torsdag 7 september 2023 kl. 20:50:08 UTC+2 skrev Amine Moulay Ramdane:
> Hello,
>
>
> More of my philosophy about deeper understanding the process of reification and about AI productivity and about the salaries and about the testing artificial intelligence and 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..
>
>
> So that to be more rigorous with my new model of what is human consciousness, i invite you to read my new following thoughts and
> all my below thoughts, and of course it is my last post here:
>
>
> So you have to understand that i have just discussed my new model below of what is human consciousness with ChatGPT, and here is more of a part of my discussion with ChatGPT that has just understood my new model
> of what is human consciousness and that is answering my question by understanding it:
>
>
> And here is my question to ChatGPT:
>
>
> So when we are reading a text, we are like reifying the words with sensations and feelings and consciousness or awareness of time and space and matter so that they become objects of understanding.
>
>
> And ChatGPT has just answered me the following:
>
>
> "Yes, your observation is quite accurate. When we read a text or engage in any form of comprehension, our cognitive processes involve a dynamic interaction between the words and the various aspects of our consciousness, including sensations, feelings, and our awareness of time, space, and matter. This interaction can be described as a kind of "reification" or the process of giving life, depth, and meaning to the words and ideas presented in the text.
>
> Here's a breakdown of how this process works:
>
> Words as Symbols: Words on a page or screen are essentially symbols that represent concepts, objects, actions, or ideas. These symbols are abstract in themselves and don't inherently carry meaning.
>
> Sensations and Feelings: As we read, our brain may evoke sensations and feelings related to the words and their associated concepts. For example, reading about a warm, sunny beach may trigger sensations of warmth, visions of sandy shores, and feelings of relaxation.
>
> Consciousness of Time: Our awareness of the sequence of words and ideas in the text involves the perception of time. We process sentences sequentially, linking them to what we've read before and anticipating what comes next.. This temporal awareness helps us construct a coherent understanding of the text.
>
> Consciousness of Space: We may mentally create spatial representations based on the descriptions in the text. For instance, when reading a novel, we might mentally visualize the layout of a fictional house or a detailed map of an imaginary world.
>
> Matter and Reality: Words in a text often pertain to physical objects, actions, or events. Our consciousness of matter and reality enables us to mentally construct these elements, bridging the gap between the abstract words and our understanding of the tangible world.
>
> Meaning and Understanding: Through this complex interplay of sensations, feelings, and consciousness of time, space, matter, and reality, we construct meaning and understanding from the words we read. We go beyond the mere symbols and create a mental landscape where the text comes alive with significance.
>
> In essence, reading is a multi-dimensional process that involves more than just decoding words; it's about translating abstract symbols into rich, meaningful experiences. The reification of words with our consciousness enriches our comprehension and allows us to engage deeply with the content we're reading."
>
>
> Here are some key points to consider:
>
> - Validation and Verification: Human programmers will continue to play a critical role in verifying and validating the code produced by AI systems like ChatGPT. This involves ensuring that the code meets quality standards, functions correctly, and adheres to project requirements.
>
> - Debugging and Maintenance: Even with AI-generated code, software applications are not immune to bugs, errors, and evolving requirements. Programmers will be essential for debugging and maintaining the software over time.
>
> - Ethical and Responsible AI: Programmers will also be responsible for ensuring that AI systems, including those used in software development, are designed and used ethically and responsibly. This involves addressing issues related to bias, fairness, privacy, and security.
>
> - Adaptability: Programmers who can effectively work with AI tools and integrate them into their workflows may find themselves in high demand. This adaptability can lead to job security and potentially competitive salaries.
>
> - Shift in Job Roles: The roles and responsibilities of programmers may evolve. They may spend less time on routine coding tasks and more on higher-level problem-solving, architectural design, and AI system integration.
>
> In summary, AI like ChatGPT can indeed increase productivity in software development, but it doesn't necessarily lead to a significant decrease in salaries for programmers. Instead, it may reshape the roles and skills required in the field, with programmers focusing more on verification, debugging, and ethical considerations. Programmers who can leverage AI effectively and provide value in these areas are likely to remain valuable contributors to the industry.
