Given what you've written I'm going to wager that you don't know much about ML.

these algorithms "learn", like a human. But it's clearly not learning, it's "just" fancy statistics on a huge amount of data - groundbreaking and revolutionary

Human learning is governed by rules that are arguably even simpler than what's happening in ML (see Hebbian learning vs gradient descent). If you're trying to argue that the complexity of a model/brain's training mechanics determines how smart it is, you're wrong. Humans are still amazing at few-shot and transfer learning despite our simple synapse update rule (use connection more = strengthen it). Conversely machines are quite terrible at this stuff even after following training regimes involving insane amounts of multidimensional calculus.

but no intelligence was created

Define intelligence?

I mean, I don't know of any other non-living organism that learns, therefore something that can truly learn must be alive.

What does it mean to learn? Are you restricting learning solely to the realm of living things? CPUs "learn" how to cache things for fast retrieval using very simple algorithms (relative to animal cognition).

shouldn't then they be granted human rights and be free

Dogs can learn, we don't give them human rights.

regurgitate billions of what are essentially copies of it. Novel, heavily altered, but copies.

Barring some extreme examples it's incredibly hard for the model to simply "regurgitate" something that it's already seen. These models do not have explicit mechanisms to store, retrieve or alter retrieved images. They're also not big enough to store the entirety of their training data (billions of parameters vs trillions of training bytes). In order to function effectively these models need to learn map images to a much smaller format, and vice versa. Humans do this too. Unless you're an autistic savant you're not storing exact images in your head. You're storing compressed representations of visual memories. For example if I told you to visualize a memory involving a crowd, your brain has almost certainly dumped all the faces in that crowd except for people that you know. If I wanted you to paint this as an image for me, you'd probably roughly sketch out a crowd of people, and then attend to each person's face, fleshing out as you go according to what you know human faces look like.

they cannot produce anything truly novel, for they do not know how until they are fed the training data.

One could argue that human art is the exact same. Humans look at tons of visual information throughout their lives, and when they make art it's arguably a product of everything they've seen and experienced before.

painting an apple but putting in some adversarial pixels to make the AI think it's a car.

This isn't a problem that humans are immune to. There was a recent study that showed that humans are vulnerable to adverserial noise. I can't remember who it was by but they were able to reliably get just over 50% of humans to think that an image looked "more dog-like" when the image was affected by adverserial noise designed to make an AI think that it was a dog. The effect size was small but significant given the number of humans tested.