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the primary challenge on the road to epistemically-rigorous generative AI is an internal ontological scheme capable of encoding the provenance of training data & representing it in a single internally-consistent knowledge graph, which is fundamentally a data normalization problem
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RT @pee_zombie
my life work is to build an information architecture system capable of representing, with maximal fidelity, the entire causality stack invol…
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it's entirely possible that rather than needing to explicitly design and build this into the model architecture, it may emerge organically during training within the model weights as a metabolic optimization, aka the "grokking" phenomenon

bounded-regret.ghost.io/future

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in fact, I'd suggest that any realworld agent naturally develops a fucntionally-equivalent internal structure in the process of world-modeling and prediction, as a metabolic optimization, given that such a structure is useful for optimizing actions given limited information

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the most adaptive epistemics are those straddling the inflection point between the competing requirements of metabolic load & executive utility

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RT @pee_zombie
much of human behavior is made legible when examined through the lens of materialist computationalism, namely the fact that information processing has a real physical cost in terms of work and metabolic energy required, & your biology resists any efforts it considered extrane…
twitter.com/pee_zombie/status/

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