Schrödinger understood the importance of aperiodicity for information capacity some time before Shannon's work explained it, anticipating key ideas about transmission and storage of information vehicles.

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

• Much of what is written by philosophers of science is either false or incomplete because they do not take account of the implications of Schrödinger's ideas for chemistry and biology.claim0.784
  Sloman's broad methodological critique of philosophy of science.
• Aperiodic structure of genetic molecules enables exponential diversity of encodable information compared to periodic crystals.claim0.780
  Schrödinger argues that non-repeating molecular structure (aperiodic solid) allows information density far exceeding periodic/crystalline alternatives.
• Information Capacity of Aperiodic Sequencesconcept0.772
  The exponential growth in combinatorial possibilities with sequence length, allowing vast genetic information storage.
• Schrödinger's thought experiment suggests that color sensation might directly penetrate the realm of the single mind or ground, and inner light intensifies this bridge.claim0.767
• Agents and their interactions are intrinsic to studying information flow in computation, not peripheral to classical information theory.claim0.766
  Central thesis: traditional static information theories fail to capture dynamic interaction necessary for understanding modern computing.
• Unnoticed consequences of Schrödinger's ideas support claims about layers of mechanism in which the higher level layers require control mechanisms whose functions cannot be described in the language of fundamental physics.claim0.761
  Sloman's extension of Schrödinger's argument to brain mechanisms and virtual machinery.
• Newtonian mechanics augmented by statistical mechanics cannot explain chemical bond stability or genetic persistence; quantum mechanics is necessary.claim0.758
• The upper bound of what can be learned from a dataset is not the most capable trajectory, but the conditional structure of the universe implicated by their sum.claim0.757
  Key insight about predictive learning's potential.