Keynote Abstract The culmination of the British Industrial Revolution in the 1840s brought with it a growing anxiety about the ever-increasing complexity and sophistication of machines. Even at that time, concerns were raised about the future implications of developing machines that can make other machines. These worries were compounded with the publication of ” On the Origin of Species ” in 1859; within a year of its publication, we see discussion of whether Darwin's ideas of evolution might also be applied to machines. The 1860s and 1870s saw multiple authors explicitly discuss the possibility of self-reproducing machines that could evolve to become more sophisticated and intelligent over time. In this talk, I will trace the early development of the idea of self-reproducing and evolving machines, from the 1800s up to the 1960s (from which point the subsequent history is better known). Along the way, I'll highlight contributions ranging from literary and pulp sci-fi authors to scientists and engineers, including work from eastern Europe and Russia as well as from western Europe and the US. I will end the talk by discussing the relevance of these early ideas to contemporary research in, and concerns about, the development of artificial life.

Keynote Abstract We often think of ageing as a natural, inevitable process. However, viewed biologically, ageing is the world's leading cause of death and suffering. That's because it's the main cause of the world's biggest killers, like cancer, heart disease and dementia. The good news is that we can rise to the challenge with science: diets, drugs and genetic changes are just some of dozens of ways we have to slow and even reverse the ageing process in the lab, and some ideas—like the removal of aged ‘senescent’ cells—are already making their way into human trials. Computational science will be essential to fulfil the incredible promise of this field: from data analysis, to machine learning, to systems biology, to artificial life. And a deeper, data-driven understanding of the ageing process could lead to the greatest revolution in the history of medicine—one that has the potential to improve billions of lives, save trillions of dollars, and transform the human condition.

Keynote Abstract All the constituents for life's emergence – CO 2 , H 2 , CH 4 NO − 2 , HPO 3− 4 , HS − , Fe ; Ni , Co , Zn , Mo – were focused at a submarine alkaline vent 4,4 billion years ago. And the redox and pH disequilibria across the vent precipitates – 0.8 volts and 5 units – were also appropriate drivers. (There was no land and thus no “warm little ponds”) (Russell, 2021). In these conditions carboxylic and amino acids, the active centers of the metalloenzymes and condensed phosphates with reactive short peptides as their nests, are known to form, and hydrazine, bases, and methyl alcohol are likely. So far, so apparently simple. But of course the concatenations of life are not chemistry, or not just chemistry and we can take it from Darwin that “in the beginning was complexity” (Morfino and Thomas, 2017). For example, nanoengines and pumps are mandatory as processors to convert the available ‘free’ energy for (proto)biosynthesis through reciprocal gating mechanisms (Branscomb and Russell, 2013), implying the involvement of what Carter and Wills call, in their far-reaching paper, “Strange Loops in Bioenergetics, Genetics, and Catalysis” (Carter and Wills, 2021). How might such requirements for complex disequilibria converters be satisfied, not only for life's emergence, but also to guarantee its evolvable continuities? Well, the main precipitate at the alkaline vents would have comprised billions upon billions of the 2D, double layered, variable valence, redox sensitive, physically flexible, solid electrolyte mineral, green rust or fougerite, a hydrated ferrous/ferric oxyhydroxide ( [ Fe II 4 Fe III 2 ( OH ) 12 ][ CO 3 ].3 H 2 O ) dosed with a plethora of transition metals and capable of absorbing a variety of anions (Russell, 2018; Duval et al., 2019). These nanoengines have been shown to mediate the reactions just mentioned. These are remarkable and unexpected characteristics for a mineral known to have been the precursor of the Banded Iron Formations that are the resource for the first industrial revolution. But couldn't such an abundant and yet complex mineral – or a ‘compendium’ of such a mineral – suggest potential for emergent autonomous computing and thereby providing a code to offspring? (Harding et al., 2006; Bartlett and Beckett, 2019) Can we envision a convergence of endeavours cleaving the emergence of life research with aspects of Alife? (Cardoso et al., 2020)

