Sometimes you come across things that you wrote not so long ago that merely serve to reinforce how fast the world has moved.
Such as this from November 2022, just as ChatGPT was released to the world and I was getting my head around foundation models and what they were:
The connection that I see is that perhaps a foundation model trained for text summarisation could be specialised on a corpus of notes and used to summarise search results, progressing us beyond the “list of results”. Instead of answering the question “show me the notes containing X”, we can more directly ask our likely underlying question “what have I learned about X over time”.
I imagine an answer that summarises, over time, one’s notes, including references to source notes – for digging deeper – as it goes. In essence, can one use machine learning as a virtual librarian for one’s notes? While text summarisation is a poor man’s librarian, very few people are rich enough to afford a real human to look through their old notebooks! I can see this ability to summarise becoming a cornerstone of a workflow, and a way to start combining the best parts of incremental and evergreen note-taking models.
I can imagine taking this a further step forward with more advanced versions of models like ChatGPT, where one could hold conversation with the model to request more information on given topics in the summary. In this way, we take a step towards the types of interactions we see within shows like Star Trek, with a conversation tailored to one’s immediate needs.
How much more we can do today, just three years later: I have been using Claude to write a vector search for my Obsidian notes. AI is not just writing a summary, but is coding the application to use AI to write a summary 🙃