Hello,

More of my philosophy about David Shapiro and about artificial intelligence and about Generative AI and about Reinforcement learning and about creativity 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..

I invite you to read the following new article (And you can translate the article from french to english):

"Better, faster, cheaper, safer: 4 reasons why AI should replace all human employees", according to David Shapiro

https://intelligence-artificielle.developpez.com/actu/348094/-Meilleure-plus-rapide-moins-couteuse-plus-sure-les-4-raisons-pour-lesquelles-l-IA-devrait-remplacer-tous-les-employes-humains-d-apres-David-Shapiro-d-avis-que-ce-futur-est-tout-proche/

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 my new model that explains what is human consciousness is the key, since the model of artificial intelligence is lacking the deep understanding with the "meaning" from human consciousness and here is my thoughts about it so that you understand why i say that artificial intelligence will not replace software programmers or software development jobs:

The inability of Large Language Models such as ChatGPT to invent new algorithms is primarily due to its training data and the nature of its architecture. ChatGPT, like other machine learning models, is trained on a vast dataset that consists of text from the internet, books, articles, and other sources. It learns patterns and associations within this data to generate human-like text and answer questions based on what it has seen and learned during training.

Here are a few key reasons why ChatGPT cannot invent new algorithms:

- Lack of Creativity: ChatGPT does not possess creativity or the ability to generate truly novel ideas. It relies on patterns and information present in its training data to generate responses. It doesn't have the capability to create new algorithms or solutions that go beyond its training data.

- No Understanding of Algorithmic Concepts: While ChatGPT may have some knowledge of existing algorithms and mathematical concepts based on its training data, it lacks a deep understanding of these concepts. It doesn't have the capacity to develop a fundamental understanding of algorithms or invent new ones.

- Data Dependency: ChatGPT's responses are heavily dependent on the data it was trained on. If a particular algorithm or concept is not well-represented in its training data, it is unlikely to provide insightful or innovative solutions related to that topic.

- Limited Scope: ChatGPT's training data is limited to text, and it lacks the ability to interact with the physical world or perform experiments. Many algorithmic inventions involve experimentation, mathematical proofs, and insights that go beyond the scope of textual data.

In summary, ChatGPT is a powerful language model for generating human-like text and providing information based on existing knowledge, but it is not a creative problem solver or algorithm inventor. Its responses are constrained by its training data and the patterns it has learned from that data. Inventing new algorithms requires creative thinking, deep understanding of mathematical and computational principles, and the ability to go beyond the limitations of pre-existing data, which are capabilities that AI models like ChatGPT currently lack.

And the deep understanding is crucial because it enables an entity, whether human or artificial intelligence, to not only apply knowledge in a rote or memorized manner but also to:

- Generalize: Deep understanding allows one to generalize knowledge to new, unseen situations. Rather than relying on memorized facts, a deep understanding of underlying principles and concepts allows for the application of knowledge in novel contexts.

- Problem-Solve: Understanding the fundamentals of a concept or field allows for creative problem-solving. It enables the generation of new solutions, adaptations, and innovations, even in situations where existing knowledge doesn't provide a direct answer.

- Critical Thinking: Deep understanding fosters critical thinking. It allows one to analyze information, identify patterns, and evaluate the strengths and weaknesses of different approaches or solutions. This is important in complex decision-making.

- Flexibility: When someone deeply understands a concept, they are more flexible in their thinking and can adapt their knowledge to various scenarios. They are not limited to rigidly following predefined procedures or solutions.

- Teaching and Communication: People with deep understanding can effectively teach and communicate complex ideas to others because they grasp the nuances and can explain concepts in various ways to facilitate learning.

Innovation: Many breakthroughs and innovations come from a deep understanding of existing knowledge, allowing individuals to see gaps or opportunities for improvement.

In the context of inventing new algorithms, deep understanding of mathematical and computational principles, as well as the ability to apply this understanding creatively, is essential. Algorithms often involve intricate mathematical or logical concepts, and a deep understanding enables the development of novel approaches, optimizations, and insights. Without such understanding, it's challenging to go beyond the boundaries of existing algorithms and come up with innovative solutions to complex problems.

