Message-ID:

"Seed me, Seymour" -- a random number generator meets the big green mother from outer space

Hello,

More of my philosophy about reinforcement learning and about artificial intelligence and about global warming and about China's Semiconductor advances to 5nm 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 have just noticed , i have just invented a new model of what is human consciousness so that to show the limitations of artificial intelligence of like it will not attain artificial general intelligence , and you can read it in my below thoughts, also i have just showed you another important
limitation of the transformers like the one of ChatGPT , and you can read about it in my below thoughts , and now here is other of my interesting questions to ChatGPT so that to show another important limitation of ChatGPT and of the next artificial intelligence of Google that is called Gemini and that will compete against the next GPT-5:

So here is my first question to ChatGPT:

Does reinforcement learning in ChatGPT takes a lot of time for good refinements , and can you transfer the refinements to the next version of ChatGPT ?

And ChatGPT has just answered me the following:

"Reinforcement learning for refining a language model like ChatGPT can indeed take a significant amount of time and resources to achieve good results. The training process involves interacting with users and receiving feedback, which can be time-consuming and require substantial computational power. The model needs to learn from a large amount of data and user interactions to improve its performance and responsiveness.

Regarding transferring refinements to the next version of ChatGPT, it depends on the specific policies and practices of the organization that develops and maintains the model. Generally, improvements and refinements made to a model can be used as a basis for enhancing future versions, but it's not a direct transfer. Developers can incorporate lessons learned from previous versions and apply new techniques to improve the model's performance in subsequent releases.

It's important to note that ChatGPT and similar models are often fine-tuned on specific datasets and use reinforcement learning from human feedback, which means they can be adapted and improved over time, but the process is iterative and may involve starting from a pretrained model and fine-tuning it with new data and feedback for each new version. It's also essential to address ethical and safety concerns in the development and refinement of AI models like ChatGPT."

And here is my second question to ChatGPT:

So then good refinements in ChatGPT can not be guaranteed , so it is also an important weakness in ChatGPT.

And ChatGPT has just answered me the following:

"Achieving a "good" refinement in AI models like ChatGPT is a complex and ongoing process, and there are no guarantees of perfection. ChatGPT and similar models have both strengths and weaknesses.

Weaknesses and challenges in refining models like ChatGPT include:

1- Bias and Ethical Concerns: Language models may inherit biases from the training data, and addressing bias issues is an ongoing challenge. Ensuring that the model responds appropriately to various inputs and avoids harmful or discriminatory behavior is an ongoing concern.

2- Ambiguity and Errors: Language models can make mistakes or produce answers that are plausible-sounding but incorrect or nonsensical. Achieving high accuracy in all responses is difficult.

3- Lack of Common Sense: AI models like ChatGPT often lack true common sense reasoning and may struggle with tasks that require a deep understanding of the world.

4- Scalability: Providing real-time interactions at scale can be challenging, and it may limit the ability to have nuanced, context-aware conversations.

5- Fine-tuning Challenges: The reinforcement learning and fine-tuning process depends on the quality and quantity of feedback data, and the model's behavior can be sensitive to the data it's trained on.

6- User-Specific Requirements: Meeting individual user preferences and specific use cases can be challenging, as not all users have the same needs or expectations.

While these are challenges, they do not make ChatGPT or similar models inherently weak. They can still provide valuable assistance, answer questions, and facilitate a range of applications. Continuous research and development are aimed at addressing these issues and improving the capabilities of such models.

It's important to use these models responsibly, to acknowledge their limitations, and to combine them with human oversight when necessary. While perfection cannot be guaranteed, ongoing refinement and feedback can lead to improvements in the model's performance and behavior over time."

So here is my interesting discussion with ChatGPT about the transformers
like of the transformer of ChatGPT that is the core system of ChatGPT, and a transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.

So here is my first question to ChatGPT:

So i think i understand what is doing the transformers , so they understand the lower level patterns and they understand the higher level patterns , so then they understand it all.

