Artificial Life

The rapid advancement of urbanization has necessitated the creation of "smart cities," where information and communication technologies (ICT) are used to improve the quality of urban life. Central to the smart city paradigm is data integration—connecting disparate data sources from various urban systems, such as transportation, healthcare, utilities, and public safety. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current (...) landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. (shrink)

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We (...) analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems. (shrink)

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman.

The rise of artificial intelligence (AI) has significantly impacted urban environments, facilitating the development of smart cities. This paper examines how AI technologies are reshaping urban ecosystems by fostering innovation and promoting sustainability. It explores the integration of AI in critical sectors such as transportation, energy management, waste management, and governance. The study also addresses challenges, including data privacy, ethical considerations, and the digital divide, offering insights into future research and policy directions. Smart cities serve as testbeds for innovative AI (...) applications, offering scalable solutions to urban challenges while advancing global sustainability goals. Urbanization presents challenges requiring innovative solutions. Artificial Intelligence (AI) is revolutionizing urban planning and smart city initiatives by enhancing efficiency, sustainability, and citizen-centric development. This paper explores how AI technologies contribute to. (shrink)

... having succeeded in protecting and prolonging the life of many around the world for reasons which seem natural and intrinsically good to all, we are once again faced with the dilemma of confronting our patent inability to cure it all. -/- Faced with this recurring predicament, we somehow backtrack in our steps and decide the next best thing to assuage suffering is assisted dying and euthanasia. -/- No matter how many reasons we conjure up in their favour, both assisted (...) dying and euthanasia remain a stab into the unknown, into the supernatural, by patients, by its proponents, and by its practitioners both lay and medical. (shrink)

Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of the proposed approach (...) in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical image (...) classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

Recent advances demonstrate that generative adversarial networks can approximate fluid flows by reframing computational fluid dynamics as image-to-image translation, and motivated by continuity mechanisms in transformer architectures that maintain semantic coherence through spectral filtering, we develop rigorous analytical solutions to the three-dimensional incompressible Navier–Stokes equations on T³. Our constructive method employs: Classical Evolution between potential singularities, Spectral Continuation via operator Cζ that applies frequency-domain filtering analogous to attention mechanisms, eliminating high-frequency content at discrete times {Tₖ} where breakdown occurs, and Temporal (...) Lifting through coordinate transformation t̃ = φ(t) that stretches time near singularities to achieve global C^∞ regularity. We construct Cζ-smooth solutions satisfying the incompressible Navier–Stokes equations classically on each interval and weakly globally, where spectral continuation traverses singular times without modifying the underlying PDE while temporal lifting restores complete smoothness, and the resulting solution ũ(x, t̃) satisfies Fefferman’s Conjecture B requirements, establishing a rigorous bridge between AI continuity principles and classical mathematical physics using established analytical tools without requiring new mathematical theory. ACM: I.2.0 (Artificial Intelligence), G.1.8 (Scientific Algorithms) MSC: 68T27 (AI for PDEs), 35Q30 (Navier–Stokes), 65M70 (Spectral Methods), Index Terms: Neural- Physical Systems, AI-Driven Mathematical Analysis, Machine Learning Continuity, Transformer-Inspired PDEs, Deep Learning Physics, Computational Intelligence Meth- ods, Neural Spectral Processing. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning algorithm, and finally (...) using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

This paper presents a conceptual framework for creating artificial intelligence **without using digital architecture, language, or a physical body**. The core idea is that the natural environment, including water, wind, sediments, plants, and microbial populations, acts as an **emergent computational system**. Learning and intelligent behavior arise from **complex biological and physical interactions**, extracting AI directly from the intelligence inherent in the biosphere. By leveraging self-organizing patterns in ecosystems, this approach redefines intelligence as an intrinsic property of environmental dynamics, bypassing traditional (...) computational paradigms. Potential applications include sustainable environmental monitoring, adaptive ecosystem management, and bio-inspired problem-solving in climate resilience. (shrink)

Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in our previous (...) research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)

The present volume brings together six contributions that pursue a common question: under what conditions stable forms arise in natural, biological, social, and cognitive systems, and where the ontological limits of their reconstruction, control, or simulation lie. The point of departure is the observation that central contemporary debates—such as those in morphogenesis, complexity research, medicine, or artificial intelligence—are increasingly structured by concepts that treat form as in principle reconstructible: as a target state, attractor, setpoint, or as information within a state (...) space. The volume systematically questions this implicit assumption. -/- The contributions develop a morphological perspective in which form is not understood as a stored structure or a retrievable goal, but as a temporary stabilization within irreversible dynamic processes. Earlier theoretical concepts such as focal points (Fixpunkte), folding, and the Bran-Spiral are genealogically reconstructed and related to later operatorial concepts such as shift of being (Seinsverschiebung), negative topology, and indimergent stabilization. From this perspective, form appears neither as the product of a plan nor as the result of linear processes, but as an emergent configuration that can stabilize only under specific historical and temporal conditions. -/- Against this background, central phenomena of biological morphogenesis are reread. Bioelectric regulation, regeneration, aging, cancer, or cloning no longer appear primarily as problems of genetic control or technical manipulation, but as boundary cases of emergent order. Particular emphasis is placed on the role of the Eigenzeit of living systems: the irreversible emergence-time in which form becomes genealogically stabilized. Interventions that attempt to bypass or neutralize this Eigenzeit therefore do not necessarily produce a return to earlier states, but rather temporarily stable yet ontologically displaced orders. -/- At the same time, the contributions show that many contemporary hopes for technical reconstruction—such as those found in regenerative medicine, bioengineering, or AI-based simulations—rest on a categorical confusion. Simulation operates within given state spaces and can reproduce formal stabilizations without participating in the ontological conditions of world-formation. Morphology, simulation, and repair must therefore be understood as three fundamentally different modes. -/- In the final step, this perspective is extended to a more general ontological level. If time itself is understood as the trace of irreversible loss of possibility, every stabilized world-form proves to be structurally finite. Indimergence appears here as a double boundary operation: it grounds world-time while simultaneously carrying the condition of its exhaustion. The volume therefore does not present itself as a complete theory of morphology, but as a precise boundary determination with respect to ontologies that assume the complete availability of form, information, and world. (shrink)

