Hello,


More of my philosophy about GPT-4 memory and about Canada and about agile methodology and 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..


I invite you to read the following article so that to understand an important limitation of Large Language Models such as GPT-4:

GPT-4 has the memory of a Goldfish

https://www.theatlantic.com/technology/archive/2023/03/gpt-4-has-memory-context-window/673426/


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 the above article is not explaining correctly , so i will explain: So another important problem is how
to solve the above problem, since Large Language Models such as GPT-4 can only discover patterns with the attention mechanisms etc. only inside a window of the maximum limit of tokens when they are trained , so the problem remains of how to ensure that
the global patterns are discovered, since for example an average book typically contains around 80,000 to 100,000 words, so GPT-4 has a window of maximum token limit of 32,000 (equivalent to 25,000 words), so then
there is local patterns that require a small window , but there is also global patterns that require a large window, so for example discovering the global patterns in the training data can require one book or two books or more, so then you are noticing
that it is an important limitation of Large Language Models such as GPT-4, since the size of the context window can indeed impact the capacity of a language model to understand nuanced concepts and leverage common sense knowledge, since a smaller window
may limit the model's ability to capture long-range dependencies, complex relationships, and broader contextual information that are also crucial for nuanced understanding, but making the window of maximum token limit bigger than that of GPT-4, so that
to solve efficiently the problem, comes with too much computational limitations and with too much increased inference. So i invite you to read my below previous thoughts so that to understand my views on it and on artificial intelligence:


How Canada could become the third or fourth largest economy in the world by the 2040s

Read more here:

https://www.nextbigfuture.com/2023/06/how-canada-could-become-the-third-or-fourth-largest-economy-in-the-world-by-the-2040s.html#more-183725


So as you have just noticed, i have just talked yesterday about my new thoughts on programming by saying the following:


"So in programming so that to not make the system harder to understand, test, and maintain, you have to implement what you need and you have to minimize at best complexity and you should avoid the duplication of code in your application and you should
encapsulate data and behavior in your classes and objects, and take advantage of object-oriented programming (OOP) concepts such as inheritance, composition, and polymorphism to create modular, manageable, and organized code, and of course you have to
minimize at best coupling and maximize at best cohesion, and you should well document code so that it be much easier to manage, maintain, and debug and you should run unit tests often, and you have to use meaningful names, and of course you should
refactor your code regularly by improving code quality since refactoring makes the code far easier to maintain over time."


But i think i have also to talk about the most important ideas of agile methodology, so of course agile methodology is used so that to adapt efficiently to the changing environment or so that to adapt efficiently to change, so here is my interesting
thoughts about agile methodology , so read them carefully:

Here are some important steps of software Evolutionary Design methodology with agile:


1- Taking a little extra time during the project to write solid code and
fix problems today, they create a codebase that’s easy to maintain
tomorrow.

2- And the most destructive thing you can do to your project is to build
new code, and then build more code that depends on it, and then still
more code that depends on that, leading to that painfully familiar
domino effect of cascading changes...and eventually leaving you with
an unmaintainable mess of spaghetti code. So when teams write code,
they can keep their software designs simple by creating software
designs based on small, self-contained units (like classes, modules,
services, etc.) that do only one thing; this helps avoid the domino
effect.

3- Instead of creating one big design at the beginning of the project
that covers all of the requirements, agile architects use incremental
design, which involves techniques that allow them to design a system
that is not just complete, but also easy for the team to modify as
the project changes.

4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.

5- And an iterative process in agile is one that makes progress through successive refinement. A development team takes a first cut
at a system, knowing it is incomplete or weak in some (perhaps many)
areas. They then iteratively refine those areas until the product is satisfactory. With each iteration the software is improved through
the addition of greater detail.


And I invite you to look at step 4 of my below thoughts of software Evolutionary Design methodology with agile, here it is:

4- When in agile a team breaks a project into phases, it’s called
incremental development. An incremental process is one in which
software is built and delivered in pieces. Each piece, or increment,
represents a complete subset of functionality. The increment may be
either small or large, perhaps ranging from just a system’s login
screen on the small end to a highly flexible set of data management
screens. Each increment is fully coded Sprints, Planning, and
Retrospectives.

And you will notice that it has to be done by "prioritizing" the pieces of the software to be delivered to the customers, and here again in agile you are noticing that we are also delivering prototypes of the software, since we often associate prototypes
with nearly completed or just-before launch versions of products. However, designers create prototypes at all phases of the design process at various resolutions. In engineering, students are taught to and practitioners think deeply before setting out to
build. However, as the product or system becomes increasingly complex, it becomes increasingly difficult to consider all factors while designing. Facing this reality, designers are no longer just "thinking to build" but also "building to think." By
getting hands on and trying to create prototypes, unforeseen issues are highlighted early, saving costs related with late stage design changes. This rapid iterative cycle of thinking and building is what allows designers to learn rapidly from doing.
Creating interfaces often benefit from the "build to think" approach. For example, in trying to layout the automotive cockpit, one can simply list all the features, buttons, and knobs that must be incorporated. However, by prototyping the cabin does one
really start to think about how the layout should be to the driver in order to avoid confusion while maximizing comfort. This then allows the designer iterate on their initial concept to develop something that is more intuitive and refined. Also
prototypes and there demonstrations are designed to get potential customers interested and excited.


More of philosophy about Democracy and the Evolutionary Design methodology..

I will make a logical analogy between software projects and Democracy,
first i will say that because of the today big complexity of software
projects, so the "requirements" of those complex software projects are
not clear and a lot could change in them, so this is why we are using an Evolutionary Design methodology with different tools
such as Unit Testing, Test Driven Development, Design Patterns,
Continuous Integration, Domain Driven Design, but we have to notice
carefully that an important thing in Evolutionary Design methodology is
that when those complex software projects grow, we have first to
normalize there growth by ensuring that the complex software projects
grow "nicely" and "balanced" by using standards, and second we have to
optimize growth of the complex software projects by balancing between
the criteria of the easy to change the complex software projects and the performance of the complex software projects, and third you have to
maximize the growth of the complex software projects by making the most
out of each optimization, and i think that by logical analogy we can
notice that in Democracy we have also to normalize the growth by not
allowing "extremism" or extremist ideologies that hurt Democracy, and we
have also to optimize Democracy by for example well balancing between "performance" of the society and in the Democracy and the "reliability"
of helping others like the weakest members of the society among the
people that of course respect the laws.


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