Idk about anyone else but its a bit long. Up to q10 i took it seriously and actually looked for ai gen artifacts (and got all of them up to 10 correct) and then I just sorta winged it and guessed and got like 50% of them right. OP if you are going to use this data anywhere I would first recommend getting all of your sources together as some of those did not have a good source, but also maybe watch out for people doing what I did and getting tired of the task and just wanting to see how well i did on the part i tried. I got like 15/20

For anyone wanting to get good at seeing the tells, focus on discontinuities across edges: the number or intensity of wrinkles across the edge of eyeglasses, the positioning of a railing behind a subject (especially if there is a corner hidden from view, you can imagine where it is, the image gen cannot). Another tell is looking for a noisy mess where you expect noisy but organized: cross-hatching trips it up especially in boundary cases where two hatches meet, when two trees or other organic looking things meet together, or other lines that have a very specific way of resolving when meeting. Finally look for real life objects that are slightly out of proportion, these things are trained on drawn images, and photos, and everything else and thus cross those influences a lot more than a human artist might. The eyes on the lego figures gave it away though that one also exhibits the discontinuity across edges with the woman’s scarf.

Thats how its supposed to work and in practice it kinda does, but the people with the money want positive results and the people doing the work have to do what they can to stay alive and relevant enough to actually do the work. Which means that while most scientists are willing to change their minds about something once they have sufficient evidence, gathering that evidence can be difficult when no one is willing to pay for it. Hard to change minds when you can’t get the evidence to show some preconceived notion was wrong.

I guess I just don’t really know what feature-rich means in this context but being proprietary, not fully cross platform, and banned on most private trackers seems like huge downsides for power users compared to customization, built in search, and integrated chat.

I get this chart probably not made for people like me in mind though.

Yeah, seems weird that simple “it downloads torrents” client gets a D. It gets the job done, is easy to figure out, and doesnt fuck about with features I would never touch. Maybe thats not enough for a power user but for me its exactly what I want.

(but then why is Tixati in B, seems to have mostly downsides?)

Wdym? Pregnancy is the original lootbox, never know what kind of kids you’re gonna get.

ENHANCE

Outside of the costs of hardware, its just power. Running these sorts of computations is getting more efficient, but the sheer amount of computation means that its gonna take a lot of electricity to run.

they know it’s impossible to do

There is some research into ML data deletion and its shown to be possible, but maybe not on larger scales and maybe not something that is actually feasible compared to retraining.

While you are overall correct, there is still a sort of “black box” effect going on. While we understand the mechanics of how the network architecture works the actual information encoded by training is, as you have said, not stored in a way that is easily accessible or editable by a human.

I am not sure if this is what OP meant by it, but it kinda fits and I wanted to add a bit of clarification. Relatedly, the easiest way to uncook (or unscramble) an egg is to feed it to a chicken, which amounts to basically retraining a model.

Always has been. The laws are there to incentivize good behavior, but when the cost of complying is larger than the projected cost of not complying they will ignore it and deal with the consequences. For us regular folk we generally can’t afford to not comply (except for all the low stakes laws that you break on a day to day basis), but when you have money to burn and a lot is at stake, the decision becomes more complicated.

The tech part of that is that we don’t really even know if removing data from these sorts of model is possible in the first place. The only way to remove it is to throw away the old one and make a new one (aka retraining the model) without the offending data. This is similar to how you can’t get a person to forget something without some really drastic measures, even then how do you know they forgot it, that information may still be used to inform their decisions, they might just not be aware of it or feign ignorance. Only real way to be sure is to scrap the person. Given how insanely costly it can be to retrain a model, the laws start looking like “necessary operating costs” instead of absolute rules.

ChatGPT is amazing for describing what you want, getting a reasonable output, and then rewriting nearly the whole thing to fit your needs. It’s a faster (shittier) stack overflow.

Python is the connective tissue holding together library calls and some of our most advanced AI research is reliant on that. mildly concerning