Classically, AI as a field was all about how to encode human knowledge in a form accessible to logical inference techniques. This involved a lot of manual entering of rules, logical propositions, fact databases, etc.
The rise of statistical learning, which can benefit from a lot more un-preprocessed data, is comparatively new.
It's not that AI didn't use data before, just that the classical models relied on a lot of data being meticulously encoded by hand. As you can imagine, this was fine for academic pursuits, but real applications quickly hit the limits of what was feasible to tackle this way due the amount of (expert) labour needed. The success of the machine learning approach is largely due to the massive scaling afforded by not requiring human intervention in every individual data point.
To expand on this, it wasn't a consensus until comparatively recently that artificial intelligence required learning techniques.
Now it seems obvious but for long time there was the idea that logic programming and knowledge engineering with large ontologies was the route to intelligence.
I doubt anyone in the AI field now makes any distinction between AI and ML. This of course implies the use of data. There's no attempt to hide this by anyone I'm aware of - it's just how it works.
The question was more about the distinction wrt AI I think.
I understood the original comment to argue that it can't be sentient because its only created by ingesting insane amounts of human interactions.
Though I doubt we'll see artificial sentience during my lifetime, i strongly disagree with that take. From my point of view Humans need that too to become smart and learn language etc, so that shouldn't be an argument. Neither for nor against.
