Mind Readings: How Generative AI Models Work Inside, Part 2

Welcome back to part two in our series of how the bloody guts of a language model work in AI. In part one, we walked through the whole visualization and we got through this section here of tokenization and embedding where we talked about the analogy of a of a newspaper, and the editor in chief rushes into the writer's room with an assignment, and and the writers all break the assignment up into little pieces, and then they start to say asking what do they know about their piece, and then how does it relate to the rest of the assignment? What is the the priority of the order? And that embedding is sort of the project plan for the writers to say, okay, now we got to work on the story. So from there, let's go ahead and move into what happens next.

So what happens next is the writers have to go and spend some time thinking. And what we see in the diagram here is query weights, key weights, and value weights. We have a query bias, key bias, value bias, and then you have the associated vectors and a layer norm. That's probably a whole bunch of words that mean nothing. The QKV query, query key and value.

Imagine you're one of the writers in this writer's room, and you get a small part of this assignment. Maybe you have to uh in the previous episode we were talking about how maybe the editors in chief wants an investigation of corruption at the shipping docks in Long Beach, and you are given the part of the assignment that says go to Daewoo Industries uh dock and and check out what's coming in on their cargo ships. So the writer, you would sit down and go, huh? What do I know about this? Maybe get out some post-it notes.

And you first write down, what do you know about Daewoo Industries? What do you know about shipping containers? What do you know about docs, right? What do you know about this topic? That's the query.

That is something that gets turned into kind of a set of post-it notes, if you will. Then you look at your post-it notes and you look at the portion of the assignment you got, and you say, okay, well, where might I find information about this? Right? And then the value part is where you get more post-it notes out, and you start saying, Well, I would know, I know where the address of the docs is. I know who runs the stocks.

May you do some Googling. Um, and you end up with this big old pile of post-it notes, right? This this huge pile of post-notes of all the all the questions you have, all the places you might go to look for those answers, and then the answers themselves. And what's happening in the AI model as this is that this whole team of writers is repeating this process over and over again. Everybody is getting out their post-it notes, they got different colored post-notes for different kinds of things.

And then in the model itself, right? When we get to this section here, the attention matrix, right, and and the projection weights, the intention output. What is happening here is that the writer's room gets together, everybody puts their post-it notes on you know a huge wall. Remember that scene from It's Always Sunny in Philadelphia, or uh the summer scene in in Sherlock, where you got the wall of notes and stuff, and there's red yarn string connecting everything, and you know the people are ranting on about the conspiracy. That's kind of what's happening here.

So the editor has given the assignment, the writers have broken up the assignment to little pieces. They've all written down what they what the question is they want to ask, where they might find the information, and then the answers to that. And then everybody gets together and says, okay, well, what do we got? Let's compare notes. What questions are you asking?

Where are you going? I'm gonna go to the docs. I'm gonna go to this this company here, I'm gonna go to the LAPD, and then the values of all that information and pulling all this information together. What you do then is as everyone's putting all their notes on the board, you're getting out the red yarn, right? And you're saying, Well, okay, I've got my sticky notes here.

Whose sticky notes are the closest match to mine? Maybe I can maybe I can you know share an Uber with Sally, and because we're both heading in the same general direction, and you start putting yarn on all the sticky notes. This is the attention matrix where you're trying to where the model is essentially trying to figure out what of all this content that we have, how does it relate to itself? Right? How do we figure out what's important?

Like Bob over there, Bob got a part of the assignment, which is you know, looking at at manifests, shipping manifests. Me, I'm going to check out Daeu Industries at in Long Beach. We're not really on the same page. So if I'm focused on my assignment, Bob's focused on his assignment, our we're not gonna have a lot of red yarn between us. As the model starts making decisions, it's gonna it's gonna keep those things kind of separate.

So after the attention matrix occurs, you're gonna get the uh the sent down here from the ascent the attention residual sent down into a layer norm with the MLP, the multilayer perceptron. What's happening here? Every writer has gone out and gone to their sources and they've all assembled your notes. We got a big old pile of post-it notes and red yarn everywhere. And at this point, we're now ready to start putting together the story.

But the writers aren't gonna do it anymore. Now it's gonna hand it off to the editing room. So that's a good place to stop. So to recap, uh, so far what's happened in our story. Um the editor-in-chief has come in to the writer's room at our fictional newspaper and said, Hey, I got a story about corruption at the docks.

Hands out the story to all the writers, the writers break it up into pieces, the writers figure out uh who's doing what, um, and how every writer's piece uh relates to the rest of the story. Then they all go and get their information, they figure out what they need to know, they figure out where they need to go get it, and then they go get the answer and they put all that together. Then after that, in this section here, they go and they get into a big room and they start putting together the story. It's it's it's the the Pepe Silvia thing from uh it's always sunny in Philadelphia where you're putting your red yarn everywhere, and the ultimate output of this is kind of like an ugly first draft. Everyone's put together their stuff, you discarded things that that are less important, and now you've got kind of a first draft.

Now, what is this section about the query key values? Um, how does that impact our usage of AI? You can see how computationally expensive this is. If every writer has to talk to every other writer and everybody's got to have all their post-notes on one board, that's a big old board, one thing, right? And it means that if we have irrelevant stuff, if you got you know, Bob is over there uh getting drunk at the local saloon, he's gonna put bring back a bunch of garbage.

Um that's not gonna help us put together a good story. So even though we say more data is better when you're using generative AI in your prompts, it has to be relevant data. You don't want to just toss everything in. You want to toss in things that will advance the story, if you will, with the relevant specific information. Jargon is really helpful because that's like giving a writer a very focused piece of some uh story to go hunt down.

So that's a good place to stop for for part two. In part three, we're gonna talk about the editor's room, and then ultimately sort of typesetting in in part four and then part five. We'll we'll wrap up with what does this all mean. So stay tuned for the next part. If you enjoyed this video, please hit the like button.

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Christopher S. Penn is one of the world's leading experts on AI and marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI, particularly for uses of AI in marketing, AI and consulting, AI and management Consulting, AI and Business, AI strategy. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers, an introduction in Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world.

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