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This article is an afterword to the book Rise of the Self-Replicators: Early Visions of Machines, AI and Robots That Can Reproduce and Evolve, coauthored by Tim Taylor and Alan Dorin (2020). The book covered the early history of thought about self-reproducing and evolving machines, from initial speculations in the 17th century up to the early 1960s (from which point onward the more recent history is already well covered elsewhere). This article supplements the material discussed in the book by presenting several relevant sources that have come to the author's attention since the book was published. The most significant additions to the history are from the German-born, 19th-century inventor and utopian John Adolphus Etzler in the 1830s–1840s, the Hungarian author and satirist Frigyes Karinthy in 1916, and the U.S. mathematician and computer scientist Fred Stahl in 1960. [ABSTRACT FROM AUTHOR]

On 25 July 2023, a workshop entitled "Molecular Communication Approaches for Wetware Artificial Life" took place as a satellite event at the international conference ALIFE 2023 (The 2023 Conference on Artificial Life). In this report, we comment on the workshop by focusing on the main theme and the motivations that led us to develop this initiative. In particular, we highlight how recent progress in synthetic biology and in the study of molecular communication from an engineering perspective can be fruitfully joined to provide a powerful platform to develop frontier research lines in "wetware" Artificial Life. The talks presented at the workshop are briefly summarized. This report is, ultimately, an opportunity to promote an emerging field that calls for collaborative efforts of scholars from multiple disciplines, from chemistry to molecular biology, from communication engineering to nanotechnology, and up to those interested in more theoretical aspects about complex artificial systems that mimic natural ones. [ABSTRACT FROM AUTHOR]

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism's traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints. [ABSTRACT FROM AUTHOR]

The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called "synthetic cells" (SCs) (or "artificial cells", or "protocells"), lead us to consider the role of wetware technologies in the "Sciences of Artificial", where they constitute the third pillar, alongside the more well-known pillars hardware (robotics) and software (Artificial Intelligence, AI). In this article, it will be highlighted how wetware approaches can help to model life and cognition from a unique perspective, complementary to robotics and AI. It is suggested that, through SB, it is possible to explore novel forms of bio-inspired technologies and systems, in particular chemical AI. Furthermore, attention is paid to the concept of semantic information and its quantification, following the strategy recently introduced by Kolchinsky and Wolpert. Semantic information, in turn, is linked to the processes of generation of "meaning", interpreted here through the lens of autonomy and cognition in artificial systems, emphasizing its role in chemical ones. [ABSTRACT FROM AUTHOR]

"Anthropology of the Artificial" is a special section of articles by a collective of anthropologists, sociologists, digital media artists, and computer scientists that address the ever increasing encounters between humans and new kinds of machine systems. The contributions draw on a series of artistic and scientific research programs which call for new ways of thinking about the relations between human and artificial systems, focusing on anthropological encounters with artworks, software/hardware, and the public encounters with such systems in settings ranging from research labs to museums. [ABSTRACT FROM AUTHOR]

This work concerns a many-body deterministic model that displays life-like properties such as emergence, complexity, self-organization, self-regulation, excitability and spontaneous compartmentalization. The model portraits the dynamics of an ensemble of locally coupled polar phase oscillators, moving in a two-dimensional space, that under certain conditions exhibit emergent superstructures. Those superstructures are self-organized dynamic networks, resulting from a synchronization process of many units, over length scales much greater than the interaction range. Such networks compartmentalize the two-dimensional space with no a priori constraints, due to the formation of porous transport walls, and represent a highly complex and novel non-linear behavior. The analysis is numerically carried out as a function of a control parameter showing distinct regimes: static pattern formation, dynamic excitable networks formation, intermittency and chaos. A statistical analysis is drawn to determine the control parameter ranges for the various behaviors to appear. The model and the results shown in this work are expected to contribute to the field of artificial life. [ABSTRACT FROM AUTHOR]

The depiction of stones that speak has long been used as a literary and philosophical device to reflect upon the limitations of human language (i.e., language as a petrification of thought and action). Jacques Rancière has described stone's capacity to bear witness as a form of "mute speech," noting how "any stone can also be language," as a part of the "testimony that mute things bear to mankind's activity." In exploring the character of this form of testimony, and asking how we hear it, this article examines the function of the Stone Guest in the legend of Don Juan, across Molière's and Pushkin's theatrical versions, a short film version by Marina Fomenko, and Mozart and Da Ponte's operatic version, so revered by Kierkegaard and others. The character of the Stone Guest is often seen as casting judgement against the aesthetic mode of life, yet the power of the character lies not in his ghostly humanity or sense of retribution, but in his stoniness – his capacity to bear witness, just as a stone monument bears witness or commemorates a past trauma. In a number of versions of the Don Juan story, the Stone Guest is announced by approaching footsteps or knocking. This acousmatic device mirrors the uncanny separation of sound and source in opera – the way in which music mediates between the conflicting imperatives of language and the corporeal or material aspects of the singing voice, lending opera its vaunted mechanical qualities. Using stones that speak as a heuristic, the article draws together ideas about the limitations of language and the mechanical qualities of opera in order to excavate the auditory affordances of the stone's form of testimony, in all its inorganic liveliness. [ABSTRACT FROM AUTHOR]

