They are just completely different things: ML and GOFAI.
It's unfortunate that we seem to have decided to call anything that we don't quite yet know how to make computers do "AI". Good for hype tho
It's unfortunate that we seem to have decided to call anything that we don't quite yet know how to make computers do "AI". Good for hype tho
People in the 1940’s don’t call radar controlled fire control computers AI because the term meant something else back then. Instead the initial idea of AI got so expanded that almost anything newer than roughly the 1970’s got called AI at some point.
Arguably it isn’t even that the meaning changed. It’s just buzzwords for grant applications the same way most basic robotics research gets suggested as beneficial for search and rescue.
It's not like they are that wildly different in tone.
The fine tuning is certainly destructive, but the general tonal biases are typically reflective of the pretrained layers.
Imagine what the pie chat makeup of textual data that was used for training would be, look at the normal part of the distribution curve, and that's pretty much exactly what you end up getting with some contextual biasing around prompting.
The problem is less that the pretrained models are somehow sinister and more that they create output that is hyper emotional, stubborn, and quick to escalate - much like nearly every thread on social media. Which is contrary to user expectations from AI.
A large part of the alignment fine tuning has less to do with safety in terms of keeping a LLM from nuking the world and more to do with preventing a LLM from telling you to go die in a fire after saying its emo poem needed improvement.
So is the argument that if you aren’t doing it using fully symbolic programming, it’s not ‘AI’? No matter how intelligent it gets?