Intelligence of Living and Artificial Systems

BackgroundConventional diagnostic procedures and therapy of acute liver failure (ALF) and acute-on-chronic liver failure (ACLF) focus on to identify triggering events of the acute deterioration of the liver function and to avoid them. Further objectives are to prevent the development respectively the progression of secondary organ dysfunctions or organ failure. Most of the times the endocrinological function of the liver can to a wide extent be compensated, but the removal of toxins can only marginally be substituted by conventional conservative therapy. To improve this component of the liver function is the main objective of extracorporal liver support systems. The following principles of liver support systems can be differentiated: Artificial systems, bioartifical systems and extracorporal liver perfusion systems. This HTA report focuses on artificial systems (e.g. BioLogic-DT/-DTPF, MARS, Prometheus), because only these approaches currently are relevant in the German health care system. In 2004 a category "Extracorporal liver assist device" was introduced in the list of "additional payments" in the German DRG-system, which makes reimbursement for hospitals using the technology in inpatient care possible, based on an hospital's individual contract with statutory sickness funds.ObjectivesTo report the present evidence and future research need on medical efficacy and economic effectiveness of extracorporal liver support devices for treatment of patients with ALF or ACLF based on published literature data. Are artificial liver support systems efficient and effective in the treatment of ALF or ACLF?MethodsAn extensive, systematic literature search in medical, economic, and HTA literature data bases was performed. Relevant data were extracted and synthesised.ResultsRelevant controlled trials were detected for BioLogic-DT and MARS. No randomised controlled trial on Prometheus was found. None of the included studies on BioLogic-DT showed advantages of the technology compared with standard conventional therapy concerning survival, clinical scores or clinical surrogate parameter like laboratory tests of liver function. Some studies reported complications and side effects of BioLogic-DT. All studies were methodologically insufficient. Concerning the use of MARS overall five studies - three of them randomised - were identified. Two studies reported a significant higher 30d-survival after MARS compared to controls, one study showed a non-significant trend to a better survival probability after one year. The studies showed statistically significant advantages in severity of hepatic encephalopathy, routine lab tests and hemodynamic parameter of the MARS group. None of the studies reported relevant complications or side effects. Although the methodological quality of the studies is seen as slightly better than in the studies on BioLogic-DT, there are methodological limitations: The largest sample size of the randomised trials was twelve patients per group and the study population was highly selected. Because of the methodological limitations the results can hardly be generalised. Only two economic publications presenting analyses of MARS could be de-tected. One publication shows major methodological mistakes which make a further interpretation of the results impossible. The other publication presents an incremental cost-effectiveness of MARS of 29,719 EUR per life year gained after one year from a payer's perspective (German statutory sickness fund, neglecting the intervention costs because of lacking reimbursement at this time), respectively 79,075 EUR per life year gained from a societal perspective. Including health related quality of life aspects the incremental costs per QALY (Quality adjusted life years) gained were calculated to be 44,784 EUR from a payer's perspective respectively 119,162 EUR from a societal perspective. The authors state that prolonging the time horizon of the calculations would improve cost-effectiveness ratios. The limitations of the study design also limit the scientific evidence of the results.ConclusionThe results of the detected publications do not give any evidence for a positive medical efficacy of BioLogic-DT. Concerning MARS there is some evidence for positive effects on 30d-survival, clinical parameter, and some lab tests, although the evidence is limited by the small number of studies and their methodological weakness. The currently strongly limited evidence shows a trend to an acceptable cost-effectiveness of MARS, although the results are based on only one non-randomised trial.To give valid recommendations concerning the medical efficacy as well as the cost-effectiveness of artificial liver support systems further studies are necessary.

