From the Publisher: In this age of DNA computers and artificial intelligence, information is becoming disembodied even as the "bodies" that once carried it vanish into virtuality. While some marvel at these changes, envisioning consciousness downloaded into a computer or humans "beamed" Star Trek-style, others view them with horror, seeing monsters brooding in the machines. In How We Became Posthuman, N. Katherine Hayles separates hype from fact, investigating the fate of embodiment in an information age. Hayles relates three interwoven stories: how information lost its body, that is, how it came to be conceptualized as an entity separate from the material forms that carry it; the cultural and technological construction of the cyborg; and the dismantling of the liberal humanist "subject" in cybernetic discourse, along with the emergence of the "posthuman." Ranging widely across the history of technology, cultural studies, and literary criticism, Hayles shows what had to be erased, forgotten, and elided to conceive of information as a disembodied entity. Thus she moves from the post-World War II Macy Conferences on cybernetics to the 1952 novel Limbo by cybernetics aficionado Bernard Wolfe; from the concept of self-making to Philip K. Dick's literary explorations of hallucination and reality; and from artificial life to postmodern novels exploring the implications of seeing humans as cybernetic systems. Although becoming posthuman can be nightmarish, Hayles shows how it can also be liberating. From the birth of cybernetics to artificial life, How We Became Posthuman provides an indispensable account of how we arrived in our virtual age, and of where we might go from here.
Современная научная деятельность характеризуется экспоненциальным ростом объема публикаций и данных, что создает значительные трудности в систематизации, анализе и интерпретации информации. В этих условиях технологии искусственного интеллекта (ИИ) становятся ключевым инструментом автоматизации процессов научного исследования. В статье рассматриваются современные подходы к применению методов машинного обучения, глубоких нейронных сетей и обработки естественного языка (NLP) для анализа научной литературы, выявления скрытых закономерностей, генерации гипотез и планирования экспериментальной работы. Особое внимание уделено практическим примерам применения ИИ в биоинформатике, химии, медицине, физике и компьютерных науках, а также анализу ограничений, связанных с интерпретируемостью моделей, надежностью выводов и соблюдением этических норм. Обсуждаются перспективы развития гибридных систем, обеспечивающих совместную работу человека и ИИ, и возможности повышения аналитических компетенций исследователей в условиях цифровизации науки.
PurposeThis bibliometric mapping study aimed to provide comprehensive insights into the global research landscape of cybernetics. Utilizing the biblioshiny function in R Studio, we conducted an analysis spanning 1958 to 2023, sourcing data from Scopus. This research focuses on key terms such as cybernetics, cybernetics systems, complex adaptive systems, viable system models (VSM), agent-based modeling, feedback loops and complexity systems.Design/methodology/approachThe analysis leveraged R Studio’s biblioshiny function to perform bibliometric mapping. Keyword searches were conducted within titles, abstracts and keywords, targeting terms central to cybernetics. The timespan, 1958–2023, provides a comprehensive overview of the evolution of cybernetics-related literature. The data were extracted from Scopus to ensure a robust and widely recognized source.FindingsThe results revealed a rich and interconnected global research network in cybernetics. The word cloud analysis highlights prominent terms such as “agent-based modeling,” “complex adaptive systems,” “feedback loop,” “viable system model” and “cybernetics.” Notably, the journal Kybernetes has emerged as a focal point, with significant citations, solidifying its position as a key source within the cybernetics research domain. The bibliometric map provides visual clarity regarding the relationships between various concepts and their evolution over time.Originality/valueThis study contributes original insights by employing advanced bibliometric techniques in R Studio to map the cybernetics research landscape. The comprehensive analysis sheds light on the evolution of key concepts and the global collaborative networks shaping cybernetics research. The identification of influential sources, such as Kybernetes, adds value to researchers seeking to navigate and contribute to the dynamic field of cybernetics. Furthermore, this study highlights that cybernetics not only provides a useful framework for understanding and managing major economic shocks but also offers perspectives for understanding phenomena in various fields such as economics, medicine, environmental sciences and climate change.
Bianca Cibu, Camelia Delcea, Adrian Domenteanu
et al.
