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Q And it's like, yep, the whole job is trying to figure out how the generative AI systems work. And it's like, you're putting this out there, people are relying on them. And then, oh, I mean, I guess, I don't know. And along the way, you're, you're trying to like figure out how they work. You're trying to interpret them. Like, isn't that backwards?
A Yeah, it is, and something really interesting, it's, it's, it's a bit of a metaphor, so I'm not saying it's exactly the same thing, but we can really learn a lot from ancient Greece and ancient Rome, because our current, you know, we started this conversation by just pointing out how much we're relying on prediction, and we've always relied on prediction, but I think there are times in history when that goes up and goes down. I think this is a peak, and another peak was ancient Greece and ancient Rome, and if you were to interview an ancient Greek person and say, what do you think about the Oracle of Delphi? They would say, oh, it's, it's cutting edge technology. You know, it's, it's the best we have to make decisions. And how does it work? Well, we're trying to interpret it, right? And the same thing with astrology. It was a very technical thing about how, how to read the stars, how to measure the distance between the stars. So in, in a way we've seen this before, even though the technology is different, the political role is actually quite similar.
AI assessment note: “Yeah, it is, and something really interesting, it's, it's, it's a bit of a metaphor”
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Q Um, so if we have these systems of prediction in our world, I mean, again, like people who are building gen AI tools, they, they care very much about prediction, predicting the next word, predicting outcomes. And when they can predict outcomes, then their agents can take the next step. Where is that leading?
A Yes. So some authors make this distinction between predictive AI and generative AI, and I am not sure it makes sense, because both kinds of AI are essentially predictive. We might use them differently, and they might look differently, but essentially, they're both machine learning, and what machine learning broadly does is it has some data, and it projects data that it doesn't have based on data that it does have, roughly. Whether it's predicting the next word, or predicting whether somebody's going to be a good employee or not. And in the case of generative AI, it's fascinating. I don't know where to start. It's fascinating how it got trained. That, I mean, that's one thing, you know, with, with copyrighted material, with personal data, and so that's one kind of thing. We can spark it, but just notice. And in the way it works, it's a very sycophantic system, as we know. It likes to please people, because that's the way it gets you to be engaged. And so, It will tell you things like, oh, that's a brilliant idea, and it will continually validate you. Um, they were built, they were designed to do that. They were designed to make people feel satisfied, instead of being designed for another thing, for, for example, for being truth tracking, which would be much more useful if, say, you're a researcher. And I think sometimes we lose sight of that, and one way to put it is in, in the …
AI assessment note: “both kinds of AI are essentially predictive.”