The rich variety of biological forms and behaviours results from one evolutionary history on Earth, via frozen accidents and selection in specific environments. This ubiquitous baggage in natural, familiar model species obscures the plasticity and swarm intelligence of cellular collectives. Significant gaps exist in our understanding of the origin of anatomical novelty, of the relationship between genome and form, and of strategies for control of large-scale structure and function in regenerative medicine and bioengineering. Analysis of living forms that have never existed before is necessary to reveal deep design principles of life as it can be. We briefly review existing examples of chimaeras, cyborgs, hybrots and other beings along the spectrum containing evolved and designed systems. To drive experimental progress in multicellular synthetic morphology, we propose teleonomic (goal-seeking, problem-solving) behaviour in diverse problem spaces as a powerful invariant across possible beings regardless of composition or origin. Cybernetic perspectives on chimaeric morphogenesis erase artificial distinctions established by past limitations of technology and imagination. We suggest that a multi-scale competency architecture facilitates evolution of robust problem-solving, living machines. Creation and analysis of novel living forms will be an essential testbed for the emerging field of diverse intelligence, with numerous implications across regenerative medicine, robotics and ethics. [ABSTRACT FROM AUTHOR]

The aim of this article is to explore the conception of artificial life forms and the interactions we have with them by paying a particular attention to the analogies that characterize them and the mental processes they give rise to. The article adopts a crossed perspective, focusing on the representations conveyed by artificial life but also on the way we deal with the presence of so-called intelligent or social machines. Based on a multi-sited ethnography of design practices and human-machine interaction experiments, this article hypothesizes that robots and AI constitute a symbolic means of addressing problems regarding our understanding of what life could be whether it is biological or social. Starting from the history of automata, this article will first address the modalities by which an "artificial life" is conceived by analogy with vital processes. It will then focus on the way these processes come into play in an experimental interaction situation. [ABSTRACT FROM AUTHOR]

The article presents the DigiHive system, an artificial chemistry simulation environment, and the results of preliminary simulation experiments leading toward building a self-replicating system resembling a living cell. The two-dimensional environment is populated by particles that can bond together and form complexes of particles. Some complexes can recognize and change the structures of surrounding complexes, where the functions they perform are encoded in their structure in the form of Prolog-like language expressions. After introducing the DigiHive environment, we present the results of simulations of two fundamental parts of a self-replicating system, the work of a universal constructor and a copying machine, and the growth and division of a cell-like wall. At the end of the article, the limitations and arising difficulties of modeling in the DigiHive environment are presented, along with a discussion of possible future experiments and applications of this type of modeling. [ABSTRACT FROM AUTHOR]

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing"—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering. [ABSTRACT FROM AUTHOR]

Biological genotypes do not code directly for phenotypes; developmental physiology is the control layer that separates genomes from capacities ascertained by selection. A key aspect is cellular competency, since cells are not passive materials but descendants of unicellular organisms with complex context-sensitive behavioral capabilities. To probe the effects of different degrees of cellular competency on evolutionary dynamics, we used an evolutionary simulation in the context of minimal artificial embryogeny. Virtual embryos consisted of a single axis of positional information values provided by cells' 'structural genes', operated upon by an evolutionary cycle in which embryos' fitness was proportional to monotonicity of the axial gradient. Evolutionary dynamics were evaluated in two modes: hardwired development (genotype directly encodes phenotype), and a more realistic mode in which cells interact prior to evaluation by the fitness function ("regulative" development). We find that even minimal ability of cells with to improve their position in the embryo results in better performance of the evolutionary search. Crucially, we observed that increasing the behavioral competency masks the raw fitness encoded by structural genes, with selection favoring improvements to its developmental problem-solving capacities over improvements to its structural genome. This suggests the existence of a powerful ratchet mechanism: evolution progressively becomes locked in to improvements in the intelligence of its agential substrate, with reduced pressure on the structural genome. This kind of feedback loop in which evolution increasingly puts more effort into the developmental software than perfecting the hardware explains the very puzzling divergence of genome from anatomy in species like planaria. In addition, it identifies a possible driver for scaling intelligence over evolutionary time, and suggests strategies for engineering novel systems in silico and in bioengineering. [ABSTRACT FROM AUTHOR]

Simple Summary: One of the great challenges in science is determining when, where, why, and how life first arose as well as the form taken by this life. In the present study, life was assumed to be (1) bounded, (2) replicating, (3) able to inherit information, and (4) able to metabolize energy. The various existing hypotheses provide little explanation of how these four conditions for life were established. Indeed, "how" a chemical process that simultaneously satisfies all four conditions emerged after the materials for life were in place is not always clear. In this study, a multiset chemical lattice model, which allows for virtual molecules of multiple types to be placed in each cell on a two-dimensional space, was considered. Using only the processes of molecular diffusion, reaction, and polymerization and modeling the chemical reactions of 15 types of molecules and 2 types of polymerized molecules, as well as using the morphogenesis rule of the Turing model, the process of emergence of a cell-like form with all three conditions except evolution ability was modeled and demonstrated. Although numerous reports using methods such as molecular dynamics, cellular automata, and artificial chemistry have clarified the process connecting non-life and life on protocell simulations, none of the models could simultaneously explain the emergence of cell shape, continuous self-replication, and replication control solely from molecular reactions and diffusion. Herein, we developed a model to generate all three conditions, except evolution ability, from hypothetical chains of chemical and molecular polymerization reactions. The present model considers a 2D lattice cell space, where virtual molecules are placed in each cell, and molecular reactions in each cell are based on a multiset rewriting rule, indicating stochastic transition of molecular species. The reaction paths of virtual molecules were implemented by replacing the rules of cellular automata that generate Turing patterns with molecular reactions. The emergence of a cell-like form with all three conditions except evolution ability was modeled and demonstrated using only molecular diffusion, reaction, and polymerization for modeling the chemical reactions of 15 types of molecules and 2 types of polymerized molecules. Furthermore, controlling self-replication is possible by changing the initial arrangement of a specific molecule. In summary, the present model is capable of investigating and refining existing hypotheses on the emergence of life. [ABSTRACT FROM AUTHOR]

