Hollo~Sphare

Welcome to my mind. 

[homo-sex-shoe-whale]

I said what I said and I did not stutter

[homo-sex-shoe-whale]

Motherhood should not be the punishment for having sex. Not all girls are able to receive appropriate sexual education.

[homo-sex-shoe-whale]

Also, children deserve to be born WANTED. Children deserve to be born into families that are financially and emotionally ready to receive them into the world. Pro life is just pro birth and anti choice- pass it on

[egalitarianchica]

It seems “trendy” nowadays to trash men on social media so let’s set the record straight. Hating men isn’t quirky or cute, it’s sexist and disgusting.

[yessoupy]

GOD I just want to be CREATIVE but all my energy is being used to survive

[cries-havoc]

this is one of those "you put it into words" kind of posts. right on.

[biggest-gaudiest-poltergeist]

we gotta get back into revolving bookcases i'm begging

truly we allow the pinnacles of human achievement to wither and collapse into ashes in the wind

[anablepsian]

These are the most fuckable bookshelves I’ve ever seen

[biggest-gaudiest-poltergeist]

[strawberrylovely]

i actually do think it’s anti-feminist to be fatphobic and yes i do mean that

[thememedaddy]

[susanoos-wife]

Warbler and Roses, by Katsushika Hokusai, ca. 1834

[3000s]

[hyperrbolic-orange]

babygirl I don't know how to tell you this but there just isn't a way to do mass dehumanization in like, a "progressive leftist" kinda way.

[donniehaspronouns]

this might piss some people off but I don’t think some of you actually ever tried to unlearn your hatefulness. you just came out as queer and decided your new targets really truly deserve it this time.

[donniehaspronouns]

you. you get it.

[calidotgov-deactivated20240108]

congratulations piracy

[livefromtheloam]

Ad agency: Please don't steal the King's potatoes, no matter how easy it is.

Regular people: Wait, the King has easily stolen potatoes? How do I get in on this?

Internet users who have been stealing potatoes for years: We made a machine that picks so many potatoes and also that machine is free. Enjoy!

[commandtower-solring-go]

Ad agency: you wouldn't steal a movie?

10 year old me with 0 income and no movie: YOU CAN STEAL MOVIES????

[can-i-make-image-descriptions]

[Image ID: Headline from IFLScience reading: "You Wouldn't Steal a Movie" Advert May Have Led To More People Stealing Movies /End ID]

[geekysteven]

[teaboot]

Fun fact! Both the music and the font in that ad were incorrectly sourced and did not provide compensation to the creators

[contemptible-scoundrel]

when all your homies are talking about homoerotic sword fights where both parties are covered in shallow cuts and bruises and sweaty and panting and so on and so forth but your combat style revolves around sneak attacks and instant kill poison

[thememedaddy]

[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.

[durnesque-esque]

Just a quick grab for some of what's being discussed:

"4. UNFATHOMABLE TRAINING DATA The size of data available on the web has enabled deep learning models to achieve high accuracy on specific benchmarks in NLP and computer vision applications. However, in both application areas, the training data has been shown to have problematic characteristics [18, 38, 42, 47, 61] resulting in models that encode stereotypical and derogatory associations along gender, race, ethnicity, and disability status [11, 12, 69, 69, 132, 132, 157]. In this section, we discuss how large, uncurated, Internet-based datasets encode the dominant/hegemonic view, which further harms people at the margins, and recommend significant resource allocation towards dataset curation and documentation practices."

"6. STOCHASTIC PARROTS In this section, we explore the ways in which the factors laid out in §4 and §5 — the tendency of training data ingested from the Internet to encode hegemonic worldviews, the tendency of LMs to amplify biases and other issues in the training data, and the tendency of researchers and other people to mistake LM-driven performance gains for actual natural language understanding — present real-world risks of harm, as these technologies are deployed. After exploring some reasons why humans mistake LM output for meaningful text, we turn to the risks and harms from deploying such a model at scale. We find that the mix of human biases and seemingly coherent language heightens the potential for automation bias, deliberate misuse, and amplification of a hegemonic worldview."

[epicallyepicnerdery]

[btc-official-deactivated2024071]

im gonna fucking cry

[greelin]

they’re burning me at the stake tomorrow. If anyone wants to come