she/they, microbiologist & general science nerd, queer af, dreaming of a solarpunk future but mainly reblogging random shit, tagging varies wildly depending on my energy level
when you're at the humiliation ritual competition and then niall kennedy shows up and invites the guy he frequented chemsex parties with, the girl he went to college with who he kept asking for money to pay off his fuck ups in adulthood, the ex who he got pregnant and cheated on a bunch of times, to his wedding with the guy his brother beat half to death and put in coma in college, and then said brother shows up at said wedding so they can kill each other
Several AI services (chatbots ) are purposely addictive, the same way people can become addicted to gambling or shopping. We’ve literally seen in real time how ChatGPT has caused psychosis and delusions in people; it can have a huge affect on someones’s mental stability. Just because it isn’t substance-based doesn’t mean that doesn’t count as an addiction, and shaming people who are trying to move on and improve themselves is counterproductive. Im proud of that dude and his 4 month mark!
Then I'll mention the predatory chatbots who do it on purpose! Character.ai is one of many AI chatbot websites that're designed to be addictive.
None of the signup methods require a password. It only takes email and birthday. Minimizing time on the signin or signup screen makes it harder for people quitting to avoid relapse.
"Characters" on the website will send messages "on their own" (prompted by the site) to try to invite inactive users back after as soon as 1 day of inactivity. This is likely to force FOMO, or make users feel more like they owe the bots a response. Unhealthy attachment stuff.
Account deletion is an essential part of every service that should go smoothly, right? Right? Wrong. It takes 1-2 weeks for a Character AI account deletion to be finalized, and account deletion requests have a high chance to not go through if you're not using the app.
Rephrasing: People leaving Character.AI are pushed to download the app in order to delete their accounts, if they haven't already. This makes it harder for people to quit and stay gone. Failing to quit an addiction makes it harder to quit successfully in the future, so this feels like a feature, not a bug.
On top of that, the delete account menu reads like this:
Tell me THAT doesn't sound like a bad ex. It's a carefully crafted yet hostile environment to those who are already addicted to the technology. I am so so SO happy, downright delighted that they've managed to quit, and I wish the best for others in recovery spaces or considering quitting as well!! While AI addiction is an emerging condition, there are already therapists and other mental health professionals trained to help people plan to quit and do so a bit easier.
(If anyone seeing this is in need of them, there are several tumblr Communities here devoted to quitting, too. They provide a mix of advice, venting spaces, and proof that you aren't alone.)
fantasies about vampires coming to your town to fall in love with you and make hot vampire sex to you: old, outdated, boring
fantasies about vampires coming to your town to improve the public infrastructure, destroy the rich families milking you for money, and perhaps also making hot vampire sex to you (or helping you make hotter sex with your partner of many years): rejuvenating, what we need in this day and age, modern
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.
I hate the videoification of everything. If I have to hear one more video of someone speaking closely into their shitty mic and I have to have all their yucky wet mouth noises and plosives and nose whistles and throat clearings and sniffles I am going to dig a vertical hole the exact dimensions of my body and I’m going to slither in head first
as someone with misophonia, the widespread popularization of asmr audio editing + people that are being pushed to make video content with no formal training and have no idea how to edit their audio (ex college professors, average joe tiktokers, etc) is literally my nightmare scenario. this is hell I am in hell
There's this video of nuns talking about their favourite things to do outside of nun activities and one of them says "ultimate frisbee" and the other one goes "and sister you are so good at that." I literally cannot get "and sister you are so good at that" out of my head. Out of all my stims this one is my fav lolol
you don’t realize how important lunch is until you’re wandering around thinking about how unloveable and untalented and uniquely cursed you are and then it’s 4pm and you finally eat lunch and you go Oh. oh right.
lot of people commenting on this post like "who eats lunch at 4pm that's a terrible time to eat lunch" yes. that is the point. 4pm lunch is inadvisable. 4pm lunch is not the ideal. 4pm lunch makes the mind demons real.
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