Project connecting Critique of Pure Reason to insights from computational philosophy

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

• "Computational processes are abstract beings that inhabit computers... A computational process is indeed much like a sorcerer's idea of a spirit. It cannot be seen or touched. It is not composed of matter at all."quote0.734
  Load-bearing quote from SICP framing computation as spirit-like; grounds the cyberanimism framework
• We hypothesize that general computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness, and that successful implementation can be established via analysis or testing.hypothesis0.725
  The central hypothesis of the paper
• We adopt computational functionalism as a working hypothesis.claim0.723
  Assumption that implementing the right computations is necessary and sufficient for consciousness.
• Process theories can be derived from variational principles in a straightforward manner with biological plausibility.hypothesis0.718
  Paper's core methodological hypothesis: gap between normative and process-level theories can be bridged.
• Buddhist thought offers an approach uniquely suited to understanding intelligence across computer science, bioengineering, and cognitive science without privileging specific embodiments.claim0.716
  Paper's key integration claim: Buddhism's emphasis on care and non-essentialism provides the conceptual lens needed for post-anthropocentric intelligence science.
• The fundamental process can be compressed to the instruction: Whatever you make must be a being.claim0.714
  Summary claim that the entire process reduces to the single rule of making beings at all scales.
• Computation is a game of empowerment and, therefore, the investment in opening the design process remains a critical challenge for contemporary architecture.claim0.714
  Final normative claim: computation’s nature as a game demands architectural openness, a challenge yet to be met.
• 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.claim0.712
  Asserts that the time is ripe for formal models.