ENTERPRISE AI ANALYSIS

Biology struggles with definitions for concepts like 'gene' or 'species' due to diversity and evolving knowledge. The 'biological species concept' fails for asexual or hybridizing organisms. Defining 'life' is even harder due to the single origin of Earth life (making universal criteria hard to distinguish from contingent ones), and the existence of 'borderline' entities like viruses, transposable elements, and prions. The distinction between 'living'/'dead' vs. 'living'/'non-living' is also discussed, highlighting complexities.

The paper critiques existing approaches (try harder, wait and see, accept multiple definitions) as unsatisfactory. It proposes a family resemblance approach using statistical modeling (cluster analysis, PCA, linear discriminant analysis) on a dataset of criteria and entities. Preliminary results show living and non-living entities generally cluster, but with ambiguous cases like red blood cells, sperm, and viruses complicating clear separation. Criteria like 'autocatalytic cycles' and 'enzymes' are strong predictors, while 'mutation' and 'adaptation to environment' are weak. The iterative application of this method is suggested to refine definitions.