This paper studies the chat-bot LaMDA developed by Google and the experience by the engineer B. Lemoine. The issue brings forth a problem, namely can artificial life feel? The discussions held to-date are set out, openly or tacitly, on the Turing test (1950) focused on intelligence. This paper highlights the phenomenological stance over any other approach to the subject. LaMDA does not just think, it feels; moreover, it senses. It is indeed a sentient person. The highly sensitive question concerns the complexity of the mind. It is claimed here that feelings and/or sensations are already cognitive acts by themselves. [ABSTRACT FROM AUTHOR]

Segmentation of tumors in the ultrasound (US) images of the breast is a critical problem in medical imaging. Due to the poor quality of the US images and varying specifications of the US machines, the segmentation and classification of the abnormalities present difficulties even for trained radiologists. Nevertheless, the US remains one of the most reliable and inexpensive tests. Recently, an artificial life (ALife) model based on tracing agents and fusion of the US and the elasticity images (F-ALife) has been proposed and analyzed. Under certain conditions, F-ALife outperforms state-of-the-art including the selected deep learning (DL) models, deformable models, machine learning, contour grouping and superpixels. Apart from the improved accuracy, F-ALife requires smaller training sets. The strongest competitors of the F-ALife are hybrids of the DL with conventional models. However, the current DL methods require a large amount of data (thousands of annotated images), which often is not available. Moreover, the hybrids require that the conventional model is properly integrated into the DL. Therefore, we offer a new DL-based hybrid with ALife. It is characterized by a high accuracy, requires a relatively small dataset, and is capable of handling previously unseen data. The new ideas include (1) a special image mask to guide ALife. The mask is generated using DL and the distance transform, (2) modification of ALife for segmentation of the US images providing a high accuracy. (These ideas are motivated by the “vehicles” of Braitenberg (Vehicles, experiments in synthetic psychology, MIT Press, Cambridge, 1984) and ALife proposed in Karunanayake et al. (Pattern Recognit 108838, 2022), (3) a two-level genetic algorithm which includes training by an individual image and by the entire set of images. The training employs an original categorization of the images based on the properties of the edge maps. The efficiency of the algorithm is demonstrated on complex tumors. The method combines the strengths of the DL neural networks with the speed and interpretability of ALife. The tests based on the characteristics of the edge map and complexity of the tumor shape show the advantages of the proposed DL-ALife. The model outperforms 14 state-of-the-art algorithms applied to the US images characterized by a complex geometry. Finally, the novel classification allows us to test and analyze the limitations of the DL for the processing of the unseen data. The code is applicable to breast cancer diagnostics (Automated Breast Ultra Sound), US-guided biopsies as well as to  projects related to automatic breast scanners. A video demo is at https://tinyurl.com/3xthedff. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comprehensive analysis on the superiority of Y. lipolytica. • Up-to-date overview of synthetic biology tools in Y. lipolytica. • First review on synthesis of diverse natural products by Y. lipolytica. • In-depth discussion on challenges and future perspectives for the production of natural products. With the rapid development of synthetic biology, researchers can design, modify, or even synthesize microorganisms de novo , and microorganisms endowed with unnatural functions can be considered "artificial life" and facilitate the development of functional products. Based on this concept, researchers can solve critical problems related to the insufficient supply of natural products, such as low yields, long production cycles, and cumbersome procedures. Due to its superior performance and unique physiological and biochemical characteristics, Yarrowia lipolytica is a favorable chassis cell used for green biomanufacturing by numerous researchers. This paper mainly reviews the development of synthetic biology techniques for Y. lipolytica and summarizes the recent research progress on the synthesis of natural products in Y. lipolytica. This review will promote the continued innovative development of Y. lipolytica by providing theoretical guidance for research on the biosynthesis of natural products. [ABSTRACT FROM AUTHOR]

