Protein interaction networks across >1800 species exhibit macro-scale nodes with lower noise and higher resilience; eukaryotes show stronger CE than archaea.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Biological networks exhibit the lowest EI among real networks and show the most significant causal emergence after coarse-graining.finding0.781
  Finding from Klein & Hoel (2020) on real network analysis.
• Biology's robustness, open-endedness, evolvability, and unique complexity likely depend on the fact that evolution works with an agential material.claim0.762
  Central claim linking life's properties to the inherent competencies of its material substrate.
• Bioelectric networks discovered by evolution ~time of bacterial biofilms; served as ideal medium for scaling computation and information synthesis before neural systems.finding0.758
  Evidence that pre-neural bioelectric infrastructure predates and likely precedes neurobiology; supports continuity of intelligence across substrates.
• The number of atoms involved in the responsible gene structure is likely to be very small, yet it displays a most regular and lawful activity with a durability that borders upon the miraculous, remaining unperturbed by the disordering tendency of heat motion for centuries.claim0.753
  Schrödinger's statement of the puzzle that quantum mechanics resolves.
• Conceptual advances in the links between machine learning and evolution now provide quantitative formalisms with which to begin to develop testable models of collective intelligence across scales.claim0.751
  Asserts that the time is ripe for formal models.
• Cells and cellular networks have problem-solving capacities that allow them to navigate entirely novel stressors (e.g., barium) without prior selection, implying a general competency beyond hardwired responses.claim0.750
  Derived from the planarian barium adaptation finding.
• The evolutionary process contains an intelligence ratchet: as competency increases, selection struggles to see the structural genome, further driving selection for competency mechanisms.claim0.748
  Explains why planaria with messy genomes have robust morphologies.
• Collective nucleation dynamics in Hebbian-learned molecular interaction systems can perform pattern recognition by assembling different structures in response to different concentration patternshypothesis0.747
  Theoretical prediction that molecular systems with proximity-based learning can recognize patterns; has mathematical connections to Hopfield associative memory