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[interestingmistakes]

Reblogging this manually. Op doesn't want credit for fear of being terminated.

[left-lateral-incisor]

[ID: A picture of the progress pride flag. The chevron with the stripes for trans people and people of color, as well as the intersex flag, have been replaced with the Tumblr "This content has been removed for violating Tumblr's Community Guidelines." /End ID]

[movementnudge]

Movement nudge, back pain, tight hips

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[mckitterick]

hey writers, desk workers, and others who sit too much:

if you want to retain mobility and don't want a permanently stiff back, this is important!

[userfish]

this pride month we’re all going to be radically pro transgender. or else.

[userfish]

hey so this means radically pro ALL transgender. don’t put limitations on this. all trans people are radically accepted here.

[belltothethirdpower]

yes, even the ppl with xenogenders. yes, even those who use neopronouns. we're all in this together

[quasi-normalcy]

The 40 Most Rage-Inducing Problems in Tech

The bugs, broken apps, and nightmare customer-service bots we can't escape, presented as a blessed and sacred addendum to Pope Leo XIV’s new

[binturong-of-the-day]

The binturong of watermelon

[captain-price-returns]

I maintain that this man's entire existence is elaborate performance art to call attention to the elite impunity problem in this country in the most garish way and it's still not working

[anarchistmemedistro]

[noneedtofearorhope]

"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."

Alabama profits off prisoners who work at McDonald’s but deems them too dangerous for parole

No state has a longer, more profit-driven history of contracting prisoners out to private companies than Alabama.

[justalurkr]

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.

[irispurpurea]

This is the paper. It's excellent, highly recommend reading it.

I remember reading about Gebru's firing but I had no idea this was the paper she was fired over.

[fuckyeahmarxismleninism]

[fuckyeahmarxismleninism]

May 13, 1985: A crowd watches as a bomb dropped by Philadelphia police on the home of the MOVE family on Osage Avenue ignites and destroys two full blocks of homes and kills 11 people, including five children.

Never forget. Never forgive.

“May 13th, 1985 is more than a day of infamy, when a city waged war on its own alleged citizens, but also when the city committed massacre and did so with perfect impunity, when babies were shot and burned alive with their mothers and fathers, and the killers rewarded with honors and pensions, while politicians talked and the media mediated mass murder. On that day, the city, armed and assisted by the US government, dropped a bomb on a house and called it law. The fire department watched buildings ignite like matches in the desert and cut off water. The courts of the land turned a blind eye, daubed mud in their socket, and prosecuted Ramona Africa for having the nerve to survive an urban holocaust, jailing her for the crime of not burning to death. Eleven men, women and children died, and not one killer was even charged with a misdemeanor.

“But on that day, more than MOVE members died. The city died, too. Its politicians died, its media died, its courts died, and its churches and houses of worship died, for they ceased to function, and they served power and money. In a very real sense, the city massacred itself, for one’s faith in such institutions died. They became empty, hollow and dead, but for the shell. May 13th, 1985 is a day that shall live in infamy, but for far more reasons than the obvious. It was the death knell of a system committing suicide. It proved that a man called John Africa spoke powerful truths when he spoke about the nature of the system as corrupt, as flawed, as poisoned. Every day past that date has only proved it even more.”

- Mumia Abu-Jamal, political prisoner and revolutionary journalist, on the 25th anniversary of the May 13, 1985, Philadelphia police bombing of the MOVE family house.

[kropotkindersurprise]

[tatzelwurming]

in the dungeon you will have to face a monster with the nose of a tiger, the teeth of a tiger, the ears of a tiger the eyes of a tiger , the cheeks of a tiger , the neck of a tiger, the torso of a tiger, the arms of a tiger, the paws of a tiger, the belly of a tiger the back of a tiger, the the legs of a tiger, the claws of a tiger, the ankles of a tiger, the tail of the tiger, the mind of a tiger, and the power of a tiger

[0gl1tch0]

It’s @rickybabyboy

[angelicdewdrop]

[binturong-of-the-day]

The binturong of silliness

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