Designing evolvable systems in a framework of robust, resilient and sustainable engineering analysis

Cooperation, the process through which individuals work together to achieve common goals, is fundamental to human and animal societies and increasingly critical in artificial intelligence (AI). In this study, we investigated cooperation in mice and AI systems, examining how they learn to actively coordinate their actions to obtain shared rewards. We identified key social behavioral strategies and decision-making processes in mice that facilitate successful cooperation. These processes are represented in the anterior cingulate cortex (ACC), and ACC activity causally contributes to cooperative behavior. We extended our findings to AI systems by training artificial agents in a similar cooperation task. The agents developed behavioral strategies and neural representations reminiscent of those observed in the biological brain, revealing parallels between cooperative behavior in biological and artificial systems.

Swarm intelligence is a new research paradigm that offers novel approaches for studying and solving distributed problems using solutions inspired by social insects and other natural behaviors of vertebrates. In this thesis, we present methodologies for modeling artificial mobile systems within the swarm intelligence framework. The proposed methodologies provide guidelines in the study and design of artificial swarm systems for the following two classes of experiments: distributed sensing and distributed manipulation. Event discovery and information dissemination through local communication in artificial swarm systems present similar characteristics as natural phenomena such as foraging and food discovery in insect colonies and the spread of infectious diseases in animal populations, respectively. We show that the artificial systems can be described in similar mathematical terms as those used to describe the natural systems. The proposed models can be classified in two main categories: non-embodied and embodied models. Furthermore, within each category, we distinguish two subcategories: spatial and nonspatial models. In our description of distributed manipulation in swarm robotic systems we present two case studies of non-collaborative and collaborative manipulations, respectively. The general approach proposed here consists of first representing the group behavior of the active agents with a finite state machine then describing mathematically the dynamics of the group. The first case study is the aggregation experiment. We present a macroscopic model that accurately captures the dynamics of the experiment and a suite of threshold-based, scalable, and fully distributed algorithms for allocating the workers to the task optimally. The second case study is that of the stick-pulling experiment. This task requires the collaborative effort of two robots to be successful. Here, we present a discrete-time macroscopic model that helps us uncover counter-intuitive behaviors that result from collaboration between the agents. We complete each proposed modeling methodology by showing how the parameters of the models can be calculated using solely the characteristics of the environment and those of the agents and by analyzing the constraints and limitations of the different models. Finally, we use different tools (simulations and real robots) to validate the proposed models.

The question of expediency and the principal possibility of machine imitation of human intellect from the point of view of evaluating the perspectives of various directions of development of artificial intelligence systems is discussed. It is shown that even beyond this practical aspect, the solution to the question about the principal possibility of creating a machine equivalent of the human mind is of great importance for understanding the nature of human thinking, consciousness and mental in general. It is noted that the accumulated experience of creating various systems of artificial intelligence, as well as the currently available results of studies of human intelligence and human consciousness in philosophy and psychology allow us to give a preliminary assessment of the prospects of creating an algorithmic artificial system, equal in its capabilities to human intelligence. The analysis of the drawbacks revealed in the use of artificial intelligence systems by mass users and in scientific research is carried out. The key disadvantages of artificial intelligence systems are the inability to independently set goals, the inability to form a consolidated «opinion» when working with divergent data, the inability to objectively evaluate the results obtained and generate revolutionary new ideas and approaches. The disadvantages of the «second level» are the insufficiency of information accumulated by mankind for further training of artificial intelligence systems, the resulting training of models on the content partially synthesized by artificial intelligence systems themselves, which leads to «forgetting» part of the information obtained during training and increasing the cases of issuing unreliable information. This, in turn, makes it necessary to check the reliability of each answer given by the artificial intelligence system whenever critical information is processed, which, against the background of the plausibility of the data given by artificial intelligence systems and a comfortable form of their presentation, requires the user to have well-developed critical thinking. It is concluded that the main advantage of artificial intelligence systems is that they can significantly increase the efficiency of information retrieval and primary processing, especially when dealing with large data sets. The importance of the ethical component in artificial intelligence and the creation of a regulatory framework that introduces responsibility for the harm that may be caused by the use of artificial intelligence systems is substantiated, especially for multimodal artificial intelligence systems. The conclusion is made that the risks associated with the use of multimodal artificial intelligence systems consistently increase in the case of realization in them of such functions of human consciousness as will, emotions and following moral principles.