The question of the origin of life is a tenacious question that challenges many branches of science but is also extremely multifaceted. While prebiotic chemistry and micropaleontology reformulate the question as that of explaining the appearance of life on Earth in the deep past, systems chemistry and synthetic biology typically understand the question as that of demonstrating the synthesis of novel living matter from nonliving matter independently of historical constraints. The objective of this contribution is to disentangle the different readings of the origin-of-life question found in science. We identify three main dimensions along which the question can be differently constrained depending on context: historical adequacy, natural spontaneity, and similarity to life-as-we-know-it. We argue that the epistemic status of what needs to be explained—the explanandum—varies from approximately true when the origin-of-life question is the most constrained to entirely speculative when the constraints are the most relaxed. This difference in epistemic status triggers a shift in the nature of the origin-of-life question from an explanation-seeking question in the most constrained case to a fact-establishing question in the lesser-constrained ones. We furthermore explore how answers to some interpretations of the origin-of-life questions matter for other interpretations. [ABSTRACT FROM AUTHOR]

In the last few years there has been a lively debate on humanoid robots interacting with humans in fields where human appearance and likeness may be essential. The debate has been bolstered by advancing AI technologies as well as increasing economic interest and public attention. The feasibility, inevitability, or ethical opportunity of companionship, love, and sex robots has been discussed. I propose a philosophical and cultural approach, applying the strategies of aesthetics and literary theory to the field of artificial beings, in order to understand reasons, use, limits, and possibilities expressed by the technology applied to companionship, love, and sex robots in the contemporary cultural and social context. In dealing with aesthetics, I will state how cognitive, biological, and ethical aspects are involved, how beauty is relatable to a robot's physical appearance, and how the aesthetics of artificial beings may offer new existential experiences. [ABSTRACT FROM AUTHOR]

Intelligence is a central feature of human beings' primary and interpersonal experience. Understanding how intelligence originated and scaled during evolution is a key challenge for modern biology. Some of the most important approaches to understanding intelligence are the ongoing efforts to build new intelligences in computer science (AI) and bioengineering. However, progress has been stymied by a lack of multidisciplinary consensus on what is central about intelligence regardless of the details of its material composition or origin (evolved vs. engineered). We show that Buddhist concepts offer a unique perspective and facilitate a consilience of biology, cognitive science, and computer science toward understanding intelligence in truly diverse embodiments. In coming decades, chimeric and bioengineering technologies will produce a wide variety of novel beings that look nothing like familiar natural life forms; how shall we gauge their moral responsibility and our own moral obligations toward them, without the familiar touchstones of standard evolved forms as comparison? Such decisions cannot be based on what the agent is made of or how much design vs. natural evolution was involved in their origin. We propose that the scope of our potential relationship with, and so also our moral duty toward, any being can be considered in the light of Care—a robust, practical, and dynamic lynchpin that formalizes the concepts of goal-directedness, stress, and the scaling of intelligence; it provides a rubric that, unlike other current concepts, is likely to not only survive but thrive in the coming advances of AI and bioengineering. We review relevant concepts in basal cognition and Buddhist thought, focusing on the size of an agent's goal space (its cognitive light cone) as an invariant that tightly links intelligence and compassion. Implications range across interpersonal psychology, regenerative medicine, and machine learning. The Bodhisattva's vow ("for the sake of all sentient life, I shall achieve awakening") is a practical design principle for advancing intelligence in our novel creations and in ourselves. [ABSTRACT FROM AUTHOR]

Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a predefined distribution of gene activation patterns. This sampling method introduces stochasticity, in turn inducing dynamicity. This dynamicity is imposed on top of an already complex fitness landscape. So where sampling is used, it is important to understand which effects arise from the structure of the fitness landscape, and which arise from the dynamicity imposed on it. Stochasticity of the fitness function also causes difficulties in reproducibility and in post-experimental analyses. We develop a deterministic distributional fitness evaluation by considering the complete distribution of gene activity patterns, so as to avoid stochasticity in fitness assessment. This fitness evaluation facilitates repeatability. Its determinism permits us to ascertain theoretical bounds on the fitness, and thus to identify whether the algorithm has reached a global optimum. It enables us to differentiate the effects of the problem domain from those of the noisy fitness evaluation, and thus to resolve two remaining anomalies in the behaviour of the problem domain of Espinosa-Soto and A. Wagner (2010). We also reveal some properties of solution GRNs that lead them to be robust and modular, leading to a deeper understanding of the nature of the problem domain. We conclude by discussing potential directions toward simulating and understanding the emergence of modularity in larger, more complex domains, which is key both to generating more useful modular solutions, and to understanding the ubiquity of modularity in biological systems. [ABSTRACT FROM AUTHOR]

