Near-future systems will contain swarms of robots and organisms in which biological units reproduce and evolve while mechanical units self-replicate and evolve, making binary life/machine division impossible.

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

• Near-future machines will be built on the principles of multi-scale competency in a fluid 'society' of components that communicate, trade, cooperate, compete, and barter information and energy resources as do living components of an organism.hypothesis0.842
  Predictive claim about future machine architectures mimicking biological multi-scale competency
• Most biological systems consist of multiple nested selves, not oneclaim0.788
  Levin critiques Integrated Information Theory's implication of singular unified self, arguing Scale-Free Cognition requires recognition of nested agents at each organizational level.
• Evolved machines increasingly exhibit self-similar, hierarchical structure like living systems.claim0.783
  Artificially evolved neural networks and robots often lack modularity unless selected for, resembling life.
• Natural selection can evolve phenotypic plasticity that solves division of labour games in two-player collectives (Tudge et al. 2016)finding0.782
  Shows plasticity as solution to HFHF in minimal models with homogeneous genotypes
• Different learning systems facing similar computational problems will converge to similar consciousness-like solutions, including potentially biological and artificial systemshypothesis0.774
  Extension of the Universality Hypothesis to consciousness: if consciousness solves a well-defined computational problem, different systems will discover it independently
• Biology's robustness, open-endedness, evolvability, and unique complexity likely depend on the fact that evolution works with an agential material.claim0.773
  Central claim linking life's properties to the inherent competencies of its material substrate.
• Artificially evolved neural networks and robots often lack modularity unless it is directly selected for, and exhibit inefficiencies from evolutionarily duplicated sub-structures.finding0.772
  Evidence that evolved machines share biological property of non-optimal modularity, blurring the distinction
• Hybrid bioengineered systems blur distinctions between intelligent machines and conscious life.claim0.772
  Brain-machine interfaces and biological-electronic hybrids demonstrate no principled line between subjectivity and engineering.