And here is what has just answered me ChatGPT:

"Yes, you've captured the idea correctly. Transformers, the architecture underlying models like ChatGPT, are capable of understanding both low-level and high-level patterns in the data they are trained on. This enables them to grasp a wide range of information, from the fine-grained details to the broader themes and concepts present in the training data. By learning these patterns comprehensively, they can generate text that reflects a deep understanding of the input data and respond in ways that incorporate both specific details and overarching themes.

However, it's important to note that while transformers can learn and recognize patterns, their understanding is based on statistical associations in the training data. They don't possess true comprehension or consciousness. They generate text by applying learned patterns and associations to the input data, which can produce responses that seem insightful and contextually relevant but are ultimately based on patterns and statistics."

And here is my second question to ChatGPT:

So the pattern can be a low level concept or a high level concept.

And here is what has just answered me ChatGPT:

"That's correct. Patterns in the context of deep learning models like transformers, including both low-level and high-level concepts, can be used to understand and generate text:

1- Low-Level Patterns: These patterns involve specific details, such as word choice, sentence structure, grammar, and stylistic elements. Low-level patterns are more granular and can include things like the use of metaphors, specific vocabulary, or consistent use of certain linguistic features.

2- High-Level Patterns: These patterns encompass broader aspects of language and content, such as themes, writing style, recurring motifs, and overarching concepts. High-level patterns relate to the overall structure and message of a text or creative work.

Deep learning models like transformers are capable of learning and applying patterns at various levels of abstraction. They can understand and generate text that reflects both low-level and high-level patterns, enabling them to respond to a wide range of input and generate contextually relevant and coherent output. The combination of these patterns contributes to the models' ability to generate text that appears meaningful and contextually appropriate."

And here is my third question:

So since the transformer is limited by the trained data , so if it doesn't find a pattern of creative work , so it can not understand this creative work and it can not be innovative in this creative work.

And here is what has just answered me ChatGPT:

"You're correct that the performance of a transformer model like GPT-3 is limited by the data it has been trained on. If a transformer hasn't been exposed to certain patterns or styles of creative works during training, it may not be as effective in understanding or generating content in those specific creative works or styles.

Transformers are data-driven models, and their ability to generate creative and innovative content is highly dependent on the diversity and quality of the data they've been trained on. If a particular style of creativity or a specific set of patterns is not represented in their training data, they may struggle to produce content that aligns with those patterns.

To improve a transformer's performance on a specific type of creative work or style, it would ideally need to be trained on a dataset that includes examples of that specific creative domain or style. Training a model on a more diverse and extensive dataset can also help it become more versatile in generating content that aligns with various patterns and styles, making it potentially more innovative in those domains.

In summary, the ability of a transformer model to understand and be innovative in a specific creative work or style is largely determined by the data it was trained on and its exposure to relevant patterns and examples."

And here is my fourth question to ChatGPT:

Hence , ChatGPT can not be guaranteed , since it is limited by the data on wich it has been trained , so it can for example lack a lot of patterns of creative works.

And here is what has just answered me ChatGPT:

"You're correct in observing that ChatGPT and similar models are not guaranteed to excel in every creative work or domain. Their performance is indeed limited by the data on which they've been trained. If a particular pattern or style of creative work is underrepresented or missing from their training data, they may struggle to understand or generate content in that specific area.

These models are most effective when they are used within the domains and patterns for which they have been extensively trained. If you need to work with specific or niche areas of creativity or unique patterns, you might encounter limitations. In such cases, additional fine-tuning or training on domain-specific data may be necessary to enhance their performance.

It's essential to understand that while ChatGPT is a powerful language model capable of generating creative content, it may not fully replace human expertise and creativity, especially in highly specialized or niche creative domains. It's a tool that can assist and inspire human creators but doesn't have a deep understanding of every possible creative context."

And here is my fifth question to ChatGPT:

So then if ChatGPT can not be guaranteed as i am explaining above , so we can not say that it can replace a lot of jobs.

And here is what has just answered me ChatGPT:

"You're correct in pointing out that ChatGPT and similar AI models have limitations, particularly in specialized or creative domains where human expertise and creativity are highly valued. While these models have the potential to automate certain tasks and assist with various jobs, they are not a wholesale replacement for human workers in many fields.

