But the end result is that it produces a ranked list of words that might follow, together with “probabilities”:Īnd the remarkable thing is that when ChatGPT does something like write an essay what it’s essentially doing is just asking over and over again “given the text so far, what should the next word be?”-and each time adding a word. ChatGPT effectively does something like this, except that (as I’ll explain) it doesn’t look at literal text it looks for things that in a certain sense “match in meaning”. Imagine scanning billions of pages of human-written text (say on the web and in digitized books) and finding all instances of this text-then seeing what word comes next what fraction of the time. So let’s say we’ve got the text “ The best thing about AI is its ability to”. The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.” (And the essence of what I’ll say applies just as well to other current “large language models” as to ChatGPT.) I should say at the outset that I’m going to focus on the big picture of what’s going on-and while I’ll mention some engineering details, I won’t get deeply into them. But how does it do it? And why does it work? My purpose here is to give a rough outline of what’s going on inside ChatGPT-and then to explore why it is that it can do so well in producing what we might consider to be meaningful text.

That ChatGPT can automatically generate something that reads even superficially like human-written text is remarkable, and unexpected. “Wolfram|Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT” » A discussion about the history of neural nets »

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