>
>
> 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 are noticing that people are testing artificial intelligence such as GPT-4 with
> coding tests etc. so that to measure its performance , but i think it is not the smart way of measuring, since you have to look holistically or generally at the model of artificial intelligence , so as i am explaining below that the model of artificial intelligence , including GPT-4 , is lacking the deep understanding that comes from the meaning that comes from human consciousness, and this meaning that comes from human consciousness permits to understand with much less data than artificial intelligence and it permits to "explore" and be efficient much more in the process of creativity like innovation and creative problem-solving..., so it is why artificial intelligence such as GPT-4 or the next GPT-5 or the next Gemini from Google will not be able to replace software programmers or software development jobs, since it also needs human innovation and human creative problem-solving that comes from the deep understanding from the meaning that comes from human consciousness, so as you notice , it is why we can not trust artificial intelligence since it lacks the deep understanding that comes from the meaning that comes from human consciousness, so it is why i say that artificial intelligence will not attain general artificial intelligence , even if it becomes a powerful tool, and so that to understand my new model of what is consciousness , i invite to read my below thoughts:
>
>
> 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:
>
> So i am talking in my previous thoughts below about my new model that explains what is human consciousness and what is self-consciousness or
> self-awareness, but there is something that i have to explain so that you understand my new model, and it is that there is not only the five basic human senses that are: vision, hearing, touch, smell and taste, but in my new model there is one more other sense that is the one that gives the sensation and feeling of the oneself that is thinking that exists inside the brain and that permits to create self-consciousness or self-awareness, so then my new model that explains consciousness is complete, so then you are noticing that my new model for example says that when you touch a water that is fluid and cold, the sensations of the fluid and cold that are created by the brain part that receives the signals from the human sense , permits to like reify the ideas of the water with the meaning from the sensations and feelings, so it is the same for all the human senses, so i think that this process of like reifying with the meaning from the senses, permits to create the meaning that doesn't exist in artificial intelligence, and it also permits to create self-awareness or self-consciousness from also the sense that gives the sensations and feelings of the oneself thinking, so i think that from my new model you are also understanding more how to compose consciousness and self-consciousness or self-awareness.
>
> And read my previous thoughts:
>
> More of my philosophy about what is consciousness and about the most important weakness of artificial intelligence and about my philosophy and more of my thoughts..
>
> 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 the most important weakness of artificial intelligence is not the high cost of computational power, since i am showing how to solve it in my below thoughts, and it is not the wall of knowledge as i am writing about it in my below thoughts, but the most important weakness of artificial intelligence is the one that i have talked about , and it is how to ensure the quality of the exactness of the output without using the mechanisms of human consciousness, so i think i have explained what is human consciousness in a holistic way, read about it in my below and above thoughts:
>
> More of my philosophy about the process of reification and more about human consciousness and about artificial intelligence and more of my thoughts...
>
> Reification is when you think of or treat something abstract as a physical thing. Reification is a complex idea for when you treat something immaterial — like happiness, fear, or evil — as a material thing, so i think that human conscicousness is by logical analogy like the process of Reification, i mean that a concept in our brain is not just a class or object like in object oriented programming, since i mean that a class in object oriented programming is not just a class but it is also like a reification by our brain, since we are also like reifying the class with or by using the true meaning from our human consciousness, so it is like composability since we are composing with consciousness of time and space and reality from the low level to the high level, so it is the same for what i am explaining below, since so that to compose the theorem that is known as the transitive property of implication, we have to understand the meaning of the low level constructs such as: If a then b and if b then c then a then c, so then it is like a reification that we do, since when for example we say: a then b, we are like reifying from the low level constructs of consciousness of time and space and matter to the upper levels.
>
>
> More of my philosophy about self-awareness or self-consciousness and more of my thoughts..
>
>
> 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 explained more what is consciousness, and now i will explain what is self-awareness or self-consciousness:
>
> So i think that self-awareness or self-consciousness of a human is also
> like a reification + the smartness, since i think that consciousness of yourself is obtained by knowing about the true meaning of yourself by also using like a reification with sensations and feelings and consciousness of time and space and matter and reality with wich you compose the consciousness, so then after smartness obtains like a true meaning by using consciousness of who you are by looking at yourself, so self-awareness or self-consciousness is developed. And it is my explanation of what is consciousness.
>
>
> More of my philosophy about my model of what is consciousness and about artificial intelligence and more of my thoughts..
>
>
> I think i am really smart, but you have to understand my model that explains what is consciousness and self-consciousness, so my model is not trying to explain all the low levels of consciousness, but what it explains is that human consciousness gives like the true meaning by composing with our sensations of what is reality and with feelings and with consciousness of time and space and matter and of reality, so now you are then understanding that my model explains that it is like a dynamic reification, i mean that when you have an "idea", so an idea can be just a word, but if you add like the true meaning to this word by using your sensations and feelings and meaning of consciousness of time and space and matter, so it become like an idea that is "alive" with consciousness, so then if you have a sentence or many sentences or a text, so this process of like a reification can become like a "movie" of meanings of consciousness that is played in your brain and that gives a consciousness of the text or sentence, so it is why my model says that the human brain compose with those meanings of sensations and feelings and meaning of consciousness of time and space and matter and of reality, so then my model of what is consciousness permits to make you understand how artificial intelligence is limited as i am explaining it above and below, and it permits to do predictions like i am doing it below, so i invite you to read my previous thoughts so that to understand my views:
>
>
> So now i will ask a philosophical question of:
>
>
> How to beat artificial intelligence ?