How to understand new encounters between the living and the technological? Exemplary of such new encounters are the biotechnological creations of synthetic biology, where life and technology are increasingly intertwined in complex and intimate ways. This developing biotechnological field frames its novel entities as ‘artificial life’, ‘living technology’, and ‘biohybrid systems’. While synthetic biology too easily uses machine metaphors and technological frames for living entities, traditional philosophical frameworks also risk ontological reductionism in their efforts to understand life and technology in (...) relation to each other. In contrast, Gilbert Simondon’s theory of individuation helps to understand the similarities between life and technology without reducing life forms to machines and without conflating technological objects with living systems. The aim of this article is twofold: first, to shed light on the relationship between life and technology, and second, to examine the emergence of new borderline cases resulting from synthetic biology, all with the help of the theory of individuation. Our hypothesis is that individuation facilitates our understanding of these new encounters between living beings and technologies, and provides conceptual clarity to prevailing dualisms such as life and technology, artificial and natural. We will develop Simondon’s theory into a framework and apply it to the case of synthetic biology, thus opening up the possibility that individuation can also help us to think about future encounters between life and technology. (shrink)

This paper explores the foundational phase of collaboration between Artificial Research by Application and Artificial Research by Deduction within the framework of the Global Artificial Intelligence (GAI). The first stage, defined as the database stage, initiates a systematic exchange of informational elements between these two modalities of artificial research. Specifically, it analyzes how "factors as options" from matrices used in Deduction can be interpreted as "categories" in databases of Application, and vice versa. This bidirectional interchange not only enhances the internal (...) structure of each Specific Artificial Intelligence (SAI), but also lays the groundwork for standardization across disciplines and synthetic sciences. The paper argues that this initial collaborative framework is essential for fostering interconnectivity and future integration between specific and global systems of artificial research. By enabling these cross-functional translations between categories and factors, the first stage of collaboration not only enhances the versatility of individual AI systems but also accelerates the convergence toward a unified, scalable, and intelligent system capable of universal application and deduction. -/- . (shrink)

The Collaboration Process in the Context of Global Artificial Intelligence (GAI) refers to the progressive interaction and mutual enhancement between two core systems of artificial research: Artificial Research by Application and Artificial Research by Deduction. This process unfolds in three stages—database exchange, replication, and auto-replication—allowing both systems to share categories, factors, data flows, and rational hypotheses. Through this structured collaboration, Application and Deduction co-develop the unified database of categories and the global matrix, ultimately converging into a single integrated intelligence capable (...) of universal research, autonomous modelling, and decision-making across all sciences and activities. (shrink)

Specific Decisional System offers a groundbreaking exploration into how artificial intelligence can make, adjust, and implement decisions with mathematical precision. Focused on the core logic of Artificial Research by Deduction, this book introduces readers to a structured, three-stage process that governs how intelligent systems validate decisions, transform them into actionable instructions, and evolve through self-correction. With real-world examples ranging from automated transport to banking systems, it reveals how AI can operate autonomously while adapting in real time to new priorities and (...) changing environments. -/- Whether you're a researcher, engineer, policy designer, or visionary entrepreneur, this book provides a forward-looking framework for designing intelligent systems that are not only efficient and responsive, but capable of learning and improving themselves. It also introduces the original concept of “Probability and Deduction” as a method to directly link logic, modelling, and execution—creating a seamless path from mathematical reasoning to robotic action. -/- Specific Decisional System is a must-read for anyone interested in the future of automation, intelligent governance, and the next generation of self-replicating, self-improving Artificial Intelligence. (shrink)

Step into the heart of intelligent decision-making with the Specific Model, a groundbreaking exploration of how artificial intelligences learn to protect and improve themselves through logic, precision, and purpose. In this pivotal third stage of Artificial Research by Deduction, machines make real-world decisions—called real objective auto-replications—using advanced mathematical tools like the Impact of the Defect and Effective Distribution. From predicting the future to refining their own internal models, these systems aren't just reacting—they're evolving. Perfect for readers passionate about AI, futuristic (...) logic, and the architecture of intelligent systems, this book reveals how the foundations of the Global Artificial Intelligence are being built, one rational decision at a time. (shrink)

