Understanding a rules-based system (chess) means to be able to learn non-probabilistic rules (an abstraction over the concrete world). Humans are a mix of symbolic and probabilistic learning, allowing them to get a huge boost in performance by admitting rules. It doesn't mean a human will never make an illegal move, but it means a much smaller probability of illegal move based on less training data. Asymptotically, performance from humans and purely probabilistic systems converge. But that also means that in appropriate situations, humans are hugely more data-efficient.
> in appropriate situations, humans are hugely more data-efficient
After spending some years raising my children I gave up the notion that humans are data efficient. It takes a mind numbing amount of training to get them to learn the most basic skills.
Compared to the amount of data needed to train an even remotely impressive 'AI' model , that is not even AGI and hallucinates on a regular basis ? On the contrary, it seems to me that humans and their children are hugely efficient.
I'd say the kid knows more about the world than ChatGPT, yes. For starters, the kid has representations of concepts such as 'blue color' because eyes... ChatGPT can answer difficult questions for sure, but overall I'd say it's much more specialized and limited than a kid. However, I also think that's mostly comparing apples and oranges, and that one's judgement about that is very personal. So, in the end I don't know.
A baby learns to walk and talk in 1 year. Compared to the number of PHDs and compute training these models, the baby is so far ahead in efficiency I marvel way more at their pace.
