193
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 23 Aug 2024
193 points (100.0% liked)
TechTakes
1485 readers
151 users here now
Big brain tech dude got yet another clueless take over at HackerNews etc? Here's the place to vent. Orange site, VC foolishness, all welcome.
This is not debate club. Unless it’s amusing debate.
For actually-good tech, you want our NotAwfulTech community
founded 1 year ago
MODERATORS
I was thinking about this after reading the P(Dumb) post.
All normal ML applications have a notion of evalutaion, e.g. the 2x2 table of {false,true}x{positive,negative}, or for clustering algorithms some metric of "goodness of fit". If you have that you can make an experiment that has quantifiable results, and then you can do actual science.
I don't even know what the equivalent for LLMs is. I don't really have time to spare to dig through the papers, but like, how do they do this? What's their experimental evaluation? I don't seen an easy way to classify LLM outputs into anything really.
The only way to do science is hypothesis->experiment->analysis. So how the fuck do the LLM people do this?
Right? "AI" is great if you want to sort a few million images of galaxies into their various morphological classifications and have it done before the end of the decade. A++, good job, no notes.
You can't grift off of that very easily, though.
I’d really like to know too, especially given how many times we’ve already seen LLMs misused in scientific settings. it’s starting to feel like the LLM people don’t have that notion — but that’s crazy, right?