Hello,


More of my philosophy about the future and about automation and AI and
about the manufacturing sector and the service sector the new Gemini pro
1.5 and about AI-generated software and 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..


And as you have just noticed , i have just talked about the important limitations of artificial intelligence and about automation and about
AI, read it below , and now i will talk more about the subject of
automation , so i invite you to read carefully my following thoughts and writing:


"A study by researchers from MIT and Boston University claims that
automation is responsible for more than half of the increase in the
income gap between the most educated and the least educated workers in
the United States. The study estimates that automation reduced the wages
of men without a high school diploma by 8.8% and of women without a high
school diploma by 2.3%. These figures have been adjusted for inflation. According to the study by Acemoglu and Restrepo, growing income
inequality could also stem from, among other things, the decline in the prevalence of unions (a highly sensitive topic today in technology
companies), market concentration resulting in a lack of competition for
labour, or other types of technological change.
Acemoglu and Restrepo's study comes at a time when the debate over
whether or not to tax robots is heating up. More and more voices rise to
call for a tax on robots to combat the effects of automation on income inequality. In this regard, a study published last month by economists
at MIT suggests that introducing a tax on robot labor, preferably a
modest tax, would incentivize companies to retain workers, while
offsetting some of the payroll taxes lost through downsizing. Of course,
the conclusions of the study are not unanimous.

According to economists' calculations, an effective tax on robots would probably be between 1% and 3.7%. The report estimates that if the tax is
much higher, it would exaggerate the role that robots play in the
operational routines of companies; and if it is lower, companies would
have no incentive to retain human employees at all."

Read more here (and you can translate the web page from french to english):

Study claims automation has caused more than half of US income
inequality since 1980

https://embarque.developpez.com/actu/340711/Une-etude-affirme-que-l-automatisation-est-a-l-origine-de-plus-de-la-moitie-de-l-inegalite-des-revenus-aux-Etats-Unis-depuis-1980-les-personnes-les-moins-diplomees-semblent-les-plus-touchees/


And following are some of the important advantages of automation:

1. Automation is the key to the shorter workweek. Automation will allow
the average number of working hours per week to continue to decline,
thereby allowing greater leisure hours and a higher quality life.

2. Automation brings safer working conditions for the worker. Since
there is less direct physical participation by the worker in the
production process, there is less chance of personal injury to the worker.

3. Automated production results in lower prices and better products. It
has been estimated that the cost to machine one unit of product by
conventional general-purpose machine tools requiring human operators may
be 100 times the cost of manufacturing the same unit using automated mass-production techniques. The electronics industry offers many
examples of improvements in manufacturing technology that have
significantly reduced costs while increasing product value (e.g., colour
TV sets, stereo equipment, calculators, and computers).

4. The growth of the automation industry will itself provide employment opportunities. This has been especially true in the computer industry,
as the companies in this industry have grown (IBM, Digital Equipment
Corp., Honeywell, etc.), new jobs have been created.
These new jobs include not only workers directly employed by these
companies, but also computer programmers, systems engineers, and other
needed to use and operate the computers.

5. Automation is the only means of increasing standard of living. Only
through productivity increases brought about by new automated methods of production, it is possible to advance standard of living. Granting wage increases without a commensurate increase in productivity
will results in inflation. To afford a better society, it is a must to
increase productivity.


"In its research, Forrester predicts that automation and AI will replace
4.9% of US jobs by 2030. This means that 0.6% of workers in the US might
lose their jobs annually. Of this percentage of jobs lost to automation, generative AI specifically will account for 30% of the losses."

