Hello,



More of my philosophy about the deep learning network and about
artificial general intelligence and about 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 i have just talked in my below previous thoughts about how AlphaGo
works technically and about artificial intelligence , but i have to
explain why i am saying below that the patterns are recorded in the deep learning network , so here is my question to ChatGPT that makes you
understand it:


Here is my question to ChatGPT:

So the learned patterns that are implicitly encoded in the model's
parameters of the deep learning network is also a kind of recording the patterns in it.


And ChatGPT has just answered me the following:

"Yes, you can think of the learned patterns that are encoded in the
model's parameters as a form of recording within the deep learning
network. The parameters of the model essentially serve as a
representation or storage of the patterns learned from the training
data. During the training process, the model adjusts these parameters to
better capture the underlying structure of the data, effectively
"recording" the learned patterns within the network's architecture.

This recorded information can then be utilized during inference or other
tasks, where the model can draw upon its learned patterns to generate
new data, make predictions, or perform other tasks relevant to the
problem domain. So, in essence, the parameters of the deep learning
network act as a repository for the learned patterns, enabling the
network to apply its knowledge to new data or tasks."


So i invite you to read carefully my below previous thoughts about
artificial 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
you have to know that AlphaGo combines raw speed, deep learning, and reinforcement learning , but i have just looked at the following
tutorial about how AlphaGo works technically and i am quickly
understanding it , so here is the tutorial and read it carefully: https://jonathan-hui.medium.com/alphago-how-it-works-technically-26ddcc085319
, so i am discovering patterns with my fluid intelligence in this
technical tutorial and it is that since it is using Deep learning so it
is by logical analogy like generative AI , i mean that AlphaGo is
constructed by training it to discover the "patterns" of the winning
games in form of higher level patterns and lower level patterns that are recorded in the lower and higher level layers of the Deep learning
network, after that it is "refined" by reinforcement learning by AlphaGo playing against itself , i mean that by the reward mechanism of
reinforcement learning , i think it refines by retaining the good
patterns of the best moves that are useful , and i think it is the most important steps of how to construct AlphaGo , but i think that the most important thing in artificial intelligence is what we call a "pattern" discovery , i mean that a pattern discovery in artificial intelligence
is done by making statistical and number calculations , but i think it
is the big deficiency of artificial intelligence , since we have to
prove if the pattern discovery in its form in artificial intelligence is sufficient to make artificial general intelligence , so i have just
invented a new model of what is human consciousness that you can read in
my following thoughts that shows that the pattern discovery of
artificial intelligence is not sufficient and that shows that we will
not attain artificial general intelligence , so my new model also shows
that the pattern discovery of the higher level patterns or the parent
pattern needs human consciousness that uses what we call a reification
with the human sensations and with consciousness of time , so you have
to carefully read my new model of what is human consciousness in my
following thoughts about artificial intelligence so that to understand
my new model:


So you have to understand that i have just grouped my important
thoughts below about the important limitations of artificial
intelligence , and also i have just invented a new model of what is
human consciousness , that you can read below , so that to show you how artificial intelligence will not attain artificial general intelligence
, also i have just added my thoughts about another limitation of
artificial intelligence that you can read below:


Here is my new question to ChatGPT:

So the Large Language Models such as ChatGPT are trained and they
discover lower level patterns and higher level patterns so that to
understand it all , but i think that the main limitation of
reinforcement learning is that it can not innovate by inventing outside
the trained data of the lower level patterns and the higher level
patterns, so it is the humans that have to innovate by inventing outside it.


And here is what has just answered me ChatGPT:

"Yes, that's a fair assessment. Reinforcement learning (RL) algorithms
operate within the confines of the data they are trained on and the
reward signals provided by humans or predefined criteria. They learn to optimize their behavior based on the patterns present in the training
data and the feedback they receive during interaction with the
environment. However, RL algorithms typically do not possess the ability
to spontaneously innovate or invent entirely new concepts, strategies,
or algorithms that are beyond the scope of their training data or reward signals.

