Death and shelling and casualties everywhere, and the mutilated bodies of children, and the evacuation and destruction of homes and the displacement of entire residential areas. Reconnaissance and combat aircraft and helicopters all the time, and targets in the streets and markets and the expansion of control zones.
Feelings of fear and anxiety.. and the terror of children returned quickly, after we had at least been in some kind of safety and calm, believing that all this had passed and ended.
All of this happens in silence, without announcement, without labels of "military operations" or "return of war," without statements.. and quietly.. without intervention or attempts or statements or pressures.
And thus we are left alone.. abandoned, being slaughtered, and the scene itself not even broadcast on screens because there are things more important.
Here is my brother PayPal account
WE APPRECIATE ANY HELP YOU GUYS CAN DO 🫶🏼🙏
Go to paypal.me/bushrabo and type in the amount. Since it’s PayPal, it's easy and secure. Don’t have a PayPal account? No worries.
13 martyrs have lost their lives in the Gaza Strip since this morning; each of them had a name, a story, and a family waiting for a return that will never happen.
everyone's afraid of looking busted everyone's afraid of making mistakes everyone's afraid of looking stupid everyone's afraid of embarrassment everyone's afraid of imperfections because of the damn PANOPTICON that is current society and it's fucking STUPID!!!!!!!!!!!!!!!!!!
If you're feeling anxious or depressed about the climate and want to do something to help right now, from your bed, for free...
Start helping with citizen science projects
Public participation in science is increasing, and citizen science has a central part in this. It is a contribution by the public to researc
What's a citizen science project? Basically, it's crowdsourced science. In this case, crowdsourced climate science, that you can help with!
You don't need qualifications or any training besides the slideshow at the start of a project. There are a lot of things that humans can do way better than machines can, even with only minimal training, that are vital to science - especially digitizing records and building searchable databases
Like labeling trees in aerial photos so that scientists have better datasets to use for restoration.
Or counting cells in fossilized plants to track the impacts of climate change.
Or digitizing old atmospheric data to help scientists track the warming effects of El Niño.
Or counting penguins to help scientists better protect them.
Those are all on one of the most prominent citizen science platforms, called Zooniverse, but there are a ton of others, too.
Oh, and btw, you don't have to worry about messing up, because several people see each image. Studies show that if you pool the opinions of however many regular people (different by field), it matches the accuracy rate of a trained scientist in the field.
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I spent a lot of time doing this when I was really badly injured and housebound, and it was so good for me to be able to HELP and DO SOMETHING, even when I was in too much pain to leave my bed. So if you are chronically ill/disabled/for whatever reason can't participate or volunteer for things in person, I highly highly recommend.
Next time you wish you could do something - anything - to help
Remember that actually, you can. And help with some science.
Yup, these are actually *really* important. And a small bit of work helps, so it’s doable even if you’re snowed under with survival work or in too much pain to concentrate for longer periods.
It’s multiply-checked by more than one person, so don’t worry about fucking it up because your concentration is fucked. Your input is valuable but not the only input.
I find Zooniverse very good, and it does Citizen Historian work too - I spent time digitising concentration camp records because a) families still don’t know what happened to some of their loved ones b) this makes the records available for historians without travelling to archives in person, which I can testify is *invaluable* for disabled historians and helps cut the need for overseas travel to do vital historical work.
It unexpectedly helped me with learning how to decipher premodern handwriting too, which proved really useful in my academic stuff. You *will* pick up valuable skills doing this. Put it on your CV.
Other places you can go to do citizen science, from the notes
(Thanks to everyone who left these in the notes! If you know more, put them in the notes, and I might add them! And ty @enbycrip for the fantastic addition that covered a bunch of details I didn't get to)
MapSwipe (collaboration between several Red Cross organizations and Doctors Without Borders, update vital geospatial data)
Smithsonian archives (transcriptions, many subjects)
Cornell Bird Lab (birds)
FoldIt (folding proteins)
Fathomverse (sea animals)
Project Monarch (butterflies)
In person
Bioblitz (nature)
Species watch (species)
Audobon Society (birds)
Also:
Even if you don't have time to spend, but do have some processor cycles to spare, check out the projects available at BOINC's Compute for Science: https://boinc.berkeley.edu/
The National Archive also reached out recently, looking for volunteer help transcribing old documents that are in cursive, because apparently that's a dying skillset
Fucking dire, man. If this isn't a call to action to stomp your consumerist urges and return to the DIY days of yore, I don't know what is.
Festive friends, I implore you. You don't need this stuff. There's a dozen reasons you don't need to buy this kind of mass produced seasonal decor and AI creeping into it is just the newest reason. Consider instead a) buying from reputable/independent/local artists, b) thrifting/buying vintage or c) just making it yourself. There is nothing I would love more than seeing a mass rejection of AI usher in a new DIY art/arts and crafts movement.
Anyway shoutout to John Williams, amazing composer and probably the one who made the other 50% of "holy shit amazing" soundtracks (Jurassic Park, Star Wars, Superman, and incidentally the original Harry Potter theme and score) who famously worked closely with the first openly transgender woman to be nominated for an Academy Award, Angela Morley. He respected her, and so far as I can see, has never made transphobic remarks.
I feel like simply calling JK Rowling a transphobe isn't strong enough anymore. Like. This is not your grandpa calling you by your deadname at a restaurant kind of transphobic. This is her wanting to eradicate all trans people (with an extra special hatred towards trans women specifically). This is her trying just that by personally funding transphobic hate groups with millions to push around laws in the UK. It is not hyperbolic to call her a dangerous, genocidal maniac.
It's not about cancelling a problematic writer. It's about literally trying to save lives by denying her as much money and power as possible.
For those who don't know: Ikumi Nakamura is the woman who was senior artist on Bayonetta, and designed the titular character along with Hideki Kamiya. Their greatest moment of bonding was over their insistence that Bayonetta keep her glasses on at all times.
Nakamura cannot go to horny jail. She is the warden.
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.
It was flagged as "Eksplicit", shadowbanned, and every reblog turned invisible. The flagging is also unappealable (unless that is an error, thanks tumblr)
Happy Trans Day of Invisibility from your tumblr mods!
Only found out because I tried to reblog with an update from your local trans unicorn siblings!
I cannot even LINK to my old comic without every new post getting deleted.
And yet, we will CONTINUE to exist, and grow, and find each other. We will be visible up to and beyond our own deaths. Because we stand for love. We love ourselves and each other and that just makes us stronger.
The original post was pardoned, but the shadowban still persisted and every reblog was hidden from the dash and flagged explicit. I'm not sure if it recovered by now, or if it's always going to be at risk of banning.
So whatever staff reviewed my complaint agreed that it wasn't explicit. So thank you for that, moderator. This highlights that the problem starts with bad actors reporting anything they don't like, and that it's a tossup if your appeal is seen by a bot, a normal human, or a bigot.
I'm grateful I can still touch people and spread positivity. Many of my queer siblings, brothers, and sisters - especially trans sisters - can't say the same.
But no matter what happens, we will always have each other. Every letter in our alphabet is a pillar holding up a beautiful world that only stands if we're all in it together. The rest of society may call you a monster, but it calls me one too. Maybe you won't hear it from the rest of them, but listen and you will hear it from me.
I love you.
I always will.
A Disaster in A Dress @skyfyrecity - Tumblr Blog | Tumgag