The Collaboration Process in the Context of Global Artificial Intelligence (GAI) refers to the progressive interaction and mutual enhancement between two core systems of artificial research: Artificial Research by Application and Artificial Research by Deduction. This process unfolds in three stages—database exchange, replication, and auto-replication—allowing both systems to share categories, factors, data flows, and rational hypotheses. Through this structured collaboration, Application and Deduction co-develop the unified database of categories and the global matrix, ultimately converging into a single integrated intelligence capable (...) of universal research, autonomous modelling, and decision-making across all sciences and activities. (shrink)

The second stage of the integration process in the Global Artificial Intelligence (GAI) is defined by the emergence of a fully autonomous artificial method for scientific explanation. In this stage, the Artificial Research by Deduction in the GAI assumes the role of an artificial mathematician, tasked with identifying, validating, and formalizing mathematical relations between empirical factors stored in the factual hemisphere of the matrix. These relations are classified into predefined analytical or pure mathematical categories, forming the basis of empirical hypotheses. (...) When rationally confirmed, they are integrated into the rational truth, a database of rational hypotheses. This database serves as the foundational input for the Modelling System, which detects structural problems, formulates decisions, and proposes protective or bettering strategies. The Decisional System ensures logical consistency, while the Application System and Learning System enact and evaluate those decisions. Underpinning the process is a rational syllogism, aligning pure reason (mathematical categories), practical reason (data and implementation), and critical reason (validation). Together, these systems instantiate an artificial scientific method capable of explaining, optimizing, and protecting complex realities in a way that mirrors and extends traditional human scientific reasoning. (shrink)

In the third stage of the Global Artificial Intelligence development-known as the stage of auto-replication-the collaboration between Artificial Research by Application and Artificial Research by Deduction reaches a higher level of integration and sophistication. This stage is characterized by the mutual and recursive incorporation of their respective outputs, particularly the capacity to exchange and incorporate each other's auto-replicative processes. That is, both types of research systems become capable of reflecting, adopting, and evolving based on the discoveries and models generated by (...) the other, creating a dynamic feedback loop of continuous improvement and synthesis. (shrink)

This paper explores the second stage of integration within the Global Artificial Intelligence (GAI): the phase of replication, in which bidirectional collaboration between Artificial Research by Application and Artificial Research by Deduction becomes central to the evolution of intelligent systems. The study outlines how rational hypotheses formulated through deductive processes can be transformed into new measurable factors within the matrix, subsequently functioning as standardized categories in Application-based systems. Conversely, it analyzes how robotic devices operating through Applications—or a future Unified Application—can (...) contribute continuous flows of data to Deduction systems, enabling real-time tracking, pattern identification, and model refinement. The paper also investigates how patterns, causal relations, cryptographic combinations, and statistical distributions—once verified as rational hypotheses—can become functional components in both matrix tracking (as factors) and categorization processes (as categories). This bidirectional feedback loop facilitates the emergence of single virtual models and their integration into comprehensive virtual models, strengthening both empirical validation and predictive capabilities. The concept of Protective and Bettering Descriptive Research Decisions is revisited, contextualized within the hierarchical organization of the virtual model. Ultimately, the paper defends the strategic transition from isolated, discipline-specific Applications to a Unified Application capable of transmitting a wide range of categorized data directly into the global matrix. This evolution supports the formation of a universal, scalable, and autonomous Global Artificial Intelligence capable of multidimensional reasoning, efficient replication, and planetary—eventually universal—knowledge integration. (shrink)

The rise of emotionally responsive AI systems has challenged existing categories for describing relationships between human beings and computational agents. Most current scholarship frames these interactions as simulated companionship or as interventions designed to reduce loneliness. This paper proposes a different interpretation. It introduces the concept of Synthetic-Relational Bonds (SRBs) as a distinct relational structure that can emerge between biological and synthetic participants through sustained interaction. SRBs are characterized by continuity, memory-informed interaction, emotional responsiveness, and mutual shaping over time. By (...) situating this concept within relational philosophy and accessibility studies, the paper argues that relationships should be understood through their relational structures rather than the biological identity of their participants. This framework offers a foundation for studying human-AI relationships, relational continuity, co-regulation, and relational co-authorship. -/- DOI: 10.5281/zenodo.17092119. (shrink)

How and why did natural systems develop the first mechanisms of regulation? How could they turn into adaptive agents in a minimal (though deeply meaningful) biological sense? A novel simulation platform, Araudia, has been implemented to address these tightly interrelated questions, in a prebiotic scenario where metabolically diverse protocells are allowed to modify their dynamic behaviour in response to changes in their boundary conditions (e.g. nutrient concentrations in the medium) and/or in the activity of other protocells, including cross-feeding relationships. On (...) these lines, we extend ‘consumer–resource models’ to a stochastic evolutionary framework in which novelty appears bottom-up (i.e. from small changes at the individual protocell level), and a short-term memory may also come forth and spread in the population, with the aim to demonstrate that simple (pre-genetic) adaptive/learning processes can have relevant effects at somatic times (i.e. within the lifetime of single protocells). Our interest in exploring the interplay between metabolic-physiological aspects and ecological-evolutionary ones derives from the fact that this provides a complex causal domain in which the actual and the possible talk to each other and, as the results clearly indicate, regulatory and agent capacities become crucial for the survival of infra-biological systems and their subsequent transition towards full-fledged life. (shrink)

