Made the foolish decision to listen to the Skyrim soundtrack while working, and now I crave Skyrim, except my Skyrim mod setup is in an unplayable state such that I would need to scrap the whole thing and start over to actually play. Sure, I could scrap the whole thing and play base game Skyrim, but I've played that to death. I probably shouldn't play any more Skyrim. I have 600 hours in it and there are so many other games I haven't gotten around to playing yet. Why must it be so tempting?
Swinging at the hornets nest here (the “sex work is work” crowd won’t like this one lol) but there was a post going around about like “if you work in hospitality and you think you see a sex worker, no you didn’t. Keep your mouth shut and let them make their money” is honestly sending me. As many of you know, one of my jobs is working front desk at a hotel and like no lads…we can’t “look the other way” at times. Yes, we know when the working girls check in (many are regulars who are open about it, plus after working their long enough you Know™️ when a woman is because you can clock the signs) and of course we can’t stop them but there are times when you literally have to say something. Here are a couple of instances that have happened on my shifts alone:
-I was checking in two girls and knew something was off and asked to see both their ID’s. Only one had a passport and her 18th birthday was literally a week ago. The other one handed me her high school student card. We had to call the police when their pimp came in and started making threats bc we refused to check them in.
-a sex worker checked in the night before and a housekeeper went into the room the next morning and found the woman in a bloody mess. There were chunks of human tissue scattered throughout the room (the housekeeper quit after that. Honestly, housekeepers are literally on the front lines of the bullshit but that’s another story)
-something about a “couple” was offfffffff at check in. The woman was standing with her hands behind her back, had her eyes glued to the floor, and wouldn’t answer questions despite her name being on the reservation. The man was answering for her. When I asked to see her ID, he hands it to me, and when I handed it to her, her arms were covered in bruises. We didn’t let them check in
-there are two properties and one of them has an agreement with a women’s shelter. I’ve checked in women into one property only to see them come in months later to the other property under the womens shelters’ reservation with bruises and broken limbs (many of whom are indigenous women).
So yeah bottom line: with the World Cup soon approaching, all of us -front desk, maintenance, housekeeping, the kitchen staff, sales, you name it all departments- have had to do extensive modules on clocking the signs of human trafficking. You can’t just look the other way because it can mean missing the underaged girl or preventing horrific violence. Yes we have our regulars and yes sometimes it is what it is but no you can’t just look the other way and let them “secure the bag” because I guarantee there’s a pimp in the lobby/parking lot who’s the one getting the bag at her expense.
Normally when my cat curls up in my lap she does so in precarious ways that force me to grab her with one arm so she doesn't fall off, which makes it hard for me to keep using my computer or whatever. But for the moment she's on some sedatives and suddenly she's fully capable of napping on my lap in a completely secure position. It's almost like her normal precariousness is some kind of feigned incompetence or something.
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.
#all of the issues raised in this paper are patently obvious if you think about it for one second#I don’t have an tech background at all and I instantly recognized all of these issues except the environmental one when I first learned#about AI back in 2021#that’s not meant to be flex either#i think any tech bro or ai pusher is lying about these issues#I don’t think that they see it differently#I think they outright know all of this is an issue and don’t care because they want to make money
You'd think that a human would be able to beat a slug in any given race. But alas, the slugs keep beating me to my strawberries as they become ripe. Today I decided to just pick the strawberries I was considering leaving for another day because I knew the slugs would get them if I didn't. And now I'm spending a lot of time in the bathroom because they were not in fact ripe. Damn you slugs!
dead wife montage but it's a henchman reminiscing about da boss after he got put six feet under. picking flowers before hiding the bodies, wiping cocaine from your nose after a big night, that long drive down the beach to find the bookie who squealed. where did the days go
pixelskylines
jacobvanloon
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dededos
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