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Introduction

Defining life has long defied a single, universally accepted formulation. As one survey notes, biology textbooks often list “life” by characteristic features, yet “it is difficult to non-controversially define life,” and experts in fields like astrobiology, synthetic biology, and philosophy routinely confront this challenge. The quest to delineate life becomes especially urgent when searching for life beyond Earth or creating new life-like systems, since different definitions carry deep assumptions about what should count as living. Recent work by Bender et al. (2025) used natural language processing and clustering of 68 expert definitions of life to map this conceptual terrain. Their “t-SNE Definitional Landscape” (Figure 1 below) reveals distinct clusters or “islands” of meaning that reflect varied scientific and philosophical perspectives. In this paper, we analyze these clusters (such as Perceptual Categorization, Cognitive Autonomy, Dissipative Self-Organizing Systems, Informational Self-Replication, among others), discuss the assumptions underlying them, and explore the implications of such definitional pluralism for astrobiology, artificial life, and life-detection missions. We also trace the historical evolution of life definitions from antiquity to the present, and consider whether striving for a single consensus or embracing productive disagreement will better serve humanity’s cosmic life-search.

*Figure 1. “Life – t-SNE Definitional Landscape.” Each point represents a proposed definition of life by an expert; points cluster by conceptual similarity. Clusters are labeled by theme (colors) ranging from Perceptual Categorization (red) to Cognitive Autonomy (cyan) to Dissipative Self-Organizing Systems (teal) and Informational Self-Replication (purple), among others. (Reproduced from Bender et al. 2025.)

Conceptual Clusters in the Life-Definition Map

Figure 1 shows a “landscape” of life definitions in two-dimensional t-SNE space, with eight conceptual clusters identified. Notably, Perez (2025) describes four of these clusters as follows:

• Thermodynamic (Self-Replicating Thermodynamic) Systems cluster: Emphasizes energy flows, entropy-reduction, and far-from-equilibrium processes. These definitions view life principally as a specialized thermodynamic process (echoing Schrödinger’s negentropy idea and Prigogine’s dissipative structures). For instance, life is sometimes characterized as a self-maintaining, entropy-decreasing system that sustains its organization by exporting entropy to the environment.
• Dissipative Self-Organizing Systems cluster: Focuses on spontaneous self-organization, boundary maintenance (e.g. membranes), and dynamic exchange with the environment. Here life is seen as a kind of persistent pattern – a dissipative structure – that autonomously maintains its identity amid material flux. These approaches draw on non-equilibrium thermodynamics and systems theory: a living entity is a network of processes that organizes itself by dissipating energy (e.g. a cell’s metabolism or a convection pattern).
• Cognitive Autonomy cluster: Centers on information processing, goal-directed behavior, and adaptive decision-making. Life is defined in terms of autonomy or “agency”: a living system has internal goals, can sense and respond to its environment, and even learn. This view treats life as a kind of mini-robot or cybernetic agent – an information-processing system (often with a “mind” of sorts) that maintains its organization and pursues self-defined objectives. For example, some definitions require “planning, learning, and abstraction” as hallmarks of living systems.
• Informational Self-Replication cluster: Highlights genetic or code-based replication and evolution. In this perspective, the essence of life is the ability to encode, copy, and transmit information. Life is seen as a self-replicating information-processing system: DNA and digital algorithms are archetypes. Tegmark (2018), for instance, famously defines life as “a self-replicating information-processing system whose information… determines its behavior (software) and the blueprints for its hardware.”. This captures the idea that life’s key feature is the storage and faithful copying of instructions (genes, genomes, computer code) that carry the potential for Darwinian evolution.
• Perceptual Categorization cluster: Treats life as fundamentally tied to perception or observer recognition. Definitions here emphasize that “liveness” is partly an emergent, relational concept: something is alive if an appropriate observer (organism, sensor, or scientist) classifies it as such. This cluster sees life as an observer-dependent category. Bender et al. note that this cluster regards life-claims as “vulnerable mutual dances” – life arises through relational recognition between systems. In effect, life is a property conferred by cognitive categorization or pattern-recognition (for example, toddlers or animals can “point out” living things by cues like self-motion or coherence, much as computer vision systems might be trained).
• Self-Sustaining Dynamic Patterns cluster: Emphasizes persistent organizational dynamics over time. These definitions see living systems as self-maintaining spatiotemporal patterns or dynamic networks (sometimes linked to autopoiesis). As Perez (2025) remarks, definitions in this cluster focus on “pattern and process” – life is a continuously regenerated pattern (chemical, structural, or behavioral) that sustains itself against perturbations. For example, life may be described as a network of reactions that keeps cycling and rebuilding itself (akin to von Neumann’s self-reproducing automata concept or modern artificial-life simulations).
• Dynamic Relational Process cluster: Focuses on life as ongoing becoming. Here the stress is on process-relational philosophy: life is an enduring process, not a static thing. Such definitions highlight continuous self-creation and interdependence. Perez notes that “self-creating” ideas emphasize life’s incessant dynamic nature; for this cluster, life is primarily about continuous autopoiesis and co-creation with the environment. These perspectives often draw on Whiteheadian or process philosophical traditions, seeing organisms as ever-evolving processes.
• Pragmatic Definitional Skepticism cluster: Includes thinkers who argue a strict definition is impossible or unnecessary. Members of this cluster caution that attempting to nail down life by necessary-and-sufficient conditions is misguided. Cleland and Chyba (2002, 2010) famously argue defining life is a “fundamentally misguided” pursuit; Machery (2012) goes so far as to say life definition projects have “wasted a lot of time, energy and money”. Instead, these voices treat “life” as a fuzzy, context-driven concept and focus on practical categories or diagnostics rather than a single essence.

These clusters are not entirely isolated. As Figure 1 suggests, certain definitions act as bridges across themes. For instance, Noble’s definition of life as “self-creating agency” spans multiple clusters: “self-creating” speaks to dynamic relational and self-sustaining aspects, while “agency” evokes cognitive autonomy. Similarly, Watson’s poetic “life is the pattern and process of love – a deeply vulnerable mutual dance” resonates across perceptual, pattern-oriented, and autonomy views. Such boundary-crossing definitions hint that the clusters share conceptual overlaps, but also underscore that each cluster privileges different features.

In summary, the t-SNE map reveals that definitions of life form a continuum of perspectives rather than a single category. The four main clusters mentioned above (thermodynamic, dissipative, cognitive, informational) illustrate fundamentally different ways to frame “life,” reflecting decades of work in physics, biology, cognitive science, and philosophy. Each emphasizes certain life-features while deemphasizing others – for example, a thermodynamic definition will likely downplay cognition, whereas an informational definition may not mention energy flows at all.

Philosophical Assumptions and Methodologies

These definitional clusters reflect deeper philosophical and methodological commitments. For instance, the Thermodynamic/Dissipative cluster rests on a mechanistic/materialist worldview: life is seen as a physical process governed by physics (entropy, energy, matter). Implicit is an assumption that life is essentially about physical organization far from equilibrium. In this camp, one treats organisms as complex machines or organized chemical factories. Methodologically, this leads to approaches that emphasize physics-based models, e.g. non-equilibrium thermodynamics (Prigogine, Deacon), or computational models of self-assembly. Epistemologically, it often implies reductionism: life can be understood by reducing it to chemical and physical laws.

In contrast, the Cognitive Autonomy cluster takes a more functional/teleonomic stance. Here life’s essence is autonomy and goal-directedness – often implying a quasi-mental or cybernetic dimension. This view aligns with philosophical traditions like cybernetics and cognitive science, and sometimes with vitalistic-sounding ideas about purpose (if couched in information terms). Scientists in this camp (e.g. AI researchers, some biologists) often use computational or theoretical modeling, agent-based simulations, or robotics: they look for markers of autonomy and information processing (neural networks, gene regulatory networks, signaling pathways). Epistemologically, this cluster tends to treat life as a systems property not reducible to chemistry alone. Defining life thus may rely on observed functions (e.g. does the system exhibit learning or survival-driven behavior) rather than on specific material composition.

The Informational Self-Replication cluster shares some overlap with Cognitive Autonomy but with a strict emphasis on heredity and evolution. It leans on the gene-centered view (Dawkins’ replicators) and on Shannon-information theory. The philosophical assumption is that life is fundamentally about replicating bits of information that can evolve. This view often glosses over the “physical substrate” (so long as information is preserved). Methodologically, it has led to studies of digital organisms (e.g. genetic algorithms, Tierra/Avida) and origin-of-life research focusing on nucleic acids or other information polymers. Epistemologically it often conflates life with evolvability; here the criterion for aliveness is the presence of an information-replicating system.

The Perceptual Categorization cluster, by contrast, shifts toward a phenomenological/relational philosophy. Its assumption is that life is not purely an objective fact but also involves an observer. This aligns loosely with pragmatism or social constructivism: we “see” life through our evolutionary-tuned perceptual filters (Green algae look alive because they move, humans look alive because they exhibit agency, etc.). In practice, such thinkers might draw on cognitive psychology (how do organisms recognize living vs nonliving objects?) or on participatory epistemologies. The methodology here might involve experiments in vision, culture, or AI (e.g. how do classifiers identify living things?), rather than a purely bottom-up biochemistry approach.

The Self-Sustaining Pattern and Dynamic Relational Process clusters carry a systems theory/holistic flavor. They assume life is an organizational state or process not easily pinned to a single molecule or sub-system. These perspectives often invoke ideas like autopoiesis (Maturana & Varela) or process philosophy (Heraclitus/Whitehead). Methodologically, one finds network theory, dynamical systems modeling, and synthetic biology experiments (creating minimal cells or protocells to test what network dynamics can sustain themselves) as typical. Epistemologically they blur the line between organism and environment, sometimes seeing life as a nested hierarchy of processes.

The Pragmatic Skepticism cluster is rooted in linguistic/philosophical analysis. Its proponents often take a Wittgensteinian or Carnapian stance: “life” may be a family-resemblance concept or a tool of discourse, not a rigid essence. They emphasize the utility of different definitions over any single “true” definition. For example, Cleland and Chyba argue that definitions of life should be seen as heuristic tools for organizing research rather than natural kinds. Accordingly, operational or diagnostic definitions (what instruments will measure on missions) become more important than abstract essences. Methodologies here involve conceptual analysis and historical study of how “life” has been used in practice (e.g. examining life detection protocols, classification schemes).

In sum, the clusters reflect tensions between reductionism vs holism, objective vs observer-dependent, and theoretical vs operational approaches. For instance, a thermodynamic-reductionist mindset might clash with an evolutionary-informational one about what to prioritize. A key epistemological divide is essence vs utility: some assume life has a hidden essence waiting to be uncovered, while others use a family resemblance or cluster view (life as an overlapping set of features). The definitional map shows these approaches are currently coexisting, often with little cross-talk; as Perez notes, a physicist’s definition of life (entropy focus) can seem “to inhabit a separate conceptual world” from a cognitive scientist’s.

Historical Evolution of Life Definitions

To contextualize these modern perspectives, we trace how the concept of life has unfolded historically. In antiquity, Plato and Aristotle laid early groundwork: Plato divided existence into mineral, vegetable, animal, and rational life (animals had sensation, humans had reasoning). Aristotle refined this by positing that living things possess a form or soul that drives self-motion and self-preservation. He emphasized that resisting perturbations (a proto-homeostasis) distinguished the living from the inanimate. (Aristotle’s vital notion of an immaterial “soul” was later secularized.) These classical ideas treated life as an intrinsic form or essence in things like plants and animals.

In the early modern era, Descartes famously drew a radical line: animals were automata (complex machines) with no inner life, while only humans had the rational soul. Descartes’ mechanistic view broke with medieval teleology and foreshadowed a purely physicalist biology. It prompted a reaction in the form of Vitalism, the 17th–19th-century doctrine that life requires special forces or principles beyond chemistry. Vitalists (e.g. Stahl, Bergson) proposed immaterial élan vital or unique biochemical rules. The turning point came in 1828 when Friedrich Wöhler synthesized urea from inorganic chemicals, undermining the notion of a life-specific force. That success signaled that organic compounds (once thought the province of life) followed the same chemistry, paving the way for a unified life‑science.

In the 19th century, advances in microscopy and evolutionary theory produced the modern biological worldview. Figures like Schleiden and Schwann formulated cell theory (life is cellular and cells arise from cells). Charles Darwin’s theory of descent with modification (1859) gave life a historical dimension: all organisms share a common ancestor. Michel Foucault later remarked that the very concept of a unified “Life” was essentially an 18th–19th century invention, arising when biologists began noticing commonalities (genetic, morphological, ecological) across diverse organisms. Darwin’s work emphasized variation and evolution as key traits of living lineages (one of the clusters today). By the late 19th century, spontaneous generation was disproven (Pasteur), reinforcing the view that life comes from life.

The 20th century added new layers. Schrödinger (1944) asked “What is Life?” in a popular physics memoir, proposing that heredity is encoded in “aperiodic crystals” and that living organisms feed on negative entropy. His thermodynamic insight influenced the Thermodynamic/Dissipative view. Mid-century molecular biology (Watson & Crick’s DNA, the genetic code) reinforced the Informational Self-Replication approach: life became understood as information in nucleic acids. Cybernetics and systems biology (Wiener, von Neumann) brought ideas of information processing and self-reproducing automata into the picture, dovetailing with today’s cognitive/autonomy cluster. Synthetic biology and computational models (1960s-onwards) introduced artificial analogues of life, further challenging old notions.

Meanwhile, philosophy of biology began explicitly questioning the concept of life. The mid-late 20th century saw debate over essentialism: was life a “natural kind” like water = H₂O, or something looser? Cleland & Chyba (2002) would later note that in contrast to water, any definition of life remained ambiguous with boundary cases. By the 21st century, new fields – astrobiology, artificial life (ALife), origin-of-life research – revived interest. In particular, exobiology (Arnold, Hogan, NASA in 1960s–70s) asked how to recognize life unlike Earth’s. The NASA “working definition” (circa 1990s) – “a self-sustaining chemical system capable of Darwinian evolution” – tried to capture key features of Earth life, yet it was already admitted to be Earth-centric. Generalizations abounded: e.g., Bartlett & Wong (2020) proposed “lyfe” (dissipation, autocatalysis, homeostasis, learning) to encompass any possible life process, reflecting a modern pluralistic impulse. Work on digital life and ALife (from von Neumann’s self-replicating automata through Langton’s ALife workshop, 1987) often uses definitions focused on information and evolution. In short, as biology matured from classification (Linnaean era) to molecular synthesis to systems science, each phase brought new criteria for life – and no single view prevailed.

Implications for Astrobiology, Artificial Life, and SETI

The diversity of life-definitions carries significant implications for fields that hunt or simulate life. In astrobiology, researchers must decide operationally what to look for on Mars, Europa, exoplanets, etc. If one defines life narrowly (say, terrestrial biochemistry), one might miss radically different organisms; if too broadly, one risks false positives (e.g. strange crystals or nonliving chemistry). As NASA itself notes, the standard working definition explicitly reflects Earth life – “a self-sustaining chemical system capable of Darwinian evolution” – but acknowledges the danger: we might land on Mars and not recognize life because it doesn’t fit our DNA/Lipid/Water paradigm. This concern motivates astrobiologists to focus on broad biosignatures (organic compounds, chemical disequilibria, metabolic byproducts) rather than a single test. For example, NASA’s Ladder of Life Detection enumerates multiple features (organic molecules, redox imbalances, pigments, waste heat, etc.) each of which might indicate life. The Ladder’s emphasis on many complementary indicators recognizes that any one sign can be ambiguous (“many life-used molecules can form without life”). Projects like the Mars 2020 Perseverance rover and the planned Europa Clipper are thus outfitted to search for a suite of habitability markers and organic matter, not just one definitive proof.

In artificial life (ALife) research, definitional pluralism is similarly evident. The field was born in the late 20th century precisely out of uncertainty about life’s criteria, aiming to create life-like phenomena in silico or in vitro. Practitioners come from biology, computer science, engineering, and often lack consensus. As one review observes, ALife is “harder to recognize” and “in which the central concept—life itself—is vexingly undefined”. The lack of shared tenets means ALife projects proceed in diverse ways – some build evolving digital organisms, others chemical protocells, others robotic systems – mirroring the very discontinuity and evolution they study. On the positive side, this pluripotency fosters creativity: by trying many embodiments, ALifers explore the parameter space of life. On the other hand, as Scientific American notes, the absence of consensus “doesn’t help” by leaving ALife as a “meandering array of projects” lacking common standards. This situation contrasts with AI, where definitional clarity (even if flawed) has guided benchmarks; ALife itself is in flux because everyone’s using their own yardstick for “alive”.

In SETI (Search for Extraterrestrial Intelligence), the focus is usually on intelligent or technological life rather than life per se, but definitional diversity still looms. SETI implicitly assumes intelligent life must share certain properties (e.g. technological radio use). If a definition of life focuses on intelligence or communication (a subset of Cognitive Autonomy), SETI will be satisfied. If life is broader (microbes, chemotrophs), then SETI programs alone miss these. Conversely, SETI researchers must consider: could a radio signal come from something we wouldn’t have called “life” otherwise? This creates tension between looking for life by oxygen/biology signatures (astrobiology) versus looking for “agents” (SETI). The definitional landscape suggests that SETI might benefit from engaging other perspectives: for example, a truly advanced entity (perhaps a “post-biological” AI) might not fit any classical definition of life, raising philosophical questions. As with astrobiology, definitional pluralism means SETI strategists must be clear what they are searching for (e.g. intelligence versus life in general), but also remain open to surprises.

Overall, definitional diversity means that astrobiology, ALife, and SETI are each grappling with a menu of possible “life criteria.” Astrobiologists must design instruments to detect a broad set of biosignatures (as in NASA’s Ladder framework) rather than one silver bullet. Artificial life researchers often sidestep the definition question by declaring “if it evolves and adapts, we’ll call it alive,” but this practical stance can limit dialogue between subfields. And SETI’s search for “technosignatures” may need to reconcile what counts as a living intelligence in the broader sense. In each case, the varieties of definition shape the research agendas: they determine which worlds we deem “habitable,” which lab efforts count as creating life, and which signals we ignore.

Definitional Pluralism in Life-Detection Missions

The implications of pluralism are perhaps most acute when planning concrete life-detection missions. On Mars, past and future landers must decide what to look for. The Viking missions (1976) famously carried biology experiments based on terrestrial microbes: they tested for metabolism and organic molecules. Critically, the designers assumed Martian life (if any) would behave like Earth microbes (e.g. require liquid water, metabolize nutrients). This Earth-centric assumption may have doomed Viking’s search or led to ambiguous results. Space.com reports suggest that by assuming water-based metabolism, Viking might have “accidentally kill[ed] life on Mars” by using too harsh conditions. In any case, Viking’s mixed signals (one experiment was positive, others negative) highlighted how the definition of life used in the test guided interpretation. Today’s Mars strategies reflect pluralism: rovers like Perseverance carry instruments (SHERLOC, PIXL, etc.) to detect organic molecules and chemical gradients, as well as to collect samples for future return. Mission planners consider both geological contexts and biosignatures (e.g. methane, organics, sediment structures) in tandem. In essence, they build a life-detection “Toolkit” rather than a single litmus test, anticipating life might manifest along multiple clues.

For Europa and Enceladus, the targets are icy ocean worlds. Cassini’s discovery of organic-rich, salty plumes on Enceladus (2015) and Hubble’s imaging of Europa’s plumes have raised hopes of subsurface life. Mission concepts (Europa Clipper, possible Enceladus orbiter/lander) must decide which biosignatures are diagnostic. Scientists have proposed looking for amino acids, lipids, or metabolic byproducts in plumes, as well as geochemical signals of life-sustaining processes. The definitional issue is acute: life in an ocean beneath kilometers of ice might not have “tell-tale” surface features. Thus, explorers focus on environmental context plus subtle chemical disequilibria. The pluralism here translates into multi-pronged detection: not just “am I finding cells?” but “are these chemical ratios only explainable by biology?” (e.g. disproportionate carbon-13 levels, or unusual redox combinations). NASA’s Ladder philosophy holds here too: no single biosignature is conclusive, especially on worlds where we cannot sample directly, so a suite of possible indicators is used.

On exoplanets, life detection is remote and purely observational. Definitions translate into which atmospheric or surface signs we target. Contemporary exoplanet science looks for biosignature gases (O₂, O₃, CH₄, etc.), surface reflectance features (the “red edge” of vegetation), and climate indicators of habitability. These targets stem from life-as-we-know-it: Earth analogies. But definitional pluralism reminds us of imposters (abiotic oxygen from photochemistry, false “vegetation” from minerals) and of life beyond analogs (life that doesn’t use oxygen or any of the “usual” metabolisms). Researchers have thus proposed looking for chemical disequilibrium: an atmosphere out of balance (e.g. coexisting CH₄ and O₂) could hint at life’s non-random activity. Again, no single molecule seals the deal; context (stellar type, geochemistry) must be accounted for. The challenge is enormous – it was long noted that truly convincing evidence (like detecting an evolving biosphere) is likely beyond current technology. Yet mission planners for telescopes (JWST, future LUVOIR/HabEx) incorporate what we learn from Earth-centric definitions while remaining open to surprises (for instance, noting that non-carbon solvents could exist).

Definitional pluralism thus pushes mission designers toward flexible, multi-wavelength, multi-signature strategies. Instead of assuming “one feature = life,” scientists build decision trees. For example, NASA’s astrobiology strategy explicitly urges studying Earth’s origin-of-life environments to guide what signatures a mission should find. In other words, understanding different chemical pathways to life on Earth informs what might count as life on Mars or icy moons. Similarly, exoplanet researchers are exploring the full space of possible life chemistries, looking for general principles (like thermodynamic disequilibrium or information flow) that any “life” might share. Thus, pluralism translates into plural search methods: spectroscopy, microscopy, geochemical analysis, and even high-throughput computations to simulate alternative biochemistries.

In sum, definitional pluralism complicates but enriches life-detection: complicates because each mission must justify why its chosen biomarkers correspond to some definition of life; enriches because multiple definitions invite multiple detection avenues, increasing the odds of catching an unexpected lifeform. A consensus definition might simplify planning, but it risks narrowing the search. As NASA’s astrobiologists warn, we do not want to mistake life for its absence by using overly narrow criteria. Instead, the current approach is broadly pluralistic: plan for a range of possibilities and interpret data with flexible frameworks.

The Quest for Consensus Versus Embracing Pluralism

The final question is whether the field should strive for a single unified definition of life or accept “productive disagreement” among definitions. Historically, efforts at strict consensus have largely failed. As noted earlier, Cleland and Chyba observed in 2002 that “no broadly accepted definition of ‘life’” exists despite decades of effort. Their critique (and Machery’s subsequent support) was that seeking necessary-and-sufficient conditions may be futile or misleading. Indeed, the t-SNE analysis suggests that experts naturally cluster into different camps, not converging on a midpoint. From this viewpoint, no single definition will satisfy all purposes – what serves a chemist does not serve an AI researcher.

Proponents of pluralism argue that disagreement has pragmatic value. Knuuttila and Loettgers (2017) note that definitions can serve different epistemic roles: theoretical (framing understanding), transdisciplinary (bridging fields), or diagnostic (guiding experiments). In practice, scientists routinely use a working definition that suits their task (e.g. a field biologist’s vs an engineer’s) and switch context as needed. Bich and Green (2017) similarly defend the utility of operational definitions that can be tested and manipulated in the lab. From this perspective, forcing a consensus might suppress innovation: each approach has revealed insights about life (e.g. thermodynamics teaches about metabolism, information theory teaches about heredity, cognitive models teach about behavior).

On the other hand, a consensus definition could clarify communication and missions. If NASA and ESA agreed on broad criteria (even if not precise necessary conditions), educational and public understanding would benefit. For instance, having a shared baseline – say, that life involves some combination of metabolism, reproduction, and homeostasis – could align mission priorities and avoid confusion (“this signal is or isn’t life by our agreed standards”). Too much pluralism risks paralysis or equivocation: if any phenomenon can be shoehorned into some definition of life, the term loses discriminatory power. Indeed, philosophers of science caution that scientific concepts work best when anchored by a core criteria, with peripheral exceptions clearly delineated.

However, given the current state of knowledge, pluralism may still be more productive. The “boundary objects” concept from the t-SNE analysis suggests a way forward: instead of a single definition, adopt bridge concepts that capture commonalities across views. For example, the notion of “autopoietic agency” or “adaptive self-organizing system” might satisfy both a systems theorist and a cognitive scientist at once. Encouraging such interdisciplinary definitions could foster dialogue between camps. It’s notable that the most useful life-detection criteria in astrobiology (like the Ladder) already embody multiple aspects (chemistry, thermodynamics, complexity) rather than a single essence.

In the end, our cosmic quest may benefit from both approaches: aim for a loose consensus on certain minimal criteria (e.g. life uses energy flows and information replication) to guide cooperative efforts, while still valuing the rich “productive disagreements” that yield new ideas. The definitional landscape itself suggests this hybrid: clusters are contiguous and overlapping, not islands drifting apart entirely. In fact, Bender et al. propose that definitions form a “unified conceptual latent space” rather than a binary split. This metaphor implies that by studying the full topology (as we have with t-SNE), researchers can communicate across traditions even while preserving diversity of views.

Conclusion

The endeavor to define and discover life in the cosmos is as multifaceted as life itself. The t-SNE “life map” underscores that definitions reflect different intuitions and disciplinary lenses: physics, chemistry, computation, and perception each carve “life” in their own way. Historically, life as a concept has expanded from Aristotelian souls and vital forces to Darwinian evolution, molecular information, and cybernetic autonomy – each era adding new pillars (metabolism, genetics, cognition, etc.). Today, no single definition commands consensus, and perhaps none can, given the breadth of possible living phenomena. Rather than lamenting this pluralism, scientists and philosophers increasingly view it as a resource. By recognizing the assumptions behind each cluster, we can design better life-search strategies (as NASA’s Ladder exemplifies) and richer artificial life experiments (as ALife’s diversity shows).

Ultimately, whether future explorers reach a consensus definition or continue in dialogue through fruitful disagreement, the key will be clarity about which definition is in play. Transparency about assumptions – e.g. “We define life here as any self-sustaining chemical metabolism” – will help interpret findings on Mars or beyond. And the plural landscape suggests: even as we specialize, we should keep an eye on the conceptual map, using bridge-definitions to ensure our insights are commensurable. In this way, humanity’s quest to know what life is and where it might be found becomes a meta-process itself – one that learns about cognition, chemistry, and the cosmos in parallel with seeking life.

Sources: Scholarly analyses of life’s definitions, recent astrobiology literature, and the cited clustering study.