Re:Show HN: ARC Prize – a $1M+ competition towards open AGI ...
by interloxia on Wednesday June 12, 2024 @02:53AM
(#40655345)
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.
Re:Show HN: ARC Prize – a $1M+ competition towards open AGI ...
by TeMPOraL on Wednesday June 12, 2024 @05:53AM
(#40656330)
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.
Re:Show HN: ARC Prize – a $1M+ competition towards open AGI ...
by HarHarVeryFunny on Wednesday June 12, 2024 @07:34AM
(#40656877)
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.
Re:Show HN: ARC Prize – a $1M+ competition towards open AGI ...
by lynx23 on Wednesday June 12, 2024 @06:33AM
(#40656528)
And once they learn sarcasm, small dogs are cats again :-)
Re:Show HN: ARC Prize – a $1M+ competition towards open AGI ...
by pea on Wednesday June 12, 2024 @12:07PM
(#40659681)
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.