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.

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• Evolution is more intelligent than we realised, equivalent to connectionist learningclaim0.809
  Interpretive stance that the formal equivalence implies cognitive capabilities in evolution
• Evolution learns to generalize beyond default morphologies, producing problem-solving machines.claim0.808
  Argues that evolutionary learning goes beyond specific adaptations.
• Open-ended evolution of intelligence is possible because agents are collectives without fixed essencehypothesis0.808
  Follows from observation that intelligent systems lack context-transcendent core; their maxima are not contingent on permanent character.
• 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.claim0.807
  Central claim about the power of connectionism.
• The evolutionary process contains an intelligence ratchet: as competency increases, selection struggles to see the structural genome, further driving selection for competency mechanisms.claim0.804
  Explains why planaria with messy genomes have robust morphologies.
• Progress in machine behavior science and biohybrid engineering breaks down the simplistic dualism of life vs. machine, revealing a continuum of emergence, rational control, and agency.claim0.801
  Second central claim: life and machine form a continuous multidimensional space, not discrete bins
• Evolution often produces general-purpose problem-solving machines whose capacities cannot be inferred from the default invariant course of development.claim0.800
  A claim about the outcome of the MCA-enhanced process.
• The key to the idea that will allow a system of workable morphogenetic sequences to evolve in a not-too great length of time, is highlighted in the genetic ideas of John Holland.quote0.799
  Explicitly credits Holland's work as the inspiration for the snippable genes approach.