What Is ChatGPT Doing ... And Why Does It Work?

In the past there were plenty of tasks—including writing essays—that we’ve assumed were somehow “fundamentally too hard” for computers. And now that we see them done by the likes of ChatGPT we tend to suddenly think that computers must have become vastly more powerful—in particular surpassing things they were already basically able to do (like prog

neural net can readily do—say from many examples of distances between cities—won’t be enough; there’s an actual computational algorithm that’s needed.

Or put another way, there’s an ultimate tradeoff between capability and trainability: the more you want a system to make “true use” of its computational capabilities, the more it’s going to show computational irreducibility, and the less it’s going to be trainable. And the more it’s fundamentally trainable, the less it’s going to be able to do soph

last embedding in this collection, and “decode” it to produce a list of probabilities for what token should come next.

that’s empirically been found to be true, at least in certain domains. But it’s a key reason why neural nets are useful: that they somehow capture a “human-like” way of doing things. Show yourself a picture of a cat, and ask “Why is that a cat?”. Maybe you’d start saying “Well, I see its pointy ears, etc.” But it’s not very easy to explain how you

So after going through all these attention blocks what is the net effect of the transformer? Essentially it’s to transform the original collection of embeddings for the sequence of tokens to a final collection. And the particular way ChatGPT works is then to pick up the

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

OK, so what do the attention heads do? Basically they’re a way of “looking back” in the sequence of tokens (i.e. in the text produced so far), and “packaging up the past” in a form that’s useful for finding the next token.

So at any given point, it’s got a certain amount of text—and its goal is to come up with an appropriate choice for the next token to add.