- cross-posted to:
- technology@lemmy.ml
Avram Piltch is the editor in chief of Tom’s Hardware, and he’s written a thoroughly researched article breaking down the promises and failures of LLM AIs.
Avram Piltch is the editor in chief of Tom’s Hardware, and he’s written a thoroughly researched article breaking down the promises and failures of LLM AIs.
I like the point about LLMs interpolating data while humans extrapolate. I think that’s sums up a key difference in “learning”. It’s also an interesting point that we anthropomorphise ML models by using words such as learning or training, but I wonder if there are other better words to use. Fitting?
“Plagiarizing” 😜
What about tuning, to align with “finetuning?”
Isn’t interpolation and extrapolation the same thing effectively, given a complex enough system?
Depending on the geometry of the state space, very literally yes. Think about a sphere, there’s a straight line passing from Denver to Guadalajara, roughly hitting Delhi on the way. Is Delhi in between them (interpolation), or behind one from the other (extrapolation)? Kind of both, unless you move the goalposts to add distance limits on interpolation, which could themselves be broken by another geometry