Earlier quoted context omitted.
It absolutely can generate new data, it does so all the time. If you are claiming otherwise I think we need a more formal definition of what you mean by new data. Are you suggesting because it can't predict the future it can't generate novel data?
It's not just the future, though the examples I gave were future oriented. But it's all very interpolation/summarization-focused. A "song lyrics in the style of Taylor Swift" isn't an actual song by Taylor Swift. A summary of the history of Texas isn't actually vetted by any historian to ensure accuracy. The answer to a math problem may not be correct. To me, those things don't qualify as "new data." They aren't suit…
One of the worst problems in the "Expert Systems" age of A.I. was reasoning over uncertainty, for instance this system
https://en.wikipedia.org/wiki/Mycin
had a half-baked approach that worked well enough for a particular range of medical diagnosis. In general it is an awful problem because it involves sampling over a joint probability distribution. If you have 1000 variables you have to sample a 1000-dimensional space, to do it the brute force way you'd have sample the data in an outrageous number of hypercubes.
Insofar as machine learning is successful it is that we have algorithms that take a comparatively sparse sample and make a good guess of what the joint p.d. is. The success of deep learning is particularly miraculous in that respect.