Keynote Abstract To possess a telescope without its other essential half, the microscope, seems to me a symbol of the darkest incomprehension. The task of the right eye is to peer into the telescope, while the left eye peers into the microscope. –Leonora Harrington, Down Below, 2017 Biology is replete with rich empirical descriptions of microscale (individual level) interactions for a wide range of systems from molecular to societal. Although many models and simulations like Conway's Game of Life elegantly show complexity can arise from simplicity, the empirically grounded microscopic descriptions we now have for biological and social systems suggest complexity begets complexity. Cells, whole organisms, and cities are more similar in complexity than textbook diagrams suggest and, although complexity might increase in a Russian doll sense as we move up organizational levels, it is not at all clear that complexity increases in any other sense—new solutions are computed and new stuff is produced but old stuff is also jettisoned or ignored. In this talk I sketch the beginnings of a new theory of emergence—The Hourglass Theory—in which there is indeed a role for simplicity but not quite the one typically ascribed to it. Rather simplicity and novelty result as adaptive systems coarse-grain microscale complexity to reduce uncertainty and use these sometimes-accurate-sometimes-error-prone perceived regularities to tune behavior.

Keynote Abstract Humanity has become a geological force. The amount of sediments and rocks moved by humans exceed that transported by all the world's rivers. Humans have radically altered nearly all of the Earth's ice-free surface predominately via agriculture to grow food for a population that has increased from 1 billion at the start of the 19th Century to 7.6 billion today. This land use has already produced a global mass extinction event unprecedented for tens of millions of years. Via fossil-fuelled industrialisation, humans have increased concentrations of carbon dioxide in the Earth's atmosphere and in doing so raised average global surface temperatures by over 1°C. These and other impacts have recently been explored by the technosphere concept. Coined by US geoscientist Peter Haff in 2014, the technosphere consists of individual humans, human societies, information networks, and stuff. In terms of stuff, humans have produced an extraordinary 30 trillion metric tons of things. From skyscrapers to CDs, fountains to fondue sets. A good deal of this is infrastructure, such as roads and railways, which links humanity together. The technosphere can be seen as the latest major evolutionary transition of not just life on Earth, but the Earth system itself. Adopting this planetary-scale perspective provides new insights into our current sustainability challenges. For example, rather than technological innovation being for the direct benefit of humans, it can instead be seen as an element in an autocatalytic system that increases the technosphere's energy and material consumption. Homo sapiens in that respect become elements, rather directors, of the technosphere. While multiple generations collectively built the technosphere it may prove to be largely autonomous and insensitive to human agency in terms of large-scale processes and developmental trajectory. If that is an accurate description, then we may be unable to avert climate change that would devastate communities around the world. Rather than being a cause of despair, embracing the technosphere concept could offer a vital opportunity for humanity to gain important agency and help forge a viable route to a more sustainable future. In order for that to happen, new and quite radical reassessments are required when it comes to human's relationships to technology and the technosphere. To understand you are in a prison, you must first be able to see the bars.

Keynote Abstract While the last 60 years have defined the field of industrial robots, and empowered hard bodied robots to execute complex assembly tasks in constrained industrial settings, the next 60 years will be ushering in our time with Pervasive robots that come in a diversity of forms and materials, helping people with physical tasks. The past 60 years have mostly been inspired by the human form but the animal kingdom with its form diversity has broader potential. For example the boneless octopus is legendary for its ability to squeeze through cracks. With the development of soft materials, machines and materials are coming closer together with machines becoming compliant and fluid like materials and materials becoming more intelligent and this raises an interesting question: what is a robot? Traditionally we have considered industrial manipulators and robots on wheels but what about robots made our of food or paper or ice, or giving everyday objects the ability to move and compute so they become intelligent and autonomous? Soft materials allow us to expand the diversity of robots. In this talk I will discuss (1) what is a soft robot, (2) how do we build soft robots, (3) how do we control soft robots, and (4) what are the challenges and opportunities around soft robots.

