Hello,


More of my philosophy about Machine programming and about oneAPI from Intel company..

I am a white arab from Morocco, and i think i am smart since i have also invented many scalable algorithms and algorithms..


I will say that when you know C and C++ moderately, it will not be so difficult to program OpenCL(Read about OpenCL here: https://en.wikipedia.org/wiki/OpenCL) or CUDA, but the important question is what is the difference between FPGA and GPU ? so i
invite you to read the following interesting paper about GPU vs FPGA Performance Comparison:

https://www.bertendsp.com/pdf/whitepaper/BWP001_GPU_vs_FPGA_Performance_Comparison_v1.0.pdf

So i think from this paper above that GPU is the good way when you
want performance and you want too cost efficiency.

So i think that the following oneAPI from Intel company that wants with it to do all the heavy lifting for you, so you can focus on the algorithm, rather than on writing OpenCL calls, is not a so smart way of doing, since as i said above that OpenCL and
CUDA programming is not so difficult, and as you will notice below that oneAPI from Intel permits you to program FPGA in a higher level manner, but here again from the paper above we can notice that GPU is the good way when you want performance and cost
efficiency, then so that to approximate well the efficiency and usefulness of oneAPI from Intel you can still use efficient and useful libraries.

Here is the new oneAPI from Intel company, read about it:

https://codematters.online/intel-oneapi-faq-part-1-what-is-oneapi/

And now i will talk about another interesting subject and it is
about the next revolution in the software industry that is Machine programming, so i invite you to read carefully the following new article about it:

https://venturebeat.com/2021/06/18/ai-weekly-the-promise-and-limitations-of-machine-programming-tools/

So i think that Machine programming will be limited to AI-powered assistants that is not so efficient, since i think that connectionism
in artificial intelligence is not able to make emerge common sense reasoning, so i invite you to read my following thoughts about it
so that to understand why:

More of my philosophy about the limit of the connectionist models in artificial intelligence and more..

I think i am smart and i will say that the connectionist model like
of deep learning has not the same nature as of the human brain, since
i can say that the brain is not just connections of neurons like
in deep learning, but it is also a "sense" like the sense of touch,
and i think that this sense of the brain is biologic,
and i think that this kind of nature of the brain of being
also a sense is giving the emergence of consciousness and self-awareness and a higher level of common sense reasoning, this
is why i think that the connectionist model in artifical intelligence is showing its limits by not being able to make emerge common sense reasoning, but as i said below that the hybrid connectionist + symbolic model can make emerge common sense reasoning.

And here is what i said about human self-awareness and awareness:

So i will start by asking a philosophical question of:

Is human self-awareness and awareness an emergence and what is it ?

So i will explain my findings:

I think i have found the first smart pattern with my fluid intelligence and i found also the rest and it is the following:

Notice that when you touch a cold water you will know about the essence
or nature of the cold water and you will also know that it is related
to senses of humans, so i think that the senses of a human give life
to ideas, it is like a "reification" of an idea, i mean that an idea
is alive since it is like reified with the senses of humans that senses time and space and matter, so this reification gives the correct meaning since you are like reifying with the human senses that gives the meaning, and i say that this capacity of
this kind of reification with the human senses is an emergence that comes from the human biology, so i am smart and i will say that the brain is a kind of calculator that calculates by using composability with the meanings that come also from this kind
of reification with the human senses, and i think that self-awareness comes from the human senses that senses our ideas of our thinking, and it is what gives consciousness and self-awareness, so now you are understanding that what is missing in
artificial intelligence is
this kind of reification with the human senses that render the brain much more optimal than artificial intelligence, and i will explain more
the why of it in my next posts.

More of my philosophy about the future of artificial intelligence and more..

I will ask a philosophical question of:

Can we forecast the future of artificial intelligence ?

I think i am smart, and i am quickly noticing that connectionism in artificial intelligence like with deep learning is not working because it is not able to make emerge common sense reasoning, so i invite you to
read the following article from ScienceDaily so that to notice it, since it is speaking about the connectionist models(like the ones of deep learning or the transformers that are a kind of deep learning) in artificial intelligence:

https://www.sciencedaily.com/releases/2020/11/201118141702.htm

Other than that the new following artificial intelligence connectionist models like from Microsoft and NVIDIA that are better than GPT-3
has the same weakness , since i think that they can not make emerge
common sense reasoning, here they are:

"Microsoft and Nvidia today announced that they trained what they claim is the largest and most capable AI-powered language model to date: Megatron-Turing Natural Language Generation (MT-NLP). The successor to the companies’ Turing NLG 17B and Megatron-
LM models, MT-NLP contains 530 billion parameters and achieves “unmatched” accuracy in a broad set of natural language tasks, Microsoft and Nvidia say — including reading comprehension, commonsense reasoning, and natural language inferences."