Read more here in the following article:

Generative AI “will replace 2.5 million jobs in the U.S. by 2030”

https://www.techopedia.com/generative-ai-will-replace-2-5-million-jobs-in-the-u-s-by-2030


And i invite you to read my below interesting thoughts:


So i think i am also a new philosopher , and you can read my new ideas
of my philosophy below and in the below web link, and now i will talk
about an important subject and it is the following:

The service sector in USA is contributing around 80% or more to the
country's GDP (Gross Domestic Product) , and the manufacturing sector,
while still significant, represented a smaller portion of the GDP,
typically around 10-20% , the manufacturing sector in USA and other
western countries etc. has declined and the service sector has grown,
the tendency of the service sector growing and the manufacturing sector shrinking is not unique to the USA but is observed globally,
particularly in many advanced economies , and several factors have
contributed to the decline of the manufacturing sector in the USA and
the growth of the service sector:

1- Globalization: The advent of globalization has led to increased
competition from low-wage countries, making it cheaper for companies to outsource manufacturing operations to countries with lower labor costs ,
but i invite you to carefully look at the benefits or advantages of
outsourcing in my thoughts just below so that you understand.

2- Technological advancements: Automation and technological innovations
have significantly increased productivity in manufacturing, leading to a reduced need for human labor in many manufacturing processes. This has
resulted in job losses and decreased employment in the manufacturing sector.

3- Shift in consumer preferences: There has been a shift in consumer preferences towards services such as healthcare, education,
entertainment, and information technology. This has increased demand for services and reduced demand for manufactured goods.

4- Rise of the knowledge economy: The growth of the knowledge economy,
driven by advancements in technology and the increasing importance of intellectual capital, has led to a greater emphasis on services such as research and development, consulting, and information technology services.

5- Government policies: Government policies, such as trade agreements
and taxation policies, have also played a role in shaping the relative
sizes of the manufacturing and service sectors. Policies that promote
free trade may encourage outsourcing of manufacturing, while policies
that support the service sector may contribute to its growth.

6- Cost of labor and regulation: The cost of labor and regulatory
burdens in the USA, such as environmental regulations and labor laws,
can make it more expensive for companies to manufacture goods
domestically compared to outsourcing production to countries with lower
labor costs and fewer regulatory requirements.

Overall, a combination of these factors has led to the decline of the manufacturing sector and the growth of the service sector in the USA.

And outsourcing manufacturing operations to countries like China can
offer several benefits to companies:

1- Lower Labor Costs: One of the primary reasons for outsourcing to
countries like China is the significantly lower labor costs compared to developed countries like the USA. This allows companies to produce goods
at a much lower cost, thus increasing profit margins or enabling them to
offer products at competitive prices in the global market.

2- Access to Skilled Workforce: Countries like China have large
populations with a growing number of skilled workers, particularly in industries like electronics, textiles, and manufacturing. Outsourcing to
these countries allows companies to tap into this skilled labor pool,
often at a fraction of the cost of employing similarly skilled workers
in developed countries.

3- Economies of Scale: Outsourcing to countries with well-developed manufacturing infrastructures allows companies to benefit from economies
of scale. Manufacturing facilities in countries like China often have
the capacity to produce goods in large quantities efficiently, reducing per-unit production costs.

4- Proximity to Suppliers: Many manufacturing facilities in countries
like China are located close to suppliers of raw materials and
components. This proximity can reduce transportation costs and lead
times, making the supply chain more efficient and responsive to changes
in demand.

5- Infrastructure and Technology: Some countries, like China, have
invested heavily in developing their manufacturing infrastructure and technology capabilities. Outsourcing to these countries allows companies
to leverage state-of-the-art facilities and equipment without having to
make significant capital investments themselves.

6- Market Access: Outsourcing to countries like China can also provide companies with better access to local and regional markets. Setting up manufacturing operations in these countries can help companies navigate regulatory requirements, trade barriers, and cultural differences,
enabling them to establish a stronger presence in those markets.

Overall, outsourcing manufacturing operations to countries like China
can offer significant cost savings, access to skilled labor and
resources, and improved market access, allowing companies to remain
competitive in an increasingly globalized economy.


So the new Gemini pro 1.5 is here , and i think that it has now solved
the problem with Gemini pro 1.0 that i am talking about in my below
previous thoughts , and the new Gemini pro 1.5 has a context window that
goes up to 10 million tokens in research, and will have up to 1 million
tokens for regular consumers. That larger context window will cost
money, but the free version of Gemini 1.5 Pro will still come with a
128K context window. For reference, GPT-4 Turbo has a 128K context
window too, and both Gemini Pro now and regular GPT-4 have a context
window of 32K. 1 million token is a first of its kind in the industry.

