Lipid vesicles are often used as compartment structures for preparing cell‐like systems and models of protocells, the hypothetical precursor structures of the first cells at the origin of life. Although the various artificially made vesicle systems are already remarkably complex, they are still very different from and much simpler than any known living cell. Nevertheless, the preparation and study of the structure and the dynamics of functionalized vesicle systems may contribute to a better understanding of (...) biological cells, in particular of the essential features of a living cell that are not found in the non‐living form of matter. The study of protocell models may possibly lead to a better understanding of the origin of the first cells. To avoid misunderstanding in this field of research, it would be useful if generally accepted definitions of terms like “artificial cells,” “synthetic cells,” “minimal cells,” “protocells,” and “primitive cells” exist. Editor's suggested further reading in BioEssaysSynthetic cells and organelles: compartmentalization strategies Abstract. (shrink)

Synthetic biology is an emerging engineering discipline that attempts to design and rewire biological components, so as to achieve new functions in a robust and predictable manner. The new tools and strategies provided by synthetic biology have the potential to improve therapeutics for neurodegenerative diseases. In particular, synthetic biology will help design small molecules, proteins, gene networks, and vectors to target disease‐related genes. Ultimately, new intelligent delivery systems will provide targeted and sustained therapeutic benefits. New treatments will arise from (...) combining ‘protect and repair’ strategies: the use of drug treatments, the promotion of neurotrophic factor synthesis, and gene targeting. Going beyond RNAi and artificial transcription factors, site‐specific genome modification is likely to play an increasing role, especially with newly available gene editing tools such as CRISPR/Cas9 systems. Taken together, these advances will help develop safe and long‐term therapies for many brain diseases in human patients. (shrink)

Nowadays, batteries play an important role in a lot of different applications like energy storage, electro-mobility, consumer electronic and so on. All the battery types have a common factor that is their complexity, independently of its nature. Usually, the batteries have an electrochemical nature. Several different test are accomplished to check the batteries performance, and commonly, it is predictable how they work depending of their technology. The present research describes the hybrid intelligent system created to accomplish fault detection over a (...) Lithium Iron Phosphate—LiFePO4 power cell type, commonly used in electro-mobility applications. The approach is based on the cell temperatures behaviour for voltage and current specific values. Taken into account the operating range of a real system based on a LiFePO4 cell, a large set of points of operation have been used to achieve the dataset. The different behaviour zones have been obtained by clustering as a first step. Then, different regression techniques have been used over each cluster. Polynomial regression, artificial neural networks and support vector regression were the combined techniques to develop the hybrid intelligent model proposed. The intelligent system gives very good results over the operating range, detecting all the faults tested during the validation. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. (...) There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

Systems evolve in order to adjust and survive. The paper’s contribution is that this evolvement is inadequate without an evolutionary telos. It is argued that without the presence of self-destruction in multiple levels of our existence and surroundings, our survival would have been impossible. This paper recognises an appreciation of auto-catastrophe at the cell level, in human attitudes (both as an individual and in societies), and extended to Earth and out to galaxies. Auto-Catastrophic Theory combines evolution with auto-catastrophic (...) behaviours and innovative machinery by offering new themes of understanding their merging such as (a) protogenic and deuterogenic auto-catastrophic processes, (b) protogenic and deuterogenic survival processes, and (c) ‘alive’, partially ‘alive’, and non-‘alive’ systems. The value of self-destruction relies on a numerical stability within a system and correlational relationships between systems. Auto-Catastrophic Theory provides novel justifications why artificial intelligence: (a) is crucial to overcome the extinction of humanity (via H+), (b) is crucial to offer indirect survival of humanity (by saving its history), and (c) is at the same time a threat for humanity. These novel justifications and themes, developed by combining evolution with catastrophe as well as innovative machinery, expand our knowledge of how best to handle fresh challenges ahead. (shrink)

There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives (...) that structure their development. This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings. (shrink)

Two reports have shown that mammalian artificial chromosomes (MAC) can be constructed from cloned human centromere DNA and telomere repeats, proving the principle that chromosomes can form from naked DNA molecules transfected into human cells. The MACs were mitotically stable, low copy number and bound antibodies associated with active centromeres. As a step toward second-generation MACs, yeast and bacterial cloning systems will have to be adapted to achieve large MAC constructs having a centromere, two telomeres, and genomic copies (...) of mammalian genes. Available construction techniques are discussed along with a new P1 artificial chromosome (PAC)-derived telomere vector (pTAT) that can be joined to other PACs in vitro, avoiding a cloning step during which large repetitive arrays often rearrange. The PAC system can be used as a route to further define the optimal DNA elements required for efficient MAC formation, to investigate the expression of genes on MACs, and possibly to develop efficient MAC-delivery protocols. BioEssays 1999;21:76–83. © 1999 John Wiley & Sons, Inc. (shrink)