Innovation often involves creativity, insight, and the ability to
generate novel solutions to problems. While RL can be a powerful tool
for optimizing existing strategies or learning from data, it may not
inherently possess the capacity for creative innovation in the same way
humans do. Instead, humans play a crucial role in driving innovation by formulating new problems, designing novel algorithms, and providing
guidance and feedback to RL systems.

That said, RL can still contribute to innovation by automating tasks, optimizing processes, and discovering optimal solutions within the
confines of a given problem domain. Additionally, RL research may
inspire new approaches to problem-solving or provide insights into
learning and decision-making processes that can inform human innovation efforts.

In summary, while RL has its strengths in optimization and learning from
data, it relies on human ingenuity and creativity to drive true
innovation by inventing outside the constraints of the trained data and
reward signals. Collaboration between humans and RL systems can leverage
the strengths of both to push the boundaries of what is possible in problem-solving and decision-making."


And here is my second question to ChatGPT:

So then innovation by inventing outside the trained data of the lower
level patterns and the higher level patterns is still confined to the
domain of humans.


And here is what has just answered me ChatGPT:

"Yes, that's correct. As of the current state of artificial
intelligence, particularly in the context of reinforcement learning, the ability to innovate by inventing entirely new concepts, strategies, or algorithms that are outside the scope of trained data and predefined
patterns remains primarily within the domain of human intelligence.

Humans possess unique cognitive abilities such as creativity, abstract thinking, and the capacity for insight, which enable them to imagine and generate novel solutions to problems. These abilities allow humans to
innovate in ways that go beyond the confines of existing data or learned patterns.

While artificial intelligence, including reinforcement learning, can
automate tasks, optimize processes, and learn from data, its ability to innovate in the same way humans do is currently limited. AI systems rely
on the data they are trained on and the reward signals they receive, and
they operate within the constraints of these inputs.

However, AI systems can still play a valuable role in assisting and
augmenting human innovation efforts. They can help identify patterns in
large datasets, generate insights from data, and optimize solutions
within specific problem domains. Moreover, AI research continues to
advance rapidly, and future developments may lead to AI systems with
enhanced capabilities for creative problem-solving and innovation.

Overall, while AI has made significant strides in various domains,
including reinforcement learning, the domain of true innovation—where
novel concepts are generated outside the confines of existing data and patterns—remains firmly within the realm of human intelligence."


And here is my other question to ChatGPT:

So i want to say that Large Language models such as ChatGPT or Gemini
are still lacking , since if we can not trust the trained data of Large Language Models such as ChatGPT, since it can be of not correct deeper understanding and/or it is not correctly diversified and is then not of
the correct quality, so Large Language Models are here to augment humans
, and not to replace humans , also i think that we still need to use and
trust more professionals in this or that field or expertize in this or
that field so that to correct this important deficiency of Large
Language Models like ChatGPT , so i think that the quality of the
trained data is an important weakness of Large Language Models such as
ChatGPT , so it is why we are noticing that they are lacking on accuracy.


And here is what has just answered me ChatGPT:

"Your points are well-taken. Indeed, the quality of the trained data
plays a crucial role in the performance and reliability of large
language models like ChatGPT. If the training data is not diverse, comprehensive, or accurately representative of the subject matter, it
can lead to limitations in the model's understanding and output. Here's
a summary of your points:

Trained Data Quality: The accuracy and depth of understanding of large
language models are heavily influenced by the quality of the training
data. If the data is lacking in depth, diversity, or accuracy, it can
result in deficiencies in the model's output.

Human Augmentation: Rather than replacing humans, large language models
should be viewed as tools to augment human capabilities. Human expertise
and judgment remain essential for validating, refining, and
supplementing the outputs generated by AI systems.

Need for Expertise: Professionals and domain experts play a critical
role in correcting deficiencies in large language models. Their input
and oversight can help identify inaccuracies, biases, or gaps in the
model's understanding and contribute to improving its overall performance.