In this study, we undertake a comprehensive bibliometric analysis of the cybernetics research field. We compile a dataset of 4856 papers from the ISI Web of Science database spanning 1975–2022, employing keywords related to cybernetics. Our findings reveal an annual growth rate of 7.56% in cybernetics research over this period, indicating sustained scholarly interest. By examining the annual progression of scientific production, we have identified three distinct periods characterized by significant disruptions in yearly publication trends. These disruptions have been thoroughly investigated within the paper, utilizing a longitudinal analysis of thematic evolution. We also identify emerging research trends through keyword analysis. Furthermore, we investigate collaborative networks among authors, their institutional affiliations, and global representation to elucidate the dissemination of cybernetics research. Employing n-gram analysis, we uncover diverse applications of cybernetics in fields such as computer science, information science, social sciences, sustainable development, supply chain, knowledge management, system dynamics, and medicine. The study contributes to enhancing the understanding of the evolving cybernetics landscape. Moreover, the conducted analysis underscores the versatile applicability across various academic and practical domains associated with the cybernetics field.
We believe that the ongoing global pandemic has highlighted the need for comprehensive approaches to address issues that transcend geographical and cultural boundaries. Therefore, this article aims to provide a general but abstract review to allow readers of a broad spectrum to learn the basic principles of three related concepts: systems, cybernetics, and complexity. Additionally, to better exemplify these concepts, we offer a review of works from the last decade that use systems theory, complexity, and cybernetics for their development. In this context, the result of this review will allow for breaking down the barriers of reductionist silos of knowledge and fostering a multidisciplinary and interdisciplinary dialogue.
“Molecular Cybernetics” is an emerging research field aiming the development of “Chemical AI”, artificial intelligence with memory and learning capabilities based on molecular communication. It is originated from “Molecular Robotics,” which studies molecular systems that comprise of the three basic elements of robots; Sensing, Planning, and Acting. Development of an Amoeba-type molecular robot (unicellular artificial cell,) motivated the construction of multicellular artificial cell systems mimicking nerve systems. The major challenges in molecular cybernetics are molecular communication over two lipid-bilayer compartments, amplification of molecular information in a compartment, and large deformation of the compartment triggered by molecular signal, etc. Recently reported molecular devices and systems that contributes to the realization of Chemical AI are overviewed.
Plasticity in animal behaviour relies on the ability to integrate external and internal cues from the changing environment and hence modulate activity in synaptic circuits of the brain. This context-dependent neuromodulation is largely based on non-synaptic signalling with neuropeptides. Here, we describe select peptidergic systems in the Drosophila brain that act at different levels of a hierarchy to modulate behaviour and associated physiology. These systems modulate circuits in brain regions, such as the central complex and the mushroom bodies, which supervise specific behaviours. At the top level of the hierarchy there are small numbers of large peptidergic neurons that arborize widely in multiple areas of the brain to orchestrate or modulate global activity in a state and context-dependent manner. At the bottom level local peptidergic neurons provide executive neuromodulation of sensory gain and intrinsically in restricted parts of specific neuronal circuits. The orchestrating neurons receive interoceptive signals that mediate energy and sleep homeostasis, metabolic state and circadian timing, as well as external cues that affect food search, aggression or mating. Some of these cues can be triggers of conflicting behaviours such as mating versus aggression, or sleep versus feeding, and peptidergic neurons participate in circuits, enabling behaviour choices and switches.
Research on so‐called “chemical artificial intelligence” (CAI) is an emerging field with the aim of constructing information‐processing systems with learning capabilities based on chemical methodologies. This can be regarded as an attempt to reconstruct Cybernetics using molecular based systems. Many chemical reaction systems with computational abilities are proposed, but most are fixed functions that deliver molecular output for a given molecular input. On the other hand, chemical AI is a system with learning capability; namely, the output should be variable and gradually change upon repeated molecular inputs. In this paper, a compartmentalization approach for implementing cellular chemical AI using liposomes is discussed. The existing studies in terms of the methods used for assembling systems consisting of many liposomes with different functions, methods for achieving recursiveness and plasticity in chemical reaction systems, and methods for reconfiguring the network topology by liposome deformation are reviewed. Issues that must be addressed in order to realize chemical AI are also identified.
The article considers the problem of finding the optimal on average control of the trajectories of continuous stochastic systems with incomplete feedback. This class of problems includes control problems in which the initial states are described by a given distribution law; random effects on the control object are taken into account; and it is also assumed that information is available only about some coordinates of the state vector. As special cases, the problems of determining the optimal open-loop control and control with complete feedback in the presence of information about all state vector coordinates are considered. A method for parameterization of the control law based on expansions in various systems of basis functions is described. The problem of parametric optimization obtained is solved using a new metaheuristic multi-agent algorithm based on the use of extended PID (Proportional-Integral-Derivative) controllers to control the movement of agents. Solutions of three model examples of control of nonlinear continuous stochastic systems with interval constraints on the amount of control for all possible cases of state vector awareness are presented.