Strong AI Model, Knowledge Representation Language

1. Theory of Meaningful Information We live in the age of applied sciences. Consider the role of applied biology responsible for the unbelievable progress in medicine or applied physics and applied chemistry, forming the basis of modern industry and modern infrastructure in general. It is nearly impossible to imagine our society without the applications of social sciences like economics, sociology, social psychology, political science, criminology and so on. Frequently ignored, however, is the fact that the boom of applied sciences was only possible because of the fundamental sciences, without which the development of the last 150 years would never have occurred. The only major type of activities remaining without adequate theoretical conceptualization is that which is concerned with meaningful information. While technical aspects of information transmission are effectively covered by the information theory of Claude Shannon, the theory of meaningful information never possessed any relevant theoretical support. Despite more than a hundred years of research devoted to the formalization of meaning, no research dedicated to the subject was ever able to produce an applicable theory having any impact on the practical information-related activities Introduction

2. Theory of Meaningful Information The word “information” is derived from the Latin word “informatio” meaning to form, to put in shape, which designated physical forming as well as the forming of the mind, i.e. education and the accumulation of knowledge. This relatively broad meaning was narrowed down in the middle Ages when information equated into education. It had been continually refined up until the first half of the twentieth century when it was equated with “transmitted message”. Today, various kinds of information have been intensively studied by numerous information-related sciences like communication media, information management, computing, cognitive sciences, physics, electrical engineering, linguistics, psychology, sociology, epistemology and medicine. While every discipline concentrates on specific features of information and ignores others, they produce multiple incompatible approaches making generalizations of the studied subject virtually impossible. This is even more complicated by the widespread practice of defining Meaningful information with the help of other concepts like knowledge and meaning, while simultaneously ignoring related concepts like data, signs, signals. Taking into account that the absolute majority of informational studies are trivial research without any pretension for broad generalizations, it is no wonder that the Information Age ― as often referred to as nowadays ― has failed until now to produce a general information theory despite several ambitious attempts in developing universal approaches. Problem Statement

3. Theory of Meaningful Information The theory was very efficient in solving practical problems of electrical engineering. Though the designation “Information Theory”, under which Shannon’s work is commonly known, is mainly a misnomer spontaneously produced by the scientific community on the surge of popularity of his approach. Shannon himself never referred to his work in such a manner. The name Shannon gave his theory in the first draft in 1940 was “Mathematical theory of communication” and he retained the same name in a publication in 1963 (Claude Elwood Shannon and Weaver 1963). He also deliberately evaded consideration of the semantic characteristics of information stressing the aforementioned publication: “Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem” (Claude Elwood Shannon and Weaver 1963). The citation of Weaver from the same publication “The word information, in this theory, is used in the special sense that must not be confused with its ordinary usage. In particular, information must not be confused with meaning...”. These citations actually define the limits of Shannon’s theory concerned with the processes of information transmission. This theory however showed no interest in studying the main characteristic of information, which is the ability to designate meaning. Information Theory of Claude Shannon

4. Theory of Meaningful Information According to the definition in Wikipedia (“Artificial Intelligence” 2018) “Artificial intelligence (AI, also machine intelligence, MI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. In computer science, AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Though normally this online encyclopedia is not considered as scientifically reliable it is exactly the right choice, in this particular case, because one can await that the site getting circa 10000 clicks everyday probably contains the pretty adequate definition of its subject. The definition made on basis of (Poole, Mackworth, and Goebel 1998, 17; Russell and Norvig 2010; Nilsson 1998; Legg and Hutter 2007), demonstrates two key specifics of the modern AI research; first, AI is understood as the antipode of NI, which is the only form of the true intelligence known at this time; second, the task of explaining the phenomenon of intelligence is completely ignored because the focus of the research is concentrated on the study of the artificial intellectual entities no matter how “intelligent” they in reality are. The Achilles' heel of this approach is that the feature of being intelligent is not a primary characteristic of someone (something) but rather an individual assessment of some observer judging the behavior of a watched thing. Attributing a certain thing with intelligence is exactly the same as attributing it with lightness, softness, bigness or goodness. In its genuine form, it is just an individual opinion assessing some particular feature, like “lightness”: - a thing which light for one person can be very heavy to another The Intelligence Theory

5. Theory of Meaningful Information The fundamental problem hindering understanding of languages is the incompatible approaches to the different language types. Even though easy-to-use-natural-language-like-notation was one of the original reasons for developing the first high level programming languages, the theory and practice of programming never intersected with the linguistics research. The highly mathematized theory of formal language achieved outstanding results in formalizing miscellaneous syntax structures, but was never successful in explaining the nature of the language’s semantics nor the links existing between the syntax and the meaning. Finally, it failed due to its almost total inability to understand the real features of real programming languages and the inability to answer also the most obvious questions like why programmers prefer to compose code in such languages as C++ and Pascal and not in Lisp and Prolog. In lack of a working theory, developers created new formal languages on the basis of miscellaneous practical and theoretical considerations, which however always concentrated on programming and never on language. The only reference to natural languages was restricted to the periodical prophecies about a supposed next programming language generation, which could do everything better by enabling the code to be composed in a natural language (so-called natural language programming). These declarations however were never pursued even by a single attempt to understand why these two kinds of languages are or appear to be that The Universal Representation Language