oh edward elric bleeding out and nearly dying impaled by a massive steel beam shaving pieces off his soul and years off his life because he chose not to kill the objectively worst guy he's ever met but he's just like. ah. if this is the price i have to pay for mercy i'm glad to pay it. 16 years old, unshakeable belief in the value of a life, shortest fuse in the world, mad as hell at all times, heart of gold for all of humanity. no one is doing it like him
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You may think the phrase "He's just a little guy" comes from internet memes, but you would be wrong.
The true origin of the phrase belongs to celebrated author Tamora Pierce, in her 1983 novel Alanna: The First Adventure, when Gary says, "Still—what can Alan do for you? He's just a little guy"(49).
Thus, whenever we say of someone or some creature that they are "just a little guy," what we are really saying is that they are a short redheaded knight-in-training with some sort of Gender going on who will kick your butt given half a chance.
Thank you for coming to my TED Talk. May we all be the little guys we wish to see in the world.
Pierce, Tamora. Alanna: The First Adventure. New York, Random House, 1983.
i was like 'it's weird the way everyone is doing free pr for the catholic church rn just bc the pr for the new pope has already been that good' and someone was like 'wait? why don't you like the pope? did something happen?' i feel insane i'm not exaggerating i didn't even know what to say?? 'did something happen?' yes the last 2 thousand years of global history, and no, going back 2,000 years in catholic history is not dramatic because it's remained consistently that bad throughout and in fact i do think the church should answer for every single century of its sins while all of its assets are returned to the people from which they were stolen and its global power is dissolved.
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Happy Pride! JSTOR Daily has a round up of LGBTQ+ articles happening this month - check them out: https://daily.jstor.org/lgbtq-pride-month-editors-picks/
Faust is back for the 5th time! If you want to use the flag of your choice as an avatar, they're under the cut. They're free to use as long as it's for personal use only.
Imagine being the gays at a pride event in 2004 living their lives when someone grabs the microphone and announces to the room that Ronald Reagan was pronounced dead. Can you even imagine the hype, the celebration, the pure elation
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The reactionary backlash to media analysis is a natural part of the wider "fascists hate anything intellectual" phenomenon, btw.
Wanting you to ignore the politics of Star Wars comes from the same exact place that wants you to substitute the germ theory of disease with the 'sickness comes from failure to be a good christian and most people who claim to be sick are just faking anyway' myth.
They don't want these complexities to exist, and by talking about them, you make them exist. It's a form of magical thought. Talking about police brutality wills police brutality into existence. A disruption of the status quo is seen as a disruption of the natural order. The problem they see is that no-one has made those people shut up. That is what they want: someone to come in and make those people shut up and go away, to put things back "where they belong."
[...]
Their will is a hammer that they are using to beat reality itself into a shape of their choosing, a simple world where reality is exactly what it looks like through their eyes, devoid of complexity, devoid of change, where they are right and their enemies are silent. They are trying to build a flat earth.
there are too many things happening this summer that i'm thinking we are going to need an extra 6-12 months of june and possibly another 3-4 months of july. probably no extra august as the problem should hopefully sort itself out by then. we are also looking into extending the day night cycle to 55 hours and extending the human lifespan to 10000 years.
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.
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wow dude jts so awesome that your car is loud as fuck and smells worse when it drives past. thags fucking epic man. i really like how it hurts to listen to you drive past and it scares people. thats awesome man. i really like your car that makes a loud as fuck fart sound. fucking epic dude
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