> kids can easily learn their new favorite animal in as little as a single example
Until they encounter a similar animal and get confused, at which point you understand the implicit heuristic they were relying on. (Eg. They confused a dairy cow as a zebra, which means their heuristic was a black-and-white quadrupedal)
Doesn't this seem remarkably close to how LLMs behave with one-shot or few-shot learning? I think there are a lot more similarities here than you give it credit for.
Also, I grew up in South Korea where early math education is highly prioritized (for better or for worse). I remember having to solve 2 dozen arithmetic problems every week after school with a private tutor. Yes, it was torture and I was miserable, but it did expose me to thousands more arithmetic questions than my American peers. All that misery paid off when I moved to the U.S. at the age of 12 and realized that my math level was 3-4 years above my peers. So yes, I think human intelligence accuracy also does improve with more training data.
Not many zebras where I live but lots of little dogs. Small dogs were clearly cats for a long time no matter what I said. The training can take a while.
This. My 2.5 y.o. still argues with me that a small dog she just saw in the park is a "cat". That's in contrast to her older sister, who at 5 is... begrudgingly accepting that I might be right about it after the third time I correct her.
The thing is that the labels "cat" and "dog" reflect a choice in most languages to name animals based on species, which manifests in certain physical/behavioral attributes. Children need to learn by observation/teaching and generalization that these are the characteristics they need to use to conform to our chosen labelling/distinction, and that other things such as size/color/speed are irrelevant.
Of course it didn't have to be this way - in a different language animals might be named based on size or abilities/behavior, etc.
So, your daughter wanting to label a cat-sized dog as a cat is just a reflection of her not having aligned her generalization of what you are talking about when you say "cat" vs "dog" with her own.
My favourite part of this is when they apply their new words to things that technically make sense, but don't. My daughter proudly pointed at a king wearing a crown as "sharp king" after learning about knives, saws, etc.
Until they encounter a similar animal and get confused, at which point you understand the implicit heuristic they were relying on. (Eg. They confused a dairy cow as a zebra, which means their heuristic was a black-and-white quadrupedal)
Doesn't this seem remarkably close to how LLMs behave with one-shot or few-shot learning? I think there are a lot more similarities here than you give it credit for.
Also, I grew up in South Korea where early math education is highly prioritized (for better or for worse). I remember having to solve 2 dozen arithmetic problems every week after school with a private tutor. Yes, it was torture and I was miserable, but it did expose me to thousands more arithmetic questions than my American peers. All that misery paid off when I moved to the U.S. at the age of 12 and realized that my math level was 3-4 years above my peers. So yes, I think human intelligence accuracy also does improve with more training data.