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On J. Searle’s «Chinese Room» from the Hybrid Model of the Artificial Cognitive Agents Design

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Abstract

The article presents a review of the phenomenon of understanding the meaning of the natural language and, more broadly, the meaning of the situation in which the cognitive agent is located, considering the context. A specific definition of understanding is given, which is at the intersection of neurophysiology, information theory and cybernetics. The scheme of an abstract architecture of the cognitive agent (of arbitrary nature) is offered, which states that an agent with such architecture can understand in the sense described in the paper. It also provides a critique of J. Searle’s mental experiment “The Chinese Room” from the point of view of the construction of artificial cognitive agents within a hybrid paradigm of artificial intelligence. The novelty of the presented work is based on the application of the author’s methodological approach to the construction of artificial cognitive agents. It not only considers the perception of external stimuli from the environment, but also the philosophical problem of “understanding” by the artificial cognitive agent of its sensory inputs. The relevance of the work follows from the renewed interest of the scientific community in the theme of Strong Artificial Intelligence (or AGI). The author's contribution consists in comprehensive treatment from different points of view of the theme of understanding perceived by artificial cognitive agents. It involves the formation of prerequisites for the development of new models and the theory of understanding within the framework of artificial intelligence, which in the future will help to build a holistic theory of the nature of human mind. The article will be interesting for specialists working in the field of artificial intellectual systems and cognitive agents construction, as well as for scientists from other scientific fields – first of all, philosophy, neurophysiology and psychology.
桌子
... Вместе с тем, в работах Г. С. Осипова дается расширение семантического треугольника Фреге для "тетраэдра", в состав которого включается персональное значение знака для когнитивного агента, получаемое на основе личного опыта [5,6]. Подобные же выводы подтверждаются исследованиями автора [7]. Таким образом, расширенное представление взаимоотношений базовых понятий этой работы -семантический тетраэдр, который показан на рис. 2. ...
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The article describes the author’s approach to solving the problem of symbol grounding, which can be used in the development of artificial cognitive agents of the general level. When implementing this approach, such agents can receive the function of understanding the sense and context of the situations in which they find themselves. The article gives a brief description of the problem of understanding the meaning and sense. In addition, the author’s vision is given of how the symbol grounding should occur when the artificial cognitive agent uses sensory information flows of various modality. Symbol grounding is carried out by building an associative-heterarchical network of concepts, with the help of which the hybrid architecture of an artificial cognitive agent is expanded. The novelty of the article is based on the author’s approach to solving the problem, which is represented by several important principles — these are multisensory integration, the use of an associative-heterarchical network of concepts and a hybrid paradigm of artificial intelligence. The relevance of the work is based on the fact that today the problem of constructing artificial cognitive agents of a general level is becoming more and more important for solving, including within the framework of national strategies for the development of artificial intelligence in various countries of the world. The article is of a theoretical nature and will be of interest to specialists in the field of artificial intelligence, as well as to all those who want to stay within the framework of modern trends in the field of artificial intelligence.
... In general, this scheme really allows us to neutralize the negative aspects of both paradigms. However, to create an artificial general intelligence agent in a hybrid architecture of artificial intelligence, there is a lack of an essential characteristic that the human mind hasunderstanding the situation in which the agent finds itself, and understanding it taking into account the context and personal experience [Dushkin, 2020]. Updating knowledge bases «on the fly» and the ability to choose from several variants of meaning available both in personal experience and in the knowledge of all mankindthese are still unsolved problems of the hybrid paradigm in particular and of artificial intelligence in general. ...
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The article describes the author’s proposal on cognitive architecture for the development of a general-level artificial intelligent agent («strong» artificial intelligence). New principles for the development of such an architecture are proposed — a hybrid approach in artificial intelligence and bionics. The architecture diagram of the proposed solution is given and descriptions of possible areas of application are described. Strong artificial intelligence is a technical solution that can solve arbitrary cognitive tasks available to humans (human-level artificial intelligence) and even surpass the capabilities of human intelligence (artificial superintelligence). The fields of application of strong artificial intelligence are limitless — from solving current problems facing the human to completely new problems that are not yet available to human civilization or are still waiting for their discoverer. The novelty of the work lies in the author’s approach to the construction of cognitive architecture, which has absorbed the results of many years of research in the field of artificial intelligence and the results of the analysis of cognitive architectures of other researchers.
... Moreover, to create an artificial general intelligence agent in a hybrid architecture of artificial intelligence, there is a lack of an essential characteristic that the human mind has -understanding the situation in which the agent finds itself, and understanding it taking into account the context and personal experience [Dushkin, 2020]. Updating knowledge bases «on the fly» and the ability to choose from several variants of meaning available both in personal experience and in the knowledge of all mankind -these are still unsolved problems of the hybrid paradigm in particular and of artificial intelligence in general. ...
Chapter
The article describes the author's proposal on cognitive architecture for the development of a general-level artificial intelligent agent («strong» artificial intelligence). New principles for the development of such an architecture are proposed — a hybrid approach in artificial intelligence and bionics. The architecture diagram of the proposed solution is given and descriptions of possible areas of application are described. Strong artificial intelligence is a technical solution that can solve arbitrary cognitive tasks available to humans (human-level artificial intelligence) and even surpass the capabilities of human intelligence (artificial superintelligence). The fields of application of strong artificial intelligence are limitless — from solving current problems facing the human to completely new problems that are not yet available to human civilization or are still waiting for their discoverer. The novelty of the work lies in the author's approach to the construction of cognitive architecture, which has absorbed the results of many years of research in the field of artificial intelligence and the results of the analysis of cognitive architectures of other researchers. The relevance of the work is based on the indisputable fact that current research in the field of weak artificial intelligence is starting to slow down due to the impossibility of solving general problems, and most national strategies for the development of technologies in the field of artificial intelligence declare the need to develop new artificial intelligence technologies, including Artificial General Intelligence. The work will be of interest to scientists, engineers, and researchers working in the field of artificial intelligence in general, as well as to any interested readers seeking to keep abreast of modern technologies.
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Chapter
The article describes the author’s approach to solving the problem of symbol grounding, which can be used in the development of artificial cognitive agents of the general level. When implementing this approach, such agents can receive the function of understanding the sense and context of the situations in which they find themselves. The article gives a brief description of the problem of understanding the meaning and sense. In addition, the author’s vision is given of how the symbol grounding should occur when the artificial cognitive agent uses sensory information flows of various modality. Symbol grounding is carried out by building an associative-heterarchical network of concepts, with the help of which the hybrid architecture of an artificial cognitive agent is expanded. The novelty of the article is based on the author’s approach to solving the problem, which is represented by several important principles—these are multisensory integration, the use of an associative-heterarchical network of concepts and a hybrid paradigm of artificial intelligence. The relevance of the work is based on the fact that today the problem of constructing artificial cognitive agents of a general level is becoming more and more important for solving, including within the framework of national strategies for the development of artificial intelligence in various countries of the world. The article is of a theoretical nature and will be of interest to specialists in the field of artificial intelligence, as well as to all those who want to stay within the framework of modern trends in the field of artificial intelligence. KeywordsMeaningSenseFrege’s triangleSymbol groundingSemanticsUnderstandingArtificial intelligenceMultisensory integrationAssociative-heterarchical networkHybrid cognitive architecture
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