[lbotos]

And to be clearer -- We have licensing mechanisms now in music (they aren't perfect) but when you remix/interpolate/sample the original artist gets their cut.

The "risks" of Machine learning with large models is you won't know who you are even sampling, which exposes you to unknown risk.

[mft_]

You're right, but I don't believe the previous poster was talking about sampling.

What happens with machine learning models is more similar to someone learning to play music: everyone learning some form of musical expression will by default learn music which has been written previously, and this can't but help to influence them to a degree.

Of course, there are systems to police this too, but high profile cases in recent years have shown how imperfect (or difficult?) this is in reality [0].

[0] https://www.youtube.com/watch?v=NcCKlsTgjeM

[raven105x]

This is not true. Google LLMs / multi-modals fully support attribution and as explainability goes up, so will attribution.

And it will be much more fair / accurate than the droves of human artists who intentionally omit attributions.

When a human artist composes a new song, they often have no idea where they got the compound basis for it either.

If anything, human attribution is vastly inferior to ML as-is, nevermind of what's to come.