Keynote Abstract Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. This talk presents a broad scientific research agenda to study machine behavior. It then summarizes a number of studies of human-machine behavioral dynamics, as well as human perception and expectations of machine behavior.

Keynote Abstract In 2021, we celebrate 100th anniversary of an invention of a ROBOT, the modern symbol of artificial life. In my presentation, I will introduce the 100 years of Robot history and 100 ways of robot existence. Meet the Robot – a concept, an embodied symbol, and an agent of (r)evolution, a drama character, a figure of automation of industrial production, the hero of science fiction, the embodiment of the human fear of (becoming) a machine, the representation of Otherness, and the protagonist of different model situations of human-machine interaction. The list of examples of robot metamorphoses will be framed by theories that will make it possible to understand the transformations of the appearance and the meaning of the Robot in different contexts and in different forms: as a dramatic character, as a literary character and as a machine within robotic art.

Keynote Abstract Consciousness and the concept of internal mental models are foundational topics in neuroscience and psychology. Yet, we do not understand them well enough to engineer artificial lifeforms that are conscious. In this talk, I will be discussing a line of work on developing “world models” for artificial agents. Such world models construct an abstract representation of the agent’s world that helps it navigate in its environment. We will be discussing the use of world models and attention as a form of bottleneck for an artificial agent, connecting this line of work with ideas and techniques from computational evolution and artificial life. The goal of the talk is to encourage the development of artificial life that incorporates a form of internal mental model, which will be a stepping stone for creating conscious machines.

Today's engineered robots are often made from reliable yet dumb parts, which greatly limits their adaptive functionality but ensures that their subsystems do not defect from the overall purpose. In contrast, a key aspect of Life is that biological systems have competency at each level - they are made of collectives of cells, tissues, organs, etc. each of which has local goals, which orchestrates the noise and fragility at lower levels towards highly robust system-level behaviors. The cooperation and competition across scales in living systems results in great plasticity, and in basal cognition - memory and decision-making outside the brain that can provide essential inspiration for artificial life and robotics. In this talk, I will outline the remarkable properties of complex body regeneration in some species, in which cellular collectives remember and work toward a specific anatomical outcome. We have now uncovered some of the mechanisms by which cells represent target morphologies and execute the anatomical homeostasis that enables them to reach these goals despite radical perturbations. The mechanism of this error reduction loop and pattern memory is bioelectrical, and I will describe the new tools with which we can now directly read out these anatomical setpoints in all cell types. Best of all, we can now re-write them in vivo, producing lines of 2-headed flatworms and other drastically altered animal anatomies by brief modulation of the bioelectric patterning software running on genomically un-edited (wild-type) cellular hardware. By cracking the morphogenetic code and understanding how anatomical decisions are implemented by distribute bioelectrical computations in tissues, we get closer to our endgame: a reverse anatomical compiler that will enable top-down design of living form at the level of patterning modules, not by micromanaging the molecular machine code on which much of biology is focused today. I will conclude by sketching out the implications of this field for not only biomedicine but also for new machine learning architectures and for the creation of computer-designed living organisms. The future belongs to a deep consilience of computer science, cognitive science, and biology to understand the plasticity of multi-scale computational systems and greatly broaden the boundaries of life-as-it-could-be.

Soft robots have the potential to adapt their morphology, properties, and behavioral control policies towards different tasks or changing environments. This adaptive capability is often inspired by biological systems. For example, octopus tentacles can access nearly infinite trajectories, yet also form joint-like structures to adapt articulated limb control strategies. Caterpillars display undulation and inchworm gaits but can rapidly curl themselves into a wheel and propel themselves away from predators. The armadillo can change from a walking gait on legs to a rolled-up ball as a defense mechanism. During this talk, I will present recent work towards particulate composites that address distributed sensing, variable stiffness properties, and variable trajectory motions inspired by these capabilities in animals. I will contextualize the materials within robotic skins,which are thin, elastic membranes with embedded robotic function. Robotic skins can be wrapped around arbitrary deformable objects to induce the desired motions and deformations, therefore enabling a multitude of robots with different morphologies and functions. Finally, I will show how merging these material discoveries with robotic skins can be used to achieve new shape-shifting capabilities in next-generation soft robots.

