THE MOST COMMON ARTIFICIAL INTELLIGENCE MISCONCEPTIONS

[Azamat Abdoullaev]

Its misconception consists in the widely established cognitive bias that AI must have the same nature as human intelligence or human mind or human brains, while "most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals".

Its misnomer comes from a strong desire of its initiators to label it differently from cybernetic intelligence or machine intelligence or computing intelligence or techno-intelligence by any ways, regardless of logical inconsistency from its original definition:

"It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable".

https://www.bbntimes.com/technology/the-most-common-artificial-intelligence-misconceptions

https://www.linkedin.com/pulse/why-has-artificial-intelligence-gotten-bad-name-ai-azamat-abdoullaev/

[Marco Neumann]

I think it comes down to the question what kind of a science AI is or has as a foundation. If it does have one that is. I currently think the cognitive and brain research provide as with strong contenders here to give AI scientific merit.

BTW as information scientist for me ontologies are not exclusively related to AI. We have used controlled vocabularies, catalogs, taxonomies and thesauri (even with strong formal logic foundations) etc long before John McCarthy came up with the marketing label AI.

Best,

Marco

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Marco Neumann
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[Dr. Lars Ludwig]

Dear Azamat,

Azamat> Its misconception consists in the widely established cognitive bias that AI must have the same nature as human intelligence or human mind or human brains, ... It is the science and engineering of making intelligent machines, especially intelligent computer programs.

I agree that AI does not have to imitate human intelligence, mind, or brains.

However, any current technology is an extension (often automation) of human thought (mechanics, logics, calculations, etc.). Thus, any technology is  - per se - artificial and intelligent, or artificial intelligence - in this broad sense.

As soon as we start to think about AI as st. really autonomous (independent of human intelligence, minds, brains, and thus independent of human intentions), as soon as there is nothing it is good for (no longer extending human intentions) besides itself (as an embodied process), we would better have stopped earlier, as we'd have created a new type of autopoietic system (perhaps an organism-machine-chimera) competing for resources in life's environment. 

If we keep thinking of AI as human intention-bound, ensuring the close correpondence of technology-based substitution processes (technology application) and intentions, we'd better make sure that technology serves its purpose as physical mind extension and focus on the basic challenges at hand (see e.g. chapter "4.3.3 Basic Memory-Process Problems in Epistemological Consideration" in the extended artificial memory book https://d-nb.info/1045194794/34). 

Best, 

Lars Ludwig

https://www.linkedin.com/in/dr-lars-ludwig/

 

    

[Azamat Abdoullaev]

I think it comes down to the question what kind of a science AI is or has as a foundation. If it does have one that is. 

Right. 

Real AI is the case of transdisciplinary science transgressing computer science, cognitive sciences, linguistics, mathematics and statistics, engineering, philosophical sciences (ontology, epistemology, ethics, logic, semantics,...).  

I currently think the cognitive and brain research provide as with strong contenders here to give AI scientific merit.

It is a sort of cognitive computing AI much promoted by IBM, hardly having big prospects, as being too subjective and anthropomorphic. 

AI needs to be more ontologized...

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[Marco Neumann]

Azamat>Real AI is the case of transdisciplinary science transgressing computer science, cognitive sciences, linguistics, mathematics and statistics, engineering, philosophical sciences (ontology, epistemology, ethics, logic, semantics,...).  

I very much like this place for AI as a truly interdisciplinary discipline. Not sure if I share the details of what that means with many of the contemporary AI protagonists though.

 

Again when it comes to formal ontologies I think they can comfortably exist outside of AI research. 

AI at the moment is first of all a business and commercial research topic. Possibly becoming more of an industry in the future. I don't think we have much of a science yet.

Take for example grad schools. How many universities provide advanced degrees in AI? If you try to answer that question you will quickly notice that most of these institutions supporting such a course are based in the US or the UK. If you take a closer look at the description of these courses and their curricula it becomes much less discernible what is actually the subject of interest here. You will find topics such as image processing, web development, algorithm design, automation, NN, database development, robotics, search engine optimization, natural language processing, and computational statistics etc and from time to time the whimsical attempts at elements of philosophy and ethics.

A more blistering critique is currently proposed by Yarden Katz in "Artificial Whiteness" published 2020. He goes a step further and locates AI primarily in the service of what he calls "empire". Born and raised with pentagon funds to ultimately serve the military industrial nexus and help to achieve its geopolitical primat du jour.

The votes are still out as far as the book goes but I wouldn't dismiss Yarden's critique out of hand; he tries to support his position systematically and AI is very much part of the contemporary security and governance debate.

Marco

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[Azamat Abdoullaev]

Marco > I very much like this place for AI as a truly interdisciplinary discipline. Not sure if I share the details of what that means with many of the contemporary AI protagonists though.

Ye. Please note the difference, AI is the first case of Transdisciplinary Science and Technology (Trans-AI), which covers interdisciplinary sciences and technologies, as computer science and technology, cognitive science and technology, philosophical sciences and technologies...

 

Marco > Again when it comes to formal ontologies I think they can comfortably exist outside of AI research. 

Ontologies are great tools to ontologize AI data and models, thus making them more metaphysically grounded.

AI at the moment is first of all a business and commercial research topic. Possibly becoming more of an industry in the future. I don't think we have much of a science yet.