Read more here:

https://venturebeat.com/2021/10/11/microsoft-and-nvidia-team-up-to-train-one-of-the-worlds-largest-language-models/

Because i also said the following:

I think i am quickly understanding the defects of Megatron-Turing Natural Language Generation (MT-NLP) that is better than GPT-3, and it is that "self-attention" of the transformers in NLP, even if they scale to very long sequences, they have a limited
expressiveness, as they cannot process input sequentially they can not model hierarchical structures and recursion, and hierarchical structure is widely thought to be essential to modeling natural language, in particular its syntax, so i think that
Microsoft Megatron-Turing Natural Language Generation (MT-NLP) and GPT-3 too will be practically applied to limited areas, but they can not make emerge common sense reasoning or the like that are necessary for general artificial intelligence.

Read the following paper so that to understand the mathematical proof of it:

https://aclanthology.org/2020.tacl-1.11.pdf

So i think that the model that will have much more success to or can make emerge common sense reasoning is like the following hybrid model in
artificial intelligence of connectionism + symbolism that we call COMET, read about it here:

Common Sense Comes Closer to Computers

https://www.quantamagazine.org/common-sense-comes-to-computers-20200430/

And here is what i also said about COMET:

I have just read the following article about neuroevolution
that is a meta-algorithm in artificial intelligence, an algorithm for
designing algorithms, i invite you to read about it here:

https://www.quantamagazine.org/computers-evolve-a-new-path-toward-human-intelligence-20191106/

So notice that it says the following

"In neuroevolution, you start by assigning random values to the weights
between layers. This randomness means the network won’t be very good at
its job. But from this sorry state, you then create a set of random
mutations — offspring neural networks with slightly different weights —
and evaluate their abilities. You keep the best ones, produce more
offspring, and repeat."

So i think that the problem with neuroevolution above is that the
"evaluate the abilities of the offspring neural networks" lacks common
sense.

So read the following interesting article that says that artificial intelligence has also brought a kind of common sense to Computers, and
read about it here:

https://arxiv.org/abs/1906.05317

And read about it in the following article:

"Now, Choi and her collaborators have united these approaches. COMET
(short for “commonsense transformers”) extends GOFAI-style symbolic reasoning with the latest advances in neural language modeling — a kind
of deep learning that aims to imbue computers with a statistical “understanding” of written language. COMET works by reimagining common-sense reasoning as a process of generating plausible (if
imperfect) responses to novel input, rather than making airtight
deductions by consulting a vast encyclopedia-like database."

Read more here:

https://www.quantamagazine.org/common-sense-comes-to-computers-20200430/



Thank you,
Amine Moulay Ramdane.

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

You are a donkey's cock.



Amine Moulay Ramdane kirjutas teisipäev, 2. november 2021 kl 01:29:17 UTC+2:

Hello,

More of my philosophy about Machine programming and about oneAPI from Intel company..

I am a white arab from Morocco, and i think i am smart since i have also invented many scalable algorithms and algorithms..

I will say that when you know C and C++ moderately, it will not be so difficult to program OpenCL(Read about OpenCL here: https://en.wikipedia.org/wiki/OpenCL) or CUDA, but the important question is what is the difference between FPGA and GPU ? so i

invite you to read the following interesting paper about GPU vs FPGA Performance Comparison:

https://www.bertendsp.com/pdf/whitepaper/BWP001_GPU_vs_FPGA_Performance_Comparison_v1.0.pdf

So i think from this paper above that GPU is the good way when you
want performance and you want too cost efficiency.

So i think that the following oneAPI from Intel company that wants with it to do all the heavy lifting for you, so you can focus on the algorithm, rather than on writing OpenCL calls, is not a so smart way of doing, since as i said above that OpenCL

and CUDA programming is not so difficult, and as you will notice below that oneAPI from Intel permits you to program FPGA in a higher level manner, but here again from the paper above we can notice that GPU is the good way when you want performance and
cost efficiency, then so that to approximate well the efficiency and usefulness of oneAPI from Intel you can still use efficient and useful libraries.

