Why this term shows up in this channel{'Within the chapter on AI for science and medicine, high-dimensional embedding spaces name the distributed representations that modern AI models use to encode meaning. Rather than storing knowledge as explicit, checklist-style rules, a model places each item it processes at a point in a space with many numeric coordinates, so that similarity between items becomes closeness in that space.'}{'This framing is the through-line of the chapter: the move from checklist-style rules to learned, distributed, predictive representations is what reshapes medicine, psychiatry, and scientific discovery at once [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@236] [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@1539].'}{''}Key points{'- High-dimensional embedding spaces hold items as vectors with many coordinates; distance in the space stands for similarity.'}{'- They contrast with explicit rules or checklists: knowledge is distributed across the representation rather than spelled out.'}{'- In this chapter they are the mechanism behind the shift from rules to learned intuition and prediction in medicine and science [^4caa6fda-b8ae-5bfd-ad34-331a298cb088@236].'}