Like it or not, AI is here to stay. How shall we use it?
Sep 24, 2026 · 29 min · 12 segments
We talk with Grant Erickson from IntelliTect and John Shovic from the University of Idaho-Coeur d'Alene.
Sep 24, 2026 · 29 min · 12 segments
We talk with Grant Erickson from IntelliTect and John Shovic from the University of Idaho-Coeur d'Alene.
These new data techniques that we're using, these analytical data techniques like genetic algorithms and decision trees and things like that, versus large language models, they do have some commonalities.
One of the things I'm sure you've heard is large language models, chat GPT and whatnot, will hallucinate.
Why does it hallucinate? It's because it's got data that points it in the wrong direction.
On the other side, when you start looking at these analytical, these 23 million data points, you can come up with answers that are also kind of hallucinations.
You look at that and an engineer looks at that and says, no, that can't be the problem.
So there's always the human and the algorithm going back and forth and back and forth.
You go back and forth, back and forth, trying to get the machine to do what you want it to do.
These new data techniques that we're using, these analytical data techniques like genetic algorithms and decision trees and things like that, versus large language models, they do have some commonalities.
One of the things I'm sure you've heard is large language models, chat GPT and whatnot, will hallucinate.
Why does it hallucinate? It's because it's got data that points it in the wrong direction.
On the other side, when you start looking at these analytical, these 23 million data points, you can come up with answers that are also kind of hallucinations.
You look at that and an engineer looks at that and says, no, that can't be the problem.
So there's always the human and the algorithm going back and forth and back and forth.
You go back and forth, back and forth, trying to get the machine to do what you want it to do.
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