It turns out our word for "surface statistics" is "world model".

> Do they merely memorize training data and reread it out loud, or are they picking up the rules of English grammar and the syntax of C language?

This is a false dichotomy. Functionally the reality is in the middle. They "memorize" training data in the sense that the loss curve is fit to these points but at test time they are asked to interpolate (and extrapolate) to new points. How well they generalize depends on how well an interpolation between training points works. If it reliably works then you could say that interpolation is a good approximation of some grammar rule, say. It's all about the data.

I’m reminded of the Holographic Principle in physics: https://en.m.wikipedia.org/wiki/Holographic_principle

Sometimes a sufficiently good model of a surface is completely identical to a model of the volume.

Idk from when even id this article? Got me LLMs currently are broke and the majority is already aware of this.

Copilot fails the cleanly refactor complex Java methods in a way that I’m better of writing that stuff by my own as I have to understand it anyways.

And the news that they don’t scale as predicted is too bad compared to how weak they currently perform…

Big thread at the time https://news.ycombinator.com/item?id=34474043