Alife has made fundamental contributions to our understanding of how evolutionary processes work. I will highlight a few of these instances, as well as ongoing work by my group and others embracing digital organisms within more traditional biological boundaries. However, there is a history of artificial life studies that are often overlooked by related disciplines. I don't mean this as a critique of either field. Instead, I would argue it's more of an opportunity. Artificial life has always been pushing the boundaries of truly interdisciplinary science, and as traditional fields expand their own horizons, old discoveries from the artificial life community are waiting to be newly embraced. This has been the promise of interdisciplinary fields, and Alife is well positioned to deliver.

Currently there exists no general theory for what life is. This makes it challenging to anticipate how a more fundamental understanding of life could inform the design (or evolution) of artificial life forms and/or artificial intelligences, or what the role of these will play in the future evolution of Earth and its biosphere. For artificial systems, designed in software, the role of information is clear, whereas for biological and other physical systems it is less so. Unifying the long history of biological evolution with what is happening currently on our planet, or with what might happen in the future due to the technological advances we are mediating, will require new paradigms for understanding what information is and does in natural systems. In this talk, I discuss quantitative approaches aimed at developing a new theory for understanding life based on the idea that life is fundamentally about information (life itself is an abstraction) and how that information interacts with the physical world. I discuss how this leads to new approaches to understand the abstraction that was the last universal common ancestor of known life on Earth, through the evolution of our biosphere to its current technologically mediated form and beyond.

In 1955, John McCarthy and colleagues proposed an AI summer research project with the following aim: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” More than six decades later, all of these research topics remain open and actively investigated in the AI community. While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and robotics, current AI systems still almost entirely lack the ability to form humanlike concepts and abstractions. Some cognitive scientists have proposed that analogy-making is a central mechanism for conceptual abstraction and understanding in humans. Douglas Hofstadter called analogy-making “the core of cognition”, and Hofstadter and co-author Emmanuel Sander noted, “Without concepts there can be no thought, and without analogies there can be no concepts.” In this talk I will reflect on the role played by analogy-making at all levels of intelligence, and on how analogy-making abilities will be central in developing AI systems with humanlike intelligence.

In my laboratory we are interested in creating the conditions that allow an artificial life form to emerge. But how do we know when our chemical system is really on the path to life? Will bottom- up (prebiotic) and top-down (programmed) be intrinsically different types of artificial life forms? In this lecture I will describe three areas of work in my laboratory: 1) how to measure how alive an artificial life form is; 2) our attempts to emerge a bottom up life form; 3) a top down chemically embodied life form. To achieve the top-down life form we had to build a chemical computer that was able to be digitally programmed, error correcting, and ability to do computations using a chemical-logic-machine. We believe this represents the first example of chemical artificial life.

In 2012, Press and Dyson discovered a strategy set, called Zero-determinant (ZD) strategies, which enforces a linear payoff relationship between a focal player and the opponent regardless of the opponent's strategy in the repeated prisoner's dilemma (RPD) game. In the RPD game, a discount factor and observation errors are both important because they often happen in society. Here, we examined strategies that enforce linear payoff relationships in the RPD game considering both a discount factor and observation errors. As a result, we first revealed that the payoffs of two players can be represented by the form of determinants even with these two factors. Then, we searched for all possible strategies that enforce linear payoff relationships and found that both ZD strategies and unconditional strategies are the only strategy sets which satisfy the condition.

