Reading through all your private documents and extracting the useful information to answer an open ended question like this is still a little way off.
Specifically, LLM's can generally only take into account ~8000 words of context when deciding on a response. Summarizing the question and all necessary information for the answer into 8000 words is hard when the user might have millions of words in their inbox.
Having said that, I don't think it's far off. There are already prototypes of LLM's which have information retrieval abilities (ie. it could do a keyword search of your inbox to find a few relevant documents to read to decide on a response). There are also promising efforts to make that 8000 word number far larger.
> Fun exercise for the reader: how much of this is actually possible with LLMs and how much is not.
I have no idea.
But it would be nice?* if this LLM stuff could be incorporated into the telephone system for my bank, pharmacy, etc.
It's obvious that these organizations don't want to connect me with a $killed human, so instead of having me interact with an infuriating "pretend" human, maybe CVS can fuse their phone system with ChatGPT to make the experience a little less maddening.
"Hey, I already gave you my birth date. No need to ask again. And I told you 30 seconds ago that I don't need to schedule a COVID vaccine. Just tell the pharmacist <xyz>."
