my aunt is watching some generic daytime tv talkshow and the hosts are very cheerfully going "yknow this AI thing isnt actually as cool as i thought it was- i keep noticing the google overview getting facts wrong! isnt that weird?" and its very funny watching regular middle aged people finally catch on. we did it lads it took long enough.
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we gotta get back to torrent distribution, i just watched someone eat eight grand in bandwidth charges because they ran a direct-download piracy site with local file hosting through cloudflare. torrents were invented literally for this exact reason
i have a file or folder on my pc that i want to share with other people. let's call it gayshit.mp3
unfortunately gayshit.mp3 is 750mb and im not paying for discord nitro so i need another way to send it
i put it into qbittorrent and it makes a torrent file. this is essentially a very small file that points to gayshit.mp3 so other computers can find it. kinda like a treasure map
i send this tiny file to my friend, who loads it into qbittorrent. their computer takes a moment to find mine over the vast expanse of cyberspace and then (as long as my pc is running and the file is still where it should be), it gets copied from my hard drive to theirs
this is the cool part: if somebody else loads that tiny file, they can download it from both of us. if i'm offline but my friend is on, the third person can still get it. this also means that if two people have separate halves of the file, they can download the other half from each other. as long as some combination of people have the pieces between them, they can all have the whole thing.
crucially this does not require a server!!! you can just upload the file to a few people and as long as they keep it, it's still accessible. as long as somebody, somewhere is still connected, it's available forever. the only way it goes away is if everybody disconnects from it.
Every day I handle more money than I will ever make. Every day.
At the start of my employment, my boss showed me videos of people stealing, and we both had a chuckle about it. How silly they were! There was a camera overhead, and itās not to watch the shoppers. See, we canāt actually stop shoplifters. They get away with it maybe nine out of ten times. But we, who are watched and tallied and witnessed? We are always caught.
At first it was hard to hold one hundred dollars bills. An amount I had never seen before. An amount that didnāt exist in my household. Itās normal now. Here is something that is not for me.
āWhat the hell, Iāll take another,ā says the man, pondering our 200 dollar watches. What the hell. Total comes to 580 and not even a flinch in his face. I have been working for 11 hours today and made only 110 dollars. It will go to my rent. Today I work for free, it feels. When I get my check, I will have 35 dollars left for food and saving.
The six hundreds he hands me go into the cash register. For a moment, I imagine having money. Then I put it away, counting out his change.
I know for a fact we sell our products for double what they are worth. That I could be making commission. That they could hand me those 580 dollars and change my life and not even mark the difference in their checkbooks. Heās not the only sale they make today, but I am the reason they made it. Heās not the only one spending 600 dollars, but if I hadnāt spent two hours with him telling me about his life, he wouldnāt have spent any. I go home. I donāt own a watch.
I have watched and rewatched a video on how to make salmon four ways. My shopping list is always the same. Pasta. Rice. Tuna. If I can afford butter it was a good week. I dream of the world I will never walk in, where I can throw the best fish fillet in the cart with a shrug. I hold hundreds in my hand and look up at the camera. I put them under the cash drawer.
I go to work. I scrap together my savings. I eat my bowl of rice slowly. My manager takes a paid week off from work just for his birthday. He owns a yacht.Ā
i wrote this while i was working at orlandoās walt disney world parks.
i was part of their college program. i moved to the state for it. they legally owned the building i was living in and still charged me rent. i ostensibly was being charged to work for them. it was a 2 bedroom apartment and they placed 6 adult women in it in forced triples.
as many as one in ten disney employees have experienced homelessness while working for the company. despite huge efforts to unionize, strike, or otherwise demand fair treatment; disney has refused to increase employee quality of life.
disney admits publicly that a good portion of their success is because the employees (ācast membersā) are dedicated, passionate, and selfless. this is never reflected in pay. even āfaceā characters (ie those that are princesses etc) make barely above a minimum wage.
at the time that i worked there, i made $8.50 an hour. at one point i was asked to create a human shield around a bag because a bomb dog had alerted to it. for eight fucking dollars an hour.
i now work a very cushy office job. i have bought the salmon and cooked it all four ways.
i go to the store. i am nice to the person behind the counter. she looks up at the camera while she counts out my change. there is nothing fundamentally different about her and i.
Love that Ryland Grace is the opposite of so many male protagonist "heroes" in media and yet he's still so incredibly brave, resilient, and strong. That flimsy little science teacher saves the day.
But he also,
Throws fits when things don't go his way. Not a "I'm a bad bitch" destroying everything-type fit, but tossing a trash can, breaking a screen-type fit.
Cries. A lot.
Pleads. He begs.
Doesn't answer the call to action.
Shows weakness. Being a coward and being fearful are two things he defines himself with.
Doesn't end up with the girl. In fact, that girl isn't even interested and he isn't, either.
Cherishes friendship over a romantic plot or something stereotypically brave like, "I'm going to save Rocky so I can save his world." No, he wants to save his friend, first and foremost.
Squeaks. He squeals. He screams, loudly and very high pitched. He whines. He complains. He physically struggles to open a jar. He's clumsy as hell. He makes some of the least graceful noises one can make.
Is not afraid to be the primitive species lowkey.
I love him and everything he stands for as a male protagonist. Men need to know that they can be just like Ryland Grace and still be just as much of a hero and a man.
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sometimes I think too hard about like. how the ability to record audio fundamentally changed how humans interact with music. can you imagine if the only time you ever heard music in your whole life was when you or another human being in your actual physical presence decided to create it. and 99.99% of the time that person was not a professional but just like your wife or your dad or your co-worker or church choir singing or playing whatever they happened to know. i honestly don't think we can fathom it
Everyone on this post making nonsense "WAH it must have been BETTER back then" comments is ridiculous. I have 24/7 instant high-quality access to Carly Rae Jepsen's entire catalog. Go listen to Cut To The Feeling and appreciate that miracle or get off my post.
I'm a pacifist like institutionally but I'm absolutely certain that violence solves at least some problems on a much smaller level. I don't believe in wars or nuclear weapons or military campaigns I do believe in the power of that guy who punched the nazi in the face so hard his entire media presence immediately crumbled to dust
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.
I just went looking for the post on X and got a message that it didn't exist
I searched for Guri Singh and got search results showing his account existed, but when I clicked on it I got a message his account did not exist
Does anyone know if he made his account private or if he got nuked by Elon? Or do I just suck at X (because I never go there)?
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Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
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#āel pastel promedio tiene tres lechesā es en realidad un error estadĆstico. El pastel promedio tiene 0 leches. Leches Georg#quien vive en una cueva y absorbe 10.000 leches al dĆa#es un valor atĆpico qeu no deberĆa haberse contado (via @deathbycoldopen)