Under-estimating the capacity of a system for plasticity, learning, and intelligent problem-solving greatly reduces the toolkit of techniques for understanding and controlling its behavior.

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

• If intelligence is defined by engaged concern for problem solving then the apparent limits of a system’s intelligence can be expanded by extending its sphere of concern.hypothesis0.773
  Hypothesis linking care scope to intelligence ceiling.
• Cognitive capacities exist on a spectrum and tools from behavioral sciences can be productively applied to understand and control systems far beyond familiar animals with central nervous systems.claim0.766
  Core tenet of diverse intelligence; justifies the methodological borrowing across fields.
• If intelligence is defined as engaged concern for problem solving, then the apparent limits of a system’s intelligence can be expanded by extending its sphere of concern.hypothesis0.766
  Predicts that care-driven expansion of concern leads to higher intelligence.
• If loss keeps going down on the test set, in the limit the model must be learning to interpret and predict all patterns represented in language, including common-sense reasoning, goal-directed optimization, and deployment of the sum of recorded human knowledge.hypothesis0.765
  Extrapolation of scaling predictive models to AGI.
• Scaling intelligence via expansion of cognitive boundaries through inclusion of others' stress-reduction in one's own homeostatic loops.claim0.764
  Central thesis: expanding an agent's sensors and goals outward to include others' states creates bidirectional feedback loop that scales intelligence and increases compassion.
• Both under-attributing and over-attributing consciousness to AI carry significant risks.claim0.764
  Risk summary.
• 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 benefitsclaim0.763
  Core theoretical claim establishing that locality constraints in physical learning are not fatal—they reflect biological precedent and provide advantages like robustness and scalability
• Unsupervised learning builds a low-dimensional model of the input data.claim0.762
  Clarifies what unsupervised learning does.