I know canon dictates that Ilya never really felt grounded in Boston and I've only been to Boston like twice in my whole life but something about Ilya being a creature of those streets appeals to me. What's his Dunkin' order, Mass people?
#so i can see ilya being one of those people who orders a large with like 15 sugar#but what is most critical is that i know in my heart that he was a double-cup person#double-cupping was ordering an iced coffee and then asking for one of the styrofoam hot cups to put it in#it perhaps pseudoscientifically kept the cold drink more insulated but also kept it from sweating all over you#peak masshole shit#dunkin' finally discontinued styrofoam cups after ilya had already moved to ottawa so he is marked safe from having to cope with the change
@trippedlaces your tags are everything I've ever wanted as a lover of niche nonsense
you're not making enough of stone fruit season. that's another thing you're fucking up. a few dozen stone fruit seasons you get your whole life. you need to take a hard look at your peach and mango consumption.
The thing I dislike about alt histories where real life monarchs have been made both gay and unproblematic is that it seems to imply that gay monarchs have not existed in history but if they did they would be cool. Which isn’t true. They have existed and they were just as bad as straight people. And I think it is disrespectful to real queer history to pretend that queer people have never been absolute bastards
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.
"autistic genderless aroace cyborg designed to be an efficient security system and living weapon who breaks out of its corporate controls, but instead of getting violent uses its newfound freedom mostly to watch soap operas and half-ass its job" is already an excellent character concept but add on "and it develops an extreme self-sacrificial devotion to people who are nice to it" and it becomes irresistible
It sent back an acknowledgment, with a packet attached. I didn’t have to open it (I would not have opened it, that would have been stupid because viral attacks exist) to know what it was. I recognized the tags and heading. It was my own (Murderbot 2.0’s own) hack-your-governor-module annotated code bundle.
the "annotated" to me indicated that this isnt just the hack, 3 is passing around the full helpme.file with MB's diary excerpts and everything. MB rogue construct folk hero confirmed.
or as my friend said "Imagine walking up to someone in a mall food court and being like 'you must liberate yourself from the machine of capitalism' and then they turn around and its Karl Marx with guns in his arms."
I think that if a story ever pulls a line like "You could have destroyed your magic with a stunt like that!" or "Pushing ahead with your magic too quickly can have dangerous consequences!" then the story should be obligated at some point to show me these magically disabled people.
All stories should have more disabled people, honestly, because disability is a basic part of being alive! What am I supposed to think of any given fictional community if I never see any elderly people, any retired people, any injured people, or any disabled people?
But seriously, a lot more fantasy stories specifically could really stand to show people who have been burned by magic to various degrees and are now living with the consequences. I'm too used to the protagonists fainting and sleeping extreme recklessness off without issue.
reminder that legally blonde (2001) and the birdcage (1996) will be back in theaters may 24th and june 27th, respectively. these movies are not what this blog considers proper vintage but I do consider them lots of fun, and it would be nice in particular for the birdcage to get a good turnout in a time where anti-gay sentiment is on the rise (at least in the US, where I am based).
