I’ll push back on this, at least in its current iteration. I just asked it to list some restaurants near my apartment (major intersection in San Jose) and it wasn’t particularly close. While there are several restaurants less than half a mile from the intersection, ChatGPT listed restaurants from several miles away.
Given the “weights in a matrix” architecture of ChatGPT, I’m not sure it’s possible to store enough data to make the query practical to answer. Say there are a couple hundred intersections in my city. You have to store the token of “restaurant name” “close to” “intersection” for each intersection. I don’t know the size of Google’s Maps DB, but I would guess it’s several Gigabytes per city. From my understanding of the theory, you would need to store BOTH the LLM weights AND the Maps data for ChatGPT to have a shot at generating good answers for that type of query.
I’m happy to be wrong here. If I’m misunderstanding something, please let me know.
Given the “weights in a matrix” architecture of ChatGPT, I’m not sure it’s possible to store enough data to make the query practical to answer. Say there are a couple hundred intersections in my city. You have to store the token of “restaurant name” “close to” “intersection” for each intersection. I don’t know the size of Google’s Maps DB, but I would guess it’s several Gigabytes per city. From my understanding of the theory, you would need to store BOTH the LLM weights AND the Maps data for ChatGPT to have a shot at generating good answers for that type of query.
I’m happy to be wrong here. If I’m misunderstanding something, please let me know.