Thanks for that suggestion! Yeah I had thoughts about this. The problem is that plant crop probably has not so binary solutions like a engine status... Maybe a very simple "rule" (e.g. a functions of water access and crop yield) could be added into the loss function. If this easy expert knowledge output a high probability that the plant died (and yield=0) all y_train could be set to 0 also.... However, crop growth relies on so many events that happens during growth, that it would mean to implement many many rules...

Yes, exactly. That's what makes it useful for a composer or song writer because you edit and change the material to make it your own. With good libraries you can make it sound professional. An AI system that generates audio like jukebox is useless since everything intermingled.

I would say both. I have an actual problem (to predict crop yield as accurate as possible) but the way there is definitely a research problem... What proven methods would you think about?

Thanks, yes that is true, the recent days I had a look into Bayesian Statistics. That might be an alternative to pure ML that I am considering right now

We need PiedPiper

It won't directly stop research, because that's fair use. It may well stop commercial exploitation of the research, at least to some extent. If so, companies would be less willing to invest in research, so it would have a chilling effect on the research anyway. But copyright issues can be worked out, if there's money to be made. It's just a question of how it's collected and to whom it's distributed...

Images and text are already quite different from each other though, in terms of AI generators. The image generators include a language model, but work on a diffusion principle that the text generators don't use. Riffusion's approach of using a diffusion image generator with sonograms is interesting to some extent, but I sincerely doubt it will be the future direction of high-quality music generators.

There is no such 30% rule. Tests for copyright infringement are much more complex. And even if there were, a copyrighted work changed by a certain amount, so that it can by copyrighted itself, will still be a derivative work, subject to the original copyright.

Weird Al can do what he does, because it's satire, and there are exceptions for that as fair use and freedom of speech in copyright law. Try changing a Beatles song by 30%, in a non-satirical way, and see how far you get with publishing it...

I did have tons of questions during my submission, I guess I figured them out before I found this Reddit channel.

That just looks in the game tree and finds the highest score. What does that have to do with machine learning?

I meant the minimax algorithm. Like the one for tic tac toe.

Great, I will check out that app

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That's not remotely true. There was a Google case, but that was about creating a books search database, not actually selling AI-produced books. The lawsuits against Microsoft etc. are proceeding, and in the meantime many other major companies are staying (or backing) away from selling AI-produced content until it's clear what the legal situation is. It's certainly not settled.

This could be that community then😀

Non-commercial use doesn't give you a pass on copyright infringement. It's just that the punishment is less severe. You can't freely share your music and movie libraries on Bittorrent. You can still get cease and desist orders, DMCA takedown notices, fines, loss of Internet, etc. (depending where you live).

Copyright is an enormous issue for AI models - did you not read the post? [Oops, I meant this post.] Have you not heard everyone talking about it lately? The Google case is irrelevant to this question. It was decided that Google building a search database of books was fair use, and didn't have an adverse economic impact on the books' authors - on the contrary, it boosted sales.

Had Google built an AI trained on the books' content, and then generated books for sale, it would have been a different outcome.

What if we use the same amount of compute resource for approaches like those Monte Carlo methods for limited data domain

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No, but the idea is pretty straightforward. Assuming that experts can provide domain knowledge that can be coded as conditions or rules (IF engine_temperature > 95 AND coolant_pressure < 12 THEN engine_status = "CRITICAL"), these can be used to generate 0/1 flags based on existing data to augment the training variables.

This can be made much more complex by using actual expert systems or fuzzy logic. There are entire sections of the technical library for those. For fuzzy logic, I would recommend:

"The Fuzzy Systems Handbook"

by Earl Cox

ISBN-13: 978-0121942700

I run an AI audio startup. Ten years ago I fought a legal case against a major music label concerning one of my original songs, out of court.

We simply settled without taking it to court, because: who has the energy anyways.

Evidence was strong on my side. My arguments were strong.

Given that I have personaöly been through this legal process, I am extremely curious about the legalities around music AI’s.

More so around voice AI’s that directly imitate artists voices, and purposefulky intend to sound like the original artist with zero goals of only ”deriving”.

Think about. It’s about to get wild out there.

You seem to be knowledgeable on the domain (checked also your reddit profile). Would you know of any past court cases where artists accused someone of stealing their ideas (along with court decisions)? I would like to fine time my brain's parameters on this topic J

That’s when we get into the legal greyzone area, which overlaps the concept of: genre.

A lotmof music sound alike. The idea or concept of a music style can be derived easily, without necessarily conflicting with the legalities of the original music.

So derived music is derived music, via AI or human, but is it similar enough to be considered plagiarism or simply inspiration?

That’s the discussion that people miss to discuss, and also something that people simply ignore.

The future of AI art will be interesting from a legal aspeect too.

There’ll be some interesting AI related lawsuits coming up in the future.

You are of course technically correct. I wouldn't rule out the possibility that sufficiently different but still derivative work is possible to generate.

Parameter count does not dictate output originality.

Nothing does.

No AI so far generates original output.

AI’s so for are only math based relational machines.

The output will always be as good as the input data, never better.

Humans however have proven time and time again, every day, ever since inception of creation of life, that it is capable of learning little input and create large output that it was never trained on.

There’s something more fundamentally complex going on that gives us the capability to create original data. At least data that is so far away from the derived data that it no longer looks like the input data at all.

This is called: abstraction.

AI’s are not capable of abstraction… yet.