And i invite you to look at the benchmarks that look very good of the
new Gemini pro 1.5 in the following web link:

https://www.reddit.com/r/Bard/comments/1arkc1k/gemini_1_pro_vs_15_pro_vs_ultra_10_on_benchmark/

And you can read more about the new Gemini pro 1.5 from the google
website in the following web page:

https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/


So i invite you to read my following previous thoughts:


I think i am also a new philosopher and you can read my new ideas of my philosophies below and in the below web link , and now i will talk about
an important subject , and here is my thoughts about it:

So I will say that AI-generated software , using generative AI like
ChatGPT , broadens the attack surface , and here's why:

1- Attack Surface: The attack surface refers to all the points in a
system where an attacker could potentially enter or exploit
vulnerabilities. This includes not just software but also hardware,
network connections, user interfaces, and more.

2- Expanding the Attack Surface: Introducing AI-generated software,
which often involves complex algorithms and large codebases, adds new components and functionalities to the overall system. Each of these
components represents a potential point of vulnerability that attackers
could target. Additionally, the increased complexity and interactions
within the software may create unforeseen security weaknesses.

3- Increased Complexity: AI-generated software can introduce complexity
in various ways, such as through the use of intricate machine learning
models, extensive libraries, or interactions with other systems. This complexity can make it harder to identify and mitigate security risks effectively.

4- Unintended Consequences: The introduction of AI systems can sometimes
have unintended consequences, such as unexpected behaviors or
vulnerabilities that arise due to the complexity of the system. These unintended consequences can further increase the attack surface by
providing new avenues for exploitation.


So the above has to be addressed through "robust" security measures and
testing , and it remains that it is not an easy thing to do, so then
addressing the challenges associated with AI-generated software through
robust security measures and testing is crucial but also presents its
own set of difficulties. Here are some of the challenges involved:

1- Complexity: AI-generated software can be highly complex, making it challenging to identify and mitigate security vulnerabilities
effectively. The intricate interactions between various components, the
use of sophisticated algorithms, and the sheer size of the codebase can
all contribute to this complexity.

2- Resource Intensive: Implementing robust security measures and
conducting thorough testing requires significant resources in terms of
time, expertise, and computational power. Organizations may face
constraints in allocating these resources effectively, particularly if
they are dealing with limited budgets or competing priorities.

3- Evolution of Threats: The landscape of cybersecurity threats is
constantly evolving, with attackers continuously developing new
techniques and tactics. This dynamic environment requires organizations
to stay vigilant and adapt their security measures accordingly, which
can be challenging to keep up with, especially for smaller teams or
those with limited expertise.

4- Balancing Security and Usability: Striking the right balance between security and usability is essential. While implementing stringent
security measures can help mitigate risks, they may also introduce
friction for users or impact the performance of the software. Finding
the optimal balance that ensures both security and usability can be a
delicate and ongoing process.

5- Testing Limitations: Comprehensive testing is essential for
identifying and addressing security vulnerabilities, but it's not always straightforward. AI-generated software may exhibit complex behaviors
that are difficult to test thoroughly, and traditional testing
approaches may not be sufficient. This necessitates the development of
new testing methodologies and tools tailored to the unique
characteristics of AI systems.

6- Regulatory Compliance: Depending on the industry and geographic
location, organizations may be subject to various regulatory
requirements related to cybersecurity. Ensuring compliance with these regulations adds another layer of complexity to the security process and
may require additional resources and expertise.

In summary, while implementing robust security measures and testing is essential for addressing the challenges associated with AI-generated
software, it's not without its own set of difficulties. Organizations
must navigate the complexities of AI systems while balancing security
needs with usability and resource constraints. It's an ongoing process
that requires continuous attention and adaptation to stay ahead of
emerging threats.

So then i say that the key is to write less code. Leaner software is
safer software.

So then my statement above of "The key is to write less code. Leaner
software is safer software" emphasizes the importance of simplicity and conciseness in software development for enhancing security. Here's why
this concept is significant:

1- Reduced Attack Surface: Every line of code introduces a potential vulnerability or weakness that attackers can exploit. By minimizing the
amount of code in a software system, developers can reduce the overall
attack surface, making it harder for attackers to find and exploit vulnerabilities.

