Nitpick: strictly speaking, a computer is not a universal Turing machine because it has a finite amount of memory and is therefore a finite state machine (and in particular can therefore only run finitely many programs). When we say that computers are universal Turing machines we are talking about an idealized version of a computer that acquires more memory as necessary.
Regarding the problem of determining whether a Turing machine is universal, this is undecidable by Rice's theorem, which asserts more generally that any nontrivial property (in the sense that at least one Turing machine has it and at least one Turing machine doesn't have it) of Turing machines is undecidable. The best you can do is an algorithm which returns "is a UTM" on some Turing machines, returns "is not a UTM" on others, or doesn't halt (or outputs "unknown"). For example, it might search through proofs that a given Turing machine is or is not universal (possibly up to some upper bound).
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The problem with training isn't purposely creating things that pass. It's purposely creating things that don't. In order to figure out what doesn't pass, we need a predicate function. Once we've figured out how to find things that won't pass, we've already found the answer.
Doesn't follow. Consider;
I accept that in order to classify something, we need to be able to classify it.
I'm suggesting there might be a function that classifies some things incorrectly, and is still useful.