If you’re using it as you found it, then yeah. But if I take derived data from it like word count and word frequency, it’s not exactly the same thing and we call that statistics. Now if I draw associations of how often certain words appear together, and then compound that with millions of other sources to create a map of related words and concepts, I’m no longer using the data as you described because I’m doing something entirely different with it. What LLMs do is generates new information from its underlying sources.
In my example, they would still be using the source code to create new software that is not open source, not matter how many Markov chains are behind it.
If you’re using it as you found it, then yeah. But if I take derived data from it like word count and word frequency, it’s not exactly the same thing and we call that statistics. Now if I draw associations of how often certain words appear together, and then compound that with millions of other sources to create a map of related words and concepts, I’m no longer using the data as you described because I’m doing something entirely different with it. What LLMs do is generates new information from its underlying sources.
In my example, they would still be using the source code to create new software that is not open source, not matter how many Markov chains are behind it.
That’s really stretching it, tbh. You’re arguing that the cake is made of chicken because it contained whole eggs at some point.