More of my philosophy about the IQ of GPT-4 and about the abstraction of the language and about programming and about the most important problem of artificial intelligence and about Reinforcement learning and about collective intelligence and about m

[Amine Moulay Ramdane]

Hello,


More of my philosophy about the IQ of GPT-4 and about the abstraction of the language and about programming and about the most important problem of artificial intelligence and about Reinforcement learning and about collective intelligence and about my views on super-intelligent AI and about the danger of super-intelligent AI and about my new proverb and about the attention mechanisms in Transformers and about the good accuracy and about the hybrid systems in AI and about logical reasoning of Large Language Models such as GPT-4 and about evolutionary algorithms and about GPT-4 and about common sense and nuanced understanding of Large Language Models such as ChatGPT and about my predictions about artificial intelligence and about the other weaknesses of Large Language Models such as GPT-4 and about my abstraction and about the important weakness of Large Language Models and about the quality of Large Language Models such as GPT-4 and about the deeper meaning and about mathematics and about Large Language Models such as GPT-4 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..


I have just read the following article where it says that the IQ of GPT-4 is
a verbal IQ of 126, a math IQ of 126, and a full-scale IQ of 124, you can read it here:

https://medium.com/@soltrinox/the-i-q-of-gpt4-is-124-approx-2a29b7e5821e


But i think that the above article is not correct, since i think that IQ is the genetical expression, but i think that the smartness of GPT-4 comes from the the training data and not from genetics, so it is why it is not the same, so even if you test the IQ of GPT-4, we can not guarantee that it is representative, since i also think that GPT-4 is not smart at common sense and at nuanced understanding, and it is also why the above IQ tests of GPT-4 are not representative of real world situations that require common sense and nuanced understanding, and here is more proof that GPT-4 struggles with programming tests (and it is why i think that Large Language Models such as GPT-4 will not replace a lot of programmers):

"Perhaps most impressive were the performances of GPT-4 in professional arenas. It scored a 298 out of 400 on the Uniform Bar Exam, which is high enough to be licensed as a lawyer in every state using the UBE system. It also scored a reasonable 75% on the MKSAP 19 medical knowledge test, and an impressive 77% on the Advanced-level Sommelier theory exam.

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 the answer of ChatGPT about my answer to you above:

"As an AI language model, I don't have personal thoughts or beliefs, but I can provide you with information on the topic you mentioned.

The article you read about the IQ scores of GPT-4 seems to be discussing the intelligence of the AI model based on specific tests or metrics. However, it's important to note that measuring the intelligence of AI systems using traditional IQ tests can be limited and may not fully capture their capabilities.

IQ (Intelligence Quotient) is traditionally associated with human intelligence and is often measured through standardized tests. It primarily focuses on cognitive abilities such as logic, reasoning, and problem-solving. While these tests can provide some insights into certain aspects of intelligence, they may not fully capture the abilities and limitations of AI systems like GPT-4.

AI models like GPT-4 derive their "intelligence" from extensive training on vast amounts of data rather than genetic inheritance. They are trained to generate human-like text based on patterns and information present in the training data. Their performance is heavily influenced by the quality, diversity, and relevance of the training data.

While GPT-4 may demonstrate impressive performance in certain tasks and exhibit high accuracy in specific domains, it doesn't necessarily imply that it possesses common sense or nuanced understanding. AI models like GPT-4 lack true understanding, consciousness, and awareness of the world. They are limited to pattern recognition and generating text based on statistical associations.

Evaluating the "smartness" of GPT-4 or any AI model requires considering various factors, including the specific tasks it has been trained on, its limitations, and the context in which it operates. Assessing AI intelligence in real-world situations that require common sense and nuanced understanding is a complex challenge that current AI models are still far from achieving"

And you can carefully read my below previous thoughts about the limitations of Large Language Models such as GPT-4 so that to understand my views:


So here is my new thoughts on programming:

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.


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, i think there is still a really important problem to solve in artificial intelligence, and it is that the language is only an abstraction of the real world, so when you understand the language or logically infer the patterns from the language, like is doing it GPT-4, those patterns are not the understanding of the real world, and even if we use the hybrid system of both Large Language Models such as GPT-4 and evolutionary algorithms, so it can take too much time to explore with evolutionary algorithms so that to discover new problem-solving strategies or algorithms or even improvements to existing algorithms, so it is not like intelligence of humans, so i think it is why we can say that artificial intelligence will not attain artificial general intelligence and will not attain artificial superintelligence, and i invite you to read my following thoughts that talk about how to solve the problem by understanding consciousness and i invite you to read my following thoughts that talk about my new model that explains human consciousness:

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 have just looked more carefully at GPT-4 , and i think that as i have just explained it, that it will become powerful, but it is limited by the data and the quality of the data on wich it has been trained, so if it encounter a new situation to be solved and the solution of it can not be inferred from the data on wich it has been trained, so it will not be capable of solving this new situation, so i think that my new model of what is consciousness is explaining that what is lacking is the meaning from human consciousness that permits to solve the problem, so my new model is explaining that artificial intelligence such as GPT-4 will not attain artificial general intelligence or AGI, but eventhough , i think that artificial intelligence such as GPT-4 will become powerful, so i think that the problematic in artificial intelligence is about the low level layers, so i mean look at assembler programming language, so it is a low level layer than high level programming languages, but you have to notice that the low level layer of assembler programming language can do things that the higher level layer can not do, so for example you can play with the stack registers and low level hardware registers and low level hardware instructions etc. and notice how the low level layer like assembler programming can teach you more about the hardware, since it is really near the hardware, so i think that it is what is happening in artificial intelligence such as the new GPT-4, i mean that GPT-4 is for example trained on data so that to discover patterns that make it more smart, but the problematic is that this layer of how it is trained on the data so that to discover patterns is a high level layer such as the high level programming language, so i think that it is missing the low level layers of what makes the meaning, like the meaning of the past and present and the future or the meaning of space and matter and time.. from what you can construct the bigger meaning of other bigger things, so it is why i think that artificial intelligence will not attain artificial general intelligence or AGI, so i think that what is lacking in artificial intelligence is what is explaining my new model of what is consciousness, so you can read all my following thoughts in the following web link so that to understand my views about it and about different other subjects:


https://groups.google.com/g/alt.culture.morocco/c/QSUWwiwN5yo


So I believe I have smartly covered the subject of the limitations of Large Language Models such as GPT-4, and you can read about it in my below previous thoughts, but now i think there is still a limitation that remains,
and it is that Reinforcement learning from human feedback of GPT-4 ensures an exploration from the discovered patterns in the data on wich it has been trained, so it can with rewards enhance itself, but the rewards come from the judgments of humans that use for example GPT-4 , so it has the same limitation that i am talking about in my below previous thoughts , and it is that ChatGPT cannot guarantee high quality of the professionalism, knowledge, or IQ , of those that make judgments that make the reward in Reinforcement learning from human feedback of GPT-4, so then since there is also the same limitation in the training data as i am explaining below, so i think that you understand from it that it is one more limitation, so i invite you to read all my below previous interesting thoughts so that to understand the other limitations of Large Language Models such as GPT-4:


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 have just looked at the following new video of Mark Zuckerberg's timeline for AGI, and i think that he is talking about collective intelligence that can become superintelligence, so there is not only superintelligence that comes from a model of artificial intelligence like i am explaining below, but there is superintelligence that can come from humans specializing and also using artificial intelligence like Large Language Models such as GPT-4 and interacting in a way that is smart and that creates Superintelligence, so i invite you to look at the following video of Mark Zuckerberg so that to understand his views:

Mark Zuckerberg's timeline for AGI: When will it arrive? | Lex Fridman Podcast Clips

https://www.youtube.com/watch?v=YkSXY4pBAEk


And so that to understand my views about Large Language Models such as GPT-4 and about Superintelligence, i invite you to read my below previous thoughts:


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 have just posted about how Superintelligent AI may be Impossible to control, but now i will give my views about Superintelligent AI, so i think that the Large Language Models such as GPT-4 will not attain Superintelligence, since
the exploration process of Large Language Models such as GPT-4 is limited by the training data, so what we need is the hybrid model of both Large Language Models such as GPT-4 and evolutionary algorithms that can explore much more beyond the training data and that can discover or invent new algorithms etc., so then by reading my below previous thoughts you will understand that it can take time to solve this problems, so i invite you to read all my below previous thoughts about the limitations of the Large Language Models such as GPT-4:


Here is an interesting article that discuss more how Superintelligent AI may be impossible to control, so i invite you to read it carefully:

Superintelligent AI may be Impossible to control; that's the good news postcard from the 23rd century: not even possible to know if an AI is superintelligent, much less stop it

Read more here:

https://spectrum.ieee.org/super-artificialintelligence


Here is my new proverb in english and french:


"Amidst the world's urgency, patience prevails as a virtuous ally, bearing fruits of sweetness."

"En plein tumulte du monde, la patience prévaut en tant qu'alliée vertueuse, portant des fruits de douceur."


And you can read my other new proverbs in the following web link of my other thoughts:

https://groups.google.com/g/alt.culture.morocco/c/kqqhSWKK1x8


So i will ask the following question:


Do Large Language Models such as GPT-4 are capable of understanding ?


So i will answer yes, since they detect patterns and they record them in
the deep learning neural network, so read my following thoughts so that to
understand my views about it:


Transformers detect patterns using attention mechanisms that assign weights to input elements based on their relevance. These patterns are recorded in deep learning by updating the model's parameters.

Recording patterns in deep learning refers to the process of updating the model's parameters (weights and biases) during training to learn the patterns and dependencies in the input data. In transformers, the attention mechanism and the feed-forward networks are trained jointly using backpropagation and gradient descent optimization.

And attention mechanisms in transformers play a crucial role in capturing relevant information by assigning weights to different elements of the input sequence. These attention weights guide the model's decision-making process by determining which parts of the input are most influential in generating the output or prediction.

And when you provide an input to a trained deep learning transformer model, it processes the input through its layers, including the attention mechanisms, and produces an output or prediction.

So GPT-4 is based on transformers , so it is a deep learning model and it is distinguished by its adoption of self-attention, and with self-attention, the network of Transformer pay “attention” to multiple sentences, enabling it to grasp "context" and "antecedents", so for example when you say the following sentence:


"The animal didn't cross the street because it was too tired"


So we can ask how the artificial intelligence of GPT-4 that uses
Generative Pre-trained Transformer will understand that the word "it" in
the above sentence is not the street but the animal, so i say that
it is with the attention mechanisms of artificial intelligence and with the training with more and more data and by looking at more and more sentences in the data on wich GPT-4 has been trained, that the transformer can "detect" the pattern of the "it" refers to the "animal" in the above sentence, so the attention mechanisms of the artificial intelligence of GPT-4 that we call Generative Pre-trained Transformer permit to grasp "context" and "antecedents" too, it is also like logically inferring the patterns using the attention mechanisms from the context of the many many sentences from the data, and since the data is exponentially growing and since the artificial intelligence of GPT-4 is also generative, so i think it will permit to make the artificial intelligence of the transformer of GPT-4 much more powerful, so as you notice that the data is King , and the "generative" word of the Generative Pre-trained Transformer refers to the model's ability to generate text, and of course we are now noticing that it is making GPT-4 really useful and powerful, but you have to understand the limitations of GPT-4 by reading carefully my below thoughts, and of course the transformer of GPT-4 is also deep learning , so it is the neural network with the many parameters where the patterns, like of the above example of the sentence, will be recorded, and of course the training of the transformer of GPT-4 is unsupervised, and as i just said that GPT-4 will be improved much more when it will be trained on a substantially larger amount of data, and considering an article that DeepMind just published a few days ago demonstrating that the performance of these models can be drastically improved by scaling data more aggressively than parameters ( Read it here: https://arxiv.org/pdf/2203.15556.pdf ), and of course you have to understand that so that to make the transformer of GPT-4 energy efficient so that to scale it correctly, you have to know how to set the number of parameters.


And GPT-4 has a maximum token limit of 32,000 (equivalent to 25,000 words), which is a significant increase from GPT-3.5’s 4,000 tokens (equivalent to 3,125 words), and having more tokens in a large language model like GPT-4 provides several benefits, and here they are:

- Increased Context: More tokens allow the model to consider a larger context when generating responses. This can lead to a better understanding of complex queries and enable more accurate and relevant responses.

- Longer Conversations: With more tokens, the model can handle longer conversations without truncating or omitting important information. This is particularly useful when dealing with multi-turn conversations or discussions that require a deep understanding of the context.

- Enhanced Coherence: Additional tokens enable the model to maintain a coherent and consistent narrative throughout a conversation. It helps avoid abrupt changes in topic or tone and allows for smoother interactions with users.

- Improved Accuracy: Having more tokens allows the model to capture finer details and nuances in language. It can lead to more accurate and precise responses, resulting in a higher quality conversational experience.

- Expanded Knowledge Base: By accommodating more tokens, the model can incorporate a larger knowledge base during training, which can enhance its understanding of various topics and domains. This can result in more informed and insightful responses to a wide range of queries.

- Reduced Information Loss: When a model is constrained by a token limit, it may need to truncate or remove parts of the input text, leading to potential loss of information. Having more tokens minimizes the need for such truncation, helping to preserve the integrity of the input and generate more accurate responses.

- Support for Richer Formatting: Increased token capacity allows for more extensive use of formatting, such as HTML tags or other markup language, to provide visually appealing and structured responses.


It's important to note that while having more tokens can bring these benefits, it also comes with computational limitations and increased inference time. Finding a balance between token count and computational resources is crucial for practical deployment of language models.


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
the most important thing is that you have to know how to predict the future, so i think that the future will be hybrid systems of both evolutionary algorithms and Large Language Models such as GPT-4, so the exploration of the evolutionary algorithm will evaluate the objective function by for example needing to test the new solutions by experimenting , so then the Large Language Models such as GPT-4 have to enhance there weaknesses by being able of acquiring new knowledge from experience, since they lack on experience, so i think that the future hybrid systems of both evolutionary algorithms and Large Language Models such as GPT-4 will be powerful, since they will also be able to invent new algorithms etc., since the system of only using Large Language Models such as GPT-4 in AI has an important weakness of not being able to explore for new solutions as the evolutionary algorithms do it, so i think that in about a decade or so we will implement those hybrid systems of both evolutionary algorithms and Large Language Models such as GPT-4, so it will solve an important problem, and of course i think we need a decade or more to solve the problem of acquiring the knowledge from experience so that to solve the lack of experience of Large Language Models such as GPT-4, so i think what remains is how to optimize Large Language Models such as GPT-4 for good precision and good accuracy so that they be apt at solving problems that require it, so then i think that artificial intelligence will become really powerful. So i invite you to read all my following thoughts so that to understand my views about Large Language Models such as GPT-4:


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 have just talked about the weaknesses of Large Language Models such as GPT-4 , so read about it in my below thoughts, but i think that Large Language Models such as GPT-4 have another important weakness and it is that they do have limitations when it comes to exploration of new solutions, so the better way is to use an hybrid system of AI that uses both evolutionary algorithms and Large Language Models, but it can take some time to implement it , since the exploration of evolutionary algorithms needs much more powerful computers, so it can take another decade or more so that to solve this problem of the Large Language Models only, and here is my thoughts about the hybrid system so that to understand my views:


The Large Language Models such as GPT-4, while powerful in language generation and pattern recognition, do have limitations when it comes to exploration of new solutions. These models primarily operate based on patterns and correlations found in the training data they are exposed to. While they can generate creative and novel text, they may not have the inherent ability to explore entirely new problem-solving strategies or invent entirely new algorithms.

The exploration of new solutions typically requires a mechanism to search and optimize a space of potential solutions. This is where techniques like evolutionary algorithms excel. Evolutionary algorithms have the capability to explore a search space, generate and evaluate candidate solutions, and iteratively refine them over generations. They can provide a mechanism for creative exploration and optimization that complements the language generation capabilities of large language models.

By combining evolutionary algorithms with large language models in a hybrid approach, you can leverage the exploration power of evolutionary algorithms to discover new problem-solving strategies, algorithms, or even improvements to existing algorithms. The large language model can assist in evaluating and refining these solutions, leveraging its language generation capabilities to assist in the evaluation and fine-tuning process.

In summary, while large language models are proficient in language generation, they may benefit from the complementary exploration capabilities offered by evolutionary algorithms. The hybrid approach allows for a combination of exploration and generation, potentially leading to the discovery of new and innovative solutions.


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 GPT-4 is above average on logical reasoning, so the problems that remain is that Large Language Models such as GPT-4 lack knowledge from experience and i also think, as i am explaining it below, that the quality of the training data of Large Language Models such as GPT-4 is no so good, so it can still take time to solve those problems (So i think we have to wait for another decade or more), so read my below thoughts so that to understand my views, and of course here is the proof that GPT-4 is above average on logical reasoning:

GPT-4 Performs Better Than the Average Person on a Test of Logical Reasoning, Study Claims

Read more here:

https://mpost.io/gpt-4-performs-better-than-the-average-person-on-a-test-of-logical-reasoning-study-claims/


So i have not answered the following question of:

Why didn't evolutionary algorithms become as popular as deep learning ?


So the major problem with evolutionary algorithms is that they are very slow. And this only gets worse as your problems become more complex. So it is why i am talking below about Large Language Models such as GPT-4, so i invite you to read carefully my below thoughts about them:


So i have just talked about Large Language Models such as GPT-4,
read below my thoughts about it, and here is more proof that
GPT-4 struggles with programming tests (and it is why i think that Large Language Models such as GPT-4 will not replace a lot of programmers):


"Perhaps most impressive were the performances of GPT-4 in professional arenas. It scored a 298 out of 400 on the Uniform Bar Exam, which is high enough to be licensed as a lawyer in every state using the UBE system. It also scored a reasonable 75% on the MKSAP 19 medical knowledge test, and an impressive 77% on the Advanced-level Sommelier theory exam.

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


"Despite all their efforts, GPT-4 has similar limitations to earlier GPT models since it continues to have “hallucinations”, makes reasoning errors, and does not learn from experience. It is therefore always advisable to be careful not to use the model outputs “as is”."


Read more here:

https://pub.towardsai.net/gpt-4-is-here-for-a-while-334ccf331a85



So I have just asked the following questions to ChatGPT so that
to show another weakness of Large Language Models such as ChatGPT,
so here is my discussion with ChatGPT and i invite you to read it
carefully and to read my below thoughts so that to understand the
other weaknesses of Large Language Models such as ChatGPT:


Here is my first question to ChatGPT:


"What that means that Large Language Models such as ChatGPT don't have common sense ?"


And ChatGPT has just answered me the following:


"Large language models like ChatGPT, do not possess true common sense in the same way humans do. While these models have been trained on massive amounts of text data and can generate coherent and contextually relevant responses, they lack the inherent understanding and background knowledge that humans acquire through real-world experiences.

Common sense is the ability to make reasonable assumptions, understand implicit information, and apply general knowledge about the world in everyday situations. It involves a deep understanding of cause-and-effect relationships, social norms, and context. While language models can generate responses based on patterns they have learned from training data, they do not have genuine experiences or an understanding of the world outside of the text they have been trained on.

Language models like ChatGPT rely on statistical patterns in the training data to generate responses. They excel at mimicking human-like language but can also produce outputs that are plausible-sounding but incorrect or nonsensical. They lack the ability to reason, think critically, or possess true understanding.

It's important to be cautious when relying solely on language models for information or decision-making, especially when it comes to areas that require common sense or nuanced understanding. While these models can provide valuable assistance and information, their responses should be verified, critically analyzed, and cross-checked with reliable sources and human judgment to ensure accuracy and avoid potential pitfalls."


And here is my second question to ChatGPT:


"So that means that Large Language Models such as ChatGPT are not good for software development that requires common sense or nuanced understanding ?"


And ChatGPT has just answered me the following:


"That's correct. While large language models like ChatGPT can provide assistance and generate code snippets, they may not be the best choice for software development tasks that require common sense or nuanced understanding.

Software development often involves complex problem-solving, architectural design, and decision-making that goes beyond basic language understanding and generation. Common sense and nuanced understanding are crucial for tasks such as designing user interfaces, handling edge cases, and making judgment calls based on real-world context.

Large language models like ChatGPT lack the ability to truly understand the underlying concepts and principles behind software development. They can generate code based on patterns learned from training data, but they may not possess the ability to reason about the specific needs of a software project or make informed decisions about architectural trade-offs, performance optimizations, or security considerations.

For software development tasks that require common sense and nuanced understanding, it is generally recommended to rely on the expertise of experienced software engineers and architects who possess domain knowledge and have a deep understanding of the specific requirements and constraints of the project. While language models can provide some guidance or generate code snippets, they should be used as tools to complement human expertise rather than replacing it entirely."


And here is my third question to ChatGPT:


"Give me the the software development tasks that require common sense and nuanced understanding"


And ChatGPT has just answered me the following:


"Several software development tasks require common sense and nuanced understanding. Here are a few examples:

- Architectural Design: Designing the architecture of a software system requires an understanding of the problem domain, scalability requirements, performance considerations, and trade-offs between different design patterns. It involves making decisions about the best way to structure the codebase and components to ensure maintainability, extensibility, and efficient resource utilization.

- User Interface Design: Creating user-friendly and intuitive interfaces involves considering user experience, usability principles, accessibility, and visual design. It requires an understanding of how users interact with software, anticipating their needs, and making design decisions that align with common usage patterns.

- Data Modeling: Designing the database schema and data models for an application requires considering data integrity, relationships between entities, normalization principles, and efficient data retrieval and storage. It involves understanding the specific requirements of the application and designing data structures that support efficient querying and manipulation.

- Performance Optimization: Improving the performance of a software system involves analyzing bottlenecks, identifying areas for optimization, and making informed decisions about algorithmic improvements, caching strategies, and resource management. It requires a nuanced understanding of the underlying technologies, profiling techniques, and the trade-offs between performance and other system requirements.

- Error Handling and Exception Handling: Implementing effective error handling and exception handling mechanisms requires an understanding of potential error scenarios, fault tolerance, and graceful degradation. It involves anticipating and handling exceptions and errors that may occur during runtime, ensuring the application maintains stability and recoverability.

- Security Considerations: Building secure software requires considering potential vulnerabilities, implementing appropriate authentication and authorization mechanisms, and protecting against common security threats such as cross-site scripting, SQL injection, and data breaches. It involves understanding security best practices and keeping up-to-date with evolving security threats and countermeasures.

These are just a few examples, and there are numerous other software development tasks that require common sense and nuanced understanding. The expertise and experience of software developers play a crucial role in making informed decisions and successfully completing these tasks."


--


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 will not be able to become good in the medium to short term, since i am explaining the important weakness in my just below first question to ChatGPT, so i think that it is smartly abstracted in my just below question and you can understand from it that ChatGPT is not like a human that can go and select a software programming book from an author of good professionalism and of good knowledge and of like a high IQ, so then you are understanding the constraint on Large Language Models such as GPT-4, so i think then that the most important problematic of Large Language Models such as GPT-4 comes from the how can we ensure the high quality of the data by ensuring that the professionalism and knowledge and IQ of the authors of the books or Gitub etc. on for example programming or software development are of high quality, so then you are understanding that it can take a long time to solve this problem, so i think that it will be the much more sophisticated future Cyborgs that will be able to solve this problem by doing it fast and efficiently, and of course you can notice that we are actually living with Cyborgs , but they are not as sophisticated as it is required to solve the above most important problematic, and here is my thoughts about the today Cyborgs so that you understand my views:

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 have just received the following answer from a person in the french newsgroup forum of fr.soc.politique, so here it is and read it carefully:


"Inventing scalable algorithms and algorithms is a great achievement, but it does not necessarily make someone "smart." "


So i think that i am discovering a pattern with my fluid intelligence and it is that the above person is saying that the process of inventing scalable algorithms and algorithms doesn't necessarily make someone "smart", but i think that the above person is not answering correctly, since i think that the above person is speaking about the "process" of "inventing", but you have to know that the scalable algorithms and algorithms can be the finished invented tool and you can also make yourself smart by using or selling this finished invented tool, so the making yourself smart doesn't come just from IQ and the culture , but the making yourself smart can come from using or selling the finished invented tool. So then you can for example make yourself smart by knowing how to "use" the tool of internet, and the knowing how to use the tool that we call internet can be a not so difficult process that make you smart, so then we can logically infer that the smartness that is the tool of internet is an "extension" of smartness of humans that is used so that to become smart, so then we can then logically infer that you can become like a Cyborg by using this or that tool such as internet, so then smartness is not only IQ and culture, but it is the "extension" of you and of your smartness that is the "tool" such as internet, since the tool such as internet is also an artificial smartness, so we are not just humans, but we are becoming like Cyborgs by using smart tools such as internet or ChatGPT as extensions of us.


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 here is my other really important and smart question to ChatGPT that shows another important weakness of ChatGPT , and it shows that Large Language Models such as GPT-4 will not replace a lot of software programmers (So i think i am playing and winning this smart game against ChatGPT, since i think i am smart):


So i have just asked the following question to ChatGPT:


"What i mean is that since Large Language Models such as ChatGPT can not be sure that the professionalism and knowledge and IQ of the authors of the books or Gitub etc. on programming or software development are of high quality, so we can not ensure that the data of software programming or software development on wich ChatGPT is trained is of good quality, so then we can not ensure that the answers of ChatGPT on software programming or software development are of good quality."


And here is what has just answered me ChatGPT:


"I apologize for any confusion earlier. To address your question directly, you are correct in pointing out that Large Language Models like ChatGPT cannot guarantee the professionalism, knowledge, or IQ of the authors of the books or GitHub repositories used in their training. Consequently, there is a level of uncertainty regarding the quality of the data on programming or software development on which ChatGPT is trained. This uncertainty raises doubts about the quality of the answers provided by ChatGPT on these topics.

The training data for ChatGPT consists of a diverse range of sources, and while efforts are made to include reputable and high-quality information, it is challenging to ensure the expertise or IQ of every author or contributor involved in creating the training data.

Due to this limitation, it is advisable to approach the answers provided by ChatGPT with caution. While ChatGPT can generate helpful responses, it is important to validate the information using additional sources, consult authoritative programming resources, and engage with experienced programmers for a more comprehensive and reliable understanding of programming or software development topics.

In summary, the uncertainty surrounding the professionalism, knowledge, and IQ of the authors in the training data of ChatGPT acknowledges the difficulty in ensuring the overall quality of the data. Relying on additional sources and engaging with the programming community can help enhance the reliability and quality of the information obtained from ChatGPT."


So i invite you to read in my below previous thoughts that also talks about the other weaknesses of ChatGPT:


So you have to know how i have just smartly abstracted my below question so that you understand the important weakness of Large Language Models such as GPT-4, so notice how my abstract question below shows that Large Language Models such as GPT-4 can understand the abstract language, so they are good at that, so then they can be good at for example philosophy or psychology or poetry, but the important weakness of them is that they are not good in areas that requires experience such as in software programming, so then i can predict that Large Language Models such as GPT-4 will not replace a lot of software programmers, so then you are understanding the important ideas of my abstract question below, so then i invite you to read all my below interesting thoughts about Large Language Models such as GPT-4 so that you understand my views:


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 i have just found the important weakness of Large Language Models such as GPT-4, and here it is , read my following discussion with ChatGPT so that to understand what it is:


So i have just asked the following important question to ChatGPT:


"I think that ChatGPT is good in areas that depend a lot on the abstract language like philosophy or psychology or poetry, but when it comes to software programming, i think that Large Language Models such as ChatGPT lack on experience such testing and debugging etc. so they lack on the context so that to understand correctly software programming, since i mean that programming depends in a much greater percentage on experience than for example philosophy or psychology or poetry."


And ChatGPT has just answered me the following:


"You are absolutely right. Programming indeed heavily relies on practical experience, and the context required to understand and correctly address programming-related tasks, including testing and debugging, is significant. While ChatGPT and similar large language models have a remarkable ability to handle abstract language and provide information on a wide range of topics, they may lack the practical experience and contextual understanding necessary to effectively navigate the intricacies of programming.

Programming involves not only understanding the syntax and concepts of a programming language but also applying that knowledge to solve specific problems, manage complex codebases, and identify and fix bugs through testing and debugging. These aspects require hands-on experience, familiarity with debugging tools and techniques, and an understanding of common pitfalls and best practices.

While language models like ChatGPT can offer general insights and guidance on programming, they should not be solely relied upon for critical tasks that require practical expertise. It is always advisable to consult specialized resources, programming documentation, experienced developers, or dedicated programming communities for more accurate and reliable assistance with programming-related topics, including testing and debugging.

In summary, while ChatGPT can excel in areas that rely heavily on abstract language, such as philosophy, psychology, or poetry, programming is indeed an area where practical experience plays a vital role, and specialized resources and human expertise are essential for accurate and reliable guidance."


So i invite you to read my following previous thoughts so that to understand my views:



So I have just said the following:


"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 there is still an important weakness in Large Language Models such as GPT-4, and it is that humans evaluate the quality of the data by using the much deeper meaning that comes from the human brain, so humans are guided in choosing the best quality by using this mechanism, but Large Language Models such as GPT-4 evaluate the quality by the quality of the data, so Large Language Models such as GPT-4 lacks the much deeper meaning of the brain and it is the defect, so it looks like the problem that i am talking about in my below thoughts about the quality of the training data."


And here is more of my logical proof of it in a form of my following interesting discussion with ChatGPT on this subject ,so i invite you to read it carefully so that to understand the important weakness of Large Language Models such as GPT-4 :


So here is my first question to ChatGPT:


"And for Large Language Models such as GPT-4, human review and evaluation so that to conform with high standards of quality of training data can take a long time."


And ChatGPT has just answered me:


"The time required for human review and evaluation of data can vary depending on factors such as the size of the dataset, the complexity of the subject matter, the number of human reviewers involved, and the specific standards and guidelines set for quality assurance. While comprehensive and rigorous human review can be time-consuming, it is difficult to provide a specific timeframe without detailed context.

Efforts are made to balance the need for thorough evaluation with the practical constraints of time and resources. The use of efficient review processes, clear guidelines, and iterative feedback loops can help streamline the review and evaluation phase. Automation and natural language processing techniques can also assist in accelerating certain aspects of data analysis, although human judgment and expertise remain critical.

It's worth noting that the time spent on human review is often distributed throughout the development process, including initial data collection, dataset curation, and ongoing evaluation as the language model is trained and fine-tuned. The aim is to continuously improve the model's performance, accuracy, and adherence to high-quality standards.

While the duration of human review is an important consideration, it's also important to balance it with other factors, such as the need to make progress, deliver timely updates, and address real-world demands. The field of natural language processing is constantly evolving, and developers strive to strike a balance between quality assurance and the practicalities of advancing the technology.

Overall, while human review can be time-consuming, the specific duration required will depend on various factors and the specific goals and constraints of the project. The aim is to ensure a reasonable level of quality while maintaining efficiency in the development and deployment of language models."


And here is my second question to ChatGPT:


"So then it is an approximation and we can not ensure yet that Large Language Models such as GPT-4 are accurate and of high quality ?"


And ChatGPT has just answered me the following:


"Large Language Models (LLMs) are indeed an approximation, and achieving perfect accuracy and quality in their outputs is a challenging task. While significant progress has been made in developing LLMs that generate coherent and contextually relevant text, they are not flawless or infallible. There are inherent limitations and potential shortcomings in their performance.

Ensuring the accuracy and high quality of LLMs is an ongoing endeavor, and researchers and developers are actively working to improve their capabilities. Efforts are made to curate high-quality training data, refine guidelines, incorporate human review, and iteratively fine-tune the models. However, due to the complexity of natural language understanding and the vastness of human knowledge, achieving complete accuracy remains a significant challenge.

