They don’t jump 36 feet into the fucking sky do you know how terrifying that would be the human race wouldn’t have survived because we’d have all had heart attacks while still in Africa
absolutely looosssinggg it. i'm so obsessed with movies which portray the woman MC in a highly specific job because the writers clearly think it's like "off-beat" and "quirky" but have no idea how the field works whatsoever.
i decided to try a romcom i somehow missed i the 2000s 'head over heels' and i got 3 and a half minutes in and we're introduced to the lonely MC with bad taste in men as evidenced by her extremely short list of ex boyfriends, including her first boyfriend when she was 11 or something because i guess that's still relevant in her adult life.
so she's resigned herself to never finding love and prefers to ignore men to focus all her energy into her career.
this job is immediately presented as though it's for spinsters with no hope of ever finding a man.
the mc's lesbian bestie (whose first line involves her being scolded for being too sexual in the workplace, but moving on) points out their colleagues as evidence that they're doomed to a romance-less, sexless life if they don't switch up their shared career path. the colleagues are three old women, so-dubbed "the menopause triplets":
these women are presented as if they have no idea what's going on at any given moment. this is 2001, and presumably this is an entry level job requiring low effort and no experience.
then their boss bursts into the room, unceremoniously bumping a large painting into the door jam and walls, announcing that it's a new project for our MC.
our MC is thrilled to see the painting. apparently it's a light in the daily slog at her dreary job for loser women with nothing going on in their lives.
And that job is? Conservator of paintings (specializing in Renaissance) at the New York City Metropolitan Museum of Art.
The painting being handled like an old couch on its way to the curb?
The Bacchanal of the Andrians by Titian.
Her lesbian colleague who is presumably also a a highly trained & skilled curator finds it depressing that the MC is so excited about the painting.
it's a quirk unique to this MC that she cares so much about paintings, in her department at the metropolitan museum of art, where her colleagues find all that art business rather dreary. because we all know that's what conservators in extremely competitive museum positions are like.
I'm not saying there can't be lifelong love in here somewhere but I also just feel like the monogamous heterosexual marriage you're fantasizing about isn't necessarily best represented by the bacchanal. and that's okay. but i do stand by that.
i enter the shower. hours pass. i emerge from the shower, having mixed all of my soaps and scrubs and lotions and conditioners and shampoos and body washes together in the tub in precise alchemical quantities. i smell like 314 different herbs and spices. my hair will not need washing for the next 500 days. my skin has developed protective chitinous scales. i step out of the tub and immediately slip and fall on a stray puddle of mane 'n tail and sprain my pussy
since becoming a barista i have noticed a few very distinct typologies among my customers. such as:
the woke left: young and fashionable. visible tattoos. often enjoys matcha, lavender flavoring, oat milk, and cold foam. pretty decent customers.
sweet old man: drinks very sweet iced lattes, pays in cash, puts all of his change in the tip jar. sometimes orders hot coffee and i get scared that his shaky old man hands will spill it and he'll get burned but that has not yet happened and god willing never shall.
evil old man: only wants drip coffee and declares it ridiculous that any other form of coffee exists. some variants only want americanos and these variants are even scarier. watch out.
sweet old woman: might need her daughter's help to order but is very bubbly and open to trying new things. compliments baristas freely and frequently.
evil old woman: does not want coffee and only wants sweet tea or soda. will not tip even if she spends three hours in the shop repeatedly asking baristas to fetch things for her.
errand husband: either stiltedly recites an order to you or shows you the order in their texts/notes app. needs to step out of line and make a phone call if you ask any follow-up questions.
grindset girlie: always wearing scrubs, an apron, and/or a name tag. orders the exact same thing every day and knows the exact change she'll need to pay for it. her regular order is both extremely caffeinated and extremely sweet.
#mamabear: is actively wrangling two to four children while ordering. order changes repeatedly because the children cannot decide if they want a muffin or a cookie or apple juice or chocolate milk etc. for some reason these women are always wearing an article of clothing or carrying some personalized item that says "mama" on it.
schoolchildren: band of two to eight adolescents hanging out after school. extremely indecisive but generally quite polite and tip well.
amnesiac in love: grown adult who needs their partner to tell them what they like. gets asked a question about their own preferences and turns to their partner to answer for them. generally acts like a shy child looking to their guardian for behavioral cues if you try to interact with them and only wants to talk to mommy i mean their wife.
this of course is not an exhaustive list but those are just some of the most consistent Types i get. ok bye xoxo
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.
Legal Eagle asking "are you covered to have an open flame in the studio?" and a producer worriedly yelling "we're not!" as Ally goes to light a bong they filled with real whiskey is maybe the hardest I have ever laughed at an episode of Game Changer.
I love the dynamic in the Discworld fandom on this site, I think it's mainly because there are a lot of dormant fans, if you will, who've read and loved the books for years but haven't engaged much recently, who sort of reappear whenever a fun post is doing the rounds. It's fantastic. We get the cozy small fandom vibe without the screaming matches, but also get the popular posts from time to time, y'know?
To everyone tagging and commenting with some variation of “Oh, I almost forgot that it’s tomorrow!”
“Damn! Damn! Damn! Every year he forgot. Well, no. He never forgot. He just put the memories away like old silverware that you didn’t want to tarnish. And every year they came back, sharp and sparkling, and stabbed him in the heart. And today, of all days...”
#oh I need to read enough discworld to understand what emotions day is
For anyone who's confused today, I won't spoil/explain what the 25th of May is in the Discworld fandom, because I think a big part of what's special about it is the way the plot unfolds in the book.
What I will explain though is that it'll make sense when you reach a book called Night Watch, which is the 29th book in the overall series and the 6th book in the City Watch subseries. If you haven't got that far yet, just take your time and enjoy the series! If you want to start with the City Watch stories then the first book you should check out is Guards! Guards!
titsay
tangentwithtanner
gummygunk
areudirty