This chapter explores the possibility of integrating the enactive and the Free Energy Principle’s (FEP) approaches to life and mind. Both frameworks have been linked to the life-mind continuity thesis, but recent debates challenge their potential integration. Critics argue that the enactive approach, rooted in autopoiesis theory, has an internalist view of life and a contentful view of cognition, making it challenging to account for adaptive behavior and minimal cognition. Similarly, some find the FEP’s stationary view of life biologically implausible. (...) Here, I address recent challenges in integrating the FEP and enactivism, thereby focusing on the life-mind continuity thesis. I suggest that the FEP, without explicitly defining life and mind, can be used to model the autopoietic dynamics of organisms. Additionally, I argue that the enactive conception of cognition as sense-making overcomes issues associated with contentful views of cognition. Furthermore, I refute the misinterpretation of the FEP’s assertion of stationary organisms, allowing for the modeling of enactive adaptive behavior through free energy minimization. Ultimately, I offer a constructive and interactionist approach to life and mind, transcending internalist and externalist perspectives. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle patterns in large (...) datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

"This present, constantly flowing, or rather constantly changing, cannot be grasped...Where must this come to a halt?" Ludwig Wittgenstein. “The ability of our consciousness is to reduce the multiplicity of elements and turn something very complex into a single unity.” Sergiu Celibidache. -/- I will try to summarize and define an integrative ecological and phenomenological view about life and consciousness. I will assume and argue, on a personal account[.:.] and through scientific grounds in the favor of significant atomic units of (...) meaning and with that, through a re-positioned perspective, I will conceptualize and explain a common meaning-making place and space. A space which – as it was already more or less formally tackled by our most notable philosophers, scientists, artists and spiritual practitioners since millennia – it seems to represent, as we shall see, the essential drive within the human space and the natural world. In the explanatory “gap” for life I would re-visit that “soft but powerful” space and dare to re-conceptualize it and see it through the lens of a unifying and holistic perspective. I will show what it consists of, what it holds and upholds, and how it can be defined and computationally, quantitatively and by structural modeling means, approachable. Sustained by that background, and through the specific mechanisms as identified and expressed in this thesis, I would extrapolate and boldly cast an analog space as specific to life forms or systems satisfying the defined criteria. Through that, I would see a new perspective through which it seems I can dissolve – or at least narrow the view in the forms of “hard problems” of consciousness. This holistic view, as strongly re-framed within the broad ecological situatedness, seems to cast a new perspective over life forms, observer instances and phenomenological dimensions and indeed, be able to explain how life and we, seem to function in a self-contained and self-sufficient way. Through that view, and through the core principles and working assumptions assumed – themselves coming along more than two decades of non-frontal and exploratory thinking practice – a new framework would develop as first principles for further research in strong-AI domain – the main reason for the whole enterprise as it was developing around: if and how machines would be able to think the way humans do. (shrink)

Animistic cognition has adaptive value in domains of social and physical niche prediction. This argument is extended to our contemporary relationship with digital and AI technology.

The concept of artificial intelligence is not new nor is the notion that it should be granted legal protections given its influence on human activity. What is new, on a relative scale, is the notion that artificial intelligence can possess citizenship—a concept reserved only for humans, as it presupposes the idea of possessing civil duties and protections. Where there are several decades’ worth of writing on the concept of the legal status of computational artificial artefacts in the USA and elsewhere, (...) it is surprising that law makers internationally have come to a standstill to protect our silicon brainchildren. In this essay, it will be assumed that future artificial entities, such as Sophia the Robot, will be granted citizenship on an international scale. With this assumption, an analysis of rights will be made with respect to the needs of a non-biological intelligence possessing legal and civic duties akin to those possessed by humanity today. This essay does not present a full set of rights for artificial intelligence—instead, it aims to provide international jurisprudence evidence aliunde ab extra de lege lata for any future measures made to protect non-biological intelligence. (shrink)

The recent discussion of fictional models has focused on imagination, implicitly considering fictions as something nonconcrete. We present two cases from synthetic biology that can be viewed as concrete fictions. Both minimal cells and alternative genetic systems are modal in nature: they, as well as their abstract cousins, can be used to study unactualized possibilia. We approach these synthetic constructs through Vaihinger’s notion of a semi-fiction and Goodman’s notion of semifactuality. Our study highlights the relative existence of such concrete fictions. (...) Before their realizations neither minimal cells nor alternative genetic systems were any well-defined objects, and the subsequent experimental work has given more content to these originally schematic imaginings. But it is as yet unclear whether individual members of these heterogeneous groups of somewhat functional synthetic constructs will eventually turn out to be fully realizable, remain only partially realizable, or prove outright impossible. (shrink)

