Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data

Funny that it does not need that much data to train your average 20th century human genius. I'd say that if we are dreaming of the future of ai, learning and reasoning seems the greatest issue, not data. That said, the article title is about LLMs, so that's what will need changing I guess.

Humans aren't just text interfaces though. The majority of your input is not textual but is sights, sounds, feelings, etc., that LLMs don't (yet?) have access to.

Humans receive an enormous amount of training data in forms not currently available to LLMs.

If you locked baby Einstein in a room with the collected works of humanity and left him there for a lifetime, I doubt he'd have even learnt to read on his own.

The stream of data from vision does NOT explain why humans learn 1000x faster: Children who lost their sight early on, can grow up to be intelligent. They can learn English, for example. They don't need to hear 200B words, like GPT-3.

Even audio is several magnitudes larger. Uncompressed stereo is 100 kilobytes per second. So an hour is already 0.5 gigabytes. A year is ~3 TB.

> Uncompressed stereo is 100 kilobytes per second.

How much of that is cognitively useful for learning English? On top of the textual content, audio gives you emphasis and mood. Not a lot of information in that -- a few bits per sentence.

Nearly all of it. You need a lot of pictures without cats to explain what a cat is.