In Rise of the Self-Replicators we delve into the deep history of thought about machines, AI and robots that can reproduce and evolve. Although these might seem like very modern concepts, we show that people were thinking about them as far back as the mid-1600s and that the discussion gathered pace in the 1800s following the British Industrial Revolution and the publication of Darwin’s On The Origin of Species.
Behind all of the work we discuss lie two central questions:
- Is it possible to design robots and other machines that can reproduce and evolve just like biological organisms do?
- And, if so, what are the implications: for the machines, for ourselves, for our environment, and for the future of life on Earth and elsewhere?
In the absence of a vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or of highly effective pharmaceutical treatments for COVID-19, countries have implemented a large range of non-pharmaceutical interventions to control the spread of the virus.1 These interventions differ in their level of stringency (ie, the severity of the measures) and their ultimate objective (eg, prevent health systems being overwhelmed, suppress incidence to low levels, or reduce incidence to zero and keep it there). With many countries facing epidemic resurgence, evaluating the impact of different strategies implemented in the early phases of the pandemic is crucial for developing an effective long-term response.
New Zealand adopted a set of non-pharmaceutical interventions aiming to bring COVID-19 incidence to zero
Andrea Roli and Stuart A. Kauffman
Entropy 2020, 22(10), 1163
Since early cybernetics studies by Wiener, Pask, and Ashby, the properties of living systems are subject to deep investigations. The goals of this endeavour are both understanding and building: abstract models and general principles are sought for describing organisms, their dynamics and their ability to produce adaptive behavior. This research has achieved prominent results in fields such as artificial intelligence and artificial life. For example, today we have robots capable of exploring hostile environments with high level of self-sufficiency, planning capabilities and able to learn. Nevertheless, the discrepancy between the emergence and evolution of life and artificial systems is still huge. In this paper, we identify the fundamental elements that characterize the evolution of the biosphere and open-ended evolution, and we illustrate their implications for the evolution of artificial systems. Subsequently, we discuss the most relevant issues and questions that this viewpoint poses both for biological and artificial systems.
Dwight Read, Claes Andersson
We review issues stemming from current models regarding the drivers of cultural complexity and cultural evolution. We disagree with the implication of the treadmill model, based on dual-inheritance theory, that population size is the driver of cultural complexity. The treadmill model reduces the evolution of artifact complexity, measured by the number of parts, to the statistical fact that individuals with high skills are more likely to be found in a larger population than in a smaller population. However, for the treadmill model to operate as claimed, implausibly high skill levels must be assumed. Contrary to the treadmill model, the risk hypothesis for the complexity of artifacts relates the number of parts to increased functional efficiency of implements. Empirically, all data on hunter-gatherer artifact complexity support the risk hypothesis and reject the treadmill model. Still, there are conditions under which increased technological complexity relates to increased population size, but the dependency does not occur in the manner expressed in the treadmill model. Instead, it relates to population size when the support system for the technology requires a large population size. If anything, anthropology and ecology suggest that cultural complexity generates high population density rather than the other way around.
James Bell, Ginestra Bianconi, David Butler, Jon Crowcroft, Paul C.W Davies, Chris Hicks, Hyunju Kim, Istvan Z. Kiss, Francesco Di Lauro, Carsten Maple, Ayan Paul, Mikhail Prokopenko, Philip Tee, Sara I. Walker
On May 28th and 29th, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in Network Science and Epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in Network Science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.