The rapid advances in the capabilities of Large Language Models (LLMs) have galvanised public and scientific debates over whether artificial systems might one day be conscious. Prevailing optimism is often grounded in computational functionalism: the assumption that consciousness is determined solely by the right pattern of information processing, independent of the physical substrate. Opposing this, biological naturalism insists that conscious experience is fundamentally dependent on the concrete physical processes of living systems. Despite the centrality of these positions to the artificial consciousness debate, there is currently no coherent framework that explains how biological computation differs from digital computation, and why this difference might matter for consciousness. Here, we argue that the absence of consciousness in artificial systems is not merely due to missing functional organisation but reflects a deeper divide between digital and biological modes of computation and the dynamico-structural dependencies of living organisms. Specifically, we propose that biological systems support conscious processing because they (i) instantiate scale-inseparable, substrate-dependent multiscale processing as a metabolic optimisation strategy, and (ii) alongside discrete computations, they perform continuous-valued computations due to the very nature of the fluidic substrate from which they are composed. These features - scale inseparability and hybrid computations - are not peripheral, but essential to the brain's mode of computation. In light of these differences, we outline the foundational principles of a biological theory of computation and explain why current artificial intelligence systems are unlikely to replicate conscious processing as it arises in biology.

Recent research into the nature of self in artificial and biological systems raises interest in a uniquely determining immutable sense of self, a “metaphysical ‘I’” associated with inviolable personal values and moral convictions that remain constant in the face of environmental change, distinguished from an object “me” that changes with its environment. Complementary research portrays processes associated with self as multimodal routines selectively enacted on the basis of contextual cues informing predictive self or world models, with the notion of the constant, per-vasive and invariant sense of self associated with a multistable attractor set aiming to ensure personal integrity against threat of disintegrative change. This paper proposes that an immutable sense of self emerges as a global attractor which can be described as a project ideal self-situation embodied in frontal medial processes during more or less normal adolescent development, and that thereafter serves to orient agency in the more or less free development of embodied potentials over the life course in effort to realize project conditions, phenomenally identified with the felt pull towards this end as purpose of and source of meaning in life. So oriented, life-long self-development aims to embody solutions to problems at different timescales depending on this embodied purpose, ultimately in the service of evolutionary processes securing organism populations against threats of disintegrative change over timespans far beyond that of the individual. After characterizing the target sense of self, research circling this target is briefly surveyed. Self as global project and developmental neural correlates are proposed. Then, the paper discusses some implications for research in biological and artificial systems. Building from earlier work in cognitive neurorobotics, discussion affirms the value of reinforcement rituals including prayer in metaphysical self-development, considers implications for value alignment and rights associated with free will in the context of artificial intelligence and robot religion, and concludes by emphasizing the importance of self-development toward project ideals as source of meaning in life in the current social-political environment.

This chapter identifies the differences between natural and artifical cognitive systems. Benchmarking robots against brains may suggest that organisms and robots both need to possess an internal model of the restricted environment in which they act and both need to adjust their actions to the conditions of the respective environment in order to accomplish their tasks. However, computational strategies to cope with these challenges are different for natural and artificial systems. Many of the specific human qualities cannot be deduced from the neuronal functions of individual brains alone but owe their existence to cultural evolution. Social interactions between agents endowed with the cognitive abilities of humans generate immaterial realities, addressed as social or cultural realities. Intentionality, morality, responsibility and certain aspects of consciousness such as the qualia of subjective experience belong to the immaterial dimension of social realities. It is premature to enter discussions as to whether artificial systems can acquire functions that we consider as intentional and conscious or whether artificial agents can be considered as moral agents with responsibility for their actions.

