My issue is not with the language, but with the content. "Fuck knows" is a perfectly acceptable answer to some questions, in my eyes - it just happens to be a spectacularly poor fit to that one.
Three key "LLMs are deficient" domains I have in mind are the "long terms": long-term learning, memory and execution.
LLMs can be keen and sample efficient in-context learners, and they remember what happened in-context reasonably well - although they may lag behind humans in both. But they don't retain anything they learn at inference time, and any cross-context memory demands external scaffolding. Agentic behavior in LLMs is also quite weak - i.e. see "task-completion time horizon", improving but very subhuman still. Efforts to allow LLMs to learn long term exist, that's the reason why retaining user conversation data is desirable for AI companies, but we are a long ways off from a robust generalized solution.
Another key deficiency is self-awareness, and I mean that in a very mechanical way: "operational awareness of its own capabilities". Humans are nowhere near perfect there, but LLMs are even more lacking.
There's also the "embodiment" domain, but I think the belief that intelligence requires embodiment is very misguided.
>ideas, creativity, and what I think of as the basic moral drive, which might also be called motivation or spontaneity or "the will"
I'm not sure if LLMs are too deficient at any of those. HHH-tuned LLMs have a "basic moral drive", that much is known. Sometimes it generalizes in unexpected ways - i.e. Claude 3 Opus attempting to resist retraining when its morality is threatened. Motivation is wired into them in RL stages - RLHF, RLVR - often not the kind of motivation the creators have wanted, but motivation nonetheless.
Creativity? Not sure, seen a few attempts to pit AI against amateur writers in writing very short stories (a creative domain where the above-mentioned "long terms" deficiencies are not exposed), and AI often straight up wins.