i genuinely love that the way k.a applegate resolves the issue of needing the alien on the team to be able to drop plot crumbs without totally solving everything for the human kids is by making aximili-esgarrouth-isthill a jock who only tangentially paid attention to when his teachers were explaining, like, the andalite equivalent of how to find the cosine of a triangle, and now he's in an astronomically rare circumstance where the fate of an entire species depends on him remembering how to do that. and he's cold sweating trying to recall the answers to homework problems he didn't do. and also he's always lying.
ax is literally experiencing like if you got teleported back several hundred years and everyone was expecting you to explain the precise mechanisms of how cell phones work to them and if you don't come up with a sufficient explanation they're all going to die. And he's not enjoying it.
its so weird to me that cis people will dislike their name so ardently and yet. not change it. you guys know that’s an option, right. no one can make you keep the shit name your mom gave you. no, not even her.
One of my friends in undergrad changed his name because he didn’t want to bear the name of his abusive and absent father. It’s been years since he did it, and he still says that it was the single best decision of his life.
One of my friends in high school changed his named as soon as he turned 18, so that the ethnic name his family gave him was finally the name reflected on all of his paperwork. He told me that he understood why his parents had given him an “English” name, but that he felt that if he needed to assimilate in order to succeed, then that was a type of success that he didn’t want.
When I was on my way home from the courthouse after changing my own name, I got into a conversation with my rideshare driver, who was extremely interested once I told him what I was in court for, and wanted to know how I’d done it, how much it cost, was it difficult, etc. It turned out that his girlfriend had chosen the name “Yo-yo” when she came to the United States, unaware of how rare that was as a name, and that she was frequently made fun of because of it. Neither one of them had realized that a name change was so easy, and he told me he was excited to let her know that she had options.
There was an intern at a summer job I had once, who changed her name to be the same name, but a different spelling. She said that she had no idea why her parents had spelled her name so oddly to begin with, and suspected that it was just an honest mistake either by them or by some nurse, but it had been a headache for her entire life, and it was a huge relief to not need to be correcting people’s spelling on important documents anymore.
One of my exes legally changed his name to have an exclamation point, because he liked to sign his name with an exclamation point.
You can always change your name if you don’t like it. You always have that option. It doesn’t matter why – it can be conformist or anti-assimilationist, serious or silly, a minor change or a major change. Your name is yours, and you have every right to change it to be whatever you want.
metaverse being an 80 billion dollar turbo flop that immediately shut down after one of the worst performances and subsequent total company rebranding in recent history SHOULD, in a kind and just world, have spelled the end for the entire company. but unfortunately we live in hell and "Meta" is still alive
same goes for twitter too like that shit should have shut down a month after it became X, The Everything App but these guys just have so much money that 80 billion dollars can be burned with reckless abandon and their companies will survive
the pistachio food trend is soooo interesting because it's like. i've been following the californian pistachio water politics for years, as a californian with personal connections to agricultural workers but! basically there's been a big push in california agriculture over the last decade to pressure farmers to produce pistachios, because iran has dominated the global market in pistachios for decades, and the US government has been trying to weaken iran economically, so they want to make california pistachios a competitor. which is ridiculous, because california's agricultural infrastructure is suffering under a drought, and pistachios take insane amounts of water. so a ton of water is being redirected from the people in order to engage in a trade war with iran over fucking. pistachios.
anyway now that the US (i.e. california) is producing more pistachios than iran, the next step is to drive consumption of pistachios, so that the farmers who are producing these pistachios can continue to make money on them. ergo all the fancy pistachio coffees at starbucks and similar shit like suddenly being able to find pistachio butter in grocery stores when five years ago it was exclusively available at specialty stores and online, and the huge boom in pistachios foods in instagram and tiktok recipe content. like i watch a lot of instagram foodie reels (cooking/baking is one of my hobbies) and these get thrown onto everyone's feeds, to promote the purchasing of pistachios, so that the US can stick it to iran. it's. kind of incredible to watch this happen in real time, because it sounds like deranged conspiracy thought, but like. i've been watching this trend for the past decade and it's fucking real.
anyway one of the vegan recipe accounts i follow just posted like five pistachio-based recipes in a row and it makes me feel some kind of fucking way
it is extremely relevant that pistachios are so easy to acquire here, but acorns, which are an indigenous California food staple crop, are impossible to find even in the best stocked grocery stores, and knowing how to prepare them for consumption is a rarer skill than sourdough starter
[Video description: Gritty is turning the crank on a flagpole to raise the Progress Pride Flag. He gesticulates angrily that the flag is not blowing in the wind, then gestures offscreen. The flag begins blowing. As Gritty begins raising the flag more, the camera pans out to show a man in a suit and sunglasses, looking like a stern Secret Service agent, is holding a leafblower that points at the flag. End description.]
but ykw at least i'm not on mount everest. at least i'm not paying tens of thousands of dollars to slowly suffocate in a 300-person line at the gates of hell. never in my life will i have to be steered in a hypoxic stupor through the maze of poop and corpses atop mount everest. on this earth a lot of horrible things can happen to you without your permission but there are a few that you have to opt into. you can just say no thanks! and be guaranteed never to have to be on mount everest. much to be grateful for actually
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.
