Promoting Origination of Noncellular Cognizers

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-04-10 DOI:10.1134/s1054661824010164

Edward Pogossian

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Abstract

According to the hypothesis of abiogenesis, the simplest cellular, uncials, originated from chemical compounds that already existed in nature. Unfortunately, in spite of ongoing intensive research efforts, abiogenesis owns more difficulties and hopes than advances. That is why new hypotheses try to exempt its difficulties. Particularly, successful modeling of cognizing lets us assume that uncials were designed by some cognizers of the Universe, originated in nature as elementary recurrent classifiers, then evolved to attain the power of cognizing, at least, comparable with the highest human one, allowing them to design uncials analogous to the human design of robots nowadays. In parallel, molecular studying assumes that even elementary units of matter are able to communicate through the IDs of classifiers. And since the constituents of uncials are functionally analogous to those of cognizers, while communication is vital for cognizing, it is worth trying to promote the origin of constituents of cognizers by reaching in abiogenesis and communications. Thus, to promote origination of cognizers, we decompose the nuclei of cognizers to constituents, followed by examining the potential impact of constituents of uncials and molecular recurrent classifiers to the origin of functionally analogous ones of cognizers. Then recall algorithms of formation of 1-/2-place classifiers for possible clues to their origination. Finally, address to the origin of dynamicity of the nuclei of cognizers–doers, to trace dynamicity of doers to the dynamics of a variety of cases in sciences as a footstep to more general models.

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促进非细胞认知者的起源

摘要根据 "生物起源 "假说，最简单的细胞--单细胞--起源于自然界中已经存在的化合物。遗憾的是，尽管研究工作一直在深入进行，但 "生物起源 "的困难和希望多于进展。这就是为什么新的假说试图免除其困难。特别是，成功的认知模型让我们可以假定，单词是由宇宙中的某些认知者设计的，它们起源于自然界，是基本的循环分类器，然后进化到至少与人类最高认知能力相当的认知能力，使它们能够设计出类似于人类现在设计机器人的单词。与此同时，分子研究认为，即使是物质的基本单位也能够通过分类器的 ID 进行交流。由于 "分类器 "的成分在功能上类似于 "认知器 "的成分，而交流对于认知至关重要，因此，通过生物起源和交流来促进 "认知器 "成分的起源是值得尝试的。因此，为了促进认知者的起源，我们将认知者的核心分解为成分，然后研究单词和分子循环分类器的成分对功能相似的认知者起源的潜在影响。然后，回顾 1 位/2 位分类器的形成算法，寻找其起源的可能线索。最后，探讨认知者-执行者核心动态性的起源，将执行者的动态性追溯到科学中各种情况的动态性，以此作为通向更一般模型的一个步骤。

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来源期刊

PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.80

自引率

20.00%

发文量

期刊介绍： The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.