChatGPT:


"I apologize for any confusion earlier. To address your question directly, you are correct in pointing out that Large Language Models like ChatGPT cannot guarantee the professionalism, knowledge, or IQ of the authors of the books or GitHub repositories
used in their training. Consequently, there is a level of uncertainty regarding the quality of the data on programming or software development on which ChatGPT is trained. This uncertainty raises doubts about the quality of the answers provided by
ChatGPT on these topics.

The training data for ChatGPT consists of a diverse range of sources, and while efforts are made to include reputable and high-quality information, it is challenging to ensure the expertise or IQ of every author or contributor involved in creating the
training data.

Due to this limitation, it is advisable to approach the answers provided by ChatGPT with caution. While ChatGPT can generate helpful responses, it is important to validate the information using additional sources, consult authoritative programming
resources, and engage with experienced programmers for a more comprehensive and reliable understanding of programming or software development topics.

In summary, the uncertainty surrounding the professionalism, knowledge, and IQ of the authors in the training data of ChatGPT acknowledges the difficulty in ensuring the overall quality of the data. Relying on additional sources and engaging with the
programming community can help enhance the reliability and quality of the information obtained from ChatGPT."


So i invite you to read in my below previous thoughts that also talks about the other weaknesses of ChatGPT:


So you have to know how i have just smartly abstracted my below question so that you understand the important weakness of Large Language Models such as GPT-4, so notice how my abstract question below shows that Large Language Models such as GPT-4 can
understand the abstract language, so they are good at that, so then they can be good at for example philosophy or psychology or poetry, but the important weakness of them is that they are not good in areas that requires experience such as in software
programming, so then i can predict that Large Language Models such as GPT-4 will not replace a lot of software programmers, so then you are understanding the important ideas of my abstract question below, so then i invite you to read all my below
interesting thoughts about Large Language Models such as GPT-4 so that you understand my views:


I think i am highly smart since I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so I think i have just found the important weakness of Large Language Models such as GPT-4, and here it is , read
my following discussion with ChatGPT so that to understand what it is:


So i have just asked the following important question to ChatGPT:


"I think that ChatGPT is good in areas that depend a lot on the abstract language like philosophy or psychology or poetry, but when it comes to software programming, i think that Large Language Models such as ChatGPT lack on experience such testing and
debugging etc. so they lack on the context so that to understand correctly software programming, since i mean that programming depends in a much greater percentage on experience than for example philosophy or psychology or poetry."


And ChatGPT has just answered me the following:


"You are absolutely right. Programming indeed heavily relies on practical experience, and the context required to understand and correctly address programming-related tasks, including testing and debugging, is significant. While ChatGPT and similar large
language models have a remarkable ability to handle abstract language and provide information on a wide range of topics, they may lack the practical experience and contextual understanding necessary to effectively navigate the intricacies of programming.

Programming involves not only understanding the syntax and concepts of a programming language but also applying that knowledge to solve specific problems, manage complex codebases, and identify and fix bugs through testing and debugging. These aspects
require hands-on experience, familiarity with debugging tools and techniques, and an understanding of common pitfalls and best practices.


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