ML can do either facsimile or imitation far better than a human mind can.

You seem to be conflating both things and suggesting that ML only does facsimile, which is where the potential legal problems are.

No, it is not. Memorization =! understanding.

I can teach a parrot to spew the times table, good luck getting it to understand how to apply it.

And a parrot is billions upon billions of times more capable than any current AI algos.

Diffusion would appear to me to work in much the same way. It doesn't understand what's good ("works", creates acceptable output) or why, but it knows it when it sees it, and has the tools to refine it.

AI, like humans, is capable of both imitation and facsimile. It is far superior at both feats.

Your fallacy is that you are noticing AI is superior at facsimile and erroneously assuming it is “not learning”. You are also ignoring the other amazing learning feats of imitation in front of you.

Parrots are lovely animals, but it’s unclear what you think you’ve accomplished by bringing them up. The fact that they are capable of more than just memorization does not differentiate them from advanced AI models, which are rapidly gaining all sorts of abilities.

Polly the parrot would have a hard time producing a picture of Elmo with a light saber in a Superman costume riding a dragon on the moon in the style of Rembrandt (in under 300ms, at least). I also know a parrot couldn’t write a 500 word story about the image.

I am pretty sure that GPT-3 is a lot more capable than a parrot in transpiling a function written in Python to Golang, or writing a summary to a tech-magazine article.

Same as StableDiffusion is ALOT more capable than me in drawing, painting and generally making up pretty pictures.

But there are no copies. For example, the LAION-2b training data is a total of 240 TB. The pruned SD model based on this dataset, is less than 5GB.

The data isn't copied into the models, it is used to teach the models, letting them learn patterns in the dataset.

Whether or not it's identical to human brains isn't the matter, they'd need to prove how a small 5GB model trained from a huge dataset infringes their rights specifically.

I have a 1.9 GB mp4 file on my harddrive. It contains 2 hours and 15 minutes of 1080p video data at 24 fps. Assuming it was generated from 4096x2160 16-bit color depth source material, the "training data" was 10.32 TB. I bet I could even get a similar size reduction as LAION-2b if I recompressed it to 720p.

Could I not also claim that I created an advanced AI model, which did not copy but learned patterns in the dataset? Modern video compression algorithms are getting quite complicated, after all.

I think no reasonable person would agree with this, but can you prove that the AI model is doing something substantially different?

Such patterns would enable the video file to decode into a multitude of pictures not originally in the training data. Obviously, a video file cannot do that...it's just compressed data.

Generative models however can generate things that are not in its training set.

And of course, there is a fundamental difference in the source data between compressed video and a generative model: video codecs work with a sorted sequence of images, where most images are slight variations of the ones before them. The training for generative AI doesn't have these properties, the input is not an ordered sequence, and even similar pictures are not sequential variations of one another.

Relying solely "uncompressed" size does not a really good metric make (this is analogous to the raw input size of the LAION dataset): one could make a reasonable argument that there are not billions (1) of image-pairs that are effectively identical up to a minute shift. I would posit the correct basis would be the Shannon entropy of the "best fit" ordering (minimize inter-frame diff), versus the lossy-compressed video, and a similar "best fit" ordering for the LAION dataset vs. the model.

My suspicion is that one will find that the relative number of "smooth transition" pairs in LAION viz the whole will be very different from the video.

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(1) - Napkin math: There are about 194400 frames, so ~37 billion (37791165600) frame-pairs. Assuming you have runs of about 1 second between hard cuts throughout, so an incidence rate of 1/24 for non-smooth transitions, gives us about ~36 billion "smooth transition" frame-pairs. I think it is safe to assume "on the order of" 1 billion, then. This ignores long "action" scenes with significant variance in images throughout, but also ignores longer-than-1-second slower scenes, hence the order-of-magnitude shrink in the assumption as buffer.

No, but what if you then produce "your own" rendition, or "remix", of that book, song or movie and offer it to the public? E.g. you memorize a collection of Taylor Swift's latest songs, and then start performing a medley of her hits in your local clubs, you may well find yourself in trouble.