It is well established in the scientific literature that global human civilization faces multiple imminent, and potentially existential, crises. The most comprehensive survey is perhaps that of the Planetary Boundaries framework (Rockström et al., 2009; Steffen et al., 2015). The unfolding of these challenges will be very complex, and the trajectory ahead is certainly still open to significant human management and moderation. Nonetheless, it is clear that we are no longer dealing with “problems” that might be “solved”; rather, this is a predicament — an uncertain, dynamic, and at least partially chaotic, disruption in global human development (Gilding, 2012). A predicament calls not for “solution”, but for engagement, and continuous refinement of response. The purpose of this contribution is to explore how some specific concepts, tools and techniques of Artificial Life have already helped shape our understanding of this predicament; and may offer some distinctive supports in moulding our future responses. The use of computational tools to model complex biological, evolutionary, ecological and social dynamics is a foundational technique in the ALife field. Indeed, computational thinking and modelling was at the heart of the systems dynamics approach to socio-ecological modelling pioneered by Forrester (1982). This provided the basis for the famous (or infamous?) Limits to Growth (LTG) project of the Club of Rome (Meadows et al., 1972). This was the first substantive attempt to computationally model the socio-ecological dynamics of global human society and assess whether ecological impacts would be likely to limit the growth of human material activities within any practically foreseeable timeframe. While the model was necessarily crude, the robust result was that — in the absence of effective control measures to the contrary — serious limits would become apparent within the first half of the 21st century. In the almost 50 years since its original publication, the world has tracked remarkably close to the “standard run” of the LTG study (Turner, 2014). In fact, multiple lines of investigation now strongly suggest that aggregate human activity has already reached a state of significant overshoot beyond safe or sustainable ecological limits. Overshoot is a qualitatively distinct regime for the design and operation of any adaptive or mitigating interventions (Catton, 1982). Effective societal responses to date have been significantly impaired by a lack of wide understanding of this harsh ecological reality. This gap in understanding facilitates the comforting — but erroneous — notion that it is prudent to delay difficult responses until after impacts are manifest. But delay is precisely one of the principle mechanisms that actually causes overshoot, and undermines the capability to damp the subsequent “crash”. This presents both a need and an opportunity for Artificial Life practitioners to use their skills and their tools to help catalyse much wider societal understanding of the nature of ecological overshoot and mediate the desperately needed reflections on how to achieve the necessary collective, systems-level, responses (cf. Bullock, 2016). Separately and in conclusion, the presentation will briefly consider the meta-question of the ecological footprint of scholarly activity itself: and what, if any, obligations scholarly communities (such as ISAL, the International Society for Artificial Life) might have to reconsider their established practices in the face of planetary scale ecological emergency (e.g. Wilde and Nevins, 2015).

Although unable to flee predators or sub-optimal growth conditions, plants have the incredible ability to continue normal life after losing whole organs. They can also alter the expression-levels of thousands of genes, remodelling growth and metabolism and deploying an extensive molecular armoury in response to threats. These abilities provide us with food but also present a potential platform for the rapid production of complex molecules from water and light. Until recently, however, we lacked the tools and data necessary for complex engineering of plant systems. The application of engineering principles to plant biology has enabled us to establish platforms for high-throughput, automated, experimentation at nanoscales. We are combining these approaches with genome editing technologies and comparative genomics to investigate how regulatory functions are encoded in plant DNA and to engineer plants with new traits and functions. In recent work, we have shown that genome editing can be used to make plants with different carbohydrate structures, paving the way for the production of more nutritious crops. Currently, we are learning where to make precise changes to regulatory regions in order to rewire the control networks that coordinate large-scale responses to environmental signals. Beyond foods, we are developing plants as photosynthetic platforms for biomanufacturing We are interested not just in making human therapies but also in manufacturing a greater range of products to improve the sustainability of agriculture.

