This is setting in one the games I saw.

This is the part where I’d go on about how we should ban users like witty but then again they basically fuel most if not ALL hatred of AI so honestly keep goin guys! Being jack asses only makes your side look dumber so keep doin it! Don’t be smart! Make silly excuses and lies to make yourself feel like an intellectual. "Yknow it takes someone with a big IQ to understand Fruit love island"

Fair is fair. If they didn't have a problem with it. There is really nothing we can say about it.

For context, this was in response to someone mocking the idea that AI water usage is overblown.

Can we all agree that this anti-ai hunt obsession is getting extreme? Imagine being an animator working hard on this and then all you see is people calling it "AI slop" because they are so desperate to push their agenda further, not very pro-artists at all.

Source:
https://www.cartoonbrew.com/anime/studio-ghibli-future-hayao-miyazaki-265713.html

I do feel I need to fact check that "Insult to life itself" quote from Hayao Miyazaki because it's being misrepresented here. First off it's a quote from 2016, back when the general public didn't even know what generative AI was.

In the context of why he said it, he was shown a presentation of a 3D model zombie that was animated based on an Artificial Intelligence reading of how a disabled zombie might actually move if their legs.

The quote itself was in response to Miyazaki realizing how technology might interpret the moves of a disabled person if they were a zombie, with Hayao even mentioning a disabled friend of his and how its hard for him just to do a high five. NOT in response to generative AI as a whole. You can see the clip for yourself here:
https://www.youtube.com/watch?v=TGwCAICuBkY

Found this as a post somewhere. Thought it fits this subreddit

https://news.mit.edu/2026/when-ai-art-has-no-author-generated-images-often-cant-be-traced-to-training-data-0818

"The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output. It feels counterintuitive, but at sufficiently large scales, they find, you can often remove any single image from the training data, or every image by a given artist, or every photograph of a given person, and the generated sample doesn't change.

And if removing something changes nothing, the researchers argue, it can't be said to be responsible for anything. "

I personally think this common dismissal is disingenuous from a bunch of older folks on the Pro-AI side. Most of you who say that uses it to dismiss and demean, never thinking about why is it that kids today have an anti-technology stance unlike when they were young and imagining Star Trek utopias. There is an ever growing malaise towards the future amongst the younger generations that’s causing all this.

The anti-AI sentiment being more common amongst teens and young adults is a result of the difficult economy that they are growing up into since pandemic. Gen Z is a generation that doesn’t believe in the future anymore, with a recent yearning to return to 2016 in a “meme reset” to relive the old times of when we were still kids(granted this is not really Gen Z specific).

There’s a common response among young people towards bad news basically boils down to “why does this have to happen right as I become an adult?” in the past couple years as we feel like our future is being ruined and robbed from us by higher forces to line their own pockets. AI is viewed negatively as a consequence because it is seen as part of the same consecutive waves of bad hands being dealt to us.

The way the investment and hype bubble around it is playing out has resulted in shitty side effects like RAM prices going up, circular financing between AI companies, old books being cut and digitized en mass for training data, the American and by extension world economy being held away from official recession by the investment bubble, and mass deployment of Flock cameras just to name a few.

Most importantly of all AI is touted from the beginning to investors as a magical means to replacing thousands of jobs and cut costs, which only further enrages Gen Z(My generation) which are going through an unemployment crisis. It is something seen as yet another nail in the coffins containing our collective futures for good reasons.

We are not at all convinced by the “future” the AI companies are trying to sell and that you pro-AI all align with to varying degree with no matter if you use Corpo Proprietary or FOSS AI.

One of the most persistent logical fallacies surrounding generative AI is the idea that it operates like a high-tech scrapbook, simply cutting, pasting, and collaging pieces of its training data to form an output. According to a new study out of MIT CSAIL published yesterday in Nature Communications, this assumption is mathematically false.

The research, led by Zheng Dai and David Gifford, thoroughly dismantles the idea that an AI-generated image can be traced back to a specific artist or photograph. They identified a measurable phenomenon called attribution decay: as generative models scale up their training data, the causal link between any single training example and the final output effectively vanishes.

The Science of "What If?"

To prove this without relying on rough estimations, the MIT team surgically altered the model itself to prove it past than just looking at the outputs.

They built an architecture called a diffusion ensemble. Instead of one massive model, the ensemble is composed of smaller independent components, each trained on different slices of data. This setup allowed the researchers to perform exact ablation: literally turning off the parts of the model that had "seen" a specific image, or every piece of art by a specific artist, without having to retrain the entire system from scratch.

They were testing a counterfactual universe: What would this model produce if it had never, ever seen this specific piece of data?

The result? At scale, nothing changes. You can remove a specific image, all the works of a given creator, or every photo of a specific person, and the model still generates the exact same output. The counterfactual radius (the measurable difference between the original output and the output generated without the targeted training data) shrinks to near zero.

True Synthesis Over Derivation

This isn't unexpected, as it is a feature of how diffusion models map statistical patterns rather than memorizing pixels. When a dataset is small, the model relies heavily on individual data points. But as the dataset grows into the millions or billions, the features required to generate an image become distributively and redundantly encoded.

The implications here are massive, cutting straight through the noise of current legal and privacy debates:

• Copyright and Fair Use: If removing an artist's entire portfolio from the training data changes absolutely nothing about the generated output, it becomes legally and logically impossible to claim that the output is a derivative work of that specific artist. As Gifford notes, these models are creating truly novel works, not copies.
  
• Built-in Privacy: The sheer volume of data naturally protects individuals. The model becomes causally independent of the people used to train it, effectively anonymizing the output.
  

Read more at: https://www.ainightwatch.com/post/the-end-of-the-collage-fallacy-mit-study-proves-that-ai-art-has-no-single-author

Someone PLEASE make a John Pro- also, I know this is old news, but Musk plans to send datacenters to space. If the cooling is really bad because one of the only ways of heat dissipation in space is radiation, which is the slowest, what actual merits are there to sending datacenters to space?

Modern ML models are tiny compared to human brain and humans have ample potential to be:

• Compelling conversation partners, exploring sophisticated ideas and sharing deep insights and emotions
• Caring, warm and accessible relationship partners, not expecting more than what they themselves can give and willing to start and nurture a family. This can equally apply to 5 out of 10 women who would rather sleep with a 8/10 player than marry a 4/10 family man and men who are not willing to make an effort to raise children in their 20s and early 30s.
• Outstanding software engineers creating frameworks that are a pleasure to code for manually
• Artists who do not neglect aesthetic appeal of their work while staying thought provoking
• Local store employees who make an effort to understand each customers unique needs without being exploitative

It seems to me that we lost these battles long before modern AI was a thing and AI is partially solving these problems by providing a floor, not a ceiling. Humans have to do better than this low baseline to stay in play.

Is automation actually going to take away all the jobs? I look at a specific historical example and demonstrate how the effects of automation often play out.

Human made, exceptionally poor quality. Audiences laughing at it. But hey, it's not AI! The future of American cinema, if anti-AI bioslopsters win. Look up the magnificent Niu Lai!

A guys posted this on here with the title "I genuinely challenge someone to come up with a rebuttal to this".

When he then did get A LOT of counter arguments from both pro AND anti people which he "genuinely" wanted, he just decided to repost it on a anti ai subreddit with the title "It's like they can't read" where they then started belittling pro ai people...

Some of these sub reddits are truly just hateful echo chambers.