The concept of complexity has become very important in theoretical biology. It is a many faceted concept and too new and ill defined to have a universally accepted meaning. This review examines the development of this concept from the point of view of its usefulness as a criteria for the study of living systems to see what it has to offer as a new approach. In particular, one definition of complexity has been put forth which has the necessary precision (...) and rigor to be considered as a useful categorization of systems, especially as it pertains to those we call living. This definition, due to Robert Rosen, has been developed in a number of works and involves some deep new concepts about the way we view systems. In particular, it focuses on the way we view the world and actually practice science through the use of the modelling relation. This mathematical object models the process by which we assign meaning to the world we perceive. By using the modelling relation, it is possible to identify the subjective nature of our practices and deal with this issue explicitly. By so doing, it becomes clear that our notion of complexity and especially its most popular manifestations, is in large part a product of the historical processes which lead to the present state of scientific epistemology. In particular, it is a reaction to the reductionist/mechanistic view of nature which can be termed the Newtonian Paradigm. This approach to epistemology has dominated for so long that its use as a model has become implicit in most of what we do in and out of science. The alternative to this approach is examined and related to the special definition of complexity given by Rosen. Some historical examples are used to emphasize the dependence of our view of what is complex in a popular sense on the ever changing state of our knowledge. The role of some popular concepts such as chaotic dynamics are examined in this context. The fields of artificial life and related areas are also viewed from the perspective of this rigorous view of complexity and found lacking. The notion that in some way life exists at the edge of chaos is examined from the perspective of the second law of thermodynamics given by Schneider and Kay. Finally, the causal elements in complex systems are explored in relation to complexity. Rosen has shown that a clear difference in causal relations exists between complex and simple systems and that this difference leads to a uniquely useful definition of what we mean by living. Rosen makes it very clear that the class of systems which are complex is a much larger class than those which we call living. For that reason, the focus of this review will be on complexity as a stepping stone towards the deeper question of what makes a system alive. (shrink)

ABSTRACT Recent developments in generating gametes via in vitro gametogenesis (IVG) from induced pluripotent stem cells (iPSCs) and their successful use for reproductive purposes in animals strongly suggest that soon these methods could also be used in human reproduction. At least two questions emerge in this context: (a) if a legislator should permit their use and (b) if ethical claims emerge that support their provision, e.g., by public health care systems. This urges an ethical reflection of the new reproductive (...) options this technique might offer. Since the concept of reproductive freedom is a key aspect for the ethical evaluation of artificial reproductive technologies (ARTs), it is necessary to analyze if the new possibilities emerging from IVG fall within the scope of this concept. The results may constitute a morally relevant difference between different imaginable applications of IVG and potentially justify differences in claims to access this technology. (shrink)

Background Though artificial intelligence holds great value for healthcare, it may also amplify health inequalities through risks of bias. In this paper, we explore bias risks in targeted medicines manufacturing. Targeted medicines manufacturing refers to the act of making medicines targeted to individual patients or to subpopulations of patients within a general group, which can be achieved, for example, by means of cell and gene therapies. These manufacturing processes are increasingly reliant on digitalised systems which can be (...) controlled by artificial intelligence algorithms. Whether and how bias might turn up in the process, however, is uncertain due to the novelty of the development. Methods Examining stakeholder views across bioethics, precision medicine, and artificial intelligence, we document a range of opinions from eleven semi-structured interviews about the possibility of bias in AI-driven targeted therapies manufacturing. Result Findings show that bias can emerge in upstream (research and development) and downstream (medicine production) processes when manufacturing targeted medicines. However, interviewees emphasized that downstream processes, particularly those not relying on patient or population data, may have lower bias risks. The study also identified a spectrum of bias meanings ranging from negative and ambivalent to positive and productive. Notably, some participants highlighted the potential for certain biases to have productive moral value in correcting health inequalities. This idea of “corrective bias” problematizes the conventional understanding of bias as primarily a negative concept defined by systematic error or unfair outcomes and suggests potential value in capitalizing on biases to help address health inequalities. Our analysis also indicates, however, that the concept of “corrective bias” requires further critical reflection before they can be used to this end. (shrink)

