Learning neural networks can enable 'chunking' and rescale problem-solving to higher organizational levels, a mechanism intrinsic to transitions in individuality.

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• 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.claim0.807
  Derived from the planarian barium adaptation finding.
• Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.806
  The paper's central thesis statement, presented prominently after the abstract
• Scaling intelligence via expansion of cognitive boundaries through inclusion of others' stress-reduction in one's own homeostatic loops.claim0.799
  Central thesis: expanding an agent's sensors and goals outward to include others' states creates bidirectional feedback loop that scales intelligence and increases compassion.
• Cognition and learning are substrate-independent; principles familiar in artificial neural networks can be implemented by any network of signals with suitable weighted inputs.claim0.799
  Central thesis enabling unification of neural, developmental, ecological, and social networks as instances of collective intelligence.
• We hypothesize that this rescaling of the problem-solving search process is intrinsic to transitions in individuality.hypothesis0.797
  Hypothesis about chunking and ETIs.
• Physical systems are more constrained in learning abilities than in silico neural networks due to locality requirements, but this mirrors biological learning constraints and offers robustness benefitsclaim0.795
  Core theoretical claim establishing that locality constraints in physical learning are not fatal—they reflect biological precedent and provide advantages like robustness and scalability
• Associative learning occurs in gene regulatory networks and other non-neural systems, making it substrate-independent.claim0.793
• Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.790
  Central claim about the power of connectionism.