The exception is cheesy local commercials. Those should be the only ads. I will listen to someone who runs a store in my city doing an awkward rap. We once had a furniture store with these awful CGI ads and the slogan "where the deals are so low, it's almost criminal!" and then they got shut down, by the cops, because it turned out. It turned out the deals were so low because. You're not going to believe this but the prices were so low it was in fact
when i was in middle school nickelodeon on directv broke and it froze on the same frame for five hours. which would not have been so bad, except it was during the episode of spongebob where he goes to live with the jellyfish. specifically the scene where he’s naked and covered in sea urchins and flopping all over the place trying to get them off. and it froze on the frame where spongebob was facedown on the ground, naked. so he was laying there like that in complete silence for five hours. we would change the channel back every so often to see if he’d gotten up, but he was still like that when we went to bed. none of my friends had directv so when i asked them the next day they hadn’t seen it, but my brother and i were pretty convinced that spongebob was dead.
TV defeats its own purpose when it's pushing an agenda, or trying to defeat other TV, or being proud or ashamed of itself for existing. It's TV, it's comfort. It's a friend you've known so well, and for so long, you just let it be with you. And it needs to be okay for it to have a bad day or phone in a day. And it needs to be okay for it to get on a boat with Levar Burton and never come back. Because eventually, it all will.
COMMUNITY | 6.13, "Emotional Consequences of Broadcast Television"
not using AI genuinely feels like the rest of the world is experiencing some kind of mass amnesia. if someone says they never use it, the immediate response is that can't be true because "everyone" uses it to write their emails or answer their questions. saw a comment suggesting that not using chatgpt to write an essay is "like the 90s". girl I graduated in 2021 and we weren't doing that! how is it that everyone has suddenly forgotten that they were entirely capable of doing these things all by themselves for their entire lives up until the past few years!! am I going crazy!!!
you dont have to be a parent to understand the horror of walking into a room to discover that the baby crawled out of his crib and onto that pottery wheel you forgot to turn off, and while the baby is spinning around and around, the dog is sitting there all calm, like a person, gently using his paws to fashion the babys soft cartilage head into something a little more modern. it might be the classic tale of bad parenting, but lets see where the dog is going with this
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.
– 92% of respondents reported feeling somewhat to very “calm / peaceful” after visiting the Library
– 74% of respondents reported that their library use positively affects how equipped they feel to cope with the world
– 90% of respondents reported that their Library use positively affects how much they love to learn new things
– 88% of respondents reported that their Library use has supported their personal growth
General tumblr reminder, since some people don't know: If the person's URL has "deactivated" plus a string of numbers (a date) after it, that means that they manually deleted their own blog. It doesn't mean they were banned. Banned blogs don't have "deactivated" after them and will just be the normal URL you can't click on or interact with. They look very similar and function the same, but they were caused by two very different things.
they really don't make Bigass Cabinet For Computer anymore. we were at the used furniture warehouse the other day & they had so many. that's the way things should be. lock away that wretched machine when you're done looking at it
a very interesting terf objection to this one boils down to "but how would the state know who to protect?" because it speaks to the incredible privilege of being in a class the state actually ever remotely wants to protect. most oppressed groups do not want the state to have a registry of them, lol
"While those working at private companies can at least earn a little money, they face possible punishment if they refuse, from being denied family visits to being sent to higher-security prisons, which are so dangerous that the federal government filed a lawsuit four years ago that remains pending [note: article is from 2024], calling the treatment of prisoners unconstitutional.
Though they make at least $7.25 an hour, the state siphons 40% off the top of all wages and also levies fees, including $5 a day for rides to their jobs and $15 a month for laundry.
Turning down work can jeopardize chances of early release in a state that last year granted parole to only 8% of eligible prisoners — an all-time low, and among the worst rates nationwide — though that number more than doubled this year after public outcry."
No state has a longer, more profit-driven history of contracting prisoners out to private companies than Alabama.
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