The Minotaur was named that because he was the son of King Minos. Anyone with a bull head has to be named after their dad, like the Kyletaur or something.
Where was broad-forehead born, was honey-paws produced?
Born in the sky aloft, on the horns of the moon,
on the head of the sun, on the Seven stars’ back.
Fire shot in flashes from the sky, on a wheel the world turned around,
while broad-forehead being formed.
From there was he let down to earth,
on the edge of the forest-fort, in the home of the golden wilderness.
*
The bear was arguably the most important and respected animal to ancient Finns, essentially considered a deity. It was believed that humans were descendants of this deity; they both ate the same food, could stand on two feet, and shared the same wisdom and emotional intelligence.
The bear was said to have come from the sky. When it set foot on earth it was warned not to ever harm humans. But it did, and as a punishment the bear was beheaded and sent back to the sky. This is the origin story of the Big Dipper constellation.
Painted in ink, white poster color and gold medium on 300 g/sqm Lanaquarelle 100% cotton hot press watercolour paper. The painting is unframed.
Personally I hate AI because it uses slave labor, is killing the planet and is making people stupid, but that's just me. The soulless art aspect is just one little piece of my grander disdain.
wait how does AI use slave labor? Do you mean the human works that are stolen and not credited or compensated? Because technically under capitalism everything is exploited but there are varying degrees
Aside from the scraping, AI tech companies, including openAI/chatGPT, have outsourced training their models to countries in the global south, specifically Kenya in openAI's case. These workers are working in sweatshop conditions for less than 2 bucks USD per hour. I'm on mobile, but if you search 'openAI Kenya slave labor' and related keywords, you can find multiple articles about it.
I think about this so goddamn often. Even the good uses are trained on slave labor.
Wambalo and other digital workers spent eight hours a day in front of a screen studying photos and videos, drawing boxes around objects and labeling them, teaching AI algorithms to recognize them.
Human labelers tag cars and pedestrians to teach autonomous vehicles not to hit them. Humans circle abnormalities in CTs, MRIs and X-rays to teach AI to recognize diseases. Even as AI gets smarter, humans in the loop will always be needed because there will always be new devices and inventions that'll need labeling.
Humans in the loop are found not only in Kenya, but also in India, the Philippines and Venezuela. They're often countries with low wages but large populations — well educated, but unemployed.
The pay for humans in the loop is $1.50-2 an hour.
"And that is gross, before tax," Wambalo said.
Wambalo, Nathan Nkunzimana and Fasica Berhane Gebrekidan were employed by SAMA, an American outsourcing company that hired for Meta and OpenAI. SAMA, based in the California Bay Area, employed over 3,000 workers in Kenya. Documents reviewed by 60 Minutes show OpenAI agreed to pay SAMA $12.50 an hour per worker, much more than the $2 the workers actually got, though SAMA says what it paid is a fair wage for the region.
It's destroying the environment. It's taking advantage of people who're desperate. It's traumatizing them for dollars an hour--if they're lucky and they aren't denied their pay for no reason. I think about this a lot, that these people were made to look at awful and disgusting and illegal things for the sake of training these stupid AI.
"I looked at people being slaughtered," Wambalo said. "People engaging in sexual activity with animals. People abusing children physically, sexually. People committing suicide."
Berhane Gebrekidan thought she'd been hired for a translation job, but she said what she ended up doing was reviewing content featuring dismembered bodies and drone attack victims.
[...]
SAMA says mental health counseling was provided by "fully-licensed professionals." Workers say it was woefully inadequate.
It's just absurd and disgusting and infuriating. Yes the good applications are worth humans working on. It's not a bad thing--if the people employed to do the work are compensated appropriately and cared for. But so many of the uses are just unnecessary.
It just. Sucks. And all they'd have to do to make it suck just a litte bit less would be to pay people appropriately, give them access to the counseling needs they have, treat them like human beings worthy of respect and care on a basic fucking level. It wouldn't resolve the environmental issues or the fact that people are thinking less and less for themselves in the name of getting all of their answers from gen AI but at least they could do one thing to make it a little less The Worst Thing Ever.
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.
"we live in an uncaring universe." sorry the special planet full of beauty and animals and food literally growing out of the ground isnt good enough for you. i guess
An absolute legend of paleo doc media has passed away today, the composer of the Walking with series.
Everyone who grew with any of these knows the music of Bartlett. While the Walking With series is known for how groundbreaking it was for tv CGI and its presentation of dinosaurs as animals, Bartlett's music was vital to hooking you.
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snowghoul