Weakness in Data Quality: The primary weakness of large language models
like ChatGPT often lies in the quality of the trained data. Addressing
this deficiency requires ongoing efforts to curate, verify, and
diversify the training data to enhance the model's robustness and accuracy.

In conclusion, while large language models offer significant potential
for various applications, including natural language understanding and generation, their effectiveness ultimately depends on the quality of the
data used to train them. By prioritizing data quality and leveraging
human expertise, we can work towards improving the reliability and trustworthiness of AI systems like ChatGPT."

But i think that the other important limitation of artificial
intelligence that also shows that artificial intelligence will not
replace humans , but will mainly augment humans , is the following:


Human intelligence and artificial intelligence do approach pattern
discovery in distinct ways.

Human Fluid Intelligence:

1- Adaptability: Human fluid intelligence refers to the ability to
quickly adapt and solve new problems. Humans can recognize patterns and
make connections based on relatively limited data or experiences.

2- Contextual Understanding: Humans often rely on contextual
understanding, drawing on a wide range of knowledge and experiences to
identify patterns. This ability to bring diverse information into play
is a hallmark of human intelligence.

3- Innate Creativity: Humans can be highly creative and draw on
intuition, imagination, and abstract thinking to identify patterns. This creativity allows for the recognition of novel patterns or the
application of existing patterns in new contexts.

Artificial Intelligence:

1- Data Dependency: AI systems, including machine learning models, often require substantial amounts of data to identify patterns effectively.
The learning process for AI involves analyzing large datasets to discern underlying patterns and relationships.

2- Algorithmic Approach: AI relies on algorithms and mathematical models
to analyze data and identify patterns. The effectiveness of AI in
pattern recognition depends on the quality of the algorithms and the
quantity and representativeness of the training data.

3- Narrow Specialization: While AI can excel in specific tasks and
domains, it may lack the broader adaptability and creativity seen in
human fluid intelligence. AI systems are often designed for specific
purposes and may struggle with tasks outside their predefined scope.

In summary, while humans can quickly adapt, understand contexts, and
exhibit creativity in pattern recognition, AI systems depend on vast
amounts of data 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
you have to understand more my below previous thoughts about my new
model of what is human consciousness , so you have to understand that
my new model of what is human consciousness says that there is a
"primitive" human consciousness that is a "primitive" self-consciousness
and that is like a controller that controls the human hands etc. , but i
am not talking about it since it is a primitive thing , so it is why my
new model of what is human consciousness is showing that the very
important thing is the consciousness that comes from the meaning that
comes from the reification with the human senses and from consciousness
of time.. , so you have to understand it correctly by reading my new
model of what is human consciousness in the below web link , so i think
that by reading it , you will able to understand that artificial
intelligence will not attain artificial general intelligence , even if
it will become a powerful tool.

So you have to also understand that the quality of data from Generative Adversarial Networks (GANs) or such in artificial intelligence is
probabilistic in nature too , so we cannot say that data generated by
models like Generative Adversarial Networks (GANs) is 100% truth or a
perfect representation of the real-world data distribution , so then you
are understanding from my thoughts that synthetic data from Generative Adversarial Networks (GANs) or such is probabilistic in nature and Large Language Models are probabilistic in nature and Reinforcement learning
is probabilistic in nature , so it is a weakness or limitation of
artificial intelligence. So i invite you to carefully read my below
previous thoughts so that to understand my views on what is
consciousness and what is smartness and about artificial intelligence:


So from my below new model of what is human consciousness in the below
web link, i think you can logically infer by discovering a pattern with your fluid intelligence that explains what is human smartness or what is human
fluid intelligence , so here is the pattern that i have just discovered:

So you have to carefully read my below new model of what is human
consciousness so that you understand that it is also like a
"reification" with the meaning that comes from the human senses , so i
am explaining more in the below web link my new model of what is human consciousness so that you understand it correctly , so i am also
explaining that this reification with the human senses also permits
smartness to require much less data than artificial intelligence , so
then i think that the pattern that i am discovering with my fluid
intelligence is that human smartness or human fluid intelligence is like
a neural network in the human brain that also works with the "meaning"
that comes from the reification with human senses and from consciousness
of time, so then this kind of neural network in the human brain can be
more or less powerful and this has as an effect to make the human brain
able to discover patterns with much less data than artificial
intelligence , so i think it is the pattern that explains what is human smartness or human fluid intelligence , so i invite you to carefully
read my below new model of what human consciousness so that to
understand it correctly and i invite you to
read about the important limitations of artificial intelligence in my
below previous 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 invite you to read my following discussion with ChatGPT so that you understand how i am explaining to ChatGPT that the probabilistic nature
is not only inherent to Large Language Models , but it is also inherent
to Reinforcement learning , so i think it is a limitation of Large
language models such as ChatGPT from OpenAI or such the new Gemini from
Google , so i invite you to carefully read my following discussion with
ChatGPT on this important subject , and of course i invite you to
carefully read the other limitations of artificial intelligence in my
below thoughts:


So here is my first question to ChatGPT:

"So i say that the other limitation of Large language models such as
GPT-4 is that they are probabilistic in nature , so then Large language
models such as GPT-4 are not designed to be highly accurate or
consistent , and it is one of there limitation , so i think that
reinforcement learning can not solve efficiently this problem , since we
can not know for sure when using Reinforcement learning it will be
highly accurate or consistent."


And here is what has just answered me ChatGPT:

"Your statement captures a common characteristic of large language
models like GPT-4, which is their probabilistic nature. Indeed, these
models generate responses based on probabilities learned from training
data, and as a result, they may produce different outputs for the same
input on different occasions.

Reinforcement learning, in the context of language models, is typically
used to fine-tune or guide the model's behavior based on some reward
signal. While reinforcement learning can be applied to improve aspects
of model behavior, it might not inherently address the probabilistic
nature of these models. Reinforcement learning is more focused on
optimizing for a specific objective or reward, and it may not guarantee
high accuracy or consistency in all situations.

It's essential to consider that the limitations you've highlighted are
inherent to the current state of large language models, and ongoing
research aims to address these challenges. Researchers are exploring
various techniques, including refining training strategies,
incorporating reinforcement learning, and developing more sophisticated architectures to enhance the accuracy and consistency of language
models. However, achieving perfect accuracy and consistency in natural
language understanding and generation remains a complex and evolving
area of research."


And here is my second question to ChatGPT:

"But there is still a problem , since we can not know for sure when Reinforcement learning will make the Large Language Model be high
accuracy or consistency , so we can not confirm it , so it is still probabilistic in nature , so it is a weakness or it is a limitation."


And here is what has just answered me ChatGPT:

"You raise a valid point. The integration of reinforcement learning in
large language models does not eliminate the inherent probabilistic
nature of these models. Reinforcement learning can guide the model
towards better performance by optimizing for specific objectives, but it doesn't guarantee deterministic outcomes in all cases.

The probabilistic nature of language models stems from the fact that
they generate responses based on learned probabilities from training
data. Even with reinforcement learning, there is uncertainty involved,
and the model's behavior can still vary depending on the context and
input it receives.

Therefore, it's accurate to view this as a limitation. The challenge is
to strike a balance between the flexibility and creativity provided by probabilistic models and the need for consistent and accurate responses. Researchers are actively exploring ways to mitigate these limitations,
but achieving complete determinism in natural language understanding and generation remains a complex and evolving area of research.

Acknowledging and understanding these limitations is crucial for
deploying language models effectively and ensuring users are aware of
the potential variability in responses. It also underscores the
importance of ongoing research and development to address these
challenges in future iterations of language models."

