this post was submitted on 15 Nov 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I mean there are plenty of major areas in ML that LLMs cannot even begin to address (e.g. processing time-series data - XGBoost still reigns supreme, edge ML, etc.). Also, keep in mind that most of the people at major LLM groups are PhD so chances are if you wanna work even on LLMs, having a PhD will help. Afterall, scaling is good but if your research shows more efficient training pathways, the difference can be 9-figure sums for these companies.