Dexterous assistive devices constitute one of the frontiers for hybrid human-machine systems. Manipulating unstable systems requires task-specific anticipatory dynamics. Learning this dynamics is more difficult when tasks, such as carrying liquid or riding a horse, produce unpredictable, irregular patterns of feedback and have hidden dimensions not projected as sensory feedback. We addressed the issue of coordination with complex systems producing irregular behaviour, with the assumption that mutual coordination allows for non-periodic processes to synchronize and in doing so to become regular. Chaos control gives formal expression to this: chaos can be stabilized onto periodic trajectories provided that the structure of the driving input takes into account the causal structure of the controlled system. Can we learn chaos control in a sensorimotor task? Three groups practiced an auditory-motor synchronization task by matching their continuously sonified hand movements to sonified tutors: a sinusoid served as a Non-Interactive Predictable tutor (NI-P), a chaotic system stood for a Non-Interactive Unpredictable tutor (NI-U), and the same system weakly driven by the participant’s movement stood for an Interactive Unpredictable tutor (I-U). We found that synchronization, dynamic similarity, and causal interaction increased with practice in I-U. Our findings have implications for current efforts to find more adequate ways of controlling complex adaptive systems.

The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s–50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin’s The Origin of Species , and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.

While technology has brought immeasurable benefits to humankind, recent advances in artificial intelligence and autonomous systems have also led to new ethical, legal, and social issues. We now face the problem of creating a cooperative society in which autonomous systems and people can coexist. The concept of artificial life provides unique perspectives, tools, and philosophies for furthering our understanding of complex living, lifelike, or hybrid systems. However, artificial life is still difficult to comprehend for those outside the academic community. We thus created a public co-creation community called ALIFE Lab, which aims to increase awareness of artificial life in collaboration with artificial life researchers and talents from creative fields such as design, art, and fashion. As one of the community activities, we organized a workshop-based program in which participants learned about Artificial Life and used it as a tool to conceive autonomous systems with concrete vocabulary and theory. This paper reports the methodology and outcomes of the workshop.

Earth has undergone a succession of stages driven by physical, chemical, geological, biological, and social processes. Among the most significant transitions in Earth’s planetary evolution are the emergence of life and subsequent biochemical innovations, the emergence of social behavior and cognition, and the emergence of technology. After life emerged, planetary processes became much more complex due to increased diversity in what is biogeochemically possible. With the evolutionary emergence of collective behaviors, social systems, and cognition, an increasing number of planetary processes became controlled by life. Since the emergence of technology, intentional steering of the environment became possible. In each stage, new mechanisms of control, mediated by new information processing architectures, are added to existing levels of control on the planetary environment. We can classify these evolutionary stages of planets into matter-dominated, life-dominated, and agency-dominated phases, where each is distinguished by the extent to which information processing systems control planetary processes. We aim to characterize how each phase shapes planetary environments.