Marco > Take for example grad schools. How many universities provide advanced degrees in AI? If you try to answer that question you will quickly notice that most of these institutions supporting such a course are based in the US or the UK. If you take a closer look at the description of these courses and their curricula it becomes much less discernible what is actually the subject of interest here. You will find topics such as image processing, web development, algorithm design, automation, NN, database development, robotics, search engine optimization, natural language processing, and computational statistics etc and from time to time the whimsical attempts at elements of philosophy and ethics.

Taking most resources and education courses, such mono-disciplinarity and fragmentarity is the biggest obstacle to Trans-AI Science and Technology.

Marco > A more blistering critique is currently proposed by Yarden Katz in "Artificial Whiteness" published 2020. He goes a step further and locates AI primarily in the service of what he calls "empire". Born and raised with pentagon funds to ultimately serve the military industrial nexus and help to achieve its geopolitical primat du jour.

The votes are still out as far as the book goes but I wouldn't dismiss Yarden's critique out of hand; he tries to support his position systematically and AI is very much part of the contemporary security and governance debate.

I saw some negative commentary: Complete foolishness. This book says literally "AI is a technology of whiteness...a tool that serves the aims of white supremacy".

https://books.google.com.cy/books/about/Artificial_Whiteness.html?id=qN7fDwAAQBAJ&source=kp_book_description&redir_esc=y

In fact, Katz has a good point, since today's AI is not only dumb and dull, obscure and narrow, but badly biased operating with biased labelled data sets, ignoring minorities, the black, Asians, Russians, etc. but working with the developed contries data sets for facial recognition, recommendation, fraud detection, etc. Say, the self-driving Tesla's AI Autopilot, could identify a black pedestrian as a non-human thing, as happened with Google's image reading algorithm...

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[Marco Neumann]

I saw some negative commentary: Complete foolishness. This book says literally "AI is a technology of whiteness...a tool that serves the aims of white supremacy".

https://books.google.com.cy/books/about/Artificial_Whiteness.html?id=qN7fDwAAQBAJ&source=kp_book_description&redir_esc=y

In fact, Katz has a good point, since today's AI is not only dumb and dull, obscure and narrow, but badly biased operating with biased labelled data sets, ignoring minorities, the black, Asians, Russians, etc. but working with the developed contries data sets for facial recognition, recommendation, fraud detection, etc. Say, the self-driving Tesla's AI Autopilot, could identify a black pedestrian as a non-human thing, as happened with Google's image reading algorithm...

I am only a little over halfway through the book but Yarden doesn't go into any great detail on the technology side yet. And his hypothesis is explicitly harsh in general and focuses on societal and cultural aspects.  He doesn't give any credit to potentially positive contributions by AI actors and the field in general. Furthermore he questions the arbitrary and inflated distinction (from his point of view) between symbolic and non symbolic AI. Similar to what Lars has described earlier as more like anything goes as long as the frame of reference is a vague AI link.

I think I first met Yarden in the valley in the early 2000s when he was still with Jim Hendler doing research with Pellet by Clark & Parsia on Description Logics and OWL at the time. So I would suspect that he has some good basic knowledge about the technologies and concepts in the AI field in general.

Where he makes a contribution though is on the literature review side and here he provides us with a collection of anecdotal evidence not yet widely published. You have to raise an eyebrow or two when you read about some of the commentary by John McCarthy which were  unfiltered and clearly colored by the cold war rhetoric at the time. 

It's a reasonable effort but a little too light on the technology and alternatives imo. 

Marco

[John F Sowa]

I have been actively involved with AI since I took Marvin Minsky's AI course in 1968.  The term AI was coined by John McCarthy for the founding conference at Dartmouth college in 1956.  The four organizers of that conference were John McCarthy from Dartmouth, Marvin Minsky from MIT, Claude Shannon from Bell Labs (AT&T), and Nat Rochester from IBM.  I never met Shannon, but I knew the other three personally and talked with them at repeated meetings and conferences over the years. At IBM, I worked with Rochester on a research project in the 1970s

 

I have not seen Yarden's book, but I can say with complete confidence that any blanket statement or generalization about AI research and applications is almost certainly a MISCONCEPTION.  The four founders in 1956 had very different views, and they invited more participants who had an even broader range of views.  Alan Turing in the UK wrote one of the founding papers in 1950, and the British term 'machine intelligence' was widely used before and some years after 1956.

 

It is true that US DoD paid for much of the R &  D in AI, but that is a small fraction of the amount they poured into physics, engineering and space.   It is also true that white males dominated the field, but that is true of almost every subject at every university in the US and Europe, especially in the STEM fields. (At Harvard, for example, women were not permitted in the faculty dining hall until 1970.)

 

I also presented invited talks in Japan, China, India, Egypt, Mexico, Brazil, Argentina, and Columbia.  The Asian country I visited most often is Malaysia.  Among other things, I presented a five-day short course for their Knowledge Systems Group.  (See the slides at http://jfsowa.com/talks/patolog1.pdf )   And by the way, the head of that project happened to be black (a very dark-skinned man whose parents came from India).

 

John

 

 

[Azamat Abdoullaev]

As with the standard/reference ontology, there is the standard/reference man concept to be used as the narrow AI/ML models' training datasets.

According to the International Commission on Radiological Protection's 1974 definition, 

"Reference man is defined as being between 20–30 years of age, weighing 70 kg, is 170 cm in height, and lives in a climate with an average temperature of from 10°C to 20°C. 