Segmentation of tumors in ultrasound (US) images of the breast is a critical issue in medical imaging. Due to the poor quality of US images and the varying specifications of US machines, segmentation and classification of abnormalities present difficulties even for trained radiologists. The paper aims to introduce a novel AI-based hybrid model for US segmentation that offers high accuracy, requires relatively smaller datasets, and is capable of handling previously unseen data. The software can be used for diagnostics and the US-guided biopsies. A unique and robust hybrid approach that combines deep learning (DL) and multi-agent artificial life (AL) has been introduced. The algorithms are verified on three US datasets. The method outperforms 14 selected state-of-the-art algorithms applied to US images characterized by complex geometry and high level of noise. The paper offers an original classification of the images and tests to analyze the limits of the DL. The model has been trained and verified on 1264 ultrasound images. The images are in the JPEG and PNG formats. The age of the patients ranges from 22 to 73 years. The 14 benchmark algorithms include deformable shapes, edge linking, superpixels, machine learning, and DL methods. The tests use eight-region shape- and contour-based evaluation metrics. The proposed method (DL-AL) produces excellent results in terms of the dice coefficient (region) and the relative Hausdorff distance H3 (contour-based) as follows: the easiest image complexity level, Dice = 0.96 and H3 = 0.26; the medium complexity level, Dice = 0.91 and H3 = 0.82; and the hardest complexity level, Dice = 0.90 and H3 = 0.84. All other metrics follow the same pattern. The DL-AL outperforms the second best (Unet-based) method by 10–20%. The method has been also tested by a series of unconventional tests. The model was trained on low complexity images and applied to the entire set of images. These results are summarized below. (1) Only the low complexity images have been used for training (68% unknown images): Dice = 0.80 and H3 = 2.01. (2) The low and the medium complexity images have been used for training (51% unknown images): Dice = 0.86 and H3 = 1.32. (3) The low, medium, and hard complexity images have been used for training (35% unknown images): Dice = 0.92 and H3 = 0.76. These tests show a significant advantage of DL-AL over 30%. A video demo illustrating the algorithm is at http://tinyurl.com/mr4ah687.Graphical Abstract: Segmentation of tumors in ultrasound (US) images of the breast is a critical issue in medical imaging. Due to the poor quality of US images and the varying specifications of US machines, segmentation and classification of abnormalities present difficulties even for trained radiologists. The paper aims to introduce a novel AI-based hybrid model for US segmentation that offers high accuracy, requires relatively smaller datasets, and is capable of handling previously unseen data. The software can be used for diagnostics and the US-guided biopsies. A unique and robust hybrid approach that combines deep learning (DL) and multi-agent artificial life (AL) has been introduced. The algorithms are verified on three US datasets. The method outperforms 14 selected state-of-the-art algorithms applied to US images characterized by complex geometry and high level of noise. The paper offers an original classification of the images and tests to analyze the limits of the DL. The model has been trained and verified on 1264 ultrasound images. The images are in the JPEG and PNG formats. The age of the patients ranges from 22 to 73 years. The 14 benchmark algorithms include deformable shapes, edge linking, superpixels, machine learning, and DL methods. The tests use eight-region shape- and contour-based evaluation metrics. The proposed method (DL-AL) produces excellent results in terms of the dice coefficient (region) and the relative Hausdorff distance H3 (contour-based) as follows: the easiest image complexity level, Dice = 0.96 and H3 = 0.26; the medium complexity level, Dice = 0.91 and H3 = 0.82; and the hardest complexity level, Dice = 0.90 and H3 = 0.84. All other metrics follow the same pattern. The DL-AL outperforms the second best (Unet-based) method by 10–20%. The method has been also tested by a series of unconventional tests. The model was trained on low complexity images and applied to the entire set of images. These results are summarized below. (1) Only the low complexity images have been used for training (68% unknown images): Dice = 0.80 and H3 = 2.01. (2) The low and the medium complexity images have been used for training (51% unknown images): Dice = 0.86 and H3 = 1.32. (3) The low, medium, and hard complexity images have been used for training (35% unknown images): Dice = 0.92 and H3 = 0.76. These tests show a significant advantage of DL-AL over 30%. A video demo illustrating the algorithm is at http://tinyurl.com/mr4ah687. [ABSTRACT FROM AUTHOR]

Artists and designers working in the fields of generative and bio art frequently focus on designing speculative visions of how nature can be reimagined with the use of computational media and synthetic biology. Centered on the unique artistic strategies of reimagining life forms, this paper analyzes and compares a selection of generative software-based projects, in which artists are mimicking different natural phenomena and have the tendency to beautify nature and life, with bio art projects, where ethical considerations are prioritized over other aspects of the work. Experimenting with software and producing code-based generative art can be a relatively accessible creative endeavor while working with synthetic biology requires more demanding and controlled workplace conditions and specialized equipment. Despite the differences in accessibility and usability, both fields of generative and bio art challenge the boundaries of contemporary artistic practice and research by mapping, provoking and contemplating the dramatic changes of life forms, in the era when living matter can be re-engineered into a new material capable of computing. [ABSTRACT FROM AUTHOR]

Engineering synthetic minimal cells provide a controllable chassis for studying the biochemical principles of natural life, increasing our understanding of complex biological processes. Recently, synthetic cell engineering has enabled communication between both natural live cells and other synthetic cells. A system such as these enable studying interactions between populations of cells, both natural and artificial, and engineering small molecule cell communication protocols for a variety of basic research and practical applications. In this review, we summarize recent progress in engineering communication between synthetic and natural cells, and we speculate about the possible future directions of this work. [ABSTRACT FROM AUTHOR]

The challenge of robotic reproduction – making of new robots by recombining two existing ones – has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios. [ABSTRACT FROM AUTHOR]

