Humans decidedly take much more data than LLMs. Like orders of magnitude. We happen to feed only distilled data to LLMs though. That's why they appear so efficient.
You can't count by text, because text is not the only thing children learn on. So unlike the bytes comparison the text comparison is totally invalid.
> We happen to feed only distilled data to LLMs though. That's why they appear so efficient.
That is wild handwaving that really explains nothing.
But then, most of the comments on this page are wild handwaving, pontificating like a blind man describing an elephant. Very little engages with or matches the quality of Norvig's arguments.
Have you ever had kids? Either you never had, you forgot, or you are intentionally deceiving us (and perhaps yourself) to support your point. If you never had kids, I can tell you you have very poor imagination about how they learn language.
Any specific entries?
"This year, we have merged the Multimodal and Interaction tracks into the Strict and Strict-small tracks. This is for two primary reasons: Firstly, there were insufficient submissions to warrant standalone tracks. Secondly, none of the submissions in the Interaction track outperformed models submitted to the Strict track, and *none of the submissions from Multimodal outperformed our baselines*. We will still allow participants to use multimodal data or interactive training procedures if they choose, but such data or learning procedures must conform to the rules of the Strict or Strict-small tracks.
Emphasis mine.
I think that's a pretty weak point to your argument. They are saying that none of the contestants outperformed their baseline multimodal models and that there was very little multimodal submissions in the first place.