The Multiple Paths to Multiple Life

Rabrg/artificial-life

A simple (300 lines of code) reproduction of Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction

Software in the natural world: A computational approach to hierarchical emergence

Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels; however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalism that articulates how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework establishes a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled.

Functional Information: Towards Synthesis of Biosemiotics and Cybernetics

Biosemiotics and cybernetics are closely related, yet they are separated by the boundary between life and non-life: biosemiotics is focused on living organisms, whereas cybernetics is applied mostly to non-living artificial devices. However, both classes of systems are agents that perform functions necessary for reaching their goals. I propose to shift the focus of biosemiotics from living organisms to agents in general, which all belong to a pragmasphere or functional universe. Agents should be considered in the context of their hierarchy and origin because their semiosis can be inherited or induced by higher-level agents. To preserve and disseminate their functions, agents use functional information - a set of signs that encode and control their functions. It includes stable memory signs, transient messengers, and natural signs. The origin and evolution of functional information is discussed in terms of transitions between vegetative, animal, and social levels of semiosis, defined by Kull. Vegetative semiosis differs substantially from higher levels of semiosis, because signs are recognized and interpreted via direct code-based matching and are not associated with ideal representations of objects. Thus, I consider a separate classification of signs at the vegetative level that includes proto-icons, proto-indexes, and proto-symbols. Animal and social semiosis are based on classification, and modeling of objects, which represent the knowledge of agents about their body (Innenwelt) and environment (Umwelt).

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

The Law of Conservation of Information: Search Processes Only Redistribute Existing Information

Conservation of information sparked scientific interest once a recurring pattern was noticed in the evolutionary computing literature. In grappling with the creation of information through evolutionary algorithms, this literature consistently revealed that the information outputted by such algorithms always needed first to be programmed into them. Thus, the primary goal of this literature—to uncover how information could be created from scratch or de novo —was shown to be misconceived: the information was not created but instead shuffled around or smuggled in, implying that it already existed in some form or other. Information output in these situations therefore always presupposed a counterbalancing input of prior information. Once this pattern was seen, the next logical step was to quantify the amount of information inputted and outputted, demonstrating a consistent mathematical relation between the two. This led to the proof of a number of theorems about search. In these theorems, a baseline search with probability p of success gave way to an improved search with probability q of success. Typically p would be very small and close to zero, implying a practically impossible search (like searching for a needle in a haystack). By contrast, q would be much larger and close to one, implying an eminently doable search. The punchline of these theorems was that, as the improved search became itself the subject of a search (a search for a search , or S4S), the probability of finding it could not exceed  p / q , rendering success of the improved search no more probable than success of the original baseline search, in effect filling one hole by digging another. Such conservation-of-information theorems, as they came to be called, were search-space specific, adapted to different kinds of search across a range of search spaces. There was a measure-theoretic theorem in which probability measures guided search. There were also function-theoretic and fitness-theoretic theorems where mappings into the search space as well as fitness functions on the search space respectively guided search. The key insight of this paper is that all these conservation-of-information theorems are special cases of a simple probabilistic relation based on elementary probability theory. This paper identifies the underlying rationale that makes all the previous conservation-of-information theorems work. In so doing, it provides a straightforward proof and general formulation of what may rightly be called the Law of Conservation of Information.

The Year of the Software Factory

We are building LifeBuild, a personal operating system for your life where AI agents help you draft projects, prioritize your commitments, and stay on top of what matters—from health to relationships to finances and beyond. This Lab Notebook chronicles our explorations.

How Life Works: A User’s Guide to the New Biology

“Bold and intriguing.”—Wall Street Journal • “Penetrating. . . . Provocative and profound.”—Publishers Weekly (starred review) • “Offers plenty of food for thought.”—Kirkus Reviews (starred review)  “Ball’s marvelous book is both wide-ranging and deep. . . . I could not put it down.”—Siddhartha Mukherjee, author of The Song of the Cell and the Pulitzer Prize–winning The Emperor of All Maladies A new, cutting-edge vision of biology that revises our understanding of what life itself is, how to enhance it, and what possibilities it offers.   Biology is undergoing a quiet but profound transformation. Several aspects of the standard picture of how life works—the idea of the genome as a blueprint, of genes as instructions for building an organism, of proteins as precisely tailored molecular machines, of cells as entities with fixed identities, and more—have been exposed as incomplete, misleading, or wrong.   In How Life Works, Philip Ball explores the new biology, revealing life to be a far richer, more ingenious affair than we had guessed. Ball explains that there is no unique place to look for an answer to this question: life is a system of many levels—genes, proteins, cells, tissues, and body modules such as the immune system and the nervous system—each with its own rules and principles. How Life Works explains how these levels operate, interface, and work together (most of the time).   With this knowledge come new possibilities. Today we can redesign and reconfigure living systems, tissues, and organisms. We can reprogram cells, for instance, to carry out new tasks and grow into structures not seen in the natural world. As we discover the conditions that dictate the forms into which cells organize themselves, our ability to guide and select the outcomes becomes ever more extraordinary. Some researchers believe that ultimately we will be able to regenerate limbs and organs, and perhaps even create new life forms that evolution has never imagined.   Incorporating the latest research and insights, How Life Works is a sweeping journey into this new frontier of the life sciences, a realm that will reshape our understanding of life as we know it.

Systems of Global Governance in the Era of Human-Machine Convergence

Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest). We leave some open questions on how techno-social systems can effectively learn and adapt with respect to our understanding of geopolitical complexity.

Sourcegraph — Code Understanding, Oversight and Evolution

Give humans and agents complete context to understand, oversee, and evolve the world's largest, most complex codebases.

The Evolutionary Ecology of Software: Constraints, Innovation, and the AI Disruption

This chapter investigates the evolutionary ecology of software, focusing on the symbiotic relationship between software and innovation. An interplay between constraints, tinkering, and frequency-dependent selection drives the complex evolutionary trajectories of these socio-technological systems. Our approach integrates agent-based modeling and case studies, drawing on complex network analysis and evolutionary theory to explore how software evolves under the competing forces of novelty generation and imitation. By examining the evolution of programming languages and their impact on developer practices, we illustrate how technological artifacts co-evolve with and shape societal norms, cultural dynamics, and human interactions. This ecological perspective also informs our analysis of the emerging role of AI-driven development tools in software evolution. While large language models (LLMs) provide unprecedented access to information, their widespread adoption introduces new evolutionary pressures that may contribute to cultural stagnation, much like the decline of diversity in past software ecosystems. Understanding the evolutionary pressures introduced by AI-mediated software production is critical for anticipating broader patterns of cultural change, technological adaptation, and the future of software innovation.

The Optimization Trap: Why Too Much Efficiency Makes Us Fragile - The Great Simplification

In this episode, Nate is joined by biologist and biophysicist Olivier Hamant to explore why living systems prioritize robustness over performance, and what that means for a civilization built almost entirely in the opposite direction.

Journal publishes paper by video-gamers who derived scientific rules from Stanford online RNA game

Video-gamers have co-authored a paper describing a new set of rules for determining the difficulty of designing structures composed of RNA molecules.

The Next 7000 Programming Languages

Landin’s seminal paper “The next 700 programming languages” considered programming languages prior to 1966 and speculated on the next 700. Half-a-century on, we cast programming languages in a Darwinian ‘tree of life’ and explore languages, their features (genes) and language evolution from the viewpoint of ‘survival of the fittest’.

For the First Time, a Cell Built From Scratch Grows and Divides | Quanta Magazine

Scientists built a synthetic cell that combines more lifelike properties than ever before — proof of concept that it’s possible to bring nonliving materials to life, or something close to it, in the lab.