Here is the new oneAPI from Intel company, read about it:

https://codematters.online/intel-oneapi-faq-part-1-what-is-oneapi/

And now i will talk about another interesting subject and it is
about the next revolution in the software industry that is Machine programming, so i invite you to read carefully the following new article about it:

https://venturebeat.com/2021/06/18/ai-weekly-the-promise-and-limitations-of-machine-programming-tools/

So i think that Machine programming will be limited to AI-powered assistants that is not so efficient, since i think that connectionism
in artificial intelligence is not able to make emerge common sense reasoning, so i invite you to read my following thoughts about it
so that to understand why:

More of my philosophy about the limit of the connectionist models in artificial intelligence and more..

I think i am smart and i will say that the connectionist model like
of deep learning has not the same nature as of the human brain, since
i can say that the brain is not just connections of neurons like
in deep learning, but it is also a "sense" like the sense of touch,
and i think that this sense of the brain is biologic,
and i think that this kind of nature of the brain of being
also a sense is giving the emergence of consciousness and self-awareness and a higher level of common sense reasoning, this
is why i think that the connectionist model in artifical intelligence is showing its limits by not being able to make emerge common sense reasoning, but as i said below that the hybrid connectionist + symbolic model can make emerge common sense

reasoning.

And here is what i said about human self-awareness and awareness:

So i will start by asking a philosophical question of:

Is human self-awareness and awareness an emergence and what is it ?

So i will explain my findings:

I think i have found the first smart pattern with my fluid intelligence and i found also the rest and it is the following:

Notice that when you touch a cold water you will know about the essence
or nature of the cold water and you will also know that it is related
to senses of humans, so i think that the senses of a human give life
to ideas, it is like a "reification" of an idea, i mean that an idea
is alive since it is like reified with the senses of humans that senses time and space and matter, so this reification gives the correct meaning since you are like reifying with the human senses that gives the meaning, and i say that this capacity of

this kind of reification with the human senses is an emergence that comes from the human biology, so i am smart and i will say that the brain is a kind of calculator that calculates by using composability with the meanings that come also from this kind
of reification with the human senses, and i think that self-awareness comes from the human senses that senses our ideas of our thinking, and it is what gives consciousness and self-awareness, so now you are understanding that what is missing in
artificial intelligence is

this kind of reification with the human senses that render the brain much more optimal than artificial intelligence, and i will explain more
the why of it in my next posts.

More of my philosophy about the future of artificial intelligence and more..

I will ask a philosophical question of:

Can we forecast the future of artificial intelligence ?

I think i am smart, and i am quickly noticing that connectionism in artificial intelligence like with deep learning is not working because it is not able to make emerge common sense reasoning, so i invite you to
read the following article from ScienceDaily so that to notice it, since it is speaking about the connectionist models(like the ones of deep learning or the transformers that are a kind of deep learning) in artificial intelligence:

https://www.sciencedaily.com/releases/2020/11/201118141702.htm

Other than that the new following artificial intelligence connectionist models like from Microsoft and NVIDIA that are better than GPT-3
has the same weakness , since i think that they can not make emerge
common sense reasoning, here they are:

"Microsoft and Nvidia today announced that they trained what they claim is the largest and most capable AI-powered language model to date: Megatron-Turing Natural Language Generation (MT-NLP). The successor to the companies’ Turing NLG 17B and

Megatron-LM models, MT-NLP contains 530 billion parameters and achieves “unmatched” accuracy in a broad set of natural language tasks, Microsoft and Nvidia say — including reading comprehension, commonsense reasoning, and natural language
inferences."

Read more here:

https://venturebeat.com/2021/10/11/microsoft-and-nvidia-team-up-to-train-one-of-the-worlds-largest-language-models/

Because i also said the following:

I think i am quickly understanding the defects of Megatron-Turing Natural Language Generation (MT-NLP) that is better than GPT-3, and it is that "self-attention" of the transformers in NLP, even if they scale to very long sequences, they have a limited

expressiveness, as they cannot process input sequentially they can not model hierarchical structures and recursion, and hierarchical structure is widely thought to be essential to modeling natural language, in particular its syntax, so i think that
Microsoft Megatron-Turing Natural Language Generation (MT-NLP) and GPT-3 too will be practically applied to limited areas, but they can not make emerge common sense reasoning or the like that are necessary for general artificial intelligence.

