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It's easy to use deep learning to generate notes that sound like music, in the same way that it's easy to generate text that looks like natural language. But it's nearly impossible to generate *good* music that way, much like you can't generate a good 2-page story or poem
With two caveats: 1. Plagiarism. If you near-copy large chunks of a good piece, these chunks will be good. 2. Large-scale curation. If you generate thousands of samples and hand-pick the best, they may be good by happenstance (especially for music, where the space is smaller)
However, algorithms (and ML in particular) absolutely do have a role to play in music creation. What's broken is the general approach of statistical mimicry, e.g. raw deep learning. To generate good music programmatically, you need an algorithmic model of what makes music good.
If you understand what makes music good with a sufficient level of clarity, you can express it in rules form, and seek to algorithmically maximize this greatness factor.
As usual with AI, this requires first understanding the subject matter by yourself, instead of blindly throwing a large dataset at a large model -- an approach which could only ever achieve local interpolation. Find the model, don't just fit a curve.
Why is the space for music smaller? How do you quantify this? I would assume the entropy rate would be higher than for text
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