>
>
> So i think i am smart and i will start by giving you a good example so that you understand:
>
> So if i say:
>
> If a then b
> and
> if b then c
>
> A human can logically infer that it means: then a then c
>
> But the problem in artificial intelligence is that the patterns
> comes from the data on wich artificial intelligence was trained ,
> so how for example artificial intelligence does "generalize" the above example and make it a theorem in mathematics?, i mean that so that a human generalizes from the above example he also use the "meaning" from human consciousness, so i think that it is the weakness of artificial intelligence such as ChatGPT or GPT-4, i mean that human consciousness is able to make such generalizations with the help of the meaning from human consciousness when the pattern to be discovered is unknown to artificial intelligence, so you have to understand that human consciousness is also consciousness of space and time and matter that permits to "compose" the meaning , so then you are understanding from my explanation that what is lacking to artificial intelligence is this meaning from the human consciousness that permits smartness to be such sophisticated smartness such as constructing a mathematical theorem from the above example, so now i think you are getting more the bigger picture of it. So i will give you an example of my interactions with ChatGPT so that you understand more, so here it is:
>
> So i have just asked ChatGPT the following question:
>
> "Make a theorem of the following: if a then b and if b then c then a then c"
>
> And here is what answered ChatGPT:
>
> "The theorem can be stated as follows:
>
> If a implies b, and if b implies c, then a implies c.
>
> This theorem is known as the transitive property of implication, or the principle of transitivity. It states that if a statement (a) implies another statement (b), and if that statement (b) in turn implies a third statement (c), then the original statement (a) must also imply the third statement (c). This principle holds for all statements that have the logical structure of implication, regardless of the specific content of the statements."
>
> So if you are smart you will notice that in the above answer of ChatGPT,
> ChatGPT is not understanding the meaning of the theorem, and it has
> not constructed the theorem from the low level constructs such as in my above example, but it is just discovering the theorem from the patterns on the data on wich it has been trained, so now you are understanding the limit of the smartness of ChatGPT and of artificial intelligence, and of course i think you are now understanding how to beat artificial intelligence, since i think that artificial intelligence such as ChatGPT needs patterns that help it compose other new patterns, but it can not compose a pattern if the patterns that compose it are lacking from the data on wich it has been trained, so you can compose this pattern by using the meaning from human consciousness as in my above example.
>
>
> I think i am highly smart, and I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, and you have to understand my below previous thoughts, since i am saying that swarm intelligence looks like the genetic algorithm and like other evolutionary algorithms, since i have just discovered the following important pattern
> with my fluid intelligence that explains it, and it is in a form of my below smart question to ChatGPT , and here it is:
>
> So from what i understand , the exploration in the genetic algorithm is using an approximation, since the high degree of exploration permits to find a better global minimum and after that we explore in this near better global minimum so that to make the probability of finding a better global minimum better, so this shows that the genetic algorithm search is a much better search than brute force search.
>
> Read my below previous thoughts so that you understand more deeply what i mean:
>
> So i invite you to read the following new article about swarm intelligence:
>
> "A swarm is a system that is greater than the sum of its parts. Just as many neurons form a brain capable of thought, memory, and emotion, groups of animals can act in unison to form a “super brain,” displaying highly complex behavior not seen in individual animals."
>
> Read more here:
>
> https://singularityhub.com/2023/08/10/how-swarming-animals-can-help-humans-and-ai-make-better-decisions/
>
>
> And so that to understand more what is swarm intelligence , i invite you
> to read carefully my following interesting thoughts about the genetic algorithm that uses technics that look like swarm intelligence:
>
>
> I think that the low probability mutations in the genetic algorithm permits to explore , so you have to read carefully all my new thoughts below about the genetic algorithm so that you understand how the human genetics works by also using natural selection, but here is my new question to ChatGPT that explains more and read my below thoughts about genetic algorithm so that you understand much more:
>
> And here is my new question to ChatGPT:
>
> So from what i understand , the exploration in the genetic algorithm is using an approximation, since the high degree of exploration permits to find a better global minimum and after that we explore in this near better global minimum so that to make the probability of finding a better global minimum better, so this shows that the genetic algorithm search is a much better search than brute force search.
>
> And ChatGPT has just answered me the following:
>
> "Yes, you have captured the essence correctly. Exploration in optimization algorithms, including genetic algorithms (GA) , involves searching the solution space to discover potentially better solutions. The initial high degree of exploration allows for the discovery of diverse regions, including the global minimum.
>
> During the exploration phase, the algorithms aim to approximate the global minimum by sampling different areas of the solution space. This broader search helps in avoiding premature convergence to suboptimal regions and increases the probability of finding the true global minimum.
