Hello,

More of my philosophy about the deep learning network and about
artificial general intelligence 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.