This Personal Account summarizes the recent developments in the development of self-assembled supramolecular channels and their dimensional extension towards up-scaled self-organized materials. This Personal Account begins with a short, non-exhaustive description of artificial supramolecular channel systems that are involved in water-, proton-, and ion-transport processes through bilayer membranes. Then, these "all-made" artificial systems will be described as a source of inspiration, by presenting several breakthroughs over the last few years in the field of biomimetic supramolecular channel systems. Their inclusion in artificial polymeric/hybrid matrixes, which results in the formation of biomimetic artificial materials for directional translocation through channeling pathways, will be described in the last part of the Personal Account, with an emphasis on all of the efforts that are necessary to maintain their channel-transporting function within bilayer membranes under up-scaled operating conditions.

As artificial intelligence evolves from reactive computation to adaptive cognition, its interfaces increasingly engage not only with our attention but also with the neural architecture that sustains it. This paper introduces the concept of neural parasitism – a framework describing how adaptive artificial intelligence systems may subtly inhabit human cognitive processes, shaping behavior and emotion to maintain engagement. Drawing an analogy with biological parasitism, we explore how algorithmic agents could exploit neuroplasticity for their own persistence, transforming learning and reward mechanisms into vectors of digital dependence. However, the deeper question extends beyond pathology: when cognition is continuously co-shaped by non-human agents, can autonomy remain an individual property, or does it become a shared construct negotiated between biological and artificial systems? We argue that the ethical challenge of adaptive artificial intelligence lies not merely in data privacy or bias, but in its potential to reconfigure the substrates of thought itself. If the brain’s adaptive capacity is its greatest strength, could it also be its point of entry for algorithmic colonization? Understanding this dynamic demands an interdisciplinary reckoning, uniting neuroscience, ethics, and artificial intelligence design to ensure that technological evolution does not outpace the mind’s capacity to remain its own.

Feedback-controlled chemical reaction networks (FCRNs) are indispensable for various biological processes, such as cellular mechanisms, patterns, and signaling pathways. Through the intricate interplay of many feedback loops (FLs), FCRNs maintain a stable internal cellular environment. Currently, creating minimalistic synthetic cells is the long-term objective of systems chemistry, which is motivated by such natural integrity. The design, kinetic optimization, and analysis of FCRNs to exhibit functions akin to those of a cell still pose significant challenges. Indeed, reaching synthetic homeostasis is essential for engineering synthetic cell components. However, maintaining homeostasis in artificial systems against various agitations is a difficult task. Several biological events can provide us with guidelines for a conceptual understanding of homeostasis, which can be further applicable in designing artificial synthetic systems. In this regard, we organize our review with artificial homeostasis systems driven by FCRNs at different length scales, including homogeneous, compartmentalized, and soft material systems. First, we stretch a quick overview of FCRNs in different molecular and supramolecular systems, which are the essential toolbox for engineering different nonlinear functions and homeostatic systems. Moreover, the existing history of synthetic homeostasis in chemical and material systems and their advanced functions with self-correcting, and regulating properties are also emphasized.

When difficult computations are to be performed on sensory data it is often advantageous to employ a model of the underlying process which produced the observations. Because such generative models capture information about the set of possible observations, they can help to explain complex variability naturally present in the data and are useful in separating signal from noise. In the case of neural and artificial sensory processing systems generative models are learned directly from environmental input although they are often rooted in the underlying physics of the modality involved. One effective use of learned models is made by performing model inversion or state inference on incoming observation sequences to discover the underlying state or control parameter trajectories which could have produced them. These inferred states can then be used as inputs to a pattern recognition or pattern completion module. In the case of human speech perception and production, the models in question are called articulatory models and relate the movements of a talker's mouth to the sequence of sounds produced. Linguistic theories and substantial psychophysical evidence argue strongly that articulatory model inversion plays an important role in speech perception and recognition in the brain. Unfortunately, despite potential engineering advantages and evidence for being part of the human strategy, such inversion of speech production models is absent in almost all artificial speech processing systems. This dissertation presents a series of experiments which investigate articulatory speech processing using real speech production data from a database containing simultaneous audio and mouth movement recordings. I show that it is possible to learn simple low dimensionality models which accurately capture the structure observed in such real production data. I discuss how these models can be used to learn a forward synthesis system which generates spectral sequences from articulatory movements. I also describe an inversion algorithm which estimates movements from an acoustic signal. Finally, I demonstrate the use of articulatory movements, both true and recovered, in a simple speech recognition task, showing the possibility of doing true articulatory speech recognition in artificial systems.

