- cross-posted to:
- technology@lemmy.ml
- 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?”
I also like the point about interpolation vs extrapolation. It’s demonstrated when you look at art history (or the history of any other creative field). Humans don’t look at paintings and create something that’s predictable based on those paintings. They go “what happens when I take that idea and go even further?” An LLM could never have invented Cubism after looking at Paul Cezanne’s paintings, but Pablo Picasso did.
That’s not a limitation of ML, but just how it is commonly used. You can take every parameter that neural network recognizes and tweak it, make it bigger, smaller, recombine it with other stuff and marvel at the results. That’s how we got origami porn, (de)cartoonify AI, QR code art, Balenciaga, dancing statues or my 5min attempt at reinventing cubism (tell AI to draw cubes over a depthmap).