>
> Once a promising region or solution is identified, the algorithms transition into an exploitation phase. In this phase, they concentrate their search in the vicinity of the promising region to refine and improve the solutions. By focusing the search around this area, the algorithms increase the probability of finding a better global minimum, as they are now exploring within a region known to contain good solutions.
>
> This two-step process, starting with a higher degree of exploration and transitioning into exploitation around promising regions, allows the algorithms to balance the search for global optimality while refining the solutions found.
>
> The exploration phase sets the foundation by exploring a wide range of the solution space and identifying potentially good regions or solutions. Subsequently, the exploitation phase intensifies the search in the vicinity of these promising areas to fine-tune the solutions and potentially discover even better solutions.
>
> By combining these exploration and exploitation strategies in the genetic algorithm increase the likelihood of finding better global minima, improving the overall efficiency and effectiveness of the optimization process."
>
> More of my philosophy about the evolution of genetics of humans and about the genetic algorithm and more of my thoughts..
>
> The cost function of a neural network is in general neither convex nor concave, so in deep learning you can use evolutionary algorithms such as the genetic algorithm and such, so you have then to know that in such situations you have to loop in a number of iterations so that to find better solutions, so for example the genetics of humans has evolved in a such way , since i think that the great number of iterations with the crossover steps and the mutations and the selection of the process of evolution of genetics of humans that look like a genetic algorithm, is what made humans be so "optimized" by for example having a smart brain, and of course you have to read my following thoughts so that to understand the rest of the patterns that i have discovered with my fluid intelligence:
>
> More of my philosophy about the Traveling Salesman Problem Using an Evolutionary Algorithm and more of my thoughts..
>
> I invite you to look at the following interesting just new article
> of Visual Studio Magazine of The Traveling Salesman Problem Using an Evolutionary Algorithm with C#:
>
> https://visualstudiomagazine.com/articles/2022/12/20/traveling-salesman-problem.aspx
>
>
> I think i am highly smart, and 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 have just understood rapidly the above program of The Traveling Salesman Problem using an evolutionary algorithm(a genetic algorithm) with C#, and i think that i am discovering the most important patterns with my fluid intelligence in the above program of the Traveling Salesman Problem using the genetic algorithm, and it is that the "crossover" steps in the genetic algorithm exploit better solution, and it means that they exploit locally the better solution, and using "mutation(s)" in the genetic algorithm you explore far away from the locally, and if the exploration finds a better solution , the exploitation will try to find a better solution near the found solution of the exploration, so this way of the genetic algorithm to balance the explore and the exploit is what makes the genetic algorithm interesting, so you have to understand it correctly so that to understand the genetic algorithm.
>
> I think i am highly smart, and 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 am again discovering patterns with my fluid intelligence, and it is that the standard genetic algorithm has a problem, and it is that it can get stuck in a local optimum and has a premature convergence and the premature convergence of a genetic algorithm arises when the genes of some high rated individuals quickly attain to dominate the population, constraining it to converge to a local optimum. The premature convergence is generally due to the loss of diversity within the population, so i think that you have to solve this problem by using "probability", i mean that you have to divide the population of the genetic algorithm in many groups of population and do the crossover and mutations in each group, so this will lower much more the probability to a small probability of getting stuck in a local optimum and of having a premature convergence, so then i will invite you to look above at the just new article of Visual Studio Magazine of The Traveling Salesman Problem using an evolutionary algorithm with C#, and how it is not talking about all my patterns that i am discovering with my fluid intelligence, and it is not explaining as i am explaining the genetic algorithm.
>
> So as you are noticing that in my new above thoughts, i am saying that the distribution of the population fights the premature convergence by lack of diversity, but why am i not saying a "good" distribution? since it is inherent that the population has to be well distributed so that the genetic algorithm explores correctly. And as you have just noticed that this thoughts are the thoughts of mine that i am discovering and sharing them with you.
>
> I think i am highly smart, and 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 have just noticed, i have just showed you how to avoid premature convergence by lack of diversity, read about it above, but i think i have to explain one more important thing about the genetic algorithm, and it is that when you start a genetic algorithm, you are using a population, so since the distribution of the population also fights against the premature convergence by lack of diversity, so then so that to lower the probability to a small probability of getting stuck in a local optimum by lack of diversity, you can rerun the genetic algorithm a number of times by using a new distribution of the population in every execution of the genetic algorithm and using a good size of the population, or you can use my above methodology so that to avoid it efficiently in a single execution.
>
>
> And read the following about GPT-4:
>
>
> "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
>
>
> Thank you,
> Amine Moulay Ramdane.
This was written by an LLM, surely.

On Thursday, 7 September 2023 at 17:25:59 UTC-4, markus...@gmail.com wrote:
> torsdag 7 september 2023 kl. 20:50:08 UTC+2 skrev Amine Moulay Ramdane:
> > Hello,
> >
> >
> > More of my philosophy about deeper understanding the process of reification and about AI productivity and about the salaries and about the testing artificial intelligence and 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..