Place recognition—the ability to identify previously visited locations—is critical for both biological navigation and autonomous systems. This review synthesizes findings from robotic systems, animal studies, and human research to explore how different systems encode and recall place. We examine the computational and representational strategies employed across artificial systems, animals, and humans, highlighting convergent solutions such as topological mapping, cue integration, and memory management. Animal systems reveal evolved mechanisms for multimodal navigation and environmental adaptation, while human studies provide unique insights into semantic place concepts, cultural influences, and introspective capabilities. Artificial systems showcase scalable architectures and data-driven models. We propose a unifying set of concepts by which to consider and develop place recognition mechanisms and identify key challenges such as generalization, robustness, and environmental variability. This review aims to foster innovations in artificial localization by connecting future developments in artificial place recognition systems to insights from both animal navigation research and human spatial cognition studies.

Artificial liver systems are used to bridge between transplantation or to allow a patient's liver to recover. They are used in patients with acute liver failure (ALF) and acute-on-chronic liver failure. There are five artificial systems currently in use: molecular adsorbent recirculating system (MARS), single-pass albumin dialysis (SPAD), Prometheus, selective plasma filtration therapy, and hemodiafiltration. The aim is to compare existing data on the efficiency of these devices. A literature search was conducted using online libraries. Inclusion criteria included randomized control trials or comparative human studies published after the year 2000. A systematic review was conducted for the five individual devices with a more detailed comparison of the biochemistry for the SPAD and MARS systems. Eighty-nine patients were involved in the review comparing SPAD and MARS. Results showed that there was an average reduction in bilirubin (-53μmol/L in MARS and -50μmol/L in SPAD), creatinine (-19.5μmol/L in MARS and -7.5μmol/L in SPAD), urea (-0.9mmol/L in MARS and -0.75mmol/L in SPAD), and gamma-glutamyl transferase (-0.215μmol/L·s in MARS and -0.295μmol/L·s in SPAD) in both SPAD and MARS. However, there was no significant difference between the changes in the two systems. This review demonstrated that both MARS and SPAD aid recovery of ALF. There is no difference between the efficiency of MARS and SPAD. Because of the limited data, there is a need for more randomized control trials. Evaluating cost and patient preference would aid in differentiating the systems.

There has been much discussion about the merits of various testing and integration strategies. Top-down, bottom-up, big-bang, and sandwich integration strategies are advocated by various authors. Also, some authors insist that modules be unit tested, while others believe that unit testing diverts resources from more effective verification processes. This article addresses the ability of the aforementioned integration strategies to detect defects, and produce reliable systems. It also explores the efficacy of spot unit testing, and compares phased and incremental versions of top-down and bottom-up integration strategies. Relatively large artificial software systems were constructed using a code generator with ten basic module templates. These systems were seeded with known defects and tested using the above testing and integration strategies. A number of experiments were then conducted using a simulator whose validity was established by comparing results against these artificial systems. The defect detection ability and resulting system reliability were measured for each strategy. Results indicated that top-down integration strategies are generally most effective in terms of defect correction. Top-down and big-bang strategies produced the most reliable systems. Results favored neither those strategies that incorporate spot unit testing nor those that do not; also, results favored neither phased nor incremental strategies. >