2- Easier Maintenance and Review: Leaner software is generally easier to maintain and review. With fewer lines of code to manage, developers can
more easily identify and address security issues during code reviews and maintenance cycles. This makes it less likely for vulnerabilities to go unnoticed and unaddressed.

3- Reduced Complexity: Simplifying software architecture and design
leads to reduced complexity, which in turn reduces the likelihood of introducing security flaws. Complex systems are inherently harder to
secure and prone to unintended interactions or vulnerabilities, whereas
simpler systems are easier to understand and reason about.

4- Faster Response to Security Threats: Leaner software allows for more
agile development and response to security threats. When software is
built with simplicity in mind, developers can quickly identify and patch vulnerabilities, reducing the window of opportunity for attackers to
exploit them.

5- Improved Performance: In addition to security benefits, leaner
software often exhibits better performance and reliability. By
minimizing unnecessary code and optimizing performance-critical
components, developers can create software that is more resilient to
attacks and less prone to crashes or failures.

Overall, the principle of writing less code to achieve leaner software
aligns with best practices in software security and development. It
emphasizes simplicity, maintainability, and agility, all of which
contribute to creating safer and more secure software systems.


I have also just grouped all my new proverbs below , so i invite you to
read them carefully , also you can read all my thoughts of my philosophy
etc. below:


I think i am also a new smart philosopher , and you can read my new
ideas of my philosophies below , and here is my new proverb that
explains what is wisdom , and of course my new ideas of my philosophies
below have also as a goal to make you enough wise or wise, and you will understand from them that wisdom makes you convinced about wisdom and
helps you drive well in the right direction , so here is my new proverb
about wisdom and about intelligence so that you understand:

Here is my new proverb:

"Wisdom helps you drive the car well and intelligence is the engine of
the car"


So here is my smart question to CharGPT that makes you understand wisdom:

Can we say the wisdom helps you to drive the car well and intelligence
is the engine of the car.


And ChatGPT has just answered me the following:


"The analogy you've presented can provide a useful way to differentiate
between wisdom and intelligence:

1- Wisdom as Driving Skill: Wisdom, in this analogy, can be likened to
the skill and experience of driving. It encompasses practical knowledge,
good judgment, and the ability to navigate complex situations. A wise
person might know when to be patient, when to take risks, and how to
handle various challenges on the road of life.

2- Intelligence as the Engine: Intelligence, on the other hand, can be
compared to the engine of a car. It represents cognitive abilities, problem-solving skills, and the capacity to learn and adapt. Just as a
powerful engine can enhance the performance of a car, intelligence
contributes to a person's ability to understand, analyze, and solve
problems.

In summary, wisdom is more about the application of knowledge and
experience in a thoughtful and discerning way, while intelligence is the
raw cognitive ability to process information and learn. Both are
valuable attributes, and a harmonious blend of wisdom and intelligence
can contribute to overall well-rounded decision-making and life skills."

And here is my other new proverb:

"The sky and its beautiful stars on a beautiful night allow us to dream
and they give us hope, and I think it's like the human imagination that
is not a down-to-earth and it allows us to dream and it gives us hope."

"Le ciel et ses belles étoiles d'une belle nuit nous permettent de rêver
et de nous donner de l'espoir, et je pense que c'est comme l'imaginaire
humain, qui n'est pas du terre à terre, qui nous permet de rêver et de
donner de l'espoir."


Here is my other new proverb:

"I think that playing at being intelligent and in an intelligent manner
is good for creativity and innovation because it allows you to add to
both divergent and convergent thinking, making you creative and innovative."

Here is my explanation of it:

Creativity and innovation are often stimulated by a combination of
divergent and convergent thinking. Divergent thinking involves
generating a variety of ideas and solutions, while convergent thinking
involves converging towards the best possible solution or idea. By
consciously playing at being intelligent, stimulating your curiosity,
adopting intellectual habits, and developing diverse skills, you can
strengthen both divergent and convergent thinking.


