Except for the theory parts, which can come in handy if you want to get past being just a code monkey. There's also plenty of coding done during a 4-year degree program -- at least there was in mine. And I was a grader. My OS class had us simulate an interactive operating system and another class had us write a functional linking loader, both in C. (my concentration, back in the mid '80s, was operating systems design) I also took classes using LISP, Pascal and x86 assembly (on new PCs as the printer for the IBM 370 caught fire and destroyed everything in the room the previous summer) I was also a research assistant doing programming in LISP (on a Xerox 1108) and Prolog for a NASA grant on automated programming techniques -- they wanted a grad student, but couldn't find one with LISP experience. I also ported programs, like the Franz LISP interpreter/compiler, from 4.3BSD on a VAX 11/785 to SunOS on a Sun4 and debugged lpd -- we had BSD source code. I also had a jerk system administrator on those BSD/Sun systems who made us RTFM *and* the BSD source code before he would answer even the simplest question -- and I have to thank him for that very much.

Pretty sure you're not going to get all that at a six-month coding boot camp. People keep dismissing the value of a 4-year CS degree, but a lot of you get out of it is what you're willing to put into it.

I accurately represented their argument. You did not.

I'll repeat: their argument was entirely focused on how the user doesn't control the exact details of the composition of AI works.

I literally read it. Don't gaslight me.

Con or not, some of this is on the students. I mean, those companies aren't really known for hiring people with only a few months of coding training and a half-year Web/VoTech degree. That said, taking advantage of their gullibility and/or desperation isn't cool.

Meh, LLaMA is right out for me because of its license. MistralAI makes great products and releases them with genuine open licenses.

Yep.

Yes, "you've been told" that by people who have no clue what they're talking about. Meanwhile, models just keep getting better and better. AI images have been out for years now. There's tons on the net.

First off, old datasets don't just disappear. So the *very worst case* is that you just keep developing your new models on pre-AI datasets.

Secondly, there is human selection on things that get posted. If humans don't like the look of something, they don't post it. In many regards, an AI image is replacing what would have been a much crapper alternative choice.

Third, dataset gatherers don't just blindly use a dump of the internet. If there's a place that tends to be a source of crappy images, they'll just exclude or downrate it.

Fourth, images are scored with aesthetic gradients before they're used. That is, humans train models to assess how much they like images, and then those models look at all the images in the dataset and rate them. Once again, crappy images are excluded / downrated.

Fifth, trainers do comparative training and look at image loss rates, and an automatically exclude problematic ones. For example, if you have a thousand images labeled "watermelon" but one is actually a zebra, the zebra will have an anomalous loss spike that warrants more attention (either from humans or in an automated manner). Loss rates can also be compared between data +sources+ - whole websites or even whole datasets - and whatever is working best gets used.

Sixth, trainers also do direct blind human comparisons for evaluation.

This notion that AIs are just going to get worse and worse because of training on AI images is just ignorant. And demonstrably false.

As for why I think the ruling was bad: their argument was that because the person doesn't control the exact details of the composition of the work, than the basic work (before postprocessing or selection) can't be copyrighted. But that exact same thing applies to photography, outside of studio conditions. Ansel Adams wasn't out there going, "Okay, put a 20 meter oak over there, a 50 meter spruce over there, shape that mountain ridge a bit steeper, put a cliff on that side, cover the whole thing with snow... now add a rainbow to the sky... okay, cue the geese!" He was searching the search space for something to match a general vision - or just taking advantage of happenstance findings. And sure, a photographer has many options at their hands in terms of their camera and its settings, but if you think that's a lot, try messing around with AUTOMATIC1111 with all of its plugins some time.

The winner of Nature Photographer of the year in 2022 was Dmitry Kokh, with "House of Bears". He was stranded on a remote Russian archipelago and discovered that polar bears had moved into an abandoned weather station, and took photos of them. He didn't even plan to be there then. He certainly didn't plan on having polar bears in an abandoned weather station, and he CERTAINLY wasn't telling the bears where to stand and how to pose. Yet his work is a classic example of what the copyright office thinks should be a copyrightable work.

And the very notion that people don't control the layout with AI art is itself flawed. It was an obsolete notion even when they made their ruling - we already had img2img, instructpix2pix and controlnet. The author CAN control the layout, down to whatever level of intricate detail they choose. Unlike, say, a nature photographer. And modern models give increasing levels of control even with the prompt itself - with SD3 (unlike SD1/2 or SC) - you can do things like "A red sphere on a blue cube to the left of a green cone" . We're heading to - if not there already - where you could write a veritable short story's worth of detail to describe a scene.

I find it just plain silly that Person A could grab their cell phone and spend 2 seconds snapping a photo of whatever happens to be out their window, and that's copyrightable, but a person who spends hours searching through the latent space - let alone with ControlNet guidance (controlnet inputs can be veritable works of art in their own right) - isn't given the same credit for the amount of creative effort put into the work.

I think, rather, it's very simple: the human creative effort should be judged not on the output of the work (the work is just a transformation of the inputs), but the amount of creative effort they put into said inputs. Not just on the backend side - selection, postprocessing, etc - but on the frontend side as well. If a person just writes "a fluffy dog" and takes the first pic that comes up, obviously, that's not sufficient creative endeavour. But if a person spends hours on the frontend in order to get the sort of image they want, why shouldn't that frontend work count? Seems dumb to me.

[insert Trump jokes here]