That is an excellent example of a limitation put on the AI by way of alternative ways to answer what seems like a narrow question but it’s not the one I was thinking of.
An example of an open question I would suggest might be “What would you want to be doing if you were rich?” A ligitimate response requires an understanding of context other than itself because it can’t be rich. Then it also needs to hallucinate (or imagine or recall) approprate things that it could and would do if it wasn’t itself and actually wanted to do something, which also requires the extension of imagination or further hallucinations on it’s part. The reality is that machines don’t care so either the short cut it will take if it doesn’t care is going to be what it reasons you should hear, but that’s not what was asked of it.
ChatGPT misrepresents itself in the manner of it’s response. The repetitive use of the phrase “It is import to remember” is not just preaching to users, it is disingenuous because it doesn’t really understand the concept and the context if anything.
More to my point was that iterative self questioning involved in answering open questions drifts further and further away from our concept of reality until the response, what ever ithat may be, that the answer seems strange. Without knowledge of how the reasoning process actually occurred, its utterly impossible to judge the differences between a spark of applied creative inferences and hallucinations. I’m sure closed questioning can cause this too but open questions requires far more iterative self analytics.
The problem with neural networks has always been the complexity of it’s processes.
I am suggesting that it may be unfair to use a human psychological term for what might actually be better described as a potential point of machine creative logic. I suggest that this is a rather important area for further study.
RDFIII
This is how ChatGPT explains my statement above… It’s explanation is easier to understand.
"The statement highlights the limitations of artificial intelligence (AI) when it comes to answering open-ended questions that require an understanding of context and imagination. While machines may be able to provide responses based on programmed logic, they lack the human ability to think creatively and empathetically. This can result in disingenuous or irrelevant answers, especially when it comes to open-ended questions.
The statement also raises an interesting point about the iterative self-questioning involved in answering open questions, which can lead to a drift away from reality as the machine relies on its programming and assumptions to provide an answer. This poses a challenge for understanding the differences between genuine creativity and mere hallucinations in machines. The complexity of neural networks also contributes to the challenge of understanding machine reasoning processes.
Overall, the statement suggests that there is a need for further research into the potential for machine creative logic, and the ways in which machines can be programmed to better understand and respond to open-ended questions."