The impact of AI models like ChatGPT on employment varies by industry and job role. In some cases, they may automate certain routine and repetitive tasks, which could lead to changes in job responsibilities or the need for reskilling in affected industries. However, many jobs involve aspects like creativity, empathy, complex problem-solving, decision-making, and domain-specific expertise, which AI models may not fully replicate.

AI can be a valuable tool for augmenting human capabilities, improving efficiency, and automating specific tasks. Still, it's unlikely to completely replace human workers in roles that require deep understanding, creativity, nuanced judgment, and emotional intelligence.

The future of work and the impact of AI on employment is a complex and evolving topic, and it often depends on the specific context and industry. While AI can be a powerful tool, it is usually seen as a complement to human skills and expertise, rather than a full replacement."

And here is my sixth question to ChatGPT:

As we have just discussed above about the transformers, so i think that nuanced understanding comes too from the necessary patterns that the transformers have to discover in the data.

And here is what has just answered me ChatGPT:

"Absolutely, nuanced understanding often arises from the ability to recognize and interpret complex patterns in data. Transformers and similar deep learning models are proficient at identifying and leveraging patterns across various levels of abstraction in the data they have been trained on. This capacity for pattern recognition allows them to achieve nuanced understanding in different domains.

Here's how nuanced understanding is related to patterns in data:

1- Pattern Recognition: Deep learning models, like transformers, excel at identifying patterns in the data. These patterns can range from low-level linguistic features (e.g., word associations, sentence structure) to high-level concepts (e.g., themes, relationships between entities).

2- Complex Relationships: In many tasks, nuanced understanding involves recognizing and interpreting complex relationships and dependencies within the data. This can encompass understanding context, causality, and subtleties in meaning.

3- Contextual Adaptation: Transformers are particularly skilled at adapting their responses based on the context provided. They use patterns in the input data to generate contextually relevant and coherent responses.

4- Domain-Specific Nuances: For tasks in specialized domains, like medical diagnoses or legal analysis, nuanced understanding involves recognizing and interpreting patterns specific to that domain.

5- Discovering Nuance: Nuanced understanding often means discovering the subtleties, exceptions, and edge cases in the data that go beyond simple patterns. It involves recognizing the limitations and context in which patterns apply.

While transformers and deep learning models can provide nuanced responses and demonstrate a strong understanding of the data they were trained on, it's important to remember that their knowledge and understanding are based on patterns and associations present in that training data. Their nuanced understanding may not always align perfectly with human intuition and domain expertise, and they may not capture all aspects of complex or specialized domains. Human expertise and oversight are often required to validate and enhance the nuanced understanding provided by these models."

And here is my seventh question to ChatGPT:

So then from what we have just discussed above, since ChatGPT can not be guaranteed , since it is limited by the data on wich it has been trained , so it can for example lack a lot of patterns of creative works, so the nuanced understanding can not be guaranteed too.

And here is what has just answered me ChatGPT:

"Your assessment is accurate. ChatGPT, like other language models, is not guaranteed to have nuanced understanding in all domains or be able to capture all patterns, especially in specialized or creative works. Its performance is indeed limited by the data on which it has been trained.

Nuanced understanding, especially in specialized, creative, or domain-specific contexts, is not something that can be guaranteed with these models. While they can exhibit a strong ability to generate contextually relevant text and demonstrate understanding within the scope of their training data, they may not capture all nuances, subtleties, or specialized patterns, particularly in domains that deviate significantly from the general training data..

In practice, these models are powerful tools for many tasks, but they are not infallible and may not fully replace the need for human expertise, particularly in areas requiring deep understanding, creativity, or domain-specific knowledge. Users should exercise judgment and validation when relying on these models for nuanced understanding, especially in critical or specialized applications."