This article aims to examine some of the ethical questions emerging from the use of already existing biotechnological tools and the issues which might occur by synthetic biology's potential future possibilities. In the first part, the essence of synthetic biology and its relation to the contemporary biotechnological research is analyzed. In the second part, the article examines whether the new biotechnological inventions pose new or revive old moral questions about the ethics of science, engineering, and technology in general. After briefly addressing some of the various issues which are raised by experts, philosophers, but also the general public, concerning synthetic biology in general, it focuses on the topic of "artificial life creation" and presents moral reasons which may or may not allow it. The topic is approached by referring to consequentialist, deontological, but also, virtue theory arguments for and against it and the possibility of a partial permission of "artificial life" experiments, asking whether the benefits outweigh the risks and moral implications is explored. Finally, it proposes an argument in favor of the future exploration of biological innovation, underlying the need for a more balanced access to its beneficial results. [ABSTRACT FROM AUTHOR]

Birch, Ginsburg, and Jablonka lay out a very convincing case for an important transition marker: unlimited associative learning (UAL). Especially welcome are the empirical predictions. I focus here not on the question of how to infer phenomenal consciousness from this behavioral metric, but on possible novel applications of this useful and fundamental framework. Specifically, I highlight two aspects of biology that are often not considered in philosophy of mind approaches that focus on natural species and evolutionary time scales. These are (1) the ability of minds and bodies to change drastically on the time scale of an individual experiencing subject, and (2) bioengineering of novel living forms with no evolutionary history at the organism level. Both of these aspects provide interesting new contexts within which to explore UAL and its implications. [ABSTRACT FROM AUTHOR]

This article advocates further examination of the role decay aesthetics can play in artificial life (ALife or AL) and art. Opening with the poetics of decay and the shadow that decay taboo has cast in western culture, firstly, we reframe decay as a constructive process of transformation. Secondly, we perform a brief historical survey of early artistic developments in the field of ALife, assessing how these early works addressed decay. We follow with a deeper analysis of contemporary artists through a lens of decay and decomposition, identifying new tendencies of ALife art (deep time simulation, slime intelligence, molecular agents, techno resurrection and ecohybridized computation). Finally, we look to the peripheries of ALife to see how decay is rendered in current technical research and examine these projects with an eye for turbulent production in the form of 'decaying' matter. We conclude with a number of open questions on decomposition and decay aesthetics, both within the artistic and technical realms of ALife. [ABSTRACT FROM AUTHOR]

Braitenberg vehicles are simple robotic platforms, equipped with rudimentary sensor and motor components. Such vehicles have typically featured as part of thought experiments that are intended to show how complex behaviours are apt to emerge from the interaction of inner control mechanisms with aspects of bodily structure and features of the wider (extra-agential) environment. The present paper describes a framework for creating Braitenberg-like vehicles, which is built on top of a widely used and freely available game engine, namely, the Unity game engine. The framework can be used to study the behaviour of virtual vehicles within a multiplicity of virtual environments. All aspects of the vehicle's design, as well as the wider virtual environment in which the vehicle is situated, can be modified during the design phase, as well as at runtime. The result is a general-purpose simulation capability that is intended to provide the foundation for studies in so-called computational situated cognition —a field of study whose primary objective is to support the computational modelling of cognitive processes associated with the physically-embodied, environmentally-embedded, and materially-extended mind. [ABSTRACT FROM AUTHOR]

In the late 1590s, Elizabeth I and the Levant Company hoped to advance their diplomatic and mercantile agendas in the Mediterranean with the gift of a splendid mechanical organ to the Ottoman Sultan Mehmed III. Thomas Dallam, who was charged with installing this fully automated instrument in the Ottoman court, wrote a lively narrative of his journey, including his personal encounter with the Sultan. This essay argues that Dallam is more complex and suggestive writer than scholars have acknowledged, producing not a plainspoken account of his journey but a suggestive sense of belonging among the humans and machines in the Ottoman seraglio. Fueled by a combination of artisan class identity, technological wonder, anxieties about cultural difference, and an expanding sense of personal vulnerability, Dallam imagines a new life at the Topkapı Palace, integrated within an exquisite system of mechanical artifice. [ABSTRACT FROM AUTHOR]

This Essay focuses briefly on early studies elaborated by natural and chemical philosophers, and the once‐called synthetic biologists, who postulated the transition from inanimate to animate matter and even foresaw the possibility of creating artificial life on the basis of physical and chemical principles only. Such ideas and speculations, ranging from soundness to weirdness, paved however the way to current developments in areas like abiotic pattern formation, cell compartmentalization, biomineralization, or the origin of life itself. In particular, the generation of biomorphs and their relationship to microfossils represents an active research domain and seems to be the logical way to bring the historical work up to the future, as some scientists are trying to make artificial cells. The last sections of this essay will also highlight modern science aimed at understanding what life is and, whether or not, it can be redefined in chemical terms. [ABSTRACT FROM AUTHOR]

Ted Chiang's The Lifecycle of Software Objects shows how life and the lifecycles of human and nonhuman artificial life are dictated by technological determinism and market primacy. Such social forces, however, are not absolute, as Chiang explores the ways in which his characters can make choices that do not always follow the principles of utility and economy. For Chiang, such possibilities emerge from everyday life--a site that enables the gradual accretion of knowledge and experience, fostering intelligence and friendship that favor the humanist values of development, codependency, and solidarity. The characters learn to cooperate and commit themselves to each other, defying the deterministic narrative that binds nonhuman life to a short-lived, programmed lifecycle, on the one hand, and subjects human life to the future of super-artificial intelligence on the other. In times when everyday life appears to be under the impervious rule of the market and technology, Chiang's story reflects on the porosity through which more viable transspecies social relations and visions can arise. [ABSTRACT FROM AUTHOR]