Read the following paper so that to understand the mathematical proof of it:

https://aclanthology.org/2020.tacl-1.11.pdf

So i think that the model that will have much more success to or can make emerge common sense reasoning is like the following hybrid model in
artificial intelligence of connectionism + symbolism that we call COMET, read about it here:

Common Sense Comes Closer to Computers

https://www.quantamagazine.org/common-sense-comes-to-computers-20200430/

And here is what i also said about COMET:

I have just read the following article about neuroevolution
that is a meta-algorithm in artificial intelligence, an algorithm for designing algorithms, i invite you to read about it here:

https://www.quantamagazine.org/computers-evolve-a-new-path-toward-human-intelligence-20191106/

So notice that it says the following

"In neuroevolution, you start by assigning random values to the weights between layers. This randomness means the network won’t be very good at its job. But from this sorry state, you then create a set of random mutations — offspring neural networks with slightly different weights — and evaluate their abilities. You keep the best ones, produce more offspring, and repeat."

So i think that the problem with neuroevolution above is that the
"evaluate the abilities of the offspring neural networks" lacks common sense.

So read the following interesting article that says that artificial intelligence has also brought a kind of common sense to Computers, and
read about it here:

https://arxiv.org/abs/1906.05317

And read about it in the following article:

"Now, Choi and her collaborators have united these approaches. COMET
(short for “commonsense transformers”) extends GOFAI-style symbolic reasoning with the latest advances in neural language modeling — a kind
of deep learning that aims to imbue computers with a statistical “understanding” of written language. COMET works by reimagining common-sense reasoning as a process of generating plausible (if
imperfect) responses to novel input, rather than making airtight
deductions by consulting a vast encyclopedia-like database."

Read more here:

https://www.quantamagazine.org/common-sense-comes-to-computers-20200430/

Thank you,
Amine Moulay Ramdane.

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

Hello,



More of my philosophy about Machine programming and about oneAPI from Intel company and about artificial intelligence..

I am a white arab from Morocco, and i think i am smart since i have also invented many scalable algorithms and algorithms..


I think that COMET (short for “commonsense transformers”) is the future
of artificial intelligence since they are capable of common sense and common sense reasoning, you can read more about it here:

https://arxiv.org/abs/1906.05317

And read about it in the following article:

"Now, Choi and her collaborators have united these approaches. COMET
(short for “commonsense transformers”) extends GOFAI-style symbolic reasoning with the latest advances in neural language modeling — a kind
of deep learning that aims to imbue computers with a statistical “understanding” of written language. COMET works by reimagining common-sense reasoning as a process of generating plausible (if
imperfect) responses to novel input, rather than making airtight
deductions by consulting a vast encyclopedia-like database."

Read more here:

https://www.quantamagazine.org/common-sense-comes-to-computers-20200430/

And you can look at the benchmark on the following paper of how COMET is much better than GPT-3:

https://arxiv.org/pdf/2010.05953.pdf


So then i think that COMET will also render Machine programming much more successful since it will permit much better AI-powered assistants that will be much more efficient, also i think it will enhance much more productivity and it will higher much more
economic growth.

Also I will say that when you know C and C++ moderately, it will not be so difficult to program OpenCL(Read about OpenCL here: https://en.wikipedia.org/wiki/OpenCL) or CUDA, but the important question is what is the difference between FPGA and GPU ? so i
invite you to read the following interesting paper about GPU vs FPGA Performance Comparison:

https://www.bertendsp.com/pdf/whitepaper/BWP001_GPU_vs_FPGA_Performance_Comparison_v1.0.pdf

So i think from this paper above that GPU is the good way when you
want performance and you want too cost efficiency.

So i think that the following oneAPI from Intel company that wants with it to do all the heavy lifting for you, so you can focus on the algorithm, rather than on writing OpenCL calls, is not a so smart way of doing, since as i said above that OpenCL and
CUDA programming is not so difficult, and as you will notice below that oneAPI from Intel permits you to program FPGA in a higher level manner, but here again from the paper above we can notice that GPU is the good way when you want performance and cost
efficiency, then so that to approximate well the efficiency and usefulness of oneAPI from Intel you can still use efficient and useful libraries.

Here is the new oneAPI from Intel company, read about it:

https://codematters.online/intel-oneapi-faq-part-1-what-is-oneapi/

And now i will talk about another interesting subject and it is
about the next revolution in the software industry that is Machine programming, so i invite you to read carefully the following new article about it:

https://venturebeat.com/2021/06/18/ai-weekly-the-promise-and-limitations-of-machine-programming-tools/


More of my philosophy about automation and artificial intelligence and productivity and more..

I think that Donald Trump was not thinking correctly, since around 85% of jobs losses in the manufacturing sector in USA was caused by automation and not by China or such as were thinking it the people who elected Donald Trump, and you can look at the
following interesting video that makes you understand that automation also has advantages and to make you understand how Donald Trump was not right:

https://www.youtube.com/watch?v=D-rd3kW7Bc8


And following are some of the 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.