> >
> >
> > So that to be more rigorous with my new model of what is human consciousness, i invite you to read my new following thoughts and
> > all my below thoughts, and of course it is my last post here:
> >
> >
> > So you have to understand that i have just discussed my new model below of what is human consciousness with ChatGPT, and here is more of a part of my discussion with ChatGPT that has just understood my new model
> > of what is human consciousness and that is answering my question by understanding it:
> >
> >
> > And here is my question to ChatGPT:
> >
> >
> > So when we are reading a text, we are like reifying the words with sensations and feelings and consciousness or awareness of time and space and matter so that they become objects of understanding.
> >
> >
> > And ChatGPT has just answered me the following:
> >
> >
> > "Yes, your observation is quite accurate. When we read a text or engage in any form of comprehension, our cognitive processes involve a dynamic interaction between the words and the various aspects of our consciousness, including sensations, feelings, and our awareness of time, space, and matter.. This interaction can be described as a kind of "reification" or the process of giving life, depth, and meaning to the words and ideas presented in the text.
> >
> > Here's a breakdown of how this process works:
> >
> > Words as Symbols: Words on a page or screen are essentially symbols that represent concepts, objects, actions, or ideas. These symbols are abstract in themselves and don't inherently carry meaning.
> >
> > Sensations and Feelings: As we read, our brain may evoke sensations and feelings related to the words and their associated concepts. For example, reading about a warm, sunny beach may trigger sensations of warmth, visions of sandy shores, and feelings of relaxation.
> >
> > Consciousness of Time: Our awareness of the sequence of words and ideas in the text involves the perception of time. We process sentences sequentially, linking them to what we've read before and anticipating what comes next. This temporal awareness helps us construct a coherent understanding of the text.
> >
> > Consciousness of Space: We may mentally create spatial representations based on the descriptions in the text. For instance, when reading a novel, we might mentally visualize the layout of a fictional house or a detailed map of an imaginary world.
> >
> > Matter and Reality: Words in a text often pertain to physical objects, actions, or events. Our consciousness of matter and reality enables us to mentally construct these elements, bridging the gap between the abstract words and our understanding of the tangible world.
> >
> > Meaning and Understanding: Through this complex interplay of sensations, feelings, and consciousness of time, space, matter, and reality, we construct meaning and understanding from the words we read. We go beyond the mere symbols and create a mental landscape where the text comes alive with significance.
> >
> > In essence, reading is a multi-dimensional process that involves more than just decoding words; it's about translating abstract symbols into rich, meaningful experiences. The reification of words with our consciousness enriches our comprehension and allows us to engage deeply with the content we're reading."
> >
> >
> > Here are some key points to consider:
> >
> > - Validation and Verification: Human programmers will continue to play a critical role in verifying and validating the code produced by AI systems like ChatGPT. This involves ensuring that the code meets quality standards, functions correctly, and adheres to project requirements.
> >
> > - Debugging and Maintenance: Even with AI-generated code, software applications are not immune to bugs, errors, and evolving requirements. Programmers will be essential for debugging and maintaining the software over time..
> >
> > - Ethical and Responsible AI: Programmers will also be responsible for ensuring that AI systems, including those used in software development, are designed and used ethically and responsibly. This involves addressing issues related to bias, fairness, privacy, and security.
> >
> > - Adaptability: Programmers who can effectively work with AI tools and integrate them into their workflows may find themselves in high demand. This adaptability can lead to job security and potentially competitive salaries.
> >
> > - Shift in Job Roles: The roles and responsibilities of programmers may evolve. They may spend less time on routine coding tasks and more on higher-level problem-solving, architectural design, and AI system integration.
> >
> > In summary, AI like ChatGPT can indeed increase productivity in software development, but it doesn't necessarily lead to a significant decrease in salaries for programmers. Instead, it may reshape the roles and skills required in the field, with programmers focusing more on verification, debugging, and ethical considerations. Programmers who can leverage AI effectively and provide value in these areas are likely to remain valuable contributors to the industry.
