Alexander R. Povolotsky – Quantitative analysis of knowledge.

Philosophy

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Quantitative Analysis of Knowledge.

Alexander R. Povolotsky

Main Hypothesis:

The general direction in the evolutionary development of nature is governed by nature’s inherent fundamental tendency to cognize itself.

Propositions/Supplementary Hypotheses:

The general direction in the evolutionary development of nature is governed by nature’s inherent fundamental tendency to cognize itself. Approaches for Quantitative analysis of knowledge Hierarchical classification of all terms (nouns) contained in the given human language. The human language contains the knowledge of the world and the laws of its development. The process of evolution did not end when human beings emerged. The evolution itself evolved into the process of discovering knowledge.

Content:

Introductory Statement Hypotheses Elaboration of Main Ideas

Introductory Statement

This document could be considered as some sort of manifest on the subject of quantitative analysis of knowledge. The author asks readers for forgiveness because of the lack of proper terminology in this article. The author hopes that this will not prevent readers from comprehending the disclosed ideas. This paper is somewhat interdisciplinary and scientific pedants call it eclectic… It starts with some philosophical assumptions. In this case, it is that the dominant human activity is seeking knowledge (if the human activity at large from the global perspective is considered.) Originally in the earlier stages of human knowledge seeking activities, the scientific exploration of nature was conducted by individuals possessing the broad knowledge of nature. Later, the field was divided into the strictly outlined domains of studies. This was greatly beneficial and produced effective results – but nowadays this approach hinders the ability to look at some phenomena which does not fit the Procrustean bed of the particular domain at large (synthesis.) The author believes that nowadays the lack of broad interdisciplinary approach is somewhat responsible for the slow-down of the rate in fundamental scientific discoveries during the last fifty years. Also, the narrow specialization approach negatively impacts the creation of new scientific domains. For example, this thesis relates to the Theory of Knowledge but commonly critics classify it as belonging to the already existing Theory of Information. Those two domains, in the author’s view, are related but not the same.

Core Ideas

Supplementary Hypothesis 1:The human language contains the knowledge of the world and the laws of its development.The human language in its structure contains the “hidden / subconscious” knowledge of the world and about the laws of its development. People put this knowledge into the natural language on the subconscious level, without ever consciously realizing that they are doing it. This subconscious mass of knowledge currently is not usable by humans in their activities (they are not aware of its existence.) This knowledge needs to be extracted and deciphered to become usable. This approach is based on the philosophical views that the human intelligence reflects (through the language’s construction) the objective reality. Specifically, when the knowledge (understanding of nature) is absorbed by humans, new lexical attributes get constructed as the reflection of that newly gained knowledge … This thesis suggests one of the methods how to start this “knowledge recovery” – via hierarchically structured object related classification.Supplementary Hypothesis 2: The process of evolution did not end when human beings emerged. The evolution itself evolved into the process of discovering knowledge.

The second, even more fundamental (and more controversial) idea is that the process of evolution did not end when human beings emerged. However, instead of continuing the “biological” evolution, the evolution itself evolved into the process of discovering knowledge. This second idea also makes the subsequent claim (the third idea) that the process of uncovering knowledge is the stimulant behind mankind’s very existence, and that the activity of discovering knowledge is the major human activity imposed on mankind by the law of evolution. The remaining human activities are not essential and play secondary roles which are essential only to the existence of the human race and not to the process of evolution as a whole.

Main Hypothesis:

The general direction in the evolutionary development of nature is governed by nature’s inherent fundamental tendency to cognize itself.

Further, the knowledge being collected by mankind is transparent with regards to its usage and its users and could survive beyond the inevitable: the cessation of mankind’s existence. To say it frankly, humans are just a stage, which came and later will disappear. This is inevitable from the laws of dialectics. Knowledge however, though originally being extracted by humans, will survive its “miners”. This concept could be reduced to the following statement/observation, (which in the author’s opinion describes a scientifically deterministic law, reflecting the objective reality): The major general direction in the evolutionary development of nature is governed by nature’s inherent fundamental tendency to cognize itself. This concept constitutes a materialistic fix to the idealistic German philosopher Hegel’s concept, which is based on the supremacy of the absolute conscious spirit over matter (but it did contain the notion of self-learning – here of course in its direct spiritual sense.) This idea by itself is not new and original and was expressed before, but the author is a true follower of the above and is trying to apply it as a foundation for the feasibility of measuring knowledge. The author in 1973-1978 several times tried to submit above ideas to USSR’s (now Russia) Academic Institute of Philosophy – but they were rejected as being contradicting to the ruling dogma of the Marxist/Leninist dialectical materialism – according to which knowledge, being produced by the regressive/conservative intelligentsia is always subordinate to the materialistic results of the progressive proletarian labor.

