So! This is a perfect case study in situations where you should be wary of misinformation.
Take a moment and ask yourself, a project like this requires a lot of time, money and dedication of resources, why would scientists dedicate that time to something that could just be done by a tree?
The answer is they wouldn't. So that means this claim requires further investigation!
This project is called LIQUID 3, and it's not meant for cities with wide open spaces, it's meant for cities like Belgrade in Serbia. These cities are densely populated and heavily polluted, to the point where pollution actually chokes out current trees and makes creating green spaces difficult.
Liquid 3 was a PhD scientists answer to these problems. The microalgae tank is intended for spaces where you either:
Don't have enough space to plant full trees, or
Don't have enough time to plant trees and wait for them to grow up.
The tank is extremely efficient when you consider the amount of space needed compared to the amount of CO2 turned into oxygen. The tank can operate throughout the winter. And most importantly, it can be quickly set up in areas that desperately need relief from air pollution NOW not in 10 years when trees are done growing. Children currently suffocating on polluted air can't wait for trees to grow, they need to be taken care of now, and Liquid 3 is one of the ways to take care of them. Depending on the species of microalgea used, a number have shown a pretty amazing capacity to pull heavy metals out of the air which is something trees can get choked up by.
The tanks aren't just tanks either! Liquid 3 have solar panels placed on top, they have lighting and mobile phone charging, and they work as public benches. The designers of it want to encourage green spaces where there's room, but where there isn't room or time, Liquid 3 can step in. Realistically, this isn't a replacement for trees. It's replacing boring metal city benches with new, cooler benches that also clean the air (and have at least some heating during the winter).
Not only that, but the microalgea that grows is native to Serbia and all that microalgea has a ton of great uses! It makes for great fertilizer, compost, wastewater treatment, cleaner biofuels and even for helping create new tanks for further air purification. They only require a quick algae divide once a month, and the produced algae can be carted off to where ever it's needed. This makes them effective solutions for areas that can't sustain complex installations.
So yeah, there's actually quite a lot of places that would like these. Lots of people currently breathing in terrible quality air would much rather have their boring city benches replaced with really fucking cool algae tanks that clean the air and can be used to help create + sustain future green spaces in cities. I dunno about you, but I'd take that over a dumb metal bench any day. Put these at every bus stop and I'd be delighted.
Also, I'm curious if these could be placed in war zones where people live, to combat the pollutants generated by exploding shit. Like, Iraq & Afghanistan is still contaminated with toxic dust from all the depleted-uranium tipped armor-piercing bullets fired ~30 years ago.
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's very telling how many terfs think "porn addiction" is a real thing when researchers found it to be a Christian invention that has to deal with shame and not actually a real addiction.
*Gasp!* You're telling me terfs are just conservative Christians?!
I would love to share this with everyone who may happen to see this post. Please support this wonderful human being. He spent nearly a half century in prison for a crime he never committed. And the only thing that kept him going was his artistic endeavors. He deserves the best life can offer anyone ❤️
Once when I was in undergrad, someone described something as “problematic” in class and our professor was like, “That’s cool, but ‘problematic’ doesn’t really mean anything. It means that the thing you’re describing has a problem, and in and of itself that’s not bad. Art, especially, should always have problems, or else it’s not interesting and not art, either. It sounds like you’re trying to say that this is bad, but you don’t want to say ‘bad.’ Is that right?”
So from then on whenever one of us called something problematic, he would make us talk it out until we could name the “bad” thing we were hinting at. In this particular class, 7/10 it was some type of oppression, and the remainder was like, “I’m uncomfortable because this is very new/confusing/pushing boundaries that made me feel safe.”
Once we stopped calling things “problematic” and stopping at that, class got way more interesting and... we all had to say, like, “that’s racist” or “that’s misogynistic” or “ew capitalism gross” out loud, which a lot of us had never done in a classroom before. Or we had to be like, “Uhhh... I’m not sure what’s so bad?” and confront our own beliefs and that was maybe even more useful.
Anyway. Whenever I see the word problematic, I can’t help but think of this professor being like, “Good starting point, now let’s get specific.” I think when we have to commit to saying “that’s ___” it requires a lot more careful thought about the truth and impact and complexities of whatever we’re claiming. Sometimes there really is some bullshit afoot, and also sometimes it’s art, and it should be full of problems, because that’s what art is.
#'this is present in the text' is often a good first step #but those second and third ones (naming it; describing its function) are vital (via @elucubrare)
so called feminists: "actually women are naturally inferior at everything, including quiz game shows, beauty pageants, chess, and video games. we need a society that is segregated so that anyone who would ever have to potential to win against a woman is eliminated. this is actually far better than organizing sports into weight classes or different athletic/skill level based categories that ignore sex, like what wrestling and video/board game tournaments already do. I cannot accept the reality that men and women are the same species. this is actually how we truly protect women. by calling them weak pathetic losers who could never win against even the worst opponent, and preventing them from even playing at all."
actually conversion I had with one of these nerds:
me: "so you think men are better at literally everything, even things that do not require any physical ability whatsoever?"
terf: "yes! those should still be segregated by sex! males should not be playing against women ever. it's an unfair advantage"
me: "so my brother has an unfair advantage over me when playing fucking monopoly?"
terf: "yes. males are statistically better and managing finances, while women are irresponsible and lenient with spending money."
hey guys. I think the people who are literally unironicly saying "women are bad at money. this means they suck at board games" aren't actually feminists
No joke, that was the rethoric the Francoist regime in Spain used to justify women being legally underage regardless of age and unable to open bank accounts without their husbands or fathers permission. That law was effective until 1975.
I know you think it’s “feminist” to hate on women who already have self esteem and image issues (through no fault of their own, as a direct result of patriarchy) for doing something to help them cope with their fleeting youth - which may or may not have been stripped from them against their will due to years of depression, abuse, oppression, or whatever else - but the truth is that the freedom to get Botox is actually a right feminists have fought for - whether intentionally or unintentionally. Women used to get thrown away when we outlived our youth and beauty - now we have the option to prolong it and change the way we are perceived and treated by society as a whole. We ALL had to grow up too fast. We ALL have to cope with a childhood that ended too soon. I’m no less a feminist than you are simply because I choose to hold onto my youth and beauty because it helps me feel better about my own loss of innocence. This is all just another excuse to hate on women and tear us down. You’re literally letting the patriarchy win by judging women for their personal choices which harm absolutely no one. Please grow up. Not everyone is conventionally attractive or ages gracefully. Some women can cope with that and some can’t. Both deserve love and respect. Have some decency. You’re not edgy for this. You’re hating just like a man would.
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