And as you have just noticed , i have just quickly constructed a new
proverb that i think generalizes more the key idea of Sun Tzu that was a Chinese military strategist and philosopher , and here is my new proverb
and i also invite you to read my new question to CharGPT below so that
to understand:


And here is my new proverb:

"So you have not to look at complexity and hardness from the stupid
point of view , since the wise way is to first find the weakness(es) of
the complex problem that make(s) it much less complex , or find the weakness(es) of the hard problem that make(s) it much less hard , and i
think it is the smart way to do , and it is by logical analogy the same
as in fighting , so when you want to fight against a stronger man than
you , you have for example to do the same by for example learning the
martial art techniques that learn you how to win the fight against the
stronger man than you by defeating him from his weakness(es)."

And here is my question to ChatGPT:

Can we say that the main idea of Sun Tzu , that was a Chinese military strategist and philosopher , is to win against the stronger than you
from his weakness(es).


And ChatGPT has just answered me the following:

"Yes, one could distill a key aspect of Sun Tzu's philosophy of war as
the art of winning against stronger adversaries by exploiting their
weaknesses and maximizing one's own strengths. Sun Tzu emphasizes the importance of strategic thinking, adaptability, and a deep understanding
of both oneself and the enemy. By identifying and capitalizing on the vulnerabilities of the opponent while minimizing one's own weaknesses,
it is possible to achieve victory even when facing a stronger force.

The notion of "winning without fighting" is also central to Sun Tzu's philosophy. If a conflict can be resolved through strategy, diplomacy,
or other means without direct confrontation, it is often considered a
superior approach. This aligns with the idea of achieving objectives
with minimal cost and resources.

In summary, Sun Tzu's philosophy involves a strategic, nuanced approach
to warfare, emphasizing intelligence, adaptability, and the exploitation
of the opponent's weaknesses to overcome apparent disadvantages and
secure victory."

And i invite you to read all my other new proverbs in the following web
link:

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


And you have to understand that i have just grouped my important
thoughts below about the important limitations of artificial
intelligence , and also i have just invented a new model of what is
human consciousness , that you can read below , so that to show you how artificial intelligence will not attain artificial general intelligence
, also i have just added my thoughts about another limitation of
artificial intelligence that you can read below:


Here is my new question to ChatGPT:

So the Large Language Models such as ChatGPT are trained and they
discover lower level patterns and higher level patterns so that to
understand it all , but i think that the main limitation of
reinforcement learning is that it can not innovate by inventing outside
the trained data of the lower level patterns and the higher level
patterns, so it is the humans that have to innovate by inventing outside it.


And here is what has just answered me ChatGPT:

"Yes, that's a fair assessment. Reinforcement learning (RL) algorithms
operate within the confines of the data they are trained on and the
reward signals provided by humans or predefined criteria. They learn to optimize their behavior based on the patterns present in the training
data and the feedback they receive during interaction with the
environment. However, RL algorithms typically do not possess the ability
to spontaneously innovate or invent entirely new concepts, strategies,
or algorithms that are beyond the scope of their training data or reward signals.

Innovation often involves creativity, insight, and the ability to
generate novel solutions to problems. While RL can be a powerful tool
for optimizing existing strategies or learning from data, it may not
inherently possess the capacity for creative innovation in the same way
humans do. Instead, humans play a crucial role in driving innovation by formulating new problems, designing novel algorithms, and providing
guidance and feedback to RL systems.

That said, RL can still contribute to innovation by automating tasks, optimizing processes, and discovering optimal solutions within the
confines of a given problem domain. Additionally, RL research may
inspire new approaches to problem-solving or provide insights into
learning and decision-making processes that can inform human innovation efforts.

In summary, while RL has its strengths in optimization and learning from
data, it relies on human ingenuity and creativity to drive true
innovation by inventing outside the constraints of the trained data and
reward signals. Collaboration between humans and RL systems can leverage
the strengths of both to push the boundaries of what is possible in problem-solving and decision-making."


And here is my second question to ChatGPT:

So then innovation by inventing outside the trained data of the lower
level patterns and the higher level patterns is still confined to the
domain of humans.


