met a woman today whose original real actual given-at-birth first name is "Vendetta." ma'am are you aware you are a videogame protagonist and/or a character in a skullduggery pleasant novel. real quick sorry to bother you miss but who exactly were your parents expecting you to avenge in their name
the worst part is I can't dare ask her that last question because then she might say something like "You don't remember, do you?" and pull out a katana and obviously I'm not stupid enough to take that risk
my professor really liked this and said that it should be 'the nucleus of a chapbook' (so like 15-30 poems of the same theme that I would attempt to get published) but now I feel awkward because I think that she thinks HE DIED? but it turns out, everything will be alright! his heart recovered! the FIP meds are working!
I couldn't believe how fast he went from death's door to running and playing once we got the GS441525 into him. I am so incredibly grateful to everyone who's spent their time researching and legislating it!!
I really do not think the average person understands how different the training resources and access are for a cis man playing sports vs everyone else. I wrote an article last month about how until 2020 they were given the Swedish women’s national hockey team expired protein bars for tournaments and only five practices leading up to it. More often than not people who aren’t cis able bodied men playing a sport do not have equipment properly fitted to them. Women’s teams do just straight up get less court or field or ice time. Coaches and trainers will refuse to train you on certain things because they just assume you cannot do something.
Idk. We have seen the athletic accomplishments of cis men come very far in the past century as they got more specialized training and the sports industry as a whole grew. Yet People still really think physical advancements and progression can only be done by cis men, and the the upper limit of what’s been recorded by everyone else is this permanent, unmovable thing. For a long time people thought it was impossible for a woman to do a triple axel until it happened.
I've known a number of non binary people in my life and I think single biggest conclusion I can draw from that is that non binary people are not the same. Like if Men fit in box A and women fit in box B, people really, really want nonbinary people to fit in a theoretical box C, and it just doesn't work like that. They are outside the boxes. They defy any simple categorization because they are not a third way of being, but every other possible way of being.
Being supportive of binary people is relatively simple, they have decided to sort themselves into one of the boxes that we have lots of experience interacting with. Being supportive of nonbinary people can be comparatively tricky, because you have to resist the urge to create box C and drop them all there. That's how we end up with various prejudices like "woman lite". Humans really, really like to categorize things. It helps us think. Unfortunately, sometimes it helps us think wrong.
If you have a non binary person in your life, I think it is important to take the extra effort to learn about them specifically.
When I was a kid watching tv was idiot coded and nerds read books, but now scrolling shortform videos is considered braindead behavior and watching tv means you're some kinda intellectual. You might think this shifting assessment of media's consumers might make readers into absolute super geniuses, but no. Only perverts read now.
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.
Break the stereotype* of tumblr sexyman always being a skinny guy in a suit. It’s time for hairy muscled short fat muscular men with big beards to shine. Vote senshi. tumblr sexyman is whoever tumblr says is the sexyman. bring in the new era
He’ll make you bread
*(people keep poiinting out that sans won who is not a skinny suit wearer. i am not saying he did not win. i'm saying it's the common stereotype of what a tumblr sexyman is. sans winning only further proves my point that senshi can win)
I'm gonna say it, I do think that even the laziest person imaginable should have a roof over their head, food in their stomach, and access to healthcare
every time someone realizes they dont have to pick between being a boy or a girl an angel gets its wings btw. and also extremely loud cheering can be heard in the distance from me specifically