Two reports have shown that mammalian artificial chromosomes (MAC) can be constructed from cloned human centromere DNA and telomere repeats, proving the principle that chromosomes can form from naked DNA molecules transfected into human cells. The MACs were mitotically stable, low copy number and bound antibodies associated with active centromeres. As a step toward second-generation MACs, yeast and bacterial cloning systems will have to be adapted to achieve large MAC constructs having a centromere, two telomeres, and genomic copies (...) of mammalian genes. Available construction techniques are discussed along with a new P1 artificial chromosome (PAC)-derived telomere vector (pTAT) that can be joined to other PACs in vitro, avoiding a cloning step during which large repetitive arrays often rearrange. The PAC system can be used as a route to further define the optimal DNA elements required for efficient MAC formation, to investigate the expression of genes on MACs, and possibly to develop efficient MAC-delivery protocols. BioEssays 1999;21:76–83. © 1999 John Wiley & Sons, Inc. (shrink)

The recent development of RNA replicating protocells and capsules that enclose complex biosynthetic cascade reactions are encouraging signs that we are gradually getting better at mastering the complexity of biological systems. The road to truly cellular compartments is still very long, but concrete progress is being made. Compartmentalization is a crucial natural methodology to enable control over biological processes occurring within the living cell. In fact, compartmentalization has been considered by some theories to be instrumental in the creation (...) of life. With the advancement of chemical biology, artificial compartments that can mimic the cell as a whole, or that can be regarded as cell organelles, have recently received much attention. The membrane between the inner and outer environment of the compartment has to meet specific requirements, such as semi‐permeability, to allow communication and molecular transport over the border. The membrane can either be built from natural constituents or from synthetic polymers, introducing robustness to the capsule. (shrink)

Is life a property of the material structure of a living system or an abstract form of organization that can be realized in other media; artificial as well as natural? One version of the Artificial Life research programme presumes, that one can separate the logical form of an organism from its material basis of construction, and that its capacity to live and reproduce is a property of the form, not the matter (Langton 1989). This seems to oppose the (...) notion of a cell within contemporary molecular biology, according to which "form" and "matter" do not represent separate realms. The information in a living cell is intimately bound to the properties of the material substrate. This condition may represent a restriction on the validity of formal theories of life. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, (...) yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

In possible scenarios on the origin of life, protocells represent the precursors of the first living cells. To study such hypothetical protocells, giant vesicles are being widely used as a simple model. Lipid vesicles can undergo complex morphological changes enabling self‐reproduction such as growth, fission, and extra‐ and intravesicular budding. These properties of vesicular systems may in some way reflect the mechanism of reproduction used by protocells. Moreover, remarkable similarities exist between the morphological changes observed in giant vesicles and (...) bacterial L‐form cells, which represent bacteria that have lost their rigid cell wall, but retain the ability to reproduce. L‐forms feature a dismantled cellular structure and are unable to carry out classical binary fission. We propose that the striking similarities in morphological transitions of L‐forms and giant lipid vesicles may provide insights into primitive reproductive mechanisms and contribute to a better understanding of the origin and evolution of mechanisms of cell reproduction.Editor's suggested further reading in BioEssays Synthesizing artificial cells from giant unilamellar vesicles: State‐of‐the art in the development of microfluidic technology Abstract. (shrink)

This article addresses three paradoxes of biomimicry. First of all: how can biomimicry be as old as technology as such and at the same time decidedly innovative and new? Secondly: how can biomimicry both entail a ‘naturalisation’ of technology and a ‘technification’ of nature? And finally: how can biomimicry be perceived as nature-friendly but at the same time (potentially at least) as a pervasive biotechnological assault on nature? Contemporary (technoscientific) biomimicry, I will argue, aims to mimic nature at the level (...) of biomolecular processes and structures: contemporary biomimicry as micro-biomimicry. Moreover, building on Aristotle, Delbrück and Schrödinger, I will emphasise that what is mimicked by contemporary (technoscientific) biomimicry, in contrast to traditional (artisanal) instances of biomimicry, is not the morphological form (εi~δος), but rather the program or formula (δóγOς) of living systems. Contemporary biomimicry is ‘in accordance with nature’, but not in the traditional sense. Rather, building on decades of biomolecular research, it strives to reconcile nature and technology against the backdrop of advanced technicity. But biomimetics will only achieve its goals if it is not pursued purely as a technological endeavour, but complemented by an ethos of sustainability and respect for nature. These claims will be elucidated with the help of two case studies: a research project (namely the BaSyC project, launched in 2017 and aimed at producing a synthetic cell) and a science novel (namely Ian McEwan's Solar, which concerns the epistemic and moral challenges involved in artificial photosynthesis). (shrink)