He is a Caucasian and is a Western European or North American in habitat and custom."

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[Marco Neumann]

Thank you John for the clarification, according to the book Claude Shannon was the least favorable to the term AI and "considered  intelligence too loaded a term". But yes I agree Yarden clearly positions himself in opposition to the AI effort. Very much in the tradition of critical and cultural studies I would say. Afterall the subtitle of the book is Politics and Ideology in Artificial Intelligence. 

The way I approach this book is to use it as an opportunity to gain another perspective on the subject. Overall it contextualizes the development of AI in its time and  makes the discourse richer and more diverse. There is no harm in considering a multitude of opinions. But there is no doubt  that if you want to learn more about AI as an emerging discipline you will have to look elsewhere. 

Marco

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[John F Sowa]

Azamat,

 

You can find stupid biases anywhere.  But I keep emphasizing that the field of AI is huge collection of different people with different personal biases and technical biases and educational and  cultural biases. 

 

 

AA> According to the International Commission on Radiological Protection's 1974 definition, "Reference man is defined as being between 20–30 years of age, weighing 70 kg, is 170 cm in height, and lives in a climate with an average temperature of from 10°C to 20°C. He is a Caucasian and is a Western European or North American in habitat and custom."

 

That is indeed a stupid bias.  And people who use stupid biases will get stupid results.  As I said in my previous note, I made repeated trips to Malaysia for several years, where their knowledge systems group was doing some good AI work.  And their situation and methods deviated from every one of those biases by a huge margin. 

 

In every field of any kind, there are as many different biases as there are people working in the field.  And the ones who make the best breakthroughs are the ones whose biases are outside of the mainstream in a better direction than most.

 

John.

 

[Marco Neumann]

On Fri, Dec 24, 2021 at 5:21 AM John F Sowa <so...@bestweb.net> wrote:

John > You can find stupid biases anywhere.  But I keep emphasizing that the field of AI is huge collection of different people with different personal biases and technical biases and educational and  cultural biases. 

Nonetheless an important contribution to the philosophy of AI by Joseph Weizenbaum in "Computer Power and Human Reason: From Judgment to Calculation" (1977). To give it a name and add it firmly to the discourse was a first step but as we know the tech industry is still struggling to come to terms with it.

 

 

 

AA> According to the International Commission on Radiological Protection's 1974 definition, "Reference man is defined as being between 20–30 years of age, weighing 70 kg, is 170 cm in height, and lives in a climate with an average temperature of from 10°C to 20°C. He is a Caucasian and is a Western European or North American in habitat and custom."

 

That is indeed a stupid bias.  And people who use stupid biases will get stupid results.  As I said in my previous note, I made repeated trips to Malaysia for several years, where their knowledge systems group was doing some good AI work.  And their situation and methods deviated from every one of those biases by a huge margin. 

 

John> In every field of any kind, there are as many different biases as there are people working in the field.  And the ones who make the best breakthroughs are the ones whose biases are outside of the mainstream in a better direction than most.

Indeed I consider bias now even as a prime source to model data's inherent context. 

 

 

John.

 

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[Azamat Abdoullaev]

JS > You can find stupid biases anywhere.  But I keep emphasizing that the field of AI is huge collection of different people with different personal biases and technical biases and educational and  cultural biases. 

Indeed. 

All human life is full of stupid things, predilections and prejudices, decisions, acts and doings. But there are unharmful, harmful and lethal stupid biases. The range of stupidity is numberless, from the highly academic stupidity of simulating human intelligence/brains/mind/behavior in machines to the geopolitical tension between NATO and Russia, where both sides are stuck in their socio-ideological partialities, preconceptions, prepossessions, predilections, presumptions, preferences, pretensions, or biases to the prejudice of the whole world. 

 

JS > That is indeed a stupid bias.  And people who use stupid biases will get stupid results.  As I said in my previous note, I made repeated trips to Malaysia for several years, where their knowledge systems group was doing some good AI work.  And their situation and methods deviated from every one of those biases by a huge margin.

Agree.

In toxicology, the median lethal dose (MLD) as an indication of the lethal toxicity of a given toxin, radiation or pathogen, as the Omicron variant, is based on the standard person concept, not existing in reality. As a result, we have the never-ending pandemic, as figured below

 

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[John F Sowa]

Azamat> I think it comes down to the question what kind of a science AI is or has as a foundation. If it does have one that is. 

Right.

 

Nobody can predict what kinds of innovations will be discovered in the future.  That's why I complain about the hype that surrounds deep neural nets and the deliberately lobotomized language called OWL.  That's also the theme of my notes about different strokes for different folks.

 

The field of AI has developed a huge toolkit of different technologies over the past 50 years, and the hype about DNNs and the limited results by the DAML project have killed or diverted attention away from much more promising directions.  I am not claiming that we should revive the old tools, but I am saying that they should be revisited and when appropriate used as  a starting point for new directions.

 

For a summary of promising directions that  have much greater potential than OWL and DNNs, see http://jfsowa.com/ikl .

 

And by the way, I am strongly AGAINST any claims about foundations.  Science does not have foundations.  The most basic science (physics) is constantly developing new foundations and fundamental directions.   A foundation is  a straight jacket that limits new developments.

