If the task is ill-defined, then it's a bit unfair to make it sound like the problem is that an LLM can't be configured to do something, if a human would have an equally hard time with the same task. The statement "it's impossible to configure the weights to..." should really be something more broad like "it's impossible to...".

I have no comment about whether it's impossible to determine the intentions of a person asking for assistance through a textual conversation with that person.

> Because that’s what I am seeing emerge from the various efforts to build LLM safety tools.

Something having not been obtained so far is not a logical argument it is impossible to obtain that thing.

> LLM != human? They don’t even use the same reasoning process.

There are a finite number of possible input strings of a given length. For any set of input strings, it is possible to build a deterministic mapping that produces "correct" answers, where those correct answers exist. Ergo anything a human can do correctly with a certain set of text inputs, it is possible to build an LLM that performs equally well. You can think of this as hardcoding the right answers into the model. The model itself can get very large, but it is always possible (not necessarily feasible).

It's only impossible for an LLM to do something right if we cannot decide what it means for the answer to BE right in a stable way, or if it requires an unbounded amount of input. No real-world tasks require an unbounded input.