Gardner’s theory of multiple intelligences has been further developed to focus on the research of human cognitive activities. Thus, the concept of emotional intelligence, which is the topic of the current paper, was introduced by John D. Mayer, Peter Salovey and Daniel Goleman. General intelligence can be defined as the capacity to carry out abstract reasoning to understand meanings, to recognize the similarities and differences between two concepts and to make generalizations. Emotional intelligence is not a part of general intelligence. Emotional intelligence can be defined as an ability of a human to perceive oneself and interact with others with the help of obtained and processed emotional information. Language acquisition is mediated by the necessity to communicate with others. Consequently, the ability to manage the process of communication is of utmost importance in learning a language. Virtual learning environment reduces dramatically the immediate interaction of the participants of the process of education. It undoubtedly affects the process of acquisition and demands to reconsider the distribution of different learning activities.
The article considers modern transformations of the ideas concerning subject’s cognitive abilities towards object because of the emergence and development of artificial intelligence (AI) technologies. The developments of scientists and engineers from National Research University of Electronic Technology (Moscow, Russia) in the field of artificial intelligence have been taken as a foundation and material of this research. Their analysis allows making a conclusion that the humanity is rather far from the realization of ‘strong artificial intelligence’. We need a qualitative breakthrough in AI material and technological basis for it. Applying ‘model-dependent realism’ by S. Hawking in the course of the speculations in the article, the author concludes that the purpose of cognition in modern science is not to describe an objective reality, but to organize its subjective perception by some definite way. So, the purpose of cognition is the perfection of cognition models permitting a subject ‘to capture’ the reality. This idea is completely confirmed by Maturana and Varela’s autopoiesis theory and von Foerster’s ‘second-order cybernetics’. The article drafts some prospects of moving away from post-nonclassical scientific thinking in its current understanding, because the development of AI technologies results in ‘blurring’ borders of subjectivity in the cognition process. The author concludes that the modern world is moving in another epistemological paradigm where a new scientific revolution is inevitable.
The development of the mind follows the path of biological evolution towards the accumulation and transmission of information with increasing efficiency. In addition to the cognitive constants of speech (Solntsev, 1974), which greatly improved the transmission of information, people have created computing devices, from the abacus to the quantum computer. The capabilities of computers classified as artificial intelligence are developing at a rapid pace. However, at the present stage, artificial intelligence (AI) lacks an emotion module, and this makes AI fundamentally different from human intelligence, since the life of the mind in humans cannot be separated from their feelings (Damasio, 2010; Panksepp, 1997). Consciousness itself is formed through the sensory and motor systems, that is, it is embodied (Foglia & Wilson, 2013), which means that our mental life is inseparable from our sensory motor experience (Wellsby & Pexman, 2014). Evolutionarily, our minds rely on ancient survival mechanisms that influence our decisions and choices. Hence, for example, the question whether the choice of Artificial Intelligence will always be favorable for humanity.
The paper aims to discuss the results of testing a neural network which classifies the vowels of the vocalic system with the [ATR] (Advanced Tongue Root) contrast based on the data of Akebu (Kwa family). The acoustic nature of the [ATR] feature is yet understudied. The only reliable acoustic correlate of [ATR] is the magnitude of the first formant (F1) which can be also modulated by tongue height, resulting in significant overlap between high [-ATR] vowels and mid [+ATR] vowels. Other acoustic metrics which had been associated with the [ATR], such as F1 bandwidth (B1), relative intensity of F1 to F2 (A1-A2), etc., are typically inconsistent across vowel types and speakers. The values of four metrics – F1, F2, A1-A2, B1 – were used for training and testing the neural network. We tested four versions of the model differing in the presence of the fifth variable encoding the speaker and the number of hidden layers. The models which included the variable encoding the speaker achieved slightly higher accuracy, whereas the precision and recall metrics of the three-layer model were generally higher than those with two hidden layers.
The functioning of a subject in a changing environment is most effective from the point of view of survival if the subject can form, maintain and use internal representations of the external world for decision-making. These representations are also called reflection in a broad sense. Using it, one can win in reflexive games since an internal representation of the enemy allows predicting their future moves. The goal is to assess the reflexive potential of heuristic model objects – artificial neural networks – in the reflexive games “Even-Odd” (or “Matching pennies”) and “Rock-Paper-Scissors”. We used homogeneous fully connected neural networks of small sizes (from 8 to 45 neurons). Games were played between neural networks with different configurations and parameters (size, step size for modifying weight coefficients). A set of reflexivity criteria is presented, corresponding to different levels of consideration: neuronal, behavioral, formal. The transitivity of formal success in the game is shown. The most successful configurations, however, may not meet other criteria of reflexivity. We hypothesize that the best compliance with the criteria and, as a consequence, universal success in reflection tasks is achievable for heterogeneous configurations with a structure in which the formation of hierarchical systems of attractors is possible.
This article is devoted to the study of the peculiarities of the functioning of the connection between the brain and the body, the analysis of possible disturbances in the transmission of information impulses from the brain to the body and consideration of the causes of distortion and defects in the implementation of this connection. The specifics of the connection between the brain and the body are considered from the point of view of various areas: neurophysiological, anatomical, psychological. Examples of communication distortion and reasons for incorrect transmission of the reaction are given. An analysis of all possible factors influencing the implementation of the connection under consideration is carried out, various types of violations are listed, and methods for correcting these violations are also presented. The result of this study is the structuring of information about the functioning of the connection between the brain and the body, consideration of all special cases of defectiveness of this connection and an explanation of the reasons for their occurrence. The article will be of practical benefit to specialists in the field of neurobiology.
The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills.
Technology has advanced significantly over the past decades. Significant changes have occurred in the field of translation with the development of programs such as Google.translate and Yandex.translator. The presented applications are already being actively implemented in translation agencies to optimize translation activities, where written translations of documents, articles, annotations, etc. must be provided to customers as quick as possible. While working with popular science text, online programs help translators gain time, but this requires to edit the text. The artistic style requires more concentration and dedication, because. the means of expression presented in it require taking into account the context and nuances of the use of certain units of language. Machine translation has the potential to become an indispensable assistant in the hands of a translator. This article discusses machine translation of expressive means, namely metaphors. The study is illustrated with examples and a comparative analysis of the translation of metaphorical units is carried out, the classification of metaphors is identified and an analysis is carried out with which means of expression the translator is able to cope with. The difficulties of translating metaphors are analyzed.
The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector. . It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one.
Science as a social institution today is experiencing a phase of profound transformation. Objects, methods, research technological tools, methods of institutional communication and mechanisms for commercializing new knowledge are changing. The creation of new interdisciplinary communication platforms is more relevant today than ever before. This review pro[1]vides key information about the First Conference «Mind, Body, Intelligence, Language in the Age of Cognitive Technologies». The organizers created an event that brought together IT developers, academic researchers, and business representatives.