 

John

[Avril Styrman]

Hi John, I don’t fully understand what you mean by your hyper-anti-foundationalism:

And by the way, I am strongly AGAINST any claims about foundations.  Science does not have foundations.  The most basic science (physics) is constantly developing new foundations and fundamental directions.   A foundation is  a straight jacket that limits new developments.

For, Kuhn showed that normal science is practiced under some foundations, until they are changed into new foundations. I’d accept that science will always have some foundations, for people by nature wish to understand how it works, but I’d also like to establish institutional criteria for evaluating theories with different foundations. This would break the straight jacket, if there is a better one available. 

Cheers,

Avril 

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[John F Sowa]

Avril,

 

Before saying anything further, I'd like to recommend Wittgenstein's term "Sprachspiel", which is usually translated as "language game', but a better translation might be "language play", "language practice", or just "language context".     One of  Ludwig W's ,most relevant discussions was his course on Foundations of Mathematics, which Alan Turing attended.  It began as a course on philosophy at Cambridge, but it became a dialog between Wittgenstein and Turing with occasional questions by the other attendees.  I strongly agree with LW, although I admit that AT made some important points.

 

All the following terms could be called Sprachspiele, since they involve a common set of terminology and ways of using the terms to describe, analyze, and explain some ways of talking about something::  foundation, paradigm, theory, discipline, policy, practice, convention, guideline, standard, school of thought...

 

Avril>, I don’t fully understand what you mean by your hyper-anti-foundationalism:

 

JFS> I am strongly AGAINST any claims about foundations.  Science does not have foundations.  The most basic science (physics) is constantly developing new foundations and fundamental directions.   A foundation is  a straight jacket that limits new developments.

 

Avril>  For, Kuhn showed that normal science is practiced under some foundations, until they are changed into new foundations.

 

JFS>  Kuhn did not use the word 'foundation'.  He used the word 'paradigm'.  That is a much weaker word, which can be changed more quickly without making other paradigms obsolete for the things that they explain quite well.   For example, Newtonian mechanics is more widely used for everyday purposes, even though physicists have known for over a century that it is only approximately correct for those applications.. 

 

Avril>  I’d accept that science will always have some foundations, for people by nature wish to understand how it works, but I’d also like to establish institutional criteria for evaluating theories with different foundations. This would break the straight jacket, if there is a better one available.

 

No.  There is no solid foundation for physics, chemistry, biology, psychology, sociology, economics, archaeology, anthropology, medicine, pharmacology, computer science, artificial intelligence, ontology, metaphysics, ethics, aesthetics...

 

There are many different paradigms, practices, etc.  Various practitioners prefer different versions, but most are willing to change, modify, or adapt their methods for different cases, problems, and applications.

 

Mathematics is one of the few sciences for which some people have strongly argued for foundations.  But there have also been strong arguments against foundations.  And today, there is no single foundation that all mathematicians can agree on.  Practicing mathematicians (people who have serious problems they need to solve) ignore foundational issues as irrelevant to any practical problems they need to solve.

 

As just one example, the Bourbaki attempted to establish a solid foundation for mathematics in the mid 20th century.  They published a bunch of textbooks that contain interesting material.  But no mathematicians today take their foundation seriously. 

 

For more about the Bourbaki, see "The ignorance of the Bourbaki" by Adrian Mathias, https://www.dpmms.cam.ac.uk/~ardm/bourbaki.pdf .  You can Google "Adrian Mathias" and "Bourbaki" for other discussions. 

 

John

 

 

[Azamat Abdoullaev]

JS> There is no solid foundation for physics, chemistry, biology, psychology, sociology, economics, archaeology, anthropology, medicine, pharmacology, computer science, artificial intelligence, ontology, metaphysics, ethics, aesthetics...

Science is the sum of universal knowledge. Knowledge has real value if only coordinated and systematized.

You can use automatic feature selection techniques of deep machine learning (DNNs) to cluster key concepts of the numbered knowledge fields. As a result, each branch of science will come with its foundational terms, which could be classified further up, with top classifications or meta-categories, as 

substance/object/matter/body/mind, state/property/quality/quantity/fact/energy, change/action/process/event/computation, relationship/causality/time/space/correlation/communication/function, data/information/knowledge.

This is a scientific, inductive, statistical or empirical way to generate the standard ontology [don't mix it with the mentioned standard white male to create the poison scale].

It could be visualized as the Venn diagrams depicting how sciences relate to each other against an overall backdrop, universe, data set, or environment, which is the standard ontology.

https://www.linkedin.com/pulse/why-has-artificial-intelligence-gotten-bad-name-ai-azamat-abdoullaev/?published 

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[Alex Shkotin]

Colleagues,

Let me add my 5 cents. The foundation of science is an experiment. There are two kinds of experiments: material and mental. L.D. Landau said, "theorists without experimenters are weakening." 

An experiment in the matter is a repeatable material algorithm with the same result on some level of precision. We use these results to develop useful or nasty technologies or just predict something important or interesting.

When we have a theory, primary terms are those for which the creator of theory can't give definitions, just axioms (inherent properties) in the best case.

For example, in geology, the concept of the rock sample is primary, as only well educated and experienced person can pick up a small piece of matter as a right representative of the whole rock layer.

An experiment is not the only way of cognition, Brodsky said in his Nobel lecture: "There are, as we know, three methods of cognition: analytical, intuitive and the method used by the biblical prophets - through revelation."

The foundation for a particular science is its part of reality (materiality or ideality:-)

And the fundamental question is why the material algorithm is possible?

