Bryanna!

Bryanna, Just here to look around.

[asiptoremember]

Idgaf! Nothing nor anyone in this world will ever convince me that it is okay to shoot and kill a literal CHILD over a bottle of water. That baby's life was worth more than that and most importantly his life mattered.

Rest in Power Cyrus Carmack Belton 💖

[icaseybaybee]

[ashwantsafreepalestine]

Tell Your Senators: Support Sen. Bernie Sanders’ Resolutions to Block Arms to Israel

Most U.S. voters want to stop U.S. arms sales to Israel and end Israel’s war on Gaza.¹ The Biden administration hasn’t listened. But that c

To all my American friends, the time is now.

Please, execute a BOMBARDMENT. Call your representatives!

[hailraykin-deactivated20230814]

Reblog to give mutuals a break from whatever they're been going through

[2087]

Peabo Bryson, Can You Stop the Rain, 1991

[bideogaymes]

im currently negative and would be welcome to help getting back to, at least, a zero balance. hoping this job lead ive applied to works out soon since i know someone who already works there 🤞🏽:

paypal.me/sleepisforlovers

venmo: sleepisforlovers

Chip in to "Laid Off Job & Lost Grandma In The Same Month" until Jun 1, 2026

Chip in to "Laid Off Job & Lost Grandma In The Same Month" until Jun 1, 2026

[bideogaymes]

thank you to everyone who shared and donated to help me pay that 80% discount on my debt from a years old loan i took out to pay rent at my first apt!

in the interim of me still looking for work and dealing with chronic health issues, id appreciate any continuing support in raising money to pay rent for this month ❤️‍🔥

0/$2000

as of 5/21/26

[thatgirltei]

I APOLOGIZE FOR DISRUPTING YOUR SCROLL… but YOU ARE going to have lots of wins for JUNE.

[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.

[nostalgic-teddyy]

we are losing what the meaning of the word “Thick” is. When someone says thick, y’all think it’s just a nice ass with a tablespoon of hips. When I hear thick, I think double chin, round pretty face, flappy arms, stretch marks, wide biscuit-busting hips, large thighs, tummy/belly, big fuck you tiddies, side rolls, pear figure, and much more. if you want slim thicc, there’s nothing wrong with that, but please…leave the ancient, sacred texts alone…thank you for coming to my ted talk.

[groovysarity]

[brandneweyescd]

rb to tell ur mutuals ur fond of them

[algorithmist]

"the massive boom in the construction of data centers seems to unilaterally benefit tech companies and their owners and require an increasingly disproportionate amount of electricity which has further knock-on effects. We're therefore skeptical that so many resources should be devoted to them" versus "don't worry about it"

[lagonegirl]

[stainedinink-deactivated2026041]

reblog to give the pervious person a nice rock

[ruesdeluxe]

[cantfightfatetoo]

The Racist Fandom Starter Pack

A canon interracial couple exists "I just don't see their chemistry" proceeds to fanon ship [insert white character] instead, claiming they have more of a connection.

"It's not their skin color, I just don't like them for some reason, btw, here are my top 5 [insert white actors] I think are better suited"

"Not everything is about race". They say, as they prop up every white character over any character of color.

"Please stop bring race drama into the fandom". Which translates to, I'm comfortable in my racism and don't appreciate any criticism, thanks!

White person, "Yeah, I wrote character of color as an animal, doesn't make me racist".

"I love character of color, here's a fic where they act like as [insert white characters] servant/therapist".

Character of color is such a bad friend for having opinions of their own/taking care of themselves instead of putting their friend [insert white character] first.

A character of color is morally ambiguous or a villain, gets redemption arc. "They're awful and haven't been condemned enough of their crimes". Stans [insert white characters] who did the exact same crimes or was a villain who's redemption arc is praised.

"Can't believe that character of color had the nerve not to die as a selfless sacrifice for [insert white character]".

"POC actress/actor is too ethnic and they'd ruin the character".

"I haven't seen racism in the fandom therefore it isn't there".

"Character of color is the main character of the show sure, but let's be honest, everyone came for [insert white character]

"Character of color is alright but I can only relate to [insert white character]".

Feel free to add more, that you've come across. Sadly, it all runs together after awhile of being in a lot of fandoms. Special shout out to all who helped me with these comments!

[qillermeme]