And here is what has just answered me ChatGPT:

"Yes, that's correct. As of the current state of artificial
intelligence, particularly in the context of reinforcement learning, the ability to innovate by inventing entirely new concepts, strategies, or algorithms that are outside the scope of trained data and predefined
patterns remains primarily within the domain of human intelligence.

Humans possess unique cognitive abilities such as creativity, abstract thinking, and the capacity for insight, which enable them to imagine and generate novel solutions to problems. These abilities allow humans to
innovate in ways that go beyond the confines of existing data or learned patterns.

While artificial intelligence, including reinforcement learning, can
automate tasks, optimize processes, and learn from data, its ability to innovate in the same way humans do is currently limited. AI systems rely
on the data they are trained on and the reward signals they receive, and
they operate within the constraints of these inputs.

However, AI systems can still play a valuable role in assisting and
augmenting human innovation efforts. They can help identify patterns in
large datasets, generate insights from data, and optimize solutions
within specific problem domains. Moreover, AI research continues to
advance rapidly, and future developments may lead to AI systems with
enhanced capabilities for creative problem-solving and innovation.

Overall, while AI has made significant strides in various domains,
including reinforcement learning, the domain of true innovation—where
novel concepts are generated outside the confines of existing data and patterns—remains firmly within the realm of human intelligence."


And here is my other question to ChatGPT:

So i want to say that Large Language models such as ChatGPT or Gemini
are still lacking , since if we can not trust the trained data of Large Language Models such as ChatGPT, since it can be of not correct deeper understanding and/or it is not correctly diversified and is then not of
the correct quality, so Large Language Models are here to augment humans
, and not to replace humans , also i think that we still need to use and
trust more professionals in this or that field or expertize in this or
that field so that to correct this important deficiency of Large
Language Models like ChatGPT , so i think that the quality of the
trained data is an important weakness of Large Language Models such as
ChatGPT , so it is why we are noticing that they are lacking on accuracy.


And here is what has just answered me ChatGPT:

"Your points are well-taken. Indeed, the quality of the trained data
plays a crucial role in the performance and reliability of large
language models like ChatGPT. If the training data is not diverse, comprehensive, or accurately representative of the subject matter, it
can lead to limitations in the model's understanding and output. Here's
a summary of your points:

Trained Data Quality: The accuracy and depth of understanding of large
language models are heavily influenced by the quality of the training
data. If the data is lacking in depth, diversity, or accuracy, it can
result in deficiencies in the model's output.

Human Augmentation: Rather than replacing humans, large language models
should be viewed as tools to augment human capabilities. Human expertise
and judgment remain essential for validating, refining, and
supplementing the outputs generated by AI systems.

Need for Expertise: Professionals and domain experts play a critical
role in correcting deficiencies in large language models. Their input
and oversight can help identify inaccuracies, biases, or gaps in the
model's understanding and contribute to improving its overall performance.

Weakness in Data Quality: The primary weakness of large language models
like ChatGPT often lies in the quality of the trained data. Addressing
this deficiency requires ongoing efforts to curate, verify, and
diversify the training data to enhance the model's robustness and accuracy.

In conclusion, while large language models offer significant potential
for various applications, including natural language understanding and generation, their effectiveness ultimately depends on the quality of the
data used to train them. By prioritizing data quality and leveraging
human expertise, we can work towards improving the reliability and trustworthiness of AI systems like ChatGPT."

But i think that the other important limitation of artificial
intelligence that also shows that artificial intelligence will not
replace humans , but will mainly augment humans , is the following:


Human intelligence and artificial intelligence do approach pattern
discovery in distinct ways.

Human Fluid Intelligence:

1- Adaptability: Human fluid intelligence refers to the ability to
quickly adapt and solve new problems. Humans can recognize patterns and
make connections based on relatively limited data or experiences.

2- Contextual Understanding: Humans often rely on contextual
understanding, drawing on a wide range of knowledge and experiences to
identify patterns. This ability to bring diverse information into play
is a hallmark of human intelligence.

3- Innate Creativity: Humans can be highly creative and draw on

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