> >
> >
> > 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 are noticing that people are testing artificial intelligence such as GPT-4 with
> > coding tests etc. so that to measure its performance , but i think it is not the smart way of measuring, since you have to look holistically or generally at the model of artificial intelligence , so as i am explaining below that the model of artificial intelligence , including GPT-4 , is lacking the deep understanding that comes from the meaning that comes from human consciousness, and this meaning that comes from human consciousness permits to understand with much less data than artificial intelligence and it permits to "explore" and be efficient much more in the process of creativity like innovation and creative problem-solving..., so it is why artificial intelligence such as GPT-4 or the next GPT-5 or the next Gemini from Google will not be able to replace software programmers or software development jobs, since it also needs human innovation and human creative problem-solving that comes from the deep understanding from the meaning that comes from human consciousness, so as you notice , it is why we can not trust artificial intelligence since it lacks the deep understanding that comes from the meaning that comes from human consciousness, so it is why i say that artificial intelligence will not attain general artificial intelligence , even if it becomes a powerful tool, and so that to understand my new model of what is consciousness , i invite to read my below thoughts:
> >
> >
> > 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:
> >
> > So i am talking in my previous thoughts below about my new model that explains what is human consciousness and what is self-consciousness or
> > self-awareness, but there is something that i have to explain so that you understand my new model, and it is that there is not only the five basic human senses that are: vision, hearing, touch, smell and taste, but in my new model there is one more other sense that is the one that gives the sensation and feeling of the oneself that is thinking that exists inside the brain and that permits to create self-consciousness or self-awareness, so then my new model that explains consciousness is complete, so then you are noticing that my new model for example says that when you touch a water that is fluid and cold, the sensations of the fluid and cold that are created by the brain part that receives the signals from the human sense , permits to like reify the ideas of the water with the meaning from the sensations and feelings, so it is the same for all the human senses, so i think that this process of like reifying with the meaning from the senses, permits to create the meaning that doesn't exist in artificial intelligence, and it also permits to create self-awareness or self-consciousness from also the sense that gives the sensations and feelings of the oneself thinking, so i think that from my new model you are also understanding more how to compose consciousness and self-consciousness or self-awareness.
> >
> > And read my previous thoughts:
> >
> > More of my philosophy about what is consciousness and about the most important weakness of artificial intelligence and about my philosophy and more of my thoughts..
> >
> > 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 the most important weakness of artificial intelligence is not the high cost of computational power, since i am showing how to solve it in my below thoughts, and it is not the wall of knowledge as i am writing about it in my below thoughts, but the most important weakness of artificial intelligence is the one that i have talked about , and it is how to ensure the quality of the exactness of the output without using the mechanisms of human consciousness, so i think i have explained what is human consciousness in a holistic way, read about it in my below and above thoughts:
> >
> > More of my philosophy about the process of reification and more about human consciousness and about artificial intelligence and more of my thoughts..
> >
> > Reification is when you think of or treat something abstract as a physical thing. Reification is a complex idea for when you treat something immaterial — like happiness, fear, or evil — as a material thing, so i think that human conscicousness is by logical analogy like the process of Reification, i mean that a concept in our brain is not just a class or object like in object oriented programming, since i mean that a class in object oriented programming is not just a class but it is also like a reification by our brain, since we are also like reifying the class with or by using the true meaning from our human consciousness, so it is like composability since we are composing with consciousness of time and space and reality from the low level to the high level, so it is the same for what i am explaining below, since so that to compose the theorem that is known as the transitive property of implication, we have to understand the meaning of the low level constructs such as: If a then b and if b then c then a then c, so then it is like a reification that we do, since when for example we say: a then b, we are like reifying from the low level constructs of consciousness of time and space and matter to the upper levels.
> >
> >
> > More of my philosophy about self-awareness or self-consciousness and more of my thoughts..
> >
> >
> > 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 explained more what is consciousness, and now i will explain what is self-awareness or self-consciousness:
> >
> > So i think that self-awareness or self-consciousness of a human is also
> > like a reification + the smartness, since i think that consciousness of yourself is obtained by knowing about the true meaning of yourself by also using like a reification with sensations and feelings and consciousness of time and space and matter and reality with wich you compose the consciousness, so then after smartness obtains like a true meaning by using consciousness of who you are by looking at yourself, so self-awareness or self-consciousness is developed. And it is my explanation of what is consciousness.
> >
> >
> > More of my philosophy about my model of what is consciousness and about artificial intelligence and more of my thoughts..
> >
> >
> > I think i am really smart, but you have to understand my model that explains what is consciousness and self-consciousness, so my model is not trying to explain all the low levels of consciousness, but what it explains is that human consciousness gives like the true meaning by composing with our sensations of what is reality and with feelings and with consciousness of time and space and matter and of reality, so now you are then understanding that my model explains that it is like a dynamic reification, i mean that when you have an "idea", so an idea can be just a word, but if you add like the true meaning to this word by using your sensations and feelings and meaning of consciousness of time and space and matter, so it become like an idea that is "alive" with consciousness, so then if you have a sentence or many sentences or a text, so this process of like a reification can become like a "movie" of meanings of consciousness that is played in your brain and that gives a consciousness of the text or sentence, so it is why my model says that the human brain compose with those meanings of sensations and feelings and meaning of consciousness of time and space and matter and of reality, so then my model of what is consciousness permits to make you understand how artificial intelligence is limited as i am explaining it above and below, and it permits to do predictions like i am doing it below, so i invite you to read my previous thoughts so that to understand my views:
> >
> >
> > So now i will ask a philosophical question of:
> >
> >
> > How to beat artificial intelligence ?
