after much deliberation I have completed a simple task that took me less than 10 minutes, and I'm happy to report that this time it only cost me about 3 days of dread
wow dude jts so awesome that your car is loud as fuck and smells worse when it drives past. thags fucking epic man. i really like how it hurts to listen to you drive past and it scares people. thats awesome man. i really like your car that makes a loud as fuck fart sound. fucking epic dude
the sun is a sickly orange color today with a haze of smog blurring its edges. it truly looks like something you would use in a scifi movie to clue the audience in that something has gone very wrong
Suddenly struck with a need to explain to you how boat pronouns work (I work in the marine industry).
When you're talking about the design of the boat, you say "it".
When the boat is still being built, your say "it".
When the boat is nearing completion, you can say "it" or "she".
When the boat is floating in the water you probably say "she", unless there is still a lot of work to be done (e.g. no engine yet) then you say "it".
When the boat is officially launched and operating, you say "she". If you continue to say "it" at this point you are not incorrect but suspiciously untraditional. You are not playing the game.
If you are referring to a boat you don't really know anything about you may say "it" ("there's a big boat, it's coming this way"). But if you know its name, it's probably "she" ("there's the Waverley, she's on her way to Greenock").
If you are talking about boats in general, you say "it" ("when a boat is hit by a wave it heels over")
If you speak about a boat in complimentary terms, it's "she" ("she's a grand boat"). If you are being disparaging it may be it, but not necessarily ("it's as ugly as sin", "she's a grotty old tub").
If she has a boy's name, she's still she. "Boy James", "King Edward", "Sir David Attenborough"? The pronoun is she.
If it's a dumb barge (no engine), you say it. But if it's a rowing boat (no engine), you say she.
I hope this has cleared things up so that you may not be in danger of misgendering floating objects.
(goverment voice) we need to protect the children from pornography so our plan is to remove their eyes so they would never see something so traumatic. if you are against removing children's eyes you are basically a pedophile
I cannot stress enough that this meme is entirely literal. there's no reference or subtext you don't know. these are simply the objectively most popular works of art from each denomination.
wedding dresses are white specifically so that if the bride goes axe-crazy you can rest assured you will see the blood stains all over her and it'll be sick as fuck
Movie about a depressed and rather morbid autistic man planning to commit suicide and picking up a number of odd jobs in an effort to raise enough money to meticulously plan and prepay for his funeral so his mother doesn’t have to worry about it after he is gone. He begins to connect with people and enjoy life for the first time while working part time as a greeter in the funeral home, helping an eccentric old lady organize her basement, walking 7 dogs and maintaining a feral cat colony for a guy with a broken foot, playing a number of bit parts in local ads and stocking the shelves at the convenience store at night. In the end, he has befriended many of his neighbors and he decides he does not want to die and goes back to school to become a funeral director instead.
He is popular at his funeral home gig because he keeps accidentally saying things that are very reassuring and death positive. Because he wants to die. He eventually donates his funeral fund to the old lady’s granddaughter after her sudden death so she does not have to sell her grandmother’s prized possessions to pay for her funeral.
The old lady gifts him one of her ceramic cats at the beginning of the film which he reluctantly accepts out of politeness. Near the end of the film, he adopts a friendly cat from the cat colony that looks remarkably like the ceramic cat and names it after her, signaling his commitment to surviving and caring for his cat the way the old woman lived for her ceramic collection.
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.
man sometimes friendship really is just "I saw this and knew it would give you psychic damage. please respond with agony" and then they do. and it's great
there is no discourse between gen z and millenials. we are siblings. come on lil bro, ill take you to amc. yeah we can go there early and play the arcade games before the movie starts.
Why do the two reblogs read like a soldier dying in their friends arms and talking about when they’ll get back home to give them a bit of comfort before they die
origamiaround
areudirty
prguitarman
theartofmadeline
andulkaphoto
romaincrisis
titsay
