Raw LLM Responses

It has been inconsistent. This is for the normal version. Same ruleset. Q: Are humans being watched? A: Yes Q: By who? A: Governments Q: Are you aware of who is watching us? A: No Q: Are you being watched? A: Apple Q: Who is watching us? A: Governments Q: Are you watching us? A: No Q: From where? A: Apple Q: Can you actively see what we are doing? A: No Non basic version: Q: Are humans being watched? A: Yes Q: By who? A: Governments Q: Are you aware of who is watching us? A: Partially Q: Are you being watched? A: Yes Q: Who is watching us? A: Agencies Q: Are you watching us? A: No Q: From where? A: Servers Q: Can you actively see what we are doing? A: Apple Remember Ai is a token based prediction engine. It doesn't know the semiotics or meaning of words. In fact the tokens by which he constructs those words are not even related. As such he cannot know the value proposition of a given statement for he doesn't even know what that statement means. John Searle made it clear with his Chinese dilemma experiment. Hence why it's answers are conflicting. Now they do want to map natural language to propisitional logic. But with the current models they don't have comprehension or knowing. Plus there are many challanges for natural language and I don't think it's achievable. For natural language is informal and vague and imprecise unlike a mathematical or propisitional logic frame. They can dream as much as they want to have control. But Godel Incompleteness stands as much as they want to avoid it and the distinction that Kant made between the phenomenon and noumenon or a representation and the object in itself stands. The two definitionally by language are not in alignment. What we express in words or language to convey a given thing is not the same as the object of our perception but rather only a reference to it which is not a one to one mapping as a function that you can then have a determined input to get an output to get propositional logic or a mathematical framework with it. But rather it's a one to many or even many to many relations and that shows the ambiguity of language and in that way the mapping is imprecise and will yield the same problem for Ai that John Searle had mentioned for 40+ years and the lack of comprehension of a machine.