5 ms·
I suspect small specific purpose models are actually a better idea for quite a lot of use cases. However you need a bunch more understanding to train and run o
by mst 3y ago
I suspect small specific purpose models are actually a better idea for quite a lot of use cases.
However you need a bunch more understanding to train and run one.
So I expect OpenAI will continue to be seen as the default for "how to do LLM things" and some people and/or companies who actually know what they're doing will use small models as a competitive advantage.
Or: OpenAI is going to be 'premium mediocre at lots of things but easy to get started with' ... and hopefully that'll be a gateway drug to people who dislike 'throw stuff at an opaque API' doing the learning.
But I don't have -that- much understanding myself, so while this isn't exactly uninformed guesswork, it certainly isn't as well informed as I'd like and people should take my ability to have an opinion only somewhat seriously.
- seanhunter 3y agoI have a slightly different take. Not all use cases are narrow use cases. OpenAI crushes the broad and/or poorly defined use cases. On those if you tried to train your own inhouse model it would be very expensive and you would produce a significantly inferior model.
- mst 3y agoI'm not sure how my "quite a lot of use cases" and your "not all use cases are narrow use cases" are meaningfully different (slightly) to you. This isn't a snipe, mind, it's me being unsure if we even disagree, especially given the latter part of your comment seems entirely correct (so far as my limited understanding goes ;).
- seanhunter 3y agoThat's not the part that's different. The part where I feel we perhaps differ is rather than being "premium mediocre" I think that openAI is really excellent where the problem space is very broad or is poorly specified. Then we both agree there are better choices where it is narrow and well specified.
- mst 3y agoAha, I see now. Thank you for the clarification.