alright I've got to do some quick math to explain attitudes towards AI to my boss.
we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)
please rb for larger sample size!
Question 1/3
What is your age, and do you feel AI is a net positive or net negative in our lives today?
Gentrification creates a stifling homogeneity in urban areas that makes it less suited for the everyday lives of the lower class and more suited towards the leisure and tourism of those with expendable income.
An old, decrepit laundromat gets replaced by an upscale bakery? And people are mad? It’s not that the poor hate organic vegan cupcakes, it’s that most of us don’t have a way to do laundry in our own home.
Run-down corner stores replaced by hand-made designer clothing boutiques? We don’t hate your eco-fabric shawl, but I can’t eat that for dinner after work like I could have a can of beans I grabbed from that corner store when I don’t have time to take the bus to the real grocery store after work.
What gentrification brings in and of itself is not typically bad, it’s that gentrification brings institutions of leisure and pleasure and makes it so that the poor have to go farther out of their way for basic necessities. It turns low-income living spaces into local tourist attractions. It can even create food deserts by putting restaurants, grocery stores, etc. in that the majority of the lower class cannot afford.
Imagine if someone totally renovated your house and turned it into a mini theme park - they took away your sleeping space, where you prepare food, where you clean yourself and get ready for your day, and replaced it with things that will please people who are visiting, who have their own homes they can go back to, who are here not for their entire life but just as a distraction from their otherwise mundane existence. It’s not that you hate theme parks, it’s not like you’ve never been to a theme park and vow to never visit one again. It’s just that you need to live! To survive! And the leisure of those who have more than you should not invalidate your existence.
I am glad this has made the rounds. Some people feel a dense misunderstanding or misinterpretation concerning gentrification, and I think it helps to hear a description/explanation of what gentrification is from those who are both affected by it and educated by the culture from which it hails. I and many others enjoy some of the delights of gentrification while simultaneously having their livelihoods threatened by it.
I don't even bother hardly to orrect my typos anymore. It is just affirmation that these words were typed by the hands of human being and not extruded by a fucnking AI
one of my professors was like (paraphrasing) "AI makes you sound the same as everybody else. The idea of a single "correct" English is racist and flattens the diversity of people. I don't care about grammar outside of the writing being understandable. I want to hear YOUR voice, Your dialect the unique way you communicate."
I have been more appreciative of the rough edges of human communication since chatGPT came to be. The misspellings and turns of phrase that real people produce.
The inherently flawed nature of an alive thing vs. the empty perfection of something that never had life in it at all
Reblogging because I tell my students exactly the same thing. Good to see other instructors are making that same point.
(At least I hope that’s the case, because the terrifying alternative is that there’s an overlap between “people i follow on tumblr” and “students i’ve taught”.)
@everything-you-feel-is-real I know by tumblr tradition that I'm to say "impossible, my posts never blow up like that," or "please don't do this to me."
But I feel in my bones that you are right. If this is to be my wife's moment of glory, I am willing to suffer notification overload, that the world may know she is funny. #MyFunnyWife
She is not directly paid for her work to vet papers, she has been hit with legal action & death threats by scientists who hate that she's exposing them and their financial fraud, and she keeps at it every single day, combing through thousands of papers to make science more fair. Please consider supporting her!
“I would eat his heart in the marketplace” is legit the most savage line I have ever heard, I’d like to personally thank Shakespeare for putting into words that feeling of rage and protectiveness women get when some fuckboy hurts another woman
Okay first off, I will always reblog this post, but secondly, I went to Shakespeare in the Park tonight to see this and all the women cheered *so loudly* when Beatrice said this line, and the guy in front of me looked around all shocked and a little scared and said “… oh wow” and it was ICONIQUE
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.
In case you just skimmed the post above and missed it, I want to reiterate and highlight Gebru's current position as Executive Director of Distributed AI Research Institute. If you're curious about what AI technology might look like when not applied in the horrifically unethical and damaging way it's currently applied, please check them out.
If we want to have nice things, decentralization is essential, and if we want to decentralize, we need to have our eyes on things that are beyond the scope of the current Big Tech narrative.
The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-roo
When The End comes for real, it’s just as Crowley supposed, with Heaven and Hell united against humanity. There are a lot of people who don’t believe what’s happening, but about half earth’s inhabitants do. And they show up for the fight.
The army humans have guns, there are doctors wielding baseball bats and taxi drivers with tire irons. Masses of youths are forming up with nothing but broken bottles and spite to defend themselves with.
Lucifer scoffs at them, his beauty already luring some people from their posts.
Not too many of them, though. Aziraphale has to believe that…
There may be millions of angels and demons among the ranks, united for a common goal, but there are billions of humans.
Crowley and Aziraphale are in the thick of it, of course. Of course. Aziraphale has come into the possession of his flaming sword again, through a series of events that Crowley really doesn’t want to think too hard about.
They stare down the Morning Star across the open expanse of the soon to be battlefield, humanity behind them, as much of it sheltered beneath their wings as they can manage.
Ok, now that we’ve had a week to think about it, here’s another, more S1-ish fix it (as the last one was desperate and had issues…but let’s be honest, so did the “season”.)
Yesssss, I love this man as Crowley. Words cannot express how much I love this man as Crowley.
Favorite bits with context:
The “Noooooo!” is when hypnotized Sister Mary tells him that all the hospital records have been burnt.
Similarly, the ending bit “Okay, that’s it, that’s it, it was worth a try. Let’s get out of here” is when Sister Mary can’t remember anything except the baby Antichrist had cute toesies. I love him, he just sounds 500% done.
“What in hell?” is when he ends up in the M25 traffic jam.
The “ha ha HA, who writes this stuff??” is when he’s driving in the flaming Bentley and turns on an American televangelist radio program about the Rapture for ‘light entertainment’. I love this because it’s the most genuine laughter we hear out of him in this version. To him, the idea that humans think there’s going to be a Rapture is funny as shit.
turnnoffyourmind
dededos
janainaart
ellievsbear