And this video above is related to my following thoughts:

And read carefully my following thoughts about Nanotechnology and about Exponential Progress:

https://groups.google.com/g/alt.culture.morocco/c/mjE_2AG1TKQ

And here is my thoughts about 3D stacking in CPUs and about Moore’s law:

https://groups.google.com/g/alt.culture.morocco/c/0VJbDNqXgHY

More of my philosophy about how to boost productivity with artificial intelligence and more..

You can boost productivity with artificial intelligence by:

1- More accurate demand forecasting using AI and machine learning
2- Predictive maintenance
3- Hyper-personalized manufacturing
4- Optimizing manufacturing processes
5- Automated material procurement

Read more here about those 5 ways artificial intelligence can boost productivity:

https://www.industryweek.com/technology-and-iiot/article/22025683/5-ways-artificial-intelligence-can-boost-productivity

More of my philosophy about the Canada Government Budget Balance and more..

So i think that Canada has not balanced the budget in the past and is not balancing the budget and it has been hit badly by
Covis-19, so you can look at the following details of Economic and Fiscal Projections of Canada that i have just read so that to understand:

https://www.budget.gc.ca/2021/report-rapport/anx1-en.html

So i think that the best way for Canada to solve the deficit's problem
and the debt-to-GDP ratio problem is to efficiently invest in artificial intelligence that could double annual economic growth rates , but
Canada has to invest efficiently in Digital and AI(artificial intelligence) literacy, so read the following so that to notice it:

"Compelling data reveal a discouraging truth about growth today. There has been a marked decline in the ability of traditional levers of production capital investment and labor to propel economic growth."

And it it says the following:

"Accenture research on the impact of AI in 12 developed economies reveals that AI could double annual economic growth rates in 2035 by changing the nature of work and creating a new relationship between man and machine. The impact of AI technologies on
business is projected to increase labor productivity by up to 40 percent and enable people to make more efficient use of their time."

Read more here so that to notice it:

https://www.accenture.com/ca-en/insight-artificial-intelligence-future-growth-canada

And McKinsey estimates that AI(Artificial intelligence) may deliver an additional economic output of around US$13 trillion by 2030, increasing global GDP by about 1.2 % annually. This will mainly come from substitution of labour by automation and
increased innovation in products and services.

Read more here:

https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf

And more of my philosophy about Digital and AI literacy and more..

I think Canada still has a problem, since it has to invest much
more in Digital and AI literacy, so read the following article so that
to understand the great importance of Digital and AI literacy:

"Digital and AI literacy is of utmost importance to help Canadian businesses scale and compete internationally. Investing in widespread digital and AI literacy for the entire population will increase domestic demand for technology and technology jobs. A
technologically literate population will create more data, which fuels AI and thus the data-driven economy as a whole. It is also necessary for workers to be able to upskill and re-skill in order to remain productive and competitive in an automated
workforce. Canadian businesses that adopt AI technology will save from lower production costs, have increased output, and be able to invest more. Increased revenue from this domestic demand, as well as Canada’s global reputation for responsible AI,
will help Canadian businesses scale globally and compete on the international level. Canada has a promising future in the data-driven economy, and strategic choices by policymakers are necessary to ensure that Canadians can benefit from an ethical and
thriving AI ecosystem."

Read more here:

Canada's Economic Future with Artificial Intelligence

https://www.kroegerpolicyreview.com/post/canada-s-economic-future-with-artificial-intelligence

And here is my following thoughts about artificial intelligence and evolutionary algorithms in artificial intelligence:

https://groups.google.com/g/alt.culture.morocco/c/joLVchvaCf0

More of my philosophy about the Debt-to-GDP ratio and about Germany and more...

Germany is well managing its debt-to-GDP ratio, even in the crisis of Covid-19, since the Debt-to-GDP ratio of Germany is the best among the G-7 countries, so i invite you to read the following article so that to notice it:

Germany to Ramp Up Borrowing With 2021 Debt of $286 Billion

https://www.bloomberg.com/news/articles/2021-03-22/germany-to-ramp-up-borrowing-with-2021-new-debt-of-286-billion


And I invite you to read the following interesting article:

China’s debt-reduction campaign is making progress, but at a cost

Read more here:

https://www.atlanticcouncil.org/blogs/new-atlanticist/chinas-debt-reduction-campaign-is-making-progress-but-at-a-cost/


Thank you,
Amine Moulay Ramdane.

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)