LLMs have general intelligence. A network of LLMs have better general intelligence.

If a single language model isn't intelligent enough for a task, but a human is, there is a good chance there exists a sufficient network of language models that is intelligent enough.

No they don't. That's the key part you keep assuming without justification. Interestingly enough you haven't responded to my other comment [1].

You asked “What intellectual tasks can humans do that language models can't?” and now that I'm thinking about it again, I think the more apt question would be the reverse:

“What intellectual tasks can a LLM do autonomously without any human supervision (direct or indirect[2]) if there's money at stake?”

You'll see that the list is going to be very shallow if not empty.

> A network of LLMs have better general intelligence.

Your argument was about tool calling for long term memory, this isn't “a network of LLM” but an LLM another tool chosen by a human to deal with LLM's limitations one one particular problem (and of you need long term memory for another problem you're very likely to need to rework both your prompt and your choice of tools to address it: it's not the LLM that solves it but your own intelligence).

[1]: https://news.ycombinator.com/item?id=43755623 [2] indirect supervision would be the human designing an automatic verification system to check LLMs output before using it. Any kind of verification that is planned in advance by the human and not improvised by the LLM when facing the problem counts as indirect supervision, even if it relies on another LLM.