1. What does inherit limitations mean?
2. How do we know something is an inherit limitation
3. Is it a problem if arguments for a particular inherit limitation also apply to humans?
From what I’ve seen people will often say things like AI can’t be creative because it’s just a statistical machine, but humans are also “just” statistical machines. People might mean something like humans are more grounded because humans react not just to how the world already works but how the world reacts to actions they take, but this difference misunderstands how LLMs are trained. Like humans LLMs get most of their training from observing the world, but LLMs are also trained with re-enforcement learning and this will surely be an active area of research.
One of many, but this is a simple one - LLMs are only limited to knowledge that is publicly available on the internet. This is "inherit" because thats how LLMs are essentially taught the information they retrieve today.
Therefore AI has to be much better than humans at the task to be considered ready to be a replacement.
——
Today robot taxis can only work in fair weather conditions in locations that are planned cities. No autonomous driving system can drive in Nigeria or India or even many european cities that were never designed for cars any time soon .
Working in very specific scenarios is useful , but hardly measure of their intelligence or candidate for replacing humans for the task
People are flippantly dismissive of the inherent limits because there ARE inherent limitations of the technology.
> Maybe because the implications are almost unthinkably profound.
Maybe because the stuff you're pointing to are just benchmarks and the definitions around things like AGI are flawed (and the goalposts are constantly moving, just like the definition of autonomous driving). I use LLMs roughly 20-30x a day - they're an absolutely wonderful tool and work like magic, but they are flawed for some very fundamental reasons.