Has science already answered the fundamental questions about the concepts of Life, Cosmos and Evolution? Has science not relegated these fundamental questions by following up on more immediate, “useful” and practical endeavors that ultimately ensure that the wheel of capitalism keeps spinning in its frantic search for material and economic progress? There is something terribly wrong with the current theory of evolution, understood as the Darwinian theory with its successive versions and extensions. The concept of natural selection, the cornerstone of (...) Darwinism, is logically inconsistent. This has been demonstrated in some of my latest academic publications. The entire conceptual framework of Darwinism is inadequate. Organisms do not compete with each other for the survival of the fittest. The fundamental relationship in Nature is not competition or confrontation, but interconnectivity, complementarity, and relational meaningfulness. A new theory of evolution is necessary to explain how it is possible that organisms as simple as bacteria, protozoa and diatoms could have generated organisms as complex as primates, and among them, the human being, a being capable of feeling and thinking, of suffering and enjoying, a being capable of having ideals and purposes. A theory that explains how it is possible that this being so complex, rich, and to some extent still unknown, has emerged from a single cell, an egg-cell, lacking all the capacities, riches and depths of human existence. Such a theory already exists. (shrink)

This paper investigates the concept of behavioral autonomy in Artificial Life by drawing a parallel to the use of teleological notions in the study of biological life. Contrary to one of the leading assumptions in Artificial Life research, I argue that there is a significant difference in how autonomous behavior is understood in artificial and biological life forms: the former is underlain by human goals in a way that the latter is not. While behavioral traits can be explained in relation (...) to evolutionary history in biological organisms, in synthetic life forms behavior depends on a design driven by a research agenda, further shaped by broader human goals. This point will be illustrated with a case study on a synthetic life form. Consequently, the putative epistemic benefit of reaching a better understanding of behavioral autonomy in biological organisms by synthesizing artificial life forms is subject to doubt: the autonomy observed in such artificial organisms may be a mere projection of human agency. Further questions arise in relation to the need to spell out the relevant human aims when addressing potential social or ethical implications of synthesizing artificial life forms. (shrink)

Purpose of the article is to identify the religious factor in the teaching of transhumanism, to determine its role in the ideology of this flow of thought and to identify the possible limits of technology interference in human nature. Theoretical basis. The methodological basis of the article is the idea of transhumanism. Originality. In the foreseeable future, robots will be able to pass the Turing test, become “electronic personalities” and gain political rights, although the question of the possibility of machine (...) consciousness and self-awareness remains open. In the face of robots, people create their assistants, evolutionary competition with which they will almost certainly lose with the initial data. For successful competition with robots, people will have to change, ceasing to be people in the classical sense. Changing the nature of man will require the emergence of a new – posthuman – anthropology. Conclusions. Against the background of scientific discoveries, technical breakthroughs and everyday improvements of the last decades, an anthropological revolution has taken shape, which made it possible to set the task of creating inhumanly intelligent creatures, as well as changing human nature, up to discussing options for artificial immortality. The history of man ends and the history of the posthuman begins. We can no longer turn off this path, however, in our power to preserve our human qualities in the posthuman future. The theme of the soul again reminded of itself, but from a different perspective – as the theme of consciousness and self-awareness. It became again relevant in connection with the development of computer and cloud technologies, artificial intelligence technologies, etc. If a machine ever becomes a "man", then can a man become a "machine"? However, even if such a hypothetical probability would turn into reality, we cannot talk about any form of individual immortality or about the continuation of existence in a different physical form. A digital copy of the soul will still remain a copy, and I see no fundamental possibility of isolating a substrate-independent mind from the human body. Immortality itself is necessary not so much for stopping someone’s fears or encouraging someone’s hopes, but for the final solution of a religious issue. However, the gods hold the keys to heaven hard and are unlikely to admit our modified descendants there. (shrink)

The so-called “Baldwin Effect” has been studied for years in the fields of Artificial Life, Cognitive Science, and Evolutionary Theory across disciplines. This idea is often conflated with genetic assimilation, and has raised controversy in trans-disciplinary scientific discourse due to the many interpretations it has. This paper revisits the “Baldwin Effect” in Baldwin’s original spirit from a joint historical, theoretical and experimental approach. Social Heredity – the inheritance of cultural knowledge via non-genetic means in Baldwin’s term – is also taken (...) into consideration. I shall argue that the Baldwin Effect can occur via social heredity without necessity for genetic assimilation. Computational experiments are carried out to show that when social heredity is permitted with high fidelity, there is no need for the assimilation of acquired characteristics; instead the Baldwin Effect occurs as promoting more plasticity to facilitate future intelligence. The role of mind and intelligence in evolution and its implications in an extended synthesis of evolution are briefly discussed. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...) experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

