Honestly it boils down to reparenting yourself & rewiring your own neuronal pathways & telling yourself a firm “stop” when you notice your mind slipping down negative loopholes & being present in the moment & enjoying being mid task rather than waiting for it to end & not thinking of inertia as your baseline and natural way of living
So tempting to keep embarking on the same self destructive cycle over & over & over again . But at some point you have to put ur foot down w ur own behaviors & be the thing that truly saves u
ok so instead of going on my usual the earth is doomed spiral I started looking into solar punk solutions and stumbled across the practice of permaculture & found a free 50 video series from the university of oregon on it if anyone else would like to learn abt ways we can actually start restoring earths whole deal
Permaculture Design Course - Oregon State University Ecampus
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
girl who has you pinned. girl who is bigger and stronger than you. girl who in spite of this is looking at you like a lost puppy. girl who is whining and trembling with need. girl who locks eyes with you and frantically mutters “pleasepleasepleaseplease” under her breath. lustdrunk mutt who waits for permission to take what it wants despite knowing it could at any time. dog who is so so so so good.
I watched some videos by that guy who set up a fake ICE hotline to get people to snitch on members of their community. Not only is this very real and useful praxis- he's preventing these ghouls from reaching the real ICE- he also handles the calls in a really amazing way.
For the most part, he doesn't make accusations or insults people, he just repeats back the appalling shit they're telling him. And they get fucking furious. The example that went viral was him fielding a call from a kindergarten teacher who wanted to report one of her student's parents.
This absolutely disgusting piece of shit thought that the parents were "illegals" who were "taking up resources" because they weren't born in the US. The child was a US citizen because he was born here, but she wanted the "ICE" agent to "look into it."
So this dude just starts repeating stuff back like "so you want me to load the parents of the 5-year-old child you teach into a van and deport them, right?" and this bitch has the gall to say "you make it sound terrible 😅" in a self-conscious way. And then when he finally makes a more direct insult by nonchalantly saying that the 5-year-old "must be a major threat to national security," she demands to speak to his supervisor (which he agrees to and then makes no effort to change his voice for lmao).
This is far from the only call where the whole "repeat their rhetoric back to them" tactic pisses the caller off, too. As rotten, immoral, and disgusting as these ghouls are, I believe there's a tiny part of them that is aware of how fucked up their beliefs and behavior are. Being forced to confront that leads to painful cognitive dissonance and they'd rather lash out at the person who criticized them than look inward and do some self-reflection. Forcing people to confront their own cognitive dissonance of "I'm a good person" clashing with "I have objectively gross and harmful beliefs" is useful, even if it will never go anywhere.
Something that also got me was how the teacher kept looking for OP to soothe and assuage her ego/conscience and got progressively more agitated when he wouldn’t. This is someone who desperately needs to think she’s a good person who is doing good things when what she’s doing is objectively heinous.
She thought she was in the right because she was trying to tear apart this family in a “polite” and indirect way. She’s not the one holding a gun and herding people into an unmarked van, after all. The fact that her call would have directly led to that outcome doesn’t register as culpability to her until OP makes her connect the dots. THAT’S why she got upset, because she was forced to acknowledge the blood on her hands.
Use 1337x.to rarbg or rutracker, highly moderated public torrent trackers, because Pirate Bay has long been shut down, unmoderated, and cloned by some shady actors AND use Mullvad VPN (or any other vpn but mullvad is the best definitely) to protect your ip address while you torrent, bind it to qbittorrent and do not use utorrent anymore either because it is littered with adware.
OR if you don’t want to pay for a VPN, use direct download or streaming sites. Please y’all just take one pass through on the r/piracy or r/freemediaheckyeah mega threads to find whatever you want for free and also not infect your computer with Trojan viruses.
With all the streaming services not only jacking up their prices lately, but making media be exclusive to a single platform; I am imploring y'all to learn how to pirate. Here is an awesome FAQ post on the aforementioned r/piracy I encourage you to pour through if you're curious of the mechanics of it all, are curious in general or have specific questions.
Here is an extremely short and simple guide as to how you can immediately start trying it out and getting your feet wet:
You'll need a single torrent client, which is the free software that actually facilitates the downloading of the files. Qbittorent, Vuze, and uTorrent are all good.
SO; to actually start downloading the files, you need to find torrenting sites.
The aforementioned 1337x.to works great, and here is a link to a ton of mirrors for it. Turn on and connect vpn (when you need t. Go to one of the torrenting sites. Search the media you want. Look through it, usually the one with the most seeds is the one you want, as it will download faster.
Once you find a torrent you're wanting, you can either use the magnet link (simple'n'easy), which directly opens your client and starts downloading it; or you download the .torrent file, then manually open it then start downloading it.
!!!!!!!!!!!!!!BUT BEFORE YOU START ON THIS TREK AND START PIRATING!!!!!!!!!
Download and use a VPN. Virtual Private Networks hide your internet traffic and will shield you from your ISP slapping you with copyright notices/fines. SERIOUSLY, make sure you are using a vpn or you're legitimately screwing yourself over with tangible real-world consequences.
I recommend using Mullvad, it's $5ish a month, pay as you go, has a very simple ui and has been perfectly dependable.
Torrenting was huge trend then it just seemed....people forgot about and stopped learning about it lol It's nothing but beneficial to learn how to do it as all streaming services are becoming more and more costly while the quality and what they offer goes in the shitter.
A Department of Homeland Security whistleblower has released the identities of about 4,500 ICE and Border Patrol employees Tuesday in what h
A whistleblower at the fascist DHS has released the names of 4,500 ICE and Border Patrol agents, providing an unprecedented means to monitor and counter their local presence
Another thing about light pollution and adjacent things is. They threatened my area with rolling blackouts last winter. Now this was of course largely because the AI datacenters are hogging all the electricity, but in the notifications about it they always specified that that residential areas would be the blacked out areas. Not offices. Not businesses. If you're at home and freezing, well, you can just go loiter in a McDonald's I guess. Never mind that this is extremely difficult for disabled people and often not allowed for pets.
Well, as winter turned into spring, I started biking home from work. A long, circuitous route that took me through residential areas, and past offices and businesses. Offices and businesses that were closed for the day. And yet their signs were still lit up. The lights were still on inside. There were TVs playing in empty breakrooms. All the computers in the school district offices stayed on, their monitors not even going to sleep, all night. Paused to take a break in an empty strip mall once and when I leaned against the glass of a restaurant I could hear the music still playing inside.
Like. There's something deeply rotten about the priorities here. These places that are so flagrantly wasting electricity will never be subject to the rolling blackouts that could freeze you out of your home. Not even at night, when no one is there, when they don't need their lights on. Their waste is prioritized over normal people's life.
