Hello,



More of my philosophy about the fountain of youth and about 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..


Energy drink ingredient 'Taurine' may extend life, shows promise in mice


"Columbia University in New York researchers conducted trials on 14-month-old mice, roughly the equivalent of human age 45.

These animals were given a daily dosage of taurine, with some put in a placebo group.

The findings revealed that middle-aged female mice witnessed a 12 percent increase in lifespan when compared to those that did not get taurine dosages. Male mice lived around 10 percent longer than female mice. All of those who got taurine reported
better health and seemed younger. In addition, taurine supplementation decreased DNA damage in old animals.


The taurine-supplemented worms witnessed a 10-23 percent increase in longevity when given to 15-year-old rhesus monkeys for six months and showed benefits in body weight, bone, blood sugar levels, and the immune system."


Read more here:

https://interestingengineering.com/science/energy-drink-ingredient-taurine-may-extend-life-shows-promise-in-mice


And read the following interesting new article:


Oxygen deficiency increases mouse lifespan by 50 percent, will it work in humans?

Read more here:

https://interestingengineering.com/science/oxygen-restriction-and-anti-aging


And I invite you to read the following new interesting article:


Researchers unlock the fountain of youth in mice through genetically modified stem cells

Read more here:

https://interestingengineering.com/health/researchers-unlock-the-fountain-of-youth-in-mice


And I have just grouped the following web links of my previous thoughts
and i invite you to read them carefully, and of course i invite
you to also read my below thoughts:


And you can read my thoughts in the following web link about cancer and about new interesting medical treatments and drugs:

https://groups.google.com/g/alt.culture.morocco/c/3HwdSeO3esc


And read more of my following thoughts about cancer and about health and about new interesting medical treatments and drugs etc.:

https://groups.google.com/g/alt.culture.morocco/c/k6_-3RpoSOA


And you can read more about my thoughts about productivity and capitalism and more in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/KN4V-rGRDdU


And i invite you to read carefully my following thoughts on different subjects and you can read my new proverbs in the following web link:

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


And of course here is more of my philosophy about the mathematical modeling of the Jackson network and about mathematics and more of my thoughts, you can read it in the following web link:

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



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 the system of only using Large
Language Models such as GPT-4 have 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.

While language models like ChatGPT can offer general insights and guidance on programming, they should not be solely relied upon for critical tasks that require practical expertise. It is always advisable to consult specialized resources, programming
documentation, experienced developers, or dedicated programming communities for more accurate and reliable assistance with programming-related topics, including testing and debugging.

In summary, while ChatGPT can excel in areas that rely heavily on abstract language, such as philosophy, psychology, or poetry, programming is indeed an area where practical experience plays a vital role, and specialized resources and human expertise are
essential for accurate and reliable guidance."


So i invite you to read my following previous thoughts so that to understand my views:



So I have just said the following:


"I think i am highly smart since I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so i think that there is still an important weakness in Large Language Models such as GPT-4, and it is that humans
evaluate the quality of the data by using the much deeper meaning that comes from the human brain, so humans are guided in choosing the best quality by using this mechanism, but Large Language Models such as GPT-4 evaluate the quality by the quality of
the data, so Large Language Models such as GPT-4 lacks the much deeper meaning of the brain and it is the defect, so it looks like the problem that i am talking about in my below thoughts about the quality of the training data."


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


So here is my first question to ChatGPT:


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


And ChatGPT has just answered me:


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

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

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

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

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


And here is my second question to ChatGPT:


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


And ChatGPT has just answered me the following:


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

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

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

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


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