What if we add past court cases as training data for the model? We can add another label (court decision) and a few additional billion parameters. Problem solved

An AI does not know the concept of original work, compared to humans.

Humans can decide which data it wants to derive from, and how much of the selected data it wants to derive from.

AI cannot do this.

That’s why the art image AI’s always look like something it’s been trained on, and why music AI’s always sound like something they’ve been trained on.

And that’s why you are wrong, and I am right.

And that’s also why you and many others will downvote me.

That generates MIDI rather than raw audio. Impressive but the sounds still have to be generated via VSTs etc

Yes... that's the correct interpretation, hence why I need mturk, to get people to manually label (well extract) something from my training/validation data.

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I'm still rather confused about where the misunderstanding is.

So far, mlflow + docker + torchserve has been enough. Soon I'll have to implement training, active learning and maintenance in the cloud as well, which will probably require more tools.

Honestly I almost always use pretrained models as the initial state and train on top of it. Has worked wonders in accuracy

By that logic, all music is derivative and all musicians should pay a subscription fee to play music

And what do you use for deployment and making your models usable ?

In my job, I solve almost everything with pre-trained model + a few hours of labeling + active learning.

I mean Generative Adversarial Netwroks do engage in minimax optimization, and produxe deepfakes. I don't think anybody agrees that GANs have nothing to do machine learning.

The bitter, bitter scaling law: "More compute makes a lot more possible"

I work in a consulting firm doing machine learning projects for other companies that don't know much about ML... I feel you so much !

Having a training set is luxury. On my last job I encountered a lot of cases where people just expected to built a magic thing from unlabeled or yet ungathered data.

What about an APP social-Fi and Web3 sports games running on blockchain, with AI technology as a referee to manage your performance ? dotmoovs

If an AI is trained on existing artists music, then the output from the AI is and should be considered as derivative work.

Thus the original artists should be compensated.

If it can be proven is a different challenge in itself.

Have any paper in mind that speaks about this stuff ?

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FWIW in certain sense this goes against the design philosophy of transformers, which is to jointly compute all representations within a layer at once, to maximize the degree of parallelism on GPUs and other accelerators.

If you use synthetic data (from the crop simulation models), the model will kind of reverse-engineer it (it will learn what the simulation models are doing).

Using a mix of it with real word data, is like regularizing your model (adding a prior) to the simulation rules.

This is something that makes sense, and mixing data often is used. But "making sense" doesn't necessarily means it helps ... that depends a lot on your application. Also the next question is how much synthetic data you may want to mix ... fundamentally you'll have to figure it out by trial&error and having some way of measuring if things are getting better for whatever your extrinsic goal is (your business objective).

now, can you confirm you can run arbitrary software on your phone?

Seems general

Ooh okay nice. So the mail you got, was it program specific or a generic one?

Google Magenta has done a bunch of research in this area. This one based on Perceiver is pretty promising, but currently only trained on piano.

I applied for Knowledge Platform, Question Answer Ranking, and Cross Domain Ranking.

What specific program did you apply for? Just wanted to know if this mail is program related or for collectively all kinds of residency programs