Hello,
More of my philosophy about the closed and open system and about AI and about the essence of artificial intelligence and about Gemini and about 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..
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 ,
[continued in next message]
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