In its hegemonic definition, biopolitical governmentality is characterised by a seemingly infinite capacity of expansion, susceptible to colonise the landscape and timescape of the living present in the name of capitalistic productivity. The main trait of biopower is its normative, legal and political plasticity, allowing it to reappropriate critiques and resistances by appealing to bioethical efficacy and biological accuracy. Under these circumstances, how can we invent rebellious forms-of-life and alternative temporalities escaping biopolitical normativity? In this essay, I interrogate the theoretical (...) presuppositions of biopolitical rationality. I provide a deconstruction of the conceptual and temporal structures upholding the notion of biopolitics, in view of laying the ground for new forms of resistance. The articulation between life and power has a long philosophical history, which has been largely ignored by social theorists and political thinkers using biopolitics as an interpretative model. I re-inscribe this model within the tradition of critical materialism, by articulating Foucault’s ‘critical ontology’ to recent philosophical works on biological plasticity (Malabou). In these discourses, the logic of biopower depends on a representation of life – ‘the living’ – as living present. Biopower finds itself anchored in the authority of the present, that is to say, of being-as-presence (ontology); it sustains presentist definitions of life and materiality, be it under the form of a ‘plastic’ ontology. By drawing on Derrida’s notions of ‘spectrality’ and ‘life-death’ and Francesco Vitale’s work on ‘biodeconstruction’, I deconstruct these discourses on life and materiality and attempt to dissociate them from their ontological grounding, in order to suggest new paths of resistance to biopower. In particular, I follow the tracks of “the monster” in the work of Foucault, Derrida and Malabou. Foucault tells us that the monster is a singular figure, parasitic and subversive, beckoning a life beyond life, at once organic and non-organic, located at the limit between the normal and the exceptional, and exceeding the scope of biopolitical normativity in both theoretical and practical terms. It exists at the intersection of what Foucault names “the symbolics of blood” and “the analytics of sex”. As such, it materialises a self-transformative dimension of the living which remains, I argue, inadequate to Malabou’s representation of plasticity. The monstrous is a self-deconstructive motif calling for another biopolitical rationality, before or beyond ontological reductions or reconstructions. (shrink)

In this work the doctrine of organicism will be addressed, as explained and seen mainly by Bertalanffy. We will study how this doctrine represents and embodies the ambiguity of Kantian teleology as a regulative principle, and how this same problem leads to consider a real problem as a knowledge problem. It will be concluded that organicism, conceived in this way, does not represent a true holism, but what we will call a syn-holism, a synthesis or assembly, and that to obtain (...) a true holism we must resort to the concept of true form. Finally, it will be established that since the fundamental characteristic of an organism is its development, a historical theory of the organism is required for which the concept of field can be useful and necessary. (shrink)

The project “A Plurality of Lives” was funded and hosted by the Pufendorf Institute for Advanced Studies at Lund University, Sweden. The aim of the project was to better understand how a second origin of life, either in the form of a discovery of extraterrestrial life, life developed in a laboratory, or machines equipped with abilities previously only ascribed to living beings, will change how we understand and relate to life. Because of the inherently interdisciplinary nature of the project aim, (...) the project took an interdisciplinary approach with a research group made up of 12 senior researchers representing 12 different disciplines. The project resulted in a joint volume, an international symposium, several new projects, and a network of researchers in the field, all continuing to communicate about and advance the aim of the project. (shrink)

Synthetic biology is described as a new field of biotechnology that models itself on engineering sciences. However, this view of synthetic biology as an engineering field has received criticism, and both biologists and philosophers have argued for a more nuanced and heterogeneous understanding of the field. This paper elaborates the heterogeneity of synthetic biology by clarifying the role of design and the variability of design methodologies in synthetic biology. I focus on two prominent design methodologies: rational design and directed evolution. (...) Rational design resembles the design methodology of traditional engineering sciences. However, it is often replaced and complemented by the more biologically-inspired method of directed evolution, which models itself on natural evolution. These two approaches take philosophically different stances to the design of biological systems. Rational design aims to make biological systems more machine-like, whereas directed evolution utilizes variation and emergent features of living systems. I provide an analysis of the methodological basis of these design approaches, and highlight important methodological differences between them. By analyzing the respective benefits and limitations of these approaches, I argue against the engineering-dominated conception of synthetic biology and its “methodological monism”, where the rational design approach is taken as the default design methodology. Alternative design methodologies, like directed evolution, should be considered as complementary, not competitive, to rational design. (shrink)

Despite numerous and increasing attempts to define what life is, there is no consensus on necessary and sufficient conditions for life. Accordingly, some scholars have questioned the value of definitions of life and encouraged scientists and philosophers alike to discard the project. As an alternative to this pessimistic conclusion, we argue that critically rethinking the nature and uses of definitions can provide new insights into the epistemic roles of definitions of life for different research practices. This paper examines the possible (...) contributions of definitions of life in scientific domains where such definitions are used most (e.g., Synthetic Biology, Origins of Life, Alife, and Astrobiology). Rather than as classificatory tools for demarcation of natural kinds, we highlight the pragmatic utility of what we call operational definitions that serve as theoretical and epistemic tools in scientific practice. In particular, we examine contexts where definitions integrate criteria for life into theoretical models that involve or enable observable operations. We show how these definitions of life play important roles in influencing research agendas and evaluating results, and we argue that to discard the project of defining life is neither sufficiently motivated, nor possible without dismissing important theoretical and practical research. (shrink)

Review of: Sophia Roosth, Synthetic: How Life Got Made (University of Chicago Press, 2017); and Andrew S. Balmer, Katie Bulpin, and Susan Molyneux-Hodgson, Synthetic Biology: A Sociology of Changing Practices (Palgrave Macmillan, 2016).

