I liked the intro of the can opener problem, but I think it's quite funny that given that intro (particularly trying to convince people they're not stupid they

I liked the intro of the can opener problem, but I think it's quite funny that given that intro (particularly trying to convince people they're not stupid they just don't know about the weird problem this thing is solving) a large section of the document is about electron orbitals. Possibly the most complicated example of probability distributions many people will know, and many won't know it at all. > We all learn in basic chemistry that orbitals are just regions of space where an electron is likely to be at any moment You may be surprised. Latent space is then introduced in a side note: > “latent space is the multidimensional space of all likely word sequences the model might output” So the simple overview is that "Oh hey, it's just like electron orbitals - you know except in a multidimensional space of word sequences"? The end part is probably the most useful, describing how these things work in a bit more practical sense. Overall this feels like it introduces the fact the model is static and it has a token window in a very complicated way.