Alphago basically generated its own training data (hence, "exceeding" is not correct) because the domain was amenable to a specific form of reinforcement learning. Programming isn't such a domain.
Alphago is proof that ML systems can exceed human abilities in at least some domains. Other domains, like identifying animal breeds/species by sight, I believe ML models are also super human. It's just not correct to say models are limited by their training data to something less than human quality. We have multiple counterexamples already.
Well, this is obviously incorrect because the training data included all the games it played against itself (good luck replicating that in the domain of programming though).
We were not talking about "exceeding human abilities in at least some domains", we were talking about whether ML (DL really) systems were able to "go beyond" their training data - but your deflection and going off point is noted.
If you're trying to argue that the model can't be better than it is, i.e. the last training data it produces, then that's true but tautologically so. ML has conquered some domains, like Go or Chess or birdwatching, and is making progress in others - like programming. There is no reason to assume ML systems can't exceed human ability at programming or any other task.
More deflection and going off point.
>is making progress in others - like programming
I already explained that programming is not amenable to the approach that allowed alphago to learn from the games it played against itself.
This is already getting pointless and tedious, should in the future know better than to respond to people that are obviously in the "scale is all you need" nonsense camp, nothing meaningful comes out of it.