they are sexually mature at ONE HUNDRED AND FIFTY YEARS OLD.
their (live!) young gestate for. wait for it. eight to eighteen (??) YEARS. can have up to 10 at a time. good grief.
longest lifespan of any vertebrate, up to five hundred years
toxic flesh
has giant eyes but is usually blind because of a weird little crustacean that's evolved to live on and eat their eyes. this doesn't seem to bother them much.
lives in deep cold water and has the lowest swim speed and tail-beat frequency for its size across all fish species. just generally lives life in extreme slow motion
largest genome of any shark
eats everything including moose and polar bears
ma'am you are delightfully strange and I'm privileged to share a planet with you
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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.
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as a denizen of this busted ass website for half my life now i do know how staff works and i’m going to lay out what they think will happen as someone who has witnessed this exact thing play out
People get very mad for a few days and then roll over and accept the change to the enclosure. This is what they are betting on. They have had meetings about this.
Getting around it: set an alarm. Get mad not just now, but in a few days, a week, two weeks. They are expecting people to sigh and get over this very odd edit to the site in a few days. Keep flooding them for more than just tonight. Be mad for the week, for the month. Leave a message every day.
like . I cant even blame them for being like “we’ll monitor for the week”. Remember when they changed the blue back in like 2015? This website acted like staff had gone into their house and shot their dog. We’re here because we’re allergic to change and therefore it’s hard to parse if people hate the change because it’s bad or because we go into thermonuclear meltdown over what color blue the background of the website is. Just continue to bug them beyond today and beyond this week.
The reblog chain is one of the things that makes Tumblr unlike anywhere else. All the notes on reblogs are attributed to the original post, no matter which branch people actually liked or reblogged. We want to keep encouraging conversations, and give contributors the recognition they deserve.
Soon, you'll be able to like, reblog, or reply to any part of a reblog chain, and that note will go to that reblog's author. Each reblog will have its own counts, instead of one aggregated number from every version of the post. And yes, you’ll be able to like multiple posts in one chain.
If a reblog doesn't add anything, the love flows up to the last person in the chain who did. Your post doesn't lose notes just because people spread it quietly.
Past notes will stay on the original post — we're only changing what happens from here on out. Retroactively re-attributing all of them would be... a lot.
This is just the beginning. More changes are coming as we keep building this out – stay tuned!
It’s very clear that you all have strong feelings about Tumblr and about this change. We hear you. The passion people have for how Tumblr works is one of the things that makes this place special.
As this rolls out over the next few days and you explore it, we’ll keep reading your replies and reblogs, so please keep sharing your questions, concerns, and ideas.
Your creativity has always been the heart of Tumblr, whether you’re the original poster or adding something brilliant in the reblogs, and nothing about this change is meant to limit that.
If you’d like to talk directly beyond the comments, leave a reply and we’ll follow up with as many of you as we can. We want to work with you to make Tumblr better.
Why are @staff such dumbasses. Do they have nothing better to do than make the little good things about this website shittier, just for what, a chance at profit which has failed times and time again? Fucking idiots
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Sorry but nothin less than painful and elaborate torture with gimpi gimpi, bullet ants, and giant Japanese hornet followed by death shall suffice for forgi haters.
I left my Orb out on the kitchen counter and now I cant find it. My orb lets me summon 7 frogs to protect me and I can't feel safe until I have it back.
Oooooh, that's why there were 7 forgies around me all of a sudden
🐸🐸🐸
😵💫🔮 🐸
🐸🐸🐸
Sorry, I didn't know. It just rolled right up to my feet. Would've questioned it sooner but I been going in and out of consciousness due to the overwhelming aura and cuteness of the forgies.