On another side, for math foundation, we should just wait a couple of years as "Her goal is to develop a theory of how mathematical knowledge is generated and shared in practice. She will show how mathematical objectivity can be achieved by human, fallible subjects." [1]

Alex

[1] https://dailynous.com/2021/12/03/philosopher-wins-over-e1-million-grant-for-project-on-mathematical-knowledge/

вс, 26 дек. 2021 г. в 07:57, John F Sowa <so...@bestweb.net>:

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[Azamat Abdoullaev]

Alex > Brodsky said in his Nobel lecture: "There are, as we know, three methods of cognition: analytical, intuitive and the method used by the biblical prophets - through revelation."

If it'd be so simple, life could be easier...

Cognition is a sample of how an experimental science transforms to the real science of general knowledge. There are a lot of special approaches to cognition:   literature, linguistics, biology, anesthesia, neuroscience, psychiatry, psychology, education, philosophy, anthropology, biology, systemics, logic, computer science, etc. All this input data are synthesized in the interdisciplinary study of the mind and its phenomena, cognitive science. Which is to be further transgressed by some general intelligence science and technology...There is no end to human cognition.

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[Alex Shkotin]

Azamat,

All arts, sciences, and technologies you mentioned are known at least as terms, but thank you for "systemics", as Mario Bunge is great! This year Andrei Rodin kept workshop [1] which may be interesting.

Alex

[1] https://philomatica.org/mario-bunge-memorial/

вс, 26 дек. 2021 г. в 14:02, Azamat Abdoullaev <ontop...@gmail.com>:

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[Azamat Abdoullaev]

Alex > ... thank you for "systemics", as Mario Bunge is great! 

You are welcome!

Mario Bunge was one of the greatest ontologists of the 20-21 centuries. But he was badly ignored to the prejudice of ontology as an exact metascience.

He has recently died as a centenarian.

I remember his letter to promote his classic Treatise on Basic Philosophy in eight volumes (1974–1989) in the USSR.

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[doug foxvog]

John Sowa,

I almost always completely agree with your comments, respect your
analysis, and find your conclusions valid.

That said, your below comment is terribly misleading. I hope you did not
intend it as such.

On Sat, December 25, 2021 23:57, John F Sowa wrote:
> ...

> Newtonian mechanics is more widely
> used for everyday purposes, even though physicists have known for over a
> century that it is only approximately correct for those applications..

Although Newtonian mechanics technically IS "only approximately correct",
the difference between the results of calculations based on Newtonian
mechanics and relativistic physics at speeds below a mile per second are
the same to more than 12 significant figures. For "everyday purposes", we
are referring to far lower speeds, and therefore the calculated values are
the same to more significant figures than that. If you are not measuring
the speed (and all other measurements) to such precision, there is NO
DIFFERENCE between the results of Newtonian and relativistic calculations.

There is NO REASON not to use Newtonian mechanics for everyday purposes.

-- doug foxvog

> John

[John F Sowa]

Alex,

 

In my reply to Avril, I made the point that there is no such thing as an ideal foundation for any major branch of science.  That would be equivalent to claiming that "All fundamental research questions in the field have been asked and answered."

 

The comments you made in your note below add more support for my claim.  Thank you.

 

See my comments on your comments.

---

From: "Alex Shkotin" <alex.s...@gmail.com>
Sent: Sunday, December 26, 2021 4:23 AM

 

Let me add my 5 cents. The foundation of science is an experiment. There are two kinds of experiments: material and mental.

 

JFS:  I agree.  But I would add that every material experiment can be mentally imagined, and the most convincing mental experiments are ones that can be carried out materially.

 

Landau said, "theorists without experimenters are weakening." An experiment in the matter is a repeatable material algorithm with the same result on some level of precision. We use these results to develop useful or nasty technologies or just predict something important or interesting. When we have a theory, primary terms are those for which the creator of theory can't give definitions, just axioms (inherent properties) in the best case.

 

JFS:  I agree.  Nothing in that paragraph conflicts with I wrote.

 

For example, in geology, the concept of the rock sample is primary, as only well educated and experienced person can pick up a small piece of matter as a right representative of the whole rock layer. An experiment is not the only way of cognition, Brodsky said in his Nobel lecture: "There are, as we know, three methods of cognition: analytical, intuitive and the method used by the biblical prophets - through revelation."

 

JFS:  I agree.  Nothing conflicts with what I wrote.

 

The foundation for a particular science is its part of reality (materiality or ideality:-)  And the fundamental question is why the material algorithm is possible?

 

JFS:  I would change "foundation for a particular science" to "foundation for a particular aspect of a science".  You can have a foundation for a particular problem or task or theory.  But nobody can claim that they have a foundation for a major science, such as physics, or chemistry, or biology, or...

On another side, for math foundation, we should just wait a couple of years as "Her goal is to develop a theory of how mathematical knowledge is generated and shared in practice. She will show how mathematical objectivity can be achieved by human, fallible subjects." [1]

 

[1] https://dailynous.com/2021/12/03/philosopher-wins-over-e1-million-grant-for-project-on-mathematical-knowledge/

 

JFS:  But she is *not* developing a foundation for mathematics.  On the contrary, she is beginning a project about how humans do mathematics.  She intends to show that nobody has established a foundation for all of mathematics.  I wish her luck, since she intends to prove my claim that no such foundation can be completed without doing an infinite amount of work.

