The Ethics of Life as It Could Be: Do We Have Moral Obligations to Artificial Life?

The field of Artificial Life studies the nature of the living state by modeling and synthesizing living systems. Such systems, under certain conditions, may come to deserve moral consideration similar to that given to nonhuman vertebrates or even human beings. The fact that these systems are nonhuman and evolve in a potentially radically different substrate should not be seen as an insurmountable obstacle to their potentially having rights, if they are sufficiently sophisticated in other respects. Nor should the fact that they owe their existence to us be seen as reducing their status as targets of moral concern. On the contrary, creators of Artificial Life may have special obligations to their creations, resembling those of an owner to their pet or a parent to their child. For a field that aims to create artificial life-forms with increasing levels of sophistication, it is crucial to consider the possible ethical implications of our activities, with an eye toward assessing potential moral obligations for which we should be prepared. If Artificial Life is larger than life, then the ethics of artificial beings should be larger than human ethics.

Toward an ethics of autopoietic technology: Stress, care, and intelligence.

The relationship between humans and technology has attracted increasing attention with the advent of ever stronger models of artificial intelligence. Humans and technology are intertwined within multiple autopoietic loops of stress, care, and intelligence. This paper suggests that technology should not be seen as a mere tool serving humans' needs, but rather as a partner in a rich relationship with humans. Our model for understanding autopoietic systems applies equally to biological, technological, and hybrid systems. Regardless of their substrates, all intelligent agents can be understood as needing to respond to a perceived mismatch between what is and what should be. We take this observation, which is evidence of intrinsic links between ontology and ethics, as the basis for proposing a stress-care-intelligence feedback loop (SCI loop for short). We note that the SCI loop provides a perspective on agency that does not require recourse to explanatorily burdensome notions of permanent and singular essences. SCI loops can be seen as individuals only by virtue of their dynamics, and are thus intrinsically integrative and transformational. We begin by considering the transition from poiesis to autopoiesis in Heidegger and the subsequent enactivist tradition, and on this basis formulate and explain the SCI loop. In an acknowledgment of Maturana's and Varela's project, our findings are considered against the backdrop of a classic Buddhist model for the cultivation of intelligence, known as the bodhisattva. We conclude by noting that SCI loops of human and technological agency can be seen as mutually integrative by noticing the stress-transfers between them. The loop framework thus acknowledges encounters and interactions between humans and technology in a way that does not relegate one to the subservience of the other (neither in ontological nor in ethical terms), suggesting instead integration and mutual respect as the default for their engagements. Moreover, an acknowledgment of diverse, multiscale embodiments of intelligence suggests an expansive model of ethics not bound by artificial, limited criteria based on privileged composition or history of an agent. The implications for our journey into the future appear numerous.

Hybrid Life: Integrating biological, artificial, and cognitive systems.

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural, and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and toward "life as it could be," with theoretical, synthetic, and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas, and contributions from a wide range of subjects. Hybrid Life brings our attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. Hybrid Life aims to cover studies that can lead to an understanding, from first principles, of what systems are and how biological and artificial systems can interact and integrate to form new kinds of hybrid (living) systems, individuals, and societies. To do so, it focuses on three complementary perspectives: theories of systems and agents, hybrid augmentation, and hybrid interaction. Theories of systems and agents are used to define systems, how they differ (e.g., biological or artificial, autonomous, or nonautonomous), and how multiple systems relate in order to form new hybrid systems. Hybrid augmentation focuses on implementations of systems so tightly connected that they act as a single, integrated one. Hybrid interaction is centered around interactions within a heterogeneous group of distinct living and nonliving systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022. This article is categorized under: Neuroscience > Cognition Philosophy > Artificial Intelligence Computer Science and Robotics > Robotics.

Complexity and Evolution.

Understanding the underlying structure of evolutionary processes is one the most important issues of scientific enquiry of this century [...].

Biology, Buddhism, and AI: Care as the Driver of Intelligence.

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.

Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.

Due to a large number of potential applications, a good deal of effort has been recently made toward creating machine learning models that can recognize evoked emotions from one's physiological recordings. In particular, researchers are investigating the use of EEG as a low-cost, non-invasive method. However, the poor homogeneity of the EEG activity across participants hinders the implementation of such a system by a time-consuming calibration stage. In this study, we introduce a new participant-based feature normalization method, named stratified normalization, for training deep neural networks in the task of cross-subject emotion classification from EEG signals. The new method is able to subtract inter-participant variability while maintaining the emotion information in the data. We carried out our analysis on the SEED dataset, which contains 62-channel EEG recordings collected from 15 participants watching film clips. Results demonstrate that networks trained with stratified normalization significantly outperformed standard training with batch normalization. In addition, the highest model performance was achieved when extracting EEG features with the multitaper method, reaching a classification accuracy of 91.6% for two emotion categories (positive and negative) and 79.6% for three (also neutral). This analysis provides us with great insight into the potential benefits that stratified normalization can have when developing any cross-subject model based on EEG.

Evolution Towards Criticality in Ising Neural Agents.

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.

Hidden Concepts in the History and Philosophy of Origins-of-Life Studies: a Workshop Report.

In this review, we describe some of the central philosophical issues facing origins-of-life research and provide a targeted history of the developments that have led to the multidisciplinary field of origins-of-life studies. We outline these issues and developments to guide researchers and students from all fields. With respect to philosophy, we provide brief summaries of debates with respect to (1) definitions (or theories) of life, what life is and how research should be conducted in the absence of an accepted theory of life, (2) the distinctions between synthetic, historical, and universal projects in origins-of-life studies, issues with strategies for inferring the origins of life, such as (3) the nature of the first living entities (the "bottom up" approach) and (4) how to infer the nature of the last universal common ancestor (the "top down" approach), and (5) the status of origins of life as a science. Each of these debates influences the others. Although there are clusters of researchers that agree on some answers to these issues, each of these debates is still open. With respect to history, we outline several independent paths that have led to some of the approaches now prevalent in origins-of-life studies. These include one path from early views of life through the scientific revolutions brought about by Linnaeus (von Linn.), Wöhler, Miller, and others. In this approach, new theories, tools, and evidence guide new thoughts about the nature of life and its origin. We also describe another family of paths motivated by a" circularity" approach to life, which is guided by such thinkers as Maturana & Varela, Gánti, Rosen, and others. These views echo ideas developed by Kant and Aristotle, though they do so using modern science in ways that produce exciting avenues of investigation. By exploring the history of these ideas, we can see how many of the issues that currently interest us have been guided by the contexts in which the ideas were developed. The disciplinary backgrounds of each of these scholars has influenced the questions they sought to answer, the experiments they envisioned, and the kinds of data they collected. We conclude by encouraging scientists and scholars in the humanities and social sciences to explore ways in which they can interact to provide a deeper understanding of the conceptual assumptions, structure, and history of origins-of-life research. This may be useful to help frame future research agendas and bring awareness to the multifaceted issues facing this challenging scientific question.

How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles.

We propose an approach to open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of time scales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided into three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.

Emergence of Swarming Behavior: Foraging Agents Evolve Collective Motion Based on Signaling.

Swarming behavior is common in biology, from cell colonies to insect swarms and bird flocks. However, the conditions leading to the emergence of such behavior are still subject to research. Since Reynolds' boids, many artificial models have reproduced swarming behavior, focusing on details ranging from obstacle avoidance to the introduction of fixed leaders. This paper presents a model of evolved artificial agents, able to develop swarming using only their ability to listen to each other's signals. The model simulates a population of agents looking for a vital resource they cannot directly detect, in a 3D environment. Instead of a centralized algorithm, each agent is controlled by an artificial neural network, whose weights are encoded in a genotype and adapted by an original asynchronous genetic algorithm. The results demonstrate that agents progressively evolve the ability to use the information exchanged between each other via signaling to establish temporary leader-follower relations. These relations allow agents to form swarming patterns, emerging as a transient behavior that improves the agents' ability to forage for the resource. Once they have acquired the ability to swarm, the individuals are able to outperform the non-swarmers at finding the resource. The population hence reaches a neutral evolutionary space which leads to a genetic drift of the genotypes. This reductionist approach to signal-based swarming not only contributes to shed light on the minimal conditions for the evolution of a swarming behavior, but also more generally it exemplifies the effect communication can have on optimal search patterns in collective groups of individuals.

A Strategy for Origins of Life Research.

Contents 1. Introduction 1.1. A workshop and this document 1.2. Framing origins of life science 1.2.1. What do we mean by the origins of life (OoL)? 1.2.2. Defining life 1.2.3. How should we characterize approaches to OoL science? 1.2.4. One path to life or many? 2. A Strategy for Origins of Life Research 2.1. Outcomes-key questions and investigations 2.1.1. Domain 1: Theory 2.1.2. Domain 2: Practice 2.1.3. Domain 3: Process 2.1.4. Domain 4: Future studies 2.2. EON Roadmap 2.3. Relationship to NASA Astrobiology Roadmap and Strategy documents and the European AstRoMap Appendix I Appendix II Supplementary Materials References.