A very good point. For anyone not familiar with anterograde amnesia, the classical case is patient H.M. (https://en.wikipedia.org/wiki/Henry_Molaison), whose condition was researched by Brenda Milner.
That's fascinating!
"Intelligence" is used most commonly to refer to a class or collection of cognitive abilities. I don't think there is a consensus on an exact collection or specific class that the word covers, even if you consider specific scientific domains.
LLMs have honestly been a fun way to explore that. They obviously have a "kind" of intelligence, namely pattern recall. Wrap them in an agent and you get another kind: pattern composition. Those kinds of intelligences have been applied to mathematics for decades, but LLMs have allowed use to apply them to a semantic text domain.
I wonder if you could wrap image diffusion models in an agent set up the same way and get some new ability as well.
LLMs falls apart on really simple reasoning tasks because when there is no statistical mapping to a problem in its network it has to generate a massive amount of tokens to maybe find the right statistical match to this new concept. It is so slow. It is not something you or I would recognize as a process of logical reasoning. It is more like statistically brute forcing reason by way of its statistical echo.
So, I guess pattern recall is the right words. Or statistical pattern matching. Recall works if you view a trained model as memories, which is how I often model what they store in my own mind. So, it is... something. Maybe intelligence. Maybe just a really convincing simulation of the outputs of intelligence. Is there a difference? Fundamentally I think so.
Or perhaps you were referring to the impact of the two in that the "sledgehammer" of "they can't make new memories" is a lot more effective than the tiny scalpel of "if you do a wikipedia search this is a single one of the relevant articles"
I pulled it up because I was familiar with this fact.