 

??, 26 ???. 2021 ?. ? 07:57, John F Sowa <so...@bestweb.net>:

[John F Sowa]

Doug,

 

We agree on the issues about theories of science and engineering.  But there are different ways of talking about practice. There is a huge difference between theoretical physics and applications of physical theories in engineering.

 

The most precise and detailed theory that all  physicists can agree on is Quantum Electrodynamics (QED).  But the mathematical computation is enormous, and the amount of precision and detail is impossible to acquire and mostly irrelevant.  Even for quantum effects, non-relativistic theory is sufficient for most practical problems.

 

In fact, most engineering projects use a huge number of inconsistent theories for different aspects of the same problem.  For example, just consider an airplane --- any typical commercial jet in the past 50 years.  The non-quantum, non-relativistic Navier-Stokes equations are far too complex for computing the flow in or around any part of the plane. 

 

Therefore, many different parts of the airflow use very different approximations:  laminar flow over the wings, turbulent flow in certain parts under certain conditions, supersonic flow in the engines, combinations of complex airflow and combustion in different parts of the engine...All those computations with different mutually inconsistent theories must be related to one another and combined.

 

Even larger problems occur in computing the whether flow for the entire globe.  There's a huge number of approximations for a huge number of different conditions at all altitudes and interfaces.

 

By the way, I did take a course in fluid mechanics many years ago.  One thing i remember is an anecdote about a professor of fluid mechanics, who was named, appropriately, Dampier Whettier.   He had an elderly aunt from the Dampier side of the family, who said that she would bequeath her fortune to him on the condition that he change his last name to Dampier.

 

He did so, and adopted the ideal name for a professor of fluid mechanics:  Dampier Whettier Dampier. That;s one of the few factoids I remember from that course, and I rarely get a chance to use it.  The only other factoid is the recommendation for Gaulois cigarettes -- not for smoking, but for wind tunnel experiments.  The Gaulois smoke is so thick that it stays in clearly visible flows in the wind tunnel.  I only knew one person who actually somked Gaulois.  He's dead, of course.

 

John

[Avril Styrman]

JFS: I made the point that there is no such thing as an ideal foundation for any major branch of science.  That would be equivalent to claiming that "All fundamental research questions in the field have been asked and answered." ... I would change "foundation for a particular science" to "foundation for a particular aspect of a science".  You can have a foundation for a particular problem or task or theory.  But nobody can claim that they have a foundation for a major science, such as physics, or chemistry, or biology, or...  

Take physics as an example. Today's physics as a whole does have some foundations that virtually everyone accepts. The clearest example is force as the basic quantity. That is not ideal. But the best we can do is to strive for the ideal. Even if we can never reach it, we still by nature strive for the ideal. Suppose that energy as the basic quantity would lead to huge methodological advances and synergy. Who knows if that would be ideal, but it would still be a lot better.

Another example. Since their birth, relativistic physics and QM have been without common foundations. But this is not a desirable state of affairs. That's why people are trying to unify them. String theory, quantum gravity and supersymmetry have not produced the wanted results thus far. Another path is to replace relativistic physics by a theory that is empirically at least as good, but more unified and understandable, and suffices as a foundation for QM too. Suppose that we would have such a foundation for QM and the domain of relativistic physics. That would not be ideal, but much better than today's situation. Or maybe it could even be ideal with respect to contemporary empirical data, but who knows what kinds of challenges new data/phenomena/perceptions raise. 

In sum, even if nobody can claim that they have a foundation for a major science, such as physics, we still by nature strive for better foundations for as wide domains as possible, whenever the current foundations lead us into irresolvable problems. That's been happening throughout history, and it's an integral part of the progress of science.

Do you agree?

Cheers,

Avril

[Alex Shkotin]

John,

I'm glad we agree on the whole, although we disagree on the details.

And let me add to yours "JFS:  I would change "foundation for a particular science" to "foundation for a particular aspect of a science".  You can have a foundation for a particular problem or task or theory.  But nobody can claim that they have a foundation for a major science, such as physics, or chemistry, or biology, or..."

We have so many different sciences and conglomerates of sciences that it's time to study their structure and interconnection and make the structure of sciences more clear and formal - to formalize sciences :-)

For any formal ontology, the question is from what sciences did you get the terms, and is any formula and text in the ontology approved by an expert of this particular science?

Ideally, we do not create formal ontology separately (silos-ly;-) but embed formal text in one or another scientific text in addition to the math formulae it has already:-)

Alex

вт, 28 дек. 2021 г. в 07:43, John F Sowa <so...@bestweb.net>:

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[Alex Shkotin]

John,

Following your "In fact, most engineering projects use a huge number of inconsistent theories for different aspects of the same problem." let me add that this is another huge story: what kind of theoretical knowledge do we have in technologies? It became more and more simple when we came to the production line and operations.

And as physics is involved let me cite L.D. Landau from [1] which I like so much to translate by Google-trans to English:

"Approximate consideration plays an enormous role in theoretical physics. First of all, perfectly exact laws are approximate, although in the vast majority of cases the accuracy they give is very high. Moreover, there is no requirement for absolute accuracy for physical laws. It is enough if there is some predetermined area of phenomena in which the accuracy of this law satisfies the task at hand. So, we calmly apply Newtonian mechanics to the movement of the shell, although we know not only that this mechanics is not absolutely accurate, but also that we have much more accurate relativistic mechanics at our disposal."

