The hexagon is nearly 30,000 km (20,000 miles) wide. This is large enough that two Earths could fit inside the storm with room to spare.
It is a standing atmospheric wave created by a powerful jet stream that wraps around the pole. Winds along its edges can exceed 300 km/h (about 220 mph).
At the very center of the hexagon lies a deep, dark polar vortex that extends hundreds of kilometers down into Saturn's atmosphere.
this is your random reminder to CHECK IF YOU'RE STILL HAVING FUN
are you enjoying scrolling tumblr? watching youtube? reading that book? playing that game? drawing that art? doing that activity? if not,
YOU CAN STOP AND DO SOMETHING ELSE
you don't have to stick to something that you are doing for fun if it isn't fun for you anymore. You can come back! If you've loved it before you are likely to love it again! but you can stop!
Don't get stuck in a loop of doing something that you think should be fun when it isn't! You can put it down for a bit! Maybe that's the very thing that will make it fun again later!
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.
The problems that irish or italian people have with most irishamericans and italoamericans isnt that they are trying to reconnect with their culture or whatever the problem is that they use their ancestry as a status of oppression that simply does not exist anymore in modern usa and that they overstate their cultural roots so much with so little care for the og mainland and the relatives they have here and the social issues of those mainlands that they just feel like they are larping. Also cant speak for the irish thing but often italoamericans will spout downright blood quantum that ends up only harming the actual immigrants in italy that have lived there for years and will be found saying shit like "ethnically italian" trying to be progressive while genuinely the only people in italy who say that italian is an ethnicity are neofascists obsessed with phrenology
smile - hollanov - @hollanovmicrofic - word count: 385 - click here for my hollanov microfic archive on ao3
Hayden's scoff could be heard all the way from the kitchen, causing Shane to pause as he got them both a drink from the fridge. "What?" he called, frowning, wondering what'd caused his best friend to be annoyed this time.
"Just can't believe it."
Confused, Shane grabbed a ginger ale for himself and a stupid energy drink for Hayden (but don't tell Jackie!) and padded back into his living room. "What, that we got our butts kicked? Because s-"
"No, that you're dating him," Hayden corrected, glowering at the TV screen.
And of course, there Ilya was, completing some interview, smirking and making lubricous comments about how Boston would wipe the floor with everyone else that season. He was in rare form, boasting about every goal he'd scored, every assist he'd made, chirping about Montreal and New York and any other team that dared get in his way. Shane, of course, felt a familiar curl of hot attraction light in his stomach, the desire to prove Ilya wrong just as strong as the desire to kiss him senseless. But still, it was hard to argue that Ilya was the sweet, thoughtful man he knew when he acted like this on TV.
But an idea occurred to him.
"No," he murmured, standing and moving to his bedroom.
He walked quickly, fishing in the back of the drawer in his bedside table for the lone polaroid shot, a risk he'd taken because Ilya just looked so perfect in the photo he'd managed to capture. It was an image of Ilya sitting by the lake, a carefree, boyish smile on his face. The angle was such that Shane's legs were visible in Ilya's lap, and Ilya's expression towards the camera was adoring, a clear reminder that for some reason, Ilya Rozanov was so in love with Shane Hollander that sometimes it burst from him like sunshine.
Shane treasured that picture more than anything.
He threw it towards Hayden, jaw set. "I'm dating him," he said firmly, gesturing.
And for a moment, Hayden's face changed. He took in the shot, the clear difference between Rozanov, star hockey player on the TV, and Ilya, Shane's boyfriend in the picture.
He softened just a little.
"Fine," he muttered, rolling his eyes and reaching to change the channel.
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