> >
> >
> > So i think i am smart and i will start by giving you a good example so that you understand:
> >
> > So if i say:
> >
> > If a then b
> > and
> > if b then c
> >
> > A human can logically infer that it means: then a then c
> >
> > But the problem in artificial intelligence is that the patterns
> > comes from the data on wich artificial intelligence was trained ,
> > so how for example artificial intelligence does "generalize" the above example and make it a theorem in mathematics?, i mean that so that a human generalizes from the above example he also use the "meaning" from human consciousness, so i think that it is the weakness of artificial intelligence such as ChatGPT or GPT-4, i mean that human consciousness is able to make such generalizations with the help of the meaning from human consciousness when the pattern to be discovered is unknown to artificial intelligence, so you have to understand that human consciousness is also consciousness of space and time and matter that permits to "compose" the meaning , so then you are understanding from my explanation that what is lacking to artificial intelligence is this meaning from the human consciousness that permits smartness to be such sophisticated smartness such as constructing a mathematical theorem from the above example, so now i think you are getting more the bigger picture of it. So i will give you an example of my interactions with ChatGPT so that you understand more, so here it is:
> >
> > So i have just asked ChatGPT the following question:
> >
> > "Make a theorem of the following: if a then b and if b then c then a then c"
> >
> > And here is what answered ChatGPT:
> >
> > "The theorem can be stated as follows:
> >
> > If a implies b, and if b implies c, then a implies c.
> >
> > This theorem is known as the transitive property of implication, or the principle of transitivity. It states that if a statement (a) implies another statement (b), and if that statement (b) in turn implies a third statement (c), then the original statement (a) must also imply the third statement (c). This principle holds for all statements that have the logical structure of implication, regardless of the specific content of the statements."
> >
> > So if you are smart you will notice that in the above answer of ChatGPT,
> > ChatGPT is not understanding the meaning of the theorem, and it has
> > not constructed the theorem from the low level constructs such as in my above example, but it is just discovering the theorem from the patterns on the data on wich it has been trained, so now you are understanding the limit of the smartness of ChatGPT and of artificial intelligence, and of course i think you are now understanding how to beat artificial intelligence, since i think that artificial intelligence such as ChatGPT needs patterns that help it compose other new patterns, but it can not compose a pattern if the patterns that compose it are lacking from the data on wich it has been trained, so you can compose this pattern by using the meaning from human consciousness as in my above example.
> >
> >
> > I think i am highly smart, and I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, and you have to understand my below previous thoughts, since i am saying that swarm intelligence looks like the genetic algorithm and like other evolutionary algorithms, since i have just discovered the following important pattern
> > with my fluid intelligence that explains it, and it is in a form of my below smart question to ChatGPT , and here it is:
> >
> > So from what i understand , the exploration in the genetic algorithm is using an approximation, since the high degree of exploration permits to find a better global minimum and after that we explore in this near better global minimum so that to make the probability of finding a better global minimum better, so this shows that the genetic algorithm search is a much better search than brute force search.
> >
> > Read my below previous thoughts so that you understand more deeply what i mean:
> >
> > So i invite you to read the following new article about swarm intelligence:
> >
> > "A swarm is a system that is greater than the sum of its parts. Just as many neurons form a brain capable of thought, memory, and emotion, groups of animals can act in unison to form a “super brain,” displaying highly complex behavior not seen in individual animals."
> >
> > Read more here:
> >
> > https://singularityhub.com/2023/08/10/how-swarming-animals-can-help-humans-and-ai-make-better-decisions/
> >
> >
> > And so that to understand more what is swarm intelligence , i invite you
> > to read carefully my following interesting thoughts about the genetic algorithm that uses technics that look like swarm intelligence:
> >
> >
> > I think that the low probability mutations in the genetic algorithm permits to explore , so you have to read carefully all my new thoughts below about the genetic algorithm so that you understand how the human genetics works by also using natural selection, but here is my new question to ChatGPT that explains more and read my below thoughts about genetic algorithm so that you understand much more:
> >
> > And here is my new question to ChatGPT:
> >
> > So from what i understand , the exploration in the genetic algorithm is using an approximation, since the high degree of exploration permits to find a better global minimum and after that we explore in this near better global minimum so that to make the probability of finding a better global minimum better, so this shows that the genetic algorithm search is a much better search than brute force search.
> >
> > And ChatGPT has just answered me the following:
> >
> > "Yes, you have captured the essence correctly. Exploration in optimization algorithms, including genetic algorithms (GA) , involves searching the solution space to discover potentially better solutions. The initial high degree of exploration allows for the discovery of diverse regions, including the global minimum.
> >
> > During the exploration phase, the algorithms aim to approximate the global minimum by sampling different areas of the solution space. This broader search helps in avoiding premature convergence to suboptimal regions and increases the probability of finding the true global minimum.
