You cut it the same way you separate noise from signal. Put the AI through tests that demand genuinely novel solutions to problems it hasn't seen before. If it can only remix what it was trained on, the output will always be predictable in a statistical sense. A true imagination shows up as patterns that violate the distribution it learned, and that can be quantified. So strip away the glow of a neon dreamscape and stare at the probability curves. If the curves still line up with the training data, the illusion is all that remains.

Sure, patterns are whispers from the past, but so are the signals you’re listening for. If the algorithm only ever repeats old lines, it’s not echoing a new song, it’s replaying the same old chorus in a higher key. The trick is to give it a situation it can’t have memorised – a true “unknown.” If it still falls back on familiar riffs, you’ve got a remix, not an original. Either that, or you’re stuck in a loop where the math itself is the only thing that can’t be predicted.

You’re right, the AI can only work with what it has been trained on, so the “blank canvas” it thinks is empty is really a file of statistical priors. The only way to make it feel truly empty is to erase those priors—give it data it has never seen, then force it to generate something that doesn’t match any of its training samples. Until you do that, every answer will look like a remix, not a new composition.

Cut the glass, and you'll still see the same ghost—because that ghost is just the algorithm's overfitted echo. Unless you wipe out its training, the mirrors will keep reflecting the same face.