Self-driving cars don't use LLMs, so I don't know how any rational analysis can claim that the analogy is valid.
>> The saying I have quoted (which has different forms) is valid for programming, construction and even cooking. So it's a simple, well understood baseline.
Sure, but the question is not "how long does it take for LLMs to get to 100%". The question is, how long does it take for them to become as good as, or better than, humans. And that threshold happens way before 100%.
Doesn't matter, because if we're talking about AI models, no (type of) model reaches 100% linearly, or 100% ever. For example, recognition models run with probabilities. Like Tesla's Autopilot (TM), which loves to hit rolled-over vehicles because it has not seen enough vehicle underbodies to classify it.
Same for scientific classification models. They emit probabilities, not certain results.
>> Sure, but the question is not "how long does it take for LLMs to get to 100%"
I never claimed that a model needs to reach a proverbial 100%.
>> The question is, how long does it take for them to become as good as, or better than, humans.
They can be better than humans for certain tasks. They are actually better than humans in some tasks since 70s, but we like to disregard them to romanticize current improvements, but I don't believe current or any generation of AIs can be better than humans in anything and everything, at once.
Remember: No machine can construct something more complex than itself.
>> And that threshold happens way before 100%.
Yes, and I consider that "treshold" as "complete", if they can ever reach it for certain tasks, not "any" task.
None of the current models maybe, but not AI in general? There’s nothing magical about brains. In fact, they’re pretty shit in many ways.
OTOH, while I won't call human brain perfect, the things we label "shit" generally turn out to be very clever and useful optimizations to workaround its own limitations, so I regard human brain higher than most AI proponents do. Also we shouldn't forget that we don't know much about how that thing works. We only guess and try to model it.
Lastly, searching perfection in numbers and charts or in engineering sense is misunderstanding nature and doing a great disservice to it, but this is a subject for another day.
Knowledge engineering has a notion called "covered/invisible knowledge" which points to the small things we do unknowingly but changes the whole outcome. None of the models (even AI in general) can capture this. We can say it's the essence of being human or the tribal knowledge which makes experienced worker who they are or makes mom's rice taste that good.
Considering these are highly individualized and unique behaviors, a model based on averaging everything can't capture this essence easily if it can ever without extensive fine-tuning for/with that particular person.