While AI models like ChatGPT can provide information based on the patterns they've learned from training data, their responses are typically shallow and lack the depth of understanding that humans possess. They can't engage in true creative problem-solving or algorithm invention because they lack the capacity for deep comprehension and creative insight.

I invite you to read the following new article that says that a team of US and Lithuanian researchers has just published a study that found ChatGPT can rival the creative abilities of the top-performing 1% of human participants in a standard test of creativity.

Read the new article here (And you can translate it from french to english):

https://intelligence-artificielle.developpez.com/actu/347371/Une-etude-rapporte-que-les-performances-de-ChatGPT-le-classent-parmi-les-1-pourcent-de-personnes-les-plus-creatives-au-monde-mais-des-critiques-affirment-que-l-IA-ne-peut-pas-faire-preuve-d-originalite/

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, and i am finding the above researchers not so smart, so i have just discovered patterns with my fluid intelligence in the above article and they are the following:

So i say the above researchers are not thinking correctly, since creativity
of Generative AI such as ChatGPT is limited in its "exploration" by the data
on wich it has been trained, so it is limited by the patterns that it has discovered and the discovering of patterns is also limited by the context window of ChatGPT when it is trained, so since you can not enlarge sufficiently the context window so that you can discover all the global patterns , so it is also a limitation in generative AI , so Reinforcement learning with Generative AI such as in ChatGPT or in the next artificial intelligence that is called Gemini of Google is limited by the data on wich it has been trained , so the future ChatGPT such as GPT-5 or the next artificial intelligence of Google that is called Gemini will have the same limitations, so you have to understand what is exploration in Generative AI and in Reinforcement learning in artificial intelligence since i have just talked about the exploration and exploitation of the genetic algorithm in a sophisticated manner, read it in my below thoughts, but the exploration of smartness of humans is not limited as the Generative AI such as ChatGPT, since smartness of humans uses the real human "meaning" from human consciousness and it uses human experience , so it is why creativity of humans is much better than generative AI such as ChatGPT because of the deep understanding that comes from the meaning from human consciousness, and so that to understand about the limitation that is the lack of the real human "meaning" from human consciousness, i invite you to read my following thoughts about my new model of what is consciousness of humans so that to understand my views:

So i have just looked more carefully at GPT-4 , and i think that as i have just explained it, that it will become powerful, but it is limited by the data and the quality of the data on wich it has been trained, so if it encounter a new situation to be solved and the solution of it can not be inferred from the data on wich it has been trained, so it will not be capable of solving this new situation, so i think that my new model of what is consciousness is explaining that what is lacking is the meaning from human consciousness that permits to solve the problem, so my new model is explaining that artificial intelligence such as GPT-4 will not attain artificial general intelligence or AGI, but eventhough , i think that artificial intelligence such as GPT-4 will become powerful, so i think that the problematic in artificial intelligence is about the low level layers, so i mean look at assembler programming language, so it is a low level layer than high level programming languages, but you have to notice that the low level layer of assembler programming language can do things that the higher level layer can not do, so for example you can play with the stack registers and low level hardware registers and low level hardware instructions etc. and notice how the low level layer like assembler programming can teach you more about the hardware, since it is really near the hardware, so i think that it is what is happening in artificial intelligence such as the new GPT-4, i mean that GPT-4 is for example trained on data so that to discover patterns that make it more smart, but the problematic is that this layer of how it is trained on the data so that to discover patterns is a high level layer such as the high level programming language, so i think that it is missing the low level layers of what makes the meaning, like the meaning of the past and present and the future or the meaning of space and matter and time.. from what you can construct the bigger meaning of other bigger things, so it is why i think that artificial intelligence will not attain artificial general intelligence or AGI, so i think that what is lacking in artificial intelligence is what is explaining my new model of what is consciousness, so you can read all my following thoughts about my new model of what is
human consciousness: