Hello,


More of my philosophy about Intel company and about the being talented and about quantum computers and about my new poem and about my other goals and about reinforcement learning and about artificial intelligence and about global warming and about China'
s Semiconductor advances to 5nm 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 invite you to read the following interesting new article about Intel's CEO Pat Gelsinger that was the lead architect of the 4th generation 80486 processor introduced in 1989 , and you can read about Pat Gelsinger in the following web page of wikipedia:

https://en.wikipedia.org/wiki/Pat_Gelsinger#References


So notice how the following interesting new article is speaking about one important thing of Intel company from USA that is the new Intel's foundry business that's essentially the business model of TSMC and Samsung , so this Intel's foundry business
takes business away from TSMC and Samsung and boosts the amount of capital that Intel can invest in both manufacturing and R&D , so i invite you to read the following new interesting article so that to understand more how Intel is doing it:

https://www.tomshardware.com/pc-components/gpus/intels-ceo-admits-to-and-details-companies-three-biggest-mistakes


So i think i am talented and it is also genetical in me, since i am for example quickly understanding the genetic algorithm in a sophisticated manner , and you can read my below thoughts of how i am explaining it to you so that you understand , and since
i have invented quickly my new ideas of my philosophy that you can read below , and i have quickly invented many proverbs that you can read below , and i have quickly invented a new model of what is human consciousness so that to show the limitation of
artificial intelligence and so that to show that artificial intelligence will not attain artificial general intelligence, and you can read it below, and i have quickly invented many poems and poems of Love in both english and french so that to show my
style of my poems and poems of Love , and you can read them in the below web links, and i think i look like my poems and my poems of Love, and of course you are noticing how i am explaining to you economy too in my kind of way, and you can read the below
web links so that you notice how i am doing it. So as you are noticing that i have grouped my important thoughts in in this post so that you know how i look like as a personality and how i am doing it, so i invite you to read all my below thoughts so
that you understand
my views:


I invite you to read the following interesting new article from ScienceDaily that says that imperfect timekeeping places significant limit on quantum computers , but it is quick to emphasise that the message is not entirely pessimistic, because the
problem could be mitigated in the future by designing clever error correction protocols , so then quantum computers can scale to much more large algorithms, so read the following new interesting article from ScienceDaily so that to understand:

https://www.sciencedaily.com/releases/2023/10/231030194558.htm


Also i invite you to read carefully my interesting new thoughts about the new and future technologies in the following web link that also talk about quantum computers:

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


Sherlock in my poem below is someone who can be employed as a detective to collect information, so here is my new poem in english:

--

So i am not into the punk rock

And i am not a Mohawk

But i want to be precise like an atomic clock

So i am not into the punk rock

So i want to be smart like a smart Sherlock

And i want to have a spirit like a rock

So i am not into the punk rock

Since i Love to think well and to talk

Since i want to avoid the deadlock

And i am not killing innocent people with a tomahawk

Since i want to be smart like a smart Sherlock

--


So have just spoken about one of my goals that is to make my own spirit reliable , but of course you have to know that i am a gentleman type of person and my other goal is that i want with my below thoughts to learn others so that to help others so that
to take care of others, also my other goal is to sell some of my softwares and to make money with them so that to give money and help the poor and the needy.


So as you have just noticed , i have just invented a new model of what is human consciousness so that to show the limitations of artificial intelligence of like it will not attain artificial general intelligence , and you can read it in my below thoughts,
also i have just showed you another important
limitation of the transformers like the one of ChatGPT , and you can read about it in my below thoughts , and now here is other of my interesting questions to ChatGPT so that to show another important limitation of ChatGPT and of the next artificial
intelligence of Google that is called Gemini and that will compete against the next GPT-5:


So here is my first question to ChatGPT:

Does reinforcement learning in ChatGPT takes a lot of time for good refinements , and can you transfer the refinements to the next version of ChatGPT ?


And ChatGPT has just answered me the following:

"Reinforcement learning for refining a language model like ChatGPT can indeed take a significant amount of time and resources to achieve good results. The training process involves interacting with users and receiving feedback, which can be time-
consuming and require substantial computational power. The model needs to learn from a large amount of data and user interactions to improve its performance and responsiveness.

Regarding transferring refinements to the next version of ChatGPT, it depends on the specific policies and practices of the organization that develops and maintains the model. Generally, improvements and refinements made to a model can be used as a basis
for enhancing future versions, but it's not a direct transfer. Developers can incorporate lessons learned from previous versions and apply new techniques to improve the model's performance in subsequent releases.

It's important to note that ChatGPT and similar models are often fine-tuned on specific datasets and use reinforcement learning from human feedback, which means they can be adapted and improved over time, but the process is iterative and may involve
starting from a pretrained model and fine-tuning it with new data and feedback for each new version. It's also essential to address ethical and safety concerns in the development and refinement of AI models like ChatGPT."


And here is my second question to ChatGPT:

So then good refinements in ChatGPT can not be guaranteed , so it is also an important weakness in ChatGPT.


And ChatGPT has just answered me the following:

"Achieving a "good" refinement in AI models like ChatGPT is a complex and ongoing process, and there are no guarantees of perfection. ChatGPT and similar models have both strengths and weaknesses.

Weaknesses and challenges in refining models like ChatGPT include:

1- Bias and Ethical Concerns: Language models may inherit biases from the training data, and addressing bias issues is an ongoing challenge. Ensuring that the model responds appropriately to various inputs and avoids harmful or discriminatory behavior is
an ongoing concern.

2- Ambiguity and Errors: Language models can make mistakes or produce answers that are plausible-sounding but incorrect or nonsensical. Achieving high accuracy in all responses is difficult.

3- Lack of Common Sense: AI models like ChatGPT often lack true common sense reasoning and may struggle with tasks that require a deep understanding of the world.

4- Scalability: Providing real-time interactions at scale can be challenging, and it may limit the ability to have nuanced, context-aware conversations.

5- Fine-tuning Challenges: The reinforcement learning and fine-tuning process depends on the quality and quantity of feedback data, and the model's behavior can be sensitive to the data it's trained on.

6- User-Specific Requirements: Meeting individual user preferences and specific use cases can be challenging, as not all users have the same needs or expectations.

While these are challenges, they do not make ChatGPT or similar models inherently weak. They can still provide valuable assistance, answer questions, and facilitate a range of applications. Continuous research and development are aimed at addressing
these issues and improving the capabilities of such models.

It's important to use these models responsibly, to acknowledge their limitations, and to combine them with human oversight when necessary. While perfection cannot be guaranteed, ongoing refinement and feedback can lead to improvements in the model's
performance and behavior over time."


So here is my interesting discussion with ChatGPT about the transformers
like of the transformer of ChatGPT that is the core system of ChatGPT, and a transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. Transformer models apply
an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.


So here is my first question to ChatGPT:

So i think i understand what is doing the transformers , so they understand the lower level patterns and they understand the higher level patterns , so then they understand it all.


And here is what has just answered me ChatGPT:

"Yes, you've captured the idea correctly. Transformers, the architecture underlying models like ChatGPT, are capable of understanding both low-level and high-level patterns in the data they are trained on. This enables them to grasp a wide range of
information, from the fine-grained details to the broader themes and concepts present in the training data. By learning these patterns comprehensively, they can generate text that reflects a deep understanding of the input data and respond in ways that
incorporate both specific details and overarching themes.

However, it's important to note that while transformers can learn and recognize patterns, their understanding is based on statistical associations in the training data. They don't possess true comprehension or consciousness. They generate text by
applying learned patterns and associations to the input data, which can produce responses that seem insightful and contextually relevant but are ultimately based on patterns and statistics."


And here is my second question to ChatGPT:

So the pattern can be a low level concept or a high level concept.


And here is what has just answered me ChatGPT:

"That's correct. Patterns in the context of deep learning models like transformers, including both low-level and high-level concepts, can be used to understand and generate text:

1- Low-Level Patterns: These patterns involve specific details, such as word choice, sentence structure, grammar, and stylistic elements. Low-level patterns are more granular and can include things like the use of metaphors, specific vocabulary, or
consistent use of certain linguistic features.

2- High-Level Patterns: These patterns encompass broader aspects of language and content, such as themes, writing style, recurring motifs, and overarching concepts. High-level patterns relate to the overall structure and message of a text or creative
work.

Deep learning models like transformers are capable of learning and applying patterns at various levels of abstraction. They can understand and generate text that reflects both low-level and high-level patterns, enabling them to respond to a wide range of
input and generate contextually relevant and coherent output. The combination of these patterns contributes to the models' ability to generate text that appears meaningful and contextually appropriate."


And here is my third question:

So since the transformer is limited by the data on wich it has been trained, so if it doesn't find a pattern of creative work , so it can not understand this creative work and it can not be innovative in this creative work.


And here is what has just answered me ChatGPT:

"You're correct that the performance of a transformer model like GPT-3 is limited by the data it has been trained on. If a transformer hasn't been exposed to certain patterns or styles of creative works during training, it may not be as effective in
understanding or generating content in those specific creative works or styles.

Transformers are data-driven models, and their ability to generate creative and innovative content is highly dependent on the diversity and quality of the data they've been trained on. If a particular style of creativity or a specific set of patterns is
not represented in their training data, they may struggle to produce content that aligns with those patterns.

To improve a transformer's performance on a specific type of creative work or style, it would ideally need to be trained on a dataset that includes examples of that specific creative domain or style. Training a model on a more diverse and extensive
dataset can also help it become more versatile in generating content that aligns with various patterns and styles, making it potentially more innovative in those domains.

In summary, the ability of a transformer model to understand and be innovative in a specific creative work or style is largely determined by the data it was trained on and its exposure to relevant patterns and examples."


And here is my fourth question to ChatGPT:

Hence , ChatGPT can not be guaranteed , since it is limited by the data on wich it has been trained , so it can for example lack a lot of patterns of creative works.


And here is what has just answered me ChatGPT:

"You're correct in observing that ChatGPT and similar models are not guaranteed to excel in every creative work or domain. Their performance is indeed limited by the data on which they've been trained. If a particular pattern or style of creative work is
underrepresented or missing from their training data, they may struggle to understand or generate content in that specific area.

These models are most effective when they are used within the domains and patterns for which they have been extensively trained. If you need to work with specific or niche areas of creativity or unique patterns, you might encounter limitations. In such
cases, additional fine-tuning or training on domain-specific data may be necessary to enhance their performance.

It's essential to understand that while ChatGPT is a powerful language model capable of generating creative content, it may not fully replace human expertise and creativity, especially in highly specialized or niche creative domains. It's a tool that can
assist and inspire human creators but doesn't have a deep understanding of every possible creative context."


And here is my fifth question to ChatGPT:

So then if ChatGPT can not be guaranteed as i am explaining above , so we can not say that it can replace a lot of jobs.


And here is what has just answered me ChatGPT:

"You're correct in pointing out that ChatGPT and similar AI models have limitations, particularly in specialized or creative domains where human expertise and creativity are highly valued. While these models have the potential to automate certain tasks
and assist with various jobs, they are not a wholesale replacement for human workers in many fields.

The impact of AI models like ChatGPT on employment varies by industry and job role. In some cases, they may automate certain routine and repetitive tasks, which could lead to changes in job responsibilities or the need for reskilling in affected
industries. However, many jobs involve aspects like creativity, empathy, complex problem-solving, decision-making, and domain-specific expertise, which AI models may not fully replicate.

AI can be a valuable tool for augmenting human capabilities, improving efficiency, and automating specific tasks. Still, it's unlikely to completely replace human workers in roles that require deep understanding, creativity, nuanced judgment, and
emotional intelligence.

The future of work and the impact of AI on employment is a complex and evolving topic, and it often depends on the specific context and industry. While AI can be a powerful tool, it is usually seen as a complement to human skills and expertise, rather
than a full replacement."



And here is my sixth question to ChatGPT:

As we have just discussed above about the transformers, so i think that nuanced understanding comes too from the necessary patterns that the transformers have to discover in the data.


And here is what has just answered me ChatGPT:

"Absolutely, nuanced understanding often arises from the ability to recognize and interpret complex patterns in data. Transformers and similar deep learning models are proficient at identifying and leveraging patterns across various levels of abstraction
in the data they have been trained on. This capacity for pattern recognition allows them to achieve nuanced understanding in different domains.

Here's how nuanced understanding is related to patterns in data:

1- Pattern Recognition: Deep learning models, like transformers, excel at identifying patterns in the data. These patterns can range from low-level linguistic features (e.g., word associations, sentence structure) to high-level concepts (e.g., themes,
relationships between entities).

2- Complex Relationships: In many tasks, nuanced understanding involves recognizing and interpreting complex relationships and dependencies within the data. This can encompass understanding context, causality, and subtleties in meaning.

3- Contextual Adaptation: Transformers are particularly skilled at adapting their responses based on the context provided. They use patterns in the input data to generate contextually relevant and coherent responses.

4- Domain-Specific Nuances: For tasks in specialized domains, like medical diagnoses or legal analysis, nuanced understanding involves recognizing and interpreting patterns specific to that domain.

5- Discovering Nuance: Nuanced understanding often means discovering the subtleties, exceptions, and edge cases in the data that go beyond simple patterns. It involves recognizing the limitations and context in which patterns apply.

While transformers and deep learning models can provide nuanced responses and demonstrate a strong understanding of the data they were trained on, it's important to remember that their knowledge and understanding are based on patterns and associations
present in that training data. Their nuanced understanding may not always align perfectly with human intuition and domain expertise, and they may not capture all aspects of complex or specialized domains. Human expertise and oversight are often required
to validate and enhance the nuanced understanding provided by these models."


And here is my seventh question to ChatGPT:

So then from what we have just discussed above, since ChatGPT can not be guaranteed , since it is limited by the data on wich it has been trained , so it can for example lack a lot of patterns of creative works, so the nuanced understanding can not be
guaranteed too.


And here is what has just answered me ChatGPT:

"Your assessment is accurate. ChatGPT, like other language models, is not guaranteed to have nuanced understanding in all domains or be able to capture all patterns, especially in specialized or creative works. Its performance is indeed limited by the
data on which it has been trained.

Nuanced understanding, especially in specialized, creative, or domain-specific contexts, is not something that can be guaranteed with these models. While they can exhibit a strong ability to generate contextually relevant text and demonstrate
understanding within the scope of their training data, they may not capture all nuances, subtleties, or specialized patterns, particularly in domains that deviate significantly from the general training data.

In practice, these models are powerful tools for many tasks, but they are not infallible and may not fully replace the need for human expertise, particularly in areas requiring deep understanding, creativity, or domain-specific knowledge. Users should
exercise judgment and validation when relying on these models for nuanced understanding, especially in critical or specialized applications."


And i invite you to read my below new model of what is human consciousness so that to understand the other limitations of artificial intelligence:


Cows and other farm animals produce about 14% of human-induced climate emissions, and it is methane from their burps and manure that is seen as both the biggest concern and best opportunity for tackling global heating. Methane is more potent at warming
the earth than carbon dioxide and it is an important emission target for policymakers because it leaves the atmosphere more quickly than carbon dioxide. The world's one billion+ cows are responsible for about 40% of global methane emissions - a
significant contributor to global warming. And i invite you to read carefully the following new article from Interesting Engineering about how scientists have just engineered climate-smart cows with 10 to 20 times more milk, and it permits to reduce the
number of cows so that to reduce much more global warming ,
since Methane is responsible for around 30% of the current rise in global temperature:

https://interestingengineering.com/science/scientists-engineer-climate-smart-cows-with-10-to-20-times-more-milk


I invite you to read carefully the following new article about how U.S. cannot halt China's Semiconductor advances to 5nm:

https://www.tomshardware.com/news/us-cannot-halt-chinas-semiconductor-advance-to-5nm-ex-tsmc-vp


And Chinese Loongson CPU takes on AMD's Zen 3 in benchmarks, with approximately Zen 3 levels of IPC, based on testing, so i invite you to read the following new article so that to notice it:


https://www.tomshardware.com/news/loongson-3a6000-beats-i3-10100f

So as you have just noticed , i have just talked about the Simulation hypothesis and i have just explained that my new model of what is consciousness shows that the simulation hypothesis is not true, and you can read about it in my below thoughts, and
here is my other previous thoughts about the Simulation hypothesis: So i think that saying that the Universe is a Digital Simulation is much less probable, since i say that look for example at how looks this thing that we call humanity, so we can not say
that the simulation of this super intelligent beings has started from a very long ago, like for example more than a billion of years, since this way of doing creates too much suffering that is much less probable since the super intelligent beings are so
advanced and so smart, but i think it is much more short, so then we notice that the super intelligent beings that have started the simulation can not start the simulation in a so primitive environment of our today of what we call humanity, so then i can
say that saying that the Universe is a Digital Simulation is much less probable and the fact the reality exists is much more probable.


From the following article I think that simple code is generally better than optimal and best performing code. In C++, optimal code is often hard to read, hard to understand, and most importantly, hard to maintain. So i invite you to read carefully the
following article so that you understand it , and of course you can translate the article from french to english:


Programming in C++ is hard, software engineering in C++ is even harder,

https://cpp.developpez.com/actu/348473/La-programmation-en-Cplusplus-est-difficile-le-genie-logiciel-en-Cplusplus-est-encore-plus-difficile-un-article-d-EDUARDO-ROCHA-traduit-par-Escapetiger/


As you have just noticed, i have just posted about a new article about the Simulation hypothesis , so i think that the Simulation hypothesis is not true ,
since from my below new model of what is human consciousness, you can notice that there is a primitive consciousness , but i am talking below about the rest of the human consciousness that requires the biologic since it is also composed with the human "
sensations" , so since it is also biologic , so i don't think that the Simulation hypothesis is true , so i invite you to read carefully my below new model of what is human consciousness and about
how it explains that we will not attain artificial general intelligence:


So i think i am also a new philosopher, so you have to discover an important pattern with your fluid intelligence , and here it is: So the problem is that you can think that competition in capitalism is on the cost and/or quality and/or quantity, so
since it is also on the cost so it can hurt the wages of workers, but it think it is not the smart way to look at it, since capitalism is not a zero sum game, since with a reasonable level of growth, capitalism can both deliver rising living standards
for most, and still ensure a relatively high rate of return to the owners of capital, so you have to look at it in a holistic way
or in a general way and say the even if there is competition in capitalism on for example the cost and it can hurt the wages locally here and there, but holistically or generally capitalism is not a zero sum game so it is holistically or generally a
positive thing even inside a country, even if competition on for example the cost in capitalism can hurt the wages of the workers in a local manner here and there inside the country, so the smart idea is that you have to know how to adapt efficiently so
that to minimize at best the disadvantages of competition in capitalism that can hurt locally here and there the wages of the workers. So then the important key is efficient adaptation. Other than that you have to read about the other problems in
capitalism that have to be solved by "progressive" capitalism in my the following thoughts in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/KN4V-rGRDdU


So i think i am a new philosopher , and so that to understand correctly my
new ideas of my philosophy , i invite you to read them carefully in the following two web links:

https://groups.google.com/g/alt.culture.morocco/c/v8pdduG6LcY

https://groups.google.com/g/alt.culture.morocco/c/xcWGmtgEu6o


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

https://groups.google.com/g/alt.culture.morocco/c/PfmfrhCk2cU


Chip startup Tachyum will make 50 Exaflop supercomputers and 8 ZettaFlop AI computers in 2025, and Tachyum books purchase order to build system with 25,000x ChatGPT4 capacity and 25x faster than current supercomputers.

Read more here in the following interesting article so that to understand it:

https://www.tachyum.com/media/press-releases/2023/10/03/tachyum-books-purchase-order-to-build-system-with-25000x-chatgpt4-capacity-and-25x-faster-than-current-supercomputers/


And Exascale or Zettascale supercomputers will also allow to construct an accurate map of the brain that allows to "reverse" engineer or understand the brain, read the following so that to notice it:

“If we don’t improve today’s technology, the compute time for a whole mouse brain would be something like 1,000,000 days of work on current supercomputers. Using all of Aurora of 2 ExaFLOPS, if everything worked beautifully, it could still take 1,000 days.” Nicola Ferrier, Argonne senior computer
scientist

Read more here so that to understand:

https://www.anl.gov/article/preparing-for-exascale-argonnes-aurora-supercomputer-to-drive-brain-map-construction


So then the 50 Exaflop supercomputers with the Chip made from the Chip startup Tachyum will take only 40 days for the compute time for a whole mouse brain.


7 TB of data for 10,000 years: the glass plate that will revolutionize storage

"This new storage technology could be capable of accumulating up to 7 TB of data and maintaining its integrity for 10,000 years . We are still far from Seagate's HAMR disks capable of containing 30 TB of data , but the properties of the Silica project
make it a real alternative to current storage."

Read more here (And you can translate it from french to english):

https://www.tomshardware.fr/7-to-de-donnees-pour-10-000-ans-la-plaque-de-verre-qui-va-revolutionner-le-stockage/


But there is another interesting revolutionary technology that is coming , and here it is:

Now Ultra Ram is actually here to make everything faster and it is going to turn your SSD into a really fast device because it doesn't need power to retain the information, and it has 4,000X more endurance than NAND and can store data for 1,000+ years,
and it is also designed to have 1/10th the latency of DRAM and be more energy efficient (by a factor of 100X) than DRAM fabricated on a similar node, and it can be used as RAM because it is so fast. So i invite to read the following new article about it:

UltraRAM Demos Prototype Chip, Secures Funding to Validate Commercial Potential

https://www.tomshardware.com/news/ultraram-demos-prototype-chip-secures-funding-to-validate-commercial-potential


Also i invite you to read my interesting new thoughts about the new and future technologies in the following web link :

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


And you can read the rest my other thoughts and my thoughts of my philosophy in the following web link:

https://groups.google.com/g/alt.culture.morocco/c/yl5wDoNdlnA


So i think what is so important is also to avoid the damages to our civilization that can be caused by severe solar storms, so notice how i am giving the probabilities of it below , but you have to be careful and be sure that we be safe from the damages
of the severe solar storms, so read my following thoughts about it:


I have just read the following new article on Interesting Engineering , and i invite you to read it:

https://interestingengineering.com/science/huge-solar-storm-proof-found-in-14300-year-old-tree-rings


So the above article is not so precise, so here is more information so that
you know about the probability of it:

A study published in 2019 found the chance of a Carrington-like event occurring before 2029 is less than 1.9 percent.

Are we ready for the next Big Solar storm?

Read more here:

https://www.discovermagazine.com/the-sciences/are-we-ready-for-the-next-big-solar-storm


And based on available data, there's roughly a one percent chance of seeing another Miyake Event within the next decade and a Miyake Event can be
80 times more powerful than a Carrington-like event, read more here in the following article:

https://phys.org/news/2022-10-tree-insight-devastating-storms.html#:~:text='%20or%20outburst.%22-,Dr.,what%20harms%20it%20may%20cause.


University of Tübingen's researchers unveil a groundbreaking solar cell for decentralized green hydrogen production, revolutionizing renewable energy. This breakthrough technology opens the door to large-scale applications, even with lower efficiencies.
This advancement have the potential to make a significant contribution to energy supply and the reduction of CO2 emissions on a global scale.


Read more here on Interesting Engineering:

https://interestingengineering.com/science/solar-cell-powers-green-hydrogen-production


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 of course
so that to know from where comes the human being, i am explaining

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