Yeah, I have to agree with you. For example, I would have no problem using a decently tested LLMs for engineering simply because Engineering usually accounts for errors and uses appropriate factors to accommodate them. Sure LLMs could be get more accurate in future, but I believe the error will reduce asymptotically. Essentially, more accurate LLMs get, it will get that much harder to increase the accuracy. There is always a price to pay, IMO.
“There’s always a price to pay” is basically what engineering is.
Anybody could build a bridge to last 100 years, or to survive a barge ship crashing into it, but it takes an engineer to build a bridge that will barely last 100 years, or barely survive a bridge crashing into it (which you could kind of say the F.S.Key bridge did, since only really the middle section was taken out).
Put another way, in the real world, there are budgets and sacrifices.
Neural networks are magical anywhere that near misses are good enough.
Companies keep using them as if they’re infallible, when lives and fortunes are at stake.
Tech is not the problem.
If you give out hammers to everyone, some people will end up with smashed balls.
Yeah, I have to agree with you. For example, I would have no problem using a decently tested LLMs for engineering simply because Engineering usually accounts for errors and uses appropriate factors to accommodate them. Sure LLMs could be get more accurate in future, but I believe the error will reduce asymptotically. Essentially, more accurate LLMs get, it will get that much harder to increase the accuracy. There is always a price to pay, IMO.
“There’s always a price to pay” is basically what engineering is.
Anybody could build a bridge to last 100 years, or to survive a barge ship crashing into it, but it takes an engineer to build a bridge that will barely last 100 years, or barely survive a bridge crashing into it (which you could kind of say the F.S.Key bridge did, since only really the middle section was taken out).
Put another way, in the real world, there are budgets and sacrifices.