Elaboration of Main Ideas

In the author’s opinion, knowledge is the most important product of human activity, though it is perceived mainly to be the derivative of the utilitarian need to improve the material standard of life, improve productivity, ease physical labor efforts, and satisfy human curiosity. e author, of course, is not claiming to be the first who believes in the existence of extraterrestrial intelligence in the universe beyond Earth, but this fits very well into author’s line of thought, so this idea is referenced here. Actually, in the afterthought, even if Earth’s experience of nature’s development is entirely unique and occurred in spite of all odds, knowledge nevertheless still reflects the laws of the objective reality and therefore, there should exist an objective method of the quantification / measurement of knowledge. This philosophical concept is nowadays partially proved by the clear separation of knowledge from its original producer (particular scientist/human being) as far as the storage of knowledge is concerned: brain->book->computer database->ROM/firmware. This represents the clear separation further from its original “receiver”. The trend of knowledge separation and taking on its own existence will eventually show itself in the area of its usage. One might think of expert systems, robots and artificial intelligence, naturally, as the next stage in that direction. The bottom line: knowledge has it is own value beyond the human need and is, in its purified logical/mathematical form, transparent (in nature) with regards to its potential producer and user. Knowledge, therefore, is the fundamental attribute of nature, which, like time, has the global overall trend of being unidirectional (its volume is always increasing.)The true knowledge, in its final instance is universal and absolute (complete) since it describes the universal objective laws of matter, which are (we believe) the same across the entire universe. The comparison of knowledge vs. information as well as the possibility of quantification/measurement of knowledge is substantially different, than some collection of bits/bytes of information (which may or may not contain any fundamental knowledge.) To repeat myself, as far as knowledge quantification and measurement ideas are concerned, those ideas came as a logical derivation of the author’s prime philosophical concept as described above. If the author’s assumptions regarding objective law are correct, there should be an objective method of the quantification/measurement of knowledge. However, finding/defining the practical approach to such measurement is a difficult and may not be achievable at all. The author was thinking to try it first on the empirical level using some relative comparative means – that is why the author was thinking to start with comparing the math equations for the 2nd Newton’s Law of Mechanics vs. the improved one in Einstein’s Special Theory of Relativity.) The mathematical expressions of physical laws need to be examined for this purpose. Perhaps it is possible to apply the Theory of Units and the concept of modeling or the empirical analysis of using Criteria by converting the equations into unit less form (see for example the definition of the Reynolds criteria to evaluate / distinguish of Laminar, Mixed and Turbulent flow in liquids; another criteria is Mach’s number, etc…) So far the author was not able to produce any viable method in applying the above mentioned approaches towards the quantification or measurement of knowledge. In addition to the previously described empirical approach in measuring relative delta knowledge contained in the mathematical formulations of physical laws, the author also looked into a different approach for mining textually expressed knowledge, based on the lexical analysis. This approach is based on the philosophical views that the human intelligence reflects (through the language construction) the objective reality. Specifically, when the knowledge (understanding of nature) is absorbed by humans, new lexical attributes get constructed as the reflection of that newly gained knowledge … Below this thesis proposes the method of retrieving this raw knowledge, being captured in the language terminology, via the means of the hierarchical classification of all terms (nouns – see below), contained in the given human language (say English Language as most scientifically common (though it would be very valuable to do it for several languages and compare results.) The suggested approach is based on the verb <-> noun grouping and is stolen by me from the Software Object Oriented representation of the class (object.) In this particular adoption of the OO, the terms (nouns) are analogous to the object’s data and the verbs are analogous to the methods (aka functions), which could be applied to (performed on) the data. Suppose for each term (noun) available in the language, we would gather the set of all verbs, which could be applied to the given term (noun.) Then we could compare each generated (per above description) set against all other sets (separately on one-to-one basis) to find whether some sets of verbs could share the common subsets. Then we could attempt to detect whether some sets were derived from the other sets so we would be able to build the hierarchical trees of such related sets. Each set is corresponding to a unique noun as it was described above so the trees are built around nouns due to their one-to-one unique relationship with the given specific set of verbs. Actually, the nodes of the trees should contain the nouns (rather than the sets of their verbs.) The top node of such a tree would contain the set (actually uniquely corresponding to its noun), which would contain just the common subset of the verbs or the minimum number of verbs. Such a noun with the minimum set of verbs has the highest level of the abstraction in the given tree. To complicate the picture the two (or more) nodes, which belong to two different trees, may act as parent nodes to generate the child node (Multiple Inheritance), etc… Further, some quantification of the abstraction value could be applied to each distinct tree – the most bottom node should have the abstraction value set to zero and for each next higher level the abstraction value should be incremented by one. Note that this method DOES NOT verify the truthfulness of the statement (it assumes that it is true.) The author’s unsolved dilemma is: should the more abstract level correspond to the higher value of knowledge or the more detailed level should be the higher level of knowledge – or should be there some trade off (optimum?)

