Call Of The Mild

Mal - 31 - they/she

[fullyarmoredbattlesturgeon]

[xxspronkus420xx]

I need this study

[unrequirede]

It looks like it's Anarchist Direct Actions: A Challenge for Law Enforcement. It was published in 2004

[writefinch]

It's worth pointing out that cops in the US adapted to these problems through using grand juries to cast wide nets and do punitive fishing expeditions in the wake of any serious suspected left-wing actions.

Here's how it works:

Someone starts a fire at an army draft office.

The cops look through their files for anyone who might be in the political orbit of someone who'd want to do that. People picked up at protests, for drug charges, vandalism, anyone who is already on their radar. They look into their known associates, anyone they live with, anyone they drink with.

Then they start subpoenaing these people for a grand jury summons. They give you immunity (but only for the matter of the grand jury!) so that you can't exercise the fifth amendment against self incrimination. If you say nothing, they can imprison you almost indefinitely for contempt of court. If they catch you in a lie, that's criminal perjury.

They'll ask you for information on everyone you know. Obviously they'll ask about their involvement in any crimes, but they're casting a wide net. Who knows who, where do they hang out, who talked to who about what and when. They'll ask you to spill interpersonal stuff, whether anyone is cheating on someone, whether people have substance abuse problems or embarrassing personal issues, if anyone is closeted, anything they consider dirt. Anyone you name is gonna get subpoenaed and they'll be asked for all this information on everyone they know, including you, and although you have immunity from your own testimony, you don't get immunity from each others.

Assuming you didn't personally do anything they can prosecute you for in the matter of the grand jury, they'll go after you based on what they know. The cops will arrest you on any little thing they have suspicion of, even if they know they can't prosecute you, just so that they can keep you in jail for a few days while you miss two shifts at work and your friends have to scramble to raise bail. They'll leak any embarrassing info that comes out, to your boss or your family or even the local press. Whatever they can do to make your life a little harder.

They will lean fucking hard on anyone who is involved in the scene but had second thoughts or felt like they were dragged into something they never wanted to do in the first place by their friends. The cops will say 'do you want to get your life ruined by people who did something stupid over something you barely even believe in' and sometimes that's a very compelling argument! If people have dependents or kids who they think won't be looked after if they go to prison, there's a lot of pressure to cooperate.

It's important to note two things:

1) Based on the ratio of actual prison sentences to maximum possible sentences for the charges, it's better for people not to cooperate with the jury both individually and as a group. People who talk still get sentenced, with the information they helped provide.

2) These aren't surgical strikes, they're an artillery barrage designed to destroy infrastructure and send people running for cover. Cops don't want you to have friends, they don't want you to hang out and have fun, they don't want you getting or providing food or shelter through anything you can't get fired from. They don't want committed direct-action people swimming freely through a sea of friendly people. They're not scared of the flower, they're scared of the soil that grows it.

[noneedtofearorhope]

Surviving a Grand Jury

Narratives from three people who successfully stood up to grand jury indictments: one who served jail time for resisting, one who went on th

[anarchistmemedistro]

Green Scared?

Green Scared?

Audible Anarchism

Philosophy Podcast · Updated weekly · A podcast broadcasting Anarchist texts and audiobooks

[catchymemes]

[enbaluka-tokyo-drift]

[depsidase]

[auxphono]

Needed a clean graphic for a project, and figured while I was vectoring I would put sex and magic back in.

Happy Pride Month

[souvenirs]

[spitfaggot]

happy birthday, gilbert baker. (june 2, 1951 — march 31, 2017)

[gregtom]

[k-simplex-deactivated20241001]

Happy Pride

[comikbook]

happy pride month dykes i love you dykes

[carlosmarcosapproved]

[depsidase]

[cloudabserk]

WARNING do NOT start reading books and comics or watching movies or looking at art!!! you will start wanting to create art yourself. or god forbid. writing.

[cambriancrew]

[willow-flowers]

Tracy Chapman

[carlithiel]

The X-Files + Scully’s Darkness Falls Jacket

[delightfullyvague]

(source)

[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.

[undertheinfluencex1]

[robertdinero]

[theehorsepusssy]

[soymoss]

starling