Living systems are more robust, diverse, complex, and supportive of human life than any technology yet created. However, our ability to create novel lifeforms is currently limited to varying existing organisms or bioengineering organoids in vitro. Here we show a scalable pipeline for creating functional novel lifeforms: AI methods automatically design diverse candidate lifeforms in silico to perform some desired function, and transferable designs are then created using a cell-based construction toolkit to realize living systems with the predicted behaviors. Although some steps in this pipeline still require manual intervention, complete automation in future would pave the way to designing and deploying unique, bespoke living systems for a wide range of functions. [ABSTRACT FROM AUTHOR]

We have developed autonomously moving pine-cone robots, which are made of multiple joined pine-cone scales for outdoor natural environments. We achieved these natural robots by using pine cones as both natural hygromorphic actuators and components of the mechanisms. When they are put in outdoor places where moist periods (e.g., rain) and dry periods repeatedly occur, they can move up and down on the spot or move forward. This article describes the motivation behind our research, the design and implementation of three different hygromorphic actuators, and applications for autonomously moving robots in nature. [ABSTRACT FROM AUTHOR]

Criticality is thought to be crucial for complex systems to adapt at the boundary between regimes with different dynamics, where the system may transition from one phase to another. Numerous systems, from sandpiles to gene regulatory networks to swarms to human brains, seem to work towards preserving a precarious balance right at their critical point. Understanding criticality therefore seems strongly related to a broad, fundamental theory for the physics of life as it could be, which still lacks a clear description of how life can arise and maintain itself in complex systems. In order to investigate this crucial question, we model populations of Ising agents competing for resources in a simple 2D environment subject to an evolutionary algorithm. We then compare its evolutionary dynamics under different experimental conditions. We demonstrate the utility that arises at a critical state and contrast it with the behaviors and dynamics that arise far from criticality. The results show compelling evidence that not only is a critical state remarkable in its ability to adapt and find solutions to the environment, but the evolving parameters in the agents tend to flow towards criticality if starting from a supercritical regime. We present simulations showing that a system in a supercritical state will tend to self-organize towards criticality, in contrast to a subcritical state, which remains subcritical though it is still capable of adapting and increasing its fitness. [ABSTRACT FROM AUTHOR]

This article describes activities carried out at the computational art laboratory and discusses a question about place. To think of place as the place of universal, it's ground, the place where you live, it isn't only a residence place, a construction of exploration, but also the planet as a possible place of survival. Therefore, we will present artworks that, in the name of a conception inspired by ecocentrism, propose to eliminate the ontological and axiological difference between all living beings and, for this reason, considers the biosphere as an important biotic unit in the context of artworks. Here the development of affective computer interfaces that seek to promote interactions is also described: a process by which an external or an internal stimulus causes a specific reaction, producing a perception, considering that the idea, in the Deleuzian and Guattarian sense, goes through creative activities. The idea arises in three distinct forms: in the philosophical context, in the form of concepts and in visual production. We describe below the results of collaborative research that has in common the poetics of interactivity between living beings and machines through graphical and machine interfaces, provided by computational processes and methods that approach several ideas involving affective mapping, body, nature and artificial life. [ABSTRACT FROM AUTHOR]

Hadn't this question already been answered? We all know about computation-universal Turing Machines. And we know that any such machine can simulate a space–time dynamics not unlike von Neumann's cellular automaton (CA), which is computation- and construction-universal and among other things can play host to self-replicating machines. And that self-replication sprinkled with a bit of randomness should inexorably lead to descent with variation, competition, and thence to evolution and all that. "So what?" Enrico Fermi would have asked, "Where is your emergent life?" (and note that our understanding of both natural and artificial life has much advanced in the 50 years since). It turns out that life is by its very nature a marginal, fragile, and ephemeral kind of phenomenon. For a substrate or a "culture medium" to be able to support the emergence from scratch of a lifelike lineage, computation- and construction-universality are necessary—but by no means sufficient! Paraphrasing Fermi, what automata that you know—including von Neumann's CA and Conway's very game of Life—have managed so far to "capture for us on film" the origin of some kind of life? What questions, then, should we ask of a prospective medium—be it a Turing machine, a CA, or some other kind of automaton—that will best probe its capabilities to originate (as well as sustain) some form of life, and which will provide us with a sense of direction to help us more quickly converge in this quest. [ABSTRACT FROM AUTHOR]

Unicellular organisms can engage in a process by which a cell purposefully destroys itself, termed programmed cell death (PCD). While it is clear that the death of specific cells within a multicellular organism could increase inclusive fitness (e.g., during development), the origin of PCD in unicellular organisms is less obvious. Kin selection has been shown to help maintain instances of PCD in existing populations of unicellular organisms; however, competing hypotheses exist about whether additional factors are necessary to explain its origin. Those factors could include an environmental shift that causes latent PCD to be expressed, PCD hitchhiking on a large beneficial mutation, and PCD being simply a common pathology. Here, we present results using an artificial life model to demonstrate that kin selection can, in fact, be sufficient to give rise to PCD in unicellular organisms. Furthermore, when benefits to kin are direct—that is, resources provided to nearby kin—PCD is more beneficial than when benefits are indirect—that is, nonkin are injured, thus increasing the relative amount of resources for kin. Finally, when considering how strict organisms are in determining kin or nonkin (in terms of mutations), direct benefits are viable in a narrower range than indirect benefits. Open Research Badges: This article has been awarded Open Data and Open Materials Badges. All materials and data are publicly accessible via the Open Science Framework at https://github.com/anyaevostinar/SuicidalAltruismDissertation/tree/master/LongTerm. [ABSTRACT FROM AUTHOR]

