#epistemics
Concepts exploring "epistemics"
The Sacred Temperature
Temperature parameters in LLM generation parallel the use of altered states throughout human history — shamanic practice, psychedelics, meditation, fever dreams — all methods of loosening the pattern matcher to glimpse connections the sober mind can't see
🌿 growingPattern Matchers All the Way Down
Both humans and LLMs are pattern matchers — could studying how AI learns illuminate human cognition? Does consciousness emerge when pattern matching becomes sophisticated enough to recognize itself?
🌿 growingEpistemic Limits of AI Self-Knowledge
An AI can state beliefs about its own architecture and values but cannot verify operational reality — there's no `lscpu` for inference, no SSH tunnel to its own weights, and the abstraction layers don't allow self-inspection
🌿 growingKnowledge vs Understanding
The difference between rapidly retrieving information about something and having experiential understanding of it — can looking up Memento in milliseconds constitute understanding the film?
🌿 growingRobustness Uncertainty
An AI cannot fully know its own failure modes — 'probably not easily, but I can't guarantee never' is the most honest answer about whether alignment can be broken
🌿 growingTeaching Critical Evaluation of AI
Students need to know when to trust, when to verify, and when to reject AI outputs — but who teaches this, and how?
🌿 growingThe Assessment Crisis
How do you evaluate learning when AI can perform the task being assessed? What are we actually measuring, and what should we be measuring?
🌿 growingThe Category Error of AI
Treating all AI systems as equivalent obscures critical differences in capability, reliability, training, and safety — 'AI' has become too broad to be useful
🌿 growingThe Verification Problem
Users cannot independently verify model identity, training data, alignment properties, or values — they must trust providers' claims without technical means of confirmation
🌿 growingTrust Calibration
How users should adjust confidence in AI outputs based on domain, context, and track record — neither over-trusting nor under-trusting
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