And i invite you to read my below new model of what is human consciousness so that to understand the other limitations of artificial intelligence:

Cows and other farm animals produce about 14% of human-induced climate emissions, and it is methane from their burps and manure that is seen as both the biggest concern and best opportunity for tackling global heating. Methane is more potent at warming the earth than carbon dioxide and it is an important emission target for policymakers because it leaves the atmosphere more quickly than carbon dioxide. The world's one billion+ cows are responsible for about 40% of global methane emissions - a significant contributor to global warming. And i invite you to read carefully the following new article from Interesting Engineering about how scientists have just engineered climate-smart cows with 10 to 20 times more milk, and it permits to reduce the number of cows so that to reduce much more global warming ,
since Methane is responsible for around 30% of the current rise in global temperature:

https://interestingengineering.com/science/scientists-engineer-climate-smart-cows-with-10-to-20-times-more-milk

I invite you to read carefully the following new article about how U.S. cannot halt China's Semiconductor advances to 5nm:

https://www.tomshardware.com/news/us-cannot-halt-chinas-semiconductor-advance-to-5nm-ex-tsmc-vp

And Chinese Loongson CPU takes on AMD's Zen 3 in benchmarks, with approximately Zen 3 levels of IPC, based on testing, so i invite you to read the following new article so that to notice it:

https://www.tomshardware.com/news/loongson-3a6000-beats-i3-10100f

So as you have just noticed , i have just talked about the Simulation hypothesis and i have just explained that my new model of what is consciousness shows that the simulation hypothesis is not true, and you can read about it in my below thoughts, and here is my other previous thoughts about the Simulation hypothesis: So i think that saying that the Universe is a Digital Simulation is much less probable, since i say that look for example at how looks this thing that we call humanity, so we can not say that the simulation of this super intelligent beings has started from a very long ago, like for example more than a billion of years, since this way of doing creates too much suffering that is much less probable since the super intelligent beings are so advanced and so smart, but i think it is much more short, so then we notice that the super intelligent beings that have started the simulation can not start the simulation in a so primitive environment of our today of what we call humanity, so then i can say that saying that the Universe is a Digital Simulation is much less probable and the fact the reality exists is much more probable.

From the following article I think that simple code is generally better than optimal and best performing code. In C++, optimal code is often hard to read, hard to understand, and most importantly, hard to maintain. So i invite you to read carefully the following article so that you understand it , and of course you can translate the article from french to english:

Programming in C++ is hard, software engineering in C++ is even harder,

https://cpp.developpez.com/actu/348473/La-programmation-en-Cplusplus-est-difficile-le-genie-logiciel-en-Cplusplus-est-encore-plus-difficile-un-article-d-EDUARDO-ROCHA-traduit-par-Escapetiger/

As you have just noticed, i have just posted about a new article about the Simulation hypothesis , so i think that the Simulation hypothesis is not true ,
since from my below new model of what is human consciousness, you can notice that there is a primitive consciousness , but i am talking below about the rest of the human consciousness that requires the biologic since it is also composed with the human "sensations" , so since it is also biologic , so i don't think that the Simulation hypothesis is true , so i invite you to read carefully my below new model of what is human consciousness and about
how it explains that we will not attain artificial general intelligence:

So i think i am also a new philosopher, so you have to discover an important pattern with your fluid intelligence , and here it is: So the problem is that you can think that competition in capitalism is on the cost and/or quality and/or quantity, so since it is also on the cost so it can hurt the wages of workers, but it think it is not the smart way to look at it, since capitalism is not a zero sum game, since with a reasonable level of growth, capitalism can both deliver rising living standards for most, and still ensure a relatively high rate of return to the owners of capital, so you have to look at it in a holistic way
or in a general way and say the even if there is competition in capitalism on for example the cost and it can hurt the wages locally here and there, but holistically or generally capitalism is not a zero sum game so it is holistically or generally a positive thing even inside a country, even if competition on for example the cost in capitalism can hurt the wages of the workers in a local manner here and there inside the country, so the smart idea is that you have to know how to adapt efficiently so that to minimize at best the disadvantages of competition in capitalism that can hurt locally here and there the wages of the workers. So then the important key is efficient adaptation. Other than that you have to read about the other problems in capitalism that have to be solved by "progressive" capitalism in my the following thoughts in the following web link:

