Hello,

More of my philosophy about my new ideas of my philosophy and about evolutionary algorithms and the genetic algorithm and about the formal and about the closed and open system and about AI and about the essence of artificial intelligence and 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 as you are noticing , i am explaining below the genetic algorithm and i am showing you below my new model of what is human consciousness so that you understand the limitations of artificial intelligence, so of course you have to read it carefully, but i think i am also a new philosopher , and you can look at my new ideas of my philosophy in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/xcWGmtgEu6o

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 you have just seen me talking quickly about Hamas and about some important laws in Islam, but i think i am a new philosopher and i will now discover important patterns with my fluid intelligence about the genetic algorithm,
and they are the following: So i think that the most important thing to understand in the genetic algorithm is that when you do a crossover of the genes of the parents, you have the tendency to obtain new childs that look like the parents, so it is why in optimization we can say that the genetic algorithm explores locally near the local optima with the crossover steps and we call it exploitation, and in the genetic algorithm , with the mutations of the genes after the crossover steps that can be of a probability of around 10% or so, permits to explore far away for better genes and it permits to not get stuck in a local optima and to continu to converge towards the global optima and it permits to not lack on diversity of the genes of the population, so this way in the genetic algorithm of balancing the exploitation with the crossover steps and with the exploration with mutations is a much better search than brute force search, and it is why the genetic algorithm is also used in optimization problems with success, and of course you have to understand that the the crossover steps can be elitistic, i mean that you can crossover the parents that have the best genes, and so that to understand the rest of my ideas about the genetic algorithm, you can read my below thoughts, and i invite you to also read carefully my below new thoughts about my new model of what is human consciousness and about the limitations of artificial intelligence:

So as you have just noticed , i have just explained my new model of what is consciousness etc., but now you have to understand an important thing,
and it is that the way of artificial intelligence such as ChatGPT is different
than the Symbolic or the formal way of doing with mathematics and programming, i mean the formal way of mathematics and programming is the way that goes from our human brains that put rules and patterns as human intelligence into the formal way of mathematics and programming, but it is what wants to avoid artificial intelligence such as ChatGPT, since i am explaining below that it is not scalable, i mean that artificial intelligence such as ChatGPT wants to go from the data on wich it is trained and discover and extracts the rules and patterns of intelligence and in a Self-supervised learning way put them into the deep learning network, so as i am explaining below that it is the scalable way of doing, also it can be fully parallelized using GPUs or the like so that to be scalable, so i think that we have not to be pessimistic, since i think that artificial intelligence such as ChatGPT will become a powerful tool, but as i am explaining below that it will not attain artificial general intelligence.

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 as you notice
i have just invented a new model of what is human consciousness, read it in my below thoughts, and i think it is much more precise and i think it permits to predict that artificial intelligence will not attain artificial general intelligence, but i have to explain one more important thing, so as you are noticing i am speaking in my below thoughts about the much more deeper meaning that comes from human consciousness, and i am explaining what is it in my new model below of what is human consciousness , but i think
you have to look at it from another perspective, and it is that the meaning from human consciousness not only permits to learn with much less data
than artificial intelligence, but it permits the human smartness to not be
a closed system like of artificial intelligence, i mean that human consciousness permits our smartness to be an open system that permits
to explore much more efficiently, so it is like an open system, and it is
why artificial intelligence will not attain general artificial intelligence since
it is a closed system.

So you have to understand more my thoughts below about artificial intelligence , so i have not talked about the connectionism and the symbolic way of doing in artificial intelligence, so i can talk about it in my kind of way, so i think that the previous way of doing of artificial intelligence was the Symbolic way of doing like with expert systems or with the prolog programming language, but the Symbolic way of doing is like the formal way of doing in mathematics or programming , i mean that they can not be scalable by being Self-supervised learning, so it is why we have followed the way of connectionism with deep learning and with for example transformers like in ChatGPT or the next Gemini, since this connectionism way of doing of for example ChatGPT or the next Gemini can be scalable by using Self-supervised learning, so i think that it is the main important advantage of it, is that it can scale rapidly.

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 to talk
about the limitations of the next AI system from Google that is called Gemini that will compete against GPT-5 from Microsoft and OpenAI, so first i will say that the window context size will be 1 million tokens, so then i think that the long-range global patterns will no more be a limitation for it, but here is the other limitation of Gemini in a form of my smart question to ChatGPT, and of course you have to read carefully my below new model of what is consciousness so that to know about the other limitations of artificial intelligence:

Here is my new smart question to ChatGPT:

But the reinforcement learning (RL) with the reward is a refining process, so i don't think adding AlphaGo's techniques such as reinforcement learning and tree search to Large Language Models like is doing it the next AI system of Google that we call Gemini will make it invent new things since it is limited by the data on wich it has been trained , so there can be refinement , but it is limited by the data on wich it has been trained.

And ChatGPT has just answered me the following:

"You raise a valid point. Integrating techniques like reinforcement learning and tree search into large language models does indeed improve their ability to generate responses and make decisions, but these models are fundamentally limited by the data on which they have been trained. Let's delve into this limitation further:

Data Dependency:

Large language models, including those enhanced with reinforcement learning and tree search, rely heavily on the data they've been trained on. They learn patterns, facts, and reasoning abilities from the text in their training data. As a result, their responses are inherently tied to the information and patterns present in that data.

No Invention of New Knowledge:

Language models, regardless of their enhancements, do not possess the ability to invent new knowledge or information. They can only generate responses based on the patterns, facts, and language structures they've learned from the training data. If a piece of information or a concept is not present in the training data, the model will not be able to invent it.

Refinement and Adaptation:

While language models cannot invent new information, they can refine and adapt the information they've learned. Techniques like reinforcement learning can help models generate more contextually appropriate and coherent responses by optimizing for user satisfaction or task completion. This means they can be better at utilizing their existing knowledge.

Data Biases and Limitations:

Language models inherit biases and limitations from their training data. They may generate biased or incorrect responses if their training data contains such biases or inaccuracies. Reinforcement learning can mitigate some of these issues, but it cannot completely eliminate them.

Generalization:

Language models can generalize to some extent. They can apply their learned knowledge to a wide range of tasks and domains. However, this generalization is limited by the diversity and coverage of the training data. If a task or domain is significantly different from the training data, the model's performance may degrade.

In summary, while reinforcement learning and tree search techniques can enhance the decision-making and response generation capabilities of large language models, they are fundamentally constrained by the data on which they have been trained. These models can refine and adapt their knowledge but cannot invent entirely new information or concepts. Their effectiveness and reliability depend on the quality and diversity of their training data, and they may still exhibit biases and limitations present in that data. Researchers and developers must be mindful of these constraints when using and deploying such models."