No comparison. The previous theory of AI was that we would accumulate and structure expert knowledge so that it could be repeated deterministically -- to train

No comparison. The previous theory of AI was that we would accumulate and structure expert knowledge so that it could be repeated deterministically -- to train computers on things like medical diagnosis. The current theory of AI is best described by The Bitter Lesson[0] essay and it's the exact opposite -- don't store any knowledge but make predictions from weighted models. This new version is much more exciting to the public and to investors because it isn't constrained by expertise, knowledge, or anything else! Without "training" anything on Shakespeare or science, we can ask for a science paper in The Bard's voice. This seemingly miraculous parlor trick is what gets all the attention and makes people think that, with a enough of a Big Black Box, we could get anything and everything to come out the other side. More succinctly, the ability of LLMs to posit on any topic and gleefully make up convincing and fake answers has lit a fire under the hype machine where before AI was only thought of or needed for "expert" use cases. [0] http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html [1] Source: I worked on an expert system at IBM and now make RAG apps.