Hello,


More of my philosophy about the maximum token limit and about the other limitations of Large Language Models such as GPT-4 and about future of artificial intelligence and about the multimodality of Large Language Models such as GPT-4 and about my
philosophy and about my contributions here...

I am a white arab from Morocco, and i think i am smart since i have also invented many scalable algorithms and algorithms..


So GPT-4 has a window of maximum token limit of 32,000 (equivalent to 25,000 words), but it can still be not sufficient to detect or discover global patterns as i am explaining it below, and it's important to note that while having more tokens can bring
benefits, it also comes with computational limitations and increased inference time. So finding a balance between token count and computational resources is crucial for practical deployment of language models such GPT-4.


So here is another limitation of Large Language Models such as GPT-4:


So here is my question to ChatGPT:


"So from what i understand , discovering patterns of the language can require the discovering of local or global patterns, but the global patterns can be difficult to detect since they can require the scanning of multiple documents or papers, so then
even a large window of the maximum tokens can not be sufficient ?"


And here is the answer of ChatGPT:


"Yes, you have summarized it correctly. Discovering patterns in language involves identifying both local and global patterns. Local patterns refer to dependencies and relationships within a smaller context window, while global patterns span across larger
sections of text.

While a larger window size, such as a larger maximum token limit, can potentially capture more extensive context and aid in detecting some global patterns, it may not be sufficient on its own. The discovery of global patterns often requires the analysis
of multiple documents or papers, which can be challenging and time-consuming.

Even with a larger window size, there are limitations to how much context can be considered, and there may still be relevant information outside the window that influences the global patterns. Additionally, the sheer volume of data, such as vast
collections of documents or papers, makes it impractical to analyze every piece of information exhaustively.

Therefore, despite the advantages of a larger window size, the discovery of global patterns can still be challenging and may require additional strategies such as sampling techniques, prioritization, collaboration, or meta-analyses.

It's important to recognize that language models, including those with large windows, have limitations in their ability to comprehensively capture all global patterns. Balancing the available computational resources, practical considerations, and
research methodologies is crucial in effectively discovering and interpreting patterns in language."



And here is the other information from ChatGPT about its limitations:



"Technically, large language models like GPT-3.5 lack true comprehension and struggle with common sense for a few reasons:

Statistical patterns: Language models are trained using a process called unsupervised learning, where they predict the next word in a sentence based on the patterns observed in the training data. They learn to generate text that is statistically similar
to the training data, but this process does not involve true understanding of the meaning behind the words. The models primarily rely on statistical associations and surface-level patterns, rather than deep semantic understanding.

Lack of external knowledge: Language models typically do not possess external knowledge beyond what is present in their training data. They lack a comprehensive understanding of the world, common knowledge, or access to real-time information. Without
this broader context, they may struggle to reason effectively or demonstrate common-sense understanding.

Absence of reasoning abilities: While attention mechanisms help language models capture relationships between words or tokens in a sequence, they do not provide explicit reasoning capabilities. Language models cannot engage in logical reasoning, perform
deductive or inductive reasoning, or make use of background knowledge to draw accurate conclusions. They are limited to what they have learned from the training data.

Lack of grounding in the physical world: Language models like GPT-3.5 do not have direct sensory perception or embodied experiences. They operate purely on text input and lack the ability to perceive the world through visual, auditory, or tactile
modalities. This lack of grounding in the physical world further limits their comprehension and common-sense reasoning abilities.

Overall, the current architecture and training methods of large language models are primarily focused on generating text that mimics human-like responses based on statistical patterns. While they can produce impressive outputs, they lack the deeper
understanding and reasoning capabilities that come naturally to human language comprehension."



And here is the other information from ChatGPT about its limitations:


"Certainly! The weakness of relying solely on statistical associations and surface-level patterns in language models like GPT-3.5 is that they can lead to limitations and inaccuracies in understanding and generating text. Here are some key aspects to
consider:

- Lack of semantic understanding: Statistical associations and surface-level patterns do not capture the underlying meaning and semantics of language. Language models primarily learn to associate certain words or phrases with others based on their co-
occurrence in the training data. While this enables them to generate text that appears similar to the training data, it does not reflect a deep comprehension of the concepts and relationships expressed by the words.

- Difficulty with context-dependent interpretations: Language models struggle with context-dependent interpretations because they primarily rely on local context within a limited window of surrounding words. They may miss out on broader contextual cues
or fail to consider the discourse or conversation history, leading to potential misinterpretations or inadequate responses.

- Sensitivity to training data biases: Language models tend to inherit biases present in the training data. If the data contains biased language or stereotypes, the models may inadvertently learn and reproduce these biases in their outputs. This can
perpetuate social, gender, or racial biases, and result in unfair or discriminatory responses.

- Inability to handle rare or novel patterns: Language models trained solely on statistical associations may struggle with rare or novel patterns that were not sufficiently represented in the training data. If the model encounters phrases or concepts
that deviate significantly from the training data's patterns, it may produce inaccurate or nonsensical responses, as it lacks a deeper understanding of the underlying meaning.

- Vulnerability to adversarial examples: Adversarial examples are carefully crafted inputs designed to mislead or confuse a model. Language models relying on statistical associations can be susceptible to adversarial attacks. By exploiting the models'
tendency to generate responses based on statistical patterns, adversaries can craft inputs that lead to unexpected or undesired outputs, potentially compromising the model's reliability and trustworthiness."

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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, so i think that
Large Language Models such as GPT-4 have inputs and outputs to the outside world, so they can be connected to the outside world , so then you have to understand my below logical proof, so my logical proof below doesn't mean that artificial intelligence
will not become powerful, but it just means that something is missing that is the meaning from human consciousness that also allows to learn with much less data than artificial intelligence, so then you are noticing that artificial intelligence will
become powerful even if it will not attain artificial general intelligence , for the simple reason that the language that is an abstraction of the real world is also really interesting, since it permits to be translated from human to human by adding the meaning from human consciousness to it, so i think that multimodality with
images and videos