Living systems are characterised by an ability to sustain chemical reaction networks far‐from‐equilibrium. It is likely that life first arose through a process of continual disruption of equilibrium states in recursive reaction networks, driven by periodic environmental changes. Herein, we report the emergence of proto‐enzymatic function from recursive polymerisation reactions using amino acids and glycolic acid. Reactions were kept out of equilibrium by diluting products 9:1 in fresh starting solution at the end of each recursive cycle, and the development of complex high molecular weight species is explored using a new metric, the Mass Index, which allows the complexity of the system to be explored as a function of cycle. This process was carried out on a range of different mineral environments. We explored the hypothesis that disrupting equilibrium via recursive cycling imposes a selection pressure and subsequent boundary conditions on products. After just four reaction cycles, product mixtures from recursive reactions exhibit greater catalytic activity and truncation of product space towards higher‐molecular‐weight species compared to non‐recursive controls. [ABSTRACT FROM AUTHOR]

The theory of autopoiesis has been influential in many areas of theoretical biology, especially in the fields of artificial life and origins of life. However, it has not managed to productively connect with mainstream biology, partly for theoretical reasons, but arguably mainly because deriving specific working hypotheses has been challenging. The theory has recently undergone significant conceptual development in the enactive approach to life and mind. Hidden complexity in the original conception of autopoiesis has been explicated in the service of other operationalizable concepts related to self-individuation: precariousness, adaptivity, and agency. Here we advance these developments by highlighting the interplay of these concepts with considerations from thermodynamics: reversibility, irreversibility, and path-dependence. We interpret this interplay in terms of the self-optimization model, and present modeling results that illustrate how these minimal conditions enable a system to re-organize itself such that it tends toward coordinated constraint satisfaction at the system level. Although the model is still very abstract, these results point in a direction where the enactive approach could productively connect with cell biology. [ABSTRACT FROM AUTHOR]

Abstract Novelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k -nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Droplet systems remain the subject of a constant fascination in science and technology. Here we focus on organic droplets floating on the surface of aqueous surfactant solutions. These droplets can exhibit intriguing interactions. Recently we have found independently in two laboratories that we can observe almost the same complex collective behaviour in two different droplet systems. The aim of this paper is to compare both droplets systems, present their differences and show their similar oscillatory behaviour. The first system consists of decanol droplets floating on a sodium decanoate solution. In the second one, the droplets consist of a mixture of ethyl salicylate and liquid paraffin and they are placed on the surface of an aqueous sodium dodecyl sulphate solution. Although the mechanism of these spatio-temporal interactions of droplets is not fully understood yet, we believe that this behaviour is based on the same phenomena. [ABSTRACT FROM AUTHOR]

We review software for exploring biological evolution from a fun and educational perspective. Our goal is to provide a background explanation of the methods used, terminology, and user experience, and learning outcomes of desktop and mobile evolutionary simulators. Freeware and commercial programs are detailed, with a discussion of how certain software packages can be used in introducing evolutionary theory. [ABSTRACT FROM AUTHOR]

In order to be fully autonomous, robots have to be resilient so that they can recover from damages and operate for a long period of time with no human assistance. To be resilient, existing approaches propose to change the robots' behavior using a different control system when a hardware fault or damage occurs. These approaches are used for robots which have fixed morphologies. However, we cannot assume which morphology would be optimal for a given problem and which morphology allows resilience. In the present paper, we introduce a new approach that generates resilient artificial modular robots by evolving the robot morphology along with its controller. We used a multi-objective evolutionary algorithm to optimize two objectives at a time, which are the traveled distance of a damage-free robot and the traveled distance of the same robot with damaged parts. The result of preliminary experiments demonstrates that during evaluation, when robots are deliberately faced to motor failures, the evolution process can optimize and generate new morphologies for which the robot's behavior is less affected by damage. This makes the robot capable to recover its ability to move forward. [ABSTRACT FROM AUTHOR]

Abstract The construction of an embryo from a single cell precursor is a highly complex process. Evolutionary emergence of the first embryos is even more complex, and involves both a transition to multicellularity along with the establishment of developmental mechanisms. We propose that embryogenesis relies on a community of cells conforming to a regulatory model of emergent multicellularity. This model draws together multiple threads in the scientific literature, from complexity theory to cybernetics, and from thermodynamic entropy to artificial life. All of these strands come together to inform a model of goal-oriented regulation for emergent structures in early life. This is an important step in the evolution of early life, as well as the emergence of complex life in the earliest habitats. Our model, called the cybernetic embryo, allows for a systems-level view of the embryogenetic process. [ABSTRACT FROM AUTHOR]