Artificial Immune Systems (AIS) are intelligent algorithms derived on the principles inspired by human immune system. In this research work, electroencephalography (EEG) signals for four distinct motor movement of human limbs are detected and classified using Negative Selection Classification Algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV - Graz dataset 2a, comprising 9 subjects) has been used. Mel Frequency Cepstral Coefficients (MFCCs) are extracted as selected feature from recorded EEG signals. Dimensionality (...) reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic Algorithm (GA) optimized detectors (Artificial Lymphocytes) are trained using Negative Selection Algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with optimized radius of detector as a non-self detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over 9 subjects with a maximum individual subject classification accuracy of 97.5 % for subject number eight. (shrink)

After a long and successful career in tracheal surgery and lung cancer, Paolo Macchiarini became very famous in 2008 with the transplantation of a trachea from a cadaver that then apparently used the patient’s own stem cells to supposedly regenerate new trachea, i.e., tissue-engineered tracheae. Among the nine patients that received this revolutionary treatment, using biological or artificial tracheae, under Macchiarini’s supervision, six have reportedly died. Although several critics had expressed concerns with the procedures, allegations of misconduct against Macchiarini (...) first arose in August of 2014 by four Karolinska Institutet (KI) colleagues, and an independent investigation was called for by KI based on claims made in seven published papers. Among the claims were the fact that the procedure constituted a high risk, information on the patients was incomplete and that there was no or incomplete ethical approval, thus constituting misconduct. His CV was also shown to contain inaccuracies. By September 10, 2016, most of these claims have now proved to be true, and Macchiarini was found guilty of misconduct by KI. This paper looks primarily at earlier published papers by Macchiarini and his collaborators in a search for clues to better understand the evolution of altruism, or narcissism. An assessment of the controversial papers, and of letters written by critics and skeptics like Pierre R. Delaere, indicate that insufficient experimental evidence was presented for several case studies, and that claims made about the success of the procedures exceeded what was shown by the evidence. A domino effect of personal and professional tragedies ensued, in rapid succession, between 2014 and 2016. The effect on the field of stem cell research has been chilling, and the side-effects have taken their toll, with several high-profile resignations, primarily at KI, within the Swedish education system and in the Nobel Committee. This case has mesmerized the bioethics and biomedical communities for years. (shrink)

All species, extant and extinct, from the simplest unicellular prokaryotes to humans, have an existential consciousness. Without sentience, the first cells that emerged some 4 billion years ago would have been evolutionary dead-ends, unable to survive in the chaotic, dangerous environment in which life first appeared and evolved. In this book, Arthur Reber's theory, the Cellular Basis of Consciousness (CBC), is outlined and distinguished from those models that argue that minds could be instantiated on artificial entities and those that (...) maintain consciousness requires a nervous system. The CBC framework takes a novel approach to classic topics such as the origin-of-life, philosophy of mind, the role of genes, the impact of cognition, and how biological information is processed by all species. It also calls for a rethinking of a variety of issues including the moral implications of the sentient capacities of all species, how welfare concerns need to be expanded beyond where they currently are, and critically, how all life is intertwined in a coordinated cognitive ecology. The Sentient Cell explores this revolutionary model, which updates the standard neo-Darwinian framework within which current approaches operate and examines the underlying biomolecular features that are the likely candidates for the "invention" of consciousness and outline their role in cellular life. (shrink)

This paper investigates the claim that artificial Intelligence Systems cannot be held morally responsible because they do not have an ability for agential self-awareness e.g. they cannot be aware that they are the agents of an action. The main suggestion is that if agential self-awareness and related first person representations presuppose an awareness of a self, the possibility of responsible artificial intelligence systems cannot be evaluated independently of research conducted on the nature of the self. Focusing (...) on a specific account of the self from the phenomenological tradition, this paper suggests that a minimal necessary condition that artificial intelligence systems must satisfy so that they have a capability for self-awareness, is having a minimal self defined as ‘a sense of ownership’. As this sense of ownership is usually associated with having a living body, one suggestion is that artificial intelligence systems must have similar living bodies so they can have a sense of self. Discussing cases of robotic animals as examples of the possibility of artificial intelligence systems having a sense of self, the paper concludes that the possibility of artificial intelligence systems having a ‘sense of ownership’ or a sense of self may be a necessary condition for having responsibility. (shrink)

The declared goal of this paper is to fill this gap: “... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress.” – the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the ‘challenges’ was originally developed (http://www.eucognition.org). So, we stick out (...) our neck and formulate the challenges for artificial cognitive systems. These challenges are articulated in terms of a definition of what a cognitive system is: a system that learns from experience and uses its acquired knowledge (both declarative and practical) in a flexible manner to achieve its own goals. (shrink)

Mercier and Sperber (M&S) suggest that human reasoning is reflective and has evolved to support social interaction. Cognitive agents benefit from being able to reflect on their beliefs whether they are acting alone or socially. A formal framework for argumentation that has emerged from research on artificial cognitive systems that parallels M&S's proposals may shed light on mental processes that underpin social interactions.