The google-doc is open for comments:-) about English first;-)

Alex

[1] https://docs.google.com/document/d/1AnnRns8DqHQdfcUL-kAOtiyu__K4rKK1ulyLTK-sEzw/edit?usp=sharing 

вт, 28 дек. 2021 г. в 08:58, John F Sowa <so...@bestweb.net>:

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[John F Sowa]

Avril and Alex,

 

 

We have a fair amount of agreement about science and technology. The major disagreement is about the word 'foundation'. Scientists and engineers rarely, if ever, use the word 'foundation' when they are developing a theory or applying one or more theories to an application.

 

People who talk about foundations are usually outsiders -- except for professionals who are actually building stuff out of rock and concrete. Any metaphorical use of the term is usually by amateurs or by professionals in some other field (such as philosophy) who are talking about a science in which they are amateurs.

 

Please find, quote, and cite peer-reviewed publications in science or engineering where you can find the word 'foundation'. If it's not made of rock, concrete, or other hard material, send us the quotation.

 

Avril: Today's physics as a whole does have some foundations that virtually everyone accepts. The clearest example is force as the basic quantity.

 

The word 'force' has different definitions in different theories and applications. Professionals who write precise definitions talk about the symbols, words, axioms, and theorems of a particular theory. But they avoid the word 'foundation' because it has no precise meaning. I suggest that you do the same.

 

Avril: In sum, even if nobody can claim that they have a foundation for a major science, such as physics, we still by nature strive for better foundations for as wide domains as possible,

 

Yes. That's a goal. But every major discovery in every science opens up more questions than it answers. The discoveries about the universe in the past century have opened up an immense amount of new results and theories about them. And every new space launch opens up completely new and unexpected results. Nobody talks about or expects anything anyone could call a foundation.

 

Alex: See https://docs.google.com/document/d/1AnnRns8DqHQdfcUL-kAOtiyu__K4rKK1ulyLTK-sEzw/edit

 

An excerpt from that book by Landau: "the establishment of general laws is possible only on the basis of experimental data, even finding consequences from general laws requires a preliminary experimental study of the phenomena."

 

Note that he talks about "general laws", not "foundations". \The term "general law" can be applied to many different special cases. But no physicist would claim that any law known today will never be revised or extended in the future.

 

By the way, I have a couple of English translations of books by Landau gathering dust in my basement.

 

John

[Alex Shkotin]

John,

Your answer to Doug is not about the foundation. But your email thread - you are the boss. For me, this email thread is not about the foundation, except the title:-)

It is about theories, engineering and practice.

By the way, there is a subtle difference in Google representation of messages in Gmail client and google group client:

-in Gmail I have a new thread beginning [1] and for me, this is a new discussion:-)

-in ontolog-forum google group we are still in the initial discussion [2] started by Azamat:-)

Funny!

John, if you have this particular text of Landau in English it is great! It is short and important to have a professional translation:-)

Alex

[1] 

[2]

вт, 28 дек. 2021 г. в 20:35, John F Sowa <so...@bestweb.net>:

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[Alex Shkotin]

ADDITION: another intriguing topic - unification is to find out the one axiomatic theory for all material phenomena. 

вт, 28 дек. 2021 г. в 20:35, John F Sowa <so...@bestweb.net>:

Avril and Alex,

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[John F Sowa]

Alex,

 

What you're asking for is a universal theory of everything (TOE) from which the answer to every possible question can be computed:

 

AS> ADDITION: another intriguing topic - unification is to find out the one axiomatic theory for all material phenomena.

 

There is noting wrong with having that as a goal.  But you have to remember that every major discovery in every branch of science has always opened up many many more questions than it answered.

 

Furthermore, every major discovery gets into areas that are many times more difficult to observe and measure than the areas that have already been studied -- those areas are always smaller, larger,  or farther away than the areas that have been explored. 

 

And the cost and complexity of the equipment needed to study them is many orders of magnitude more expensive.   Neutrinos, Higgs bosons, and dark matter are the smallest and most elusive things that scientists have been searching for.  But the equipment needed to detect them is huge.

 

Conclusions:

 

1. Don't expect a final TOE within the 21st century -- probably not within the next few millennia, if ever.

 

2. Even if a TOE were discovered tomorrow, we couldn't do anything useful with it for a long, long looooong time, if ever.  Just look at how many new inventions were made that use the Higgs boson:  ZERO.

 

3. It's OK to talk about a TOE at a beer party.  But don't expect  to get anything more useful from that party than beer.

 

John

[John F Sowa]

Alex>Your answer to Doug is not about the foundation. But your email thread - you are the boss. For me, this email thread is not about the foundation, except the title:-) It is about theories, engineering and practice.

 

That is true.  As I have repeated many, many times:  It's impossible for any science (or ontology) to have a solid, stable foundation until every possible theoretical question has been asked and definitively answered.

 

That cannot happen for a long, long, loooong time -- if ever.

 

Therefore, this topic of foundations is irrelevant.

 

That is the best possible statement that can be made about FOUNDATIONS,

 

John

[Alex Shkotin]

John,

You know my task: formalization of existing sciences and technologies. For me, TOE means one more thing to formalize:-)

I just mentioned that unification is the same class of topic as foundation;-) - for beer party :-)

Alex

чт, 30 дек. 2021 г. в 08:34, John F Sowa <so...@bestweb.net>:

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[Avril Styrman]

John, that sounds very strange. Let us first put terminological confusions aside and talk about basic ontological commitments of theories, i.e., about their basic law hypotheses or basic structures. This, inter alia, is what I meant by foundations. 

