Because the DMCA lawyers are aggressively litigious and have laws/precedent to back it up. Artists have limited legal recourse and fewer laws to protect them.

Man, all these years I was listening to music to train my learning model I had no idea that my ears are illegal.

Isn't SoundCloud basically danbooru for music?

There might not be a nicely accessible dataset yet, but that probably won't stop major players

It's not controversial as long as you don't share it and make money with it, you are pretty much free to do whatever you want.

If you plan to share the output, meaning here what's generated, not just the code and checkpoints, or a training set that's under copyright, publicly then it's another question entirely and if you are serious about that I recommend seeking legal advice.

Currently we have exascale computers, e.g. 1e18 flops at around 50e6 watts.

The power output of the sun is about 4e26 watts. That's 20 orders of magnitude on the table.

This paper claims that energy of computation can theoretically be reduced by another 22 orders of magnitude. https://arxiv.org/pdf/quant-ph/9908043.pdf

So physics (our current understanding) seems to allow at least 42 orders of magnitude bigger (computationally) learning machines than current generation foundation models, without leaving this solar system, and without converting mass into energy...

Not your fault, but it seems reasonable to interpret "validation samples" as samples for your validation set (e.g., you wanted people to manually label something for your training/validation data).

Not the one doing the downvoting, but, isn't that same thing?

Validating a sample and having validation samples, when your problem has a "known solution" or is close to, is equivalent.

ah it does say that, and noncommercial license at the top as well.

Doesn't matter much, you can't realistically use it anyways.

The source is there, but I don't think it's a pre-trained model, and it sounds really slow (like a second of audio will be like an hour of processing). It'd probably cost less to commission a real musician.

MuseNet from OpenAI uses GPT-2. IMO it is the best musical idea generator that exists. I used it to compose a number of classical solo piano pieces.

>No portion of the Software, nor any content created with the Software, may be used for commercial purposes.

Not really. Attribution only, no other restrictions.

https://github.com/openai/jukebox/blob/master/LICENSE

Copyright issues aren't problematic. The case law is well settled until there is new legislation. Some parties don't like it and some journalists want to write about it. 🤷

Copyright is not a big obstacle for generative AI. Research Author's Guild vs Google.

I find it hilarious jukebox trained on a fuckton of copyrighted shit and now OpenAI points the finger at the potential users.

Jukebox license is different from the one you're thinking of.

It’s like saying that creators of database will create all SaaS products

For end user, many more things matter

>Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models.

Expert systems as in hand-crafted rule-based expert system?

Yes. Look at stable diffusion and riffusion for an example of this. Music isn't fundamentally different from images and text in terms of how modern AI works.

I think what I'm talking would be an algorithm that understands the sounds of different instruments, their tonality, rhythm, and so on, in much the same way ChatGPT understands the relationship between words or presumably Vall-E understands phonemes -- and then understands how to put them together in the style of various artists.

I'll have to check out Riffusion, though, as I'm unfamiliar with it, thanks.

>> "Al does get permission"

> By obtaining official copyright permission from the artists, Weird Al’s attorneys negotiate royalties which vary from a flat fee buyout to royalty participation.

Link

I.e. a licence

The first bitter lesson was "people who focused on 'more domain-specific algorithms' lost out to the people who just waited for massive compute power to become available." I think the second bitter lesson is intended to be Robotics-specific and it is "people who focus on 'robotics-specific algorithms' will lose out to the people who leverage large foundation models from non-robotics fields, like large language models."

I think it's better to do various elements of music separately and combine them rather than try to do the whole song at once. It's a challenging problem. There is much less training data for example. I've worked on melodies so far: https://www.melodies.ai/, https://www.youtube.com/playlist?list=PLoCzMRqh5SkFPG0-RIAR8jYRaICWubUdx.

How many team members would it take ChatLawGPT and feed it tons of Hebrew content? Isn't the whole point that it can learn domain knowledge?

All the numbers in your comment added up to 69. Congrats!

  6
+ 55
+ 8
= 69

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