my prized possession is this loveseat I bought from a divorced dad who couldn’t tell me anything about it and in the years i’ve owned it i’ve never been able to find out who made it or where it came from. it’s got nails and finger creases and palm lines but they’re all kinda hard to see in this pic.
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.
A rule change pushed by White House officials would slash benefits or end support for as many as 400,000 Supplemental Security Income recipi
The Trump administration wants to make a rule change that would decrease or end benefits for SSI recipients who live with their family. As many as 400,000 disabled people may have their benefits cut if this rule change occurs.
It's worth noting as always that as things currently stand, the MAXIMUM benefit a person can receive from SSI is $994 a month.
If two people on SSI are married, the MAXIMUM they can receive is $1,491 a month, total, for both of them. (Meaning marriage to another SSI recipient reduces your maximum income possible to $745 a person each month.)
Could you live on that?
Could you live on that without living with family?
The cops very clearly planted evidence on him because they had to make an arrest because all eyes were on them and whoever actually did the deed was making them look stupid.
Why would the real killer hero have kept the weapon on his person and traveled two states over while carrying it and a manifesto in his bag, conveniently turning the crime into a federal matter? The same guy whose bag they found in a park, filled with monopoly money? Why did the police turn off their bodycams, take Luigi's stuff, drive a block away, turn their bodycams back on, go back into the restaurant, and then arrest him?
From the moment of his arrest, even left-of-center media has been presuming his guilt without examining anything (e.g. calling him "the killer" instead of "alleged" or "accused") and then when I say he didn't do it, the nearest person chimes in with some quip that tells me they think he did do it but should go free anyway. Don't get me wrong, I would have the same attitude if he had done it. But he didn't. It makes me feel like the only sane person in the world, even among my staunchly leftist friends.
Every homeless person you see on the streets has been put there by force or the threat of force by the police to protect the profits of landowners and landlords and less directly to act as an example and threat for workers who might otherwise be less willing to accept unjust wages and working conditions.
@3liza they didn’t account for it because they didn’t realize it was an issue. The particles the gloves are shedding are not microplastics but they are similar enough in composition and under an electron microscope that because the scientists didn’t know to account for them, they didn’t realize that’s what they were seeing.
Also they DID account for contamination in WET sample preparation, because they’d already learned previously that the nitrile gloves could contaminate wet samples, but this was the first discovery that they were contaminating DRY or AIRBORNE samples as well.
All of this was very clearly laid out in just the last provided link - i didn’t even have to read all of them to learn this, I read like 3 paragraphs of the nautil.us article and I was able to learn what happened and why it hadn’t been accounted for.
Like, I understand the frustration, but this is the sort of thing that has to be DISCOVERED before it can be accounted for, and this is what that kind of discovery looks like, and berating scientists for not already knowing something science hadn’t yet learned is kind of a pointless and bad faith approach to things.
We’ve learned that a lot of the studies done on microplastics in our environment were not actually accurate, and had unintentionally incorrectly inflated numbers of microplastics in their samples due to this issue, which means that while microplastics are still obviously a problem, they’re not as overwhelming large of a problem than we thought! This is a good thing! Science has done its job and we have learned new things and can now do even better science! There’s no reason to be angry at or berate the scientists who’ve gone before. We know better now. That’s the important part.
“I will have an undergraduate class, let’s say a young white male student, politically-correct, who will say: “I am only a bourgeois white male, I can’t speak.” … I say to them: “Why not develop a certain degree of rage against the history that has written such an abject script for you that you are silenced?” Then you begin to investigate what it is that silences you, rather than take this very determinist position-since my skin colour is this, since my sex is this, I cannot speak… From this position, then, I say you will of course not speak in the same way about the Third World material, but if you make it your task not only to learn what is going on there through language, through specific programmes of study, but also at the same time through a historical critique of your position as the investigating person, then you will have earned the right to criticize, you be heard. When you take the position of not doing your homework- “I will not criticize because of my accident of birth, the historical accident” - that is the much more pernicious position.”
2087
prguitarman
turnnoffyourmind
areudirty
dailybridgerton