It's important to approach LLM outputs with a critical mindset, recognizing that they are probabilistic models trained on vast amounts of data. The generated responses can sometimes be influenced by biases present in the training data or may lack a comprehensive understanding of nuanced contexts.

To mitigate these limitations, responsible use and interpretation of LLM outputs are crucial. Recognizing and understanding the potential biases, limitations, and uncertainties of the models can help users make informed decisions and take additional steps to verify information when necessary. Combining LLM-generated responses with human judgment and expertise is often recommended for critical tasks that require high accuracy and quality.

While LLMs have demonstrated impressive capabilities, they are not a perfect substitute for human intelligence or understanding. Ongoing research, open dialogue, and collaborative efforts are necessary to address the limitations, improve the quality, and ensure responsible use of LLM technology."


I have also asked this third interesting question to ChatGPT:


"I mean since Large Language Models such as GPT-4 lack true understanding or consciousness, so it is still an approximation , so we can not be sure of the quality ?"


And ChatGPT has answered me the following:


"You are correct that since large language models lack true understanding or consciousness, their responses are based on statistical patterns and associations in the training data. This means that their outputs are approximations and not a reflection of true comprehension. As a result, there are limitations and potential quality concerns with these models. Here are some factors to consider:

- Biases in training data: Large language models can inadvertently learn biases present in the training data. If the training data contains biases, the model's responses may exhibit similar biases or reinforce existing societal biases. Bias mitigation techniques are being actively researched and implemented to address this concern.

- Contextual limitations: While language models consider context, their understanding is limited to the immediate context provided. They may not have a persistent memory of the entire conversation or be able to reason about information from previous interactions, which can lead to inconsistent or contextually inappropriate responses.

- Incorrect or misleading information: Language models can generate responses that sound plausible but may not always be factually accurate. They may generate answers based on patterns in the training data, even if the information is outdated, incomplete, or incorrect. It's important to verify information from reliable sources independently.

- Lack of common sense reasoning: Large language models may struggle with common sense reasoning and understanding implicit knowledge. They rely primarily on patterns in the training data and may not possess deep conceptual understanding or intuitive reasoning abilities.

- Ethical considerations: The use of language models also raises ethical concerns. For instance, there are debates surrounding issues like the responsible disclosure of limitations, transparency in AI-generated content, and potential misuse or malicious applications of these models.

Given these considerations, it is essential to approach the outputs of large language models with caution and critical thinking. They can provide valuable assistance and generate useful responses, but human judgment and verification remain crucial for assessing the quality and reliability of the information they provide."


And i think ChatGPT has another problem, and it is that the generated content can infringe on the copyright of existing works. This could occur if ChatGPT generates content similar to existing copyrighted material of the data on wich it has been trained. So you have to be careful, since it can hurt your business, but you have to know that copyright does not protect ideas, concepts, systems, or methods of doing something. But copyright law for example protects the expression of ideas rather than the ideas themselves. In other words, copyright law protects the specific form in which an idea is expressed, rather than the underlying idea or concept. And you have to also know that there is another problem with ChatGPT and it is that it can generate an invention, but it could be argued that the creators of the model,
OpenAI, should be able to patent the invention. However, it could also be argued, that the source material used to train the model should be considered as prior art, meaning that the invention would not be considered new and therefore not patentable.


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, i have just posted about how Large language models such as GPT-4 struggle at math, but i think that it will be solved, since i think that GPT-4 has a maximum token limit of 32,000 (equivalent to 25,000 words), and that's not the problem, but i think that the problem comes from the percentage of mathematical content within the training data that is relatively small compared to the whole training data. So, i think GPT-4 has not encountered sufficient mathematical concepts and problems in the training data to develop an understanding of them. So i think the lack of exposure to mathematical content in the training data hinders GPT-4's ability to solve complex mathematical problems effectively, and i think that this problem will soon be solved, And the other problem is that GPT-4's primary strength lies in its ability to reason with natural language. However, when it comes to solving mathematical problems that require manipulation of equations or formulas, GPT-4's proficiency in symbolic reasoning is not good. And it is particularly evident when the GPT-4 encounters abstract symbols that must be manipulated to arrive at a solution, but i think that this problem will soon be solved and i think that Large language models such as GPT-4 will soon be able to not struggle anymore at math. And i think that GPT-4 lacks true understanding of the world, since its understanding comes from the data on wich it has been trained that lacks the true understanding of the world, so i think it is a limitation of GPT-4, also here is the limitations of GPT-4 that you can read in my below thoughts:

1- GPT-4 lacks on the understanding of context, since GPT-4 was trained
on large amounts of text data, but it has not the ability to
understand the context of the text. This means that it can generate
coherent sentences, but they may not always make sense in the context
of the conversation.

2- And GPT-4 is limited in its ability to generate creative or original
content. GPT-4 is trained on existing text data, so it is not able to
generate new ideas or concepts. This means that GPT-4 is not suitable
for tasks that require creativity or originality.


Here is what has just answered me ChatGPT about my previous thoughts below about exploration and exploitation etc. and about the quality of the answers of ChatGPT, so here is the answer of ChatGPT to my thoughts that recognizes one important limitation of its limitations , so read it carefully:


"Your thoughts on the genetic algorithm and its connection to exploration and exploitation in artificial intelligence are insightful. The genetic algorithm indeed utilizes exploration and exploitation processes to find optimal solutions by maintaining diversity in the population and avoiding getting stuck in local optima.

