I like contemplating everything, being ridiculous, stuffing my soul with chocolate, and hunting Pokemon and bears and shit in the woods. Me in a Nutshell: -Creative -Honest -Deep Thinker -Sensitive -Silly -Idealistic Interests and Activities: Literature,...
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.
I just went looking for the post on X and got a message that it didn't exist
I searched for Guri Singh and got search results showing his account existed, but when I clicked on it I got a message his account did not exist
Does anyone know if he made his account private or if he got nuked by Elon? Or do I just suck at X (because I never go there)?
Reasons for hope: Lots of amazing people did a ton of work to make this fantastic, fully interactive resource available - because no matter how bleak things seem, there are millions, and millions of people doing everything they can to protect both the world and their own communities.
You can use this to view and subscribe to updates, project statuses, and for at least some of them even whole dossiers. This is an amazing resource, I highly recommend checking it out
krillin from dragon ball super is genuinely one of the most courageously unhinged characters in all of anime and i will not be taking criticism
okay so people sleep on krillin constantly and i think it's because they're measuring him by the wrong standard
like yes obviously he can't compete with goku or vegeta or literally anyone in the top tier of dragon ball super
he knows that
HE KNOWS THAT
that's the whole point
he's not standing up because he thinks he can win. he's not deluded. he has done the math and the math said sit down and he looked at the math and said
no
and then walked into the fight anyway
fully informed. completely aware. zero illusions about his chances.
that specific flavour of courage — knowing exactly how bad the odds are and showing up regardless — is so much rarer and more impressive than the courage of someone who just doesn't understand the danger they're in
krillin understands. krillin goes anyway.
he is built completely different and i will not hear otherwise 🫡
* using they/them pronouns to refer to Jax, as they never fully got to transition and is still using he/him irl it seems.
One of the most tragic things about Jax’s abstraction is that they not only could’ve turned to Pomni for support, but literally any human in the circus.
They could’ve talked to Ragatha about having a complicated relationship with one’s mother.
They could’ve talked to Zooble about gender/body dysphoria.
They could’ve talked to Gangle about masking, anxiety, and depression.
They could’ve talked to Kinger while lucid, who could’ve been the father figure they never had and helped them unlearn harmful aspects of toxic masculinity (plus he’s a loving girl dad).
I know the point is that some people can’t be saved from themselves. Ribbit really, really tried to be there for them and Jax pushed her away (and everyone else).
I wish fans would stop woobifying or damning Jax. The idea that Jax *could’ve* been redeemed and that their fate was self-inflicted can coexist.
the fact that we only have “herculean task” and “sisyphean task” feels so limiting. so here’s a few more tasks for your repertoire
icarian task: when you have a task you know you’re going to fail at anyways, so why not have some fun with it before it all comes crashing down
cassandrean task: when you have to deal with people you KNOW won’t listen to you, despite having accurate information, and having to watch them fumble about when you told them the solution from the start (most often witnessed in customer service)
feel free to chime in i ran out of ideas much faster than i anticipated
Promethean task: opposite of a Cassandraean task. You have the right information, and SOMEONE has to share it. But it's all in the delivery and if you're the person to identify the problem you WILL be hated forever.
Oedipal Task: (1) Attempting to avoid an unspeakably awful outcome and in doing so creating the circumstances that will bring it about.
(2) Trying to solve an problem and discovering that you are in fact the problem you are trying to solve.
damoclean task: the thing you've been putting off long enough that it becomes a constantly hanging doom over your head
pyrrhic task: you can get it done but it's going to cost you
medean task: you can get it done and you don't care what it costs you
dionysian task: task that might not be -better- if you do it drunk, but -will- definitely be more fun
hegelochic task: it was a simple job, but your name will be recorded in the annals of history for how impressively you fucked it up
task of theseus: a project for which the parameters have changed so many times that you're not sure it IS still the same task
gordian task: ok technically there Is a Right Way to do this but it's going to be fiddly and awful and take forever and what if. what if you just said fuck it. and started slicing
Rumination is probably the most common type of OCD compulsion, but I rarely see anyone talking about it. I've talked to multiple people diagnosed with OCD who didn't even recognize it as a compulsion.
Basically, if you have OCD you have terrible intrusive thoughts. They can be about anything, but common themes are fear of being a bad person, fear of hurting someone, fear of contamination. etc.
Rumination is when you get stuck in a spiral. Rumination is when you spend hours catastrophizing, overthinking, analyzing, telling yourself it's going to be okay.
I'll say it again:
Rumination is a compulsion.
Rumination is a compulsion, and that means you have to stop doing it.
I did ERP (exposure response prevention) for my OCD with a therapist! For 9 months! And it did help, but the idea didn't really click until I found this website a couple years later.
And Oh My God. It made things make so much more sense, and I was able to pull myself out of an episode even though I wasn't in therapy or on meds at the time.
Genuinely if you have OCD, or even if you suspect you have OCD, I'm begging you to read some of these articles.
Like this was genuinely life changing for me.
Here are some of the ones that were most helpful to me:
Just want to add that if you're on the spectrum, you may also experience Autistic Rumination, which is distinct from the obsessive variety, despite the two having some overlapping characteristics!
“For example, if you’re trying to convince people to boycott a segregated store, your object is to convince them that boycotting the store will have a strategic effect, not that desegregation is morally important. For whatever reason, on a cognitive level human beings have a really hard time with this. Smucker cites an example of a Lefty roleplaying session where people were tasked with selling an action to people who agreed with them on principle but didn’t see the strategic merit of the action. Surprisingly, the sellers couldn’t make the conceptual switch to sell strategic merit: instead, they doubled down on THIS ISSUE IS IMPORTANT — even though it had been stressed to them that the people they were selling to bought into the importance of the issue. People react poorly to “this is important, so do WHATEVER I SAY”; they want to be convinced that what you’re proposing will work.”
“Bob Wing, a grassroots organizer, explains this nicely: “If winning feels impossible, then righteousness can seem like the next best thing.” But righteousness is not conducive to getting normies to join your team if your team cannot demonstrate ability to, at least sometimes, win. Nor does righteousness help you make real inroads with regular people.”
origamiaround
ellievsbear
annaxmalina
romaincrisis
vaporwave
kaledo-art
tangentwithtanner
areudirty