In a spellbinding narrative that skillfully weaves together cutting-edge research among today's foremost scientists, theoretical physicist Michio Kaku--author of the bestselling book Hyperspace --presents a bold, exhilarating adventure into the science of tomorrow. In Visions, Dr. Kaku examines in vivid detail how the three scientific revolutions that profoundly reshaped the twentieth century--the quantum, biogenetic, and computer revolutions--will transform the way we live in the twenty-first century. The fundamental elements of matter and life--the particles of the atom and the nucleus of (...) the cell--have now been decoded, closing one of the great chapters of scientific history. But this is just the preface to an even more far-reaching scientific revolution, as we make the transition from being passive observers of the mysteries of nature to becoming masters of nature, able to manipulate matter, life, and intelligence to remold the world around us. In the first part of Visions, Dr. Kaku discusses the cyber future, when millions of microprocessors are scattered throughout our environment; when the iron principle that has ruled the computer industry, Moore's Law, finally collapses, forcing scientists to adopt startling new designs like DNA computers and quantum computers; and when artificial intelligence systems finally arrive. In the next section, Dr. Kaku shows how the decoding of DNA will allow us to conquer devastating genetic diseases, defeat many cancers at the molecular level, synthesize new medicines using virtual reality, grow new organs, conquer aging and reshape our genetic inheritance. Finally, he explores how quantum physicists will perfect new ways to harness the cosmic energy of the universe--from molecular machines to supermagnets that may energize a second industrial revolution, to powerful fusion engines that one day may take us to the stars. What makes Michio Kaku's vision of the future of science so compelling and authoritative is that it is based on the groundbreaking research already underway at leading laboratories around the world. Weaving interviews with over 150 scientists--several of them Nobel laureates--into a rich, inspiring narrative, Dr. Kaku reveals the growing consensus among key scientists about how science will likely evolve through the early, middle, and late years of the twenty-first century. An intimate, thrilling tour through the next century of science, Visions is a riveting, essential map to how scientists will reshape our future. (shrink)

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, (...) class='Hi'>cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the (...) subjective nature of ethical decision-making. The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

In Sherrington's Loom, Alan McComas provides a historical account of the research that has led to recognition of key mechanisms underlying consciousness. Evidence is assembled from a rich variety of sources--neurological patients, animal behavior, laboratory studies, and especially brain stimulation and recording in humans and animals. Among the remarkable advances in the field has been the ability to identify nerve cells in the human brain that store memories of specific people, places, and objects. In addition to dealing with the issue (...) of "free will," the book assembles the information into possible working models for sensations, intentions, and actions. McComas concludes by considering the possibility of consciousness in artificially intelligent systems. (shrink)

Since the early nineteenth century a membrane or wall has been central to the cell’s identity as the elementary unit of life. Yet the literally and metaphorically marginal status of the cell membrane made it the site of clashes over the definition of life and the proper way to study it. In this article I show how the modern cell membrane was conceived of by analogy to the first “artificial cell,” invented in 1864 by the (...) chemist Moritz Traube (1826–1894), and reimagined by the plant physiologist Wilhelm Pfeffer (1845–1920) as a precision osmometer. Pfeffer’s artificial cell osmometer became the conceptual and empirical basis for the law of dilute solutions in physical chemistry, but his use of an artificial analogue to theorize the existence of the plasma membrane as distinct from the cell wall prompted debate over whether biology ought to be more closely unified with the physical sciences, or whether it must remain independent as the science of life. By examining how the histories of plant physiology and physical chemistry intertwined through the artificial cell, I argue that modern biology relocated vitality from protoplasmic living matter to nonliving chemical substances—or, in broader cultural terms, that the disenchantment of life was accompanied by the (re)enchantment of ordinary matter. (shrink)

Public entities around the world are increasingly deploying artificial intelligence and algorithmic decision-making systems to provide public services or to use their enforcement powers. The rationale for the public sector to use these systems is similar to private sector: increase efficiency and speed of transactions and lower the costs. However, public entities are first and foremost established to meet the needs of the members of society and protect the safety, fundamental rights, and wellbeing of those they serve. (...) Currently AI systems are deployed by the public sector at various administrative levels without robust due diligence, monitoring, or transparency. This paper critically maps out the challenges in procurement of AI systems by public entities and the long-term implications necessitating AI-specific procurement guidelines and processes. This dual-prong exploration includes the new complexities and risks introduced by AI systems, and the institutional capabilities impacting the decision-making process. AI-specific public procurement guidelines are urgently needed to protect fundamental rights and due process. (shrink)