Our socio-technical environment is becoming increasingly complex all the while we have less and less time for making decisions. The advent of computers in general and AI in particular has helped us to deal with complexity and time constraints. Virtually no industry and sector remains untouched by this development: The private sector, government, non-profits; manufacturing, advertisement, logistics, healthcare, defense; finance, compliance, customer care, human resources and many more. Why is it then that, I wonder, we still have very little satisficing solutions to our worlds most pressing problems as for example stated in the sustainable development goals: Climate change, poverty, sustainable cities and so forth. One reason for this, I believe, is grounded in the false trust in the abundance of data and ubiquity of computational power. More data and brute force doesnt necessarily mean better insights and thus decisions. Sustainable and robust decisions depend on our understanding why things happen and our ability to think in plausible futures. Causality and scenarios can be generated through the symbiosis of human brain ingenuity and computational simulations. This implies that humans embrace digitalization as an opportunity and invest in the further development of cognitive humanmachine interaction. I will suggest and critically discuss a framework for constructing and applying a computational decision support framework that furthers this vision. Technically the framework hinges on data fusion, simulation and insight generation. Successful application for decision-making, however, relies on consequent stakeholder integration, which requires building trust in a simulations underlying causal model from which openness to internalize insights in organizational decision making processes derives. I will present the concrete steps of building a computational simulation designed for decision support against the background of two case studies representing pressing social problems, along with the process of taking the client on this journey and how it helped her to improve business critical decisions and thus outcomes. The quintessence is as simple as it remains futuresque: Tomorrows successful organization represents itself and the environment it operates in in a form of holodeck, enabling it and its employees to play through and train for the future that will challenges us.

Modern biological cells are endowed with effective mechanisms which control their division, ensuring that it does not take place before the duplication of the genetic material has been completed. It is unlikely that similar sophisticated mechanisms were in place in primitive protocells, which were much simpler than their present-day descendants. So a major question concerns the way in which reproduction of the whole protocell might take place together with replication of its genetic molecules, absent any kind of high-level control. This might happen if the rate of duplication of the genetic material and that of fission of the protocell are the same, i.e. if the two processes are synchronized. This possibility can be studied using simplified models of reaction networks (among replicators), assuming that one or more replicators can affect the growth and fission rates of their lipid container. Surprisingly enough, such synchronization does not necessarily require a careful assembly of reactions with very specific reaction rates. On the contrary, it turns out to be a property which emerges spontaneously in a broad set of models, with different parameters, different reaction networks and even different protocell architectures. Note that synchronization, while being a widespread property, is not always achieved for all the models and reaction types. The conditions for emergent synchronization will be discussed, reviewing previous work and showing some new results. These results are based upon dynamical models which assume that the reactions are known a priori. On the other hand, in models of the origin of life it is often assumed that not all the important chemicals are there since the very beginning, but that some of them are synthesized at later stages. The appearance of new chemicals makes new reactions possible, which may in turn lead to the synthesis of new chemicals, etc. Dealing with this kind of problems requires the choice of a particular model of the replicators and of their interactions; in this paper the random binary polymer model proposed by S. Kauffman, where the replicators are polymers which can undergo cleavage or condensation, will be considered. This model allows, in principle, the appearance in time of polymers of increasing length. Another aspect which has to be taken into account, in order to properly model these phenomena, is that new chemical species may be initially present in very low concentrations, which require a stochastic treatment like the one allowed by the well-known Gillespie algorithm. The random binary polymer model can give rise in time to collectively autocatalytic sets, which are able to self-replicate; if some chemicals which belong to the core or to the periphery of these sets are coupled to the growth of the lipid container, this may lead to emergent synchronization. However, the interactions can be quite complicated and the overall behaviour can be counterintuitive. Some examples of dynamical behaviours which have been observed in simulations will be presented and discussed, with particular emphasis on features which are always, or frequently, observed. It will be argued that studying the dynamical interaction of autocatalytic sets with the growth and splitting dynamics of the lipid container is crucial to understand the possibility that a population of protocells undergo sustainable growth and evolution.