It operates in three basic stages. First, it takes the sequence of tokens that corresponds to the text so far, and finds an embedding (i.e. an array of numbers) that represents these. Then it operates on this embedding—in a “standard neural net way”, with values “rippling through” successive layers in a network—to

Well, it should be a list of 50,000 or so numbers that effectively give the probabilities for each of the possible “fill-in” words. And once again, to find an embedding, we want to “intercept” the “insides” of the neural net just before it “reaches its conclusion”—and then pick up the list of numbers that occur there,

And what we see is that if the net is too small, it just can’t reproduce the function we want.

And a key idea in the construction of ChatGPT was to have another step after “passively reading” things like the web: to have actual humans actively interact with ChatGPT, see what it produces, and in effect give it feedback on “how to be a good chatbot”.

see “how similar” the “environments” are in which different words appear. So, for example, “alligator” and “crocodile” will often appear almost interchangeably in otherwise similar sentences, and that means they’ll be placed nearby in the embedding. But “turnip” and “eagle” won’t tend to appear in otherwise similar sentences, so they’ll be placed f

array above is the positional embedding—with its somewhat-random-looking structure being just what “happened to be learned” (in this case in GPT-2).

Because what’s actually inside ChatGPT are a bunch of numbers—with a bit less than 10 digits of precision—that are some kind of distributed encoding of the aggregate structure of all that text.

its most notable feature is a piece of neural net architecture called a “transformer”.

And so, for example, we can think of a word embedding as trying to lay out words in a kind of “meaning space” in which words that are somehow “nearby in meaning” appear nearby in the embedding.