The Limits of Machine Knowledge

AI systems know things in a fundamentally different way from humans. A language model trained on millions of medical papers might generate a perfectly structured diagnosis, but it has never examined a patient, felt the anxiety of a health scare, or understood the difference between a textbook case and the messy reality of someone's actual symptoms. Its knowledge is statistical - derived from patterns in text - not experiential. This matters because many important decisions require the kind of knowledge that comes from lived experience, professional judgement, and understanding context that isn't captured in data. AI is remarkably good at synthesising information, identifying patterns, and generating plausible outputs across almost any domain. But it has no way to know what it doesn't know, no ability to recognise when a situation is genuinely novel rather than superficially similar to something in its training data, and no mechanism for the kind of intuitive expertise that experienced professionals develop over years of practice. Using AI well means understanding these boundaries and knowing when to rely on human judgement instead.