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

So i think i am a new philosopher , and so that to understand correctly my
new ideas of my philosophy , i invite you to read them carefully in the
following two web links:

https://groups.google.com/g/alt.culture.morocco/c/v8pdduG6LcY

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

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

https://groups.google.com/g/alt.culture.morocco/c/PfmfrhCk2cU

Chip startup Tachyum will make 50 Exaflop supercomputers and 8 ZettaFlop AI computers in 2025, and Tachyum books purchase order to build system with 25,000x ChatGPT4 capacity and 25x faster than current supercomputers.

Read more here in the following interesting article so that to understand it:

https://www.tachyum.com/media/press-releases/2023/10/03/tachyum-books-purchase-order-to-build-system-with-25000x-chatgpt4-capacity-and-25x-faster-than-current-supercomputers/

And Exascale or Zettascale supercomputers will also allow to construct an accurate map of the brain that allows to "reverse" engineer or understand the brain, read the following so that to notice it:

“If we don’t improve today’s technology, the compute time for a whole
mouse brain would be something like 1,000,000 days of work on current
supercomputers. Using all of Aurora of 2 ExaFLOPS, if everything worked beautifully, it could still take 1,000 days.” Nicola Ferrier, Argonne senior computer
scientist

And based on available data, there's roughly a one percent chance of seeing another Miyake Event within the next decade and a Miyake Event can be
80 times more powerful than a Carrington-like event, read more here in the following article:

https://phys.org/news/2022-10-tree-insight-devastating-storms.html#:~:text='%20or%20outburst.%22-,Dr.,what%20harms%20it%20may%20cause.

University of Tübingen's researchers unveil a groundbreaking solar cell for decentralized green hydrogen production, revolutionizing renewable energy. This breakthrough technology opens the door to large-scale applications, even with lower efficiencies. This advancement have the potential to make a significant contribution to energy supply and the reduction of CO2 emissions on a global scale.

Read more here on Interesting Engineering:

https://interestingengineering.com/science/solar-cell-powers-green-hydrogen-production

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 of course
so that to know from where comes the human being, i am explaining
below the genetic algorithm , so of course you have to read it carefully below , so notice that the genetic algorithm explains how human being has been constructed by nature , i mean that there is two things in human genetics ,
so there is the mutations and there is the crossover of the human genes and there is the selection process like with the natural selection , so the selection process can be elitistic or more elitistic so that it looks like the natural selection in the evolution process of humans , and notice carefully that the human genetic mutations comes from two things: from the solar storms that can mutate our genetics and from the genetic mutations that can happen inside the body , and of course the two can happen with a low probability , but it permits to explore , and of course you have to read my following thoughts of what is the genetic algorithm so that to understand the rest:

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 5% 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."

And i think ChatGPT has another problem, and it is that the generated content can infringe on the copyright of existing works. This could occur if ChatGPT generates content similar to existing copyrighted material of the data on wich it has been trained. So you have to be careful, since it can hurt your business, but you have to know that copyright does not protect ideas, concepts, systems, or methods of doing something. But copyright law for example protects the expression of ideas rather than the ideas themselves. In other words, copyright law protects the specific form in which an idea is expressed, rather than the underlying idea or concept. And you have to also know that there is another problem with ChatGPT and it is that it can generate an invention (but it is limited by the data on wich it has been trained), but it could be argued that the creators of the model, OpenAI, should be able to patent the invention. However, it could also be argued, that the source material used to train the model should be considered as prior art, meaning that the invention would not be considered new and therefore not patentable.

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."

Subject	Replies	Author
More of my philosophy about reinforcement learning and about By: Amine Moulay Ramdane on Wed, 1 Nov 2023	0	Amine Moulay Ramdane

"Seed me, Seymour" -- a random number generator meets the big green mother from outer space

tech / sci.math / More of my philosophy about reinforcement learning and about artificial intelligence and about global warming and about China's Semiconductor advances to 5nm and more of my thoughts..