Microfluidic technology – the manipulation of fluids at micrometer scales – has revolutionized many areas of synthetic biology. The bottom‐up synthesis of “minimal” cell models has traditionally suffered from poor control of assembly conditions. Giant unilamellar vesicles (GUVs) are good models of living cells on account of their size and unilamellar membrane structure. In recent years, a number of microfluidic approaches for constructing GUVs has emerged. These provide control over traditionally elusive parameters of vesicular structure, such as size, lamellarity, (...) membrane composition, and internal contents. They also address sophisticated cellular functions such as division and protein synthesis. Microfluidic techniques for GUV synthesis can broadly be categorized as continuous‐flow based approaches and droplet‐based approaches. This review presents the state‐of‐the‐art of microfluidic technology, a robust platform for recapitulating complex cellular structure and function in synthetic models of biological cells. (shrink)

Artificial intelligence systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will (...) function. This means that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process. (shrink)

In this paper we challenge the notion of ‘normativity’ used by some enactive approaches to cognition. We define some varieties of enactivism and their assumptions and make explicit the reasoning behind the co-emergence of individuality and normativity. Then we argue that appealing to dispositions for explaining some living processes can be more illuminating than claiming that all such processes are normative. For this purpose, we will present some considerations, inspired by Wittgenstein, regarding norm-establishing and norm-following and show that attributions of (...) normativity to non-social agents are deeply paradoxical. The main conclusions of our discussion are: (1) circular and internal explanations centred on the stability of living systems are insufficient to account for processes where the environment plays an important role, such as adaptation. Enactivism is not an explanatory alternative to evolutionary biology but needs it as a complement to accounts focused on the internal self-assembly of organisms; (2) though we share enactivism’s anti-representational spirit, we argue that ecological psychology can offer a better account of perception. (shrink)

The article explores a set of issues determining the synergetics of artificial cognitive systems: conditions for the realization of non-equilibrium stability of systems, synthesis options of artificial cognitive system, as well as mechanisms of self-organization of consciousness formed on its basis. Artificial cognitive systems are proposed to include not only artificial intelligence systems imitating human thinking, but any multilevel systems that perform the functions of recognizing and remembering information, decision-making, storage, explanation, (...) understanding and production of new knowledge. The defining property of a cognitive system is the ability to make decisions. It is shown that the content of the cognitive system is consciousness, interpreted within the framework of the information concept as an information environment in which the extended model of reality is realized. Consciousness can be qualified as an open dynamic system. The state of such systems is determined by the processes occurring in them (thought processes – in the case of consciousness). For such systems, called "living", there is a realization of mechanisms of stable disequilibrium. The implementation of an artificial cognitive system is technically carried out on the basis of an artificial neural network, and organizationally – according to the actor or reactor model. Self-organization processes determining the synergetics of consciousness are a special case of the extreme principle, which is a consequence of the law of excessive reaction of supersystems, regulating the existence of dissipative systems at the expense of supersystem resources. The initiation of self-organization of consciousness is carried out by the process of thinking, which, due to the non-equilibrium stability of consciousness, does not stop. Thus thinking can be interpreted as a process of varying system parameters in search of suitable ones for extended modeling of reality. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can (...) arise in some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (shrink)

Artificial intelligence systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5–10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation for medical error. It argues that the introduction of AI systems will provide an additional argument pointing towards no-fault compensation as the better legal solution to compensation for medical error in modern health care systems. The paper falls into four parts. The first part rehearses (...) the main arguments for and against no-fault compensation. The second explains why it is likely that AI systems will be widely introduced. The third part analyses why it is difficult to fit AI systems into fault-based compensation systems while the final part suggests how no-fault compensation could provide a possible solution to such challenges. (shrink)

Are intelligent machines really intelligent? Is the underlying philosoph- ical concept of intelligence satisfactory for describing how the present systems work? Is understanding a necessary and sufficient condition for intelligence? If a machine could understand, should we attribute subjectivity to it? This paper addresses the problem of deciding whether the so-called ”intelligent machines” are capable of understanding, instead of merely processing signs. It deals with the relationship between syntax and semantics. The main thesis concerns the inevitability of semantics for (...) any discussion about the possibility of building conscious machines, condensed into the following two tenets: ”If a machine is capable of understanding (in the strong sense), then it must be capable of combining rules and intuitions”; “If semantics cannot be reduced to syntax, then a machine cannot understand.” Our conclusion states that it is not necessary to attribute understanding to a machine in order to explain its exhibited “intelligent” behavior; a merely syntactic and mechanistic approach to intelligence as a task-solving tool suffices to justify the range of operations that it can display in the current state of technological development. (shrink)

