I couldn’t find it easily, what age range is this intended for? The images make it seem elementary-school-ish, but I’m not sure if elementary school kids have the foundations for interpreting scatterplots, let alone scatterplots with logarithmic axes. I’ve been out of education for a while though, so maybe I’m misremembering.

This is great and has lots of practical stuff.

Some of the takeaways feel over-reliant on implementation details that don’t capture intent. E.g. something like “the LLM is just trying to predict the next word” sort of has the explanatory power of “your computer works because it’s just using binary”—like yeah, sure, practically speaking yes—but that’s just the most efficient way to lay out their respective architectures and could conceivably be changed from under you.

I wonder if something like neural style transfer would work as a prelude. It helps me. Don’t know how you’d introduce it, but with NST you have two objectives—content loss and style loss—and can see pretty quickly visually how the model balances between the two and where it fails.

The bigger picture here is that people came up with a bunch of desirable properties they wanted to see and a way to automatically fulfill some of them some of the time by looking at lots of examples, and it’s why you get a text box that can write like Shakespeare but can’t tell you whether your grandma will be okay after she went to the hospital an hour ago.