And I invite you to read carefully the following new article from InfoWorld that shows how help for generative AI is on the way by using for example knowledge graph and such:

https://www.infoworld.com/article/3712266/help-for-generative-ai-is-on-the-way.html


But i think that the above article is not speaking about the limitations
of this way of doing , so here is my thoughts below in the form of my discussion with ChatGPT so that you understand the limitations of it:


Here is my first question to ChatGPT:

But you are saying the following: Knowledge graphs provide a structured representation of information, enabling AI systems to cross-reference
and verify facts more accurately , but i mean that AI systems such as
LLMs are still limited by there limitation to verify facts with there
smartness , since there smartness is limited by the training data , so
it is like circular.


And here is what has answered me ChatGPT:

"You're correct in highlighting the circularity inherent in relying
solely on LLMs, especially when it comes to fact-checking and
verification. While knowledge graphs can provide structured information
for verification, if an LLM's understanding is inherently limited by its training data, it may not effectively utilize the structured information provided by the knowledge graph to verify facts accurately.

This limitation underscores the importance of a holistic approach to
building AI systems. While LLMs have demonstrated impressive
capabilities in natural language understanding and generation, they
should be complemented with other techniques, such as knowledge graphs,
expert systems, or external databases, to verify facts and provide
reliable information to users.

Furthermore, efforts to mitigate biases and limitations in LLMs'
training data, as well as ongoing research in natural language
processing and AI, are essential to improving the accuracy and
reliability of AI systems in fact-checking and verification tasks.

In summary, while knowledge graphs can enhance the structured
representation of information for fact-checking, addressing the
limitations of LLMs' training data and inherent biases is crucial for effectively utilizing this information and improving the reliability of
AI systems."

And here is my second question to ChatGPT:

So i think you are understanding now what i want to mean , and it is
that fact checking and verification on knowledge graphs so that to
increase accuracy , is limited by the smartness of the LLMs that is
limited by the training data that is limited by the lack of context and
the lack quality etc.


And here is what has just answered me ChatGPT:

"Yes, I understand your point now. You're highlighting a critical aspect
of the limitations of fact-checking and verification tasks, especially
when utilizing knowledge graphs with large language models (LLMs) like GPT.

Indeed, the accuracy of fact-checking and verification performed by LLMs
is inherently limited by several factors:

1- Training Data: LLMs are trained on vast amounts of text data, but
this data may contain biases, inaccuracies, or lack contextual nuances.
As a result, the model's understanding of language and ability to verify
facts may be limited by the quality and breadth of the training data.

2- Contextual Understanding: LLMs may struggle with understanding
context and nuance in language, which is crucial for accurate
fact-checking and verification. Without a deep understanding of the
context surrounding a statement or claim, LLMs may misinterpret
information or provide inaccurate assessments.

3- Quality of Information: Knowledge graphs provide structured
information, but the quality and reliability of the data within the
knowledge graph can vary. If the knowledge graph contains inaccuracies
or outdated information, LLMs may propagate these inaccuracies during fact-checking and verification tasks.

4- Scope of Knowledge: LLMs have limitations in their knowledge base, as
they can only generate responses based on the information present in
their training data. If a fact or claim falls outside the scope of the
model's training data, it may struggle to provide accurate verification.

Given these limitations, it's important to approach fact-checking and verification tasks with caution and to consider the broader context and
sources of information. While knowledge graphs can provide structured
data for verification, addressing the limitations of LLMs' training data
and ensuring the quality and relevance of information are essential
steps in improving the accuracy of fact-checking and verification
processes. Additionally, integrating multiple sources of information and employing human judgment and expertise can further enhance the
reliability of fact-checking efforts."


