i hate this cause i did japanese for like a year and this explains the use of the -ne particle WAYYYY better than my teachers ever did. it took me ages to comprehend what this post makes abundantly clear.
my teachers: its like a, a little rise at the end of a sentence, to show that you are seeking a response, while not warranting the -ka particle which would make it a proper question.
fun fact: afaik, "-ne" was inherited from the Portuguese settlers/priests that stayed in Japan in the 16th century. It comes from "né?", which the contraction of "não é?", "isn't it?".
No one did better than random chance, even though they only included people in the study who are experienced with astrology and stated that they expect themselves to do better than random chance
They gave every astrologer a set of 50 things about a person and 5 birth charts to choose from. They weren’t even coming up with the chart themselves!
After taking the test, most thought they nailed it. Zero out of 152 did better than 5 out of 12. None nailed it
Astrologers who rated themselves highly experienced (“world class experts”) did the same or worse as those who said they have limited experience. Both performed the same as random chance
#i appreciate how genuine and non-judgemental this comic feels
#like left one is not upset at right one for caring while being powerless
#and right one seems genuinely distraught and not performative
I'm glad the facial expressions are coming across accurately!
It can feel so absurd to say gosh I hope the torment maze removes some fire and rusty nails soon, but alas, sometimes that's all one can do.
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)?
Listening to Andy Weir talk about eridians is so funny because fans are always talking about Rocky and Adrian as these “soft” adorable aliens but Weir won’t ever let us forget that their species are apex predators on their planet. Not like humans who became apex predators by inventing weapons, but natural top of the foodchain like lions or polar bears. So far I haven’t found an interview where Weir explains who ate eridians in the ancient past that caused them to watch over each other while they slept; another predator species or rivaling eridians.
Grace is joking around with a selectively violent creature that can rip his soft squishy body apart in an instant!
But it’s also a lot of fun to hear Weir talk about all the stuff he wants to include in a possible sequel, like the fact that eridians can have several conversations at once even with the same eridian. He imagine Rocky and Adrian bickering in one conversation while having a nice conversation at the same time that slowly turns into a fight and all of a sudden they’re yelling at each other in two conversations about different things.
He also says they have terrible spacial memory because they can see everything around them all the time thanks to their echo location so to them it’s crazy that humans can only see in one direction but still remember what’s behind them and even what the last room they were in looks like. Apparently eridians mostly just remember that the room exists and that it has the computer in it but if you asked them where the computer is placed in the room they’ll struggle to give a precise answer.
And Rocky got scared when Grace hugged him because eridians don’t have a concept of expressing affection with physical touch. To them it’s only neutral or violent because thanks to their hard shell they can’t really feel much. They only use it to move each other around or to break through their prey’s shell to get to the soft insides. So in their inter-species friendship only Grace would feel any desire to touch Rocky. It makes it very cute that Rocky joins in on Grace’s hugging ritual. It’s purely for Grace’s sake.
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