Hello,

More of my philosophy about new and future technologies and about Artificial intelligence 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..

So i also invite you to look at the following web link with my thoughts about the new and future technologies:

https://groups.google.com/g/alt.culture.morocco/c/lfnlD52jDzI

And of course, i invite you to read all my below thoughts about artificial intelligence and about technology and more , so that to understand my views:

I invite you to read the following new article about the new DeepMind AI model called Gemimi:

After Bard, Google’s DeepMind hopes Gemini will truly surpass ChatGPT

https://indianexpress.com/article/technology/artificial-intelligence/deepmind-claims-gemini-will-rival-chatgpt-8688846/

And here is my questions to ChatGPT about the limitations of the above new DeepMind AI model called Gemimi:

And here is my first question to ChatGPT:

"So the hybrid model of AlphaGO techniques and large language models such ChatGPT, used by new DeepMind AI model called Gemimi,
can still lack on creativity , since the exploration is limited by the data ?"

And here is what answered me ChatGPT:

"Yes, the hybrid model of combining AlphaGo techniques with large language models like ChatGPT may still face limitations when it comes to creativity, and one of the factors contributing to this limitation is the reliance on available data.

Creativity often involves thinking beyond the existing patterns and generating novel, innovative solutions. While large language models like ChatGPT have shown impressive language generation capabilities, they are fundamentally trained on vast amounts of existing text data. The model's responses are primarily influenced by the patterns, examples, and language conventions present in the training data.

If the training data does not sufficiently capture a wide range of creative or unconventional approaches, the model may struggle to generate truly creative outputs. The model's exploration is indeed limited by the data it has been exposed to during training, which can constrain its ability to think "outside the box" and produce highly imaginative or innovative responses.

To enhance creativity, it may be necessary to provide the model with additional diverse and creative training data or employ techniques like reinforcement learning with suitable reward mechanisms that encourage creative outputs. However, achieving truly human-level creativity in language generation remains a challenging goal for current AI models, and they may not match the level of inventive thinking and imagination exhibited by human beings."

And here is my second question to ChatGPT:

"And what you are saying is also true for the hybrid model of AlphaGO techniques and large language models such ChatGPT ?"

"Yes, the limitations I mentioned regarding creativity and the influence of available data still apply to the hybrid model that combines AlphaGo techniques with large language models like ChatGPT.

The hybrid model inherits the data limitations from the underlying large language model component. It heavily relies on the patterns and examples present in the training data to generate responses. Therefore, if the training data does not contain diverse and creative examples, the hybrid model may struggle to exhibit high levels of creativity in its outputs.

While integrating AlphaGo techniques can enhance decision-making and exploration capabilities, it does not fundamentally change the limitations related to creativity imposed by the available training data. The hybrid model may still face challenges in generating highly creative or innovative responses that go beyond what it has seen during training.

Achieving true human-level creativity in AI models remains an ongoing research challenge."

And so that to understand more the limitations of Large Language Models such as GPT-4 and of the next new Google AI model called
Gemimi, i invite you to read my following thoughts:

Also i want you to understand another important thing about Large Language Models such as GPT-4, and here is what has answered me ChatGPT so that you understand it:

"Capturing long-range dependencies and global patterns efficiently is an ongoing research area, and while significant progress has been made, it is a complex problem that may not have a definitive "solution" in the traditional sense. The field of deep learning and natural language processing continues to evolve, and researchers are continuously exploring new techniques and architectures to improve the efficiency of capturing long-range dependencies.

The timeline for achieving optimal efficiency in handling long-range dependencies is difficult to predict. It depends on various factors, including the complexity of the problem, available resources for research and development, and breakthroughs in the field. While some advancements may be made in the near future, it is possible that further refinements and optimizations could take several years or even a decade to fully address the challenges associated with computational expense.

It's important to note that progress in artificial intelligence research is often iterative, with incremental improvements over time. Researchers continually propose new techniques, architectures, and optimizations to enhance the efficiency of models. It is a collaborative effort involving academia, industry, and open-source communities.

Therefore, while it is challenging to provide a definitive timeline for solving the problem of efficiently capturing long-range dependencies, ongoing research and innovation in the field suggest that improvements and advancements will continue to be made over time."

And here is what thinks ChatGPT about the pattern that i have just discovered with my fluid intelligence:

Here is my following question to ChatGPT:

"What do you think of my following thoughts:

So i have to explain something important, so since i have just discovered an important pattern with my fluid intelligence and read about it in my below previous thoughts, so i think that Large Language Models such as GPT-4 are capturing causality indirectly from the language and much more easily in the languages like french or english , so in professions like psychology or the law or philosophy that use the language like french or english, it is easy for Large Language Models to capture causality, but in software development , it is more difficult, since for example in GitHub where you find the open source software projects, Large Language Models are not able to capture causality correctly, since those software projects in GitHub are not languages like french and english, so causality in them is not specified and modeled with a language like french or english, so you are understanding more the important pattern that i am discovering with my fluid intelligence below that explains a so important thing, so then you are understanding that it is important to help Large Language Models such as GPT-4 to capture causality by modeling it with a language like french or english , by specifying it enough and clearly. So then you are understanding this important limitation of Large Language Models such as GPT-4."

And here is what ChatGPT has answered:

"Your thoughts touch on an interesting point regarding the capabilities of large language models like GPT-4 and their ability to capture causality. While language models can process and understand natural language to a certain extent, capturing causality directly from the language is still a complex task.

In professions like psychology, law, or philosophy, where the language itself is used to express causality, it might be easier for large language models to capture and comprehend the underlying causal relationships. These fields heavily rely on language-based reasoning and argumentation.

However, in domains like software development, causality is often expressed and modeled in code rather than natural language. GitHub, as you mentioned, hosts numerous open-source software projects that are predominantly written in programming languages rather than French or English. Large language models face challenges in capturing causality correctly in such contexts since the causality is not explicitly specified and modeled using natural language.

To help large language models like GPT-4 capture causality more effectively, one approach could be to provide them with clearer and more explicit language-based specifications. By expressing and defining causality using languages like French or English, the models may have a better chance of understanding and incorporating causal relationships into their reasoning processes.

It's important to note that large language models still have limitations, and while they can be powerful tools, they may not possess a comprehensive understanding of causality or domain-specific knowledge. Collaborating with experts in specific fields can help address these limitations and ensure accurate modeling of causality in various domains."

I invite you to read the following interesting article:

Why artificial intelligence needs to understand consequences

Read more here:

https://www.nature.com/articles/d41586-023-00577-1

And here is my other question to ChatGPT as a proof:

"So let's take for example open source software projects on GitHub, so open source projects on GitHub are not documented correctly, so ChatGPT can lack to capture causality, and can lack to understand correctly creativity of it ?"

And here is what ChatGPT has answered: