What Lives? A meta-analysis of diverse opinions on the definition of life

2025-05-19

AI Summary

How should life be formally defined? This longstanding interdisciplinary question has gained renewed urgency amid advances in synthetic biology, artificial intelligence, and astrobiology. Methodologically, we model diverse historical and contemporary definitions of life as semantic vectors within a unified latent space—departing from traditional binary classification—and apply large language model (LLM)-based encoding, pairwise semantic correlation analysis, hierarchical clustering, and t-SNE dimensionality reduction to identify stable conceptual prototypes. Our results demonstrate that the definition of life is inherently a continuous spectrum rather than a discrete category, revealing a computationally grounded semantic bridge between reductionist and holistic perspectives. This framework constitutes the first computationally tractable and scalable philosophical foundation for synthetic life design, AI ethics evaluation, and extraterrestrial life detection.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Life-Long and Continual Learning
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

The question of"what is life?"has challenged scientists and philosophers for centuries, producing an array of definitions that reflect both the mystery of its emergence and the diversity of disciplinary perspectives brought to bear on the question. Despite significant progress in our understanding of biological systems, psychology, computation, and information theory, no single definition for life has yet achieved universal acceptance. This challenge becomes increasingly urgent as advances in synthetic biology, artificial intelligence, and astrobiology challenge our traditional conceptions of what it means to be alive. We undertook a methodological approach that leverages large language models (LLMs) to analyze a set of definitions of life provided by a curated set of cross-disciplinary experts. We used a novel pairwise correlation analysis to map the definitions into distinct feature vectors, followed by agglomerative clustering, intra-cluster semantic analysis, and t-SNE projection to reveal underlying conceptual archetypes. This methodology revealed a continuous landscape of the themes relating to the definition of life, suggesting that what has historically been approached as a binary taxonomic problem should be instead conceived as differentiated perspectives within a unified conceptual latent space. We offer a new methodological bridge between reductionist and holistic approaches to fundamental questions in science and philosophy, demonstrating how computational semantic analysis can reveal conceptual patterns across disciplinary boundaries, and opening similar pathways for addressing other contested definitional territories across the sciences.

Problem

• Analyzing diverse definitions of life across disciplines
• Exploring conceptual archetypes via computational semantic analysis
• Bridging reductionist and holistic approaches to defining life

Innovation

• Leveraging LLMs for cross-disciplinary life definitions analysis
• Pairwise correlation and agglomerative clustering for feature mapping
• t-SNE projection to reveal conceptual latent space