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this post was submitted on 25 Sep 2023
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Well that's exactly what I was thinking when these companies were making these claims... like HOW could they possibly handle this locally on a CPU or GPU when there must be a massive database that (I assume) is constantly being updated? Didn't make sense.
EDIT: this entire website can go fuck off. You ask a simple question about some reasonably new tech, and you get downvoted for having the audacity to learn some new stuff. People on here are downright pathetic.
"AI" doesn't use databases per se, they are trained models built from large amounts of training data.
Some models run fine on small devices (like the model running on phones to make better pictures) but others are huge like Open AI's LLM.
Wouldn't that data be stored in some kind of database?
No, the data will influence the model.
Some of the data may be found in the model itself (i.e. the AI generated images outputting mangled author signatures from the original works that were used during training) but not in the traditional form of a database. You can't directly retrieve that data back in its original form even if some models can be coerced to do something similar.
It's basically a statistical model built from training data.
The training of these huge models also cost a fortune. Allegedly in the millions of $ in a data and processing-intensive process.
The training data isn't stored in the model. You can take an existing model and fine tune it on a whole bunch of additional data and the model size won't change.
The other answers are a bit confusing...
Yes, that's in a database.
However, you can think of it like a large library of books on how to best tune a ukulele. There might be a lot of information to figure out how to tone the ukulele and a lot of skill to put all that knowledge to use, but the ukulele once tuned, is quite small and portable.
You're right. Run an llm locally adjacent to your application sandboxes and local user apps and your office will lower its heating bills.