The other limitation of Large language models such as GPT-4 is that they
are probabilistic in nature , so then Large language models such as
GPT-4 are not designed to be highly accurate or consistent , and it is
one of there limitation , so i invite you to carefully read the
following interesting article that talks about it:

Are large language models wrong for coding?

https://www.infoworld.com/article/3697272/are-large-language-models-wrong-for-coding.html


Also so that to know the other important limitations of artificial
intelligence , i invite you to read my following thoughts:

I invite you to take a look at my new model of what is human
consciousness that i have just invented that permits to predict and that
shows the important limitations of artificial intelligence and that
shows that artificial intelligence will not attain artificial general intelligence (AGI) , but it will become a powerful tool that will
augment humans , so the first prediction of my new model of what is
human consciousness is that artificial intelligence will not attain
artificial general intelligence but it will become a powerful tool,
second prediction of my new model is that artificial intelligence will
then mainly augment humans , but it will not replace humans , and third prediction of my model is that we have to decrypt the human brain so
that we understand deeply the human consciousness so that we augment
artificial intelligence with consciousness so that it solves the problem
and so that artificial intelligence becomes artificial general
intelligence or super intelligence , and fourth prediction is that my
new model shows that until the next step we are more safe , since in the
next step of understanding deeply human consciousness , we will be so
powerful since humanity is progressing in an exponential progress , so i
think then we will be able to help effectively humans even if artificial intelligence will be augmented with consciousness and will replace
humans. So i invite you to read my new model of what is human
consciousness in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/s53zucweUIQ


And i invite you to read carefully all my following previous thoughts:


A study by AI researchers at Princeton and the University of Chicago
suggests that LLMs are a long way from being able to solve common
software engineering problems.

Read more here in the following new article:

https://leaddev.com/tech/researchers-say-generative-ai-isnt-replacing-devs-any-time-soon


And read the following about GPT-4:

"In programming tests, GPT-4 did worse; the AI struggles with code, it
seems. GPT-4 was able to get 31 out of 41 correct solutions in the
"easy" Leetcode test, but got just 21/80 on the medium test and only 3
correct questions on the hard test. Meanwhile, its Codeforces rating is
a measly 392, placing it below the 5th percentile of users.""

Read more here:

https://hothardware.com/news/openai-gpt-4-model


And here is my question to ChatGPT:

I think the important weakness of generative AI is that when you want to compose a higher pattern from lower level patterns , if generative AI
like ChatGPT doesn't find the parent pattern , it will not work , other
than that the parent pattern has to have a meaning that guides like the
human meaning so that to be constructed efficiently , but it is not the
case in generative AI like ChatGPT.


And ChatGPT has just answered me the following:

"You raise an important point about the limitations of generative AI
like ChatGPT. Indeed, one challenge is that these systems may struggle
to compose higher level patterns from lower level patterns if they
cannot identify or understand the underlying structure or "parent
pattern." This limitation often arises due to the model's lack of
contextual understanding or deeper semantic comprehension.

Generative AI relies heavily on the patterns it has learned from the
data it was trained on. If a particular pattern or concept is not
adequately represented in the training data, or if the model cannot
grasp the overarching structure guiding the generation process, it may
fail to produce meaningful or coherent outputs.

Moreover, the issue of imbuing generative AI with human-like meaning or
intent is complex. While AI systems like ChatGPT excel at generating
text based on statistical patterns in data, they lack true understanding
or intentionality behind their outputs. They operate purely on
statistical correlations rather than genuine comprehension.

Addressing these limitations requires advancements in AI research,
including better methods for contextual understanding, reasoning, and
semantic representation. Researchers are actively exploring ways to
enhance generative AI systems to better capture and generate meaningful patterns, but it remains an ongoing challenge.

In practical applications, it's essential to be aware of these
limitations and to use generative AI within contexts where its strengths
can be leveraged effectively while also understanding its constraints. Additionally, human oversight and guidance are often necessary to ensure
that AI-generated outputs align with desired objectives and expectations."


And here is my second question to ChatGPT:

So then the meaning in like the meaning in the human brain , permits to
guide the construction of the higher level or the parent pattern from
the lower level patterns , so if the higher level layers in the deep
learning don't contain the higher level pattern , it will not work , so
the meaning from the human brain is an efficient way that constructs the
higher level pattern or the parent pattern and this way of doing doesn't
exist in generative AI like ChatGPT , so generative AI like ChatGPT can
not be as creative as humans , so i don't think that generative AI like
ChatGPT will replace humans , but it will augment humans.



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