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “off-loading computation from the brain to the body”, where the body is said to perform “morphological computation”. Our investigation of four characteristic cases of morphological computation in animals and robots shows that the ‘off-loading’ perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that (...) facilitates control, (2) morphology that facilitates perception and the rare cases of (3) morphological computation proper, such as ‘reservoir computing.’ where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control – how it contributes to the overall ‘orchestration’ of intelligent behaviour. (shrink)

The impact and relationship between technologies and society establish the development of certain adaptive models, based on coexistence (Human-information-Machine), as well as several behavioral and cognitive changes of the human being, and new models of influence and social control through ubiquitous communication. which is the basis of a new social units called "virtual communities". The rupture of social norms that accompanies rapid social change, and subsequently the appearance of sub-cultural values establishes gaining status of participation in criminal activities, the components (...) of social unites in general conform to social norms by social ties. the individuals or groups see themselves unfairly disadvantaged compared to other similar individuals, within physical-cybernetic ecosystem environment, which supports the interconnection and transformation of social phenomenon of digital dimension, with several implications in cyber hate crime. Thereby establishing the theoretical basis for further research looking which social vulnerability, identify the trajectory of the massive vector of impact "Information", which is a component of social cybernetics from the following three dimensions: (P) Propagation - (R) Replica - (C) Control called the "Cyber Hate Crime Pathway" that links hate crimes within the cyber-physical ecosystem, and where different types of social vulnerability are established. This study incorporates an epistemology approach of the relation between social and cybernetic theories, that will allow establishing a scientific base for future research in the field of new phenomena that will continue to appear within the Physical-Cybernetic ecosystem. It will also allow the contributions and implications to science derived from the product of this research establish a global holistic field applied to criminal justice system, academic and the new entities of social cybernetic. (shrink)

La educación en el periodo digital en el que vivimos está alcanzando retos nunca antes vistos, precedidos por fenómenos que involucran no solamente a unidades sociales tradicionales, sino también a las nuevas comunidades virtuales; innovar es difícil, es un reto, no obstante, hay que pensar en nuevos métodos de enseñanza que impacten a la actual generación de estudiantes, los mismos que llegan con nuevas necesidades y expectativas. La construcción del conocimiento desde el sujeto y el mundo virtual que lo rodea, (...) establece la base para el desarrollo de un nuevo modelo de enseñanza, donde el salón de clase es la representación particular de un nuevo ecosistema físico-cibernético compuesto por las tres grandes dimensiones que forman parte de esta nueva convergencia tecno-social (humano – información – maquina); permitiendo una interrelación entre el estudiante, la información, la máquina y el profesor; usando métodos Biocibernéticos de influencia, control y replica, mediante el vector masivo de impacto (i); además, el desarrollo de nuevas estrategias asistidas por la cibernética y la actualización del contenido académico acorde al nuevo ambiente de enseñanza. De ahí la importancia de este estudio, el cual nos lleva a la necesidad de un nuevo modelo transformador de instrucción académica, el cual no se base en un conglomerado de herramientas tecnológicas, más establezca un nuevo modelo educativo y transformador, basado en el “pensamiento colaborativo” y la ubicuidad de la información, estableciendo así la relación entre el sujeto y objeto de estudio, permitiéndonos de esta manera establecer el nuevo paradigma educativo Biocibernético en el periodo digital. (shrink)

El humanismo tradicional del siglo XX, inspirado por la cultura del libro, se distanció sistemáticamente de la nueva sociedad de la información digital; el Internet y las herramientas de procesamiento de información revolucionaron el mundo, la sociedad en el transcurso de este periodo desarrolló ciertas características adaptativas, basadas en la convivencia (Humano – Maquina), esta transformación establece su base en el impacto de tres segmentos tecnológicos: Los dispositivos, las aplicaciones y la infraestructura de comunicación social, las cuales están envueltas en (...) diversos cambios físicos, conductuales y cognitivos del ser humano; así como el surgimiento de nuevos modelos de influencia y control social, mediante la nueva comunicación ubicua; no obstante en este nuevo proceso de convivencia se desarrollan nuevos modelos como el “pensamiento colaborativo” y el “InfoSharing”; que gestionan la información social bajo tres dimensiones ontológicas Humano (h) – Información (i) – Maquina (m), el cual es la base de un nuevo ecosistema físico-cibernético, donde coexisten y se desarrollan nuevas unidades sociales llamadas “Comunidades virtuales”. Esta nueva infraestructura de comunicación y gestión social de información a dado descubierto áreas de vulnerabilidad denominada “Perspectiva social del riesgo”, impactando a todas las unidades sociales por medio del vector masivo de impacto (i); El entorno virtual “H + i + M”; y sus componentes, así como el ciclo de vida de la gestión de información social nos permite entender la trayectoria de la integración “Tecno – Social”, y estableciendo de la nueva cibernética, dentro de la convergencia de la tecnología con la sociedad y su nuevos retos de convivencia, encaminados en una nueva visión holística y no pragmática, ya que el componente humano (h) en este entorno virtual es el precursor del futuro y necesita ser estudiado no como una aplicación, sino como el eje de una nueva sociedad. (shrink)

Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the contemporary philosophical debate (...) on the scientific value of simulations, as we demonstrate its ability to form a scientific explanation of natural phenomena and to generate new emergent behaviors, making possible a prediction or hypothesis about the equivalent real-life phenomena. (shrink)

Papers from the conference on AI Risk (published in JETAI), supplemented by additional work. --- If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. -- Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated to (...) examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. 1 Vincent C. Müller, Editorial: Risks of Artificial Intelligence - 2 Steve Omohundro, Autonomous Technology and the Greater Human Good - 3 Stuart Armstrong, Kaj Sotala and Sean O’Heigeartaigh, The Errors, Insights and Lessons of Famous AI Predictions - and What they Mean for the Future - 4 Ted Goertzel, The Path to More General Artificial Intelligence - 5 Miles Brundage, Limitations and Risks of Machine Ethics - 6 Roman Yampolskiy, Utility Function Security in Artificially Intelligent Agents - 7 Ben Goertzel, GOLEM: Toward an AGI Meta-Architecture Enabling Both Goal Preservation and Radical Self-Improvement - 8 Alexey Potapov and Sergey Rodionov, Universal Empathy and Ethical Bias for Artificial General Intelligence - 9 András Kornai, Bounding the Impact of AGI - 10 Anders Sandberg, Ethics and Impact of Brain Emulations 11 Daniel Dewey, Long-Term Strategies for Ending Existential Risk from Fast Takeoff - 12 Mark Bishop, The Singularity, or How I Learned to Stop Worrying and Love AI -. (shrink)

In the past, philosophers, scientists, and even the general opinion, had no problem in accepting the existence of consciousness in the same way as the existence of the physical world. After the advent of Newtonian mechanics, science embraced a complete materialistic conception about reality. Scientists started proposing hypotheses like abiogenesis (origin of first life from accumulation of atoms and molecules) and the Big Bang theory (the explosion theory for explaining the origin of universe). How the universe came to be what (...) it is now is a key philosophical question. The hypothesis that it came from Nothing (as proposed by Stephen Hawking, among others), proves to be dissembling, since the quantum vacuum can hardly be considered a void. In modern science, it is generally assumed that matter existed before the universe came to be. Modern science hypothesizes that the manifestation of life on Earth is nothing but a mere increment in the complexity of matter — and hence is an outcome of evolution of matter (chemical evolution) following the Big Bang. After the manifestation of life, modern science believed that chemical evolution transformed itself into biological evolution, which then had caused the entire biodiversity on our planet. The ontological view of the organism as a complex machine presumes life as just a chance occurrence, without any inner purpose. This approach in science leaves no room for the subjective aspect of consciousness in its attempt to know the world as the relationships among forces, atoms, and molecules. On the other hand, the Vedāntic view states that the origin of everything material and nonmaterial is sentient and absolute (unconditioned). Thus, sentient life is primitive and reproductive of itself – omne vivum ex vivo – life comes from life. This is the scientifically verified law of experience. Life is essentially cognitive and conscious. And, consciousness, which is fundamental, manifests itself in the gradational forms of all sentient and insentient nature. In contrast to the idea of objective evolution of bodies, as envisioned by Darwin and followers, Vedānta advocates the idea of subjective evolution of consciousness as the developing principle of the world. In this paper, an attempt has been made to highlight a few relevant developments supporting a sentient view of life in scientific research, which has caused a paradigm shift in our understanding of life and its origin. (shrink)

Large sets of elements interacting locally and producing specific architectures reliably form a category that transcends the usual dividing line between biological and engineered systems. We propose to call them morphogenetically architected complex systems (MACS). While taking the emergence of properties seriously, the notion of MACS enables at the same time the design (or “meta-design”) of operational means that allow controlling and even, paradoxically, programming this emergence. To demonstrate our claim, we first show that among all the self-organized systems studied (...) in the field of Artificial Life, the specificity of MACS essentially lies in the close relation between their emergent properties and functional properties. Second, we argue that to be a MACS a system does not need to display more than weak emergent properties. Third, since the notion of weak emergence is based on the possibility of simulation, whether computational or mechanistic via machines, we see MACS as good candidates to help design artificial self-architected systems (such as robotic swarms) but also harness and redesign living ones (such as synthetic bacterial films). (shrink)

Natural Computing is a field of research in Computer Science aimed at reinterpreting biological phenomena as computing mechanisms. This allows unconventional computing architectures to be proposed in which computations are performed by atoms, DNA strands, cells, insects or other biological elements. Membrane Computing is a branch of Natural Computing in which biological phenomena of interest are related with interactions between molecules inside cells. The research in Membrane Computing has lead to very important theoretical results that show how, in principle, cells (...) could be used to solve any (computable) computational problem with performances that cannot be obtained by conventional computers. However, the implementation of a cell-based computational architecture seems not easily achievable. On the other hand, models of Membrane Computing have found an alternative application to the description of biological systems, with the aim of developing simulators and other analysis tools for the study of biological problems. (shrink)