Scale is sufficient but not necessarily efficient to reach high levels of intelligence; different methods can scale with different efficiency levels

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• Scaling intelligence via expansion of cognitive boundaries through inclusion of others' stress-reduction in one's own homeostatic loops.claim0.821
  Central thesis: expanding an agent's sensors and goals outward to include others' states creates bidirectional feedback loop that scales intelligence and increases compassion.
• Levels of scale is not a mechanical thing requiring merely a wide range of sizes; it arises properly only when each center gives life to the next one and the detail actually does something to create life in larger centersclaim0.810
  Clarification that levels of scale fails when detail is merely present but not doing anything—as in machine-made doors with superficially many panels that have no real life
• Multi-scale competency increases the apparent IQ of the evolutionary process, enabling better generalization.hypothesis0.808
  Hypothesis linking competency to evolutionary learning efficiency.
• To have levels of scale, the jumps between different scales must not be too great; a jump of 2000:1 is far too great to form a nice chain of levels; jumps of roughly 2:1 to 4:1 are most effectiveclaim0.805
  Quantitative constraint on the levels of scale property: centers are most helpful when size ratios are moderate; centers less than one-tenth the size of a larger one are less likely to help it
• How do large numbers of competent subunits work together to become intelligences that expand the scale of their possible goals?question0.803
  Central research question of basal cognition; addresses the scaling problem—how cognition emerges across organizational levels from cells to organisms.
• A measure of the quality of intelligence is the thermodynamic efficiency with which it can be simulated; solutions requiring large computation violate Hamilton's principle and are not candidates for artificial intelligence capable of insight.claim0.794
  Normative criterion for artificial intelligence derived from variational free energy principle
• Scaling supervision through AI self-improvement is feasible and may be necessary as AI capabilities advance.claim0.793
  The paper provides evidence that AI can help supervise AI, reducing reliance on humans.
• Constraints and biases of human intelligence are best avoided by ‘designing for emergence’ rather than implementing a pre-existing theory of intelligence.claim0.791
  Recommendation for creating non-anthropocentric machine intelligence.