@Cosmik it looks like the discussion has gone in a different direction since you quoted my post, so I'll keep it brief.
To your first example, you gave the hypothesis:
a neural network can be programmed to judge good vs bad art.
What's lacking here is
empiricism. The hypothesis is not empirical because "good" and "bad" are value judgements and therefore cannot be falsified empirically. You need to revise the hypothesis to something more empirical; perhaps "a neural network can be programmed to mimic human judgements of art."
Secondly, your prediction (the specific thing you try to falsify) does not fit your hypothesis, because it introduces a ranking system (1-10) whereas your hypothesis posits a binary system (good/bad). Thirdly, the conclusion you draw ("I speculate that I have mimicked the way the human brain views art...") does not relate to your hypothesis, which contained no statement about the functioning of the human brain or how humans perceive anything.
Finally, a positive result in one experiment does not prove a hypothesis. It just means that the prediction was not falsified under the experimental conditions and therefore the hypothesis has not been
disproven. The experiment needs to be repeated and (more importantly) additional empirically falsifiable predictions that follow from the hypothesis need to be posited and tested, before you can start to become more confident in your hypothesis.
Science can never prove anything is "true". It is, rather, a rigorous way of arriving at the best explanation subject to current limitations.
The scientific method itself is rigorous and unchanging, but the validity of the theories it leads to are certainly not absolute; these are always limited by both the intelligence and creativity of those who develop them, and their capacity (e.g. technologically) to test them. The most a scientific theory can hope to be is the
current best explanation.
So science really does make only rather modest claims compared to what your expectations seemed to be