Recent comments in /f/MachineLearning

currentscurrents t1_j49u28o wrote

Reply to comment by mycall in [D] Is MusicGPT a viable possibility? by markhachman

I don't think it's that simple - whether or not generative AI is considered "transformative" has not yet been tested by the courts.

Until somebody actually gets sued over this and it goes to court, we don't know how the legal system is going to handle it. There is currently a lawsuit against Github Copilot, so we will probably know in the next couple years.

5

Kafke t1_j49tiot wrote

Reply to [D] Is MusicGPT a viable possibility? by markhachman

well it wouldn't be "MusicGPT", but yes, ai music is already a thing. Such as with riffusion (stable diffusion trained on spectrograms to create music).

you're correct about the controversy though. All modern AI work in the same way: by curating a dataset that's used to train a neural network's weights, and then those weights are used to produce something related to the dataset. So just as art AI uses images in the training that people yell about copyright issues, music AI will use music in the training that people may also complain about.

13

Mystiic_Madness t1_j49oy05 wrote

Reply to comment by mycall in [D] Is MusicGPT a viable possibility? by markhachman

Fun fact Weird Al still liceneses music from the orginal artists

> Al does get permission from the original writers of the songs that he parodies. While the law supports his ability to parody without permission, he feels it’s important to maintain the relationships that he’s built with artists and writers over the years. Plus, Al wants to make sure that he gets his songwriter credit (as writer of new lyrics) as well as his rightful share of the royalties. - Weird Al's site

The reason is because of "Write a word, Get a third" where if you write or change a lyric in a song you are entitled to a 1/3rd of the royalties.

5

bloc97 t1_j49ft0g wrote

Reply to comment by mugbrushteeth in [D] Bitter lesson 2.0? by Tea_Pearce

My bet is on "mortal computers" (term coined by Hinton). Our current methods to train Deep Nets are extremely inefficient. CPU and GPUs basically have to load data, process it, then save it back to memory. We can eliminate this bandwidth limitation by printing basically a very large differentiable memory cell, with hardware connections inside representing the connections between neurons, which will allow us to do inference or backprop in a single step.

2