The article critiques traditional epistemology, arguing that categories like truth and belief don't apply to ChatGPT, which lacks human grounding and verification abilities. Instead, it produces linguistic continuations based on training data patterns. The author advocates for an interaction design that acknowledges its limitations while harnessing its unique generative capabilities. By interpreting ChatGPT through a liminal space lens, the discussion shifts to how knowledge co-emerges from user interactions, underscoring the importance of participatory engagement rather than fixed epistemological frameworks.
The traditional epistemological categories - truth, belief, inference, and justification - are ill-suited to describe ChatGPT, which does not possess beliefs or human grounding.
ChatGPT generates plausible continuations of linguistic sequences based on patterns from its training data, yet knowledge emerges during interactions with it.
This philosophical distinction matters greatly for how we design our interactions with the model: quick and easy definitive answers or responses caveated with limitations.
ChatGPT operates in a liminal space - a threshold where human intentions and algorithmic outputs converge, emphasizing knowledge as emerging from active engagement.
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