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ARXIV:2602.17116 · AI PHILOSOPHY · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2602.17116AI PHILOSOPHYSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Develop a new epistemological framework for understanding generative AI's impact on knowledge production.
Opportunity summary
Pain Develop a new epistemological framework for understanding generative AI's impact on knowledge production.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Develop a new epistemological framework for understanding generative AI's impact on knowledge production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure,…
Generative AI presents an unprecedented challenge to our understanding of knowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Building on Peirce semiotics and Papert constructionism, it reconceptualizes generative models as navigators of learned manifolds and proposes navigational knowledge as a third mode…
AI Philosophy moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Develop a new epistemological framework for understanding generative AI's impact on knowledge production.
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Paper Pack
10.48550/arXiv.2602.17116Develop a new epistemological framework for understanding generative AI's impact on knowledge production.
Abstract
Generative AI presents an unprecedented challenge to our understanding of knowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure, and without such understanding, its responsible integration into science, education, and institutional life cannot proceed on a principled basis. This paper argues that the missing account must begin with a paradigmatic break that has not yet received adequate philosophical attention. In the Turing-Shannon-von Neumann tradition, information enters the machine as encoded binary vectors, and semantics remains external to the process. Neural network architectures rupture this regime: symbolic input is instantly projected into a high-dimensional space where coordinates correspond to semantic parameters, transforming binary code into a position in a geometric space of meanings. It is this space that constitutes the active epistemic condition shaping generative production. Drawing on four structural properties of high-dimensional geometry concentration of measure, near-orthogonality, exponential directional capacity, and manifold regularity the paper develops an Indexical Epistemology of High-Dimensional Spaces. Building on Peirce semiotics and Papert constructionism, it reconceptualizes generative models as navigators of learned manifolds and proposes navigational knowledge as a third mode of knowledge production, distinct from both symbolic reasoning and statistical recombination.
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Proof status
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PROBLEM
Develop a new epistemological framework for understanding generative AI's impact on knowledge production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic...
METHOD
Generative AI presents an unprecedented challenge to our understanding of knowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic ch...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Building on Peirce semiotics and Papert constructionism, it reconceptualizes generative models as navigators of learned manifolds and proposes navigational knowledge as a third mode of knowledge productio...
WHY NOW
AI Philosophy moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop a new epistemological framework for understanding generative AI's impact on knowledge production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure, and without such understanding, its responsible integration into science, education, and institutional life cannot proceed on a principled basis.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative AI presents an unprecedented challenge to our understanding of knowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure, and without such understanding, its responsible integration into science, education, and institutional life cannot proceed on a principled basis.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Building on Peirce semiotics and Papert constructionism, it reconceptualizes generative models as navigators of learned manifolds and proposes navigational knowledge as a third mode of knowledge production, distinct from both symbolic reasoning and statistical recombination.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Philosophy moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop a new epistemological framework for understanding generative AI's impact on knowledge production.
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AI Philosophy
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