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    Carmelics

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    LoyalLoyalJusticeJustice
    Made withinDC&Austin
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    Home/Original/inverse
    See Original
    Inverse View

    It is not the case that Classical syntactic engines operate over discrete, context-independent symbol tokens and cannot represent the holistic, context-sensitive background of embodied know-how.

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    1 perspective
    Reason for
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    • 1.Neural networks manipulate discrete parameters but achieve context-sensitive behavior without explicit symbols, suggesting discreteness isn't the limiting factor.
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    • 2.Embodied know-how can be functionally reconstructed through high-dimensional representations; background knowledge needn't be phenomenologically present.
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    • 3.The claim conflates implementation substrate (discrete vs. continuous) with representational capacity; symbol systems can reference embodied states indirectly.
      ?

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    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Embodied skills like riding a bike involve motor memory and proprioceptive feedback that discrete symbols cannot capture or manipulate.
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    • 2.Context-sensitivity requires understanding unstated background assumptions; symbol systems lack access to the agent's lived situation.
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    • 3.Human experts demonstrate intuitive judgment in novel situations that exceeds what can be derived from explicit symbolic rules alone.
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