Speaker A critiques the use of the term "hallucination" for AI model errors, arguing it misleads people about model behavior. They contrast traditional relational databases—precise but inflexible—with modern AI systems that interpolate, adapt, and transfer abstractions across domains, enabling fuzzy, generative outputs that fill gaps between knowledge points.
Do you see a pathway to getting to a place where we can trust what's coming out of these models? Sometimes a new trend gets a brand name or a label, and then it confuses everybody about what's happened. You know, in some ways, it causes, like, the entire field to hallucinate about what they're actually seeing. It's a really weird word, hallucinate, because this is of relational databases that have basically built pretty much all the value of software for 40 or 50 years. Its big weakness is that it can't adapt, it can't change. It doesn't have any fuzziness. You only get precisely what you put in. You only get out precisely what you put in. Whereas this now kind of solves a bit of that problem because it has this, like, fuzzy, adaptive sort of abstraction that it can transfer knowledge from one domain to another, that it can interpolate. So, you know, you have two knowledge points, and it can join those knowledge by finding the gap in the middle, which is what you see when it's generating a new image that's in the style of something, for example. So hallucination is just an unfortunate word.
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