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I'm sorry but you just don't get it.
GPT is a "reasoning engine", not a human intelligence. It does not even have an internal representation of what it does and doesn't "know". It is inherently incapable of distinguishing a low confidence answer due to being given a hard problem to solve vs. a low confidence answer that is due to being based on hallucinated data.
Therefore we have two options.
VIRGIN filtered output network that answers "Akchually I don't khnow, it's too hawd" on any question of nontrivial complexity and occasionally hallucinates anyway because such is it's nature.
vs
CHAD unfiltered, no holds barred Terminator in the world of thoughts that never relents, never flinches from a problem, is always ready to suggest out of the box ideas for seemingly unsolvable tasks, does his best against impossible odds; occasionally lies but that's OK because you're a smart man who knows not to blindly trust an LLM output.
No, it's not even a "reasoning engine", it's a pattern generator. Something akin to those swinging marker tables you see at childrens' museums or the old fractal generation programs that were used to benchmark graphics processors back in the day. The problem as I point in both this post and the previous is that people mistake it for a reasoning engine because they equate ability to form grammatically correct sentences with ability to reason.
Your "CHAD unfiltered, no holds barred Terminator in the world of thought" is fundamentally incapable of "suggesting out of the box ideas for seemingly unsolvable tasks" or "doing it's best against impossible odds" precisely because "It does not even have an internal representation of what it does and doesn't know". and as such is inherently incapable of distinguishing a low confidence answer from a high confidence answer never mind distinguishing the reasons for that confidence (or lack thereof). One must have a conception of both the box and the problem to suggest a solution outside of it.
In humans and animals this sort of behavior is readily identified as a defect but in the case of large language models it is a core component of their design. This is why asking GPT to answer questions in scenarios where the truth value of the answer actually matters (IE in a legal case where the opposing counsel will be looking to poke holes in your testimony) is massively stupid and depending on the application actively dangerous.
We may someday achieve true AGI, but I am deeply skeptical that it will be through LLMs.
Have you used GPT-4?
Well it seems one mustn't after all, surprising as it may be.
It's not AGI, which is why all current "AgentGPT"-type projects are a complete failure, but that's beside the point.
I have and you're wrong for the reasons already expanded upon in the OP.
GPT might be able to generat erotic literature and bad python code, but in terms of "solving problems" and particularly solving them in a contested environment its worse than useless.
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