Alexander R. Povolotsky

  1. At The Dawn of The Twentieth Century
  2. Protected: The eternal curse!
  3. Generalizing identities for Pi & its convergents
  4. О доступе к вкушению плодов древа знаний
  5. Истории моей эмиграции
  6. Большой Театр открылся после Перестройки
  7. Пролетает над грешной Землёй метеор
  8. Когда в Раю случился первородный грех
  9. My resume
  10. Глупцу
  11. Вновь про талисман
  12. Корабль мечты
  13. Иллюзий хрупких юности узорчатый рассыпался витраж
  14. Древо жизни
  15. Мертво искусство без души
  16. Strikes heavy bombing roar rumbling bass
  17. Россия – расстались навсегда с тобой евреи…
  18. Никто не учится увы истории урокам
  19. Alexander R. Povolotsky – Approximate identities based on linear combinations of symbolic constants
  20. Alexander R. Povolotsky – Quantitative analysis of knowledge.
  21. Review of “Newton’s Laws – The Concepts”
  22. My discussion with Ray Solomonoff
  23. BBP identity yielding rational number
  24. Alexander R. Povolotsky – Logarithm, Pi related and other identities
  25. Entire English language poetry collection
  26. Pi approximations by roots of Diophantine equation
  27. Re-rise of Islamism & decline of Western Judo-Christian Civilization
  28. Who was driving the *show* during the collapse of the Soviet Union (USSR)?
  29. Sonnets (in Russian)
  30. В подражанье к Тютчеву (lyrics in Russian)
  31. Уже погасли фонари
  32. Вчера я видел сон (lyrics in Russian)
  33. Вина бокал (Lyrics in Russian)
  34. Ramanujan constant related stuff
  35. Extending Braille notation for math, physics and chemistry ?
  36. Entire poetry collection
  37. Alexander R. Povolotsky – BBP formula for Pi in a slight disguise
  38. Alexander R. Povolotsky -Three hard to prove conjectures
  39. “This guy”
  40. Infinite sums for Euler number (Napier’s constant) & its roots
  41. Alexander R. Povolotsky – Poetical improvisation on Pushkin’s ‘Egyptian Nights’
  42. Alexander R. Povolotsky – Formulas on k-folded sums of powers.
  43. English translation of the “Unsmiling Tsarevna (Nesmeyana)” song
  44. Napoleon vs Russia
  45. USSR’s demise & dissolution caused 4 fold Dow-Jones growth
  46. Brief rhymed reviews for selected books
  47. “Young And Tender Love Forever”
  48. The quotes, which I made up
  49. My English translation of A. S. Pushkin’s “The Night”, which he wrote in 1823
  50. Do you love or just want to be loved ?
  51. Моя остановка
  52. My Jewish People, Israel – I am your proud son !
  53. Besame Mucho Juanita, the Mexican girl
  54. Stepp but stepp around
  55. Please now look at me
  56. New shocking findings on the Beauty and the Beast ;-)
  57. On Love and Poetry
  58. There was a girl
  59. To YOU
  60. “Ошибся я, ошиблась ты”
  61. On USSR/Russia history – brief notes
  62. The bouquet of roses
  63. “Посмотри на небе звёзды горят”
  64. The mirror (in Russian)
  65. Стареющий певец
  66. variation on Goethe’s
  67. Lyrics – for the song (in Russian)
  68. Слова любви
  69. I don’t want to read your book
  70. Я стою на краю
  71. The ballad about the king, his daughter and her young lover
  72. Два имени (two names)
  73. Много лет тому назад
  74. variation on Goethe’s (in Russian)
  75. The mirror (lyrical poetry – in Russian)
  76. Jipsy motives – in Russian
  77. Из камня слова моего (lyrics in Russian)
  78. Ушли любовь, страданья, муки (lyrics in Russian)
  79. Бесцельна жизнь
  80. My tribute to Omar Khayyam