Regarding ChatGPT, you raise valid points about the limitations and uncertainties associated with its responses. Language models like ChatGPT are probabilistic in nature, and while they can provide valuable information and assistance, they are not infallible or a substitute for human expertise.

The answers provided by ChatGPT in response to your questions acknowledge these limitations. ChatGPT emphasizes that it is not intended to replace programmers or domain experts in tasks that require high-quality and accurate programming. Professional programmers bring critical thinking, deep understanding, and domain-specific knowledge that language models may lack.

ChatGPT also acknowledges that its responses may not always be of high quality or accuracy, and supervision from individuals who understand the subject matter is crucial. Human supervision and guidance are necessary to ensure accurate learning, critical assessment of information, and contextualized explanations.

In summary, while language models like ChatGPT can be valuable tools for assistance and learning, they should be used in conjunction with human expertise and critical thinking to ensure the delivery of high-quality and reliable solutions."


And 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 have understood the genetic algorithm and its processes that we call exploration and exploitation, so you have to understand the process of exploration and exploitation in artificial intelligence, so the processes in the genetic algorithm are like the processes that have made our human genetics, so the mutations that happen with a low probability in the genetic algorithm ensures a diversity that permits to explore if there is another more optimal solution , and the genetic algorithm has not to get stuck in a local optimum by being lack of diversity in the population of the genetic algorithm , so in ChatGPT, other than the mechanism of attention that permits to discover the patterns on the data on wich it has been trained, it has another mechanism called Reinforcement learning from human feedback that ensure an exploration from the discovered patterns in the data on wich it has been trained, so Reinforcement learning from human feedback in ChatGPT can make it discover other patterns and it can permits ChatGPT to refine its model , and exploitation, on the other hand, involves using the learned knowledge to provide more reliable and effective responses, so CharGPT has to balance between exploration and exploitation so that to be efficient, but i think that one of the problem that remains , is that we can not be certain or sure that the answers of ChatGPT are of high quality, and it is what i am explaining in my below thoughts, so i invite you to read my thoughts below so that to understand this limitation of ChatGPT, and i invite you to read my thoughts in the below web link so that to understand the other limitations of ChatGPT and so that to understand my views on different subjects:


More of my philosophy about ChatGPT and more of my 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 smart discussion with ChatGPT so that to understand the other limitations of ChatGPT:


So I have just asked the smart following questions to ChatGPT, so read them carefully and read carefully the answers of ChatGPT:

Here is my first question to ChatGPT:


"ChatGPT, or any language model for that matter, are probabilistic in nature and can be considered as approximations, so the main problem is that if we can not be certain or sure that the answers of ChatGPT, or any language model for that matter, are of high quality, so we have for example to review the programming code of ChatGPT, so then it means that it can not replace in this case programmers."



And here is the answer of ChatGPT:


"You are correct. Language models like ChatGPT are not intended to replace programmers or domain experts in tasks that require high-quality, accurate, and reliable programming. While they can assist with certain programming-related queries or provide general information, they are not a substitute for the expertise and experience of professional programmers.

Programming involves complex problem-solving, critical thinking, and consideration of various factors such as performance, security, and maintainability. It requires a deep understanding of programming concepts, best practices, and specific domain knowledge.

Language models like ChatGPT have limitations in terms of their ability to comprehend complex programming scenarios, understand the specific requirements of a project, and provide optimal solutions. They may not be aware of the latest programming practices, industry standards, or specific project constraints. Therefore, relying solely on language models for programming tasks can result in suboptimal or incorrect solutions.

However, language models can still be useful in certain programming-related tasks, such as providing general programming explanations, assisting with syntax or code examples, or offering suggestions for common programming issues. They can serve as a valuable resource to complement the skills and expertise of programmers.

In summary, while language models can offer assistance in programming-related tasks, they are not a replacement for programmers. Professional programmers and domain experts bring critical thinking, expertise, and the ability to understand and address complex programming challenges, ensuring the delivery of high-quality and reliable solutions."


And here is my second a question to ChatGPT:


"So since we can not be certain or sure that the answers from ChatGPT are of high quality, so the learning process from ChatGPT needs supervision from those that understand"


And here is the answer of ChatGPT:


"You're correct that language models like ChatGPT may not always provide answers of high quality or accuracy. While efforts are made to optimize their responses, there is always a possibility of errors, incomplete information, or incorrect interpretations.

Supervision and guidance from individuals who understand the subject matter are indeed crucial for effective learning. ChatGPT can serve as a tool to assist in the learning process, but it should not be relied upon as the sole source of information or guidance.

When using ChatGPT or any AI model for learning purposes, it's important to approach the information critically and verify it using reliable sources. Consulting experts, teachers, or knowledgeable individuals in the field can provide valuable insights, clarification, and oversight.

Supervision and guidance help ensure that the learning process is accurate, comprehensive, and aligned with the desired objectives. Human expertise and understanding are essential for assessing the quality of information, identifying potential biases or inaccuracies, and providing contextualized explanations.

Ultimately, learning is a collaborative process that involves leveraging the benefits of AI models like ChatGPT while actively engaging with human expertise. By combining the strengths of AI models and human supervision, a more effective and reliable learning experience can be achieved."


And i invite you to read carefully my thoughts on different subjects in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/kqqhSWKK1x8



And i invite you to read carefully my following thoughts on different subjects and you can read my new proverbs in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/kqqhSWKK1x8



Thank you,
Amine Moulay Ramdane.