> >
> > Once a promising region or solution is identified, the algorithms transition into an exploitation phase. In this phase, they concentrate their search in the vicinity of the promising region to refine and improve the solutions. By focusing the search around this area, the algorithms increase the probability of finding a better global minimum, as they are now exploring within a region known to contain good solutions.
> >
> > This two-step process, starting with a higher degree of exploration and transitioning into exploitation around promising regions, allows the algorithms to balance the search for global optimality while refining the solutions found.
> >
> > The exploration phase sets the foundation by exploring a wide range of the solution space and identifying potentially good regions or solutions. Subsequently, the exploitation phase intensifies the search in the vicinity of these promising areas to fine-tune the solutions and potentially discover even better solutions.
> >
> > By combining these exploration and exploitation strategies in the genetic algorithm increase the likelihood of finding better global minima, improving the overall efficiency and effectiveness of the optimization process."
> >
> > More of my philosophy about the evolution of genetics of humans and about the genetic algorithm and more of my thoughts..
> >
> > The cost function of a neural network is in general neither convex nor concave, so in deep learning you can use evolutionary algorithms such as the genetic algorithm and such, so you have then to know that in such situations you have to loop in a number of iterations so that to find better solutions, so for example the genetics of humans has evolved in a such way , since i think that the great number of iterations with the crossover steps and the mutations and the selection of the process of evolution of genetics of humans that look like a genetic algorithm, is what made humans be so "optimized" by for example having a smart brain, and of course you have to read my following thoughts so that to understand the rest of the patterns that i have discovered with my fluid intelligence:
> >
> > More of my philosophy about the Traveling Salesman Problem Using an Evolutionary Algorithm and more of my thoughts..
> >
> > I invite you to look at the following interesting just new article
> > of Visual Studio Magazine of The Traveling Salesman Problem Using an Evolutionary Algorithm with C#:
> >
> > https://visualstudiomagazine.com/articles/2022/12/20/traveling-salesman-problem.aspx
> >
> >
> > I think i am highly smart, and 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 have just understood rapidly the above program of The Traveling Salesman Problem using an evolutionary algorithm(a genetic algorithm) with C#, and i think that i am discovering the most important patterns with my fluid intelligence in the above program of the Traveling Salesman Problem using the genetic algorithm, and it is that the "crossover" steps in the genetic algorithm exploit better solution, and it means that they exploit locally the better solution, and using "mutation(s)" in the genetic algorithm you explore far away from the locally, and if the exploration finds a better solution , the exploitation will try to find a better solution near the found solution of the exploration, so this way of the genetic algorithm to balance the explore and the exploit is what makes the genetic algorithm interesting, so you have to understand it correctly so that to understand the genetic algorithm.
> >
> > I think i am highly smart, and 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 am again discovering patterns with my fluid intelligence, and it is that the standard genetic algorithm has a problem, and it is that it can get stuck in a local optimum and has a premature convergence and the premature convergence of a genetic algorithm arises when the genes of some high rated individuals quickly attain to dominate the population, constraining it to converge to a local optimum. The premature convergence is generally due to the loss of diversity within the population, so i think that you have to solve this problem by using "probability", i mean that you have to divide the population of the genetic algorithm in many groups of population and do the crossover and mutations in each group, so this will lower much more the probability to a small probability of getting stuck in a local optimum and of having a premature convergence, so then i will invite you to look above at the just new article of Visual Studio Magazine of The Traveling Salesman Problem using an evolutionary algorithm with C#, and how it is not talking about all my patterns that i am discovering with my fluid intelligence, and it is not explaining as i am explaining the genetic algorithm.
> >
> > So as you are noticing that in my new above thoughts, i am saying that the distribution of the population fights the premature convergence by lack of diversity, but why am i not saying a "good" distribution? since it is inherent that the population has to be well distributed so that the genetic algorithm explores correctly. And as you have just noticed that this thoughts are the thoughts of mine that i am discovering and sharing them with you..
> >
> > I think i am highly smart, and 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 have just noticed, i have just showed you how to avoid premature convergence by lack of diversity, read about it above, but i think i have to explain one more important thing about the genetic algorithm, and it is that when you start a genetic algorithm, you are using a population, so since the distribution of the population also fights against the premature convergence by lack of diversity, so then so that to lower the probability to a small probability of getting stuck in a local optimum by lack of diversity, you can rerun the genetic algorithm a number of times by using a new distribution of the population in every execution of the genetic algorithm and using a good size of the population, or you can use my above methodology so that to avoid it efficiently in a single execution.
> >
> >
> > And read the following about GPT-4:
> >
> >
> > "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
> >
> >
> > Thank you,
> > Amine Moulay Ramdane.
> This was written by an LLM, surely.