the novelty of having pets really does never wear off i’ve had my cat for ten years and i still look at him strolling around like can you believe this. a cat. is everyone seeing this. he’s alive he has bones and all. unbelievable
everybody: you’ve got to advocate for yourself in medical settings!
medical professionals when a patient advocates for themselves in a medical setting (x100 if that patient is a part of any minority): damn. you’re a hypochondriac crazy bitch who has every mental illness and is seeking every narcotic in existence. that’s the only reason you’d be disappointed in the care you’re receiving here. in retaliation, we will be even less helpful and less sympathetic. our jobs are hard. people are dying. we don’t have time to deal with anyone who is slightly inconvenient for us.
Yeahhhhhh. Whenever someone comes online to speak about a negligent, discriminatory or extremely unprofessional and uncomfortable experience they had in a medical setting, medical professionals tend to swarm them just to remind them over and over again that their job is sooooooo hard and patients are soooooo mean to them and that there are soooooooo many sicker people, like the sicker patients aren’t also being humiliated and abused, like hospitals do not stabilize people and dump them back on the street to die, like deaths due to medical errors aren’t a major problem, like racism, ableism, classism and misogyny isn’t rampant in the medical system from the top down, perpetuated systemically and individually by providers. So many doctors and nurses hate to admit they’re wrong, they hate to admit they’re complacent, they hate to admit that their patients are all people, often having the worst day of their life.
some hyper famous artists like Van Gogh transcend overratedness and become underrated because they're so normalized. Like I'll look at a van Gogh and I'm like wait this really is amazing you guys don't get it
When I was training to be a battered women’s advocate, my supervisor said something that really blew my mind:
“You can always assume one thing about your clients; and that is that they are doing their best. Always assume everyone is doing their best. And if they’re having a day where their best just isn’t that great, or their best doesn’t look like your best, you have to be okay with that.”
Any now whenever anyone in my life, either a friend or a client, frustrates me, disappoints me, or pisses me off, I just tell myself They are doing their best. Their best isn’t that great today, but I have days where my best isn’t that great either.
Op I’d like to thank you for sharing this. Ever since the first time I’ve read it I’ve held it in my mind and it really has helped me to be kinder to others and to myself.
truly the most american thing is Big Drink. more than late stage capitalism, more than an unparalleled cultural focus on individualism, more than 9/11 jokes
what binds all americans together culturally is Big Drink
and you might be saying "is this fat shaming" or "but mayor bloomberg outlawed Big Drink in nyc" or "gays are so annoying about their iced coffee" or some other dumb comment but no open your minds, Big Drink isn't just sugary or caffeinated beverages
every day i see one of you hydration bitches (affectionate) on the train with a water bottle so big a toddler could drown in it. that too is Big Drink.
we literally invented a bigger beer can (tall boy) in wisconsin in the 60s in the service of Big Drink
anyway i never feel more american then when i have Big Drink in my hands
can you imagine what it will be like the day it finally happens. no one will be posting about anything else. category 10 posting event. if it happens because of someone else their gofundme page will reach over $500,000 within a day. #hopecore
“Why don’t you use ai” idk man beyond the obvious environmental and “this machine causes psychosis and encourages people to kill themselves” thing I think asking the equivalent of a solid D student who is also a pathological liar if they can answer my question/do the work for me seems pretty fucking stupid
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.
in the past i've described my experience of being an ace with a sex drive as being hungry with no appetite, but actually my experience is more like being hungry and never going out to eat because i always have all the tools and ingredients to make exactly what i want, exactly how i want it, at home. i don't want other people in my kitchen and i certainly don't want to be in anyone else's kitchen. love reading about fictional kitchens, though.
Doomed siblings have me on a chokehold in a way no trope can ever achieve. Something so gut wrenchingly beautiful about two kids going through unimaginable trauma and coming out damaged forever. I always eat it up. Maybe they stay together and find solace only in each other, used and kicked out by society! Maybe they end up on opposite sides of the war! Maybe they end up betraying one another! Maybe one of them dies haunting the narrative forever! Maybe one of them destroys the world for the other! So many endless tragic possibilities!
germansingergirl
snowghoul
vaporwave
ellievsbear
pixelskylines
jbbartram-illu
dailybridgerton
gummygunk
