THIS WAS A TRIUMPH

you have the guts to do what's right when everyone else just runs away

[thebirdandhersong]

Absolutely horrid that a 10hr sleep does not cure you of all that ails you

[alex51324]

it doesn't even cure me of being tired

[zaprowsdower27]

It should work like in RPGs. I want to be able to go to bed exhausted, poisoned, and missing half of my blood and wake up at 100% fully recovered

[fairymascot]

Do you believe in destiny?

[nomeki]

[tigkres]

I decided to join tumblr n post my art here too hi ladies

[graciehart]

idiots (affectionate)・[36/?] ⤷ 2.21 — “The Calusari”

[dumnslut]

[soupy-sez]

PITCH BLACK (2000) dir. David Twohy

[sloppystyle]

Happy Send Me On My Way Sunday

[thunderbottle]

he’s on his way

[billymayslesbian]

imagining a universe where porn is a marketable genre so you have to deal with raycon ads while trying to jerk your shit

[torchichate]

You’re an easy slut, aren’t you kitten? Almost as easy as dinner with Hellofresh

[belindas-chandra]

DOCTOR WHO (2005—)

S02E10: Love and Monsters

[twistedingenue]

Not to be former Catholic-blogging on main but Chicago Pope can WRITE, y'all

Breaking the chains of new forms of slavery 173. This distorted view of the human person is reflected today in various forms of servitude directly linked to the digital economy. Nothing in the world of AI is immaterial or magical. Every seemingly immediate and flawless response is the result of a long chain of mediation, involving vast networks of natural resources, energy infrastructure and, above all, people. A significant part of the digital economy’s functioning relies on the silent work of millions of people engaged in essential yet largely unseen activities, such as data labeling, model training and content moderation, often involving disturbing material. In many cases, these workers are young people, predominantly women, working under demanding conditions for minimal wages. Added to this invisible labor is the even harsher work of extracting the resources required for the production of the devices and microprocessors on which AI depends. In some regions of the world, children and adolescents work in dangerous conditions, crushing the materials from which rare earth elements are extracted. The bodies of these people are scarred, injured and worn down so that computational flow may continue uninterruptedly. Furthermore, criminal networks use online platforms, messaging systems, anonymous payment methods and profiling techniques in order to recruit, control and transport victims of trafficking — very often minors — reducing men and women to “data” to be tracked and “packages” to be moved around within the same digital circuits that support much of the global economy. This reality deeply challenges the moral conscience of our time. It is not enough to invoke efficiency, nor to celebrate the benefits of innovation, if they are built on a chain of exploitation that remains deliberately hidden. If technology promises emancipation, yet produces new forms of global subordination, it stands in contradiction to the fundamental principle of human dignity.

MAGNIFICA HUMANITAS OF HIS HOLINESS POPE LEO XIV ON SAFEGUARDING THE HUMAN PERSON IN THE TIME OF ARTIFICIAL INTELLIGENCE

[unifixation]

This reality deeply challenges the moral conscience of our time. It is not enough to invoke efficiency, nor to celebrate the benefits of innovation, if they are built on a chain of exploitation that remains deliberately hidden. If technology promises emancipation, yet produces new forms of global subordination, it stands in contradiction to the fundamental principle of human dignity.

i went and read/skimmed through the encyclical and damn. bangers all around:

[fernsnailz]

the x-files is fun because any time you get a legitimately good episode they make sure to follow it up with some shit like The Ghoul That Caused The Challenger Disaster or Amish Pheromones. really cleanses the palate

[sadmortician]

wanted to draw my beautiful wife dana evans because she’s coming back to me in 4 days <3

[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.

[dragongirlsweetie]

ok i am curious. how long is the longest song in your library (not counting tracks that are like several songs in one file like a full album mix or symphony recording or whatever) (also if it is longer than 20 minutes say the name in the tags i am curious)

how long

< 3:00

3:00–3:59

4:00–4:59

5:00–5:59

6:00–6:59

7:00–7:59

8:00–11:59

12:00–15:59

16:00–20:59

21:00–24:59

25:00–30:00

≥ 30:00

Voting ended onJun 1

[dragongirlsweetie]

ok i would like to clarify it has to be music and it can't just be a short song that's been looped a bunch. that still counts as several songs in one file, it's just several of the same song in one file. no audiobooks no podcasts no plants vs zombies theme 2 hour loop

[lookalivealivealive]

[theehorsepusssy]