This article examines the transformative role of Generative Artificial Intelligence (GAI) in education, exploring diverse disciplines such as engineering, medicine, programming, and information systems. GAI emerges as a revolutionary force, promising to fundamentally change educational practices. The PRISMA 2020 methodology (Page et al., 2021) was used to evaluate the relevant scientific literature. The systematic review identified research that applied specific criteria in the analysis of IAG in education. The selected papers covered a variety of disciplines, providing a comprehensive (...) view of the impact of IAG in education. The results show a diversity of approaches and institutional responses to IAG in education. From significant changes in teaching practices to the identification of specific opportunities in disciplines such as computer science and engineering, GAI stands out as a catalyst for transformation. The adaptation of tertiary education, the need for specific skills to work with GAI, and ethical responses emerge as key themes. It focuses on the need for a deeper and more consensual understanding of the role of GAI in education. Concrete research-supported opportunities are highlighted, but ethical limitations and concerns are also underscored. Collaboration, reflection, and adaptation to open educational practices are proposed as approaches to address the challenges and fully exploit the benefits of IAG in education. This comprehensive analysis contributes to understanding the current and future landscape of GAI in education, providing valuable guidelines for educators, institutions, and educational technology developers. (shrink)

Substantial research over the last ten years has indicated that many generative artificial intelligence systems (“GAI”) have the potential to produce biased results, particularly with respect to gender. This potential for bias has grown progressively more important in recent years as GAI has become increasingly integrated in multiple critical sectors, such as healthcare, consumer lending, and employment. While much of the study of gender bias in popular GAI systems is focused on text-based GAI such as OpenAI’s ChatGPT (...) and Google’s Gemini (formerly Bard), this article describes the results of a confirmatory experiment of gender bias in visual GAI systems. The authors argue that the potential for gender bias in visual GAI systems is potentially more troubling than bias in textual GAI because of the superior memorability of images and the capacity for emotional communication that images represent. They go on to offer four potential approaches to gender bias in visual GAI based on the roles visual GAI could play in modern society. The article concludes with a discussion of how dominant societal values could influence a choice between those four potential approaches to gender bias in visual GAI and some suggestions for further research. (shrink)

Decision theory has had a long-standing history in the behavioural and social sciences as a tool for constructing good approximations of human behaviour. Yet as artificially intelligent systems (AIs) grow in intellectual capacity and eventually outpace humans, decision theory becomes evermore important as a model of AI behaviour. What sort of decision procedure might an AI employ? In this work, I propose that policy-based causal decision theory (PCDT), which places a primacy on the decision-relevance of predictors and simulations of (...) agent behaviour, may be such a procedure. I compare this account to the recently-developed functional decision theory (FDT), which is motivated by similar concerns. I also address potentially counterintuitive features of PCDT, such as its refusal to condition on observations made at certain times. (shrink)

The article presents a new method for obtaining analogues of words, characterized by simplicity and the absence of the need for preliminary training on large data as in existing methods. In the method under study, analogues are determined by their syntactic predicates using methods of distributive semantics. In the study, analogues of adjectives, nouns and verbs were obtained and analyzed. This made it possible to obtain a result that is not inferior to the results obtained using the most popular neural (...) network approach as word2vec when qualitatively comparing analogues. The demonstrated method shows that obtaining analogues is possible using methods of distributive semantics using a more interpretable method, which opens up the possibility of studying semantic analogy. This method also allows you to identify analogues on a specific topic. Based on the experimental results obtained, an original definition of analogues and cognitive schemes is formulated. The article also analyzes and substantiates the possibility of a new approach for creating artificial intelligence systems based on the researched method. According to the authors, this provides significant advantages for the creation of such systems. In particular, the proposed method allows for broader generalizations over orders of magnitude smaller data, as well as learning during use, which is not possible for neural networks. (shrink)

All information results from a process, intrinsic to living beings, of info-autopoiesis or information self-production; a sensory commensurable, self-referential feedback process immanent to Bateson’s ‘difference which makes a difference’. To highlight and illustrate the fundamental nature of the info-autopoietic process, initially, two simulations based on one-parameter feedback are presented. The first, simulates a homeostatic control mechanism (thermostat) which is representative of a mechanistic, cybernetic system with very predictable dynamics, fully dependent on an external referent. The second, simulates a homeorhetic process, (...) inherent to biological systems, illustrating a self-referenced, autonomous system. Further, the active incorporation/interference of viral particles by prokaryotic cells and the activation of CRISPR-Cas can be understood as info-autopoiesis at the most fundamental cellular level, as well as constituting a planetary network of self-referenced information. Moreover, other examples of the info-autopoietic nature of information are presented to show the generality of its applicability. In short, info-autopoiesis is a recursive process that is sufficiently generic to be the only basis for information in nature: from the single cell, to multi-cellular organisms, to consideration of all types of natural and non-natural phenomena, including tools and artificial constructions. (shrink)