You are saying that the topic is irrelevant because theories come and go, and their basic law hypotheses go with them. So, we should wait until physics is ready, if ever, until there is sense in talking about basic structures of theories?

On the contrary, we must talk about them now, in order to understand them and their defects, so that we can get forward. Why should we not talk about them?

Cheers,

Avril

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[John F Sowa]

Avril,

 

Thank you for the clarification.  The words 'theory', 'foundation'. and 'ontology' have different meanings in different contexts:

 

Avril:  Let us first put terminological confusions aside and talk about basic ontological commitments of theories, i.e., about their basic law hypotheses or basic structures. This, inter alia, is what I meant by foundations. 

 

The word 'theory' i,s good.    That has a very clear and precise meaning: (1) A set of terms (names of predicates).  (2) A set of axioms (statements in some version of logic that use those terms0. (3) Rules of inference for the logic of #2. Given those three things, a theory is defined as the deductive closure of the axioms (#2) according to the rules of inference (#3).

 

That is a very precise definition.  I suggest that you either dump the word 'foundation' or define it in terms of one or more theories and their interrelationships.

 

Next question:  What do you mean by 'ontological commitment'?    Do you mean the set of terms (#1) of a specific theory?  Or a complete theory with all its implications?  Or a large set of mutually consistent theories that all use the same terms?  If that's what you mean, you could form the conjunction of all those theories into one very large theory.

 

But as we discussed before, every science has a huge number of inconsistent theories, and every large application uses multiple inconsistent theories for a single artifact  (such as a bridge, airplane, automobile, house, road, city...).

 

When you talk about a huge artifact, such as a city, and all the sciences that are relevant to its inhabitants, visitors, workers, etc.,

you get an enormous number of inconsistent theories,

 

Is it even possible to talk about ontological commitment for something so huge and complex?

 

John

[doug foxvog]

On Tue, December 28, 2021 00:59, John F Sowa wrote:
> Doug,

> We agree on the issues about theories of science and engineering. But
> there are different ways of talking about practice. There is a huge
> difference between theoretical physics and applications of physical
> theories in engineering.

John, i agree with everything you say here. Scientists and engineers use
approximations all the time. They select which approximations to use so
that the results are indistinguishable from the more complex equations.
When the difference in results are smaller than the precision of the
measurements, the simplified equations provide just as good results and
with far less computational effort (and computing time).

My only issue with your previous post was your description of the
approximations. You did not make clear that

> non-relativistic theory is sufficient for most practical problems.

That is all.

FWIW, i slightly knew a couple of French Gaulois smokers when i was
working in the EU. I have lost track of them, so i can't say if they are
still alive.

-- doug foxvog

> The most precise and detailed theory that all physicists can agree on is
> Quantum Electrodynamics (QED). But the mathematical computation is
> enormous, and the amount of precision and detail is impossible to acquire
> and mostly irrelevant. Even for quantum effects, non-relativistic theory
> is sufficient for most practical problems.
>
> In fact, most engineering projects use a huge number of inconsistent
> theories for different aspects of the same problem. For example, just
> consider an airplane --- any typical commercial jet in the past 50 years.
> The non-quantum, non-relativistic Navier-Stokes equations are far too
> complex for computing the flow in or around any part of the plane.
>
> Therefore, many different parts of the airflow use very different
> approximations: laminar flow over the wings, turbulent flow in certain
> parts under certain conditions, supersonic flow in the engines,
> combinations of complex airflow and combustion in different parts of the
> engine...All those computations with different mutually inconsistent
> theories must be related to one another and combined.
>

> Even larger problems occur in computing the [weather] flow for the entire

> globe. There's a huge number of approximations for a huge number of
> different conditions at all altitudes and interfaces.
>
> By the way, I did take a course in fluid mechanics many years ago. One
> thing i remember is an anecdote about a professor of fluid mechanics, who
> was named, appropriately, Dampier Whettier. He had an elderly aunt from
> the Dampier side of the family, who said that she would bequeath her
> fortune to him on the condition that he change his last name to Dampier.
>
> He did so, and adopted the ideal name for a professor of fluid mechanics:
> Dampier Whettier Dampier. That;s one of the few factoids I remember from
> that course, and I rarely get a chance to use it. The only other factoid
> is the recommendation for Gaulois cigarettes -- not for smoking, but for
> wind tunnel experiments. The Gaulois smoke is so thick that it stays in
> clearly visible flows in the wind tunnel. I only knew one person who
> actually somked Gaulois. He's dead, of course.
>
> John
>

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[Avril Styrman]

John,

when I talk about ontological commitments of a scientific theory, I mean commitments to existence. Ontological commitments of theory T indicate what exists and how it exists according to T. We can classify ontological commitments of scientific theories into verified and unverified. Verified commitments are commitments to things whose existence has been empirically verified. Univerified commitments may be called metaphysical commitments: they are commitmets to existence of something that has not been thoroughly verified to exist or hold. For instance, universal law hypotheses of theories are metaphysical commitments, as they cannot be thoroughly verified.

There are no overwhelming obstacles for talking about ontological commitments of scientific theories.

Cheers,

Avril

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