> The ability to identify text via search, to summarize and read contents (with just enough contextual capability to be useful) and to output relevant results i

> The ability to identify text via search, to summarize and read contents (with just enough contextual capability to be useful) and to output relevant results is rather close, conceptually, to the potential of language models. The difference between what we actually have – competing and discrete systems owned by corporations – and LCARS (besides many orders of magnitude of greater sophistication in the fictional system) is that LCARS is presented as an integrated, holistic and scoped system. LCARS’ design is to be a library that enables access to knowledge and retrieves results based on queried criteria. But hasn't their been decades worth of work on just that sort of generic system that could work on whatever data sources you have without needing to be trained on a ridiculously huge corpus of data? But that nobody ever figured it out, and the "fuck it, train this thing on all human-generated text we can find" won out over trying to build search + summarization + relevant result generation from first principals? Sure, you can imagine that future. And people have tried to build it. But I haven't seen any reason to believe that it hasn't worked because of some sort of "too many academics captured by Google" issue. Google didn't even manage to be the first company to commercialize this alternate approach, after all!