The high predictive accuracy of contemporary machine learning-based AI systems has led some scholars to argue that, in certain cases, we should grant them epistemic expertise and authority over humans. This approach suggests that humans would have the epistemic obligation of relying on the predictions of a highly accurate AI system. Contrary to this view, in this work we claim that it is not possible to endow AI systems with a genuine account of epistemic expertise. In fact, relying (...) on accounts of expertise and authority from virtue epistemology, we show that epistemic expertise requires a relation with understanding that AI systems do not satisfy and intellectual abilities that these systems do not manifest. Further, following the Distribution Cognition theory and adapting an account by Croce on the virtues of collective epistemic agents to the case of human-AI interactions we show that, if an AI system is successfully appropriated by a human agent, a _hybrid_ epistemic agent emerges, which can become both an epistemic expert and an authority. Consequently, we claim that the aforementioned hybrid agent is the appropriate object of a discourse around trust in AI and the epistemic obligations that stem from its epistemic superiority. (shrink)

Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...) the epistemology of computer simulation are the three preoccupations addressed by the paper. (shrink)

To assess the intelligence of machines, Alan Turing proposed a test of imitation known as the imitation game, famously known as the Turing test. To assess whether artificial intelligent (AI) systems could be moral or not, Colin Allen et al. developed a test of imitation in the context of morality, a test known as the Moral Turing Test (MTT), which I will, in this paper, call the moral imitation game. There are arguments against developing any type of MTT (...) or moral imitation game. Rather than developing a moral imitation game, this paper proposes a criterion of imitation for AI systems in the moral scenario. To develop a criterion of imitation, I explore the notion of moral attribution in detail. Within the case of moral attribution, I introduce the subject‐ascriber distinction. The notion of moral attribution and the subject‐ascriber distinction is employed to address the following questions: (a) how is the assessment in MTT arranged?, (b) what is the role of the interrogator in the MTT?, and (c) what information is to be concealed from the interrogator? The first question deals with the aspect of assessment, the second question deals with the reordering of the role of the interrogator and the third one deals with the concealment aspect of the imitation game. After that, a criterion of imitation for AI systems is put forward. Using the subject‐ascriber distinction in moral attribution and David Lewis' Mad Pain and Martian Pain cases, it is argued that the notion of sentience is insignificant for the ascriber for moral attributions. (shrink)

This essay explains how, from the theoretical perspective of Basil Bernstein's three “conditions for democracy,” the current pedagogy of artificially intelligent personalized learning seems inadequate. Building on Bernstein's comprehensive work and more recent research concerned with personalized education, Natalia Kucirkova and Sandra Leaton Gray suggest three principles for advancing personalized education and artificial intelligence (AI). They argue that if AI is to reach its full potential in terms of promoting children's identity as democratic citizens, its pedagogy must go beyond (...) monitoring the technological progression of personalized provision of knowledge. It needs to pay more careful attention to the democratic impact of data-driven systems. Kucirkova and Leaton Gray propose a framework to distinguish the value of personalized learning in relation to pluralization and to guide educational researchers and practitioners in its application to socially just classrooms. (shrink)

Abstract: Many believe that, in addition to cognitive capacities, autonomous robots need something similar to affect. As in humans, affect, including specific emotions, would filter robot experience based on a set of goals, values, and interests. This narrows behavioral options and avoids combinatorial explosion or regress problems that challenge purely cognitive assessments in a continuously changing experiential field. Adding human-like affect to robots is not straightforward, however. Affect in organisms is an aspect of evolved biological systems, from the taxes (...) of single-cell organisms to the instincts, drives, feelings, moods, and emotions that focus human behavior through the mediation of hormones, pheromones, neurotransmitters, the autonomic nervous system, and key brain structures. We argue that human intelligence is intimately linked to biological affective systems and to the unique repertoire of potential behaviors, sometimes conflicting, they facilitate. Artificial affect is affect in name only and without genes and biological bodies, autonomous robots will lack the goals, interests, and value systems associated with human intelligence. We will take advantage of their general intelligence and expertise, but robots will not enter our intellectual world or apply for legal status in the community. -/- . (shrink)